From ddebecd247b92c9365516bb59313795ff9ff8b41 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Fri, 28 Jul 2023 22:16:06 +0000 Subject: [PATCH 01/13] Start adding components for the segment anything model --- keras_cv/models/__init__.py | 4 + keras_cv/models/segmentation/__init__.py | 4 + .../segmentation/segment_anything/__init__.py | 26 + .../segmentation/segment_anything/sam.py | 13 + .../segment_anything/sam_image_encoder.py | 564 ++++++++++++++++++ .../segment_anything/sam_layers.py | 106 ++++ .../segment_anything/sam_mask_decoder.py | 303 ++++++++++ .../segment_anything/sam_prompt_encoder.py | 322 ++++++++++ .../segmentation/segment_anything/sam_test.py | 292 +++++++++ .../segment_anything/sam_transformer.py | 389 ++++++++++++ 10 files changed, 2023 insertions(+) create mode 100644 keras_cv/models/segmentation/segment_anything/__init__.py create mode 100644 keras_cv/models/segmentation/segment_anything/sam.py create mode 100644 keras_cv/models/segmentation/segment_anything/sam_image_encoder.py create mode 100644 keras_cv/models/segmentation/segment_anything/sam_layers.py create mode 100644 keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py create mode 100644 keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py create mode 100644 keras_cv/models/segmentation/segment_anything/sam_test.py create mode 100644 keras_cv/models/segmentation/segment_anything/sam_transformer.py diff --git a/keras_cv/models/__init__.py b/keras_cv/models/__init__.py index 4191c07575..fc636dbdbd 100644 --- a/keras_cv/models/__init__.py +++ b/keras_cv/models/__init__.py @@ -166,5 +166,9 @@ YOLOV8Detector, ) from keras_cv.models.segmentation import DeepLabV3Plus +from keras_cv.models.segmentation.segment_anything import ImageEncoder +from keras_cv.models.segmentation.segment_anything import MaskDecoder +from keras_cv.models.segmentation.segment_anything import PromptEncoder +from keras_cv.models.segmentation.segment_anything import TwoWayTransformer from keras_cv.models.stable_diffusion import StableDiffusion from keras_cv.models.stable_diffusion import StableDiffusionV2 diff --git a/keras_cv/models/segmentation/__init__.py b/keras_cv/models/segmentation/__init__.py index 122dc4191e..4c9b7460fb 100644 --- a/keras_cv/models/segmentation/__init__.py +++ b/keras_cv/models/segmentation/__init__.py @@ -13,3 +13,7 @@ # limitations under the License. from keras_cv.models.segmentation.deeplab_v3_plus import DeepLabV3Plus +from keras_cv.models.segmentation.segment_anything import ImageEncoder +from keras_cv.models.segmentation.segment_anything import MaskDecoder +from keras_cv.models.segmentation.segment_anything import PromptEncoder +from keras_cv.models.segmentation.segment_anything import TwoWayTransformer diff --git a/keras_cv/models/segmentation/segment_anything/__init__.py b/keras_cv/models/segmentation/segment_anything/__init__.py new file mode 100644 index 0000000000..7f1fda07f0 --- /dev/null +++ b/keras_cv/models/segmentation/segment_anything/__init__.py @@ -0,0 +1,26 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from keras_cv.models.segmentation.segment_anything.sam_image_encoder import ( + ImageEncoder, +) +from keras_cv.models.segmentation.segment_anything.sam_mask_decoder import ( + MaskDecoder, +) +from keras_cv.models.segmentation.segment_anything.sam_prompt_encoder import ( + PromptEncoder, +) +from keras_cv.models.segmentation.segment_anything.sam_transformer import ( + TwoWayTransformer, +) diff --git a/keras_cv/models/segmentation/segment_anything/sam.py b/keras_cv/models/segmentation/segment_anything/sam.py new file mode 100644 index 0000000000..3992ffb59a --- /dev/null +++ b/keras_cv/models/segmentation/segment_anything/sam.py @@ -0,0 +1,13 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/keras_cv/models/segmentation/segment_anything/sam_image_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_image_encoder.py new file mode 100644 index 0000000000..eaf8462cda --- /dev/null +++ b/keras_cv/models/segmentation/segment_anything/sam_image_encoder.py @@ -0,0 +1,564 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from keras_cv.backend import keras +from keras_cv.backend import ops +from keras_cv.models.segmentation.segment_anything.sam_layers import MLPBlock +from keras_cv.models.segmentation.segment_anything.sam_layers import ( + SAMLayerNormalization, +) + + +def get_rel_pos(query_size, key_size, rel_pos): + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + + Args: + query_size (int): The number of features of the queries. + key_size (int): The number of features of the keys. + rel_pos (tensor): Relative positional embedding tensor. + + Returns: + tensor: Extracted positional embeddings according to relative + positions. + """ + max_rel_dist = 2 * max(query_size, key_size) - 1 + if rel_pos.shape[0] != max_rel_dist: + rel_pos_resized = ops.image.resize( + images=ops.reshape( + rel_pos, (1, rel_pos.shape[0], rel_pos.shape[1], 1) + ), + size=(max_rel_dist, rel_pos.shape[1]), + interpolation="bilinear", + ) + rel_pos_resized = ops.squeeze(rel_pos_resized, axis=(0, -1)) + else: + rel_pos_resized = rel_pos + query_coordinates = ops.arange(query_size, dtype="float32")[:, None] * max( + key_size / query_size, 1.0 + ) + key_coordinates = ops.arange(key_size, dtype="float32")[None, :] * max( + query_size / key_size, 1.0 + ) + relative_coordinates = (query_coordinates - key_coordinates) + ( + key_size - 1 + ) * max(query_size / key_size, 1.0) + relative_coordinates = ops.cast(relative_coordinates, dtype="int64") + return ops.take(rel_pos_resized, relative_coordinates, 0) + + +def add_decomposed_rel_pos( + attention_map, queries, rel_pos_h, rel_pos_w, query_size, key_size +): + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + + Args: + attention_map (tensor): Attention map. + queries (tensor): Queries in the attention layer with shape + `(B, q_h * q_w, C)`. + rel_pos_h (tensor): Relative position embeddings `(Lh, C)` for height + axis. + rel_pos_w (tensor): relative position embeddings `(Lw, C)` for width + axis. + query_size (tuple[int, int]): Spatial sequence size of queries with + `(q_h, q_w)`. + key_size (tuple[int, int]): Spatial sequence size of keys with + `(k_h, k_w)`. + + Returns: + tensor: attention map with added relative positional embeddings. + + References: + - https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa: E501 + """ + query_height, query_width = query_size + key_height, key_width = key_size + rel_heights = get_rel_pos(query_height, key_height, rel_pos_h) + rel_widths = get_rel_pos(query_width, key_width, rel_pos_w) + + B, _, C = queries.shape + rel_queries = ops.reshape(queries, (B, query_height, query_width, C)) + rel_heights = ops.einsum("bhwc,hkc->bhwk", rel_queries, rel_heights) + rel_widths = ops.einsum("bhwc,wkc->bhwk", rel_queries, rel_widths) + + attention_map = ops.reshape( + attention_map, (B, query_height, query_width, key_height, key_width) + ) + attention_map = attention_map + rel_heights[..., :, None] + attention_map = attention_map + rel_widths[..., None, :] + attention_map = ops.reshape( + attention_map, (B, query_height * query_width, key_height * key_width) + ) + return attention_map + + +@keras.saving.register_keras_serializable(package="keras_cv") +class MultiHeadAttentionWithRelativePE(keras.layers.Layer): + """Multi-head Attention block with relative position embeddings. + + Args: + num_heads (int): Number of attention heads. + key_dim (int): Size of each attention head for query, key, and + value. + use_bias (bool, optional): Whether to use bias when projecting + the queries, keys, and values. Defaults to `True`. + use_rel_pos (bool, optional): Whether to use relative positional + embeddings or not. Defaults to `False`. + input_size (tuple[int, int], optional): Size of the input image. + Must be provided when using relative positional embeddings. + Defaults to `None`. + + Raises: + ValueError: When `input_size = None` with `use_rel_pos = True`. + """ + + def __init__( + self, + num_heads, + key_dim, + use_bias=True, + use_rel_pos=False, + input_size=None, + **kwargs + ): + super().__init__(**kwargs) + self.num_heads = num_heads + self.key_dim = key_dim + self.scale = self.key_dim**-0.5 + self.use_bias = use_bias + + self.qkv = keras.layers.Dense( + key_dim * self.num_heads * 3, use_bias=self.use_bias + ) + self.projection = keras.layers.Dense(key_dim * self.num_heads) + + self.input_size = input_size + self.use_rel_pos = use_rel_pos + if self.use_rel_pos: + if input_size is None: + raise ValueError( + "Input size must be provided if using relative " + "positional encoding." + ) + self.rel_pos_h = self.add_weight( + name="rel_pos_h", + shape=(2 * self.input_size[0] - 1, self.key_dim), + initializer="zeros", + trainable=True, + ) + self.rel_pos_w = self.add_weight( + name="rel_pos_w", + shape=(2 * self.input_size[1] - 1, self.key_dim), + initializer="zeros", + trainable=True, + ) + + def call(self, x): + B, H, W, C = x.shape + qkv = ops.transpose( + ops.reshape( + self.qkv(x), (B, H * W, 3, self.num_heads, self.key_dim) + ), + axes=(2, 0, 3, 1, 4), + ) + qkv = ops.reshape(qkv, (3, B * self.num_heads, H * W, self.key_dim)) + queries, keys, values = ops.unstack(qkv, axis=0) + attention_map = (queries * self.scale) @ ops.transpose( + keys, axes=(0, 2, 1) + ) + + if self.use_rel_pos: + attention_map = add_decomposed_rel_pos( + attention_map, + queries, + self.rel_pos_h, + self.rel_pos_w, + (H, W), + (H, W), + ) + attention_map = ops.softmax(attention_map, axis=-1) + x = ops.reshape( + attention_map @ values, (B, self.num_heads, H, W, self.key_dim) + ) + x = ops.transpose(x, axes=(0, 2, 3, 1, 4)) + x = ops.reshape(x, (B, H, W, C)) + x = self.projection(x) + + return x + + def get_config(self): + config = super().get_config() + config.update( + { + "num_heads": self.num_heads, + "key_dim": self.key_dim, + "use_bias": self.use_bias, + "use_rel_pos": self.use_rel_pos, + "input_size": self.input_size, + } + ) + return config + + +def window_partition(x, window_size): + B, H, W, C = x.shape + pad_height = (window_size - H % window_size) % window_size + pad_width = (window_size - W % window_size) % window_size + if pad_height > 0 or pad_width > 0: + x = ops.pad(x, ((0, 0), (0, pad_height), (0, pad_width), (0, 0))) + H_padded, W_padded = H + pad_height, W + pad_width + x = ops.reshape( + x, + ( + B, + H_padded // window_size, + window_size, + W_padded // window_size, + window_size, + C, + ), + ) + windows = ops.reshape( + ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), + (-1, window_size, window_size, C), + ) + return windows, (H_padded, W_padded) + + +def window_unpartition(windows, window_size, HW_padded, HW): + H_padded, W_padded = HW_padded + H, W = HW + B = windows.shape[0] // ( + (H_padded // window_size) * (W_padded // window_size) + ) + x = ops.reshape( + windows, + ( + B, + H_padded // window_size, + W_padded // window_size, + window_size, + window_size, + -1, + ), + ) + x = ops.reshape( + ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), (B, H_padded, W_padded, -1) + ) + return x[:, :H, :W, :] + + +@keras.utils.register_keras_serializable(package="keras_cv") +class WindowedTransformerEncoder(keras.layers.Layer): + """Transformer blocks with support of window attention and residual + propagation blocks. + + Args: + project_dim (int): the dimensionality of the projection of the + encoder, and output of the `MultiHeadAttention`. + mlp_dim (int): the intermediate dimensionality of the MLP head before + projecting to `project_dim`. + num_heads (int): the number of heads for the `MultiHeadAttention` + layer. + use_bias (bool, optional): Whether to use bias to project the keys, + queries, and values in the attention layer. Defaults to `True`. + use_rel_pos (bool, optional): Whether to use relative positional + emcodings in the attention layer. Defaults to `False`. + window_size (int, optional): Window size for windowed attention. + Defaults to `0`. + input_size (tuple[int, int], optional): Height and width of the input + image as a tuple of integers. Must be provided when using relative + positional embeddings. Defaults to `None`. + activation (str, optional): the activation function to apply in the + MLP head - should be a function. Defaults to `"gelu"`. + layer_norm_epsilon (float, optional): The epsilon to use in the layer + normalization layers. Defaults to `1e-6`. + """ + + def __init__( + self, + project_dim, + mlp_dim, + num_heads, + use_bias=True, + use_rel_pos=False, + window_size=0, + input_size=None, + activation="gelu", + layer_norm_epsilon=1e-6, + **kwargs + ): + super().__init__(**kwargs) + self.project_dim = project_dim + self.mlp_dim = mlp_dim + self.num_heads = num_heads + self.use_bias = use_bias + self.input_size = input_size + self.activation = activation + self.layer_norm_epsilon = layer_norm_epsilon + self.window_size = window_size + self.use_rel_pos = use_rel_pos + + self.layer_norm1 = keras.layers.LayerNormalization( + epsilon=self.layer_norm_epsilon + ) + self.layer_norm2 = keras.layers.LayerNormalization( + epsilon=self.layer_norm_epsilon + ) + self.attention = MultiHeadAttentionWithRelativePE( + num_heads=self.num_heads, + key_dim=self.project_dim // self.num_heads, + use_bias=use_bias, + use_rel_pos=use_rel_pos, + input_size=input_size + if window_size == 0 + else (window_size, window_size), + ) + self.mlp_block = MLPBlock(project_dim, mlp_dim, activation) + + def call(self, x): + shortcut = x + x = self.layer_norm1(x) + # Window Partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + + x, HW_padded = window_partition(x, self.window_size) + + x = self.attention(x) + # Reverse Window Partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, HW_padded, (H, W)) + + x = shortcut + x + x = x + self.mlp_block(self.layer_norm2(x)) + + return x + + def get_config(self): + config = super().get_config() + config.update( + { + "project_dim": self.project_dim, + "mlp_dim": self.mlp_dim, + "num_heads": self.num_heads, + "use_bias": self.use_bias, + "use_rel_pos": self.use_rel_pos, + "window_size": self.window_size, + "input_size": self.input_size, + "activation": self.activation, + "layer_norm_epsilon": self.layer_norm_epsilon, + } + ) + return config + + +@keras.utils.register_keras_serializable(package="keras_cv") +class SAMPatchingAndEmbedding(keras.layers.Layer): + """Image to Patch Embedding using only a conv layer (without + layer normalization). + + Args: + kernel_size (tuple[int, int], optional): Kernel size of the + projection layer. Defaults to `(16, 16)`. + strides (tuple, optional): Strides of the projection layer. + Defaults to `(16, 16)`. + embed_dim (int, optional): Number of filters to use in the + projection layer i.e. projection size. Defaults to `768`. + """ + + def __init__( + self, kernel_size=(16, 16), strides=(16, 16), embed_dim=768, **kwargs + ): + super().__init__(**kwargs) + + self.projection = keras.layers.Conv2D( + embed_dim, kernel_size=kernel_size, strides=strides + ) + + self.kernel_size = kernel_size + self.strides = strides + self.embed_dim = embed_dim + + def call(self, x): + x = self.projection(x) + return x + + def get_config(self): + config = super().get_config() + config.update( + { + "kernel_size": self.kernel_size, + "strides": self.strides, + "embed_dim": self.embed_dim, + } + ) + return config + + +@keras.utils.register_keras_serializable(package="keras_cv") +class ImageEncoder(keras.models.Model): + """A ViT image encoder for the segment anything model. + + Args: + img_size (int, optional): The size of the input image. Defaults to + `1024`. + patch_size (int, optional): the patch size to be supplied to the + Patching layer to turn input images into a flattened sequence of + patches. Defaults to `16`. + in_chans (int, optional): The number of channels in the input image. + Defaults to `3`. + embed_dim (int, optional): The latent dimensionality to be projected + into in the output of each stacked windowed transformer encoder. + Defaults to `1280`. + depth (int, optional): The number of transformer encoder layers to + stack in the Vision Transformer. Defaults to `32`. + mlp_dim (_type_, optional): The dimensionality of the hidden Dense + layer in the transformer MLP head. Defaults to `1280*4`. + num_heads (int, optional): the number of heads to use in the + `MultiHeadAttentionWithRelativePE` layer of each transformer + encoder. Defaults to `16`. + out_chans (int, optional): The number of channels (features) in the + output (image encodings). Defaults to `256`. + use_bias (bool, optional): Whether to use bias to project the keys, + queries, and values in the attention layer. Defaults to `True`. + use_abs_pos (bool, optional): Whether to add absolute positional + embeddings to the output patches. Defaults to `True`. + use_rel_pos (bool, optional): Whether to use relative positional + emcodings in the attention layer. Defaults to `False`. + window_size (int, optional): The size of the window for windowed + attention in the transformer encoder blocks. Defaults to `0`. + global_attention_indices (list, optional): Indexes for blocks using + global attention. Defaults to `[7, 15, 23, 31]`. + layer_norm_epsilon (int, optional): The epsilon to use in the layer + normalization blocks in transformer encoder. Defaults to `1e-6`. + """ + + def __init__( + self, + img_size=1024, + patch_size=16, + in_chans=3, + embed_dim=1280, + depth=32, + mlp_dim=1280 * 4, + num_heads=16, + out_chans=256, + use_bias=True, + use_abs_pos=True, + use_rel_pos=False, + window_size=0, + global_attention_indices=[7, 15, 23, 31], + layer_norm_epsilon=1e-6, + **kwargs + ): + super().__init__(**kwargs) + self.img_size = img_size + self.patch_size = patch_size + self.in_chans = in_chans + self.embed_dim = embed_dim + self.depth = depth + self.mlp_dim = mlp_dim + self.num_heads = num_heads + self.out_chans = out_chans + self.use_bias = use_bias + self.use_rel_pos = use_rel_pos + self.use_abs_pos = use_abs_pos + self.window_size = window_size + self.global_attention_indices = global_attention_indices + self.layer_norm_epsilon = layer_norm_epsilon + + self.patch_embed = SAMPatchingAndEmbedding( + kernel_size=(patch_size, patch_size), + strides=(patch_size, patch_size), + embed_dim=embed_dim, + ) + if self.use_abs_pos: + self.pos_embed = self.add_weight( + name="pos_embed", + shape=( + 1, + self.img_size // self.patch_size, + self.img_size // self.patch_size, + self.embed_dim, + ), + initializer="zeros", + trainable=True, + ) + else: + self.pos_embed = None + self.transformer_blocks = [] + for i in range(depth): + block = WindowedTransformerEncoder( + project_dim=embed_dim, + mlp_dim=mlp_dim, + num_heads=num_heads, + use_bias=use_bias, + use_rel_pos=use_rel_pos, + window_size=window_size + if i not in global_attention_indices + else 0, + input_size=(img_size // patch_size, img_size // patch_size), + ) + self.transformer_blocks.append(block) + self.transformer_blocks = keras.models.Sequential( + self.transformer_blocks + ) + self.bottleneck = keras.models.Sequential( + [ + keras.layers.Conv2D( + filters=out_chans, kernel_size=1, use_bias=False + ), + SAMLayerNormalization(), + keras.layers.Conv2D( + filters=out_chans, + kernel_size=3, + padding="same", + use_bias=False, + ), + SAMLayerNormalization(), + ] + ) + + def call(self, x): + B, _, _, _ = x.shape + x = self.patch_embed(x) + if self.pos_embed is not None: + x = x + self.pos_embed + x = self.transformer_blocks(x) + return self.bottleneck(x) + + def get_config(self): + config = super().get_config() + config.update( + { + "img_size": self.img_size, + "patch_size": self.patch_size, + "in_chans": self.in_chans, + "embed_dim": self.embed_dim, + "depth": self.depth, + "mlp_dim": self.mlp_dim, + "num_heads": self.num_heads, + "out_chans": self.out_chans, + "use_bias": self.use_bias, + "use_abs_pos": self.use_abs_pos, + "use_rel_pos": self.use_rel_pos, + "window_size": self.window_size, + "global_attention_indices": self.global_attention_indices, + "layer_norm_epsilon": self.layer_norm_epsilon, + } + ) + return config diff --git a/keras_cv/models/segmentation/segment_anything/sam_layers.py b/keras_cv/models/segmentation/segment_anything/sam_layers.py new file mode 100644 index 0000000000..764aefaf01 --- /dev/null +++ b/keras_cv/models/segmentation/segment_anything/sam_layers.py @@ -0,0 +1,106 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from keras_cv.backend import keras +from keras_cv.backend import ops + + +@keras.utils.register_keras_serializable(package="keras_cv") +class MLPBlock(keras.layers.Layer): + def __init__(self, embedding_dim, mlp_dim, activation="gelu", **kwargs): + """A MLP block with architecture + `embedding_dim -> mlp_dim -> embedding_dim`. + + Args: + embedding_dim (int): The number of units in the input and the + output layer. + mlp_dim (int): The number of units in the hidden layer. + activation (str, optional): The activation of the output. + Defaults to "gelu". + """ + super().__init__(**kwargs) + self.dense_layer1 = keras.layers.Dense(mlp_dim) + self.dense_layer2 = keras.layers.Dense(embedding_dim) + self.activation_layer = keras.layers.Activation(activation) + + self.embedding_dim = embedding_dim + self.mlp_dim = mlp_dim + self.activation = activation + + self.built = False + + def build(self, input_shape=None): + self.dense_layer1.build([self.embedding_dim]) + self.dense_layer2.build([self.mlp_dim]) + + self.built = True + + def call(self, x): + return self.dense_layer2(self.activation_layer(self.dense_layer1(x))) + + def get_config(self): + config = super().get_config() + config.update( + { + "embedding_dim": self.embedding_dim, + "mlp_dim": self.mlp_dim, + "activation": self.activation, + } + ) + + +@keras.utils.register_keras_serializable(package="keras_cv") +class SAMLayerNormalization(keras.layers.Layer): + def __init__(self, epsilon=1e-6, **kwargs): + """A SAMLayerNormalization layer without moving mean and variance. + + Args: + epsilon (float, optional): Small float added to variance to + avoid dividing by zero. Defaults to 1e-6. + """ + super().__init__(**kwargs) + self.epsilon = epsilon + + def build(self, input_shape): + self.weight = self.add_weight( + name="weight", + shape=(input_shape[-1],), + initializer="ones", + trainable=True, + ) + self.bias = self.add_weight( + name="weight", + shape=(input_shape[-1],), + initializer="zeros", + trainable=True, + ) + + def call(self, x): + u = ops.mean(x, axis=-1, keepdims=True) + s = ops.mean(ops.square(x - u), axis=-1, keepdims=True) + x = (x - u) / ops.sqrt(s + self.epsilon) + x = self.weight * x + self.bias + return x + + def compute_output_shape(self, input_shape): + return input_shape + + def get_config(self): + config = super().get_config() + config.update( + { + "epsilon": self.epsilon, + } + ) + return config diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py new file mode 100644 index 0000000000..877039a9a8 --- /dev/null +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -0,0 +1,303 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from keras_cv.backend import keras +from keras_cv.backend import ops +from keras_cv.models.segmentation.segment_anything.sam_layers import ( + SAMLayerNormalization, +) + + +@keras.utils.register_keras_serializable(package="keras_cv") +class MLP(keras.layers.Layer): + """A MLP block with architecture + `input_dim -> [hidden_dim] * (num_layers - 1) -> output_dim`. + + Args: + hidden_dim (int): The number of units in the hidden layers. + output_dim (int): The number of units in the output layer. + num_layers (int): The total number of dense layers to use. + """ + + def __init__(self, hidden_dim, output_dim, num_layers, **kwargs): + super().__init__(**kwargs) + self.hidden_dim = hidden_dim + self.output_dim = output_dim + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.dense_net = [] + for hidden_dim in h: + self.dense_net.append(keras.layers.Dense(hidden_dim)) + self.dense_net.append(keras.layers.Activation("relu")) + self.dense_net.append(keras.layers.Dense(output_dim)) + self.dense_net = keras.models.Sequential(self.dense_net) + + self.built = False + + def build(self, input_shape): + self.dense_net.build(input_shape) + + self.built = True + + def call(self, x): + return self.dense_net(x) + + def get_config(self): + config = super().get_config() + config.update( + { + "hidden_dim": self.hidden_dim, + "output_dim": self.output_dim, + "num_layers": self.num_layers, + } + ) + return config + + +@keras.utils.register_keras_serializable(package="keras_cv") +class MaskDecoder(keras.models.Model): + """Mask decoder for the segment anything model. + + This lightweight module efficiently maps the image embedding and a set of + prompt embeddings to an output mask. Before applying the transformer + decoder, the layer first inserts into the set of prompt embeddings a + learned output token embedding that will be used at the decoder's output. + For simplicity, these embeddings (not including the image embedding) are + collectively called "tokens". + + The image embeddings, positional image embeddings, and tokens are passed + through a transformer decoder. After running the decoder, the layer + upsamples the updated image embedding by 4x with two transposed + convolutional layers (now it's downscaled 4x relative to the input + image). Then, the tokens attend once more to the image embedding and + the updated output token embedding are passed to a small 3-layer MLP that + outputs a vector matching the channel dimension of the upscaled image + embedding. Finally, a mask is predicted with a spatially point-wise + product between the upscaled image embedding and the MLP's output. + + Args: + transformer_dim (int): The number of input features to the transformer + decoder. + transformer (keras.layers.Layer): A transformer decoder. + num_multimask_outputs (int): Number of multimask outputs. The model + would generate these many extra masks when `multimask_output` is + `True`. + iou_head_depth (int): The depth of the dense net used to predict the + IoU confidence score. + iou_head_hidden_dim (int): The number of units in the hidden layers + used in the dense net to predict the IoU confidence score. + activation (str, optional): Activation to use in the mask upscaler + network. Defaults to "gelu". + + References: + - [Segment Anything](https://arxiv.org/abs/2304.02643) + """ + + def __init__( + self, + transformer_dim, + transformer, + num_multimask_outputs, + iou_head_depth, + iou_head_hidden_dim, + activation="gelu", + **kwargs, + ): + super().__init__(**kwargs) + self.transformer_dim = transformer_dim + self.transformer = transformer + self.num_multimask_outputs = num_multimask_outputs + self.iou_head_depth = iou_head_depth + self.iou_head_hidden_dim = iou_head_hidden_dim + self.activation = activation + + self.iou_token = keras.layers.Embedding(1, transformer_dim) + self.num_mask_tokens = num_multimask_outputs + 1 + self.mask_tokens = keras.layers.Embedding( + self.num_mask_tokens, transformer_dim + ) + + self.output_upscaling = keras.models.Sequential( + [ + keras.layers.Conv2DTranspose( + transformer_dim // 4, kernel_size=2, strides=2 + ), + SAMLayerNormalization(), + keras.layers.Activation(activation), + keras.layers.Conv2DTranspose( + transformer_dim // 8, kernel_size=2, strides=2 + ), + keras.layers.Activation(activation), + ] + ) + + self.output_hypernetworks_mlps = [ + MLP(transformer_dim, transformer_dim // 8, 3) + for _ in range(self.num_mask_tokens) + ] + + self.iou_prediction_head = MLP( + iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth + ) + + self.iou_token.build(None) + self.mask_tokens.build(None) + + self.built = False + + def build( + self, + image_embeddings_shape, + image_pe_shape, + sparse_prompt_embeddings_shape, + dense_prompt_embeddings_shape, + *args, + **kwargs, + ): + transformer_image_embed_shape = [ + None, + image_embeddings_shape[1], + image_embeddings_shape[2], + image_embeddings_shape[3], + ] + tokens_shape = [ + None, + 1 + self.num_mask_tokens + sparse_prompt_embeddings_shape[1], + self.transformer_dim, + ] + self.transformer.build( + image_embedding_shape=transformer_image_embed_shape, + image_pe_shape=transformer_image_embed_shape, + point_embedding_shape=tokens_shape, + ) + self.output_upscaling.build([None, None, None, self.transformer_dim]) + + for mlp in self.output_hypernetworks_mlps: + mlp.build([None, self.transformer_dim]) + + self.iou_prediction_head.build([None, self.transformer_dim]) + + self.built = True + + def call( + self, + image_embeddings, + image_pe, + sparse_prompt_embeddings, + dense_prompt_embeddings, + multimask_output, + ): + masks, iou_pred = self.predict_masks( + image_embeddings=image_embeddings, + image_pe=image_pe, + sparse_prompt_embeddings=sparse_prompt_embeddings, + dense_prompt_embeddings=dense_prompt_embeddings, + ) + + if multimask_output: + return masks[:, 1:, :, :], iou_pred[:, 1:] + return masks[:, :1, :, :], iou_pred[:, :1] + + def predict_masks( + self, + image_embeddings, + image_pe, + sparse_prompt_embeddings, + dense_prompt_embeddings, + ): + output_tokens = ops.concatenate( + [self.iou_token.weights[0], self.mask_tokens.weights[0]], axis=0 + ) + output_tokens = ops.broadcast_to( + output_tokens[None, ...], + shape=( + sparse_prompt_embeddings.shape[0], + output_tokens.shape[0], + output_tokens.shape[1], + ), + ) + tokens = ops.concatenate( + [output_tokens, sparse_prompt_embeddings], axis=1 + ) + + source = ops.broadcast_to( + image_embeddings, + shape=( + tokens.shape[0], + image_embeddings.shape[1], + image_embeddings.shape[2], + image_embeddings.shape[3], + ), + ) + source = source + dense_prompt_embeddings + positional_source = ops.broadcast_to( + image_pe, + shape=( + tokens.shape[0], + image_embeddings.shape[1], + image_embeddings.shape[2], + image_embeddings.shape[3], + ), + ) + B, H, W, C = source.shape + + hidden_state, source = self.transformer( + source, positional_source, tokens + ) + iou_token_out = hidden_state[:, 0, :] + mask_tokens_out = hidden_state[:, 1 : (1 + self.num_mask_tokens), :] + + source = ops.reshape(source, (B, H, W, C)) + upscaled_embeddings = self.output_upscaling(source) + hyper_in_list = [] + for i in range(self.num_mask_tokens): + hyper_in_list.append( + self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) + ) + hyper_in = ops.stack(hyper_in_list, axis=1) + B, H, W, C = upscaled_embeddings.shape + upscaled_embeddings = ops.reshape( + ops.transpose(upscaled_embeddings, axes=(0, 3, 1, 2)), + (B, C, H * W), + ) + masks = ops.reshape( + hyper_in @ upscaled_embeddings, (B, self.num_mask_tokens, H, W) + ) + + iou_pred = self.iou_prediction_head(iou_token_out) + + return masks, iou_pred + + def get_config(self): + config = super().get_config() + config.update( + { + "transformer_dim": self.transformer_dim, + "transformer": keras.saving.serialize_keras_object( + self.transformer + ), + "num_multimask_outputs": self.num_multimask_outputs, + "iou_head_depth": self.iou_head_depth, + "iou_head_hidden_dim": self.iou_head_hidden_dim, + "activation": self.activation, + } + ) + return config + + @classmethod + def from_config(cls, config): + config.update( + {"transformer": keras.layers.deserialize(config["transformer"])} + ) + return super().from_config(config) diff --git a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py new file mode 100644 index 0000000000..1cfe94600c --- /dev/null +++ b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py @@ -0,0 +1,322 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +from keras_cv.backend import keras +from keras_cv.backend import ops +from keras_cv.models.segmentation.segment_anything.sam_layers import ( + SAMLayerNormalization, +) + + +@keras.saving.register_keras_serializable(package="keras_cv") +class RandomFrequencyPositionalEmbeddings(keras.layers.Layer): + def __init__(self, num_positional_features, scale, **kwargs): + """Positional encoding using random spatial frequencies. + + This layer maps coordinates/points in 2D space to positional + encodings using random spatial frequencies. + + Args: + num_positional_features (int): Number of positional features + in the output. + scale (float): The standard deviation of the random frequencies. + """ + super().__init__(**kwargs) + self.num_positional_features = num_positional_features + self.scale = scale + init_func = lambda *a, **kw: self.scale * ops.random.normal( + shape=(2, self.num_positional_features), dtype=self.dtype + ) + self.positional_encoding_gaussian_matrix = self.add_weight( + name="positional_emcoding_gaussian_matrix", + shape=(2, self.num_positional_features), + dtype=self.dtype, + trainable=False, + initializer=init_func, + ) + + self.built = True + + def __positional_encodings(self, coords): + coords = 2 * coords - 1 + coords = coords @ self.positional_encoding_gaussian_matrix + coords = 2 * math.pi * coords + return ops.concatenate([ops.sin(coords), ops.cos(coords)], axis=-1) + + def call(self, size): + """Generate a positional encoding for an image of any given size. + + Args: + size (int): The size of the image. + + Returns: + tensor: Positional encoding of the image. + """ + H, W = size + H, W = ops.cast(H, "int64"), ops.cast(W, "int64") + grid = ops.ones(shape=(H, W), dtype=self.dtype) + y_embed = ops.cumsum(grid, axis=0) - 0.5 + x_embed = ops.cumsum(grid, axis=1) - 0.5 + y_embed = y_embed / ops.cast(H, self.dtype) + x_embed = x_embed / ops.cast(W, self.dtype) + return self.__positional_encodings( + ops.stack([x_embed, y_embed], axis=-1) + ) + + def call_with_coords(self, coords_input, image_size): + """Positionally encode points that are not normalized to `[0, 1]`. + + Args: + coords_input (tensor): 2D coordinates/points to map. + image_size (tuple[int, int]): Height and width of the image + being prompted. + + Returns: + tensor: Positional encodings of the normalized coordinates. + """ + coords_normalized = ops.stack( + [ + coords_input[..., 0] / image_size[1], + coords_input[..., 1] / image_size[0], + ], + axis=-1, + ) + return self.__positional_encodings(coords_normalized) + + def get_config(self): + config = super().get_config() + config.update( + { + "num_positional_features": self.num_positional_features, + "scale": self.scale, + } + ) + return config + + +@keras.saving.register_keras_serializable(package="keras_cv") +class PromptEncoder(keras.models.Model): + """Prompt Encoder for the segment anything model. + + The prompt encoder generates encodings for three types of prompts: + + - Point prompts: Points on the image along with a label indicating whether + the point is in the foreground (part of the mask) or in the background + (not a part of the mask). + - Box prompts: A batch of bounding boxes with format [(x1, y1), (x2, y2)] + used to determine the location of the masks in the image. + - Masks: An input mask can be passed to refine the positional embeddings + for the output mask. + + First, the point prompts and box prompts are concatenated and positional + encodings are generated using random spatial frequencies. A point is + represented as the sum of a positional encoding of the point's location + and one of two learned embeddings that indicate if the point is either in + the foreground or background. A box is represented by an embedding pair: + + (1) the positional encoding of its top-left corner summed with a learned + embedding representing "top-left corner" and + (2) the same structure but using a learned embedding indicating + "bottom-right corner". + + The box and point encodings are referred to as "sparse encodings" + + If a mask prompt is passed, a convolutional neural net is used to + downscale it to generate "dense encodings". If no mask prompt is passed, + an embedding layer is used instead to generate a "no mask" embedding. + + Args: + embed_dim (int): The number of features in the output embeddings. + image_embedding_size (int): The number of features in the image + embeddings generated by an image encoder. + input_image_size (tuple[int, int]): A tuple of the height and width + of the image being prompted. + mask_in_chans (int): The number of channels of the mask prompt. + activation (str, optional): The activation to use in the mask + downscaler neural net. Defaults to "gelu". + + References: + - [Segment Anything](https://arxiv.org/abs/2304.02643) + """ + + def __init__( + self, + embed_dim, + image_embedding_size, + input_image_size, + mask_in_chans, + activation="gelu", + **kwargs + ): + super().__init__(**kwargs) + self.embed_dim = embed_dim + self.image_embedding_size = image_embedding_size + self.input_image_size = input_image_size + self.mask_in_chans = mask_in_chans + self.activation = activation + + self.positional_embedding_layer = RandomFrequencyPositionalEmbeddings( + num_positional_features=self.embed_dim // 2, scale=1 + ) + + self.foreground_point_embed = keras.layers.Embedding(1, embed_dim) + self.background_point_embed = keras.layers.Embedding(1, embed_dim) + self.top_left_corner_embed = keras.layers.Embedding(1, embed_dim) + self.bottom_right_corner_embed = keras.layers.Embedding(1, embed_dim) + self.not_a_point_embed = keras.layers.Embedding(1, embed_dim) + + self.mask_downscaler = keras.models.Sequential( + [ + keras.layers.Conv2D( + mask_in_chans // 4, kernel_size=2, strides=2 + ), + SAMLayerNormalization(), + keras.layers.Activation(activation), + keras.layers.Conv2D(mask_in_chans, kernel_size=2, strides=2), + SAMLayerNormalization(), + keras.layers.Activation(activation), + keras.layers.Conv2D(embed_dim, kernel_size=1), + ] + ) + self.mask_downscaler.build( + [None, 4 * image_embedding_size[0], 4 * image_embedding_size[1], 1] + ) + self.no_mask_embed = keras.layers.Embedding(1, embed_dim) + + for layer in [ + self.foreground_point_embed, + self.background_point_embed, + self.top_left_corner_embed, + self.bottom_right_corner_embed, + self.not_a_point_embed, + self.no_mask_embed, + ]: + layer.build(None) + + self.built = True + + def get_dense_pe(self): + """Get positional embeddings for the image being prompted. + + Returns: + tensor: The positional embeddings of the image. + """ + # convert the image_embedding_size to a tensor since keras core + # expects the input type to be a symbolic/concrete tensor. + return self.positional_embedding_layer( + ops.convert_to_tensor(self.image_embedding_size, dtype="float32") + )[None, ...] + + def __embed_points(self, points, labels, pad): + points = points + 0.5 + if pad: + padding_point = ops.zeros((points.shape[0], 1, 2), dtype=self.dtype) + padding_label = -ops.ones((labels.shape[0], 1), dtype=self.dtype) + points = ops.concatenate([points, padding_point], axis=1) + labels = ops.concatenate([labels, padding_label], axis=1) + point_embeddings = self.positional_embedding_layer.call_with_coords( + points, self.input_image_size + ) + labels = ops.broadcast_to(labels[..., None], point_embeddings.shape) + point_embeddings = ops.where( + labels == 0, + point_embeddings + self.background_point_embed.weights[0], + point_embeddings + self.foreground_point_embed.weights[0], + ) + point_embeddings = ops.where( + labels == -1, + # TODO: for whatever reason, ops.broadcast_to doesn't work here, so + # we instead use zeros_like to broadcast to the correct shape. + self.not_a_point_embed.weights[0] + + ops.zeros_like(point_embeddings), + point_embeddings, + ) + return point_embeddings + + def __embed_box(self, box): + box = box + 0.5 + coords = ops.reshape(box, (-1, 2, 2)) + corner_embedding = self.positional_embedding_layer.call_with_coords( + coords, self.input_image_size + ) + top_left_embedding = ( + corner_embedding[:, 0, :] + self.top_left_corner_embed.weights[0] + ) + bottom_right_embedding = ( + corner_embedding[:, 1, :] + + self.bottom_right_corner_embed.weights[0] + ) + corner_embedding = ops.stack( + [top_left_embedding, bottom_right_embedding], axis=1 + ) + return corner_embedding + + def __embed_mask(self, mask): + mask_embedding = self.mask_downscaler(mask) + return mask_embedding + + def call(self, points=None, labels=None, box=None, mask=None): + if points is not None: + B = points.shape[0] + elif box is not None: + B = box.shape[0] + elif mask is not None: + B = mask.shape[0] + else: + raise ValueError("At least one of the inputs must not be None.") + sparse_embeddings = ops.zeros((B, 0, self.embed_dim), dtype=self.dtype) + if points is not None: + if labels is None: + raise ValueError("`labels` must also be provided with `points`") + point_embeddings = self.