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Error in Vision Transformer examples · Issue #1907 · keras-team/keras-io · GitHub
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examples","number":1907,"repository":{"nameWithOwner":"keras-team/keras-io","id":"MDEwOlJlcG9zaXRvcnkyNjE4NjE3MzM=","name":"keras-io","owner":{"__typename":"Organization","login":"keras-team","id":"MDEyOk9yZ2FuaXphdGlvbjM0NDU1MDQ4","url":"https://github.com/keras-team"},"hasAnyTemplates":true,"isSecurityPolicyEnabled":false,"isArchived":false,"isPrivate":false,"databaseId":261861733,"slashCommandsEnabled":false,"viewerCanInteract":false,"viewerInteractionLimitReasonHTML":"","planFeatures":{"maximumAssignees":10},"visibility":"PUBLIC","pinnedIssues":{"totalCount":0},"viewerCanPinIssues":false,"issueTypes":{"edges":[{"node":{"id":"IT_kwDOAg2-CM4AaYnk"}},{"node":{"id":"IT_kwDOAg2-CM4AaYnm"}},{"node":{"id":"IT_kwDOAg2-CM4AaYnp"}}]}},"titleHTML":"Error in Vision Transformer examples","url":"https://github.com/keras-team/keras-io/issues/1907","viewerCanUpdateNext":false,"issueType":null,"state":"OPEN","stateReason":null,"duplicateOf":null,"linkedPullRequests":{"nodes":[]},"subIssuesSummary":{"total":0,"completed":0},"__isLabelable":"Issue","labels":{"edges":[],"pageInfo":{"endCursor":null,"hasNextPage":false}},"__isNode":"Issue","assignees":{"nodes":[{"id":"MDQ6VXNlcjczMDY5MDQw","login":"sachinprasadhs","name":"Sachin Prasad","avatarUrl":"https://avatars.githubusercontent.com/u/73069040?s=64\u0026u=c73d4a1b1d1dd9d2429212c11bc8b77170692bf6\u0026v=4"}]},"milestone":null,"databaseId":2458122709,"viewerDidAuthor":false,"locked":false,"author":{"__typename":"User","__isActor":"User","login":"angelo-ml","id":"MDQ6VXNlcjIyNzc2Mzkx","profileUrl":"https://github.com/angelo-ml","avatarUrl":"https://avatars.githubusercontent.com/u/22776391?u=503102a640cc6f6238bf13706c5a6b1fca3d6d5b\u0026v=4"},"__isComment":"Issue","body":"### Issue Type\r\n\r\nDocumentation Bug\r\n\r\n### Source\r\n\r\nsource\r\n\r\n### Keras Version\r\n\r\n2.14\r\n\r\n### Custom Code\r\n\r\nYes\r\n\r\n### OS Platform and Distribution\r\n\r\nUbuntu 22.04\r\n\r\n### Python version\r\n\r\n3.10\r\n\r\n### GPU model and memory\r\n\r\nNvidia RTX4070 (12GB)\r\n\r\n### Current Behavior?\r\n\r\nHi,\r\nI've spotted a mistake in the Vision Transformer examples in Keras.io [3,4,5,6,7].\r\n\r\nIn all five of the examples below, to build the ViT architecture, the authors use a single hyper-parameter named projection_dim, which is used both as the model's hidden dimension, and as the dimension for queries, keys, and values, in the multi-head attention layer. These two hyper-parameters they shouldn't be the same. However, according to [1], they are connected:\r\n\r\nhidden dimension = number of heads * qkv dimension\r\n\r\nOne simple way to verify this issue, is to calculate the total number of trainable parameters of the model.\r\nUsing the architecture from the examples in Keras.io, and setting the same hyper-parameters with vision transformer base, the model has only 15 million parameters (while the Vision Transformer Base has 86 million [2]).\r\n\r\nTo fix this issue:\r\n1. a hidden dimension parameter can be defined as: \r\nhidden_dim = projection_dim * num_heads\r\n\r\n2. The encoded patches should be projected in the hidden dimension, instead of the projection_dim: \r\nencoded_patches = PatchEncoder(num_patches, hidden_dim)(patches)\r\n\r\n3. The transformer_units should also use the hidden dimension:\r\ntransformer_units = [hidden_dim * 2, hidden_dim, ]\r\n\r\nThen, if the same hyper-parameters used as in the original paper, the number of trainable parameters will be the same, as in the ViT base.\r\n\r\nI understand that the authors may have used alternative versions of the original model, but this particular modification, can change significantly the behaviour of the model.\r\n\r\nIf you'll need any further information, please let me know.