run formatters"

keras-team · Mar 2, 2024 · b1a5427 · b1a5427
1 parent daca84f
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Showing 4 changed files with 32 additions and 31 deletions.
diff --git a/keras_cv/models/backbones/backbone_presets.py b/keras_cv/models/backbones/backbone_presets.py
@@ -28,9 +28,9 @@
 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.video_swin import video_swin_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
-from keras_cv.models.backbones.video_swin import video_swin_backbone_presets
 
 backbone_presets_no_weights = {
     **resnet_v1_backbone_presets.backbone_presets_no_weights,

diff --git a/keras_cv/models/backbones/video_swin/video_swin_aliases.py b/keras_cv/models/backbones/video_swin/video_swin_aliases.py
@@ -13,11 +13,14 @@
 # limitations under the License.
 
 import copy
+
 from keras_cv.models.backbones.video_swin.video_swin_backbone import (
     VideoSwinBackbone,
 )
+from keras_cv.models.backbones.video_swin.video_swin_backbone_presets import (
+    backbone_presets,
+)
 from keras_cv.utils.python_utils import classproperty
-from keras_cv.models.backbones.video_swin.video_swin_backbone_presets import backbone_presets
 
 ALIAS_DOCSTRING = """VideoSwin{size}Backbone model.
 

diff --git a/keras_cv/models/backbones/video_swin/video_swin_backbone.py b/keras_cv/models/backbones/video_swin/video_swin_backbone.py
@@ -16,10 +16,10 @@
 
 import numpy as np
 from keras import layers
-from keras_cv.backend import ops
 
 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.video_swin_layers import VideoSwinBasicLayer
 from keras_cv.layers.video_swin_layers import VideoSwinPatchingAndEmbedding
 from keras_cv.layers.video_swin_layers import VideoSwinPatchMerging
@@ -33,7 +33,7 @@ class VideoSwinBackbone(Backbone):
 
     Args:
         input_shape (tuple[int], optional): The size of the input image in
-            `(depth, height, width, channel)` format. 
+            `(depth, height, width, channel)` format.
             Defaults to `(32, 224, 224, 3)`.
         input_tensor (KerasTensor, optional): Output of
             `keras.layers.Input()`) to use as image input for the model.
@@ -51,18 +51,18 @@ class VideoSwinBackbone(Backbone):
         num_heads (tuple[int]): Number of attention head of each stage.
             Default to [3, 6, 12, 24]
         window_size (int): Window size. Default to [8, 7, 7].
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. 
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
             Default to 4.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. 
+        qkv_bias (bool): If True, add a learnable bias to query, key, value.
             Default to True.
         qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
             Default to None.
         drop_rate (float): Dropout rate.
         attn_drop_rate (float): Attention dropout rate. Default: 0.
         drop_path_rate (float): Stochastic depth rate. Default: 0.2.
-        patch_norm (bool): If True, add normalization after patch embedding. 
+        patch_norm (bool): If True, add normalization after patch embedding.
             Default to False.
- 
+
     References:
         - [Video Swin Transformer](https://arxiv.org/abs/2106.13230)
         - [Official Code](https://github.com/SwinTransformer/Video-Swin-Transformer)
@@ -94,7 +94,11 @@ def __init__(
         )
 
         # Check that the input video is well specified.
-        if input_spec.shape[-4] is None or input_spec.shape[-3] is None or input_spec.shape[-2] is None:
+        if (
+            input_spec.shape[-4] is None
+            or input_spec.shape[-3] is None
+            or input_spec.shape[-2] is None
+        ):
             raise ValueError(
                 "Depth, height and width of the video must be specified"
                 " in `input_shape`."
@@ -112,11 +116,11 @@ def __init__(
             # Use common rescaling strategy across keras_cv
             x = keras.layers.Rescaling(1.0 / 255.0)(x)
 
-            # Video Swin scales inputs based on the standard ImageNet mean/stddev.
-            # Officially, Videw Swin takes tensor of [0-255] ranges. 
-            # And use mean=[123.675, 116.28, 103.53] and 
-            # std=[58.395, 57.12, 57.375] for normalization. 
-            # So, if include_rescaling is set to True, then, to match with the 
+            # VideoSwin scales inputs based on the ImageNet mean/stddev.
+            # Officially, Videw Swin takes tensor of [0-255] ranges.
+            # And use mean=[123.675, 116.28, 103.53] and
+            # std=[58.395, 57.12, 57.375] for normalization.
+            # So, if include_rescaling is set to True, then, to match with the
             # official scores, following normalization should be added.
             x = (x - ops.array([0.485, 0.456, 0.406], dtype=x.dtype)) / (
                 ops.array([0.229, 0.224, 0.225], dtype=x.dtype)
@@ -147,9 +151,9 @@ def __init__(
                 attn_drop_rate=attn_drop_rate,
                 drop_path_rate=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
                 norm_layer=norm_layer,
-                downsample=VideoSwinPatchMerging
-                if (i < num_layers - 1)
-                else None,
+                downsample=(
+                    VideoSwinPatchMerging if (i < num_layers - 1) else None
+                ),
                 name=f"videoswin_basic_layer_{i + 1}",
             )
             x = layer(x)

diff --git a/keras_cv/models/classification/video_classifier_presets.py b/keras_cv/models/classification/video_classifier_presets.py
@@ -16,42 +16,36 @@
 classifier_presets = {
     "videoswin_tiny_kinetics_classifier": {
         "metadata": {
-            "description": (
-                "videoswin_tiny_kinetics " # TODO: update
-            ),
+            "description": ("videoswin_tiny_kinetics "),  # TODO: update
             "params": 25_613_800,
             "official_name": "VideoClassifier",
             "path": "video_classifier",
         },
     },
     "videoswin_small_kinetics_classifier": {
         "metadata": {
-            "description": (
-                "videoswin_small_kinetics " # TODO: update
-            ),
-            "params": 25_613_800, # TODO: update
+            "description": ("videoswin_small_kinetics "),  # TODO: update
+            "params": 25_613_800,  # TODO: update
             "official_name": "VideoClassifier",
             "path": "video_classifier",
         },
     },
     "videoswin_base_kinetics_classifier": {
         "metadata": {
-            "description": (
-                "videoswin_base_kinetics " # TODO: update
-            ),
-            "params": 25_613_800, # TODO: update
+            "description": ("videoswin_base_kinetics "),  # TODO: update
+            "params": 25_613_800,  # TODO: update
             "official_name": "VideoClassifier",
             "path": "video_classifier",
         },
     },
     "videoswin_base_something_something_v2_classifier": {
         "metadata": {
             "description": (
-                "videoswin_base_something_something_v2 " # TODO: update
+                "videoswin_base_something_something_v2 "  # TODO: update
             ),
-            "params": 25_613_800, # TODO: update
+            "params": 25_613_800,  # TODO: update
             "official_name": "VideoClassifier",
             "path": "video_classifier",
         },
     },
-}
+}