__embed_points( + points, labels, pad=(box is None) + ) + sparse_embeddings = ops.concatenate( + [sparse_embeddings, point_embeddings], axis=1 + ) + if box is not None: + box_embeddings = self.__embed_box(box) + sparse_embeddings = ops.concatenate( + [sparse_embeddings, box_embeddings], axis=1 + ) + if mask is not None: + dense_embeddings = self.__embed_mask(mask) + else: + dense_embeddings = ops.broadcast_to( + ops.reshape( + self.no_mask_embed.weights[0], (1, 1, 1, self.embed_dim) + ), + shape=( + B, + self.image_embedding_size[0], + self.image_embedding_size[1], + self.embed_dim, + ), + ) + return sparse_embeddings, dense_embeddings + + def get_config(self): + config = super().get_config() + config.update( + { + "embed_dim": self.embed_dim, + "image_embedding_size": self.image_embedding_size, + "input_image_size": self.input_image_size, + "mask_in_chans": self.mask_in_chans, + "activation": self.activation, + } + ) + return config diff --git a/keras_cv/models/segmentation/segment_anything/sam_test.py b/keras_cv/models/segmentation/segment_anything/sam_test.py new file mode 100644 index 0000000000..4492010056 --- /dev/null +++ b/keras_cv/models/segmentation/segment_anything/sam_test.py @@ -0,0 +1,292 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import numpy as np +import pytest + +from keras_cv.backend import keras +from keras_cv.backend import ops +from keras_cv.models.segmentation.segment_anything.sam_image_encoder import ( + ImageEncoder, +) +from keras_cv.models.segmentation.segment_anything.sam_image_encoder import ( + MultiHeadAttentionWithRelativePE, +) +from keras_cv.models.segmentation.segment_anything.sam_image_encoder import ( + WindowedTransformerEncoder, +) +from keras_cv.models.segmentation.segment_anything.sam_mask_decoder import ( + MaskDecoder, +) +from keras_cv.models.segmentation.segment_anything.sam_prompt_encoder import ( + PromptEncoder, +) +from keras_cv.models.segmentation.segment_anything.sam_transformer import ( + TwoWayMultiHeadAttention, +) +from keras_cv.models.segmentation.segment_anything.sam_transformer import ( + TwoWayTransformer, +) +from keras_cv.tests.test_case import TestCase + + +class TestSAM(TestCase): + def test_multi_head_attention_with_relative_pe(self): + attention_with_rel_pe = MultiHeadAttentionWithRelativePE( + num_heads=16, + key_dim=1280 // 16, + use_bias=True, + input_size=(64, 64), + ) + x = np.ones(shape=(1, 64, 64, 1280)) + x_out = ops.convert_to_numpy(attention_with_rel_pe(x)) + self.assertEqual(x_out.shape, (1, 64, 64, 1280)) + + def test_windowed_transformer_encoder(self): + windowed_transformer_encoder = WindowedTransformerEncoder( + project_dim=1280, + mlp_dim=1280 * 4, + num_heads=16, + use_bias=True, + use_rel_pos=True, + window_size=14, + input_size=(64, 64), + ) + x = np.ones((1, 64, 64, 1280)) + x_out = ops.convert_to_numpy(windowed_transformer_encoder(x)) + self.assertEqual(x_out.shape, (1, 64, 64, 1280)) + self.assertAllClose(x_out, np.ones_like(x_out)) + + @pytest.mark.extra_large + def test_image_encoder(self): + image_encoder = ImageEncoder( + img_size=1024, + patch_size=16, + in_chans=3, + embed_dim=1280, + depth=32, + mlp_dim=1280 * 4, + num_heads=16, + out_chans=256, + use_bias=True, + use_rel_pos=True, + window_size=14, + global_attention_indices=[7, 15, 23, 31], + ) + x = np.ones((1, 1024, 1024, 3)) + x_out = ops.convert_to_numpy(image_encoder(x)) + num_parameters = sum( + np.prod(tuple(x.shape)) for x in image_encoder.trainable_variables + ) + self.assertEqual(x_out.shape, (1, 64, 64, 256)) + self.assertEqual(num_parameters, 637_026_048) + + # saving test + path = os.path.join(self.get_temp_dir(), "sam_tf_image_encoder.keras") + image_encoder.save(path) + loaded_model = keras.saving.load_model(path) + x_out_loaded = ops.convert_to_numpy(loaded_model(x)) + self.assertAllClose(x_out, x_out_loaded) + + def get_points_labels_box_mask(self, B): + prompt_encoder = PromptEncoder( + embed_dim=256, + image_embedding_size=(64, 64), + input_image_size=(1024, 1024), + mask_in_chans=16, + ) + + points = ops.convert_to_tensor( + np.random.randint(0, 1023, (B, 10, 2)), dtype="float32" + ) + labels = ops.convert_to_tensor( + 1 * (np.random.rand(B, 10) > 0.5), dtype="int64" + ) + box = ops.array( + [ + [ + [[10, 10], [500, 500]], + [[20, 20], [500, 500]], + [[30, 30], [500, 500]], + [[40, 40], [500, 500]], + [[50, 50], [500, 500]], + [[60, 60], [500, 500]], + [[70, 70], [500, 500]], + ] + ], + dtype="float32", + ) + box = box[:, :B, ...] + input_mask = ops.convert_to_tensor( + 1.0 * (np.random.rand(B, 256, 256, 1) > 0.5), dtype="float32" + ) + + return prompt_encoder, points, labels, box, input_mask + + def test_prompt_encoder(self): + ( + prompt_encoder, + points, + labels, + box, + input_mask, + ) = self.get_points_labels_box_mask(7) + + sparse_embeddings, dense_embeddings = prompt_encoder( + points=points, labels=labels, box=box, mask=input_mask + ) + + num_parameters = sum( + np.prod(tuple(x.shape)) for x in prompt_encoder.trainable_weights + ) + + sparse_embeddings = ops.convert_to_numpy(sparse_embeddings) + dense_embeddings = ops.convert_to_numpy(dense_embeddings) + + self.assertEqual(sparse_embeddings.shape, (7, 12, 256)) + self.assertEqual(dense_embeddings.shape, (7, 64, 64, 256)) + self.assertEqual(num_parameters, 6_220) + + # saving test + path = os.path.join(self.get_temp_dir(), "sam_tf_prompt_encoder.keras") + prompt_encoder.save(path) + loaded_model = keras.saving.load_model(path) + sparse_embeddings_loaded, dense_embeddings_loaded = loaded_model( + points=points, labels=labels, box=box, mask=input_mask + ) + sparse_embeddings_loaded = ops.convert_to_numpy( + sparse_embeddings_loaded + ) + dense_embeddings_loaded = ops.convert_to_numpy(dense_embeddings_loaded) + pegm_ref = ops.convert_to_numpy( + prompt_encoder.positional_embedding_layer.positional_encoding_gaussian_matrix # noqa: E501 + ) + pegm_loaded = ops.convert_to_numpy( + loaded_model.positional_embedding_layer.positional_encoding_gaussian_matrix # noqa: E501 + ) + self.assertAllClose(pegm_ref, pegm_loaded) + self.assertAllClose(sparse_embeddings, sparse_embeddings_loaded) + self.assertAllClose(dense_embeddings, dense_embeddings_loaded) + + def test_two_way_multi_head_attention(self): + ( + prompt_encoder, + points, + labels, + box, + input_mask, + ) = self.get_points_labels_box_mask(1) + image_embeddings = np.random.randn(1, 64, 64, 256) + + sparse_embeddings, _ = prompt_encoder( + points=points, labels=labels, box=box, mask=input_mask + ) + + two_way_attention = TwoWayMultiHeadAttention( + num_heads=8, + key_dim=256 // 8, + mlp_dim=2048, + skip_first_layer_pe=False, + ) + queries, keys = two_way_attention( + queries=sparse_embeddings, + keys=ops.reshape(image_embeddings, (1, 64 * 64, 256)), + query_pe=sparse_embeddings, + key_pe=ops.reshape( + prompt_encoder.get_dense_pe(), (1, 64 * 64, 256) + ), + ) + + queries, keys = map(ops.convert_to_numpy, [queries, keys]) + + self.assertEqual(queries.shape, (1, 12, 256)) + self.assertEqual(keys.shape, (1, 64 * 64, 256)) + + def test_two_way_transformer(self): + ( + prompt_encoder, + points, + labels, + box, + input_mask, + ) = self.get_points_labels_box_mask(1) + sparse_embeddings, _ = prompt_encoder( + points=points, labels=labels, box=box, mask=input_mask + ) + image_embeddings = np.random.randn(1, 64, 64, 256) + two_way_transformer = TwoWayTransformer( + depth=2, embedding_dim=256, num_heads=8, mlp_dim=2048 + ) + queries, keys = two_way_transformer( + image_embedding=image_embeddings, + image_pe=prompt_encoder.get_dense_pe(), + point_embedding=sparse_embeddings, + ) + queries, keys = map(ops.convert_to_numpy, [queries, keys]) + self.assertEqual(queries.shape, (1, 12, 256)) + self.assertEqual(keys.shape, (1, 64 * 64, 256)) + + def test_mask_decoder(self): + ( + prompt_encoder, + points, + labels, + box, + input_mask, + ) = self.get_points_labels_box_mask(1) + sparse_embeddings, dense_embeddings = prompt_encoder( + points=points, labels=labels, box=box, mask=input_mask + ) + image_embeddings = np.random.randn(1, 64, 64, 256) + mask_decoder = MaskDecoder( + transformer_dim=256, + transformer=TwoWayTransformer( + depth=2, embedding_dim=256, mlp_dim=2048, num_heads=8 + ), + num_multimask_outputs=3, + iou_head_depth=3, + iou_head_hidden_dim=256, + ) + masks, iou_pred = mask_decoder( + image_embeddings=image_embeddings, + image_pe=prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings[:1, ...], + dense_prompt_embeddings=dense_embeddings[:1, ...], + multimask_output=True, + ) + num_parameters = sum( + np.prod(tuple(x.shape)) for x in mask_decoder.trainable_variables + ) + masks, iou_pred = map(ops.convert_to_numpy, [masks, iou_pred]) + self.assertEqual(masks.shape, (1, 3, 256, 256)) + self.assertEqual(iou_pred.shape, (1, 3)) + self.assertEqual(num_parameters, 4_058_340) + + # saving test + path = os.path.join(self.get_temp_dir(), "sam_tf_mask_decoder.keras") + mask_decoder.save(path) + loaded_model = keras.saving.load_model(path) + masks_loaded, iou_pred_loaded = loaded_model( + image_embeddings=image_embeddings, + image_pe=prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings[:1, ...], + dense_prompt_embeddings=dense_embeddings[:1, ...], + multimask_output=True, + ) + masks_loaded = ops.convert_to_numpy(masks_loaded) + iou_pred_loaded = ops.convert_to_numpy(iou_pred_loaded) + self.assertAllClose(masks, masks_loaded) + self.assertAllClose(iou_pred, iou_pred_loaded) diff --git a/keras_cv/models/segmentation/segment_anything/sam_transformer.py b/keras_cv/models/segmentation/segment_anything/sam_transformer.py new file mode 100644 index 0000000000..7ea664deff --- /dev/null +++ b/keras_cv/models/segmentation/segment_anything/sam_transformer.py @@ -0,0 +1,389 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from keras_cv.backend import keras +from keras_cv.backend import ops +from keras_cv.models.segmentation.segment_anything.sam_layers import MLPBlock + + +@keras.utils.register_keras_serializable(package="keras_cv") +class MultiHeadAttentionWithDownsampling(keras.layers.Layer): + """Multi-Head Attention with downsampling. + + An attention layer that allows for downscaling the size of the embedding + after projection to queries, keys, and values. + + This layer first downscales the features of input queries, keys, and + values using a dense layer. Multi-head attention is then performed + and the attention map is projected back (upscaled) to the number of + input features. + + Args: + num_heads (int): Number of attention heads. + key_dim (int): Size of each attention head for query, key, and + value. + downsample_rate (int, optional): The factor by which to downscale the + input features i.e. the input features of size `key_dim` are + projected down to `key_dim // downsample_rate`. + """ + + def __init__(self, num_heads, key_dim, downsample_rate=1, **kwargs): + super().__init__(**kwargs) + self.num_heads = num_heads + self.key_dim = key_dim + self.downsample_rate = downsample_rate + self.internal_dims = key_dim // downsample_rate + + # Downsample + self.query_proj = keras.layers.Dense( + self.internal_dims * self.num_heads + ) + self.key_proj = keras.layers.Dense(self.internal_dims * self.num_heads) + self.value_proj = keras.layers.Dense( + self.internal_dims * self.num_heads + ) + + # Upsample + self.out_proj = keras.layers.Dense(self.key_dim * self.num_heads) + + self.built = False + + def __separate_heads(self, x): + B, N, C = x.shape + x = ops.reshape(x, (B, N, self.num_heads, C // self.num_heads)) + return ops.transpose(x, axes=(0, 2, 1, 3)) + + def __recombine_heads(self, x): + B, N_H, N_T, C_PH = x.shape + x = ops.transpose(x, axes=(0, 2, 1, 3)) + return ops.reshape(x, (B, N_T, N_H * C_PH)) + + def build(self, query_shape, value_shape, key_shape): + self.query_proj.build(query_shape) + self.key_proj.build(key_shape) + self.value_proj.build(value_shape) + self.out_proj.build([self.internal_dims * self.num_heads]) + + self.built = True + + def call(self, query, value, key): + query = self.query_proj(query) + key = self.key_proj(key) + value = self.value_proj(value) + + # Separate into heads + query = self.__separate_heads(query) + key = self.__separate_heads(key) + value = self.__separate_heads(value) + + # Attention + C_PH = query.shape[-1] + out = query @ ops.transpose(key, (0, 1, 3, 2)) + out = out / ops.sqrt(ops.cast(C_PH, dtype=self.dtype)) + out = ops.softmax(out, axis=-1) + + # Get output + attention_map = out @ value + attention_map = self.__recombine_heads(attention_map) + return self.out_proj(attention_map) + + def get_config(self): + config = super().get_config() + config.update( + { + "num_heads": self.num_heads, + "key_dim": self.key_dim, + "downsample_rate": self.downsample_rate, + } + ) + return config + + +@keras.utils.register_keras_serializable(package="keras_cv") +class TwoWayMultiHeadAttention(keras.layers.Layer): + """Two-way multi-head attention layer. + + Args: + num_heads (int): Number of attention heads. + key_dim (int): Size of each attention head for query, key, and + value. + mlp_dim (int): Number of hidden dims to use in the mlp block. + skip_first_layer_pe (bool): A boolean indicating whether to skip the + first layer positional embeddings. + attention_downsample_rate (int, optional): The downsample rate to use + in the attention layers. Defaults to 2. + activation (str, optional): The activation for the mlp block's output + layer. Defaults to "relu". + """ + + def __init__( + self, + num_heads, + key_dim, + mlp_dim, + skip_first_layer_pe, + attention_downsample_rate=2, + activation="relu", + **kwargs, + ): + super().__init__(**kwargs) + self.num_heads = num_heads + self.key_dim = key_dim + self.mlp_dim = mlp_dim + self.skip_first_layer_pe = skip_first_layer_pe + self.attention_downsample_rate = attention_downsample_rate + self.activation = activation + + self.self_attention = MultiHeadAttentionWithDownsampling( + num_heads=num_heads, key_dim=key_dim + ) + self.layer_norm1 = keras.layers.LayerNormalization(epsilon=1e-5) + self.cross_attention_token_to_image = ( + MultiHeadAttentionWithDownsampling( + num_heads=num_heads, + key_dim=key_dim, + downsample_rate=attention_downsample_rate, + ) + ) + self.layer_norm2 = keras.layers.LayerNormalization(epsilon=1e-5) + + self.mlp_block = MLPBlock(key_dim * num_heads, mlp_dim, activation) + + self.layer_norm3 = keras.layers.LayerNormalization(epsilon=1e-5) + self.cross_attention_image_to_token = ( + MultiHeadAttentionWithDownsampling( + num_heads=num_heads, + key_dim=key_dim, + downsample_rate=attention_downsample_rate, + ) + ) + self.layer_norm4 = keras.layers.LayerNormalization(epsilon=1e-5) + + self.built = False + + def build(self, queries_shape, keys_shape, query_pe_shape, key_pe_shape): + self.self_attention.build( + query_shape=queries_shape, + value_shape=queries_shape, + key_shape=queries_shape, + ) + self.layer_norm1.build(queries_shape) + self.cross_attention_token_to_image.build( + query_shape=queries_shape, + key_shape=keys_shape, + value_shape=keys_shape, + ) + self.layer_norm2.build(queries_shape) + self.mlp_block.build(queries_shape) + self.layer_norm3.build(queries_shape) + self.cross_attention_image_to_token.build( + query_shape=keys_shape, + key_shape=queries_shape, + value_shape=queries_shape, + ) + self.layer_norm4.build(keys_shape) + + self.built = True + + def call(self, queries, keys, query_pe, key_pe): + # print("Actual queries_shape:", queries.shape) + if self.skip_first_layer_pe: + queries = self.self_attention( + query=queries, value=queries, key=queries + ) + else: + queries_with_pe = queries + query_pe + attention_map = self.self_attention( + query=queries_with_pe, key=queries_with_pe, value=queries + ) + queries = queries + attention_map + queries = self.layer_norm1(queries) + + queries_with_pe = queries + query_pe + keys_with_pe = keys + key_pe + attention_map = self.cross_attention_token_to_image( + query=queries_with_pe, key=keys_with_pe, value=keys + ) + queries = queries + attention_map + queries = self.layer_norm2(queries) + + mlp_out = self.mlp_block(queries) + queries = queries + mlp_out + queries = self.layer_norm3(queries) + + queries_with_pe = queries + query_pe + keys_with_pe = keys + key_pe + attention_map = self.cross_attention_image_to_token( + query=keys_with_pe, key=queries_with_pe, value=queries + ) + keys = keys + attention_map + keys = self.layer_norm4(keys) + + return queries, keys + + def get_config(self): + config = super().get_config() + config.update( + { + "num_heads": self.num_heads, + "key_dim": self.key_dim, + "mlp_dim": self.mlp_dim, + "skip_first_layer_pe": self.skip_first_layer_pe, + "attention_downsample_rate": self.attention_downsample_rate, + "activation": self.activation, + } + ) + return config + + +@keras.utils.register_keras_serializable(package="keras_cv") +class TwoWayTransformer(keras.layers.Layer): + """A two-way cross-attention transformer decoder. + + A transformer decoder that attends to an input image using + queries whose positional embedding is supplied. + + The transformer decoder design is shown in [1]_. Each decoder layer + performs 4 steps: (1) self-attention on the tokens, (2) cross-attention + from tokens (as queries) to the image embedding, (3) a point-wise MLP + updates each token, and (4) cross-attention from the image embedding (as + queries) to tokens. This last step updates the image embedding with prompt + information. Each self/cross-attention and MLP has a residual connection + and layer normalization. + + To ensure the decoder has access to critical geometric information the + positional encodings are added to the image embedding whenever they + participate in an attention layer. Additionally, the entire original + prompt tokens (including their positional encodings) are re-added to the + updated tokens whenever they participate in an attention layer. This + allows for a strong dependence on both the prompt token's geometric + location and type. + + Args: + depth (int): The depth of the attention blocks (the number + of attention blocks to use). + embedding_dim (int): The number of features of the input image and + point embeddings. + num_heads (int): Number of heads to use in the attention layers. + mlp_dim (int): The number of units in the hidden layer of the MLP + block used in the attention layers. + activation (str, optional): The activation of the MLP block's output + layer used in the attention layers. Defaults to "relu". + attention_downsample_rate (int, optional): The downsample rate of the + attention layers. Defaults to 2. + + References: + - [Segment Anything](https://arxiv.org/abs/2304.02643) + """ + + def __init__( + self, + depth, + embedding_dim, + num_heads, + mlp_dim, + activation="relu", + attention_downsample_rate=2, + **kwargs, + ): + super().__init__(**kwargs) + self.depth = depth + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.mlp_dim = mlp_dim + self.activation = activation + self.attention_downsample_rate = attention_downsample_rate + self.layers = [] + for i in range(depth): + self.layers.append( + TwoWayMultiHeadAttention( + num_heads=num_heads, + key_dim=embedding_dim // num_heads, + mlp_dim=mlp_dim, + skip_first_layer_pe=(i == 0), + attention_downsample_rate=attention_downsample_rate, + activation=activation, + ) + ) + self.final_attention_token_to_image = ( + MultiHeadAttentionWithDownsampling( + num_heads=num_heads, + key_dim=embedding_dim // num_heads, + downsample_rate=attention_downsample_rate, + ) + ) + self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5) + + self.built = False + + def build( + self, image_embedding_shape, image_pe_shape, point_embedding_shape + ): + B, H, W, C = image_embedding_shape + image_embedding_shape = [B, H * W, C] + for layer in self.layers: + layer.build( + queries_shape=point_embedding_shape, + keys_shape=image_embedding_shape, + query_pe_shape=point_embedding_shape, + key_pe_shape=image_embedding_shape, + ) + self.final_attention_token_to_image.build( + query_shape=point_embedding_shape, + key_shape=image_embedding_shape, + value_shape=image_embedding_shape, + ) + self.final_layer_norm.build(point_embedding_shape) + + self.built = True + + def call(self, image_embedding, image_pe, point_embedding): + B, H, W, C = image_embedding.shape + image_embedding = ops.reshape(image_embedding, (B, H * W, C)) + B, H, W, C = image_pe.shape + image_pe = ops.reshape(image_pe, (B, H * W, C)) + queries = point_embedding + keys = image_embedding + + for layer in self.layers: + queries, keys = layer( + queries=queries, + keys=keys, + query_pe=point_embedding, + key_pe=image_pe, + ) + + queries_with_pe = queries + point_embedding + keys_with_pe = keys + image_pe + attention_map = self.final_attention_token_to_image( + query=queries_with_pe, key=keys_with_pe, value=keys + ) + queries = queries + attention_map + queries = self.final_layer_norm(queries) + + return queries, keys + + def get_config(self): + config = super().get_config() + config.update( + { + "depth": self.depth, + "embedding_dim": self.embedding_dim, + "num_heads": self.num_heads, + "mlp_dim": self.mlp_dim, + "activation": self.activation, + "attention_downsample_rate": self.attention_downsample_rate, + } + ) + return config From 025dd15af7553bb5cc9be895c4c9e8bc680c3630 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Sun, 30 Jul 2023 00:17:16 +0000 Subject: [PATCH 02/13] SAMLayerNormalization -> keras.layers.LayerNormalization They both behave exactly the same when moving_mean and moving_variance are None and epsilon is 1e-6 --- .../segment_anything/sam_image_encoder.py | 7 +-- .../segment_anything/sam_layers.py | 47 ------------------- .../segment_anything/sam_mask_decoder.py | 5 +- .../segment_anything/sam_prompt_encoder.py | 7 +-- 4 files changed, 5 insertions(+), 61 deletions(-) diff --git a/keras_cv/models/segmentation/segment_anything/sam_image_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_image_encoder.py index eaf8462cda..3801c75f62 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_image_encoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_image_encoder.py @@ -15,9 +15,6 @@ from keras_cv.backend import keras from keras_cv.backend import ops from keras_cv.models.segmentation.segment_anything.sam_layers import MLPBlock -from keras_cv.models.segmentation.segment_anything.sam_layers import ( - SAMLayerNormalization, -) def get_rel_pos(query_size, key_size, rel_pos): @@ -522,14 +519,14 @@ def __init__( keras.layers.Conv2D( filters=out_chans, kernel_size=1, use_bias=False ), - SAMLayerNormalization(), + keras.layers.LayerNormalization(epsilon=1e-6), keras.layers.Conv2D( filters=out_chans, kernel_size=3, padding="same", use_bias=False, ), - SAMLayerNormalization(), + keras.layers.LayerNormalization(epsilon=1e-6), ] ) diff --git a/keras_cv/models/segmentation/segment_anything/sam_layers.py b/keras_cv/models/segmentation/segment_anything/sam_layers.py index 764aefaf01..4dcde4a9b3 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_layers.py +++ b/keras_cv/models/segmentation/segment_anything/sam_layers.py @@ -13,7 +13,6 @@ # limitations under the License. from keras_cv.backend import keras -from keras_cv.backend import ops @keras.utils.register_keras_serializable(package="keras_cv") @@ -58,49 +57,3 @@ def get_config(self): "activation": self.activation, } ) - - -@keras.utils.register_keras_serializable(package="keras_cv") -class SAMLayerNormalization(keras.layers.Layer): - def __init__(self, epsilon=1e-6, **kwargs): - """A SAMLayerNormalization layer without moving mean and variance. - - Args: - epsilon (float, optional): Small float added to variance to - avoid dividing by zero. Defaults to 1e-6. - """ - super().__init__(**kwargs) - self.epsilon = epsilon - - def build(self, input_shape): - self.weight = self.add_weight( - name="weight", - shape=(input_shape[-1],), - initializer="ones", - trainable=True, - ) - self.bias = self.add_weight( - name="weight", - shape=(input_shape[-1],), - initializer="zeros", - trainable=True, - ) - - def call(self, x): - u = ops.mean(x, axis=-1, keepdims=True) - s = ops.mean(ops.square(x - u), axis=-1, keepdims=True) - x = (x - u) / ops.sqrt(s + self.epsilon) - x = self.weight * x + self.bias - return x - - def compute_output_shape(self, input_shape): - return input_shape - - def get_config(self): - config = super().get_config() - config.update( - { - "epsilon": self.epsilon, - } - ) - return config diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py index 877039a9a8..486c6cd252 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -14,9 +14,6 @@ from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.models.segmentation.segment_anything.sam_layers import ( - SAMLayerNormalization, -) @keras.utils.register_keras_serializable(package="keras_cv") @@ -133,7 +130,7 @@ def __init__( keras.layers.Conv2DTranspose( transformer_dim // 4, kernel_size=2, strides=2 ), - SAMLayerNormalization(), + keras.layers.LayerNormalization(epsilon=1e-6), keras.layers.Activation(activation), keras.layers.Conv2DTranspose( transformer_dim // 8, kernel_size=2, strides=2 diff --git a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py index 1cfe94600c..5cb1759740 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py @@ -16,9 +16,6 @@ from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.models.segmentation.segment_anything.sam_layers import ( - SAMLayerNormalization, -) @keras.saving.register_keras_serializable(package="keras_cv") @@ -183,10 +180,10 @@ def __init__( keras.layers.Conv2D( mask_in_chans // 4, kernel_size=2, strides=2 ), - SAMLayerNormalization(), + keras.layers.LayerNormalization(epsilon=1e-6), keras.layers.Activation(activation), keras.layers.Conv2D(mask_in_chans, kernel_size=2, strides=2), - SAMLayerNormalization(), + keras.layers.LayerNormalization(epsilon=1e-6), keras.layers.Activation(activation), keras.layers.Conv2D(embed_dim, kernel_size=1), ] From 43b0f2b3231fb08d9a54e550d9c8a3c948d9254f Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Wed, 2 Aug 2023 00:13:23 +0000 Subject: [PATCH 03/13] Move the image encoder to detectron2 backbone and fix for tf.keras backend --- keras_cv/layers/__init__.py | 3 + .../detectron2_layers.py} | 182 +++------------- keras_cv/layers/detectron2_layers_test.py | 48 +++++ keras_cv/models/__init__.py | 13 +- .../models/backbones/detectron2/__init__.py | 13 ++ .../detectron2/data/sam_vitdet_b_out.npz | Bin 0 -> 4194432 bytes .../detectron2/detectron2_aliases.py | 116 ++++++++++ .../detectron2/detectron2_backbone.py | 203 ++++++++++++++++++ .../detectron2/detectron2_backbone_presets.py | 107 +++++++++ .../detectron2_backbone_presets_test.py | 95 ++++++++ .../detectron2/detectron2_backbone_test.py | 45 ++++ keras_cv/models/segmentation/__init__.py | 1 - .../segmentation/segment_anything/__init__.py | 3 - .../segment_anything/sam_layers.py | 3 - .../segment_anything/sam_mask_decoder.py | 27 --- .../segmentation/segment_anything/sam_test.py | 69 +----- .../segment_anything/sam_transformer.py | 64 +----- 17 files changed, 680 insertions(+), 312 deletions(-) rename keras_cv/{models/segmentation/segment_anything/sam_image_encoder.py => layers/detectron2_layers.py} (69%) create mode 100644 keras_cv/layers/detectron2_layers_test.py create mode 100644 keras_cv/models/backbones/detectron2/__init__.py create mode 100644 keras_cv/models/backbones/detectron2/data/sam_vitdet_b_out.npz create mode 100644 keras_cv/models/backbones/detectron2/detectron2_aliases.py create mode 100644 keras_cv/models/backbones/detectron2/detectron2_backbone.py create mode 100644 keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py create mode 100644 keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py create mode 100644 keras_cv/models/backbones/detectron2/detectron2_backbone_test.py diff --git a/keras_cv/layers/__init__.py b/keras_cv/layers/__init__.py index c8b01f2769..166d553924 100644 --- a/keras_cv/layers/__init__.py +++ b/keras_cv/layers/__init__.py @@ -17,6 +17,9 @@ from tensorflow.keras.layers import RandomWidth from keras_cv.layers.augmenter import Augmenter +from keras_cv.layers.detectron2_layers import MultiHeadAttentionWithRelativePE +from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding +from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder from keras_cv.layers.feature_pyramid import FeaturePyramid from keras_cv.layers.fusedmbconv import FusedMBConvBlock from keras_cv.layers.mbconv import MBConvBlock diff --git a/keras_cv/models/segmentation/segment_anything/sam_image_encoder.py b/keras_cv/layers/detectron2_layers.py similarity index 69% rename from keras_cv/models/segmentation/segment_anything/sam_image_encoder.py rename to keras_cv/layers/detectron2_layers.py index 3801c75f62..25e148e5ba 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_image_encoder.py +++ b/keras_cv/layers/detectron2_layers.py @@ -163,6 +163,14 @@ def __init__( trainable=True, ) + self.qkv.build([self.key_dim * self.num_heads]) + self.projection.build([self.key_dim * self.num_heads]) + + self.built = True + + def compute_output_shape(self, input_shape): + return input_shape + def call(self, x): B, H, W, C = x.shape qkv = ops.transpose( @@ -326,6 +334,14 @@ def __init__( ) self.mlp_block = MLPBlock(project_dim, mlp_dim, activation) + self.layer_norm1.build([None, None, None, self.project_dim]) + self.layer_norm2.build([None, None, None, self.project_dim]) + + self.built = True + + def compute_output_shape(self, input_shape): + return input_shape + def call(self, x): shortcut = x x = self.layer_norm1(x) @@ -364,7 +380,7 @@ def get_config(self): @keras.utils.register_keras_serializable(package="keras_cv") -class SAMPatchingAndEmbedding(keras.layers.Layer): +class ViTDetPatchingAndEmbedding(keras.layers.Layer): """Image to Patch Embedding using only a conv layer (without layer normalization). @@ -390,6 +406,15 @@ def __init__( self.strides = strides self.embed_dim = embed_dim + self.built = False + + def build(self, input_shape): + self.projection.build(input_shape) + self.built = True + + def compute_output_shape(self, input_shape): + return self.projection.compute_output_shape(input_shape) + def call(self, x): x = self.projection(x) return x @@ -404,158 +429,3 @@ def get_config(self): } ) return config - - -@keras.utils.register_keras_serializable(package="keras_cv") -class ImageEncoder(keras.models.Model): - """A ViT image encoder for the segment anything model. - - Args: - img_size (int, optional): The size of the input image. Defaults to - `1024`. - patch_size (int, optional): the patch size to be supplied to the - Patching layer to turn input images into a flattened sequence of - patches. Defaults to `16`. - in_chans (int, optional): The number of channels in the input image. - Defaults to `3`. - embed_dim (int, optional): The latent dimensionality to be projected - into in the output of each stacked windowed transformer encoder. - Defaults to `1280`. - depth (int, optional): The number of transformer encoder layers to - stack in the Vision Transformer. Defaults to `32`. - mlp_dim (_type_, optional): The dimensionality of the hidden Dense - layer in the transformer MLP head. Defaults to `1280*4`. - num_heads (int, optional): the number of heads to use in the - `MultiHeadAttentionWithRelativePE` layer of each transformer - encoder. Defaults to `16`. - out_chans (int, optional): The number of channels (features) in the - output (image encodings). Defaults to `256`. - use_bias (bool, optional): Whether to use bias to project the keys, - queries, and values in the attention layer. Defaults to `True`. - use_abs_pos (bool, optional): Whether to add absolute positional - embeddings to the output patches. Defaults to `True`. - use_rel_pos (bool, optional): Whether to use relative positional - emcodings in the attention layer. Defaults to `False`. - window_size (int, optional): The size of the window for windowed - attention in the transformer encoder blocks. Defaults to `0`. - global_attention_indices (list, optional): Indexes for blocks using - global attention. Defaults to `[7, 15, 23, 31]`. - layer_norm_epsilon (int, optional): The epsilon to use in the layer - normalization blocks in transformer encoder. Defaults to `1e-6`. - """ - - def __init__( - self, - img_size=1024, - patch_size=16, - in_chans=3, - embed_dim=1280, - depth=32, - mlp_dim=1280 * 4, - num_heads=16, - out_chans=256, - use_bias=True, - use_abs_pos=True, - use_rel_pos=False, - window_size=0, - global_attention_indices=[7, 15, 23, 31], - layer_norm_epsilon=1e-6, - **kwargs - ): - super().__init__(**kwargs) - self.img_size = img_size - self.patch_size = patch_size - self.in_chans = in_chans - self.embed_dim = embed_dim - self.depth = depth - self.mlp_dim = mlp_dim - self.num_heads = num_heads - self.out_chans = out_chans - self.use_bias = use_bias - self.use_rel_pos = use_rel_pos - self.use_abs_pos = use_abs_pos - self.window_size = window_size - self.global_attention_indices = global_attention_indices - self.layer_norm_epsilon = layer_norm_epsilon - - self.patch_embed = SAMPatchingAndEmbedding( - kernel_size=(patch_size, patch_size), - strides=(patch_size, patch_size), - embed_dim=embed_dim, - ) - if self.use_abs_pos: - self.pos_embed = self.add_weight( - name="pos_embed", - shape=( - 1, - self.img_size // self.patch_size, - self.img_size // self.patch_size, - self.embed_dim, - ), - initializer="zeros", - trainable=True, - ) - else: - self.pos_embed = None - self.transformer_blocks = [] - for i in range(depth): - block = WindowedTransformerEncoder( - project_dim=embed_dim, - mlp_dim=mlp_dim, - num_heads=num_heads, - use_bias=use_bias, - use_rel_pos=use_rel_pos, - window_size=window_size - if i not in global_attention_indices - else 0, - input_size=(img_size // patch_size, img_size // patch_size), - ) - self.transformer_blocks.append(block) - self.transformer_blocks = keras.models.Sequential( - self.transformer_blocks - ) - self.bottleneck = keras.models.Sequential( - [ - keras.layers.Conv2D( - filters=out_chans, kernel_size=1, use_bias=False - ), - keras.layers.LayerNormalization(epsilon=1e-6), - keras.layers.Conv2D( - filters=out_chans, - kernel_size=3, - padding="same", - use_bias=False, - ), - keras.layers.LayerNormalization(epsilon=1e-6), - ] - ) - - def call(self, x): - B, _, _, _ = x.shape - x = self.patch_embed(x) - if self.pos_embed is not None: - x = x + self.pos_embed - x = self.transformer_blocks(x) - return self.bottleneck(x) - - def get_config(self): - config = super().get_config() - config.update( - { - "img_size": self.img_size, - "patch_size": self.patch_size, - "in_chans": self.in_chans, - "embed_dim": self.embed_dim, - "depth": self.depth, - "mlp_dim": self.mlp_dim, - "num_heads": self.num_heads, - "out_chans": self.out_chans, - "use_bias": self.use_bias, - "use_abs_pos": self.use_abs_pos, - "use_rel_pos": self.use_rel_pos, - "window_size": self.window_size, - "global_attention_indices": self.global_attention_indices, - "layer_norm_epsilon": self.layer_norm_epsilon, - } - ) - return config diff --git a/keras_cv/layers/detectron2_layers_test.py b/keras_cv/layers/detectron2_layers_test.py new file mode 100644 index 0000000000..71b5e190fd --- /dev/null +++ b/keras_cv/layers/detectron2_layers_test.py @@ -0,0 +1,48 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np + +from keras_cv.backend import ops +from keras_cv.layers.detectron2_layers import MultiHeadAttentionWithRelativePE +from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder +from keras_cv.tests.test_case import TestCase + + +class TestDetectron2Layers(TestCase): + def test_multi_head_attention_with_relative_pe(self): + attention_with_rel_pe = MultiHeadAttentionWithRelativePE( + num_heads=16, + key_dim=1280 // 16, + use_bias=True, + input_size=(64, 64), + ) + x = np.ones(shape=(1, 64, 64, 1280)) + x_out = ops.convert_to_numpy(attention_with_rel_pe(x)) + self.assertEqual(x_out.shape, (1, 64, 64, 1280)) + + def test_windowed_transformer_encoder(self): + windowed_transformer_encoder = WindowedTransformerEncoder( + project_dim=1280, + mlp_dim=1280 * 4, + num_heads=16, + use_bias=True, + use_rel_pos=True, + window_size=14, + input_size=(64, 64), + ) + x = np.ones((1, 64, 64, 1280)) + x_out = ops.convert_to_numpy(windowed_transformer_encoder(x)) + self.assertEqual(x_out.shape, (1, 64, 64, 1280)) + self.assertAllClose(x_out, np.ones_like(x_out)) diff --git a/keras_cv/models/__init__.py b/keras_cv/models/__init__.py index fc636dbdbd..5d9102d784 100644 --- a/keras_cv/models/__init__.py +++ b/keras_cv/models/__init__.py @@ -88,6 +88,18 @@ from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import ( EfficientNetV1Backbone, ) +from keras_cv.models.backbones.detectron2.detectron2_backbone import ( + ViTDetBackbone, +) +from keras_cv.models.backbones.detectron2.detectron2_aliases import ( + SAMViTDetBBackbone, +) +from keras_cv.models.backbones.detectron2.detectron2_aliases import ( + SAMViTDetLBackbone, +) +from keras_cv.models.backbones.detectron2.detectron2_aliases import ( + SAMViTDetHBackbone, +) from keras_cv.models.backbones.efficientnet_v2.efficientnet_v2_aliases import ( EfficientNetV2B0Backbone, ) @@ -166,7 +178,6 @@ YOLOV8Detector, ) from keras_cv.models.segmentation import DeepLabV3Plus -from keras_cv.models.segmentation.segment_anything import ImageEncoder from keras_cv.models.segmentation.segment_anything import MaskDecoder from keras_cv.models.segmentation.segment_anything import PromptEncoder from keras_cv.models.segmentation.segment_anything import TwoWayTransformer diff --git a/keras_cv/models/backbones/detectron2/__init__.py b/keras_cv/models/backbones/detectron2/__init__.py new file mode 100644 index 0000000000..3992ffb59a --- /dev/null +++ b/keras_cv/models/backbones/detectron2/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/keras_cv/models/backbones/detectron2/data/sam_vitdet_b_out.npz b/keras_cv/models/backbones/detectron2/data/sam_vitdet_b_out.npz new file mode 100644 index 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z^?Ut*om;b)`S-fhb)t))Eil6@#wFt0>%UQ>{v*`qR{be?AkzQ;`Shc98oAEwp(K`cAk*W9yymc;v(AF z>j0m33Ov@w)=>YTg_)^lqtC3N5He$iMDJy_WbZc(GS-Fbopvf6B~h)5Kb|Q?