\r\n\r\nBest wishes,\r\nAngelos\r\n\r\n[1] see table 3 in the original paper: https://arxiv.org/pdf/1706.03762\r\n[2] https://arxiv.org/pdf/2010.11929\r\n[3] https://keras.io/examples/vision/image_classification_with_vision_transformer/\r\n[4] https://keras.io/examples/vision/vit_small_ds/\r\n[5] https://keras.io/examples/vision/object_detection_using_vision_transformer/\r\n[6] https://keras.io/examples/vision/token_learner/\r\n[7] https://keras.io/examples/vision/vit_small_ds/\r\n\r\n\r\n\r\n### Standalone code to reproduce the issue or tutorial link\r\n\r\n```shell\r\n# Below is the ViT class (create_vit_object_detector, indentical to [3]),\r\n# with the same hyper-parameters as ViT-base, including the hidden dimension hyper-parameter.\r\n# If the hidden_dimension is set equal to projection_dim (as implied in the Keras.io examples)\r\n# the model will have 15M parameters. \r\n# If set to 768 (=projection_dim*num_heads), it will have 86M parameters, as the original model.\r\n\r\n# The code uses tensorflow 2.14 \r\n\r\n#%% Import libraries\r\nimport keras\r\nfrom keras import layers\r\nimport tensorflow as tf\r\n\r\n#%% define required functions and classes\r\ndef mlp(x, hidden_units, dropout_rate):\r\n for units in hidden_units:\r\n x = layers.Dense(units, activation=keras.activations.gelu)(x)\r\n x = layers.Dropout(dropout_rate)(x)\r\n return x\r\n\r\nclass Patches(layers.Layer):\r\n def __init__(self, patch_size):\r\n super().__init__()\r\n self.patch_size = patch_size\r\n\r\n def call(self, images):\r\n input_shape = tf.shape(images)\r\n batch_size = input_shape[0]\r\n height = input_shape[1]\r\n width = input_shape[2]\r\n channels = input_shape[3]\r\n num_patches_h = height // self.patch_size\r\n num_patches_w = width // self.patch_size\r\n patches = tf.image.extract_patches(images, \r\n [1,self.patch_size,self.patch_size,1],\r\n [1,self.patch_size,self.patch_size,1],\r\n rates=[1, 1, 1, 1],\r\n padding=\"SAME\"\r\n )\r\n patches = tf.reshape(\r\n patches,\r\n (\r\n batch_size,\r\n num_patches_h * num_patches_w,\r\n self.patch_size * self.patch_size * channels,\r\n ),\r\n )\r\n return patches\r\n\r\n def get_config(self):\r\n config = super().get_config()\r\n config.update({\"patch_size\": self.patch_size})\r\n return config\r\n\r\nclass PatchEncoder(layers.Layer):\r\n def __init__(self, num_patches, projection_dim):\r\n super().__init__()\r\n self.num_patches = num_patches\r\n self.projection = layers.Dense(units=projection_dim)\r\n self.position_embedding = layers.Embedding(\r\n input_dim=num_patches, output_dim=projection_dim\r\n )\r\n\r\n # Override function to avoid error while saving model\r\n def get_config(self):\r\n config = super().get_config().copy()\r\n config.update(\r\n {\r\n \"input_shape\": input_shape,\r\n \"patch_size\": patch_size,\r\n \"num_patches\": num_patches,\r\n \"projection_dim\": projection_dim,\r\n \"num_heads\": num_heads,\r\n \"transformer_units\": transformer_units,\r\n \"transformer_layers\": transformer_layers,\r\n \"mlp_head_units\": mlp_head_units,\r\n }\r\n )\r\n return config\r\n\r\n def call(self, patch):\r\n positions = tf.expand_dims(\r\n tf.experimental.numpy.arange(start=0, stop=self.num_patches, step=1), axis=0\r\n )\r\n projected_patches = self.projection(patch)\r\n encoded = projected_patches + self.position_embedding(positions)\r\n return encoded\r\n\r\ndef create_vit_object_detector(\r\n input_shape,\r\n patch_size,\r\n num_patches,\r\n projection_dim,\r\n num_heads,\r\n transformer_units,\r\n transformer_layers,\r\n mlp_head_units,\r\n hidden_dimension\r\n):\r\n \r\n inputs = keras.Input(shape=input_shape)\r\n # Create patches\r\n patches = Patches(patch_size)(inputs)\r\n # Encode patches\r\n encoded_patches = PatchEncoder(num_patches, hidden_dimension)(patches)\r\n\r\n # Create multiple layers of the Transformer block.\r\n for _ in range(transformer_layers):\r\n # Layer normalization 1.\r\n x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\r\n # Create a multi-head attention layer.