code reformat (#2311)

keras_cv/models/feature_extractors/__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.

keras_cv/models/feature_extractors/clip/__init__.py

@@ -1,3 +1,16 @@
+# 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.feature_extractors.clip.clip_image_encoder import (
     CLIPImageEncoder,
 )

keras_cv/models/feature_extractors/clip/clip_image_encoder.py

@@ -54,14 +54,16 @@ def __init__(
             name="residual_transformer_encoder",
         )(x)
         x = ops.transpose(x, axes=(1, 0, 2))
-
+        print(x.shape)
         x = keras.layers.LayerNormalization(epsilon=1e-6, name="ln_2")(
             x[:, 0, :]
         )
-
-        proj = keras.layers.Dense(output_dim, name="vision_projector")
+        print(x.shape)
+        proj = keras.layers.Dense(
+            output_dim, name="vision_projector", use_bias=False
+        )
         x = proj(x)
-
+        print("final", x.shape)
         output = x
 
         super().__init__(

keras_cv/models/feature_extractors/clip/clip_modelling.py

@@ -159,8 +159,8 @@ def compute_output_shape(self, inputs_shape):
 
 class CLIPAttention(keras.layers.Layer):
     """
-    - Documentation page: https://huggingface.co/docs/transformers/model_doc/clip
-    - Implementation: https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py
+    - Documentation page: https://huggingface.co/docs/transformers/model_doc/clip # noqa: E501
+    - Implementation: https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py # noqa: E501
     """
 
     def __init__(self, project_dim, num_heads, dropout=0.0, **kwargs):
@@ -171,7 +171,8 @@ def __init__(self, project_dim, num_heads, dropout=0.0, **kwargs):
         self.head_dim = self.project_dim // self.num_heads
         if self.head_dim * self.num_heads != self.project_dim:
             raise ValueError(
-                f"project_dim must be divisible by num_heads (got `project_dim`: {self.project_dim} and `num_heads`:"
+                f"project_dim must be divisible by num_heads (got `project_dim`"
+                ": {self.project_dim} and `num_heads`:"
                 f" {self.num_heads})."
             )
 
@@ -194,13 +195,15 @@ def build(self, input_shape):
 
     def _transpose_for_scores(self, tensor, batch_size):
         """
-        Copied from https://github.com/huggingface/transformers/blob/8e164c5400b7b413c7b8fb32e35132001effc970/src/transformers/models/bert/modeling_tf_bert.py#L252
+        Copied from https://github.com/huggingface/transformers/blob/8e164c5400b7b413c7b8fb32e35132001effc970/src/transformers/models/bert/modeling_tf_bert.py#L252 # noqa: E501
         """
-        # [batch_size, seq_len, all_head_dim] -> [batch_size, seq_len, num_heads, head_dim]
+        # [batch_size, seq_len, all_head_dim] ->
+        # [batch_size, seq_len, num_heads, head_dim]
         tensor = ops.reshape(
             tensor, (batch_size, -1, self.num_heads, self.head_dim)
         )
-        # [batch_size, seq_len, num_heads, head_dim] -> [batch_size, num_heads, seq_len, head_dim]
+        # [batch_size, seq_len, num_heads, head_dim] ->
+        # [batch_size, num_heads, seq_len, head_dim]
         return ops.transpose(tensor, axes=[0, 2, 1, 3])
 
     def call(
@@ -220,7 +223,6 @@ def call(
         value_layer = self._transpose_for_scores(mixed_value_layer, batch_size)
 
         # Scaled dot product between key and query = raw attention scores.
-
         attention_scores = ops.matmul(
             query_layer, ops.transpose(key_layer, axes=[0, 1, 3, 2])
         )
@@ -231,11 +233,13 @@ def call(
 
         # Apply the causal_attention_mask first
         if causal_attention_mask is not None:
-            # Apply the causal attention mask (precomputed for all layers in the call() function)
+            # Apply the causal attention mask (precomputed for all layers in
+            # the call() function)
             attention_scores = ops.add(attention_scores, causal_attention_mask)
 
         if attention_mask is not None:
-            # Apply the attention mask (precomputed for all layers in the call() function)
+            # Apply the attention mask (precomputed for all layers in the
+            # call() function)
             attention_scores = ops.add(attention_scores, attention_mask)
 
         # Normalize the attention scores to probabilities.