=q3UOB@50bf#gxc~qF literal 0 HcmV?d00001 diff --git a/keras_cv/models/backbones/detectron2/detectron2_aliases.py b/keras_cv/models/backbones/detectron2/detectron2_aliases.py new file mode 100644 index 0000000000..6002259bc4 --- /dev/null +++ b/keras_cv/models/backbones/detectron2/detectron2_aliases.py @@ -0,0 +1,116 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +from keras_cv.models.backbones.detectron2.detectron2_backbone import ( + ViTDetBackbone, +) +from keras_cv.models.backbones.detectron2.detectron2_backbone_presets import ( + backbone_presets, +) +from keras_cv.utils.python_utils import classproperty + +ALIAS_DOCSTRING = """{SAM}VitDet{size}Backbone model. + + Reference: + - [Detectron2](https://github.com/facebookresearch/detectron2) + - [Segment Anything](https://arxiv.org/abs/2304.02643) + + For transfer learning use cases, make sure to read the + [guide to transfer learning & fine-tuning](https://keras.io/guides/transfer_learning/). + + Examples: + ```python + input_data = np.ones(shape=(1, 1024, 1024, 3)) + + # Randomly initialized backbone + model = {SAM}VitDet{size}Backbone() + output = model(input_data) + ``` +""" # noqa: E501 + + +class SAMViTDetBBackbone(ViTDetBackbone): + def __new__( + cls, + **kwargs, + ): + return ViTDetBackbone.from_preset("sam_vitdet_b", **kwargs) + + @classproperty + def presets(cls): + """Dictionary of preset names and configurations.""" + return { + "sam_vitdet_b": copy.deepcopy(backbone_presets["sam_vitdet_b"]), + } + + @classproperty + def presets_with_weights(cls): + """Dictionary of preset names and configurations that include + weights.""" + return cls.presets + + +class SAMViTDetLBackbone(ViTDetBackbone): + def __new__( + cls, + **kwargs, + ): + return ViTDetBackbone.from_preset("sam_vitdet_l", **kwargs) + + @classproperty + def presets(cls): + """Dictionary of preset names and configurations.""" + return { + "sam_vitdet_l": copy.deepcopy(backbone_presets["sam_vitdet_l"]), + } + + @classproperty + def presets_with_weights(cls): + """Dictionary of preset names and configurations that include + weights.""" + return cls.presets + + +class SAMViTDetHBackbone(ViTDetBackbone): + def __new__( + cls, + **kwargs, + ): + return ViTDetBackbone.from_preset("sam_vitdet_h", **kwargs) + + @classproperty + def presets(cls): + """Dictionary of preset names and configurations.""" + return { + "sam_vitdet_h": copy.deepcopy(backbone_presets["sam_vitdet_h"]), + } + + @classproperty + def presets_with_weights(cls): + """Dictionary of preset names and configurations that include + weights.""" + return cls.presets + + +setattr( + SAMViTDetBBackbone, "__doc__", ALIAS_DOCSTRING.format(SAM="SAM", size="B") +) +setattr( + SAMViTDetLBackbone, "__doc__", ALIAS_DOCSTRING.format(SAM="SAM", size="L") +) +setattr( + SAMViTDetHBackbone, "__doc__", ALIAS_DOCSTRING.format(SAM="SAM", size="H") +) diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone.py b/keras_cv/models/backbones/detectron2/detectron2_backbone.py new file mode 100644 index 0000000000..9aa57ad811 --- /dev/null +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone.py @@ -0,0 +1,203 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +from keras_cv.backend import keras +from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding +from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder +from keras_cv.models.backbones.backbone import Backbone +from keras_cv.models.backbones.detectron2.detectron2_backbone_presets import ( + backbone_presets, +) +from keras_cv.utils.python_utils import classproperty + + +@keras.utils.register_keras_serializable(package="keras_cv.models") +class ViTDetBackbone(Backbone): + """A ViT image encoder that uses a windowed transformer encoder and + relative positional encodings. + + Args: + img_size (int, optional): The size of the input image. Defaults to + `1024`. + patch_size (int, optional): the patch size to be supplied to the + Patching layer to turn input images into a flattened sequence of + patches. Defaults to `16`. + in_chans (int, optional): The number of channels in the input image. + Defaults to `3`. + embed_dim (int, optional): The latent dimensionality to be projected + into in the output of each stacked windowed transformer encoder. + Defaults to `1280`. + depth (int, optional): The number of transformer encoder layers to + stack in the Vision Transformer. Defaults to `32`. + mlp_dim (_type_, optional): The dimensionality of the hidden Dense + layer in the transformer MLP head. Defaults to `1280*4`. + num_heads (int, optional): the number of heads to use in the + `MultiHeadAttentionWithRelativePE` layer of each transformer + encoder. Defaults to `16`. + out_chans (int, optional): The number of channels (features) in the + output (image encodings). Defaults to `256`. + use_bias (bool, optional): Whether to use bias to project the keys, + queries, and values in the attention layer. Defaults to `True`. + use_abs_pos (bool, optional): Whether to add absolute positional + embeddings to the output patches. Defaults to `True`. + use_rel_pos (bool, optional): Whether to use relative positional + emcodings in the attention layer. Defaults to `False`. + window_size (int, optional): The size of the window for windowed + attention in the transformer encoder blocks. Defaults to `0`. + global_attention_indices (list, optional): Indexes for blocks using + global attention. Defaults to `[7, 15, 23, 31]`. + layer_norm_epsilon (int, optional): The epsilon to use in the layer + normalization blocks in transformer encoder. Defaults to `1e-6`. + """ + + def __init__( + self, + img_size=1024, + patch_size=16, + in_chans=3, + embed_dim=1280, + depth=32, + mlp_dim=1280 * 4, + num_heads=16, + out_chans=256, + use_bias=True, + use_abs_pos=True, + use_rel_pos=False, + window_size=0, + global_attention_indices=[7, 15, 23, 31], + layer_norm_epsilon=1e-6, + **kwargs + ): + super().__init__(**kwargs) + self.img_size = img_size + self.patch_size = patch_size + self.in_chans = in_chans + self.embed_dim = embed_dim + self.depth = depth + self.mlp_dim = mlp_dim + self.num_heads = num_heads + self.out_chans = out_chans + self.use_bias = use_bias + self.use_rel_pos = use_rel_pos + self.use_abs_pos = use_abs_pos + self.window_size = window_size + self.global_attention_indices = global_attention_indices + self.layer_norm_epsilon = layer_norm_epsilon + + self.patch_embed = ViTDetPatchingAndEmbedding( + kernel_size=(patch_size, patch_size), + strides=(patch_size, patch_size), + embed_dim=embed_dim, + ) + if self.use_abs_pos: + self.pos_embed = self.add_weight( + name="pos_embed", + shape=( + 1, + self.img_size // self.patch_size, + self.img_size // self.patch_size, + self.embed_dim, + ), + initializer="zeros", + trainable=True, + ) + else: + self.pos_embed = None + self.transformer_blocks = [] + for i in range(depth): + block = WindowedTransformerEncoder( + project_dim=embed_dim, + mlp_dim=mlp_dim, + num_heads=num_heads, + use_bias=use_bias, + use_rel_pos=use_rel_pos, + window_size=window_size + if i not in global_attention_indices + else 0, + input_size=(img_size // patch_size, img_size // patch_size), + ) + self.transformer_blocks.append(block) + self.bottleneck = keras.models.Sequential( + [ + keras.layers.Conv2D( + filters=out_chans, kernel_size=1, use_bias=False + ), + keras.layers.LayerNormalization(epsilon=1e-6), + keras.layers.Conv2D( + filters=out_chans, + kernel_size=3, + padding="same", + use_bias=False, + ), + keras.layers.LayerNormalization(epsilon=1e-6), + ] + ) + + self.patch_embed.build( + [None, self.img_size, self.img_size, self.in_chans] + ) + self.bottleneck.build( + [ + None, + self.img_size // self.patch_size, + self.img_size // self.patch_size, + self.embed_dim, + ] + ) + + self.built = True + + def call(self, x): + B, _, _, _ = x.shape + x = self.patch_embed(x) + if self.pos_embed is not None: + x = x + self.pos_embed + for block in self.transformer_blocks: + x = block(x) + return self.bottleneck(x) + + def get_config(self): + config = super().get_config() + config.update( + { + "img_size": self.img_size, + "patch_size": self.patch_size, + "in_chans": self.in_chans, + "embed_dim": self.embed_dim, + "depth": self.depth, + "mlp_dim": self.mlp_dim, + "num_heads": self.num_heads, + "out_chans": self.out_chans, + "use_bias": self.use_bias, + "use_abs_pos": self.use_abs_pos, + "use_rel_pos": self.use_rel_pos, + "window_size": self.window_size, + "global_attention_indices": self.global_attention_indices, + "layer_norm_epsilon": self.layer_norm_epsilon, + } + ) + return config + + @classproperty + def presets(cls): + """Dictionary of preset names and configurations.""" + return copy.deepcopy(backbone_presets) + + # @classproperty + # def presets_with_weights(cls): + # """Dictionary of preset names and configurations that include + # weights.""" + # return copy.deepcopy(backbone_presets) diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py new file mode 100644 index 0000000000..9c8f8ca63c --- /dev/null +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py @@ -0,0 +1,107 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""VitDet model preset configurations.""" + +backbone_presets = { + "sam_vitdet_b": { + "metadata": { + "description": ( + "VitDet Backbone for the segment anything model with 12 " + "transformer encoders with embed dim 768 and attention layers" + " with 12 heads with global attention on encoders 2, 5, 8, " + "and 11." + ), + "params": 89_670_912, + "official_name": "VitDet", + "path": "detectron2", + }, + "class_name": "keras_cv.models>VitDetBackbone", + "config": { + "img_size": 1024, + "patch_size": 16, + "in_chans": 3, + "embed_dim": 768, + "depth": 12, + "mlp_dim": 768 * 4, + "num_heads": 12, + "out_chans": 256, + "use_bias": True, + "use_rel_pos": True, + "window_size": 14, + "global_attention_indices": [2, 5, 8, 11], + }, + # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/sam_vit_b.weights.h5", # noqa: E501 + # "weights_hash": None + }, + "sam_vitdet_l": { + "metadata": { + "description": ( + "VitDet Backbone for the segment anything model with 24 " + "transformer encoders with embed dim " + "1024 and attention layers with 16 heads with global " + "attention on encoders 5, 11, 17, and 23." + ), + "params": 308_278_272, + "official_name": "VitDet", + "path": "detectron2", + }, + "class_name": "keras_cv.models>VitDetBackbone", + "config": { + "img_size": 1024, + "patch_size": 16, + "in_chans": 3, + "embed_dim": 1024, + "depth": 24, + "mlp_dim": 1024 * 4, + "num_heads": 16, + "out_chans": 256, + "use_bias": True, + "use_rel_pos": True, + "window_size": 14, + "global_attention_indices": [5, 11, 17, 23], + }, + # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/sam_vit_l.weights.h5", # noqa: E501 + # "weights_hash": None + }, + "sam_vitdet_h": { + "metadata": { + "description": ( + "VitDet Backbone for the segment anything model " + "with 32 transformer encoders with embed dim " + "1280 and attention layers with 16 heads with global " + "attention on encoders 7, 15, 23, and 31." + ), + "params": 637_026_048, + "official_name": "VitDet", + "path": "detectron2", + }, + "class_name": "keras_cv.models>VitDetBackbone", + "config": { + "img_size": 1024, + "patch_size": 16, + "in_chans": 3, + "embed_dim": 1280, + "depth": 32, + "mlp_dim": 1280 * 4, + "num_heads": 16, + "out_chans": 256, + "use_bias": True, + "use_rel_pos": True, + "window_size": 14, + "global_attention_indices": [7, 15, 23, 31], + }, + # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/sam_vit_h.weights.h5", # noqa: E501 + # "weights_hash": None + }, +} diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py new file mode 100644 index 0000000000..7166dfd6e5 --- /dev/null +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py @@ -0,0 +1,95 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for loading pretrained model presets.""" + +import pathlib + +import numpy as np +import pytest + +from keras_cv.backend import ops +from keras_cv.models.backbones.detectron2.detectron2_aliases import ( + SAMViTDetBBackbone, +) +from keras_cv.models.backbones.detectron2.detectron2_backbone import ( + ViTDetBackbone, +) +from keras_cv.tests.test_case import TestCase + + +@pytest.mark.large +class ViTDetPresetSmokeTest(TestCase): + """ + A smoke test for ViTDet presets we run continuously. + This only tests the smallest weights we have available. Run with: + `pytest keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py --run_large` # noqa: E501 + """ + + def setUp(self): + self.input_batch = np.ones(shape=(1, 1024, 1024, 3)) + + def test_backbone_output(self): + model = ViTDetBackbone.from_preset("sam_vitdet_b") + outputs = model(self.input_batch) + + # The forward pass from a preset should be stable! + # This test should catch cases where we unintentionally change our + # network code in a way that would invalidate our preset weights. + # We should only update these numbers if we are updating a weights + # file, or have found a discrepancy with the upstream source. + + expected = np.load( + pathlib.Path(__file__).parent / "data" / "sam_vitdet_b_out.npz" + ) + # Keep a high tolerance, so we are robust to different hardware. + self.assertAllClose( + ops.convert_to_numpy(outputs), + expected, + atol=1e-5, + rtol=1e-5, + ) + + def test_applications_model_output(self): + model = SAMViTDetBBackbone() + model(self.input_batch) + + def test_applications_model_output_with_preset(self): + model = SAMViTDetBBackbone.from_preset("sam_vitdet_b") + model(self.input_batch) + + def test_preset_docstring(self): + """Check we did our docstring formatting correctly.""" + for name in ViTDetBackbone.presets: + self.assertRegex(ViTDetBackbone.from_preset.__doc__, name) + + def test_unknown_preset_error(self): + # Not a preset name + with self.assertRaises(ValueError): + ViTDetBackbone.from_preset("vitdet_nonexistant") + + +@pytest.mark.extra_large +class ViTDetPresetFullTest(TestCase): + """ + Test the full enumeration of our preset. + This tests every preset for ViTDet and is only run manually. + Run with: + `pytest keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py --run_extra_large` # noqa: E501 + """ + + def test_load_ViTDet(self): + input_data = np.ones(shape=(1, 1024, 1024, 3)) + for preset in ViTDetBackbone.presets: + model = ViTDetBackbone.from_preset(preset) + model(input_data) diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py b/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py new file mode 100644 index 0000000000..c880899d5b --- /dev/null +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py @@ -0,0 +1,45 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import numpy as np +import pytest + +from keras_cv.backend import keras +from keras_cv.backend import ops +from keras_cv.models.backbones.detectron2.detectron2_aliases import ( + SAMViTDetBBackbone, +) +from keras_cv.tests.test_case import TestCase + + +@pytest.mark.extra_large +class TestViTDetBackbone(TestCase): + def test_call_and_save(self): + model = SAMViTDetBBackbone() + x = np.ones((1, 1024, 1024, 3)) + x_out = ops.convert_to_numpy(model(x)) + num_parameters = sum( + np.prod(tuple(x.shape)) for x in model.trainable_variables + ) + self.assertEqual(x_out.shape, (1, 64, 64, 256)) + self.assertEqual(num_parameters, 89_670_912) + + # saving test + path = os.path.join(self.get_temp_dir(), "sam_tf_model.keras") + model.save(path) + loaded_model = keras.saving.load_model(path) + x_out_loaded = ops.convert_to_numpy(loaded_model(x)) + self.assertAllClose(x_out, x_out_loaded) diff --git a/keras_cv/models/segmentation/__init__.py b/keras_cv/models/segmentation/__init__.py index 4c9b7460fb..7410687444 100644 --- a/keras_cv/models/segmentation/__init__.py +++ b/keras_cv/models/segmentation/__init__.py @@ -13,7 +13,6 @@ # limitations under the License. from keras_cv.models.segmentation.deeplab_v3_plus import DeepLabV3Plus -from keras_cv.models.segmentation.segment_anything import ImageEncoder from keras_cv.models.segmentation.segment_anything import MaskDecoder from keras_cv.models.segmentation.segment_anything import PromptEncoder from keras_cv.models.segmentation.segment_anything import TwoWayTransformer diff --git a/keras_cv/models/segmentation/segment_anything/__init__.py b/keras_cv/models/segmentation/segment_anything/__init__.py index 7f1fda07f0..982989c216 100644 --- a/keras_cv/models/segmentation/segment_anything/__init__.py +++ b/keras_cv/models/segmentation/segment_anything/__init__.py @@ -12,9 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -from keras_cv.models.segmentation.segment_anything.sam_image_encoder import ( - ImageEncoder, -) from keras_cv.models.segmentation.segment_anything.sam_mask_decoder import ( MaskDecoder, ) diff --git a/keras_cv/models/segmentation/segment_anything/sam_layers.py b/keras_cv/models/segmentation/segment_anything/sam_layers.py index 4dcde4a9b3..fb55f7b5cf 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_layers.py +++ b/keras_cv/models/segmentation/segment_anything/sam_layers.py @@ -37,9 +37,6 @@ def __init__(self, embedding_dim, mlp_dim, activation="gelu", **kwargs): self.mlp_dim = mlp_dim self.activation = activation - self.built = False - - def build(self, input_shape=None): self.dense_layer1.build([self.embedding_dim]) self.dense_layer2.build([self.mlp_dim]) diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py index 486c6cd252..2df711593d 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -151,33 +151,6 @@ def __init__( self.iou_token.build(None) self.mask_tokens.build(None) - self.built = False - - def build( - self, - image_embeddings_shape, - image_pe_shape, - sparse_prompt_embeddings_shape, - dense_prompt_embeddings_shape, - *args, - **kwargs, - ): - transformer_image_embed_shape = [ - None, - image_embeddings_shape[1], - image_embeddings_shape[2], - image_embeddings_shape[3], - ] - tokens_shape = [ - None, - 1 + self.num_mask_tokens + sparse_prompt_embeddings_shape[1], - self.transformer_dim, - ] - self.transformer.build( - image_embedding_shape=transformer_image_embed_shape, - image_pe_shape=transformer_image_embed_shape, - point_embedding_shape=tokens_shape, - ) self.output_upscaling.build([None, None, None, self.transformer_dim]) for mlp in self.output_hypernetworks_mlps: diff --git a/keras_cv/models/segmentation/segment_anything/sam_test.py b/keras_cv/models/segmentation/segment_anything/sam_test.py index 4492010056..109f4a1c16 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_test.py +++ b/keras_cv/models/segmentation/segment_anything/sam_test.py @@ -15,19 +15,9 @@ import os import numpy as np -import pytest from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.models.segmentation.segment_anything.sam_image_encoder import ( - ImageEncoder, -) -from keras_cv.models.segmentation.segment_anything.sam_image_encoder import ( - MultiHeadAttentionWithRelativePE, -) -from keras_cv.models.segmentation.segment_anything.sam_image_encoder import ( - WindowedTransformerEncoder, -) from keras_cv.models.segmentation.segment_anything.sam_mask_decoder import ( MaskDecoder, ) @@ -44,63 +34,6 @@ class TestSAM(TestCase): - def test_multi_head_attention_with_relative_pe(self): - attention_with_rel_pe = MultiHeadAttentionWithRelativePE( - num_heads=16, - key_dim=1280 // 16, - use_bias=True, - input_size=(64, 64), - ) - x = np.ones(shape=(1, 64, 64, 1280)) - x_out = ops.convert_to_numpy(attention_with_rel_pe(x)) - self.assertEqual(x_out.shape, (1, 64, 64, 1280)) - - def test_windowed_transformer_encoder(self): - windowed_transformer_encoder = WindowedTransformerEncoder( - project_dim=1280, - mlp_dim=1280 * 4, - num_heads=16, - use_bias=True, - use_rel_pos=True, - window_size=14, - input_size=(64, 64), - ) - x = np.ones((1, 64, 64, 1280)) - x_out = ops.convert_to_numpy(windowed_transformer_encoder(x)) - self.assertEqual(x_out.shape, (1, 64, 64, 1280)) - self.assertAllClose(x_out, np.ones_like(x_out)) - - @pytest.mark.extra_large - def test_image_encoder(self): - image_encoder = ImageEncoder( - img_size=1024, - patch_size=16, - in_chans=3, - embed_dim=1280, - depth=32, - mlp_dim=1280 * 4, - num_heads=16, - out_chans=256, - use_bias=True, - use_rel_pos=True, - window_size=14, - global_attention_indices=[7, 15, 23, 31], - ) - x = np.ones((1, 1024, 1024, 3)) - x_out = ops.convert_to_numpy(image_encoder(x)) - num_parameters = sum( - np.prod(tuple(x.shape)) for x in image_encoder.trainable_variables - ) - self.assertEqual(x_out.shape, (1, 64, 64, 256)) - self.assertEqual(num_parameters, 637_026_048) - - # saving test - path = os.path.join(self.get_temp_dir(), "sam_tf_image_encoder.keras") - image_encoder.save(path) - loaded_model = keras.saving.load_model(path) - x_out_loaded = ops.convert_to_numpy(loaded_model(x)) - self.assertAllClose(x_out, x_out_loaded) - def get_points_labels_box_mask(self, B): prompt_encoder = PromptEncoder( embed_dim=256, @@ -189,7 +122,7 @@ def test_two_way_multi_head_attention(self): box, input_mask, ) = self.get_points_labels_box_mask(1) - image_embeddings = np.random.randn(1, 64, 64, 256) + image_embeddings = np.random.randn(1, 64, 64, 256).astype(np.float32) sparse_embeddings, _ = prompt_encoder( points=points, labels=labels, box=box, mask=input_mask diff --git a/keras_cv/models/segmentation/segment_anything/sam_transformer.py b/keras_cv/models/segmentation/segment_anything/sam_transformer.py index 7ea664deff..4850f5b916 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_transformer.py +++ b/keras_cv/models/segmentation/segment_anything/sam_transformer.py @@ -57,7 +57,12 @@ def __init__(self, num_heads, key_dim, downsample_rate=1, **kwargs): # Upsample self.out_proj = keras.layers.Dense(self.key_dim * self.num_heads) - self.built = False + self.query_proj.build([self.num_heads * self.key_dim]) + self.key_proj.build([self.num_heads * self.key_dim]) + self.value_proj.build([self.num_heads * self.key_dim]) + self.out_proj.build([self.internal_dims * self.num_heads]) + + self.built = True def __separate_heads(self, x): B, N, C = x.shape @@ -69,14 +74,6 @@ def __recombine_heads(self, x): x = ops.transpose(x, axes=(0, 2, 1, 3)) return ops.reshape(x, (B, N_T, N_H * C_PH)) - def build(self, query_shape, value_shape, key_shape): - self.query_proj.build(query_shape) - self.key_proj.build(key_shape) - self.value_proj.build(value_shape) - self.out_proj.build([self.internal_dims * self.num_heads]) - - self.built = True - def call(self, query, value, key): query = self.query_proj(query) key = self.key_proj(key) @@ -170,34 +167,14 @@ def __init__( ) self.layer_norm4 = keras.layers.LayerNormalization(epsilon=1e-5) - self.built = False - - def build(self, queries_shape, keys_shape, query_pe_shape, key_pe_shape): - self.self_attention.build( - query_shape=queries_shape, - value_shape=queries_shape, - key_shape=queries_shape, - ) - self.layer_norm1.build(queries_shape) - self.cross_attention_token_to_image.build( - query_shape=queries_shape, - key_shape=keys_shape, - value_shape=keys_shape, - ) - self.layer_norm2.build(queries_shape) - self.mlp_block.build(queries_shape) - self.layer_norm3.build(queries_shape) - self.cross_attention_image_to_token.build( - query_shape=keys_shape, - key_shape=queries_shape, - value_shape=queries_shape, - ) - self.layer_norm4.build(keys_shape) + self.layer_norm1.build([None, None, self.num_heads * self.key_dim]) + self.layer_norm2.build([None, None, self.num_heads * self.key_dim]) + self.layer_norm3.build([None, None, self.num_heads * self.key_dim]) + self.layer_norm4.build([None, None, self.num_heads * self.key_dim]) self.built = True def call(self, queries, keys, query_pe, key_pe): - # print("Actual queries_shape:", queries.shape) if self.skip_first_layer_pe: queries = self.self_attention( query=queries, value=queries, key=queries @@ -325,26 +302,7 @@ def __init__( ) self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5) - self.built = False - - def build( - self, image_embedding_shape, image_pe_shape, point_embedding_shape - ): - B, H, W, C = image_embedding_shape - image_embedding_shape = [B, H * W, C] - for layer in self.layers: - layer.build( - queries_shape=point_embedding_shape, - keys_shape=image_embedding_shape, - query_pe_shape=point_embedding_shape, - key_pe_shape=image_embedding_shape, - ) - self.final_attention_token_to_image.build( - query_shape=point_embedding_shape, - key_shape=image_embedding_shape, - value_shape=image_embedding_shape, - ) - self.final_layer_norm.build(point_embedding_shape) + self.final_layer_norm.build([None, None, self.embedding_dim]) self.built = True From ac7f30ee2cb0ee7569244cf67dd18a50ef805752 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Fri, 18 Aug 2023 14:42:22 -0700 Subject: [PATCH 04/13] Address review comments and address saving bug - Use `keras_cv.export_api.keras_cv_export` instead of `keras.saving.register_keras_serializable`. - Add a `SerializableSequential` class to address the saving bug with the `Sequential` model. - Push the helper functions in `keras_cv/layers/detectron2_layers.py` to the bottom of the file. - Add the detectron2 layers to the `keras_cv/layers/__init__.py` file. - Add a test for the `ViTDetPatchingAndEmbedding` layer. --- keras_cv/layers/__init__.py | 1 + keras_cv/layers/detectron2_layers.py | 273 +++++++++--------- keras_cv/layers/detectron2_layers_test.py | 7 + keras_cv/layers/serializable_sequential.py | 62 ++++ .../layers/serializable_sequential_test.py | 39 +++ keras_cv/models/__init__.py | 30 +- .../detectron2/detectron2_backbone.py | 13 +- .../detectron2/detectron2_backbone_test.py | 7 +- .../segment_anything/sam_layers.py | 3 +- .../segment_anything/sam_mask_decoder.py | 10 +- .../segment_anything/sam_prompt_encoder.py | 5 +- .../segment_anything/sam_transformer.py | 7 +- 12 files changed, 293 insertions(+), 164 deletions(-) create mode 100644 keras_cv/layers/serializable_sequential.py create mode 100644 keras_cv/layers/serializable_sequential_test.py diff --git a/keras_cv/layers/__init__.py b/keras_cv/layers/__init__.py index 166d553924..41f4905d3b 100644 --- a/keras_cv/layers/__init__.py +++ b/keras_cv/layers/__init__.py @@ -127,6 +127,7 @@ from keras_cv.layers.regularization.dropblock_2d import DropBlock2D from keras_cv.layers.regularization.squeeze_excite import SqueezeAndExcite2D from keras_cv.layers.regularization.stochastic_depth import StochasticDepth +from keras_cv.layers.serializable_sequential import SerializableSequential from keras_cv.layers.spatial_pyramid import SpatialPyramidPooling from keras_cv.layers.transformer_encoder import TransformerEncoder from keras_cv.layers.vit_layers import PatchingAndEmbedding diff --git a/keras_cv/layers/detectron2_layers.py b/keras_cv/layers/detectron2_layers.py index 25e148e5ba..289773fb65 100644 --- a/keras_cv/layers/detectron2_layers.py +++ b/keras_cv/layers/detectron2_layers.py @@ -12,97 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. +from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops from keras_cv.models.segmentation.segment_anything.sam_layers import MLPBlock -def get_rel_pos(query_size, key_size, rel_pos): - """ - Get relative positional embeddings according to the relative positions of - query and key sizes. - - Args: - query_size (int): The number of features of the queries. - key_size (int): The number of features of the keys. - rel_pos (tensor): Relative positional embedding tensor. - - Returns: - tensor: Extracted positional embeddings according to relative - positions. - """ - max_rel_dist = 2 * max(query_size, key_size) - 1 - if rel_pos.shape[0] != max_rel_dist: - rel_pos_resized = ops.image.resize( - images=ops.reshape( - rel_pos, (1, rel_pos.shape[0], rel_pos.shape[1], 1) - ), - size=(max_rel_dist, rel_pos.shape[1]), - interpolation="bilinear", - ) - rel_pos_resized = ops.squeeze(rel_pos_resized, axis=(0, -1)) - else: - rel_pos_resized = rel_pos - query_coordinates = ops.arange(query_size, dtype="float32")[:, None] * max( - key_size / query_size, 1.0 - ) - key_coordinates = ops.arange(key_size, dtype="float32")[None, :] * max( - query_size / key_size, 1.0 - ) - relative_coordinates = (query_coordinates - key_coordinates) + ( - key_size - 1 - ) * max(query_size / key_size, 1.0) - relative_coordinates = ops.cast(relative_coordinates, dtype="int64") - return ops.take(rel_pos_resized, relative_coordinates, 0) - - -def add_decomposed_rel_pos( - attention_map, queries, rel_pos_h, rel_pos_w, query_size, key_size -): - """ - Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. - - Args: - attention_map (tensor): Attention map. - queries (tensor): Queries in the attention layer with shape - `(B, q_h * q_w, C)`. - rel_pos_h (tensor): Relative position embeddings `(Lh, C)` for height - axis. - rel_pos_w (tensor): relative position embeddings `(Lw, C)` for width - axis. - query_size (tuple[int, int]): Spatial sequence size of queries with - `(q_h, q_w)`. - key_size (tuple[int, int]): Spatial sequence size of keys with - `(k_h, k_w)`. - - Returns: - tensor: attention map with added relative positional embeddings. - - References: - - https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa: E501 - """ - query_height, query_width = query_size - key_height, key_width = key_size - rel_heights = get_rel_pos(query_height, key_height, rel_pos_h) - rel_widths = get_rel_pos(query_width, key_width, rel_pos_w) - - B, _, C = queries.shape - rel_queries = ops.reshape(queries, (B, query_height, query_width, C)) - rel_heights = ops.einsum("bhwc,hkc->bhwk", rel_queries, rel_heights) - rel_widths = ops.einsum("bhwc,wkc->bhwk", rel_queries, rel_widths) - - attention_map = ops.reshape( - attention_map, (B, query_height, query_width, key_height, key_width) - ) - attention_map = attention_map + rel_heights[..., :, None] - attention_map = attention_map + rel_widths[..., None, :] - attention_map = ops.reshape( - attention_map, (B, query_height * query_width, key_height * key_width) - ) - return attention_map - - -@keras.saving.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.layers.MultiHeadAttentionWithRelativePE") class MultiHeadAttentionWithRelativePE(keras.layers.Layer): """Multi-head Attention block with relative position embeddings. @@ -218,55 +134,7 @@ def get_config(self): return config -def window_partition(x, window_size): - B, H, W, C = x.shape - pad_height = (window_size - H % window_size) % window_size - pad_width = (window_size - W % window_size) % window_size - if pad_height > 0 or pad_width > 0: - x = ops.pad(x, ((0, 0), (0, pad_height), (0, pad_width), (0, 0))) - H_padded, W_padded = H + pad_height, W + pad_width - x = ops.reshape( - x, - ( - B, - H_padded // window_size, - window_size, - W_padded // window_size, - window_size, - C, - ), - ) - windows = ops.reshape( - ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), - (-1, window_size, window_size, C), - ) - return windows, (H_padded, W_padded) - - -def window_unpartition(windows, window_size, HW_padded, HW): - H_padded, W_padded = HW_padded - H, W = HW - B = windows.shape[0] // ( - (H_padded // window_size) * (W_padded // window_size) - ) - x = ops.reshape( - windows, - ( - B, - H_padded // window_size, - W_padded // window_size, - window_size, - window_size, - -1, - ), - ) - x = ops.reshape( - ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), (B, H_padded, W_padded, -1) - ) - return x[:, :H, :W, :] - - -@keras.utils.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.layers.WindowedTransformerEncoder") class WindowedTransformerEncoder(keras.layers.Layer): """Transformer blocks with support of window attention and residual propagation blocks. @@ -379,7 +247,7 @@ def get_config(self): return config -@keras.utils.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.layers.ViTDetPatchingAndEmbedding") class ViTDetPatchingAndEmbedding(keras.layers.Layer): """Image to Patch Embedding using only a conv layer (without layer normalization). @@ -429,3 +297,136 @@ def get_config(self): } ) return config + + +def get_rel_pos(query_size, key_size, rel_pos): + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + + Args: + query_size (int): The number of features of the queries. + key_size (int): The number of features of the keys. + rel_pos (tensor): Relative positional embedding tensor. + + Returns: + tensor: Extracted positional embeddings according to relative + positions. + """ + max_rel_dist = 2 * max(query_size, key_size) - 1 + if rel_pos.shape[0] != max_rel_dist: + rel_pos_resized = ops.image.resize( + images=ops.reshape( + rel_pos, (1, rel_pos.shape[0], rel_pos.shape[1], 1) + ), + size=(max_rel_dist, rel_pos.shape[1]), + interpolation="bilinear", + ) + rel_pos_resized = ops.squeeze(rel_pos_resized, axis=(0, -1)) + else: + rel_pos_resized = rel_pos + query_coordinates = ops.arange(query_size, dtype="float32")[:, None] * max( + key_size / query_size, 1.0 + ) + key_coordinates = ops.arange(key_size, dtype="float32")[None, :] * max( + query_size / key_size, 1.0 + ) + relative_coordinates = (query_coordinates - key_coordinates) + ( + key_size - 1 + ) * max(query_size / key_size, 1.0) + relative_coordinates = ops.cast(relative_coordinates, dtype="int64") + return ops.take(rel_pos_resized, relative_coordinates, 0) + + +def add_decomposed_rel_pos( + attention_map, queries, rel_pos_h, rel_pos_w, query_size, key_size +): + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + + Args: + attention_map (tensor): Attention map. + queries (tensor): Queries in the attention layer with shape + `(B, q_h * q_w, C)`. + rel_pos_h (tensor): Relative position embeddings `(Lh, C)` for height + axis. + rel_pos_w (tensor): relative position embeddings `(Lw, C)` for width + axis. + query_size (tuple[int, int]): Spatial sequence size of queries with + `(q_h, q_w)`. + key_size (tuple[int, int]): Spatial sequence size of keys with + `(k_h, k_w)`. + + Returns: + tensor: attention map with added relative positional embeddings. + + References: + - https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa: E501 + """ + query_height, query_width = query_size + key_height, key_width = key_size + rel_heights = get_rel_pos(query_height, key_height, rel_pos_h) + rel_widths = get_rel_pos(query_width, key_width, rel_pos_w) + + B, _, C = queries.shape + rel_queries = ops.reshape(queries, (B, query_height, query_width, C)) + rel_heights = ops.einsum("bhwc,hkc->bhwk", rel_queries, rel_heights) + rel_widths = ops.einsum("bhwc,wkc->bhwk", rel_queries, rel_widths) + + attention_map = ops.reshape( + attention_map, (B, query_height, query_width, key_height, key_width) + ) + attention_map = attention_map + rel_heights[..., :, None] + attention_map = attention_map + rel_widths[..., None, :] + attention_map = ops.reshape( + attention_map, (B, query_height * query_width, key_height * key_width) + ) + return attention_map + + +def window_partition(x, window_size): + B, H, W, C = x.shape + pad_height = (window_size - H % window_size) % window_size + pad_width = (window_size - W % window_size) % window_size + if pad_height > 0 or pad_width > 0: + x = ops.pad(x, ((0, 0), (0, pad_height), (0, pad_width), (0, 0))) + H_padded, W_padded = H + pad_height, W + pad_width + x = ops.reshape( + x, + ( + B, + H_padded // window_size, + window_size, + W_padded // window_size, + window_size, + C, + ), + ) + windows = ops.reshape( + ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), + (-1, window_size, window_size, C), + ) + return windows, (H_padded, W_padded) + + +def window_unpartition(windows, window_size, HW_padded, HW): + H_padded, W_padded = HW_padded + H, W = HW + B = windows.shape[0] // ( + (H_padded // window_size) * (W_padded // window_size) + ) + x = ops.reshape( + windows, + ( + B, + H_padded // window_size, + W_padded // window_size, + window_size, + window_size, + -1, + ), + ) + x = ops.reshape( + ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), (B, H_padded, W_padded, -1) + ) + return x[:, :H, :W, :] diff --git a/keras_cv/layers/detectron2_layers_test.py b/keras_cv/layers/detectron2_layers_test.py index 71b5e190fd..aecea0a379 100644 --- a/keras_cv/layers/detectron2_layers_test.py +++ b/keras_cv/layers/detectron2_layers_test.py @@ -16,6 +16,7 @@ from keras_cv.backend import ops from keras_cv.layers.detectron2_layers import MultiHeadAttentionWithRelativePE +from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder from keras_cv.tests.test_case import TestCase @@ -46,3 +47,9 @@ def test_windowed_transformer_encoder(self): x_out = ops.convert_to_numpy(windowed_transformer_encoder(x)) self.assertEqual(x_out.shape, (1, 64, 64, 1280)) self.assertAllClose(x_out, np.ones_like(x_out)) + + def test_vit_patching_and_embedding(self): + vit_patching_and_embedding = ViTDetPatchingAndEmbedding() + x = np.ones((1, 1024, 1024, 3)) + x_out = vit_patching_and_embedding(x) + self.assertEqual(x_out.shape, (1, 64, 64, 768)) diff --git a/keras_cv/layers/serializable_sequential.py b/keras_cv/layers/serializable_sequential.py new file mode 100644 index 0000000000..8584c8ee0c --- /dev/null +++ b/keras_cv/layers/serializable_sequential.py @@ -0,0 +1,62 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from keras_cv.api_export import keras_cv_export +from keras_cv.backend import keras +from keras_cv.utils.python_utils import classproperty + + +# TODO(tirthasheshpatel): Use `Sequential` model once the bug is resolved. +# Temporarily substitute the `Sequential` model with this because a +# bug in Keras/Keras Core prevents the weights of a sequential model to +# load in TensorFlow if they are saved in JAX/PyTorch and vice versa. +# This only happens when the `build` method is called in the `__init__` +# step. +@keras_cv_export("keras_cv.layers.SerializableSequential") +class SerializableSequential(keras.layers.Layer): + def __init__(self, layers_list, **kwargs): + super().