\r\n attention_output = layers.MultiHeadAttention(\r\n num_heads=num_heads, key_dim=projection_dim, dropout=0.1\r\n )(x1, x1)\r\n # Skip connection 1.\r\n x2 = layers.Add()([attention_output, encoded_patches])\r\n # Layer normalization 2.\r\n x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\r\n # MLP\r\n x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)\r\n # Skip connection 2.\r\n encoded_patches = layers.Add()([x3, x2])\r\n\r\n # Create a [batch_size, projection_dim] tensor.\r\n representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\r\n representation = layers.GlobalAveragePooling1D()(representation)\r\n representation = layers.Dropout(0.3)(representation)\r\n # Add MLP.\r\n features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.3)\r\n\r\n bounding_box = layers.Dense(1024)(\r\n features\r\n ) # Final four neurons that output bounding box\r\n\r\n # return Keras model.\r\n return keras.Model(inputs=inputs, outputs=bounding_box)\r\n\r\n#%% model parameters\r\nimage_size = 224\r\npatch_size = 16\r\ninput_shape = (image_size, image_size, 3) # input image shape\r\nnum_patches = (image_size // patch_size) ** 2\r\nprojection_dim = 64\r\nnum_heads = 12\r\nhidden_dimension = projection_dim * num_heads\r\n\r\n# Size of the transformer layers\r\ntransformer_units = [\r\n hidden_dimension * 3,\r\n hidden_dimension,\r\n]\r\ntransformer_layers = 12\r\nmlp_head_units = [3072, 3072] # Size of the dense layers\r\n\r\nvit_object_detector = create_vit_object_detector(\r\n input_shape,\r\n patch_size,\r\n num_patches,\r\n projection_dim,\r\n num_heads,\r\n transformer_units,\r\n transformer_layers,\r\n mlp_head_units,\r\n hidden_dimension,\r\n)\r\n\r\nvit_object_detector.summary()\r\n```\r\n\r\n\r\n### Relevant log output\r\n\r\n_No response_","bodyHTML":"\u003ch3 dir=\"auto\"\u003eIssue Type\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003eDocumentation Bug\u003c/p\u003e\n\u003ch3 dir=\"auto\"\u003eSource\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003esource\u003c/p\u003e\n\u003ch3 dir=\"auto\"\u003eKeras Version\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003e2.14\u003c/p\u003e\n\u003ch3 dir=\"auto\"\u003eCustom Code\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003eYes\u003c/p\u003e\n\u003ch3 dir=\"auto\"\u003eOS Platform and Distribution\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003eUbuntu 22.04\u003c/p\u003e\n\u003ch3 dir=\"auto\"\u003ePython version\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003e3.10\u003c/p\u003e\n\u003ch3 dir=\"auto\"\u003eGPU model and memory\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003eNvidia RTX4070 (12GB)\u003c/p\u003e\n\u003ch3 dir=\"auto\"\u003eCurrent Behavior?\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003eHi,\u003cbr\u003e\nI've spotted a mistake in the Vision Transformer examples in Keras.io [3,4,5,6,7].\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn all five of the examples below, to build the ViT architecture, the authors use a single hyper-parameter named projection_dim, which is used both as the model's hidden dimension, and as the dimension for queries, keys, and values, in the multi-head attention layer. These two hyper-parameters they shouldn't be the same. However, according to [1], they are connected:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ehidden dimension = number of heads * qkv dimension\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOne simple way to verify this issue, is to calculate the total number of trainable parameters of the model.\u003cbr\u003e\nUsing the architecture from the examples in Keras.io, and setting the same hyper-parameters with vision transformer base, the model has only 15 million parameters (while the Vision Transformer Base has 86 million [2]).