__init__(**kwargs) + self.layers_list = layers_list + + def build(self, input_shape): + output_shape = input_shape + for layer in self.layers_list: + layer.build(output_shape) + output_shape = layer.compute_output_shape(output_shape) + self.built = True + + def call(self, x): + for layer in self.layers_list: + x = layer(x) + return x + + def get_config(self): + config = super().get_config() + layers_list_serialized = [ + keras.saving.serialize_keras_object(layer) + for layer in self.layers_list + ] + config.update({"layers_list": layers_list_serialized}) + + @classproperty + def from_config(self, config): + config.update( + { + "layers_list": [ + keras.layers.deserialize(layer) + for layer in config["layers_list"] + ] + } + ) + return super().from_config(config) diff --git a/keras_cv/layers/serializable_sequential_test.py b/keras_cv/layers/serializable_sequential_test.py new file mode 100644 index 0000000000..d49b276ca4 --- /dev/null +++ b/keras_cv/layers/serializable_sequential_test.py @@ -0,0 +1,39 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from copy import deepcopy + +import numpy as np + +from keras_cv.backend import keras +from keras_cv.layers.serializable_sequential import SerializableSequential +from keras_cv.tests.test_case import TestCase + + +class TestDetectron2Layers(TestCase): + def test_sequential_equivalence(self): + layers = [ + keras.layers.Conv2D(16, 2), + keras.layers.Flatten(), + keras.layers.Dense(5), + ] + model = SerializableSequential(layers) + model_keras = keras.Sequential(deepcopy(layers)) + model.build([None, 2, 2, 3]) + model_keras.build([None, 2, 2, 3]) + model.set_weights(model_keras.weights) + x = np.ones((1, 2, 2, 3)) + x_out = model(x) + x_expected = model_keras(x) + self.assertAllClose(x_out, x_expected) diff --git a/keras_cv/models/__init__.py b/keras_cv/models/__init__.py index 5d9102d784..5d84cbab20 100644 --- a/keras_cv/models/__init__.py +++ b/keras_cv/models/__init__.py @@ -43,6 +43,18 @@ from keras_cv.models.backbones.densenet.densenet_backbone import ( DenseNetBackbone, ) +from keras_cv.models.backbones.detectron2.detectron2_aliases import ( + SAMViTDetBBackbone, +) +from keras_cv.models.backbones.detectron2.detectron2_aliases import ( + SAMViTDetHBackbone, +) +from keras_cv.models.backbones.detectron2.detectron2_aliases import ( + SAMViTDetLBackbone, +) +from keras_cv.models.backbones.detectron2.detectron2_backbone import ( + ViTDetBackbone, +) from keras_cv.models.backbones.efficientnet_lite.efficientnet_lite_aliases import ( # noqa: E501 EfficientNetLiteB0Backbone, ) @@ -88,18 +100,6 @@ from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import ( EfficientNetV1Backbone, ) -from keras_cv.models.backbones.detectron2.detectron2_backbone import ( - ViTDetBackbone, -) -from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - SAMViTDetBBackbone, -) -from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - SAMViTDetLBackbone, -) -from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - SAMViTDetHBackbone, -) from keras_cv.models.backbones.efficientnet_v2.efficientnet_v2_aliases import ( EfficientNetV2B0Backbone, ) @@ -178,8 +178,8 @@ YOLOV8Detector, ) from keras_cv.models.segmentation import DeepLabV3Plus -from keras_cv.models.segmentation.segment_anything import MaskDecoder -from keras_cv.models.segmentation.segment_anything import PromptEncoder -from keras_cv.models.segmentation.segment_anything import TwoWayTransformer +from keras_cv.models.segmentation import MaskDecoder +from keras_cv.models.segmentation import PromptEncoder +from keras_cv.models.segmentation import TwoWayTransformer from keras_cv.models.stable_diffusion import StableDiffusion from keras_cv.models.stable_diffusion import StableDiffusionV2 diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone.py b/keras_cv/models/backbones/detectron2/detectron2_backbone.py index 9aa57ad811..833b006412 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone.py @@ -14,9 +14,11 @@ import copy +from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder +from keras_cv.layers.serializable_sequential import SerializableSequential from keras_cv.models.backbones.backbone import Backbone from keras_cv.models.backbones.detectron2.detectron2_backbone_presets import ( backbone_presets, @@ -24,7 +26,7 @@ from keras_cv.utils.python_utils import classproperty -@keras.utils.register_keras_serializable(package="keras_cv.models") +@keras_cv_export("keras_cv.models.ViTDetBackbone") class ViTDetBackbone(Backbone): """A ViT image encoder that uses a windowed transformer encoder and relative positional encodings. @@ -130,7 +132,7 @@ def __init__( input_size=(img_size // patch_size, img_size // patch_size), ) self.transformer_blocks.append(block) - self.bottleneck = keras.models.Sequential( + self.bottleneck = SerializableSequential( [ keras.layers.Conv2D( filters=out_chans, kernel_size=1, use_bias=False @@ -160,6 +162,13 @@ def __init__( self.built = True + @property + def pyramid_level_inputs(self): + raise NotImplementedError( + "The `ViTDetBackbone` model doesn't compute" + " pyramid level features." + ) + def call(self, x): B, _, _, _ = x.shape x = self.patch_embed(x) diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py b/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py index c880899d5b..5d7da1ce3f 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py @@ -25,8 +25,8 @@ from keras_cv.tests.test_case import TestCase -@pytest.mark.extra_large class TestViTDetBackbone(TestCase): + @pytest.mark.extra_large def test_call_and_save(self): model = SAMViTDetBBackbone() x = np.ones((1, 1024, 1024, 3)) @@ -43,3 +43,8 @@ def test_call_and_save(self): loaded_model = keras.saving.load_model(path) x_out_loaded = ops.convert_to_numpy(loaded_model(x)) self.assertAllClose(x_out, x_out_loaded) + + def test_pyramid_level_inputs_error(self): + model = SAMViTDetBBackbone() + with self.assertRaises(NotImplementedError, msg="doesn't compute"): + model.pyramid_level_inputs diff --git a/keras_cv/models/segmentation/segment_anything/sam_layers.py b/keras_cv/models/segmentation/segment_anything/sam_layers.py index fb55f7b5cf..1f1237ee01 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_layers.py +++ b/keras_cv/models/segmentation/segment_anything/sam_layers.py @@ -12,10 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras -@keras.utils.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.layers.MLPBlock") class MLPBlock(keras.layers.Layer): def __init__(self, embedding_dim, mlp_dim, activation="gelu", **kwargs): """A MLP block with architecture diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py index 2df711593d..dd4a3647ae 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -12,11 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. +from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops +from keras_cv.layers.serializable_sequential import SerializableSequential -@keras.utils.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.layers.MLP") class MLP(keras.layers.Layer): """A MLP block with architecture `input_dim -> [hidden_dim] * (num_layers - 1) -> output_dim`. @@ -38,7 +40,7 @@ def __init__(self, hidden_dim, output_dim, num_layers, **kwargs): self.dense_net.append(keras.layers.Dense(hidden_dim)) self.dense_net.append(keras.layers.Activation("relu")) self.dense_net.append(keras.layers.Dense(output_dim)) - self.dense_net = keras.models.Sequential(self.dense_net) + self.dense_net = SerializableSequential(self.dense_net) self.built = False @@ -62,7 +64,7 @@ def get_config(self): return config -@keras.utils.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.models.MaskDecoder") class MaskDecoder(keras.models.Model): """Mask decoder for the segment anything model. @@ -125,7 +127,7 @@ def __init__( self.num_mask_tokens, transformer_dim ) - self.output_upscaling = keras.models.Sequential( + self.output_upscaling = SerializableSequential( [ keras.layers.Conv2DTranspose( transformer_dim // 4, kernel_size=2, strides=2 diff --git a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py index 5cb1759740..46f2d27b90 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py @@ -14,11 +14,12 @@ import math +from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops -@keras.saving.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.layers.RandomFrequencyPositionalEmbeddings") class RandomFrequencyPositionalEmbeddings(keras.layers.Layer): def __init__(self, num_positional_features, scale, **kwargs): """Positional encoding using random spatial frequencies. @@ -104,7 +105,7 @@ def get_config(self): return config -@keras.saving.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.models.PromptEncoder") class PromptEncoder(keras.models.Model): """Prompt Encoder for the segment anything model. diff --git a/keras_cv/models/segmentation/segment_anything/sam_transformer.py b/keras_cv/models/segmentation/segment_anything/sam_transformer.py index 4850f5b916..e3771d2139 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_transformer.py +++ b/keras_cv/models/segmentation/segment_anything/sam_transformer.py @@ -12,12 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. +from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops from keras_cv.models.segmentation.segment_anything.sam_layers import MLPBlock -@keras.utils.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.layers.MultiHeadAttentionWithDownsampling") class MultiHeadAttentionWithDownsampling(keras.layers.Layer): """Multi-Head Attention with downsampling. @@ -107,7 +108,7 @@ def get_config(self): return config -@keras.utils.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.layers.TwoWayMultiHeadAttention") class TwoWayMultiHeadAttention(keras.layers.Layer): """Two-way multi-head attention layer. @@ -224,7 +225,7 @@ def get_config(self): return config -@keras.utils.register_keras_serializable(package="keras_cv") +@keras_cv_export("keras_cv.layers.TwoWayTransformer") class TwoWayTransformer(keras.layers.Layer): """A two-way cross-attention transformer decoder. From 360baf7311e7d47b679ed00ee282aabb45968655 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Sun, 20 Aug 2023 19:19:46 -0700 Subject: [PATCH 05/13] Make the backbone functional; unite MLP and MLPBlock --- keras_cv/layers/__init__.py | 1 + keras_cv/layers/detectron2_layers.py | 85 ++++++++++--- .../detectron2/detectron2_backbone.py | 117 ++++++++---------- .../detectron2/detectron2_backbone_presets.py | 15 ++- .../segment_anything/sam_layers.py | 57 +++++---- .../segment_anything/sam_mask_decoder.py | 47 +------ .../segment_anything/sam_prompt_encoder.py | 3 +- .../segmentation/segment_anything/sam_test.py | 2 +- .../segment_anything/sam_transformer.py | 18 ++- 9 files changed, 178 insertions(+), 167 deletions(-) diff --git a/keras_cv/layers/__init__.py b/keras_cv/layers/__init__.py index 41f4905d3b..488a29ae4e 100644 --- a/keras_cv/layers/__init__.py +++ b/keras_cv/layers/__init__.py @@ -17,6 +17,7 @@ from tensorflow.keras.layers import RandomWidth from keras_cv.layers.augmenter import Augmenter +from keras_cv.layers.detectron2_layers import AddPositionalEmbedding from keras_cv.layers.detectron2_layers import MultiHeadAttentionWithRelativePE from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder diff --git a/keras_cv/layers/detectron2_layers.py b/keras_cv/layers/detectron2_layers.py index 289773fb65..e8ba25f686 100644 --- a/keras_cv/layers/detectron2_layers.py +++ b/keras_cv/layers/detectron2_layers.py @@ -15,7 +15,7 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.models.segmentation.segment_anything.sam_layers import MLPBlock +from keras_cv.models.segmentation.segment_anything.sam_layers import MLP @keras_cv_export("keras_cv.layers.MultiHeadAttentionWithRelativePE") @@ -79,16 +79,17 @@ def __init__( trainable=True, ) + def build(self, input_shape=None): self.qkv.build([self.key_dim * self.num_heads]) self.projection.build([self.key_dim * self.num_heads]) - self.built = True def compute_output_shape(self, input_shape): return input_shape def call(self, x): - B, H, W, C = x.shape + shape = ops.shape(x) + B, H, W, C = shape[0], shape[1], shape[2], shape[3] qkv = ops.transpose( ops.reshape( self.qkv(x), (B, H * W, 3, self.num_heads, self.key_dim) @@ -200,11 +201,18 @@ def __init__( if window_size == 0 else (window_size, window_size), ) - self.mlp_block = MLPBlock(project_dim, mlp_dim, activation) + self.mlp_block = MLP( + mlp_dim, + project_dim, + num_layers=2, + activation="gelu", + ) + def build(self, input_shape=None): self.layer_norm1.build([None, None, None, self.project_dim]) self.layer_norm2.build([None, None, None, self.project_dim]) - + self.attention.build() + self.mlp_block.build([None, None, None, self.project_dim]) self.built = True def compute_output_shape(self, input_shape): @@ -274,8 +282,6 @@ def __init__( self.strides = strides self.embed_dim = embed_dim - self.built = False - def build(self, input_shape): self.projection.build(input_shape) self.built = True @@ -299,6 +305,43 @@ def get_config(self): return config +@keras_cv_export("keras_cv.layers.AddPositionalEmbedding") +class AddPositionalEmbedding(keras.layers.Layer): + def __init__(self, img_size, patch_size, embed_dim, **kwargs): + super().__init__(**kwargs) + self.img_size = img_size + self.patch_size = patch_size + self.embed_dim = embed_dim + self.pos_embed = self.add_weight( + name="pos_embed", + shape=( + 1, + img_size // patch_size, + img_size // patch_size, + embed_dim, + ), + initializer="zeros", + trainable=True, + ) + + def compute_output_shape(self, input_shape): + return input_shape + + def call(self, x): + return x + self.pos_embed + + def get_confg(self): + config = super().get_config() + config.update( + { + "img_size": self.img_size, + "patch_size": self.patch_size, + "embed_dim": self.embed_dim, + } + ) + return config + + def get_rel_pos(query_size, key_size, rel_pos): """ Get relative positional embeddings according to the relative positions of @@ -313,10 +356,10 @@ def get_rel_pos(query_size, key_size, rel_pos): tensor: Extracted positional embeddings according to relative positions. """ - max_rel_dist = 2 * max(query_size, key_size) - 1 + max_rel_dist = 2 * ops.maximum(query_size, key_size) - 1 if rel_pos.shape[0] != max_rel_dist: rel_pos_resized = ops.image.resize( - images=ops.reshape( + image=ops.reshape( rel_pos, (1, rel_pos.shape[0], rel_pos.shape[1], 1) ), size=(max_rel_dist, rel_pos.shape[1]), @@ -325,16 +368,16 @@ def get_rel_pos(query_size, key_size, rel_pos): rel_pos_resized = ops.squeeze(rel_pos_resized, axis=(0, -1)) else: rel_pos_resized = rel_pos - query_coordinates = ops.arange(query_size, dtype="float32")[:, None] * max( - key_size / query_size, 1.0 + query_coordinates = ops.arange(query_size, dtype="float32")[:, None] * ( + ops.cast(ops.maximum(key_size // query_size, 1), dtype="float32") ) - key_coordinates = ops.arange(key_size, dtype="float32")[None, :] * max( - query_size / key_size, 1.0 + key_coordinates = ops.arange(key_size, dtype="float32")[None, :] * ( + ops.cast(ops.maximum(query_size // key_size, 1), dtype="float32") ) - relative_coordinates = (query_coordinates - key_coordinates) + ( - key_size - 1 - ) * max(query_size / key_size, 1.0) - relative_coordinates = ops.cast(relative_coordinates, dtype="int64") + relative_coordinates = (query_coordinates - key_coordinates) + ops.cast( + key_size - 1, dtype="float32" + ) * ops.cast(ops.maximum(query_size // key_size, 1), dtype="float32") + relative_coordinates = ops.cast(relative_coordinates, dtype="int32") return ops.take(rel_pos_resized, relative_coordinates, 0) @@ -368,7 +411,8 @@ def add_decomposed_rel_pos( rel_heights = get_rel_pos(query_height, key_height, rel_pos_h) rel_widths = get_rel_pos(query_width, key_width, rel_pos_w) - B, _, C = queries.shape + shape = ops.shape(queries) + B, C = shape[0], shape[2] rel_queries = ops.reshape(queries, (B, query_height, query_width, C)) rel_heights = ops.einsum("bhwc,hkc->bhwk", rel_queries, rel_heights) rel_widths = ops.einsum("bhwc,wkc->bhwk", rel_queries, rel_widths) @@ -385,7 +429,8 @@ def add_decomposed_rel_pos( def window_partition(x, window_size): - B, H, W, C = x.shape + shape = ops.shape(x) + B, H, W, C = shape[0], shape[1], shape[2], shape[3] pad_height = (window_size - H % window_size) % window_size pad_width = (window_size - W % window_size) % window_size if pad_height > 0 or pad_width > 0: @@ -412,7 +457,7 @@ def window_partition(x, window_size): def window_unpartition(windows, window_size, HW_padded, HW): H_padded, W_padded = HW_padded H, W = HW - B = windows.shape[0] // ( + B = ops.shape(windows)[0] // ( (H_padded // window_size) * (W_padded // window_size) ) x = ops.reshape( diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone.py b/keras_cv/models/backbones/detectron2/detectron2_backbone.py index 833b006412..48d3c924a6 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone.py @@ -16,9 +16,10 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras +from keras_cv.layers.detectron2_layers import AddPositionalEmbedding from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder -from keras_cv.layers.serializable_sequential import SerializableSequential +from keras_cv.models import utils from keras_cv.models.backbones.backbone import Backbone from keras_cv.models.backbones.detectron2.detectron2_backbone_presets import ( backbone_presets, @@ -67,9 +68,10 @@ class ViTDetBackbone(Backbone): def __init__( self, - img_size=1024, + input_shape=(1024, 1024, 3), + input_tensor=None, + include_rescaling=False, patch_size=16, - in_chans=3, embed_dim=1280, depth=32, mlp_dim=1280 * 4, @@ -83,44 +85,39 @@ def __init__( layer_norm_epsilon=1e-6, **kwargs ): - super().__init__(**kwargs) - self.img_size = img_size - self.patch_size = patch_size - self.in_chans = in_chans - self.embed_dim = embed_dim - self.depth = depth - self.mlp_dim = mlp_dim - self.num_heads = num_heads - self.out_chans = out_chans - self.use_bias = use_bias - self.use_rel_pos = use_rel_pos - self.use_abs_pos = use_abs_pos - self.window_size = window_size - self.global_attention_indices = global_attention_indices - self.layer_norm_epsilon = layer_norm_epsilon + img_input = utils.parse_model_inputs(input_shape, input_tensor) + + # Check that the input image is well specified. + if img_input.shape[-3] is None or img_input.shape[-2] is None: + raise ValueError( + "Height and width of the image must be specified" + " in `input_shape`." + ) + if img_input.shape[-3] != img_input.shape[-2]: + raise ValueError( + "Input image must be square i.e. the height must" + " be equal to the width in the `input_shape`" + " tuple/tensor." + ) + + img_size = img_input.shape[-3] - self.patch_embed = ViTDetPatchingAndEmbedding( + x = img_input + + if include_rescaling: + # Use common rescaling strategy across keras_cv + x = keras.layers.Rescaling(1.0 / 255.0)(x) + + x = ViTDetPatchingAndEmbedding( kernel_size=(patch_size, patch_size), strides=(patch_size, patch_size), embed_dim=embed_dim, - ) - if self.use_abs_pos: - self.pos_embed = self.add_weight( - name="pos_embed", - shape=( - 1, - self.img_size // self.patch_size, - self.img_size // self.patch_size, - self.embed_dim, - ), - initializer="zeros", - trainable=True, - ) - else: - self.pos_embed = None - self.transformer_blocks = [] + )(x) + if use_abs_pos: + x = AddPositionalEmbedding(img_size, patch_size, embed_dim)(x) + for i in range(depth): - block = WindowedTransformerEncoder( + x = WindowedTransformerEncoder( project_dim=embed_dim, mlp_dim=mlp_dim, num_heads=num_heads, @@ -130,9 +127,8 @@ def __init__( if i not in global_attention_indices else 0, input_size=(img_size // patch_size, img_size // patch_size), - ) - self.transformer_blocks.append(block) - self.bottleneck = SerializableSequential( + )(x) + x = keras.models.Sequential( [ keras.layers.Conv2D( filters=out_chans, kernel_size=1, use_bias=False @@ -146,21 +142,24 @@ def __init__( ), keras.layers.LayerNormalization(epsilon=1e-6), ] - ) + )(x) - self.patch_embed.build( - [None, self.img_size, self.img_size, self.in_chans] - ) - self.bottleneck.build( - [ - None, - self.img_size // self.patch_size, - self.img_size // self.patch_size, - self.embed_dim, - ] - ) + super().__init__(inputs=img_input, outputs=x, **kwargs) - self.built = True + self.patch_size = patch_size + self.embed_dim = embed_dim + self.depth = depth + self.mlp_dim = mlp_dim + self.num_heads = num_heads + self.out_chans = out_chans + self.use_bias = use_bias + self.use_rel_pos = use_rel_pos + self.use_abs_pos = use_abs_pos + self.window_size = window_size + self.global_attention_indices = global_attention_indices + self.layer_norm_epsilon = layer_norm_epsilon + self.input_tensor = input_tensor + self.include_rescaling = include_rescaling @property def pyramid_level_inputs(self): @@ -169,22 +168,14 @@ def pyramid_level_inputs(self): " pyramid level features." ) - def call(self, x): - B, _, _, _ = x.shape - x = self.patch_embed(x) - if self.pos_embed is not None: - x = x + self.pos_embed - for block in self.transformer_blocks: - x = block(x) - return self.bottleneck(x) - def get_config(self): config = super().get_config() config.update( { - "img_size": self.img_size, + "input_shape": self.input_shape, + "input_tensor": self.input_tensor, + "include_rescaling": self.include_rescaling, "patch_size": self.patch_size, - "in_chans": self.in_chans, "embed_dim": self.embed_dim, "depth": self.depth, "mlp_dim": self.mlp_dim, diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py index 9c8f8ca63c..ae2ea4c592 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py @@ -28,9 +28,10 @@ }, "class_name": "keras_cv.models>VitDetBackbone", "config": { - "img_size": 1024, + "input_shape": (1024, 1024, 3), + "input_tensor": None, + "include_rescaling": False, "patch_size": 16, - "in_chans": 3, "embed_dim": 768, "depth": 12, "mlp_dim": 768 * 4, @@ -58,9 +59,10 @@ }, "class_name": "keras_cv.models>VitDetBackbone", "config": { - "img_size": 1024, + "input_shape": (1024, 1024, 3), + "input_tensor": None, + "include_rescaling": False, "patch_size": 16, - "in_chans": 3, "embed_dim": 1024, "depth": 24, "mlp_dim": 1024 * 4, @@ -88,9 +90,10 @@ }, "class_name": "keras_cv.models>VitDetBackbone", "config": { - "img_size": 1024, + "input_shape": (1024, 1024, 3), + "input_tensor": None, + "include_rescaling": False, "patch_size": 16, - "in_chans": 3, "embed_dim": 1280, "depth": 32, "mlp_dim": 1280 * 4, diff --git a/keras_cv/models/segmentation/segment_anything/sam_layers.py b/keras_cv/models/segmentation/segment_anything/sam_layers.py index 1f1237ee01..dac4a32f55 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_layers.py +++ b/keras_cv/models/segmentation/segment_anything/sam_layers.py @@ -14,44 +14,53 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras +from keras_cv.layers.serializable_sequential import SerializableSequential -@keras_cv_export("keras_cv.layers.MLPBlock") -class MLPBlock(keras.layers.Layer): - def __init__(self, embedding_dim, mlp_dim, activation="gelu", **kwargs): - """A MLP block with architecture - `embedding_dim -> mlp_dim -> embedding_dim`. +@keras_cv_export("keras_cv.layers.MLP") +class MLP(keras.layers.Layer): + """A MLP block with architecture + `input_dim -> [hidden_dim] * (num_layers - 1) -> output_dim`. - Args: - embedding_dim (int): The number of units in the input and the - output layer. - mlp_dim (int): The number of units in the hidden layer. - activation (str, optional): The activation of the output. - Defaults to "gelu". - """ - super().__init__(**kwargs) - self.dense_layer1 = keras.layers.Dense(mlp_dim) - self.dense_layer2 = keras.layers.Dense(embedding_dim) - self.activation_layer = keras.layers.Activation(activation) + Args: + hidden_dim (int): The number of units in the hidden layers. + output_dim (int): The number of units in the output layer. + num_layers (int): The total number of dense layers to use. + activation (str): Activation to use in the hidden layers. + Default is `"relu"`. + """ - self.embedding_dim = embedding_dim - self.mlp_dim = mlp_dim + def __init__( + self, hidden_dim, output_dim, num_layers, activation="relu", **kwargs + ): + super().__init__(**kwargs) + self.hidden_dim = hidden_dim + self.output_dim = output_dim + self.num_layers = num_layers self.activation = activation + h = [hidden_dim] * (num_layers - 1) + self.dense_net = [] + for hidden_dim in h: + self.dense_net.append(keras.layers.Dense(hidden_dim)) + self.dense_net.append(keras.layers.Activation(activation)) + self.dense_net.append(keras.layers.Dense(output_dim)) + self.dense_net = SerializableSequential(self.dense_net) - self.dense_layer1.build([self.embedding_dim]) - self.dense_layer2.build([self.mlp_dim]) - + def build(self, input_shape): + self.dense_net.build(input_shape) self.built = True def call(self, x): - return self.dense_layer2(self.activation_layer(self.dense_layer1(x))) + return self.dense_net(x) def get_config(self): config = super().get_config() config.update( { - "embedding_dim": self.embedding_dim, - "mlp_dim": self.mlp_dim, + "hidden_dim": self.hidden_dim, + "output_dim": self.output_dim, + "num_layers": self.num_layers, "activation": self.activation, } ) + return config diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py index dd4a3647ae..fab4fd1190 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -16,52 +16,7 @@ from keras_cv.backend import keras from keras_cv.backend import ops from keras_cv.layers.serializable_sequential import SerializableSequential - - -@keras_cv_export("keras_cv.layers.MLP") -class MLP(keras.layers.Layer): - """A MLP block with architecture - `input_dim -> [hidden_dim] * (num_layers - 1) -> output_dim`. - - Args: - hidden_dim (int): The number of units in the hidden layers. - output_dim (int): The number of units in the output layer. - num_layers (int): The total number of dense layers to use. - """ - - def __init__(self, hidden_dim, output_dim, num_layers, **kwargs): - super().__init__(**kwargs) - self.hidden_dim = hidden_dim - self.output_dim = output_dim - self.num_layers = num_layers - h = [hidden_dim] * (num_layers - 1) - self.dense_net = [] - for hidden_dim in h: - self.dense_net.append(keras.layers.Dense(hidden_dim)) - self.dense_net.append(keras.layers.Activation("relu")) - self.dense_net.append(keras.layers.Dense(output_dim)) - self.dense_net = SerializableSequential(self.dense_net) - - self.built = False - - def build(self, input_shape): - self.dense_net.build(input_shape) - - self.built = True - - def call(self, x): - return self.dense_net(x) - - def get_config(self): - config = super().get_config() - config.update( - { - "hidden_dim": self.hidden_dim, - "output_dim": self.output_dim, - "num_layers": self.num_layers, - } - ) - return config +from keras_cv.models.segmentation.segment_anything.sam_layers import MLP @keras_cv_export("keras_cv.models.MaskDecoder") diff --git a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py index 46f2d27b90..1d73bcf964 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py @@ -17,6 +17,7 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops +from keras_cv.layers.serializable_sequential import SerializableSequential @keras_cv_export("keras_cv.layers.RandomFrequencyPositionalEmbeddings") @@ -176,7 +177,7 @@ def __init__( self.bottom_right_corner_embed = keras.layers.Embedding(1, embed_dim) self.not_a_point_embed = keras.layers.Embedding(1, embed_dim) - self.mask_downscaler = keras.models.Sequential( + self.mask_downscaler = SerializableSequential( [ keras.layers.Conv2D( mask_in_chans // 4, kernel_size=2, strides=2 diff --git a/keras_cv/models/segmentation/segment_anything/sam_test.py b/keras_cv/models/segmentation/segment_anything/sam_test.py index 109f4a1c16..7703f54ff8 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_test.py +++ b/keras_cv/models/segmentation/segment_anything/sam_test.py @@ -33,7 +33,7 @@ from keras_cv.tests.test_case import TestCase -class TestSAM(TestCase): +class SAMTest(TestCase): def get_points_labels_box_mask(self, B): prompt_encoder = PromptEncoder( embed_dim=256, diff --git a/keras_cv/models/segmentation/segment_anything/sam_transformer.py b/keras_cv/models/segmentation/segment_anything/sam_transformer.py index e3771d2139..44f9ef477b 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_transformer.py +++ b/keras_cv/models/segmentation/segment_anything/sam_transformer.py @@ -15,7 +15,7 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.models.segmentation.segment_anything.sam_layers import MLPBlock +from keras_cv.models.segmentation.segment_anything.sam_layers import MLP @keras_cv_export("keras_cv.layers.MultiHeadAttentionWithDownsampling") @@ -58,10 +58,10 @@ def __init__(self, num_heads, key_dim, downsample_rate=1, **kwargs): # Upsample self.out_proj = keras.layers.Dense(self.key_dim * self.num_heads) - self.query_proj.build([self.num_heads * self.key_dim]) - self.key_proj.build([self.num_heads * self.key_dim]) - self.value_proj.build([self.num_heads * self.key_dim]) - self.out_proj.build([self.internal_dims * self.num_heads]) + self.query_proj.build([None, None, self.num_heads * self.key_dim]) + self.key_proj.build([None, None, self.num_heads * self.key_dim]) + self.value_proj.build([None, None, self.num_heads * self.key_dim]) + self.out_proj.build([None, None, self.internal_dims * self.num_heads]) self.built = True @@ -156,7 +156,12 @@ def __init__( ) self.layer_norm2 = keras.layers.LayerNormalization(epsilon=1e-5) - self.mlp_block = MLPBlock(key_dim * num_heads, mlp_dim, activation) + self.mlp_block = MLP( + mlp_dim, + key_dim * num_heads, + num_layers=2, + activation=activation, + ) self.layer_norm3 = keras.layers.LayerNormalization(epsilon=1e-5) self.cross_attention_image_to_token = ( @@ -170,6 +175,7 @@ def __init__( self.layer_norm1.build([None, None, self.num_heads * self.key_dim]) self.layer_norm2.build([None, None, self.num_heads * self.key_dim]) + self.mlp_block.build([None, None, self.num_heads * self.key_dim]) self.layer_norm3.build([None, None, self.num_heads * self.key_dim]) self.layer_norm4.build([None, None, self.num_heads * self.key_dim]) From d7a1eb327fcf20f28deef5ea26b45c3070b0e426 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Thu, 24 Aug 2023 20:03:38 -0700 Subject: [PATCH 06/13] Address David's review comments --- keras_cv/layers/__init__.py | 3 +- keras_cv/layers/detectron2_layers.py | 407 +++++++++++------- keras_cv/models/__init__.py | 10 +- ...{sam_vitdet_b_out.npz => vitdet_b_out.npz} | Bin .../detectron2/detectron2_aliases.py | 34 +- .../detectron2/detectron2_backbone_presets.py | 12 +- .../detectron2_backbone_presets_test.py | 10 +- .../detectron2/detectron2_backbone_test.py | 8 +- keras_cv/models/segmentation/__init__.py | 4 +- .../segmentation/segment_anything/__init__.py | 4 +- .../segment_anything/sam_layers.py | 311 +++++++++++-- .../segment_anything/sam_mask_decoder.py | 8 +- .../segment_anything/sam_prompt_encoder.py | 101 +---- .../segmentation/segment_anything/sam_test.py | 14 +- .../segment_anything/sam_transformer.py | 220 +--------- 15 files changed, 596 insertions(+), 550 deletions(-) rename keras_cv/models/backbones/detectron2/data/{sam_vitdet_b_out.npz => vitdet_b_out.npz} (100%) diff --git a/keras_cv/layers/__init__.py b/keras_cv/layers/__init__.py index 2f4627d1d1..639d685fdb 100644 --- a/keras_cv/layers/__init__.py +++ b/keras_cv/layers/__init__.py @@ -17,10 +17,11 @@ from tensorflow.keras.layers import RandomWidth from keras_cv.layers.augmenter import Augmenter -from keras_cv.layers.detectron2_layers import AddPositionalEmbedding +from keras_cv.layers.detectron2_layers import AddRelativePositionalEmbedding from keras_cv.layers.detectron2_layers import MultiHeadAttentionWithRelativePE from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder +from keras_cv.layers.detectron2_layers import WindowPartitioning from keras_cv.layers.feature_pyramid import FeaturePyramid from keras_cv.layers.fusedmbconv import FusedMBConvBlock from keras_cv.layers.hierarchical_transformer_encoder import ( diff --git a/keras_cv/layers/detectron2_layers.py b/keras_cv/layers/detectron2_layers.py index e8ba25f686..ab1d322e03 100644 --- a/keras_cv/layers/detectron2_layers.py +++ b/keras_cv/layers/detectron2_layers.py @@ -15,7 +15,171 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.models.segmentation.segment_anything.sam_layers import MLP +from keras_cv.layers.serializable_sequential import SerializableSequential + + +@keras_cv_export("keras_cv.layers.MLP") +class MLP(keras.layers.Layer): + """A MLP block with architecture + `input_dim -> [hidden_dim] * (num_layers - 1) -> output_dim`. + + Args: + hidden_dim (int): The number of units in the hidden layers. + output_dim (int): The number of units in the output layer. + num_layers (int): The total number of dense layers to use. + activation (str): Activation to use in the hidden layers. + Default is `"relu"`. + """ + + def __init__( + self, hidden_dim, output_dim, num_layers, activation="relu", **kwargs + ): + super().__init__(**kwargs) + self.hidden_dim = hidden_dim + self.output_dim = output_dim + self.num_layers = num_layers + self.activation = activation + h = [hidden_dim] * (num_layers - 1) + self.dense_net = [] + for hidden_dim in h: + self.dense_net.append(keras.layers.Dense(hidden_dim)) + self.dense_net.append(keras.layers.Activation(activation)) + self.dense_net.append(keras.layers.Dense(output_dim)) + self.dense_net = SerializableSequential(self.dense_net) + + def build(self, input_shape): + self.dense_net.build(input_shape) + self.built = True + + def call(self, x): + return self.dense_net(x) + + def get_config(self): + config = super().get_config() + config.update( + { + "hidden_dim": self.hidden_dim, + "output_dim": self.output_dim, + "num_layers": self.num_layers, + "activation": self.activation, + } + ) + return config + + +@keras_cv_export("keras_cv.layers.AddRelativePositionalEmbedding") +class AddRelativePositionalEmbedding(keras.layers.Layer): + def __init__(self, input_size, key_dim, **kwargs): + super().__init__(**kwargs) + self.input_size = input_size + self.key_dim = key_dim + self.rel_pos_h = self.add_weight( + name="rel_pos_h", + shape=(2 * self.input_size[0] - 1, self.key_dim), + initializer="zeros", + trainable=True, + ) + self.rel_pos_w = self.add_weight( + name="rel_pos_w", + shape=(2 * self.input_size[1] - 1, self.key_dim), + initializer="zeros", + trainable=True, + ) + self.built = True + + def _get_rel_pos(self, query_size, key_size, rel_pos): + """ + Get relative positional embeddings according to the relative positions + of query and key sizes. + + Args: + query_size (int): The number of features of the queries. + key_size (int): The number of features of the keys. + rel_pos (tensor): Relative positional embedding tensor. + + Returns: + tensor: Extracted positional embeddings according to relative + positions. + """ + max_rel_dist = 2 * ops.maximum(query_size, key_size) - 1 + + def _interpolate(): + rel_pos_resized = ops.image.resize( + image=ops.reshape( + rel_pos, (1, rel_pos.shape[0], rel_pos.shape[1], 1) + ), + size=(max_rel_dist, rel_pos.shape[1]), + interpolation="bilinear", + ) + rel_pos_resized = ops.squeeze(rel_pos_resized, axis=(0, -1)) + return rel_pos_resized + + rel_pos_resized = ops.cond( + rel_pos.shape[0] != max_rel_dist, + _interpolate, + lambda *a, **kw: rel_pos, + ) + query_coordinates = ops.arange(query_size, dtype="float32")[:, None] * ( + ops.cast(ops.maximum(key_size // query_size, 1), dtype="float32") + ) + key_coordinates = ops.arange(key_size, dtype="float32")[None, :] * ( + ops.cast(ops.maximum(query_size // key_size, 1), dtype="float32") + ) + relative_coordinates = (query_coordinates - key_coordinates) + ops.cast( + key_size - 1, dtype="float32" + ) * ops.cast(ops.maximum(query_size // key_size, 1), dtype="float32") + relative_coordinates = ops.cast(relative_coordinates, dtype="int32") + return ops.take(rel_pos_resized, relative_coordinates, 0) + + def call(self, attention_map, queries, query_size, key_size): + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + + Args: + attention_map (tensor): Attention map. + queries (tensor): Queries in the attention layer with shape + `(B, q_h * q_w, C)`. + query_size (tuple[int, int]): Spatial sequence size of queries with + `(q_h, q_w)`. + key_size (tuple[int, int]): Spatial sequence size of keys with + `(k_h, k_w)`. + + Returns: + tensor: attention map with added relative positional embeddings. + + References: + - https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa: E501 + """ + query_size = ops.cast(query_size, "int32") + key_size = ops.cast(key_size, "int32") + query_height, query_width = query_size[0], query_size[1] + key_height, key_width = key_size[0], key_size[1] + rel_heights = self._get_rel_pos( + query_height, key_height, self.rel_pos_h + ) + rel_widths = self._get_rel_pos(query_width, key_width, self.rel_pos_w) + + shape = ops.shape(queries) + B, C = shape[0], shape[2] + rel_queries = ops.reshape(queries, (B, query_height, query_width, C)) + rel_heights = ops.einsum("bhwc,hkc->bhwk", rel_queries, rel_heights) + rel_widths = ops.einsum("bhwc,wkc->bhwk", rel_queries, rel_widths) + + attention_map = ops.reshape( + attention_map, (B, query_height, query_width, key_height, key_width) + ) + attention_map = attention_map + rel_heights[..., :, None] + attention_map = attention_map + rel_widths[..., None, :] + attention_map = ops.reshape( + attention_map, + (B, query_height * query_width, key_height * key_width), + ) + return attention_map + + def get_config(self): + config = super().get_config() + config.update({"input_size": self.input_size, "key_dim": self.key_dim}) + return config @keras_cv_export("keras_cv.layers.MultiHeadAttentionWithRelativePE") @@ -52,31 +216,22 @@ def __init__( self.key_dim = key_dim self.scale = self.key_dim**-0.5 self.use_bias = use_bias + self.input_size = input_size + self.use_rel_pos = use_rel_pos self.qkv = keras.layers.Dense( key_dim * self.num_heads * 3, use_bias=self.use_bias ) self.projection = keras.layers.Dense(key_dim * self.num_heads) - self.input_size = input_size - self.use_rel_pos = use_rel_pos if self.use_rel_pos: if input_size is None: raise ValueError( "Input size must be provided if using relative " "positional encoding." ) - self.rel_pos_h = self.add_weight( - name="rel_pos_h", - shape=(2 * self.input_size[0] - 1, self.key_dim), - initializer="zeros", - trainable=True, - ) - self.rel_pos_w = self.add_weight( - name="rel_pos_w", - shape=(2 * self.input_size[1] - 1, self.key_dim), - initializer="zeros", - trainable=True, + self.add_decomposed_reative_pe = AddRelativePositionalEmbedding( + self.input_size, self.key_dim ) def build(self, input_shape=None): @@ -103,13 +258,11 @@ def call(self, x): ) if self.use_rel_pos: - attention_map = add_decomposed_rel_pos( + attention_map = self.add_decomposed_reative_pe( attention_map, - queries, - self.rel_pos_h, - self.rel_pos_w, - (H, W), - (H, W), + queries=queries, + query_size=(H, W), + key_size=(H, W), ) attention_map = ops.softmax(attention_map, axis=-1) x = ops.reshape( @@ -135,6 +288,74 @@ def get_config(self): return config +@keras_cv_export("keras_cv.layers.WindowPartitioning") +class WindowPartitioning(keras.layers.Layer): + def __init__(self, window_size, **kwargs): + super().__init__(**kwargs) + self.window_size = window_size + self.built = True + + def partition(self, x): + shape = ops.shape(x) + B, H, W, C = shape[0], shape[1], shape[2], shape[3] + pad_height = ( + self.window_size - H % self.window_size + ) % self.window_size + pad_width = (self.window_size - W % self.window_size) % self.window_size + if pad_height > 0 or pad_width > 0: + x = ops.pad(x, ((0, 0), (0, pad_height), (0, pad_width), (0, 0))) + H_padded, W_padded = H + pad_height, W + pad_width + x = ops.reshape( + x, + ( + B, + H_padded // self.window_size, + self.window_size, + W_padded // self.window_size, + self.window_size, + C, + ), + ) + windows = ops.reshape( + ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), + (-1, self.window_size, self.window_size, C), + ) + return windows, (H_padded, W_padded) + + def unpartition(self, windows, HW_padded, HW): + H_padded, W_padded = HW_padded + H, W = HW + B = ops.shape(windows)[0] // ( + (H_padded // self.window_size) * (W_padded // self.window_size) + ) + x = ops.reshape( + windows, + ( + B, + H_padded // self.window_size, + W_padded // self.window_size, + self.window_size, + self.window_size, + -1, + ), + ) + x = ops.reshape( + ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), + (B, H_padded, W_padded, -1), + ) + return x[:, :H, :W, :] + + def call(self, x, HW_padded=None, HW=None, mode="partition"): + if mode == "partition": + return self.partition(x) + return self.unpartition(x, HW_padded, HW) + + def get_config(self): + config = super().get_config() + config.update({"window_size": self.window_size}) + return config + + @keras_cv_export("keras_cv.layers.WindowedTransformerEncoder") class WindowedTransformerEncoder(keras.layers.Layer): """Transformer blocks with support of window attention and residual @@ -207,6 +428,7 @@ def __init__( num_layers=2, activation="gelu", ) + self.window_partitioning = WindowPartitioning(window_size) def build(self, input_shape=None): self.layer_norm1.build([None, None, None, self.project_dim]) @@ -225,12 +447,14 @@ def call(self, x): if self.window_size > 0: H, W = x.shape[1], x.shape[2] - x, HW_padded = window_partition(x, self.window_size) + x, HW_padded = self.window_partitioning(x, mode="partition") x = self.attention(x) # Reverse Window Partition if self.window_size > 0: - x = window_unpartition(x, self.window_size, HW_padded, (H, W)) + x = self.window_partitioning( + x, HW_padded=HW_padded, HW=(H, W), mode="unpartition" + ) x = shortcut + x x = x + self.mlp_block(self.layer_norm2(x)) @@ -305,7 +529,9 @@ def get_config(self): return config -@keras_cv_export("keras_cv.layers.AddPositionalEmbedding") +# TODO: Merge this with the `keras_cv.layers.PatchingAndEmbedding` class once +# it has been ported to Keras Core. +@keras_cv_export("keras_cv.layers.detectron2.AddPositionalEmbedding") class AddPositionalEmbedding(keras.layers.Layer): def __init__(self, img_size, patch_size, embed_dim, **kwargs): super().__init__(**kwargs) @@ -340,138 +566,3 @@ def get_confg(self): } ) return config - - -def get_rel_pos(query_size, key_size, rel_pos): - """ - Get relative positional embeddings according to the relative positions of - query and key sizes. - - Args: - query_size (int): The number of features of the queries. - key_size (int): The number of features of the keys. - rel_pos (tensor): Relative positional embedding tensor. - - Returns: - tensor: Extracted positional embeddings according to relative - positions. - """ - max_rel_dist = 2 * ops.maximum(query_size, key_size) - 1 - if rel_pos.shape[0] != max_rel_dist: - rel_pos_resized = ops.image.resize( - image=ops.reshape( - rel_pos, (1, rel_pos.shape[0], rel_pos.shape[1], 1) - ), - size=(max_rel_dist, rel_pos.shape[1]), - interpolation="bilinear", - ) - rel_pos_resized = ops.squeeze(rel_pos_resized, axis=(0, -1)) - else: - rel_pos_resized = rel_pos - query_coordinates = ops.arange(query_size, dtype="float32")[:, None] * ( - ops.cast(ops.maximum(key_size // query_size, 1), dtype="float32") - ) - key_coordinates = ops.arange(key_size, dtype="float32")[None, :] * ( - ops.cast(ops.maximum(query_size // key_size, 1), dtype="float32") - ) - relative_coordinates = (query_coordinates - key_coordinates) + ops.cast( - key_size - 1, dtype="float32" - ) * ops.cast(ops.maximum(query_size // key_size, 1), dtype="float32") - relative_coordinates = ops.cast(relative_coordinates, dtype="int32") - return ops.take(rel_pos_resized, relative_coordinates, 0) - - -def add_decomposed_rel_pos( - attention_map, queries, rel_pos_h, rel_pos_w, query_size, key_size -): - """ - Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. - - Args: - attention_map (tensor): Attention map. - queries (tensor): Queries in the attention layer with shape - `(B, q_h * q_w, C)`. - rel_pos_h (tensor): Relative position embeddings `(Lh, C)` for height - axis. - rel_pos_w (tensor): relative position embeddings `(Lw, C)` for width - axis. - query_size (tuple[int, int]): Spatial sequence size of queries with - `(q_h, q_w)`. - key_size (tuple[int, int]): Spatial sequence size of keys with - `(k_h, k_w)`. - - Returns: - tensor: attention map with added relative positional embeddings. - - References: - - https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa: E501 - """ - query_height, query_width = query_size - key_height, key_width = key_size - rel_heights = get_rel_pos(query_height, key_height, rel_pos_h) - rel_widths = get_rel_pos(query_width, key_width, rel_pos_w) - - shape = ops.shape(queries) - B, C = shape[0], shape[2] - rel_queries = ops.reshape(queries, (B, query_height, query_width, C)) - rel_heights = ops.einsum("bhwc,hkc->bhwk", rel_queries, rel_heights) - rel_widths = ops.einsum("bhwc,wkc->bhwk", rel_queries, rel_widths) - - attention_map = ops.reshape( - attention_map, (B, query_height, query_width, key_height, key_width) - ) - attention_map = attention_map + rel_heights[..., :, None] - attention_map = attention_map + rel_widths[..., None, :] - attention_map = ops.reshape( - attention_map, (B, query_height * query_width, key_height * key_width) - ) - return attention_map - - -def window_partition(x, window_size): - shape = ops.shape(x) - B, H, W, C = shape[0], shape[1], shape[2], shape[3] - pad_height = (window_size - H % window_size) % window_size - pad_width = (window_size - W % window_size) % window_size - if pad_height > 0 or pad_width > 0: - x = ops.pad(x, ((0, 0), (0, pad_height), (0, pad_width), (0, 0))) - H_padded, W_padded = H + pad_height, W + pad_width - x = ops.reshape( - x, - ( - B, - H_padded // window_size, - window_size, - W_padded // window_size, - window_size, - C, - ), - ) - windows = ops.reshape( - ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), - (-1, window_size, window_size, C), - ) - return windows, (H_padded, W_padded) - - -def window_unpartition(windows, window_size, HW_padded, HW): - H_padded, W_padded = HW_padded - H, W = HW - B = ops.shape(windows)[0] // ( - (H_padded // window_size) * (W_padded // window_size) - ) - x = ops.reshape( - windows, - ( - B, - H_padded // window_size, - W_padded // window_size, - window_size, - window_size, - -1, - ), - ) - x = ops.reshape( - ops.transpose(x, axes=(0, 1, 3, 2, 4, 5)), (B, H_padded, W_padded, -1) - ) - return x[:, :H, :W, :] diff --git a/keras_cv/models/__init__.py b/keras_cv/models/__init__.py index e3307479dc..34adfe98f7 100644 --- a/keras_cv/models/__init__.py +++ b/keras_cv/models/__init__.py @@ -44,13 +44,13 @@ DenseNetBackbone, ) from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - SAMViTDetBBackbone, + ViTDetBBackbone, ) from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - SAMViTDetHBackbone, + ViTDetHBackbone, ) from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - SAMViTDetLBackbone, + ViTDetLBackbone, ) from keras_cv.models.backbones.detectron2.detectron2_backbone import ( ViTDetBackbone, @@ -199,8 +199,8 @@ YOLOV8Detector, ) from keras_cv.models.segmentation import DeepLabV3Plus -from keras_cv.models.segmentation import MaskDecoder -from keras_cv.models.segmentation import PromptEncoder +from keras_cv.models.segmentation import SAMMaskDecoder +from keras_cv.models.segmentation import SAMPromptEncoder from keras_cv.models.segmentation import TwoWayTransformer from keras_cv.models.segmentation.segformer.segformer_aliases import SegFormer from keras_cv.models.segmentation.segformer.segformer_aliases import SegFormerB0 diff --git a/keras_cv/models/backbones/detectron2/data/sam_vitdet_b_out.npz b/keras_cv/models/backbones/detectron2/data/vitdet_b_out.npz similarity index 100% rename from keras_cv/models/backbones/detectron2/data/sam_vitdet_b_out.npz rename to keras_cv/models/backbones/detectron2/data/vitdet_b_out.npz diff --git a/keras_cv/models/backbones/detectron2/detectron2_aliases.py b/keras_cv/models/backbones/detectron2/detectron2_aliases.py index 6002259bc4..9f225ccbed 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_aliases.py +++ b/keras_cv/models/backbones/detectron2/detectron2_aliases.py @@ -22,7 +22,7 @@ ) from keras_cv.utils.python_utils import classproperty -ALIAS_DOCSTRING = """{SAM}VitDet{size}Backbone model. +ALIAS_DOCSTRING = """VitDet{size}Backbone model. Reference: - [Detectron2](https://github.com/facebookresearch/detectron2) @@ -36,24 +36,24 @@ input_data = np.ones(shape=(1, 1024, 1024, 3)) # Randomly initialized backbone - model = {SAM}VitDet{size}Backbone() + model = VitDet{size}Backbone() output = model(input_data) ``` """ # noqa: E501 -class SAMViTDetBBackbone(ViTDetBackbone): +class ViTDetBBackbone(ViTDetBackbone): def __new__( cls, **kwargs, ): - return ViTDetBackbone.from_preset("sam_vitdet_b", **kwargs) + return ViTDetBackbone.from_preset("vitdet_b", **kwargs) @classproperty def presets(cls): """Dictionary of preset names and configurations.""" return { - "sam_vitdet_b": copy.deepcopy(backbone_presets["sam_vitdet_b"]), + "vitdet_b": copy.deepcopy(backbone_presets["vitdet_b"]), } @classproperty @@ -63,18 +63,18 @@ def presets_with_weights(cls): return cls.presets -class SAMViTDetLBackbone(ViTDetBackbone): +class ViTDetLBackbone(ViTDetBackbone): def __new__( cls, **kwargs, ): - return ViTDetBackbone.from_preset("sam_vitdet_l", **kwargs) + return ViTDetBackbone.from_preset("vitdet_l", **kwargs) @classproperty def presets(cls): """Dictionary of preset names and configurations.""" return { - "sam_vitdet_l": copy.deepcopy(backbone_presets["sam_vitdet_l"]), + "vitdet_l": copy.deepcopy(backbone_presets["vitdet_l"]), } @classproperty @@ -84,18 +84,18 @@ def presets_with_weights(cls): return cls.presets -class SAMViTDetHBackbone(ViTDetBackbone): +class ViTDetHBackbone(ViTDetBackbone): def __new__( cls, **kwargs, ): - return ViTDetBackbone.from_preset("sam_vitdet_h", **kwargs) + return ViTDetBackbone.from_preset("vitdet_h", **kwargs) @classproperty def presets(cls): """Dictionary of preset names and configurations.""" return { - "sam_vitdet_h": copy.deepcopy(backbone_presets["sam_vitdet_h"]), + "vitdet_h": copy.deepcopy(backbone_presets["vitdet_h"]), } @classproperty @@ -105,12 +105,6 @@ def presets_with_weights(cls): return cls.presets -setattr( - SAMViTDetBBackbone, "__doc__", ALIAS_DOCSTRING.format(SAM="SAM", size="B") -) -setattr( - SAMViTDetLBackbone, "__doc__", ALIAS_DOCSTRING.format(SAM="SAM", size="L") -) -setattr( - SAMViTDetHBackbone, "__doc__", ALIAS_DOCSTRING.format(SAM="SAM", size="H") -) +setattr(ViTDetBBackbone, "__doc__", ALIAS_DOCSTRING.format(size="B")) +setattr(ViTDetLBackbone, "__doc__", ALIAS_DOCSTRING.format(size="L")) +setattr(ViTDetHBackbone, "__doc__", ALIAS_DOCSTRING.format(size="H")) diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py index ae2ea4c592..b5173aae6f 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py @@ -14,7 +14,7 @@ """VitDet model preset configurations.""" backbone_presets = { - "sam_vitdet_b": { + "vitdet_b": { "metadata": { "description": ( "VitDet Backbone for the segment anything model with 12 " @@ -42,10 +42,10 @@ "window_size": 14, "global_attention_indices": [2, 5, 8, 11], }, - # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/sam_vit_b.weights.h5", # noqa: E501 + # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/vitdet_b.weights.h5", # noqa: E501 # "weights_hash": None }, - "sam_vitdet_l": { + "vitdet_l": { "metadata": { "description": ( "VitDet Backbone for the segment anything model with 24 " @@ -73,10 +73,10 @@ "window_size": 14, "global_attention_indices": [5, 11, 17, 23], }, - # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/sam_vit_l.weights.h5", # noqa: E501 + # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/vitdet_l.weights.h5", # noqa: E501 # "weights_hash": None }, - "sam_vitdet_h": { + "vitdet_h": { "metadata": { "description": ( "VitDet Backbone for the segment anything model " @@ -104,7 +104,7 @@ "window_size": 14, "global_attention_indices": [7, 15, 23, 31], }, - # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/sam_vit_h.weights.h5", # noqa: E501 + # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/vitdet_h.weights.h5", # noqa: E501 # "weights_hash": None }, } diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py index 7166dfd6e5..2d72b5934b 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py @@ -20,7 +20,7 @@ from keras_cv.backend import ops from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - SAMViTDetBBackbone, + ViTDetBBackbone, ) from keras_cv.models.backbones.detectron2.detectron2_backbone import ( ViTDetBackbone, @@ -40,7 +40,7 @@ def setUp(self): self.input_batch = np.ones(shape=(1, 1024, 1024, 3)) def test_backbone_output(self): - model = ViTDetBackbone.from_preset("sam_vitdet_b") + model = ViTDetBackbone.from_preset("vitdet_b") outputs = model(self.input_batch) # The forward pass from a preset should be stable! @@ -50,7 +50,7 @@ def test_backbone_output(self): # file, or have found a discrepancy with the upstream source. expected = np.load( - pathlib.Path(__file__).parent / "data" / "sam_vitdet_b_out.npz" + pathlib.Path(__file__).parent / "data" / "vitdet_b_out.npz" ) # Keep a high tolerance, so we are robust to different hardware. self.assertAllClose( @@ -61,11 +61,11 @@ def test_backbone_output(self): ) def test_applications_model_output(self): - model = SAMViTDetBBackbone() + model = ViTDetBBackbone() model(self.input_batch) def test_applications_model_output_with_preset(self): - model = SAMViTDetBBackbone.from_preset("sam_vitdet_b") + model = ViTDetBBackbone.from_preset("vitdet_b") model(self.input_batch) def test_preset_docstring(self): diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py b/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py index 5d7da1ce3f..da75761cb1 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py @@ -20,7 +20,7 @@ from keras_cv.backend import keras from keras_cv.backend import ops from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - SAMViTDetBBackbone, + ViTDetBBackbone, ) from keras_cv.tests.test_case import TestCase @@ -28,7 +28,7 @@ class TestViTDetBackbone(TestCase): @pytest.mark.extra_large def test_call_and_save(self): - model = SAMViTDetBBackbone() + model = ViTDetBBackbone() x = np.ones((1, 1024, 1024, 3)) x_out = ops.convert_to_numpy(model(x)) num_parameters = sum( @@ -38,13 +38,13 @@ def test_call_and_save(self): self.assertEqual(num_parameters, 89_670_912) # saving test - path = os.path.join(self.get_temp_dir(), "sam_tf_model.keras") + path = os.path.join(self.get_temp_dir(), "model.keras") model.save(path) loaded_model = keras.saving.load_model(path) x_out_loaded = ops.convert_to_numpy(loaded_model(x)) self.assertAllClose(x_out, x_out_loaded) def test_pyramid_level_inputs_error(self): - model = SAMViTDetBBackbone() + model = ViTDetBBackbone() with self.assertRaises(NotImplementedError, msg="doesn't compute"): model.pyramid_level_inputs diff --git a/keras_cv/models/segmentation/__init__.py b/keras_cv/models/segmentation/__init__.py index 63bf8f78c7..932a0e4425 100644 --- a/keras_cv/models/segmentation/__init__.py +++ b/keras_cv/models/segmentation/__init__.py @@ -14,6 +14,6 @@ from keras_cv.models.segmentation.deeplab_v3_plus import DeepLabV3Plus from keras_cv.models.segmentation.segformer import SegFormer -from keras_cv.models.segmentation.segment_anything import MaskDecoder -from keras_cv.models.segmentation.segment_anything import PromptEncoder +from keras_cv.models.segmentation.segment_anything import SAMMaskDecoder +from keras_cv.models.segmentation.segment_anything import SAMPromptEncoder from keras_cv.models.segmentation.segment_anything import TwoWayTransformer diff --git a/keras_cv/models/segmentation/segment_anything/__init__.py b/keras_cv/models/segmentation/segment_anything/__init__.py index 982989c216..74353b1417 100644 --- a/keras_cv/models/segmentation/segment_anything/__init__.py +++ b/keras_cv/models/segmentation/segment_anything/__init__.py @@ -13,10 +13,10 @@ # limitations under the License. from keras_cv.models.segmentation.segment_anything.sam_mask_decoder import ( - MaskDecoder, + SAMMaskDecoder, ) from keras_cv.models.segmentation.segment_anything.sam_prompt_encoder import ( - PromptEncoder, + SAMPromptEncoder, ) from keras_cv.models.segmentation.segment_anything.sam_transformer import ( TwoWayTransformer, diff --git a/keras_cv/models/segmentation/segment_anything/sam_layers.py b/keras_cv/models/segmentation/segment_anything/sam_layers.py index dac4a32f55..e728bb857e 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_layers.py +++ b/keras_cv/models/segmentation/segment_anything/sam_layers.py @@ -12,55 +12,308 @@ # See the License for the specific language governing permissions and # limitations under the License. +import math + from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras -from keras_cv.layers.serializable_sequential import SerializableSequential +from keras_cv.backend import ops +from keras_cv.layers.detectron2_layers import MLP + + +@keras_cv_export("keras_cv.layers.MultiHeadAttentionWithDownsampling") +class MultiHeadAttentionWithDownsampling(keras.layers.Layer): + """Multi-Head Attention with downsampling. + + An attention layer that allows for downscaling the size of the embedding + after projection to queries, keys, and values. + + This layer first downscales the features of input queries, keys, and + values using a dense layer. Multi-head attention is then performed + and the attention map is projected back (upscaled) to the number of + input features. + + Args: + num_heads (int): Number of attention heads. + key_dim (int): Size of each attention head for query, key, and + value. + downsample_rate (int, optional): The factor by which to downscale the + input features i.e. the input features of size `key_dim` are + projected down to `key_dim // downsample_rate`. + """ + + def __init__(self, num_heads, key_dim, downsample_rate=1, **kwargs): + super().__init__(**kwargs) + self.num_heads = num_heads + self.key_dim = key_dim + self.downsample_rate = downsample_rate + self.internal_dims = key_dim // downsample_rate + + # Downsample + self.query_proj = keras.layers.Dense( + self.internal_dims * self.num_heads + ) + self.key_proj = keras.layers.Dense(self.internal_dims * self.num_heads) + self.value_proj = keras.layers.Dense( + self.internal_dims * self.num_heads + ) + + # Upsample + self.out_proj = keras.layers.Dense(self.key_dim * self.num_heads) + + self.query_proj.build([None, None, self.num_heads * self.key_dim]) + self.key_proj.build([None, None, self.num_heads * self.key_dim]) + self.value_proj.build([None, None, self.num_heads * self.key_dim]) + self.out_proj.build([None, None, self.internal_dims * self.num_heads]) + + self.built = True + + def __separate_heads(self, x): + B, N, C = x.shape + x = ops.reshape(x, (B, N, self.num_heads, C // self.num_heads)) + return ops.transpose(x, axes=(0, 2, 1, 3)) + def __recombine_heads(self, x): + B, N_H, N_T, C_PH = x.shape + x = ops.transpose(x, axes=(0, 2, 1, 3)) + return ops.reshape(x, (B, N_T, N_H * C_PH)) -@keras_cv_export("keras_cv.layers.MLP") -class MLP(keras.layers.Layer): - """A MLP block with architecture - `input_dim -> [hidden_dim] * (num_layers - 1) -> output_dim`. + def call(self, query, value, key): + query = self.query_proj(query) + key = self.key_proj(key) + value = self.value_proj(value) + + # Separate into heads + query = self.__separate_heads(query) + key = self.__separate_heads(key) + value = self.__separate_heads(value) + + # Attention + C_PH = query.shape[-1] + out = query @ ops.transpose(key, (0, 1, 3, 2)) + out = out / ops.sqrt(ops.cast(C_PH, dtype=self.dtype)) + out = ops.softmax(out, axis=-1) + + # Get output + attention_map = out @ value + attention_map = self.__recombine_heads(attention_map) + return self.out_proj(attention_map) + + def get_config(self): + config = super().get_config() + config.update( + { + "num_heads": self.num_heads, + "key_dim": self.key_dim, + "downsample_rate": self.downsample_rate, + } + ) + return config + + +@keras_cv_export("keras_cv.layers.TwoWayMultiHeadAttention") +class TwoWayMultiHeadAttention(keras.layers.Layer): + """Two-way multi-head attention layer. Args: - hidden_dim (int): The number of units in the hidden layers. - output_dim (int): The number of units in the output layer. - num_layers (int): The total number of dense layers to use. - activation (str): Activation to use in the hidden layers. - Default is `"relu"`. + num_heads (int): Number of attention heads. + key_dim (int): Size of each attention head for query, key, and + value. + mlp_dim (int): Number of hidden dims to use in the mlp block. + skip_first_layer_pe (bool): A boolean indicating whether to skip the + first layer positional embeddings. + attention_downsample_rate (int, optional): The downsample rate to use + in the attention layers. Defaults to 2. + activation (str, optional): The activation for the mlp block's output + layer. Defaults to "relu". """ def __init__( - self, hidden_dim, output_dim, num_layers, activation="relu", **kwargs + self, + num_heads, + key_dim, + mlp_dim, + skip_first_layer_pe, + attention_downsample_rate=2, + activation="relu", + **kwargs, ): super().__init__(**kwargs) - self.hidden_dim = hidden_dim - self.output_dim = output_dim - self.num_layers = num_layers + self.num_heads = num_heads + self.key_dim = key_dim + self.mlp_dim = mlp_dim + self.skip_first_layer_pe = skip_first_layer_pe + self.attention_downsample_rate = attention_downsample_rate self.activation = activation - h = [hidden_dim] * (num_layers - 1) - self.dense_net = [] - for hidden_dim in h: - self.dense_net.append(keras.layers.Dense(hidden_dim)) - self.dense_net.append(keras.layers.Activation(activation)) - self.dense_net.append(keras.layers.Dense(output_dim)) - self.dense_net = SerializableSequential(self.dense_net) - - def build(self, input_shape): - self.dense_net.build(input_shape) + + self.self_attention = MultiHeadAttentionWithDownsampling( + num_heads=num_heads, key_dim=key_dim + ) + self.layer_norm1 = keras.layers.LayerNormalization(epsilon=1e-5) + self.cross_attention_token_to_image = ( + MultiHeadAttentionWithDownsampling( + num_heads=num_heads, + key_dim=key_dim, + downsample_rate=attention_downsample_rate, + ) + ) + self.layer_norm2 = keras.layers.LayerNormalization(epsilon=1e-5) + + self.mlp_block = MLP( + mlp_dim, + key_dim * num_heads, + num_layers=2, + activation=activation, + ) + + self.layer_norm3 = keras.layers.LayerNormalization(epsilon=1e-5) + self.cross_attention_image_to_token = ( + MultiHeadAttentionWithDownsampling( + num_heads=num_heads, + key_dim=key_dim, + downsample_rate=attention_downsample_rate, + ) + ) + self.layer_norm4 = keras.layers.LayerNormalization(epsilon=1e-5) + + self.layer_norm1.build([None, None, self.num_heads * self.key_dim]) + self.layer_norm2.build([None, None, self.num_heads * self.key_dim]) + self.mlp_block.build([None, None, self.num_heads * self.key_dim]) + self.layer_norm3.build([None, None, self.num_heads * self.key_dim]) + self.layer_norm4.build([None, None, self.num_heads * self.key_dim]) + self.built = True - def call(self, x): - return self.dense_net(x) + def call(self, queries, keys, query_pe, key_pe): + if self.skip_first_layer_pe: + queries = self.self_attention( + query=queries, value=queries, key=queries + ) + else: + queries_with_pe = queries + query_pe + attention_map = self.self_attention( + query=queries_with_pe, key=queries_with_pe, value=queries + ) + queries = queries + attention_map + queries = self.layer_norm1(queries) + + queries_with_pe = queries + query_pe + keys_with_pe = keys + key_pe + attention_map = self.cross_attention_token_to_image( + query=queries_with_pe, key=keys_with_pe, value=keys + ) + queries = queries + attention_map + queries = self.layer_norm2(queries) + + mlp_out = self.mlp_block(queries) + queries = queries + mlp_out + queries = self.layer_norm3(queries) + + queries_with_pe = queries + query_pe + keys_with_pe = keys + key_pe + attention_map = self.cross_attention_image_to_token( + query=keys_with_pe, key=queries_with_pe, value=queries + ) + keys = keys + attention_map + keys = self.layer_norm4(keys) + + return queries, keys def get_config(self): config = super().get_config() config.update( { - "hidden_dim": self.hidden_dim, - "output_dim": self.output_dim, - "num_layers": self.num_layers, + "num_heads": self.num_heads, + "key_dim": self.key_dim, + "mlp_dim": self.mlp_dim, + "skip_first_layer_pe": self.skip_first_layer_pe, + "attention_downsample_rate": self.attention_downsample_rate, "activation": self.activation, } ) return config + + +@keras_cv_export("keras_cv.layers.RandomFrequencyPositionalEmbeddings") +class RandomFrequencyPositionalEmbeddings(keras.layers.Layer): + def __init__(self, num_positional_features, scale, **kwargs): + """Positional encoding using random spatial frequencies. + + This layer maps coordinates/points in 2D space to positional + encodings using random spatial frequencies. + + Args: + num_positional_features (int): Number of positional features + in the output. + scale (float): The standard deviation of the random frequencies. + """ + super().__init__(**kwargs) + self.num_positional_features = num_positional_features + self.scale = scale + init_func = lambda *a, **kw: self.scale * ops.random.normal( + shape=(2, self.num_positional_features), dtype=self.dtype + ) + self.positional_encoding_gaussian_matrix = self.add_weight( + name="positional_encoding_gaussian_matrix", + shape=(2, self.num_positional_features), + dtype=self.dtype, + trainable=False, + initializer=init_func, + ) + + self.built = True + + def __positional_encodings(self, coords): + coords = 2 * coords - 1 + coords = coords @ self.positional_encoding_gaussian_matrix + coords = 2 * math.pi * coords + return ops.concatenate([ops.sin(coords), ops.cos(coords)], axis=-1) + + def call(self, size): + """Generate a positional encoding for an image of any given size. + + Args: + size (int): The size of the image. + + Returns: + tensor: Positional encoding of the image. + """ + H, W = size + H, W = ops.cast(H, "int64"), ops.cast(W, "int64") + grid = ops.ones(shape=(H, W), dtype=self.dtype) + y_embed = ops.cumsum(grid, axis=0) - 0.5 + x_embed = ops.cumsum(grid, axis=1) - 0.5 + y_embed = y_embed / ops.cast(H, self.dtype) + x_embed = x_embed / ops.cast(W, self.dtype) + return self.__positional_encodings( + ops.stack([x_embed, y_embed], axis=-1) + ) + + def encode_coordinates(self, coords_input, image_size): + """Positionally encode points that are not normalized to `[0, 1]`. + + Args: + coords_input (tensor): 2D coordinates/points to map. + image_size (tuple[int, int]): Height and width of the image + being prompted. + + Returns: + tensor: Positional encodings of the normalized coordinates. + """ + coords_normalized = ops.stack( + [ + coords_input[..., 0] / image_size[1], + coords_input[..., 1] / image_size[0], + ], + axis=-1, + ) + return self.__positional_encodings(coords_normalized) + + def get_config(self): + config = super().get_config() + config.update( + { + "num_positional_features": self.num_positional_features, + "scale": self.scale, + } + ) + return config diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py index fab4fd1190..350a06efaa 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -15,13 +15,13 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops +from keras_cv.layers.detectron2_layers import MLP from keras_cv.layers.serializable_sequential import SerializableSequential -from keras_cv.models.segmentation.segment_anything.sam_layers import MLP -@keras_cv_export("keras_cv.models.MaskDecoder") -class MaskDecoder(keras.models.Model): - """Mask decoder for the segment anything model. +@keras_cv_export("keras_cv.models.SAMMaskDecoder") +class SAMMaskDecoder(keras.models.Model): + """Mask decoder for the Segment Anything Model (SAM). This lightweight module efficiently maps the image embedding and a set of prompt embeddings to an output mask. Before applying the transformer diff --git a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py index 1d73bcf964..6447b26a34 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py @@ -12,103 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. -import math - from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops from keras_cv.layers.serializable_sequential import SerializableSequential +from keras_cv.models.segmentation.segment_anything.sam_layers import ( + RandomFrequencyPositionalEmbeddings, +) -@keras_cv_export("keras_cv.layers.RandomFrequencyPositionalEmbeddings") -class RandomFrequencyPositionalEmbeddings(keras.layers.Layer): - def __init__(self, num_positional_features, scale, **kwargs): - """Positional encoding using random spatial frequencies. - - This layer maps coordinates/points in 2D space to positional - encodings using random spatial frequencies. - - Args: - num_positional_features (int): Number of positional features - in the output. - scale (float): The standard deviation of the random frequencies. - """ - super().__init__(**kwargs) - self.num_positional_features = num_positional_features - self.scale = scale - init_func = lambda *a, **kw: self.scale * ops.random.normal( - shape=(2, self.num_positional_features), dtype=self.dtype - ) - self.positional_encoding_gaussian_matrix = self.add_weight( - name="positional_emcoding_gaussian_matrix", - shape=(2, self.num_positional_features), - dtype=self.dtype, - trainable=False, - initializer=init_func, - ) - - self.built = True - - def __positional_encodings(self, coords): - coords = 2 * coords - 1 - coords = coords @ self.positional_encoding_gaussian_matrix - coords = 2 * math.pi * coords - return ops.concatenate([ops.sin(coords), ops.cos(coords)], axis=-1) - - def call(self, size): - """Generate a positional encoding for an image of any given size. - - Args: - size (int): The size of the image. - - Returns: - tensor: Positional encoding of the image. - """ - H, W = size - H, W = ops.cast(H, "int64"), ops.cast(W, "int64") - grid = ops.ones(shape=(H, W), dtype=self.dtype) - y_embed = ops.cumsum(grid, axis=0) - 0.5 - x_embed = ops.cumsum(grid, axis=1) - 0.5 - y_embed = y_embed / ops.cast(H, self.dtype) - x_embed = x_embed / ops.cast(W, self.dtype) - return self.__positional_encodings( - ops.stack([x_embed, y_embed], axis=-1) - ) - - def call_with_coords(self, coords_input, image_size): - """Positionally encode points that are not normalized to `[0, 1]`. - - Args: - coords_input (tensor): 2D coordinates/points to map. - image_size (tuple[int, int]): Height and width of the image - being prompted. - - Returns: - tensor: Positional encodings of the normalized coordinates. - """ - coords_normalized = ops.stack( - [ - coords_input[..., 0] / image_size[1], - coords_input[..., 1] / image_size[0], - ], - axis=-1, - ) - return self.__positional_encodings(coords_normalized) - - def get_config(self): - config = super().get_config() - config.update( - { - "num_positional_features": self.num_positional_features, - "scale": self.scale, - } - ) - return config - - -@keras_cv_export("keras_cv.models.PromptEncoder") -class PromptEncoder(keras.models.Model): - """Prompt Encoder for the segment anything model. +@keras_cv_export("keras_cv.models.SAMPromptEncoder") +class SAMPromptEncoder(keras.models.Model): + """Prompt Encoder for the Segment Anything Model (SAM). The prompt encoder generates encodings for three types of prompts: @@ -226,7 +141,7 @@ def __embed_points(self, points, labels, pad): padding_label = -ops.ones((labels.shape[0], 1), dtype=self.dtype) points = ops.concatenate([points, padding_point], axis=1) labels = ops.concatenate([labels, padding_label], axis=1) - point_embeddings = self.positional_embedding_layer.call_with_coords( + point_embeddings = self.positional_embedding_layer.encode_coordinates( points, self.input_image_size ) labels = ops.broadcast_to(labels[..., None], point_embeddings.shape) @@ -248,7 +163,7 @@ def __embed_points(self, points, labels, pad): def __embed_box(self, box): box = box + 0.5 coords = ops.reshape(box, (-1, 2, 2)) - corner_embedding = self.positional_embedding_layer.call_with_coords( + corner_embedding = self.positional_embedding_layer.encode_coordinates( coords, self.input_image_size ) top_left_embedding = ( diff --git a/keras_cv/models/segmentation/segment_anything/sam_test.py b/keras_cv/models/segmentation/segment_anything/sam_test.py index 7703f54ff8..8e144db0b5 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_test.py +++ b/keras_cv/models/segmentation/segment_anything/sam_test.py @@ -18,14 +18,14 @@ from keras_cv.backend import keras from keras_cv.backend import ops +from keras_cv.models.segmentation.segment_anything.sam_layers import ( + TwoWayMultiHeadAttention, +) from keras_cv.models.segmentation.segment_anything.sam_mask_decoder import ( - MaskDecoder, + SAMMaskDecoder, ) from keras_cv.models.segmentation.segment_anything.sam_prompt_encoder import ( - PromptEncoder, -) -from keras_cv.models.segmentation.segment_anything.sam_transformer import ( - TwoWayMultiHeadAttention, + SAMPromptEncoder, ) from keras_cv.models.segmentation.segment_anything.sam_transformer import ( TwoWayTransformer, @@ -35,7 +35,7 @@ class SAMTest(TestCase): def get_points_labels_box_mask(self, B): - prompt_encoder = PromptEncoder( + prompt_encoder = SAMPromptEncoder( embed_dim=256, image_embedding_size=(64, 64), input_image_size=(1024, 1024), @@ -184,7 +184,7 @@ def test_mask_decoder(self): points=points, labels=labels, box=box, mask=input_mask ) image_embeddings = np.random.randn(1, 64, 64, 256) - mask_decoder = MaskDecoder( + mask_decoder = SAMMaskDecoder( transformer_dim=256, transformer=TwoWayTransformer( depth=2, embedding_dim=256, mlp_dim=2048, num_heads=8 diff --git a/keras_cv/models/segmentation/segment_anything/sam_transformer.py b/keras_cv/models/segmentation/segment_anything/sam_transformer.py index 44f9ef477b..f630076e2f 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_transformer.py +++ b/keras_cv/models/segmentation/segment_anything/sam_transformer.py @@ -15,220 +15,12 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.models.segmentation.segment_anything.sam_layers import MLP - - -@keras_cv_export("keras_cv.layers.MultiHeadAttentionWithDownsampling") -class MultiHeadAttentionWithDownsampling(keras.layers.Layer): - """Multi-Head Attention with downsampling. - - An attention layer that allows for downscaling the size of the embedding - after projection to queries, keys, and values. - - This layer first downscales the features of input queries, keys, and - values using a dense layer. Multi-head attention is then performed - and the attention map is projected back (upscaled) to the number of - input features. - - Args: - num_heads (int): Number of attention heads. - key_dim (int): Size of each attention head for query, key, and - value. - downsample_rate (int, optional): The factor by which to downscale the - input features i.e. the input features of size `key_dim` are - projected down to `key_dim // downsample_rate`. - """ - - def __init__(self, num_heads, key_dim, downsample_rate=1, **kwargs): - super().__init__(**kwargs) - self.num_heads = num_heads - self.key_dim = key_dim - self.downsample_rate = downsample_rate - self.internal_dims = key_dim // downsample_rate - - # Downsample - self.query_proj = keras.layers.Dense( - self.internal_dims * self.num_heads - ) - self.key_proj = keras.layers.Dense(self.internal_dims * self.num_heads) - self.value_proj = keras.layers.Dense( - self.internal_dims * self.num_heads - ) - - # Upsample - self.out_proj = keras.layers.Dense(self.key_dim * self.num_heads) - - self.query_proj.build([None, None, self.num_heads * self.key_dim]) - self.key_proj.build([None, None, self.num_heads * self.key_dim]) - self.value_proj.build([None, None, self.num_heads * self.key_dim]) - self.out_proj.build([None, None, self.internal_dims * self.num_heads]) - - self.built = True - - def __separate_heads(self, x): - B, N, C = x.shape - x = ops.reshape(x, (B, N, self.num_heads, C // self.num_heads)) - return ops.transpose(x, axes=(0, 2, 1, 3)) - - def __recombine_heads(self, x): - B, N_H, N_T, C_PH = x.shape - x = ops.transpose(x, axes=(0, 2, 1, 3)) - return ops.reshape(x, (B, N_T, N_H * C_PH)) - - def call(self, query, value, key): - query = self.query_proj(query) - key = self.key_proj(key) - value = self.value_proj(value) - - # Separate into heads - query = self.__separate_heads(query) - key = self.__separate_heads(key) - value = self.