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo fix this issue:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003ea hidden dimension parameter can be defined as:\u003cbr\u003e\nhidden_dim = projection_dim * num_heads\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eThe encoded patches should be projected in the hidden dimension, instead of the projection_dim:\u003cbr\u003e\nencoded_patches = PatchEncoder(num_patches, hidden_dim)(patches)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eThe transformer_units should also use the hidden dimension:\u003cbr\u003e\ntransformer_units = [hidden_dim * 2, hidden_dim, ]\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eThen, if the same hyper-parameters used as in the original paper, the number of trainable parameters will be the same, as in the ViT base.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eI understand that the authors may have used alternative versions of the original model, but this particular modification, can change significantly the behaviour of the model.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you'll need any further information, please let me know.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBest wishes,\u003cbr\u003e\nAngelos\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e[1] see table 3 in the original paper: \u003ca href=\"https://arxiv.org/pdf/1706.03762\" rel=\"nofollow\"\u003ehttps://arxiv.org/pdf/1706.03762\u003c/a\u003e\u003cbr\u003e\n[2] \u003ca href=\"https://arxiv.org/pdf/2010.11929\" rel=\"nofollow\"\u003ehttps://arxiv.org/pdf/2010.11929\u003c/a\u003e\u003cbr\u003e\n[3] \u003ca href=\"https://keras.io/examples/vision/image_classification_with_vision_transformer/\" rel=\"nofollow\"\u003ehttps://keras.io/examples/vision/image_classification_with_vision_transformer/\u003c/a\u003e\u003cbr\u003e\n[4] \u003ca href=\"https://keras.io/examples/vision/vit_small_ds/\" rel=\"nofollow\"\u003ehttps://keras.io/examples/vision/vit_small_ds/\u003c/a\u003e\u003cbr\u003e\n[5] \u003ca href=\"https://keras.io/examples/vision/object_detection_using_vision_transformer/\" rel=\"nofollow\"\u003ehttps://keras.io/examples/vision/object_detection_using_vision_transformer/\u003c/a\u003e\u003cbr\u003e\n[6] \u003ca href=\"https://keras.io/examples/vision/token_learner/\" rel=\"nofollow\"\u003ehttps://keras.io/examples/vision/token_learner/\u003c/a\u003e\u003cbr\u003e\n[7] \u003ca href=\"https://keras.io/examples/vision/vit_small_ds/\" rel=\"nofollow\"\u003ehttps://keras.io/examples/vision/vit_small_ds/\u003c/a\u003e\u003c/p\u003e\n\u003ch3 dir=\"auto\"\u003eStandalone code to reproduce the issue or tutorial link\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Below is the ViT class (create_vit_object_detector, indentical to [3]),\n# with the same hyper-parameters as ViT-base, including the hidden dimension hyper-parameter.\n# If the hidden_dimension is set equal to projection_dim (as implied in the Keras.io examples)\n# the model will have 15M parameters. \n# If set to 768 (=projection_dim*num_heads), it will have 86M parameters, as the original model.\n\n# The code uses tensorflow 2.14 \n\n#%% Import libraries\nimport keras\nfrom keras import layers\nimport tensorflow as tf\n\n#%% define required functions and classes\ndef mlp(x, hidden_units, dropout_rate):\n for units in hidden_units:\n x = layers.Dense(units, activation=keras.activations.gelu)(x)\n x = layers.Dropout(dropout_rate)(x)\n return x\n\nclass Patches(layers.Layer):\n def __init__(self, patch_size):\n super().__init__()\n self.patch_size = patch_size\n\n def call(self, images):\n input_shape = tf.shape(images)\n batch_size = input_shape[0]\n height = input_shape[1]\n width = input_shape[2]\n channels = input_shape[3]\n num_patches_h = height // self.patch_size\n num_patches_w = width // self.patch_size\n patches = tf.image.extract_patches(images, \n [1,self.patch_size,self.patch_size,1],\n [1,self.patch_size,self.patch_size,1],\n rates=[1, 1, 1, 1],\n padding=\u0026quot;SAME\u0026quot;\n )\n patches = tf.reshape(\n patches,\n (\n batch_size,\n num_patches_h * num_patches_w,\n self.patch_size * self.patch_size * channels,\n ),\n )\n return patches\n\n def get_config(self):\n config = super().get_config()\n config.update({\u0026quot;patch_size\u0026quot;: self.patch_size})\n return config\n\nclass PatchEncoder(layers.Layer):\n def __init__(self, num_patches, projection_dim):\n super().__init__()\n self.num_patches = num_patches\n self.projection = layers.Dense(units=projection_dim)\n self.position_embedding = layers.Embedding(\n input_dim=num_patches, output_dim=projection_dim\n )\n\n # Override function to avoid error while saving model\n def get_config(self):\n config = super().get_config().copy()\n config.update(\n {\n \u0026quot;input_shape\u0026quot;: input_shape,\n \u0026quot;patch_size\u0026quot;: patch_size,\n \u0026quot;num_patches\u0026quot;: num_patches,\n \u0026quot;projection_dim\u0026quot;: projection_dim,\n \u0026quot;num_heads\u0026quot;: num_heads,\n \u0026quot;transformer_units\u0026quot;: transformer_units,\n \u0026quot;transformer_layers\u0026quot;: transformer_layers,\n \u0026quot;mlp_head_units\u0026quot;: mlp_head_units,\n }\n )\n return config\n\n def call(self, patch):\n positions = tf.expand_dims(\n tf.experimental.numpy.arange(start=0, stop=self.num_patches, step=1), axis=0\n )\n projected_patches = self.projection(patch)\n encoded = projected_patches + self.position_embedding(positions)\n return encoded\n\ndef create_vit_object_detector(\n input_shape,\n patch_size,\n num_patches,\n projection_dim,\n num_heads,\n transformer_units,\n transformer_layers,\n mlp_head_units,\n hidden_dimension\n):\n \n inputs = keras.Input(shape=input_shape)\n # Create patches\n patches = Patches(patch_size)(inputs)\n # Encode patches\n encoded_patches = PatchEncoder(num_patches, hidden_dimension)(patches)\n\n # Create multiple layers of the Transformer block.\n for _ in range(transformer_layers):\n # Layer normalization 1.\n x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n # Create a multi-head attention layer.\n attention_output = layers.MultiHeadAttention(\n num_heads=num_heads, key_dim=projection_dim, dropout=0.1\n )(x1, x1)\n # Skip connection 1.\n x2 = layers.Add()([attention_output, encoded_patches])\n # Layer normalization 2.\n x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\n # MLP\n x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)\n # Skip connection 2.\n encoded_patches = layers.Add()([x3, x2])\n\n # Create a [batch_size, projection_dim] tensor.\n representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n representation = layers.GlobalAveragePooling1D()(representation)\n representation = layers.Dropout(0.3)(representation)\n # Add MLP.\n features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.3)\n\n bounding_box = layers.Dense(1024)(\n features\n ) # Final four neurons that output bounding box\n\n # return Keras model.\n return keras.Model(inputs=inputs, outputs=bounding_box)\n\n#%% model parameters\nimage_size = 224\npatch_size = 16\ninput_shape = (image_size, image_size, 3) # input image shape\nnum_patches = (image_size // patch_size) ** 2\nprojection_dim = 64\nnum_heads = 12\nhidden_dimension = projection_dim * num_heads\n\n# Size of the transformer layers\ntransformer_units = [\n hidden_dimension * 3,\n hidden_dimension,\n]\ntransformer_layers = 12\nmlp_head_units = [3072, 3072] # Size of the dense layers\n\nvit_object_detector = create_vit_object_detector(\n input_shape,\n patch_size,\n num_patches,\n projection_dim,\n num_heads,\n transformer_units,\n transformer_layers,\n mlp_head_units,\n hidden_dimension,\n)\n\nvit_object_detector.summary()\"\u003e\u003cpre class=\"notranslate\"\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Below is the ViT class (create_vit_object_detector, indentical to [3]),\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e with the same hyper-parameters as ViT-base, including the hidden dimension hyper-parameter.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If the hidden_dimension is set equal to projection_dim (as implied in the Keras.io examples)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e the model will have 15M parameters. \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e If set to 768 (=projection_dim*num_heads), it will have 86M parameters, as the original model.