__separate_heads(value) - - # Attention - C_PH = query.shape[-1] - out = query @ ops.transpose(key, (0, 1, 3, 2)) - out = out / ops.sqrt(ops.cast(C_PH, dtype=self.dtype)) - out = ops.softmax(out, axis=-1) - - # Get output - attention_map = out @ value - attention_map = self.__recombine_heads(attention_map) - return self.out_proj(attention_map) - - def get_config(self): - config = super().get_config() - config.update( - { - "num_heads": self.num_heads, - "key_dim": self.key_dim, - "downsample_rate": self.downsample_rate, - } - ) - return config - - -@keras_cv_export("keras_cv.layers.TwoWayMultiHeadAttention") -class TwoWayMultiHeadAttention(keras.layers.Layer): - """Two-way multi-head attention layer. - - Args: - num_heads (int): Number of attention heads. - key_dim (int): Size of each attention head for query, key, and - value. - mlp_dim (int): Number of hidden dims to use in the mlp block. - skip_first_layer_pe (bool): A boolean indicating whether to skip the - first layer positional embeddings. - attention_downsample_rate (int, optional): The downsample rate to use - in the attention layers. Defaults to 2. - activation (str, optional): The activation for the mlp block's output - layer. Defaults to "relu". - """ - - def __init__( - self, - num_heads, - key_dim, - mlp_dim, - skip_first_layer_pe, - attention_downsample_rate=2, - activation="relu", - **kwargs, - ): - super().__init__(**kwargs) - self.num_heads = num_heads - self.key_dim = key_dim - self.mlp_dim = mlp_dim - self.skip_first_layer_pe = skip_first_layer_pe - self.attention_downsample_rate = attention_downsample_rate - self.activation = activation - - self.self_attention = MultiHeadAttentionWithDownsampling( - num_heads=num_heads, key_dim=key_dim - ) - self.layer_norm1 = keras.layers.LayerNormalization(epsilon=1e-5) - self.cross_attention_token_to_image = ( - MultiHeadAttentionWithDownsampling( - num_heads=num_heads, - key_dim=key_dim, - downsample_rate=attention_downsample_rate, - ) - ) - self.layer_norm2 = keras.layers.LayerNormalization(epsilon=1e-5) - - self.mlp_block = MLP( - mlp_dim, - key_dim * num_heads, - num_layers=2, - activation=activation, - ) - - self.layer_norm3 = keras.layers.LayerNormalization(epsilon=1e-5) - self.cross_attention_image_to_token = ( - MultiHeadAttentionWithDownsampling( - num_heads=num_heads, - key_dim=key_dim, - downsample_rate=attention_downsample_rate, - ) - ) - self.layer_norm4 = keras.layers.LayerNormalization(epsilon=1e-5) - - self.layer_norm1.build([None, None, self.num_heads * self.key_dim]) - self.layer_norm2.build([None, None, self.num_heads * self.key_dim]) - self.mlp_block.build([None, None, self.num_heads * self.key_dim]) - self.layer_norm3.build([None, None, self.num_heads * self.key_dim]) - self.layer_norm4.build([None, None, self.num_heads * self.key_dim]) - - self.built = True - - def call(self, queries, keys, query_pe, key_pe): - if self.skip_first_layer_pe: - queries = self.self_attention( - query=queries, value=queries, key=queries - ) - else: - queries_with_pe = queries + query_pe - attention_map = self.self_attention( - query=queries_with_pe, key=queries_with_pe, value=queries - ) - queries = queries + attention_map - queries = self.layer_norm1(queries) - - queries_with_pe = queries + query_pe - keys_with_pe = keys + key_pe - attention_map = self.cross_attention_token_to_image( - query=queries_with_pe, key=keys_with_pe, value=keys - ) - queries = queries + attention_map - queries = self.layer_norm2(queries) - - mlp_out = self.mlp_block(queries) - queries = queries + mlp_out - queries = self.layer_norm3(queries) - - queries_with_pe = queries + query_pe - keys_with_pe = keys + key_pe - attention_map = self.cross_attention_image_to_token( - query=keys_with_pe, key=queries_with_pe, value=queries - ) - keys = keys + attention_map - keys = self.layer_norm4(keys) - - return queries, keys - - def get_config(self): - config = super().get_config() - config.update( - { - "num_heads": self.num_heads, - "key_dim": self.key_dim, - "mlp_dim": self.mlp_dim, - "skip_first_layer_pe": self.skip_first_layer_pe, - "attention_downsample_rate": self.attention_downsample_rate, - "activation": self.activation, - } - ) - return config +from keras_cv.models.segmentation.segment_anything.sam_layers import ( + MultiHeadAttentionWithDownsampling, +) +from keras_cv.models.segmentation.segment_anything.sam_layers import ( + TwoWayMultiHeadAttention, +) @keras_cv_export("keras_cv.layers.TwoWayTransformer") From ada60406d00f657a80558491a9eaae081d1a3fb2 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Fri, 8 Sep 2023 15:44:35 -0700 Subject: [PATCH 07/13] Add SAM Task model; make MaskDecoder and PromptEncoder XLA compatible --- keras_cv/backend/tf_ops.py | 1 + keras_cv/layers/detectron2_layers.py | 52 +-- keras_cv/layers/detectron2_layers_test.py | 9 + keras_cv/models/__init__.py | 1 + .../detectron2/detectron2_backbone.py | 49 ++- .../detectron2/detectron2_backbone_presets.py | 6 + keras_cv/models/segmentation/__init__.py | 1 + .../segmentation/segment_anything/__init__.py | 3 + .../segmentation/segment_anything/sam.py | 101 +++++ .../segment_anything/sam_layers.py | 26 +- .../segment_anything/sam_mask_decoder.py | 112 +++--- .../segment_anything/sam_prompt_encoder.py | 245 +++++++----- .../segmentation/segment_anything/sam_test.py | 359 ++++++++++++------ .../segment_anything/sam_transformer.py | 50 ++- keras_cv/models/utils.py | 8 +- sam_testing.py | 32 ++ 16 files changed, 709 insertions(+), 346 deletions(-) create mode 100644 sam_testing.py diff --git a/keras_cv/backend/tf_ops.py b/keras_cv/backend/tf_ops.py index dc93712095..106c9d0a33 100644 --- a/keras_cv/backend/tf_ops.py +++ b/keras_cv/backend/tf_ops.py @@ -21,6 +21,7 @@ from keras_core.src.backend.tensorflow.numpy import * # noqa: F403, F401 # Some TF APIs where the numpy API doesn't support raggeds that we need +from tensorflow import broadcast_to # noqa: F403, F401 from tensorflow import concat as concatenate # noqa: F403, F401 from tensorflow import range as arange # noqa: F403, F401 from tensorflow import reduce_all as all # noqa: F403, F401 diff --git a/keras_cv/layers/detectron2_layers.py b/keras_cv/layers/detectron2_layers.py index ab1d322e03..6e59abcc19 100644 --- a/keras_cv/layers/detectron2_layers.py +++ b/keras_cv/layers/detectron2_layers.py @@ -12,13 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +import numpy as np + from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops from keras_cv.layers.serializable_sequential import SerializableSequential -@keras_cv_export("keras_cv.layers.MLP") class MLP(keras.layers.Layer): """A MLP block with architecture `input_dim -> [hidden_dim] * (num_layers - 1) -> output_dim`. @@ -101,34 +102,31 @@ def _get_rel_pos(self, query_size, key_size, rel_pos): tensor: Extracted positional embeddings according to relative positions. """ - max_rel_dist = 2 * ops.maximum(query_size, key_size) - 1 + max_rel_dist = 2 * max(query_size, key_size) - 1 - def _interpolate(): + if ops.shape(rel_pos)[0] != max_rel_dist: rel_pos_resized = ops.image.resize( image=ops.reshape( - rel_pos, (1, rel_pos.shape[0], rel_pos.shape[1], 1) + rel_pos, + (1, ops.shape(rel_pos)[0], ops.shape(rel_pos)[1], 1), ), - size=(max_rel_dist, rel_pos.shape[1]), + size=(max_rel_dist, ops.shape(rel_pos)[1]), interpolation="bilinear", ) rel_pos_resized = ops.squeeze(rel_pos_resized, axis=(0, -1)) return rel_pos_resized - - rel_pos_resized = ops.cond( - rel_pos.shape[0] != max_rel_dist, - _interpolate, - lambda *a, **kw: rel_pos, - ) - query_coordinates = ops.arange(query_size, dtype="float32")[:, None] * ( - ops.cast(ops.maximum(key_size // query_size, 1), dtype="float32") + else: + rel_pos_resized = rel_pos + query_coordinates = np.arange(query_size, dtype="float32")[:, None] * ( + max(key_size / query_size, 1.0) ) - key_coordinates = ops.arange(key_size, dtype="float32")[None, :] * ( - ops.cast(ops.maximum(query_size // key_size, 1), dtype="float32") + key_coordinates = np.arange(key_size, dtype="float32")[None, :] * ( + max(query_size / key_size, 1.0) ) - relative_coordinates = (query_coordinates - key_coordinates) + ops.cast( - key_size - 1, dtype="float32" - ) * ops.cast(ops.maximum(query_size // key_size, 1), dtype="float32") - relative_coordinates = ops.cast(relative_coordinates, dtype="int32") + relative_coordinates = (query_coordinates - key_coordinates) + ( + key_size - 1 + ) * max(query_size / key_size, 1.0) + relative_coordinates = relative_coordinates.astype("int32") return ops.take(rel_pos_resized, relative_coordinates, 0) def call(self, attention_map, queries, query_size, key_size): @@ -150,8 +148,6 @@ def call(self, attention_map, queries, query_size, key_size): References: - https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa: E501 """ - query_size = ops.cast(query_size, "int32") - key_size = ops.cast(key_size, "int32") query_height, query_width = query_size[0], query_size[1] key_height, key_width = key_size[0], key_size[1] rel_heights = self._get_rel_pos( @@ -345,11 +341,6 @@ def unpartition(self, windows, HW_padded, HW): ) return x[:, :H, :W, :] - def call(self, x, HW_padded=None, HW=None, mode="partition"): - if mode == "partition": - return self.partition(x) - return self.unpartition(x, HW_padded, HW) - def get_config(self): config = super().get_config() config.update({"window_size": self.window_size}) @@ -445,15 +436,14 @@ def call(self, x): x = self.layer_norm1(x) # Window Partition if self.window_size > 0: - H, W = x.shape[1], x.shape[2] - - x, HW_padded = self.window_partitioning(x, mode="partition") + H, W = ops.shape(x)[1], ops.shape(x)[2] + x, HW_padded = self.window_partitioning.partition(x) x = self.attention(x) # Reverse Window Partition if self.window_size > 0: - x = self.window_partitioning( - x, HW_padded=HW_padded, HW=(H, W), mode="unpartition" + x = self.window_partitioning.unpartition( + x, HW_padded=HW_padded, HW=(H, W) ) x = shortcut + x diff --git a/keras_cv/layers/detectron2_layers_test.py b/keras_cv/layers/detectron2_layers_test.py index aecea0a379..f56d3d98cf 100644 --- a/keras_cv/layers/detectron2_layers_test.py +++ b/keras_cv/layers/detectron2_layers_test.py @@ -15,6 +15,7 @@ import numpy as np from keras_cv.backend import ops +from keras_cv.layers.detectron2_layers import AddPositionalEmbedding from keras_cv.layers.detectron2_layers import MultiHeadAttentionWithRelativePE from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder @@ -53,3 +54,11 @@ def test_vit_patching_and_embedding(self): x = np.ones((1, 1024, 1024, 3)) x_out = vit_patching_and_embedding(x) self.assertEqual(x_out.shape, (1, 64, 64, 768)) + + def test_add_positional_embedding(self): + add_positional_embedding = AddPositionalEmbedding( + img_size=1024, patch_size=16, embed_dim=256 + ) + x = np.ones((1, 64, 64, 256)) + x_out = add_positional_embedding(x) + self.assertEqual(x_out.shape, (1, 64, 64, 256)) diff --git a/keras_cv/models/__init__.py b/keras_cv/models/__init__.py index 34adfe98f7..e1526670c3 100644 --- a/keras_cv/models/__init__.py +++ b/keras_cv/models/__init__.py @@ -201,6 +201,7 @@ from keras_cv.models.segmentation import DeepLabV3Plus from keras_cv.models.segmentation import SAMMaskDecoder from keras_cv.models.segmentation import SAMPromptEncoder +from keras_cv.models.segmentation import SegmentAnythingModel from keras_cv.models.segmentation import TwoWayTransformer from keras_cv.models.segmentation.segformer.segformer_aliases import SegFormer from keras_cv.models.segmentation.segformer.segformer_aliases import SegFormerB0 diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone.py b/keras_cv/models/backbones/detectron2/detectron2_backbone.py index 48d3c924a6..6935a6b195 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone.py @@ -33,23 +33,27 @@ class ViTDetBackbone(Backbone): relative positional encodings. Args: - img_size (int, optional): The size of the input image. Defaults to - `1024`. + input_shape (tuple[int], optional): The size of the input image in + `(H, W, C)` format. Defaults to `(1024, 1024, 3)`. + input_tensor (KerasTensor, optional): Output of + `keras.layers.Input()`) to use as image input for the model. + Defaults to `None`. + include_rescaling (bool, optional): Whether to rescale the inputs. If + set to `True`, inputs will be passed through a + `Rescaling(1/255.0)` layer. Defaults to `False`. patch_size (int, optional): the patch size to be supplied to the Patching layer to turn input images into a flattened sequence of patches. Defaults to `16`. - in_chans (int, optional): The number of channels in the input image. - Defaults to `3`. embed_dim (int, optional): The latent dimensionality to be projected into in the output of each stacked windowed transformer encoder. - Defaults to `1280`. + Defaults to `768`. depth (int, optional): The number of transformer encoder layers to - stack in the Vision Transformer. Defaults to `32`. - mlp_dim (_type_, optional): The dimensionality of the hidden Dense - layer in the transformer MLP head. Defaults to `1280*4`. + stack in the Vision Transformer. Defaults to `12`. + mlp_dim (int, optional): The dimensionality of the hidden Dense + layer in the transformer MLP head. Defaults to `768*4`. num_heads (int, optional): the number of heads to use in the `MultiHeadAttentionWithRelativePE` layer of each transformer - encoder. Defaults to `16`. + encoder. Defaults to `12`. out_chans (int, optional): The number of channels (features) in the output (image encodings). Defaults to `256`. use_bias (bool, optional): Whether to use bias to project the keys, @@ -57,35 +61,38 @@ class ViTDetBackbone(Backbone): use_abs_pos (bool, optional): Whether to add absolute positional embeddings to the output patches. Defaults to `True`. use_rel_pos (bool, optional): Whether to use relative positional - emcodings in the attention layer. Defaults to `False`. + emcodings in the attention layer. Defaults to `True`. window_size (int, optional): The size of the window for windowed - attention in the transformer encoder blocks. Defaults to `0`. + attention in the transformer encoder blocks. Defaults to `14`. global_attention_indices (list, optional): Indexes for blocks using - global attention. Defaults to `[7, 15, 23, 31]`. + global attention. Defaults to `[2, 5, 8, 11]`. layer_norm_epsilon (int, optional): The epsilon to use in the layer normalization blocks in transformer encoder. Defaults to `1e-6`. """ def __init__( self, + *, input_shape=(1024, 1024, 3), input_tensor=None, include_rescaling=False, patch_size=16, - embed_dim=1280, - depth=32, - mlp_dim=1280 * 4, - num_heads=16, + embed_dim=768, + depth=12, + mlp_dim=768 * 4, + num_heads=12, out_chans=256, use_bias=True, use_abs_pos=True, - use_rel_pos=False, - window_size=0, - global_attention_indices=[7, 15, 23, 31], + use_rel_pos=True, + window_size=14, + global_attention_indices=[2, 5, 8, 11], layer_norm_epsilon=1e-6, **kwargs ): - img_input = utils.parse_model_inputs(input_shape, input_tensor) + img_input = utils.parse_model_inputs( + input_shape, input_tensor, name="images" + ) # Check that the input image is well specified. if img_input.shape[-3] is None or img_input.shape[-2] is None: @@ -172,7 +179,7 @@ def get_config(self): config = super().get_config() config.update( { - "input_shape": self.input_shape, + "input_shape": self.input_shape[1:], "input_tensor": self.input_tensor, "include_rescaling": self.include_rescaling, "patch_size": self.patch_size, diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py index b5173aae6f..49105427c6 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py @@ -38,9 +38,11 @@ "num_heads": 12, "out_chans": 256, "use_bias": True, + "use_abs_pos": True, "use_rel_pos": True, "window_size": 14, "global_attention_indices": [2, 5, 8, 11], + "layer_norm_epsilon": 1e-6, }, # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/vitdet_b.weights.h5", # noqa: E501 # "weights_hash": None @@ -69,9 +71,11 @@ "num_heads": 16, "out_chans": 256, "use_bias": True, + "use_abs_pos": True, "use_rel_pos": True, "window_size": 14, "global_attention_indices": [5, 11, 17, 23], + "layer_norm_epsilon": 1e-6, }, # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/vitdet_l.weights.h5", # noqa: E501 # "weights_hash": None @@ -100,9 +104,11 @@ "num_heads": 16, "out_chans": 256, "use_bias": True, + "use_abs_pos": True, "use_rel_pos": True, "window_size": 14, "global_attention_indices": [7, 15, 23, 31], + "layer_norm_epsilon": 1e-6, }, # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/vitdet_h.weights.h5", # noqa: E501 # "weights_hash": None diff --git a/keras_cv/models/segmentation/__init__.py b/keras_cv/models/segmentation/__init__.py index 932a0e4425..aa4ffab4a4 100644 --- a/keras_cv/models/segmentation/__init__.py +++ b/keras_cv/models/segmentation/__init__.py @@ -16,4 +16,5 @@ from keras_cv.models.segmentation.segformer import SegFormer from keras_cv.models.segmentation.segment_anything import SAMMaskDecoder from keras_cv.models.segmentation.segment_anything import SAMPromptEncoder +from keras_cv.models.segmentation.segment_anything import SegmentAnythingModel from keras_cv.models.segmentation.segment_anything import TwoWayTransformer diff --git a/keras_cv/models/segmentation/segment_anything/__init__.py b/keras_cv/models/segmentation/segment_anything/__init__.py index 74353b1417..945955261f 100644 --- a/keras_cv/models/segmentation/segment_anything/__init__.py +++ b/keras_cv/models/segmentation/segment_anything/__init__.py @@ -12,6 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. +from keras_cv.models.segmentation.segment_anything.sam import ( + SegmentAnythingModel, +) from keras_cv.models.segmentation.segment_anything.sam_mask_decoder import ( SAMMaskDecoder, ) diff --git a/keras_cv/models/segmentation/segment_anything/sam.py b/keras_cv/models/segmentation/segment_anything/sam.py index 3992ffb59a..2efa590262 100644 --- a/keras_cv/models/segmentation/segment_anything/sam.py +++ b/keras_cv/models/segmentation/segment_anything/sam.py @@ -11,3 +11,104 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + +from keras_cv.api_export import keras_cv_export +from keras_cv.backend import keras +from keras_cv.models.backbones.detectron2.detectron2_backbone import ( + ViTDetBackbone, +) +from keras_cv.models.segmentation.segment_anything.sam_mask_decoder import ( + SAMMaskDecoder, +) +from keras_cv.models.segmentation.segment_anything.sam_prompt_encoder import ( + SAMPromptEncoder, +) +from keras_cv.models.task import Task + + +@keras_cv_export("keras_cv.models.SegmentAnythingModel") +class SegmentAnythingModel(Task): + def __init__( + self, + *, + backbone: ViTDetBackbone, + prompt_encoder: SAMPromptEncoder, + mask_decoder: SAMMaskDecoder, + **kwargs + ): + if not isinstance(backbone, ViTDetBackbone): + raise ValueError("`backbone` must be a `ViTDetBackbone`.") + if not isinstance(prompt_encoder, SAMPromptEncoder): + raise ValueError("`prompt_encoder` must be a `SAMPromptEncoder`.") + if not isinstance(mask_decoder, SAMMaskDecoder): + raise ValueError("`mask_decoder` must be a `SAMMaskDecoder`.") + + # Get the image encoder input -- Images + backbone_input = backbone.input + + # Define the prompt encoder inputs -- Prompts + prompt_inputs = { + "points": keras.Input(shape=[None, 2], name="points"), + "labels": keras.Input(shape=[None], name="labels"), + "box": keras.Input(shape=[None, 2, 2], name="box"), + "mask": keras.Input(shape=[None, None, None, 1], name="mask"), + } + + # All Inputs -- Images + Prompts + all_inputs = {"images": backbone_input} + all_inputs.update(prompt_inputs) + + # Build the prompt encoder + prompt_embeddings = prompt_encoder(prompt_inputs) + + # Define the mask decoder inputs + mask_decoder_inputs = { + "image_embeddings": backbone.output, + "image_pe": prompt_embeddings["dense_positional_embeddings"], + "sparse_prompt_embeddings": prompt_embeddings["sparse_embeddings"], + "dense_prompt_embeddings": prompt_embeddings["dense_embeddings"], + } + + # Build the mask decoder + outputs = mask_decoder(mask_decoder_inputs) + + super().__init__(inputs=all_inputs, outputs=outputs, **kwargs) + + self.backbone = backbone + self.prompt_encoder = prompt_encoder + self.mask_decoder = mask_decoder + + def fit(self, *args, **kwargs): + raise NotImplementedError( + "Segment Anything Model only supports inference for now. Training" + " the model isn't supported yet." + ) + + def get_config(self): + config = super().get_config() + config.update( + { + "backbone": keras.saving.serialize_keras_object(self.backbone), + "prompt_encoder": keras.saving.serialize_keras_object( + self.prompt_encoder + ), + "mask_decoder": keras.saving.serialize_keras_object( + self.mask_decoder + ), + } + ) + return config + + @classmethod + def from_config(cls, config): + config.update( + { + "prompt_encoder": keras.layers.deserialize( + config["prompt_encoder"] + ), + "mask_decoder": keras.layers.deserialize( + config["mask_decoder"] + ), + } + ) + return super().from_config(config) diff --git a/keras_cv/models/segmentation/segment_anything/sam_layers.py b/keras_cv/models/segmentation/segment_anything/sam_layers.py index e728bb857e..4c6b556f02 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_layers.py +++ b/keras_cv/models/segmentation/segment_anything/sam_layers.py @@ -60,20 +60,22 @@ def __init__(self, num_heads, key_dim, downsample_rate=1, **kwargs): # Upsample self.out_proj = keras.layers.Dense(self.key_dim * self.num_heads) + def build(self, input_shape=None): self.query_proj.build([None, None, self.num_heads * self.key_dim]) self.key_proj.build([None, None, self.num_heads * self.key_dim]) self.value_proj.build([None, None, self.num_heads * self.key_dim]) self.out_proj.build([None, None, self.internal_dims * self.num_heads]) - self.built = True def __separate_heads(self, x): - B, N, C = x.shape + shape = ops.shape(x) + B, N, C = shape[0], shape[1], shape[2] x = ops.reshape(x, (B, N, self.num_heads, C // self.num_heads)) return ops.transpose(x, axes=(0, 2, 1, 3)) def __recombine_heads(self, x): - B, N_H, N_T, C_PH = x.shape + shape = ops.shape(x) + B, N_H, N_T, C_PH = shape[0], shape[1], shape[2], shape[3] x = ops.transpose(x, axes=(0, 2, 1, 3)) return ops.reshape(x, (B, N_T, N_H * C_PH)) @@ -88,7 +90,7 @@ def call(self, query, value, key): value = self.__separate_heads(value) # Attention - C_PH = query.shape[-1] + C_PH = ops.shape(query)[-1] out = query @ ops.transpose(key, (0, 1, 3, 2)) out = out / ops.sqrt(ops.cast(C_PH, dtype=self.dtype)) out = ops.softmax(out, axis=-1) @@ -175,12 +177,15 @@ def __init__( ) self.layer_norm4 = keras.layers.LayerNormalization(epsilon=1e-5) + def build(self, input_shape=None): + self.self_attention.build() self.layer_norm1.build([None, None, self.num_heads * self.key_dim]) + self.cross_attention_token_to_image.build() self.layer_norm2.build([None, None, self.num_heads * self.key_dim]) self.mlp_block.build([None, None, self.num_heads * self.key_dim]) self.layer_norm3.build([None, None, self.num_heads * self.key_dim]) + self.cross_attention_image_to_token.build() self.layer_norm4.build([None, None, self.num_heads * self.key_dim]) - self.built = True def call(self, queries, keys, query_pe, key_pe): @@ -260,25 +265,28 @@ def __init__(self, num_positional_features, scale, **kwargs): initializer=init_func, ) + def build(self, input_shape=None): self.built = True def __positional_encodings(self, coords): - coords = 2 * coords - 1 + coords = coords * 2 - 1 coords = coords @ self.positional_encoding_gaussian_matrix - coords = 2 * math.pi * coords + coords = coords * (2 * math.pi) return ops.concatenate([ops.sin(coords), ops.cos(coords)], axis=-1) def call(self, size): + return self.encode_image(size) + + def encode_image(self, size): """Generate a positional encoding for an image of any given size. Args: - size (int): The size of the image. + size (tuple[int, int]): The size of the image. Returns: tensor: Positional encoding of the image. """ H, W = size - H, W = ops.cast(H, "int64"), ops.cast(W, "int64") grid = ops.ones(shape=(H, W), dtype=self.dtype) y_embed = ops.cumsum(grid, axis=0) - 0.5 x_embed = ops.cumsum(grid, axis=1) - 0.5 diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py index 350a06efaa..34d038e8c9 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -17,10 +17,13 @@ from keras_cv.backend import ops from keras_cv.layers.detectron2_layers import MLP from keras_cv.layers.serializable_sequential import SerializableSequential +from keras_cv.models.segmentation.segment_anything.sam_transformer import ( + TwoWayTransformer, +) -@keras_cv_export("keras_cv.models.SAMMaskDecoder") -class SAMMaskDecoder(keras.models.Model): +@keras_cv_export("keras_cv.layers.SAMMaskDecoder") +class SAMMaskDecoder(keras.layers.Layer): """Mask decoder for the Segment Anything Model (SAM). This lightweight module efficiently maps the image embedding and a set of @@ -41,18 +44,22 @@ class SAMMaskDecoder(keras.models.Model): product between the upscaled image embedding and the MLP's output. Args: - transformer_dim (int): The number of input features to the transformer - decoder. - transformer (keras.layers.Layer): A transformer decoder. - num_multimask_outputs (int): Number of multimask outputs. The model - would generate these many extra masks when `multimask_output` is - `True`. - iou_head_depth (int): The depth of the dense net used to predict the - IoU confidence score. - iou_head_hidden_dim (int): The number of units in the hidden layers - used in the dense net to predict the IoU confidence score. + transformer_dim (int, optional): The number of input features to the + transformer decoder. Defaults to `256`. + transformer (keras.layers.Layer, optional): A transformer decoder. + Defaults to `None`. When `None`, a + `keras_cv.models.TwoWayTransformer` layer is used. + num_multimask_outputs (int, optional): Number of multimask outputs. + The model would generate these many extra masks. The total masks + generated by the model are `1 + num_multimask_outputs`. Defaults + to `3`. + iou_head_depth (int, optional): The depth of the dense net used to + predict the IoU confidence score. Defaults to `3`. + iou_head_hidden_dim (int, optional): The number of units in the hidden + layers used in the dense net to predict the IoU confidence score. + Defaults to `256`. activation (str, optional): Activation to use in the mask upscaler - network. Defaults to "gelu". + network. Defaults to `"gelu"`. References: - [Segment Anything](https://arxiv.org/abs/2304.02643) @@ -60,16 +67,19 @@ class SAMMaskDecoder(keras.models.Model): def __init__( self, - transformer_dim, - transformer, - num_multimask_outputs, - iou_head_depth, - iou_head_hidden_dim, + *, + transformer_dim=256, + transformer=None, + num_multimask_outputs=3, + iou_head_depth=3, + iou_head_hidden_dim=256, activation="gelu", **kwargs, ): super().__init__(**kwargs) self.transformer_dim = transformer_dim + if transformer is None: + transformer = TwoWayTransformer() self.transformer = transformer self.num_multimask_outputs = num_multimask_outputs self.iou_head_depth = iou_head_depth @@ -105,53 +115,51 @@ def __init__( iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth ) - self.iou_token.build(None) - self.mask_tokens.build(None) - + def build(self, input_shape=None): + self.transformer.build() + self.iou_token.build([None]) + self.mask_tokens.build([None]) self.output_upscaling.build([None, None, None, self.transformer_dim]) - for mlp in self.output_hypernetworks_mlps: mlp.build([None, self.transformer_dim]) - self.iou_prediction_head.build([None, self.transformer_dim]) - self.built = True - def call( - self, - image_embeddings, - image_pe, - sparse_prompt_embeddings, - dense_prompt_embeddings, - multimask_output, - ): - masks, iou_pred = self.predict_masks( + def call(self, inputs): + image_embeddings = inputs["image_embeddings"] + image_pe = inputs["image_pe"] + sparse_prompt_embeddings = inputs["sparse_prompt_embeddings"] + dense_prompt_embeddings = inputs["dense_prompt_embeddings"] + + masks, iou_pred = self._predict_masks( image_embeddings=image_embeddings, image_pe=image_pe, sparse_prompt_embeddings=sparse_prompt_embeddings, dense_prompt_embeddings=dense_prompt_embeddings, ) - if multimask_output: - return masks[:, 1:, :, :], iou_pred[:, 1:] - return masks[:, :1, :, :], iou_pred[:, :1] + return {"masks": masks, "iou_pred": iou_pred} - def predict_masks( + def _predict_masks( self, image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, ): + indices_iou = ops.arange(1, dtype="int32") + indices_mask = ops.arange(self.num_mask_tokens, dtype="int32") + output_tokens = ops.concatenate( - [self.iou_token.weights[0], self.mask_tokens.weights[0]], axis=0 + [self.iou_token(indices_iou), self.mask_tokens(indices_mask)], + axis=0, ) output_tokens = ops.broadcast_to( output_tokens[None, ...], shape=( - sparse_prompt_embeddings.shape[0], - output_tokens.shape[0], - output_tokens.shape[1], + ops.shape(sparse_prompt_embeddings)[0], + ops.shape(output_tokens)[0], + ops.shape(output_tokens)[1], ), ) tokens = ops.concatenate( @@ -161,23 +169,24 @@ def predict_masks( source = ops.broadcast_to( image_embeddings, shape=( - tokens.shape[0], - image_embeddings.shape[1], - image_embeddings.shape[2], - image_embeddings.shape[3], + ops.shape(tokens)[0], + ops.shape(image_embeddings)[1], + ops.shape(image_embeddings)[2], + ops.shape(image_embeddings)[3], ), ) source = source + dense_prompt_embeddings positional_source = ops.broadcast_to( image_pe, shape=( - tokens.shape[0], - image_embeddings.shape[1], - image_embeddings.shape[2], - image_embeddings.shape[3], + ops.shape(tokens)[0], + ops.shape(image_embeddings)[1], + ops.shape(image_embeddings)[2], + ops.shape(image_embeddings)[3], ), ) - B, H, W, C = source.shape + shape = ops.shape(source) + B, H, W, C = shape[0], shape[1], shape[2], shape[3] hidden_state, source = self.transformer( source, positional_source, tokens @@ -193,7 +202,8 @@ def predict_masks( self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) ) hyper_in = ops.stack(hyper_in_list, axis=1) - B, H, W, C = upscaled_embeddings.shape + shape = ops.shape(upscaled_embeddings) + B, H, W, C = shape[0], shape[1], shape[2], shape[3] upscaled_embeddings = ops.reshape( ops.transpose(upscaled_embeddings, axes=(0, 3, 1, 2)), (B, C, H * W), diff --git a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py index 6447b26a34..569d563f81 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py @@ -21,8 +21,8 @@ ) -@keras_cv_export("keras_cv.models.SAMPromptEncoder") -class SAMPromptEncoder(keras.models.Model): +@keras_cv_export("keras_cv.layers.SAMPromptEncoder") +class SAMPromptEncoder(keras.layers.Layer): """Prompt Encoder for the Segment Anything Model (SAM). The prompt encoder generates encodings for three types of prompts: @@ -53,14 +53,17 @@ class SAMPromptEncoder(keras.models.Model): an embedding layer is used instead to generate a "no mask" embedding. Args: - embed_dim (int): The number of features in the output embeddings. - image_embedding_size (int): The number of features in the image - embeddings generated by an image encoder. - input_image_size (tuple[int, int]): A tuple of the height and width - of the image being prompted. - mask_in_chans (int): The number of channels of the mask prompt. + embed_dim (int, optional): The number of features in the output + embeddings. Defaults to `256`. + image_embedding_size (int, optional): The number of features in the + image embeddings generated by an image encoder. Defaults to + `(64, 64)`. + input_image_size (tuple[int], optional): A tuple of the height and + width of the image being prompted. Defaults to `(1024, 1024)`. + mask_in_chans (int, optional): The number of channels of the mask + prompt. Defaults to `16`. activation (str, optional): The activation to use in the mask - downscaler neural net. Defaults to "gelu". + downscaler neural net. Defaults to `"gelu"`. References: - [Segment Anything](https://arxiv.org/abs/2304.02643) @@ -68,10 +71,11 @@ class SAMPromptEncoder(keras.models.Model): def __init__( self, - embed_dim, - image_embedding_size, - input_image_size, - mask_in_chans, + *, + embed_dim=256, + image_embedding_size=(64, 64), + input_image_size=(1024, 1024), + mask_in_chans=16, activation="gelu", **kwargs ): @@ -86,11 +90,21 @@ def __init__( num_positional_features=self.embed_dim // 2, scale=1 ) - self.foreground_point_embed = keras.layers.Embedding(1, embed_dim) - self.background_point_embed = keras.layers.Embedding(1, embed_dim) - self.top_left_corner_embed = keras.layers.Embedding(1, embed_dim) - self.bottom_right_corner_embed = keras.layers.Embedding(1, embed_dim) - self.not_a_point_embed = keras.layers.Embedding(1, embed_dim) + self.foreground_point_embed = keras.layers.Embedding( + 1, embed_dim, name="foreground_point_embed" + ) + self.background_point_embed = keras.layers.Embedding( + 1, embed_dim, name="background_point_embed" + ) + self.top_left_corner_embed = keras.layers.Embedding( + 1, embed_dim, name="top_left_corner_embed" + ) + self.bottom_right_corner_embed = keras.layers.Embedding( + 1, embed_dim, name="bottom_right_corner_embed" + ) + self.not_a_point_embed = keras.layers.Embedding( + 1, embed_dim, name="not_a_point_embed" + ) self.mask_downscaler = SerializableSequential( [ @@ -103,13 +117,15 @@ def __init__( keras.layers.LayerNormalization(epsilon=1e-6), keras.layers.Activation(activation), keras.layers.Conv2D(embed_dim, kernel_size=1), - ] + ], + name="mask_downscaler", ) - self.mask_downscaler.build( - [None, 4 * image_embedding_size[0], 4 * image_embedding_size[1], 1] + self.no_mask_embed = keras.layers.Embedding( + 1, embed_dim, name="no_mask_embed" ) - self.no_mask_embed = keras.layers.Embedding(1, embed_dim) + def build(self, input_shape=None): + self.positional_embedding_layer.build() for layer in [ self.foreground_point_embed, self.background_point_embed, @@ -118,100 +134,108 @@ def __init__( self.not_a_point_embed, self.no_mask_embed, ]: - layer.build(None) - + layer.build([None]) + self.mask_downscaler.build( + [ + None, + 4 * self.image_embedding_size[0], + 4 * self.image_embedding_size[1], + 1, + ] + ) self.built = True - def get_dense_pe(self): - """Get positional embeddings for the image being prompted. - - Returns: - tensor: The positional embeddings of the image. - """ - # convert the image_embedding_size to a tensor since keras core - # expects the input type to be a symbolic/concrete tensor. - return self.positional_embedding_layer( - ops.convert_to_tensor(self.image_embedding_size, dtype="float32") - )[None, ...] + def compute_output_shape(self, input_shape): + return { + "sparse_embeddings": [None, None, self.embed_dim], + "dense_embeddings": [ + None, + self.image_embedding_size[0], + self.image_embedding_size[1], + self.embed_dim, + ], + "dense_positional_embeddings": [ + None, + self.image_embedding_size[0], + self.image_embedding_size[1], + self.embed_dim, + ], + } - def __embed_points(self, points, labels, pad): + def __embed_points(self, points, labels): points = points + 0.5 - if pad: - padding_point = ops.zeros((points.shape[0], 1, 2), dtype=self.dtype) - padding_label = -ops.ones((labels.shape[0], 1), dtype=self.dtype) - points = ops.concatenate([points, padding_point], axis=1) - labels = ops.concatenate([labels, padding_label], axis=1) + indices = ops.arange(1, dtype="int32") + point_embeddings = self.positional_embedding_layer.encode_coordinates( points, self.input_image_size ) - labels = ops.broadcast_to(labels[..., None], point_embeddings.shape) + labels = ops.broadcast_to( + labels[..., None], ops.shape(point_embeddings) + ) point_embeddings = ops.where( labels == 0, - point_embeddings + self.background_point_embed.weights[0], - point_embeddings + self.foreground_point_embed.weights[0], + point_embeddings + self.background_point_embed(indices), + point_embeddings + self.foreground_point_embed(indices), ) point_embeddings = ops.where( labels == -1, - # TODO: for whatever reason, ops.broadcast_to doesn't work here, so - # we instead use zeros_like to broadcast to the correct shape. - self.not_a_point_embed.weights[0] - + ops.zeros_like(point_embeddings), + self.not_a_point_embed(indices), point_embeddings, ) return point_embeddings def __embed_box(self, box): + shape = ops.shape(box) + B, N = shape[0], shape[1] box = box + 0.5 - coords = ops.reshape(box, (-1, 2, 2)) + indices = ops.arange(1, dtype="int32") corner_embedding = self.positional_embedding_layer.encode_coordinates( - coords, self.input_image_size - ) - top_left_embedding = ( - corner_embedding[:, 0, :] + self.top_left_corner_embed.weights[0] - ) - bottom_right_embedding = ( - corner_embedding[:, 1, :] - + self.bottom_right_corner_embed.weights[0] + box, self.input_image_size ) + top_left_embedding = corner_embedding[ + :, :, 0, : + ] + self.top_left_corner_embed(indices) + bottom_right_embedding = corner_embedding[ + :, :, 1, : + ] + self.bottom_right_corner_embed(indices) corner_embedding = ops.stack( - [top_left_embedding, bottom_right_embedding], axis=1 + [top_left_embedding, bottom_right_embedding], axis=2 ) - return corner_embedding + return ops.reshape(corner_embedding, (B, N * 2, self.embed_dim)) def __embed_mask(self, mask): mask_embedding = self.mask_downscaler(mask) return mask_embedding - def call(self, points=None, labels=None, box=None, mask=None): - if points is not None: - B = points.shape[0] - elif box is not None: - B = box.shape[0] - elif mask is not None: - B = mask.shape[0] - else: - raise ValueError("At least one of the inputs must not be None.") - sparse_embeddings = ops.zeros((B, 0, self.embed_dim), dtype=self.dtype) - if points is not None: - if labels is None: - raise ValueError("`labels` must also be provided with `points`") - point_embeddings = self.__embed_points( - points, labels, pad=(box is None) - ) - sparse_embeddings = ops.concatenate( - [sparse_embeddings, point_embeddings], axis=1 - ) - if box is not None: - box_embeddings = self.__embed_box(box) - sparse_embeddings = ops.concatenate( - [sparse_embeddings, box_embeddings], axis=1 - ) - if mask is not None: - dense_embeddings = self.__embed_mask(mask) - else: - dense_embeddings = ops.broadcast_to( + def call(self, inputs): + points, labels, box, mask = ( + inputs["points"], + inputs["labels"], + inputs["box"], + inputs["mask"], + ) + + # Get the batch shape. Since all the inputs must have the + # same batch shape, choose one input arbitrarily. + B = ops.shape(points)[0] + + # Compute point embeddings + point_embeddings = self.__embed_points(points, labels) + + # Compute box embeddings + box_embeddings = self.__embed_box(box) + + # Concatenate both into a sparse embeddings tensor + sparse_embeddings = ops.concatenate( + [point_embeddings, box_embeddings], axis=1 + ) + + # Compute the mask embeddings + _no_mask_embed = lambda: ( + ops.broadcast_to( ops.reshape( - self.no_mask_embed.weights[0], (1, 1, 1, self.embed_dim) + self.no_mask_embed(ops.arange(1, dtype="int32")), + (1, 1, 1, self.embed_dim), ), shape=( B, @@ -220,7 +244,50 @@ def call(self, points=None, labels=None, box=None, mask=None): self.embed_dim, ), ) - return sparse_embeddings, dense_embeddings + ) + + def _maybe_input_mask_embed(): + # Keras Core passes the masks as concrete tensors for both the + # true and false functions to build the output shape. So, we + # need to handle the case when 0 size mask is passed and + # dispatch the call to `_no_mask_embed`. Note that we can't call + # the lambda directly since the inputs are bound to different + # values when called with concrete values. + if mask.shape[1] == 0: + return ops.broadcast_to( + ops.reshape( + self.no_mask_embed(ops.arange(1, dtype="int32")), + (1, 1, 1, self.embed_dim), + ), + shape=( + B, + self.image_embedding_size[0], + self.image_embedding_size[1], + self.embed_dim, + ), + ) + shape = ops.shape(mask) + BM, N, H, W, C = shape[0], shape[1], shape[2], shape[3], shape[4] + return self.__embed_mask(ops.reshape(mask, (BM * N, H, W, C))) + + dense_embeddings = ops.cond( + ops.equal(ops.size(mask), 0), + _no_mask_embed, + _maybe_input_mask_embed, + ) + + # Compute the dense positional embeddings + dense_positional_embeddings = ( + self.positional_embedding_layer.encode_image( + self.image_embedding_size + )[None, ...] + ) + + return { + "sparse_embeddings": sparse_embeddings, + "dense_embeddings": dense_embeddings, + "dense_positional_embeddings": dense_positional_embeddings, + } def get_config(self): config = super().get_config() diff --git a/keras_cv/models/segmentation/segment_anything/sam_test.py b/keras_cv/models/segmentation/segment_anything/sam_test.py index 8e144db0b5..3fbeeb45cc 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_test.py +++ b/keras_cv/models/segmentation/segment_anything/sam_test.py @@ -12,12 +12,21 @@ # See the License for the specific language governing permissions and # limitations under the License. +import itertools import os import numpy as np +import pytest +from absl.testing import parameterized from keras_cv.backend import keras from keras_cv.backend import ops +from keras_cv.models.backbones.detectron2.detectron2_aliases import ( + ViTDetBBackbone, +) +from keras_cv.models.segmentation.segment_anything.sam import ( + SegmentAnythingModel, +) from keras_cv.models.segmentation.segment_anything.sam_layers import ( TwoWayMultiHeadAttention, ) @@ -34,99 +43,121 @@ class SAMTest(TestCase): - def get_points_labels_box_mask(self, B): - prompt_encoder = SAMPromptEncoder( + def setUp(self): + self.image_encoder = ViTDetBBackbone() + self.prompt_encoder = SAMPromptEncoder( embed_dim=256, image_embedding_size=(64, 64), input_image_size=(1024, 1024), mask_in_chans=16, ) - - points = ops.convert_to_tensor( - np.random.randint(0, 1023, (B, 10, 2)), dtype="float32" - ) - labels = ops.convert_to_tensor( - 1 * (np.random.rand(B, 10) > 0.5), dtype="int64" - ) - box = ops.array( - [ - [ - [[10, 10], [500, 500]], - [[20, 20], [500, 500]], - [[30, 30], [500, 500]], - [[40, 40], [500, 500]], - [[50, 50], [500, 500]], - [[60, 60], [500, 500]], - [[70, 70], [500, 500]], - ] - ], - dtype="float32", - ) - box = box[:, :B, ...] - input_mask = ops.convert_to_tensor( - 1.0 * (np.random.rand(B, 256, 256, 1) > 0.5), dtype="float32" + self.mask_decoder = SAMMaskDecoder( + transformer_dim=256, + transformer=TwoWayTransformer( + depth=2, embed_dim=256, mlp_dim=2048, num_heads=8 + ), + num_multimask_outputs=3, + iou_head_depth=3, + iou_head_hidden_dim=256, ) - return prompt_encoder, points, labels, box, input_mask + def get_prompts(self, B, prompts="all"): + rng = np.random.default_rng(0) + + points = ops.ones((B, 0, 2)) + labels = ops.ones((B, 0)) + box = ops.ones((B, 0, 2, 2)) + input_mask = ops.ones((B, 0, 256, 256, 1)) + + if "all" in prompts or "points" in prompts: + points = ops.convert_to_tensor( + rng.integers(0, 1023, (B, 10, 2)), dtype="float32" + ) + labels = ops.convert_to_tensor( + 1 * (rng.random((B, 10)) > 0.5), dtype="int32" + ) + if "all" in prompts or "box" in prompts: + x1y1 = rng.integers(0, 1022, (B, 2)) + x2y2 = rng.integers(x1y1, 1023, (B, 2)) + box = np.stack([x1y1, x2y2], axis=1) + box = ops.convert_to_tensor(box[:, None, ...], dtype="float32") + if "all" in prompts or "mask" in prompts: + input_mask = ops.convert_to_tensor( + 1.0 * (rng.random((B, 1, 256, 256, 1)) > 0.5), dtype="float32" + ) - def test_prompt_encoder(self): - ( - prompt_encoder, - points, - labels, - box, - input_mask, - ) = self.get_points_labels_box_mask(7) + return points, labels, box, input_mask - sparse_embeddings, dense_embeddings = prompt_encoder( - points=points, labels=labels, box=box, mask=input_mask + def test_prompt_encoder_simple(self): + points, labels, box, input_mask = self.get_prompts(7) + + outputs = self.prompt_encoder( + dict(points=points, labels=labels, box=box, mask=input_mask) + ) + sparse_embeddings, dense_embeddings, dense_positional_embeddings = ( + outputs["sparse_embeddings"], + outputs["dense_embeddings"], + outputs["dense_positional_embeddings"], ) - num_parameters = sum( - np.prod(tuple(x.shape)) for x in prompt_encoder.trainable_weights + trainable_parameters = np.sum( + [np.prod(x.shape) for x in self.prompt_encoder.trainable_weights] + ) + num_parameters = np.sum( + [np.prod(x.shape) for x in self.prompt_encoder.weights] ) sparse_embeddings = ops.convert_to_numpy(sparse_embeddings) dense_embeddings = ops.convert_to_numpy(dense_embeddings) + dense_positional_embeddings = ops.convert_to_numpy( + dense_positional_embeddings + ) self.assertEqual(sparse_embeddings.shape, (7, 12, 256)) self.assertEqual(dense_embeddings.shape, (7, 64, 64, 256)) - self.assertEqual(num_parameters, 6_220) + self.assertEqual(dense_positional_embeddings.shape, (1, 64, 64, 256)) + self.assertEqual(trainable_parameters, 6_220) + self.assertEqual(num_parameters, 6_476) - # saving test - path = os.path.join(self.get_temp_dir(), "sam_tf_prompt_encoder.keras") - prompt_encoder.save(path) - loaded_model = keras.saving.load_model(path) - sparse_embeddings_loaded, dense_embeddings_loaded = loaded_model( - points=points, labels=labels, box=box, mask=input_mask + @parameterized.named_parameters( + [ + ("_".join(x), x) + for x in itertools.chain( + itertools.combinations(["points", "box", "mask"], 1), + itertools.combinations(["points", "box", "mask"], 2), + ) + ] + ) + def test_prompt_encoder_partial_prompts(self, prompts): + points, labels, box, input_mask = self.get_prompts(7, prompts) + outputs = self.prompt_encoder( + {"points": points, "labels": labels, "box": box, "mask": input_mask} ) - sparse_embeddings_loaded = ops.convert_to_numpy( - sparse_embeddings_loaded + sparse_embeddings, dense_embeddings = ( + outputs["sparse_embeddings"], + outputs["dense_embeddings"], ) - dense_embeddings_loaded = ops.convert_to_numpy(dense_embeddings_loaded) - pegm_ref = ops.convert_to_numpy( - prompt_encoder.positional_embedding_layer.positional_encoding_gaussian_matrix # noqa: E501 - ) - pegm_loaded = ops.convert_to_numpy( - loaded_model.positional_embedding_layer.positional_encoding_gaussian_matrix # noqa: E501 + + self.assertAllEqual( + sparse_embeddings.shape, + (7, points.shape[1] + box.shape[1] * 2, 256), ) - self.assertAllClose(pegm_ref, pegm_loaded) - self.assertAllClose(sparse_embeddings, sparse_embeddings_loaded) - self.assertAllClose(dense_embeddings, dense_embeddings_loaded) + self.assertAllEqual(dense_embeddings.shape, (7, 64, 64, 256)) + if "mask" not in prompts: + no_mask_embed = ops.broadcast_to( + self.prompt_encoder.no_mask_embed(ops.arange(1)), + (7, 64, 64, 256), + ) + self.assertAllClose(dense_embeddings, no_mask_embed) def test_two_way_multi_head_attention(self): - ( - prompt_encoder, - points, - labels, - box, - input_mask, - ) = self.get_points_labels_box_mask(1) + points, labels, box, input_mask = self.get_prompts(1) image_embeddings = np.random.randn(1, 64, 64, 256).astype(np.float32) - sparse_embeddings, _ = prompt_encoder( - points=points, labels=labels, box=box, mask=input_mask + prompt_encoder_outputs = self.prompt_encoder( + dict(points=points, labels=labels, box=box, mask=input_mask) ) + sparse_embeddings = prompt_encoder_outputs["sparse_embeddings"] two_way_attention = TwoWayMultiHeadAttention( num_heads=8, @@ -139,7 +170,8 @@ def test_two_way_multi_head_attention(self): keys=ops.reshape(image_embeddings, (1, 64 * 64, 256)), query_pe=sparse_embeddings, key_pe=ops.reshape( - prompt_encoder.get_dense_pe(), (1, 64 * 64, 256) + prompt_encoder_outputs["dense_positional_embeddings"], + (1, 64 * 64, 256), ), ) @@ -149,77 +181,162 @@ def test_two_way_multi_head_attention(self): self.assertEqual(keys.shape, (1, 64 * 64, 256)) def test_two_way_transformer(self): - ( - prompt_encoder, - points, - labels, - box, - input_mask, - ) = self.get_points_labels_box_mask(1) - sparse_embeddings, _ = prompt_encoder( - points=points, labels=labels, box=box, mask=input_mask + points, labels, box, input_mask = self.get_prompts(1) + prompt_encoder_outputs = self.prompt_encoder( + dict(points=points, labels=labels, box=box, mask=input_mask) ) + sparse_embeddings = prompt_encoder_outputs["sparse_embeddings"] image_embeddings = np.random.randn(1, 64, 64, 256) two_way_transformer = TwoWayTransformer( - depth=2, embedding_dim=256, num_heads=8, mlp_dim=2048 + depth=2, embed_dim=256, num_heads=8, mlp_dim=2048 ) queries, keys = two_way_transformer( image_embedding=image_embeddings, - image_pe=prompt_encoder.get_dense_pe(), + image_pe=prompt_encoder_outputs["dense_positional_embeddings"], point_embedding=sparse_embeddings, ) + queries, keys = map(ops.convert_to_numpy, [queries, keys]) + self.assertEqual(queries.shape, (1, 12, 256)) self.assertEqual(keys.shape, (1, 64 * 64, 256)) def test_mask_decoder(self): - ( - prompt_encoder, - points, - labels, - box, - input_mask, - ) = self.get_points_labels_box_mask(1) - sparse_embeddings, dense_embeddings = prompt_encoder( - points=points, labels=labels, box=box, mask=input_mask + points, labels, box, input_mask = self.get_prompts(1) + prompt_encoder_outputs = self.prompt_encoder( + dict(points=points, labels=labels, box=box, mask=input_mask) ) - image_embeddings = np.random.randn(1, 64, 64, 256) - mask_decoder = SAMMaskDecoder( - transformer_dim=256, - transformer=TwoWayTransformer( - depth=2, embedding_dim=256, mlp_dim=2048, num_heads=8 - ), - num_multimask_outputs=3, - iou_head_depth=3, - iou_head_hidden_dim=256, + sparse_embeddings, dense_embeddings, dense_positional_embeddings = ( + prompt_encoder_outputs["sparse_embeddings"], + prompt_encoder_outputs["dense_embeddings"], + prompt_encoder_outputs["dense_positional_embeddings"], ) - masks, iou_pred = mask_decoder( - image_embeddings=image_embeddings, - image_pe=prompt_encoder.get_dense_pe(), - sparse_prompt_embeddings=sparse_embeddings[:1, ...], - dense_prompt_embeddings=dense_embeddings[:1, ...], - multimask_output=True, + image_embeddings = np.random.randn(1, 64, 64, 256) + outputs = self.mask_decoder( + dict( + image_embeddings=image_embeddings, + image_pe=dense_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + ) ) - num_parameters = sum( - np.prod(tuple(x.shape)) for x in mask_decoder.trainable_variables + masks, iou_pred = outputs["masks"], outputs["iou_pred"] + num_parameters = np.sum( + [np.prod(x.shape) for x in self.mask_decoder.weights] ) masks, iou_pred = map(ops.convert_to_numpy, [masks, iou_pred]) - self.assertEqual(masks.shape, (1, 3, 256, 256)) - self.assertEqual(iou_pred.shape, (1, 3)) + self.assertEqual(masks.shape, (1, 4, 256, 256)) + self.assertEqual(iou_pred.shape, (1, 4)) self.assertEqual(num_parameters, 4_058_340) - # saving test - path = os.path.join(self.get_temp_dir(), "sam_tf_mask_decoder.keras") - mask_decoder.save(path) - loaded_model = keras.saving.load_model(path) - masks_loaded, iou_pred_loaded = loaded_model( - image_embeddings=image_embeddings, - image_pe=prompt_encoder.get_dense_pe(), - sparse_prompt_embeddings=sparse_embeddings[:1, ...], - dense_prompt_embeddings=dense_embeddings[:1, ...], - multimask_output=True, - ) - masks_loaded = ops.convert_to_numpy(masks_loaded) - iou_pred_loaded = ops.convert_to_numpy(iou_pred_loaded) - self.assertAllClose(masks, masks_loaded) - self.assertAllClose(iou_pred, iou_pred_loaded) + @pytest.mark.large + def test_end_to_end_model_predict(self): + model = SegmentAnythingModel( + backbone=self.image_encoder, + prompt_encoder=self.prompt_encoder, + mask_decoder=self.mask_decoder, + ) + + points, labels, box, input_mask = self.get_prompts(1) + + inputs = { + "images": np.ones((1, 1024, 1024, 3)), + "points": points, + "labels": labels, + "box": box, + "mask": input_mask, + } + + # Check the number of parameters + num_parameters = np.sum([np.prod(x.shape) for x in model.weights]) + self.assertEqual(num_parameters, 89_670_912 + 6_476 + 4_058_340) + + # Forward pass through the model + outputs = model.predict(inputs) + masks, iou_pred = outputs["masks"], outputs["iou_pred"] + + # Check the output is equal to the one we expect if we + # run each component separately. This is to confirm that + # the graph is getting compiled correctly i.e. the jitted + # execution is equivalent to the eager execution. + features = self.image_encoder(inputs["images"]) + outputs_ex = self.prompt_encoder( + {k: v for k, v in inputs.items() if k != "images"} + ) + outputs_ex = self.mask_decoder( + { + "image_embeddings": features, + "image_pe": outputs_ex["dense_positional_embeddings"], + "sparse_prompt_embeddings": outputs_ex["sparse_embeddings"], + "dense_prompt_embeddings": outputs_ex["dense_embeddings"], + }, + ) + masks_ex, iou_pred_ex = outputs_ex["masks"], outputs_ex["iou_pred"] + + self.assertAllClose(masks, masks_ex, atol=1e-5) + self.assertAllClose(iou_pred, iou_pred_ex, atol=1e-5) + + @pytest.mark.extra_large + def test_end_to_end_model_save(self): + # Build the model + model = SegmentAnythingModel( + backbone=self.image_encoder, + prompt_encoder=self.prompt_encoder, + mask_decoder=self.mask_decoder, + ) + + # Get the inputs + points, labels, box, input_mask = self.get_prompts(1) + + inputs = { + "images": ops.ones((1, 1024, 1024, 3)), + "points": points, + "labels": labels, + "box": box, + "mask": input_mask, + } + + # Forward pass + outputs = model(inputs) + + # Save the model + save_path = os.path.join(self.get_temp_dir(), "model.keras") + model.save(save_path, save_format="keras_v3") + restored_model = keras.models.load_model(save_path) + + # Check we got the real object back. + self.assertIsInstance(restored_model, SegmentAnythingModel) + + # Check that output matches. + restored_outputs = restored_model(inputs) + self.assertAllClose(outputs, restored_outputs) + + def test_model_fit_error(self): + # Build the model + model = SegmentAnythingModel( + backbone=self.image_encoder, + prompt_encoder=self.prompt_encoder, + mask_decoder=self.mask_decoder, + ) + + # Get the inputs + points, labels, box, input_mask = self.get_prompts(1) + + inputs = { + "images": ops.ones((1, 1024, 1024, 3)), + "points": points, + "labels": labels, + "box": box, + "mask": input_mask, + } + + # Compile the model + model.compile( + optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"] + ) + + # Check that calling fit raises a NotImplementedError. + with self.assertRaises( + NotImplementedError, msg=r"only supports inference" + ): + model.fit(inputs) diff --git a/keras_cv/models/segmentation/segment_anything/sam_transformer.py b/keras_cv/models/segmentation/segment_anything/sam_transformer.py index f630076e2f..d8a8ae92cd 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_transformer.py +++ b/keras_cv/models/segmentation/segment_anything/sam_transformer.py @@ -47,17 +47,18 @@ class TwoWayTransformer(keras.layers.Layer): location and type. Args: - depth (int): The depth of the attention blocks (the number - of attention blocks to use). - embedding_dim (int): The number of features of the input image and - point embeddings. - num_heads (int): Number of heads to use in the attention layers. - mlp_dim (int): The number of units in the hidden layer of the MLP - block used in the attention layers. + depth (int, optional): The depth of the attention blocks (the number + of attention blocks to use). Defaults to `2`. + embed_dim (int, optional): The number of features of the input image + and point embeddings. Defaults to `256`. + num_heads (int, optional): Number of heads to use in the attention + layers. Defaults to `8`. + mlp_dim (int, optional): The number of units in the hidden layer of + the MLP block used in the attention layers. Defaults to `2048`. activation (str, optional): The activation of the MLP block's output - layer used in the attention layers. Defaults to "relu". + layer used in the attention layers. Defaults to `"relu"`. attention_downsample_rate (int, optional): The downsample rate of the - attention layers. Defaults to 2. + attention layers. Defaults to `2`. References: - [Segment Anything](https://arxiv.org/abs/2304.02643) @@ -65,17 +66,18 @@ class TwoWayTransformer(keras.layers.Layer): def __init__( self, - depth, - embedding_dim, - num_heads, - mlp_dim, + *, + depth=2, + embed_dim=256, + num_heads=8, + mlp_dim=2048, activation="relu", attention_downsample_rate=2, **kwargs, ): super().__init__(**kwargs) self.depth = depth - self.embedding_dim = embedding_dim + self.embed_dim = embed_dim self.num_heads = num_heads self.mlp_dim = mlp_dim self.activation = activation @@ -85,7 +87,7 @@ def __init__( self.layers.append( TwoWayMultiHeadAttention( num_heads=num_heads, - key_dim=embedding_dim // num_heads, + key_dim=embed_dim // num_heads, mlp_dim=mlp_dim, skip_first_layer_pe=(i == 0), attention_downsample_rate=attention_downsample_rate, @@ -95,20 +97,26 @@ def __init__( self.final_attention_token_to_image = ( MultiHeadAttentionWithDownsampling( num_heads=num_heads, - key_dim=embedding_dim // num_heads, + key_dim=embed_dim // num_heads, downsample_rate=attention_downsample_rate, ) ) self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5) - self.final_layer_norm.build([None, None, self.embedding_dim]) - + def build(self, input_shape=None): + for layer in self.layers: + layer.build() + self.final_attention_token_to_image.build() + self.final_layer_norm.build([None, None, self.embed_dim]) self.built = True def call(self, image_embedding, image_pe, point_embedding): - B, H, W, C = image_embedding.shape + shape = ops.shape(image_embedding) + B, H, W, C = shape[0], shape[1], shape[2], shape[3] image_embedding = ops.reshape(image_embedding, (B, H * W, C)) - B, H, W, C = image_pe.shape + + shape = ops.shape(image_pe) + B, H, W, C = shape[0], shape[1], shape[2], shape[3] image_pe = ops.reshape(image_pe, (B, H * W, C)) queries = point_embedding keys = image_embedding @@ -136,7 +144,7 @@ def get_config(self): config.update( { "depth": self.depth, - "embedding_dim": self.embedding_dim, + "embed_dim": self.embed_dim, "num_heads": self.num_heads, "mlp_dim": self.mlp_dim, "activation": self.activation, diff --git a/keras_cv/models/utils.py b/keras_cv/models/utils.py index cb573b63c3..7199823385 100644 --- a/keras_cv/models/utils.py +++ b/keras_cv/models/utils.py @@ -25,12 +25,14 @@ def get_tensor_input_name(tensor): return tensor.node.layer.name -def parse_model_inputs(input_shape, input_tensor): +def parse_model_inputs(input_shape, input_tensor, **kwargs): if input_tensor is None: - return keras.layers.Input(shape=input_shape) + return keras.layers.Input(shape=input_shape, **kwargs) else: if not keras.backend.is_keras_tensor(input_tensor): - return keras.layers.Input(tensor=input_tensor, shape=input_shape) + return keras.layers.Input( + tensor=input_tensor, shape=input_shape, **kwargs + ) else: return input_tensor diff --git a/sam_testing.py b/sam_testing.py new file mode 100644 index 0000000000..1fda6cfdba --- /dev/null +++ b/sam_testing.py @@ -0,0 +1,32 @@ +import keras_cv +from keras_cv.backend import keras +from keras_cv.backend import multi_backend +from keras_cv.models import TwoWayTransformer + +if multi_backend(): + keras.config.disable_traceback_filtering() +else: + import tensorflow as tf + + tf.debugging.disable_traceback_filtering() + +image_encoder = keras_cv.models.ViTDetBBackbone() +prompt_encoder = keras_cv.models.SAMPromptEncoder( + embed_dim=256, + image_embedding_size=(64, 64), + input_image_size=(1024, 1024), + mask_in_chans=16, +) +mask_decoder = keras_cv.models.SAMMaskDecoder( + transformer_dim=256, + transformer=TwoWayTransformer( + depth=2, embedding_dim=256, mlp_dim=2048, num_heads=8 + ), + num_multimask_outputs=3, + iou_head_depth=3, + iou_head_hidden_dim=256, +) + +model = keras_cv.models.SegmentAnythingModel( + image_encoder, prompt_encoder, mask_decoder +) From d908527b82abb3737e499479ea2db49de3e053ed Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Sat, 9 Sep 2023 16:52:11 -0700 Subject: [PATCH 08/13] Remove a stray file --- sam_testing.py | 32 -------------------------------- 1 file changed, 32 deletions(-) delete mode 100644 sam_testing.py diff --git a/sam_testing.py b/sam_testing.py deleted file mode 100644 index 1fda6cfdba..0000000000 --- a/sam_testing.py +++ /dev/null @@ -1,32 +0,0 @@ -import keras_cv -from keras_cv.backend import keras -from keras_cv.backend import multi_backend -from keras_cv.models import TwoWayTransformer - -if multi_backend(): - keras.config.disable_traceback_filtering() -else: - import tensorflow as tf - - tf.debugging.disable_traceback_filtering() - -image_encoder = keras_cv.models.ViTDetBBackbone() -prompt_encoder = keras_cv.models.SAMPromptEncoder( - embed_dim=256, - image_embedding_size=(64, 64), - input_image_size=(1024, 1024), - mask_in_chans=16, -) -mask_decoder = keras_cv.models.SAMMaskDecoder( - transformer_dim=256, - transformer=TwoWayTransformer( - depth=2, embedding_dim=256, mlp_dim=2048, num_heads=8 - ), - num_multimask_outputs=3, - iou_head_depth=3, - iou_head_hidden_dim=256, -) - -model = keras_cv.models.SegmentAnythingModel( - image_encoder, prompt_encoder, mask_decoder -) From 07cb9d6d731ff013b2f28447fbd396009ed52752 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Sat, 9 Sep 2023 22:08:46 -0700 Subject: [PATCH 09/13] Add docs for the Task model --- .../segmentation/segment_anything/sam.py | 136 ++++++++++++++++++ 1 file changed, 136 insertions(+) diff --git a/keras_cv/models/segmentation/segment_anything/sam.py b/keras_cv/models/segmentation/segment_anything/sam.py index 2efa590262..834d4024ea 100644 --- a/keras_cv/models/segmentation/segment_anything/sam.py +++ b/keras_cv/models/segmentation/segment_anything/sam.py @@ -28,6 +28,142 @@ @keras_cv_export("keras_cv.models.SegmentAnythingModel") class SegmentAnythingModel(Task): + """ + The Segment Anything (SAM) Model. + + Args: + backbone (keras_cv.models.Backbone): A feature extractor for the input + images. + prompt_encoder (keras_cv.models.SAMPromptEncoder): A Keras layer to + compute embeddings for points, box, and mask prompt. + mask_decoder (keras_cv.models.SAMMaskDecoder): A Keras layer to + generate segmentation masks given the embeddings generated by the + backbone and the prompt encoder. + + References: + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + + Examples: + + >>> import numpy as np + >>> from keras_cv.models import ViTDetBBackbone + >>> from keras_cv.models import SAMPromptEncoder + >>> from keras_cv.models import SAMMaskDecoder + + Create all the components of the SAM model: + + >>> backbone = ViTDetBBackbone() + >>> prompt_encoder = SAMPromptEncoder() + >>> mask_decoder = SAMMaskDecoder() + + Instantiate the model: + + >>> sam = SegmentAnythingModel( + ... backbone=backbone, + ... prompt_encoder=prompt_encoder, + ... mask_decoder=mask_decoder + ... ) + + Define the input of the backbone. This must be a batch of images of shape + `(1024, 1024, 3)` for the ViT backbone we are using: + + >>> image = np.ones((1, 1024, 1024, 3)) + + SAM works by prompting the input images. There are three ways to prompt: + + (1) Labelled Points: Foreground points (points with label 1) are encoded + such that the output masks generated by the mask decoder contain them + and background points (points with label 0) are encoded such that the + generated masks don't contain them. + (2) Box: A box tells the model which part/crop of the image to segment. + (3) Mask: An input mask can be used to refine the output of the mask + decoder. + + These prompts can be mixed and matched but at least one of the prompts + must be present. To turn off a particular prompt, a zero size array of the + following shapes must be passed: + + (1) For points prompts, the expected shape is `(batch, num_points, 2)`. If + no point prompt is desired, pass an input of shape `(batch, 0, 2)`. + The labels must have shape `(batch, 0)` in case of no point prompt. + (2) For box prompt, the expected shape is `(batch, 1, 2, 2)`. The second + dimension (`box.shape[1]`) represents whether a box prompt is present + or not. If no box prompt is present, an input of shape + `(batch, 0, 2, 2)` is expected. + (3) Similarly, mask prompts have shape `(batch, 1, H, W, 1)`. Here too, + the first dimension (`mask.shape[1]`) indicates the presence of a mask + prompt. To turn off mask prompts, an input of shape + `(batch, 0, H, W, 1)` must be passed. + + For example, to pass in all the prompts, do: + + >>> points = np.array([[[512., 512.], [100., 100.]]]) + >>> # For labels: 1 means foreground point, 0 means background + >>> labels = np.array([[1., 0.]]) + >>> box = np.array([[[[384., 384.], [640., 640.]]]]) + >>> input_mask = np.ones((1, 1, 256, 256, 1)) + + Prepare an input dictionary: + + >>> inputs = { + ... "images": image, + ... "points": points, + ... "labels": labels, + ... "box": box, + ... "mask": input_mask + ... } + ... + >>> outputs = sam.predict(inputs) + >>> masks, iou_pred = outputs["masks"], outputs["iou_pred"] + + The first mask in the output `masks` (i.e. `masks[:, 0, ...]`) is the best + mask predicted by the model based on the prompts. Other `masks` + (i.e. `masks[:, 1:, ...]`) are alternate predictions that can be used if + they are desired over the first one. + + Now, in case of only points and box prompts, do: + + >>> no_input_mask = np.empty((1, 0, 256, 256, 1)) + >>> inputs = { + ... "images": image, + ... "points": points, + ... "labels": labels, + ... "box": box, + ... "mask": no_input_mask + ... } + ... + >>> outputs = sam.predict(inputs) + >>> masks, iou_pred = outputs["masks"], outputs["iou_pred"] + + Anothe example is that only points prompts are present. + Note that if point prompts are present (i.e. `points.shape[1] != 0`), + but no box prompt is present (i.e. `box.shape[1] == 0`), the points must + be passed using a zero point and -1 label: + + >>> no_box = np.empty((1, 0, 2, 2)) + >>> padded_points = np.concatenate( + ... [points, np.zeros(1, 1, 2)], axis=1 + ... ) + ... + >>> padded_labels = np.concatenate( + ... [labels, -np.ones((1, 1))], axis=1 + ... ) + >>> inputs = { + ... "images": image, + ... "points": padded_points, + ... "labels": padded_labels, + ... "box": no_box, + ... "mask": no_input_mask + ... } + ... + >>> outputs = sam.predict(inputs) + >>> masks, iou_pred = outputs["masks"], outputs["iou_pred"] + + Note that the segment anything model only supports inference and training + isn't support yet. So, calling the `fit` method will fail for now. + """ # noqa: E501 + def __init__( self, *, From 343557aa34d96cad2d9a59ac0cbdddfca877e32e Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Mon, 11 Sep 2023 01:17:12 -0700 Subject: [PATCH 10/13] Add more references [skip ci] --- keras_cv/layers/detectron2_layers.py | 28 ++++++++++++--- .../detectron2/detectron2_aliases.py | 3 +- .../detectron2/detectron2_backbone.py | 7 +++- .../segment_anything/sam_layers.py | 35 +++++++++++++------ .../segment_anything/sam_mask_decoder.py | 5 +-- .../segment_anything/sam_prompt_encoder.py | 5 +-- .../segment_anything/sam_transformer.py | 5 +-- 7 files changed, 65 insertions(+), 23 deletions(-) diff --git a/keras_cv/layers/detectron2_layers.py b/keras_cv/layers/detectron2_layers.py index 6e59abcc19..c91a602b06 100644 --- a/keras_cv/layers/detectron2_layers.py +++ b/keras_cv/layers/detectron2_layers.py @@ -30,7 +30,12 @@ class MLP(keras.layers.Layer): num_layers (int): The total number of dense layers to use. activation (str): Activation to use in the hidden layers. Default is `"relu"`. - """ + + References: + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + - [Detectron2](https://github.com/facebookresearch/detectron2) + """ # noqa: E501 def __init__( self, hidden_dim, output_dim, num_layers, activation="relu", **kwargs @@ -196,7 +201,12 @@ class MultiHeadAttentionWithRelativePE(keras.layers.Layer): Raises: ValueError: When `input_size = None` with `use_rel_pos = True`. - """ + + References: + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + - [Detectron2](https://github.com/facebookresearch/detectron2) + """ # noqa: E501 def __init__( self, @@ -372,7 +382,12 @@ class WindowedTransformerEncoder(keras.layers.Layer): MLP head - should be a function. Defaults to `"gelu"`. layer_norm_epsilon (float, optional): The epsilon to use in the layer normalization layers. Defaults to `1e-6`. - """ + + References: + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + - [Detectron2](https://github.com/facebookresearch/detectron2) + """ # noqa: E501 def __init__( self, @@ -481,7 +496,12 @@ class ViTDetPatchingAndEmbedding(keras.layers.Layer): Defaults to `(16, 16)`. embed_dim (int, optional): Number of filters to use in the projection layer i.e. projection size. Defaults to `768`. - """ + + References: + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + - [Detectron2](https://github.com/facebookresearch/detectron2) + """ # noqa: E501 def __init__( self, kernel_size=(16, 16), strides=(16, 16), embed_dim=768, **kwargs diff --git a/keras_cv/models/backbones/detectron2/detectron2_aliases.py b/keras_cv/models/backbones/detectron2/detectron2_aliases.py index 9f225ccbed..c899796683 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_aliases.py +++ b/keras_cv/models/backbones/detectron2/detectron2_aliases.py @@ -26,7 +26,8 @@ Reference: - [Detectron2](https://github.com/facebookresearch/detectron2) - - [Segment Anything](https://arxiv.org/abs/2304.02643) + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning](https://keras.io/guides/transfer_learning/). diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone.py b/keras_cv/models/backbones/detectron2/detectron2_backbone.py index 6935a6b195..7d9f6c06d1 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone.py +++ b/keras_cv/models/backbones/detectron2/detectron2_backbone.py @@ -68,7 +68,12 @@ class ViTDetBackbone(Backbone): global attention. Defaults to `[2, 5, 8, 11]`. layer_norm_epsilon (int, optional): The epsilon to use in the layer normalization blocks in transformer encoder. Defaults to `1e-6`. - """ + + References: + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + - [Detectron2](https://github.com/facebookresearch/detectron2) + """ # noqa: E501 def __init__( self, diff --git a/keras_cv/models/segmentation/segment_anything/sam_layers.py b/keras_cv/models/segmentation/segment_anything/sam_layers.py index 4c6b556f02..0c1efa9801 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_layers.py +++ b/keras_cv/models/segmentation/segment_anything/sam_layers.py @@ -39,7 +39,11 @@ class MultiHeadAttentionWithDownsampling(keras.layers.Layer): downsample_rate (int, optional): The factor by which to downscale the input features i.e. the input features of size `key_dim` are projected down to `key_dim // downsample_rate`. - """ + + References: + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + """ # noqa: E501 def __init__(self, num_heads, key_dim, downsample_rate=1, **kwargs): super().__init__(**kwargs) @@ -127,7 +131,11 @@ class TwoWayMultiHeadAttention(keras.layers.Layer): in the attention layers. Defaults to 2. activation (str, optional): The activation for the mlp block's output layer. Defaults to "relu". - """ + + References: + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + """ # noqa: E501 def __init__( self, @@ -240,17 +248,22 @@ def get_config(self): @keras_cv_export("keras_cv.layers.RandomFrequencyPositionalEmbeddings") class RandomFrequencyPositionalEmbeddings(keras.layers.Layer): - def __init__(self, num_positional_features, scale, **kwargs): - """Positional encoding using random spatial frequencies. + """Positional encoding using random spatial frequencies. - This layer maps coordinates/points in 2D space to positional - encodings using random spatial frequencies. + This layer maps coordinates/points in 2D space to positional + encodings using random spatial frequencies. - Args: - num_positional_features (int): Number of positional features - in the output. - scale (float): The standard deviation of the random frequencies. - """ + Args: + num_positional_features (int): Number of positional features + in the output. + scale (float): The standard deviation of the random frequencies. + + References: + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + """ # noqa: E501 + + def __init__(self, num_positional_features, scale, **kwargs): super().__init__(**kwargs) self.num_positional_features = num_positional_features self.scale = scale diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py index 34d038e8c9..62d07df41d 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -62,8 +62,9 @@ class SAMMaskDecoder(keras.layers.Layer): network. Defaults to `"gelu"`. References: - - [Segment Anything](https://arxiv.org/abs/2304.02643) - """ + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + """ # noqa: E501 def __init__( self, diff --git a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py index 569d563f81..93410818d7 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py @@ -66,8 +66,9 @@ class SAMPromptEncoder(keras.layers.Layer): downscaler neural net. Defaults to `"gelu"`. References: - - [Segment Anything](https://arxiv.org/abs/2304.02643) - """ + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + """ # noqa: E501 def __init__( self, diff --git a/keras_cv/models/segmentation/segment_anything/sam_transformer.py b/keras_cv/models/segmentation/segment_anything/sam_transformer.py index d8a8ae92cd..efcc89b7b1 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_transformer.py +++ b/keras_cv/models/segmentation/segment_anything/sam_transformer.py @@ -61,8 +61,9 @@ class TwoWayTransformer(keras.layers.Layer): attention layers. Defaults to `2`. References: - - [Segment Anything](https://arxiv.org/abs/2304.02643) - """ + - [Segment Anything paper](https://arxiv.org/abs/2304.02643) + - [Segment Anything GitHub](https://github.com/facebookresearch/segment-anything) + """ # noqa: E501 def __init__( self, From 19312e71d426eebb61a75496baed3ae430bf88f7 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Thu, 14 Sep 2023 17:34:15 -0700 Subject: [PATCH 11/13] Remove SerializableSequential layer --- keras_cv/layers/__init__.py | 1 - keras_cv/layers/detectron2_layers.py | 3 +- keras_cv/layers/serializable_sequential.py | 62 ------------------- .../layers/serializable_sequential_test.py | 39 ------------ .../segment_anything/sam_mask_decoder.py | 3 +- .../segment_anything/sam_prompt_encoder.py | 3 +- 6 files changed, 3 insertions(+), 108 deletions(-) delete mode 100644 keras_cv/layers/serializable_sequential.py delete mode 100644 keras_cv/layers/serializable_sequential_test.py diff --git a/keras_cv/layers/__init__.py b/keras_cv/layers/__init__.py index 639d685fdb..843ee1ba89 100644 --- a/keras_cv/layers/__init__.py +++ b/keras_cv/layers/__init__.py @@ -138,7 +138,6 @@ from keras_cv.layers.segformer_multihead_attention import ( SegFormerMultiheadAttention, ) -from keras_cv.layers.serializable_sequential import SerializableSequential from keras_cv.layers.spatial_pyramid import SpatialPyramidPooling from keras_cv.layers.transformer_encoder import TransformerEncoder from keras_cv.layers.vit_layers import PatchingAndEmbedding diff --git a/keras_cv/layers/detectron2_layers.py b/keras_cv/layers/detectron2_layers.py index c91a602b06..cc4821f682 100644 --- a/keras_cv/layers/detectron2_layers.py +++ b/keras_cv/layers/detectron2_layers.py @@ -17,7 +17,6 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.layers.serializable_sequential import SerializableSequential class MLP(keras.layers.Layer): @@ -51,7 +50,7 @@ def __init__( self.dense_net.append(keras.layers.Dense(hidden_dim)) self.dense_net.append(keras.layers.Activation(activation)) self.dense_net.append(keras.layers.Dense(output_dim)) - self.dense_net = SerializableSequential(self.dense_net) + self.dense_net = keras.models.Sequential(self.dense_net) def build(self, input_shape): self.dense_net.build(input_shape) diff --git a/keras_cv/layers/serializable_sequential.py b/keras_cv/layers/serializable_sequential.py deleted file mode 100644 index 8584c8ee0c..0000000000 --- a/keras_cv/layers/serializable_sequential.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright 2023 The KerasCV Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from keras_cv.api_export import keras_cv_export -from keras_cv.backend import keras -from keras_cv.utils.python_utils import classproperty - - -# TODO(tirthasheshpatel): Use `Sequential` model once the bug is resolved. -# Temporarily substitute the `Sequential` model with this because a -# bug in Keras/Keras Core prevents the weights of a sequential model to -# load in TensorFlow if they are saved in JAX/PyTorch and vice versa. -# This only happens when the `build` method is called in the `__init__` -# step. -@keras_cv_export("keras_cv.layers.SerializableSequential") -class SerializableSequential(keras.layers.Layer): - def __init__(self, layers_list, **kwargs): - super().__init__(**kwargs) - self.layers_list = layers_list - - def build(self, input_shape): - output_shape = input_shape - for layer in self.layers_list: - layer.build(output_shape) - output_shape = layer.compute_output_shape(output_shape) - self.built = True - - def call(self, x): - for layer in self.layers_list: - x = layer(x) - return x - - def get_config(self): - config = super().get_config() - layers_list_serialized = [ - keras.saving.serialize_keras_object(layer) - for layer in self.layers_list - ] - config.update({"layers_list": layers_list_serialized}) - - @classproperty - def from_config(self, config): - config.update( - { - "layers_list": [ - keras.layers.deserialize(layer) - for layer in config["layers_list"] - ] - } - ) - return super().from_config(config) diff --git a/keras_cv/layers/serializable_sequential_test.py b/keras_cv/layers/serializable_sequential_test.py deleted file mode 100644 index d49b276ca4..0000000000 --- a/keras_cv/layers/serializable_sequential_test.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright 2023 The KerasCV Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from copy import deepcopy - -import numpy as np - -from keras_cv.backend import keras -from keras_cv.layers.serializable_sequential import SerializableSequential -from keras_cv.tests.test_case import TestCase - - -class TestDetectron2Layers(TestCase): - def test_sequential_equivalence(self): - layers = [ - keras.layers.Conv2D(16, 2), - keras.layers.Flatten(), - keras.layers.Dense(5), - ] - model = SerializableSequential(layers) - model_keras = keras.Sequential(deepcopy(layers)) - model.build([None, 2, 2, 3]) - model_keras.build([None, 2, 2, 3]) - model.set_weights(model_keras.weights) - x = np.ones((1, 2, 2, 3)) - x_out = model(x) - x_expected = model_keras(x) - self.assertAllClose(x_out, x_expected) diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py index 62d07df41d..de06dd8abe 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -16,7 +16,6 @@ from keras_cv.backend import keras from keras_cv.backend import ops from keras_cv.layers.detectron2_layers import MLP -from keras_cv.layers.serializable_sequential import SerializableSequential from keras_cv.models.segmentation.segment_anything.sam_transformer import ( TwoWayTransformer, ) @@ -93,7 +92,7 @@ def __init__( self.num_mask_tokens, transformer_dim ) - self.output_upscaling = SerializableSequential( + self.output_upscaling = keras.models.Sequential( [ keras.layers.Conv2DTranspose( transformer_dim // 4, kernel_size=2, strides=2 diff --git a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py index 93410818d7..1e6b2ec0a8 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py @@ -15,7 +15,6 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.layers.serializable_sequential import SerializableSequential from keras_cv.models.segmentation.segment_anything.sam_layers import ( RandomFrequencyPositionalEmbeddings, ) @@ -107,7 +106,7 @@ def __init__( 1, embed_dim, name="not_a_point_embed" ) - self.mask_downscaler = SerializableSequential( + self.mask_downscaler = keras.models.Sequential( [ keras.layers.Conv2D( mask_in_chans // 4, kernel_size=2, strides=2 From e5767d8c978647016c829a3011069ea37c39bd27 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Sat, 16 Sep 2023 20:49:42 -0700 Subject: [PATCH 12/13] detectron2 -> vit_det; add SAM presets; fix ViTDet presets --- keras_cv/layers/__init__.py | 10 +- ...detectron2_layers.py => vit_det_layers.py} | 25 ++++- ..._layers_test.py => vit_det_layers_test.py} | 10 +- keras_cv/models/__init__.py | 16 +-- keras_cv/models/backbones/backbone_presets.py | 3 + .../{detectron2 => vit_det}/__init__.py | 0 .../data/vitdet_base_out.npz} | Bin .../vit_det_aliases.py} | 24 ++-- .../vit_det_backbone.py} | 23 ++-- .../vit_det_backbone_presets.py} | 84 ++++++++++---- .../vit_det_backbone_presets_test.py} | 19 ++-- .../vit_det_backbone_test.py} | 21 +++- .../data/sam_base_out_iou_pred.npy | Bin 0 -> 144 bytes .../data/sam_base_out_masks.npy | Bin 0 -> 1048704 bytes .../segmentation/segment_anything/sam.py | 52 +++++---- .../segment_anything/sam_layers.py | 16 ++- .../segment_anything/sam_mask_decoder.py | 4 +- .../segment_anything/sam_presets.py | 103 ++++++++++++++++++ .../segment_anything/sam_prompt_encoder.py | 2 +- .../segmentation/segment_anything/sam_test.py | 43 +++++++- .../segment_anything/sam_transformer.py | 2 +- 21 files changed, 341 insertions(+), 116 deletions(-) rename keras_cv/layers/{detectron2_layers.py => vit_det_layers.py} (97%) rename keras_cv/layers/{detectron2_layers_test.py => vit_det_layers_test.py} (86%) rename keras_cv/models/backbones/{detectron2 => vit_det}/__init__.py (100%) rename keras_cv/models/backbones/{detectron2/data/vitdet_b_out.npz => vit_det/data/vitdet_base_out.npz} (100%) rename keras_cv/models/backbones/{detectron2/detectron2_aliases.py => vit_det/vit_det_aliases.py} (79%) rename keras_cv/models/backbones/{detectron2/detectron2_backbone.py => vit_det/vit_det_backbone.py} (92%) rename keras_cv/models/backbones/{detectron2/detectron2_backbone_presets.py => vit_det/vit_det_backbone_presets.py} (56%) rename keras_cv/models/backbones/{detectron2/detectron2_backbone_presets_test.py => vit_det/vit_det_backbone_presets_test.py} (85%) rename keras_cv/models/backbones/{detectron2/detectron2_backbone_test.py => vit_det/vit_det_backbone_test.py} (74%) create mode 100644 keras_cv/models/segmentation/segment_anything/data/sam_base_out_iou_pred.npy create mode 100644 keras_cv/models/segmentation/segment_anything/data/sam_base_out_masks.npy create mode 100644 keras_cv/models/segmentation/segment_anything/sam_presets.py diff --git a/keras_cv/layers/__init__.py b/keras_cv/layers/__init__.py index 843ee1ba89..0bfa2aa8ec 100644 --- a/keras_cv/layers/__init__.py +++ b/keras_cv/layers/__init__.py @@ -17,11 +17,6 @@ from tensorflow.keras.layers import RandomWidth from keras_cv.layers.augmenter import Augmenter -from keras_cv.layers.detectron2_layers import AddRelativePositionalEmbedding -from keras_cv.layers.detectron2_layers import MultiHeadAttentionWithRelativePE -from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding -from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder -from keras_cv.layers.detectron2_layers import WindowPartitioning from keras_cv.layers.feature_pyramid import FeaturePyramid from keras_cv.layers.fusedmbconv import FusedMBConvBlock from keras_cv.layers.hierarchical_transformer_encoder import ( @@ -140,4 +135,9 @@ ) from keras_cv.layers.spatial_pyramid import SpatialPyramidPooling from keras_cv.layers.transformer_encoder import TransformerEncoder +from keras_cv.layers.vit_det_layers import AddRelativePositionalEmbedding +from keras_cv.layers.vit_det_layers import MultiHeadAttentionWithRelativePE +from keras_cv.layers.vit_det_layers import ViTDetPatchingAndEmbedding +from keras_cv.layers.vit_det_layers import WindowedTransformerEncoder +from keras_cv.layers.vit_det_layers import WindowPartitioning from keras_cv.layers.vit_layers import PatchingAndEmbedding diff --git a/keras_cv/layers/detectron2_layers.py b/keras_cv/layers/vit_det_layers.py similarity index 97% rename from keras_cv/layers/detectron2_layers.py rename to keras_cv/layers/vit_det_layers.py index cc4821f682..78c0b0bfb6 100644 --- a/keras_cv/layers/detectron2_layers.py +++ b/keras_cv/layers/vit_det_layers.py @@ -72,7 +72,9 @@ def get_config(self): return config -@keras_cv_export("keras_cv.layers.AddRelativePositionalEmbedding") +@keras_cv_export( + "keras_cv.layers.AddRelativePositionalEmbedding", package="keras_cv.layers" +) class AddRelativePositionalEmbedding(keras.layers.Layer): def __init__(self, input_size, key_dim, **kwargs): super().__init__(**kwargs) @@ -182,7 +184,10 @@ def get_config(self): return config -@keras_cv_export("keras_cv.layers.MultiHeadAttentionWithRelativePE") +@keras_cv_export( + "keras_cv.layers.MultiHeadAttentionWithRelativePE", + package="keras_cv.layers", +) class MultiHeadAttentionWithRelativePE(keras.layers.Layer): """Multi-head Attention block with relative position embeddings. @@ -293,7 +298,9 @@ def get_config(self): return config -@keras_cv_export("keras_cv.layers.WindowPartitioning") +@keras_cv_export( + "keras_cv.layers.WindowPartitioning", package="keras_cv.layers" +) class WindowPartitioning(keras.layers.Layer): def __init__(self, window_size, **kwargs): super().__init__(**kwargs) @@ -356,7 +363,9 @@ def get_config(self): return config -@keras_cv_export("keras_cv.layers.WindowedTransformerEncoder") +@keras_cv_export( + "keras_cv.layers.WindowedTransformerEncoder", package="keras_cv.layers" +) class WindowedTransformerEncoder(keras.layers.Layer): """Transformer blocks with support of window attention and residual propagation blocks. @@ -483,7 +492,9 @@ def get_config(self): return config -@keras_cv_export("keras_cv.layers.ViTDetPatchingAndEmbedding") +@keras_cv_export( + "keras_cv.layers.ViTDetPatchingAndEmbedding", package="keras_cv.layers" +) class ViTDetPatchingAndEmbedding(keras.layers.Layer): """Image to Patch Embedding using only a conv layer (without layer normalization). @@ -540,7 +551,9 @@ def get_config(self): # TODO: Merge this with the `keras_cv.layers.PatchingAndEmbedding` class once # it has been ported to Keras Core. -@keras_cv_export("keras_cv.layers.detectron2.AddPositionalEmbedding") +@keras_cv_export( + "keras_cv.layers.AddPositionalEmbedding", package="keras_cv.layers" +) class AddPositionalEmbedding(keras.layers.Layer): def __init__(self, img_size, patch_size, embed_dim, **kwargs): super().__init__(**kwargs) diff --git a/keras_cv/layers/detectron2_layers_test.py b/keras_cv/layers/vit_det_layers_test.py similarity index 86% rename from keras_cv/layers/detectron2_layers_test.py rename to keras_cv/layers/vit_det_layers_test.py index f56d3d98cf..05c698730e 100644 --- a/keras_cv/layers/detectron2_layers_test.py +++ b/keras_cv/layers/vit_det_layers_test.py @@ -15,14 +15,14 @@ import numpy as np from keras_cv.backend import ops -from keras_cv.layers.detectron2_layers import AddPositionalEmbedding -from keras_cv.layers.detectron2_layers import MultiHeadAttentionWithRelativePE -from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding -from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder +from keras_cv.layers.vit_det_layers import AddPositionalEmbedding +from keras_cv.layers.vit_det_layers import MultiHeadAttentionWithRelativePE +from keras_cv.layers.vit_det_layers import ViTDetPatchingAndEmbedding +from keras_cv.layers.vit_det_layers import WindowedTransformerEncoder from keras_cv.tests.test_case import TestCase -class TestDetectron2Layers(TestCase): +class TestViTDetLayers(TestCase): def test_multi_head_attention_with_relative_pe(self): attention_with_rel_pe = MultiHeadAttentionWithRelativePE( num_heads=16, diff --git a/keras_cv/models/__init__.py b/keras_cv/models/__init__.py index e1526670c3..ae775ed824 100644 --- a/keras_cv/models/__init__.py +++ b/keras_cv/models/__init__.py @@ -43,18 +43,6 @@ from keras_cv.models.backbones.densenet.densenet_backbone import ( DenseNetBackbone, ) -from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - ViTDetBBackbone, -) -from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - ViTDetHBackbone, -) -from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - ViTDetLBackbone, -) -from keras_cv.models.backbones.detectron2.detectron2_backbone import ( - ViTDetBackbone, -) from keras_cv.models.backbones.efficientnet_lite.efficientnet_lite_aliases import ( # noqa: E501 EfficientNetLiteB0Backbone, ) @@ -190,6 +178,10 @@ from keras_cv.models.backbones.resnet_v2.resnet_v2_backbone import ( ResNetV2Backbone, ) +from keras_cv.models.backbones.vit_det.vit_det_aliases import ViTDetBBackbone +from keras_cv.models.backbones.vit_det.vit_det_aliases import ViTDetHBackbone +from keras_cv.models.backbones.vit_det.vit_det_aliases import ViTDetLBackbone +from keras_cv.models.backbones.vit_det.vit_det_backbone import ViTDetBackbone from keras_cv.models.classification.image_classifier import ImageClassifier from keras_cv.models.object_detection.retinanet.retinanet import RetinaNet from keras_cv.models.object_detection.yolo_v8.yolo_v8_backbone import ( diff --git a/keras_cv/models/backbones/backbone_presets.py b/keras_cv/models/backbones/backbone_presets.py index 614f85cd24..95d3ccd522 100644 --- a/keras_cv/models/backbones/backbone_presets.py +++ b/keras_cv/models/backbones/backbone_presets.py @@ -28,6 +28,7 @@ from keras_cv.models.backbones.mobilenet_v3 import mobilenet_v3_backbone_presets from keras_cv.models.backbones.resnet_v1 import resnet_v1_backbone_presets from keras_cv.models.backbones.resnet_v2 import resnet_v2_backbone_presets +from keras_cv.models.backbones.vit_det import vit_det_backbone_presets from keras_cv.models.object_detection.yolo_v8 import yolo_v8_backbone_presets backbone_presets_no_weights = { @@ -40,6 +41,7 @@ **densenet_backbone_presets.backbone_presets_no_weights, **efficientnet_lite_backbone_presets.backbone_presets_no_weights, **yolo_v8_backbone_presets.backbone_presets_no_weights, + **vit_det_backbone_presets.backbone_presets_no_weights, } backbone_presets_with_weights = { @@ -52,6 +54,7 @@ **densenet_backbone_presets.backbone_presets_with_weights, **efficientnet_lite_backbone_presets.backbone_presets_with_weights, **yolo_v8_backbone_presets.backbone_presets_with_weights, + **vit_det_backbone_presets.backbone_presets_with_weights, } backbone_presets = { diff --git a/keras_cv/models/backbones/detectron2/__init__.py b/keras_cv/models/backbones/vit_det/__init__.py similarity index 100% rename from keras_cv/models/backbones/detectron2/__init__.py rename to keras_cv/models/backbones/vit_det/__init__.py diff --git a/keras_cv/models/backbones/detectron2/data/vitdet_b_out.npz b/keras_cv/models/backbones/vit_det/data/vitdet_base_out.npz similarity index 100% rename from keras_cv/models/backbones/detectron2/data/vitdet_b_out.npz rename to keras_cv/models/backbones/vit_det/data/vitdet_base_out.npz diff --git a/keras_cv/models/backbones/detectron2/detectron2_aliases.py b/keras_cv/models/backbones/vit_det/vit_det_aliases.py similarity index 79% rename from keras_cv/models/backbones/detectron2/detectron2_aliases.py rename to keras_cv/models/backbones/vit_det/vit_det_aliases.py index c899796683..dec51207e3 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_aliases.py +++ b/keras_cv/models/backbones/vit_det/vit_det_aliases.py @@ -14,10 +14,8 @@ import copy -from keras_cv.models.backbones.detectron2.detectron2_backbone import ( - ViTDetBackbone, -) -from keras_cv.models.backbones.detectron2.detectron2_backbone_presets import ( +from keras_cv.models.backbones.vit_det.vit_det_backbone import ViTDetBackbone +from keras_cv.models.backbones.vit_det.vit_det_backbone_presets import ( backbone_presets, ) from keras_cv.utils.python_utils import classproperty @@ -48,13 +46,15 @@ def __new__( cls, **kwargs, ): - return ViTDetBackbone.from_preset("vitdet_b", **kwargs) + return ViTDetBackbone.from_preset("vitdet_base", **kwargs) @classproperty def presets(cls): """Dictionary of preset names and configurations.""" return { - "vitdet_b": copy.deepcopy(backbone_presets["vitdet_b"]), + "vitdet_base_sa1b": copy.deepcopy( + backbone_presets["vitdet_base_sa1b"] + ), } @classproperty @@ -69,13 +69,15 @@ def __new__( cls, **kwargs, ): - return ViTDetBackbone.from_preset("vitdet_l", **kwargs) + return ViTDetBackbone.from_preset("vitdet_large", **kwargs) @classproperty def presets(cls): """Dictionary of preset names and configurations.""" return { - "vitdet_l": copy.deepcopy(backbone_presets["vitdet_l"]), + "vitdet_large_sa1b": copy.deepcopy( + backbone_presets["vitdet_large_sa1b"] + ), } @classproperty @@ -90,13 +92,15 @@ def __new__( cls, **kwargs, ): - return ViTDetBackbone.from_preset("vitdet_h", **kwargs) + return ViTDetBackbone.from_preset("vitdet_huge", **kwargs) @classproperty def presets(cls): """Dictionary of preset names and configurations.""" return { - "vitdet_h": copy.deepcopy(backbone_presets["vitdet_h"]), + "vitdet_huge_sa1b": copy.deepcopy( + backbone_presets["vitdet_huge_sa1b"] + ), } @classproperty diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone.py b/keras_cv/models/backbones/vit_det/vit_det_backbone.py similarity index 92% rename from keras_cv/models/backbones/detectron2/detectron2_backbone.py rename to keras_cv/models/backbones/vit_det/vit_det_backbone.py index 7d9f6c06d1..26e2d5d190 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone.py +++ b/keras_cv/models/backbones/vit_det/vit_det_backbone.py @@ -16,18 +16,21 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras -from keras_cv.layers.detectron2_layers import AddPositionalEmbedding -from keras_cv.layers.detectron2_layers import ViTDetPatchingAndEmbedding -from keras_cv.layers.detectron2_layers import WindowedTransformerEncoder +from keras_cv.layers.vit_det_layers import AddPositionalEmbedding +from keras_cv.layers.vit_det_layers import ViTDetPatchingAndEmbedding +from keras_cv.layers.vit_det_layers import WindowedTransformerEncoder from keras_cv.models import utils from keras_cv.models.backbones.backbone import Backbone -from keras_cv.models.backbones.detectron2.detectron2_backbone_presets import ( +from keras_cv.models.backbones.vit_det.vit_det_backbone_presets import ( backbone_presets, ) +from keras_cv.models.backbones.vit_det.vit_det_backbone_presets import ( + backbone_presets_with_weights, +) from keras_cv.utils.python_utils import classproperty -@keras_cv_export("keras_cv.models.ViTDetBackbone") +@keras_cv_export("keras_cv.models.ViTDetBackbone", package="keras_cv.models") class ViTDetBackbone(Backbone): """A ViT image encoder that uses a windowed transformer encoder and relative positional encodings. @@ -208,8 +211,8 @@ def presets(cls): """Dictionary of preset names and configurations.""" return copy.deepcopy(backbone_presets) - # @classproperty - # def presets_with_weights(cls): - # """Dictionary of preset names and configurations that include - # weights.""" - # return copy.deepcopy(backbone_presets) + @classproperty + def presets_with_weights(cls): + """Dictionary of preset names and configurations that include + weights.""" + return copy.deepcopy(backbone_presets_with_weights) diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py b/keras_cv/models/backbones/vit_det/vit_det_backbone_presets.py similarity index 56% rename from keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py rename to keras_cv/models/backbones/vit_det/vit_det_backbone_presets.py index 49105427c6..825f157ed7 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets.py +++ b/keras_cv/models/backbones/vit_det/vit_det_backbone_presets.py @@ -13,20 +13,20 @@ # limitations under the License. """VitDet model preset configurations.""" -backbone_presets = { - "vitdet_b": { +backbone_presets_no_weights = { + "vitdet_base": { "metadata": { "description": ( - "VitDet Backbone for the segment anything model with 12 " + "Detectron2 ViT basebone with 12 " "transformer encoders with embed dim 768 and attention layers" " with 12 heads with global attention on encoders 2, 5, 8, " "and 11." ), "params": 89_670_912, "official_name": "VitDet", - "path": "detectron2", + "path": "vit_det", }, - "class_name": "keras_cv.models>VitDetBackbone", + "class_name": "keras_cv.models>ViTDetBackbone", "config": { "input_shape": (1024, 1024, 3), "input_tensor": None, @@ -44,22 +44,20 @@ "global_attention_indices": [2, 5, 8, 11], "layer_norm_epsilon": 1e-6, }, - # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/vitdet_b.weights.h5", # noqa: E501 - # "weights_hash": None }, - "vitdet_l": { + "vitdet_large": { "metadata": { "description": ( - "VitDet Backbone for the segment anything model with 24 " + "Detectron2 ViT basebone with 24 " "transformer encoders with embed dim " "1024 and attention layers with 16 heads with global " "attention on encoders 5, 11, 17, and 23." ), "params": 308_278_272, "official_name": "VitDet", - "path": "detectron2", + "path": "vit_det", }, - "class_name": "keras_cv.models>VitDetBackbone", + "class_name": "keras_cv.models>ViTDetBackbone", "config": { "input_shape": (1024, 1024, 3), "input_tensor": None, @@ -77,22 +75,20 @@ "global_attention_indices": [5, 11, 17, 23], "layer_norm_epsilon": 1e-6, }, - # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/vitdet_l.weights.h5", # noqa: E501 - # "weights_hash": None }, - "vitdet_h": { + "vitdet_huge": { "metadata": { "description": ( - "VitDet Backbone for the segment anything model " + "Detectron2 ViT basebone model " "with 32 transformer encoders with embed dim " "1280 and attention layers with 16 heads with global " "attention on encoders 7, 15, 23, and 31." ), "params": 637_026_048, "official_name": "VitDet", - "path": "detectron2", + "path": "vit_det", }, - "class_name": "keras_cv.models>VitDetBackbone", + "class_name": "keras_cv.models>ViTDetBackbone", "config": { "input_shape": (1024, 1024, 3), "input_tensor": None, @@ -110,7 +106,57 @@ "global_attention_indices": [7, 15, 23, 31], "layer_norm_epsilon": 1e-6, }, - # "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/vitdet_h.weights.h5", # noqa: E501 - # "weights_hash": None }, } + + +backbone_presets_with_weights = { + "vitdet_base_sa1b": { + "metadata": { + "description": ( + "A base Detectron2 ViT backbone trained on the SA1B dataset." + ), + "params": 89_670_912, + "official_name": "VitDet", + "path": "vit_det", + }, + "class_name": "keras_cv.models>ViTDetBackbone", + "config": backbone_presets_no_weights["vitdet_base"]["config"], + "weights_url": "https://storage.googleapis.com/keras-cv/models/vitdet/vitdet_base.h5", # noqa: E501 + "weights_hash": "63c0ca6ff422142f95c24a0223445906728b353469be10c8e34018392207c93a", # noqa: E501 + }, + "vitdet_large_sa1b": { + "metadata": { + "description": ( + "A large Detectron2 ViT backbone trained on the SA1B dataset." + ), + "params": 308_278_272, + "official_name": "VitDet", + "path": "vit_det", + }, + "class_name": "keras_cv.models>ViTDetBackbone", + "config": backbone_presets_no_weights["vitdet_large"]["config"], + "weights_url": "https://storage.googleapis.com/keras-cv/models/vitdet/vitdet_large.h5", # noqa: E501 + "weights_hash": "b85f73ee5a82842aecbc7c706ca69530aaa828d3324d0793a93730c94727b30e", # noqa: E501 + }, + "vitdet_huge_sa1b": { + "metadata": { + "description": ( + "A huge Detectron2 ViT backbone trained on the SA1B dataset." + ), + "params": 637_026_048, + "official_name": "VitDet", + "path": "vit_det", + }, + "class_name": "keras_cv.models>ViTDetBackbone", + "config": backbone_presets_no_weights["vitdet_huge"]["config"], + "weights_url": "https://storage.googleapis.com/keras-cv/models/vitdet/vitdet_huge.h5", # noqa: E501 + "weights_hash": "ae6e1a95acd748f783bddeadd5915fdc6d1c15d23909df3cd4fa446c7d6b9fc1", # noqa: E501 + }, +} + + +backbone_presets = { + **backbone_presets_no_weights, + **backbone_presets_with_weights, +} diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py b/keras_cv/models/backbones/vit_det/vit_det_backbone_presets_test.py similarity index 85% rename from keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py rename to keras_cv/models/backbones/vit_det/vit_det_backbone_presets_test.py index 2d72b5934b..7575ecd1d8 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone_presets_test.py +++ b/keras_cv/models/backbones/vit_det/vit_det_backbone_presets_test.py @@ -19,12 +19,8 @@ import pytest from keras_cv.backend import ops -from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - ViTDetBBackbone, -) -from keras_cv.models.backbones.detectron2.detectron2_backbone import ( - ViTDetBackbone, -) +from keras_cv.models.backbones.vit_det.vit_det_aliases import ViTDetBBackbone +from keras_cv.models.backbones.vit_det.vit_det_backbone import ViTDetBackbone from keras_cv.tests.test_case import TestCase @@ -40,7 +36,7 @@ def setUp(self): self.input_batch = np.ones(shape=(1, 1024, 1024, 3)) def test_backbone_output(self): - model = ViTDetBackbone.from_preset("vitdet_b") + model = ViTDetBackbone.from_preset("vitdet_base_sa1b") outputs = model(self.input_batch) # The forward pass from a preset should be stable! @@ -50,7 +46,7 @@ def test_backbone_output(self): # file, or have found a discrepancy with the upstream source. expected = np.load( - pathlib.Path(__file__).parent / "data" / "vitdet_b_out.npz" + pathlib.Path(__file__).parent / "data" / "vitdet_base_out.npz" ) # Keep a high tolerance, so we are robust to different hardware. self.assertAllClose( @@ -65,9 +61,14 @@ def test_applications_model_output(self): model(self.input_batch) def test_applications_model_output_with_preset(self): - model = ViTDetBBackbone.from_preset("vitdet_b") + model = ViTDetBackbone.from_preset("vitdet_base") model(self.input_batch) + def test_applications_model_predict(self): + model = ViTDetBBackbone() + # Test that the model XLA compiles + model.predict(self.input_batch) + def test_preset_docstring(self): """Check we did our docstring formatting correctly.""" for name in ViTDetBackbone.presets: diff --git a/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py b/keras_cv/models/backbones/vit_det/vit_det_backbone_test.py similarity index 74% rename from keras_cv/models/backbones/detectron2/detectron2_backbone_test.py rename to keras_cv/models/backbones/vit_det/vit_det_backbone_test.py index da75761cb1..fb12438119 100644 --- a/keras_cv/models/backbones/detectron2/detectron2_backbone_test.py +++ b/keras_cv/models/backbones/vit_det/vit_det_backbone_test.py @@ -19,15 +19,13 @@ from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - ViTDetBBackbone, -) +from keras_cv.models.backbones.vit_det.vit_det_aliases import ViTDetBBackbone from keras_cv.tests.test_case import TestCase class TestViTDetBackbone(TestCase): - @pytest.mark.extra_large - def test_call_and_save(self): + @pytest.mark.large + def test_call(self): model = ViTDetBBackbone() x = np.ones((1, 1024, 1024, 3)) x_out = ops.convert_to_numpy(model(x)) @@ -37,13 +35,26 @@ def test_call_and_save(self): self.assertEqual(x_out.shape, (1, 64, 64, 256)) self.assertEqual(num_parameters, 89_670_912) + @pytest.mark.extra_large + def teat_save(self): # saving test + model = ViTDetBBackbone() + x = np.ones((1, 1024, 1024, 3)) + x_out = ops.convert_to_numpy(model(x)) path = os.path.join(self.get_temp_dir(), "model.keras") model.save(path) loaded_model = keras.saving.load_model(path) x_out_loaded = ops.convert_to_numpy(loaded_model(x)) self.assertAllClose(x_out, x_out_loaded) + @pytest.mark.extra_large + def test_fit(self): + model = ViTDetBBackbone() + x = np.ones((1, 1024, 1024, 3)) + y = np.zeros((1, 64, 64, 256)) + model.compile(optimizer="adam", loss="mse", metrics=["mse"]) + model.fit(x, y, epochs=1) + def test_pyramid_level_inputs_error(self): model = ViTDetBBackbone() with self.assertRaises(NotImplementedError, msg="doesn't compute"): diff --git a/keras_cv/models/segmentation/segment_anything/data/sam_base_out_iou_pred.npy b/keras_cv/models/segmentation/segment_anything/data/sam_base_out_iou_pred.npy new file mode 100644 index 0000000000000000000000000000000000000000..974516d6f5c656d5d81db897381442ea65117c08 GIT binary patch literal 144 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+l>qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= oXCxM+0{I$-ItnJ5ItsN4WCJeQitD!bI!@ZP$Q-l_IFe%r08WV_-v9sr literal 0 HcmV?d00001 diff --git a/keras_cv/models/segmentation/segment_anything/data/sam_base_out_masks.npy 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a/keras_cv/models/segmentation/segment_anything/sam.py +++ b/keras_cv/models/segmentation/segment_anything/sam.py @@ -12,21 +12,24 @@ # See the License for the specific language governing permissions and # limitations under the License. +import copy + from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras -from keras_cv.models.backbones.detectron2.detectron2_backbone import ( - ViTDetBackbone, -) -from keras_cv.models.segmentation.segment_anything.sam_mask_decoder import ( - SAMMaskDecoder, +from keras_cv.models.backbones.backbone_presets import backbone_presets +from keras_cv.models.backbones.backbone_presets import ( + backbone_presets_with_weights, ) -from keras_cv.models.segmentation.segment_anything.sam_prompt_encoder import ( - SAMPromptEncoder, +from keras_cv.models.segmentation.segment_anything.sam_presets import ( + sam_presets, ) from keras_cv.models.task import Task +from keras_cv.utils.python_utils import classproperty -@keras_cv_export("keras_cv.models.SegmentAnythingModel") +@keras_cv_export( + "keras_cv.models.SegmentAnythingModel", package="keras_cv.models" +) class SegmentAnythingModel(Task): """ The Segment Anything (SAM) Model. @@ -164,21 +167,7 @@ class SegmentAnythingModel(Task): isn't support yet. So, calling the `fit` method will fail for now. """ # noqa: E501 - def __init__( - self, - *, - backbone: ViTDetBackbone, - prompt_encoder: SAMPromptEncoder, - mask_decoder: SAMMaskDecoder, - **kwargs - ): - if not isinstance(backbone, ViTDetBackbone): - raise ValueError("`backbone` must be a `ViTDetBackbone`.") - if not isinstance(prompt_encoder, SAMPromptEncoder): - raise ValueError("`prompt_encoder` must be a `SAMPromptEncoder`.") - if not isinstance(mask_decoder, SAMMaskDecoder): - raise ValueError("`mask_decoder` must be a `SAMMaskDecoder`.") - + def __init__(self, *, backbone, prompt_encoder, mask_decoder, **kwargs): # Get the image encoder input -- Images backbone_input = backbone.input @@ -248,3 +237,20 @@ def from_config(cls, config): } ) return super().from_config(config) + + @classproperty + def presets(cls): + """Dictionary of preset names and configurations.""" + return copy.deepcopy({**backbone_presets, **sam_presets}) + + @classproperty + def presets_with_weights(cls): + """Dictionary of preset names and configurations that include + weights.""" + return copy.deepcopy({**backbone_presets_with_weights, **sam_presets}) + + @classproperty + def backbone_presets(cls): + """Dictionary of preset names and configurations of compatible + backbones.""" + return copy.deepcopy(backbone_presets) diff --git a/keras_cv/models/segmentation/segment_anything/sam_layers.py b/keras_cv/models/segmentation/segment_anything/sam_layers.py index 0c1efa9801..127db266c4 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_layers.py +++ b/keras_cv/models/segmentation/segment_anything/sam_layers.py @@ -17,10 +17,13 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.layers.detectron2_layers import MLP +from keras_cv.layers.vit_det_layers import MLP -@keras_cv_export("keras_cv.layers.MultiHeadAttentionWithDownsampling") +@keras_cv_export( + "keras_cv.layers.MultiHeadAttentionWithDownsampling", + package="keras_cv.layers", +) class MultiHeadAttentionWithDownsampling(keras.layers.Layer): """Multi-Head Attention with downsampling. @@ -116,7 +119,9 @@ def get_config(self): return config -@keras_cv_export("keras_cv.layers.TwoWayMultiHeadAttention") +@keras_cv_export( + "keras_cv.layers.TwoWayMultiHeadAttention", package="keras_cv.layers" +) class TwoWayMultiHeadAttention(keras.layers.Layer): """Two-way multi-head attention layer. @@ -246,7 +251,10 @@ def get_config(self): return config -@keras_cv_export("keras_cv.layers.RandomFrequencyPositionalEmbeddings") +@keras_cv_export( + "keras_cv.layers.RandomFrequencyPositionalEmbeddings", + package="keras_cv.layers", +) class RandomFrequencyPositionalEmbeddings(keras.layers.Layer): """Positional encoding using random spatial frequencies. diff --git a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py index de06dd8abe..141f90addc 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_mask_decoder.py @@ -15,13 +15,13 @@ from keras_cv.api_export import keras_cv_export from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.layers.detectron2_layers import MLP +from keras_cv.layers.vit_det_layers import MLP from keras_cv.models.segmentation.segment_anything.sam_transformer import ( TwoWayTransformer, ) -@keras_cv_export("keras_cv.layers.SAMMaskDecoder") +@keras_cv_export("keras_cv.models.SAMMaskDecoder", package="keras_cv.models") class SAMMaskDecoder(keras.layers.Layer): """Mask decoder for the Segment Anything Model (SAM). diff --git a/keras_cv/models/segmentation/segment_anything/sam_presets.py b/keras_cv/models/segmentation/segment_anything/sam_presets.py new file mode 100644 index 0000000000..6460e5f517 --- /dev/null +++ b/keras_cv/models/segmentation/segment_anything/sam_presets.py @@ -0,0 +1,103 @@ +# Copyright 2023 The KerasCV Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SAM model preset configurations.""" + +from keras_cv.models.backbones.vit_det import vit_det_backbone_presets + +prompt_encoder_preset = { + "class_name": "keras_cv.models>SAMPromptEncoder", + "config": { + "embed_dim": 256, + "image_embedding_size": (64, 64), + "input_image_size": (1024, 1024), + "mask_in_chans": 16, + "activation": "gelu", + }, +} + +mask_decoder_preset = { + "class_name": "keras_cv.models>SAMMaskDecoder", + "config": { + "transformer_dim": 256, + "transformer": { + "class_name": "keras_cv.models>TwoWayTransformer", + "config": { + "depth": 2, + "embed_dim": 256, + "num_heads": 8, + "mlp_dim": 2048, + "activation": "relu", + "attention_downsample_rate": 2, + }, + }, + "num_multimask_outputs": 3, + "iou_head_depth": 3, + "iou_head_hidden_dim": 256, + "activation": "gelu", + }, +} + +sam_presets = { + "sam_base_sa1b": { + "metadata": { + "description": "The base SAM model trained on the SA1B dataset.", + "params": 93_735_728, + "official_name": "SAM", + "path": "segment_anything", + }, + "config": { + "backbone": vit_det_backbone_presets.backbone_presets[ + "vitdet_base" + ], + "prompt_encoder": prompt_encoder_preset, + "mask_decoder": mask_decoder_preset, + }, + "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/sam_base.h5", # noqa: E501 + "weights_hash": "5a18868ed227b6f093d4a6cb7ed689868dd11f288a8311ae69002a9a9d86d192", # noqa: E501 + }, + "sam_large_sa1b": { + "metadata": { + "description": "The large SAM model trained on the SA1B dataset.", + "params": 312_343_088, + "official_name": "SAM", + "path": "segment_anything", + }, + "config": { + "backbone": vit_det_backbone_presets.backbone_presets[ + "vitdet_large" + ], + "prompt_encoder": prompt_encoder_preset, + "mask_decoder": mask_decoder_preset, + }, + "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/sam_large.h5", # noqa: E501 + "weights_hash": "4ef43d3a8e24200c14a086a043dec8e956fef500c6171268a35029ea720305f0", # noqa: E501 + }, + "sam_huge_sa1b": { + "metadata": { + "description": "The huge SAM model trained on the SA1B dataset.", + "params": 641_090_864, + "official_name": "SAM", + "path": "segment_anything", + }, + "config": { + "backbone": vit_det_backbone_presets.backbone_presets[ + "vitdet_huge" + ], + "prompt_encoder": prompt_encoder_preset, + "mask_decoder": mask_decoder_preset, + }, + "weights_url": "https://storage.googleapis.com/keras-cv/models/segment_anything/sam_huge.h5", # noqa: E501 + "weights_hash": "3284c7c3c91274e8cb1ec2de69da3b6d6cee4483f7d8b0e17e1042b9dfc86fe5", # noqa: E501 + }, +} diff --git a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py index 1e6b2ec0a8..6a729231ca 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py +++ b/keras_cv/models/segmentation/segment_anything/sam_prompt_encoder.py @@ -20,7 +20,7 @@ ) -@keras_cv_export("keras_cv.layers.SAMPromptEncoder") +@keras_cv_export("keras_cv.models.SAMPromptEncoder", package="keras_cv.models") class SAMPromptEncoder(keras.layers.Layer): """Prompt Encoder for the Segment Anything Model (SAM). diff --git a/keras_cv/models/segmentation/segment_anything/sam_test.py b/keras_cv/models/segmentation/segment_anything/sam_test.py index 3fbeeb45cc..dfe7b3b8f5 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_test.py +++ b/keras_cv/models/segmentation/segment_anything/sam_test.py @@ -14,6 +14,7 @@ import itertools import os +import pathlib import numpy as np import pytest @@ -21,9 +22,7 @@ from keras_cv.backend import keras from keras_cv.backend import ops -from keras_cv.models.backbones.detectron2.detectron2_aliases import ( - ViTDetBBackbone, -) +from keras_cv.models.backbones.vit_det.vit_det_aliases import ViTDetBBackbone from keras_cv.models.segmentation.segment_anything.sam import ( SegmentAnythingModel, ) @@ -311,7 +310,43 @@ def test_end_to_end_model_save(self): restored_outputs = restored_model(inputs) self.assertAllClose(outputs, restored_outputs) - def test_model_fit_error(self): + @pytest.mark.large + def test_end_to_end_model_preset(self): + # Define the RNG. Don't change the seed. This seed + # was used to generate the inputs for the reference + # values. + rng = np.random.default_rng(0) + + # Generate the inputs + inputs = { + "images": 255.0 * rng.random((1, 1024, 1024, 3), dtype=np.float32), + "points": np.array( + [[[10, 10], [100, 100], [500, 500]]], dtype=np.float32 + ), + "labels": np.array([[0, 1, 0]], dtype=np.float32), + "box": np.array( + [[[[10.0, 10.0], [100.0, 100.0]]]], dtype=np.float32 + ), + "mask": (rng.random((1, 1, 256, 256, 1)) > 0.5).astype(np.float32), + } + + # Run the model + model = SegmentAnythingModel.from_preset("sam_base_sa1b") + outs = model.predict(inputs) + + # Make sure the weights have been loaded correctly. + masks_expected = np.load( + pathlib.Path(__file__).parent / "data" / "sam_base_out_masks.npy" + ) + iou_pred_expected = np.load( + pathlib.Path(__file__).parent / "data" / "sam_base_out_iou_pred.npy" + ) + self.assertAllClose(outs["masks"], masks_expected, atol=1e-2, rtol=1e-2) + self.assertAllClose( + outs["iou_pred"], iou_pred_expected, atol=1e-2, rtol=1e-2 + ) + + def test_end_to_end_model_fit_error(self): # Build the model model = SegmentAnythingModel( backbone=self.image_encoder, diff --git a/keras_cv/models/segmentation/segment_anything/sam_transformer.py b/keras_cv/models/segmentation/segment_anything/sam_transformer.py index efcc89b7b1..64d1de8575 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_transformer.py +++ b/keras_cv/models/segmentation/segment_anything/sam_transformer.py @@ -23,7 +23,7 @@ ) -@keras_cv_export("keras_cv.layers.TwoWayTransformer") +@keras_cv_export("keras_cv.models.TwoWayTransformer", package="keras_cv.models") class TwoWayTransformer(keras.layers.Layer): """A two-way cross-attention transformer decoder. From 8ad94ae6db08af3ad21e5a1b03fb355dd53ba20a Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Mon, 18 Sep 2023 13:03:58 -0700 Subject: [PATCH 13/13] Increse test tolerence for GCB Run --- keras_cv/models/segmentation/segment_anything/sam_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/keras_cv/models/segmentation/segment_anything/sam_test.py b/keras_cv/models/segmentation/segment_anything/sam_test.py index dfe7b3b8f5..1ab5a131fa 100644 --- a/keras_cv/models/segmentation/segment_anything/sam_test.py +++ b/keras_cv/models/segmentation/segment_anything/sam_test.py @@ -272,8 +272,8 @@ def test_end_to_end_model_predict(self): ) masks_ex, iou_pred_ex = outputs_ex["masks"], outputs_ex["iou_pred"] - self.assertAllClose(masks, masks_ex, atol=1e-5) - self.assertAllClose(iou_pred, iou_pred_ex, atol=1e-5) + self.assertAllClose(masks, masks_ex, atol=5e-5) + self.assertAllClose(iou_pred, iou_pred_ex, atol=5e-5) @pytest.mark.extra_large def test_end_to_end_model_save(self):

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