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e The code uses tensorflow 2.14 \u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e%% Import libraries\u003c/span\u003e\nimport keras\nfrom keras import layers\nimport tensorflow as tf\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e%% define required functions and classes\u003c/span\u003e\ndef mlp(x, hidden_units, dropout_rate):\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eunits\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e hidden_units:\n x = layers.Dense(units, activation=keras.activations.gelu)(x)\n x = layers.Dropout(dropout_rate)(x)\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e x\n\nclass Patches(layers.Layer):\n def __init__(self, patch_size):\n \u003cspan class=\"pl-en\"\u003esuper().__init__\u003c/span\u003e()\n self.patch_size = patch_size\n\n def call(self, images):\n input_shape = tf.shape(images)\n batch_size = input_shape[0]\n height = input_shape[1]\n width = input_shape[2]\n channels = input_shape[3]\n num_patches_h = height // self.patch_size\n num_patches_w = width // self.patch_size\n patches = tf.image.extract_patches(images, \n [1,self.patch_size,self.patch_size,1],\n [1,self.patch_size,self.patch_size,1],\n rates=[1, 1, 1, 1],\n padding=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eSAME\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n )\n patches = tf.reshape(\n patches,\n (\n batch_size,\n num_patches_h \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e num_patches_w,\n self.patch_size \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e self.patch_size \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e channels,\n ),\n )\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e patches\n\n def get_config(self):\n config = \u003cspan class=\"pl-en\"\u003esuper().get_config\u003c/span\u003e()\n config.update({\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epatch_size\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: self.patch_size})\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e config\n\nclass PatchEncoder(layers.Layer):\n def __init__(self, num_patches, projection_dim):\n \u003cspan class=\"pl-en\"\u003esuper().__init__\u003c/span\u003e()\n self.num_patches = num_patches\n self.projection = layers.Dense(units=projection_dim)\n self.position_embedding = layers.Embedding(\n input_dim=num_patches, output_dim=projection_dim\n )\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Override function to avoid error while saving model\u003c/span\u003e\n def get_config(self):\n config = \u003cspan class=\"pl-en\"\u003esuper().get_config().copy\u003c/span\u003e()\n config.update(\n {\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003einput_shape\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: input_shape,\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003epatch_size\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: patch_size,\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enum_patches\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: num_patches,\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eprojection_dim\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: projection_dim,\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enum_heads\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: num_heads,\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etransformer_units\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: transformer_units,\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etransformer_layers\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: transformer_layers,\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003emlp_head_units\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e: mlp_head_units,\n }\n )\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e config\n\n def call(self, patch):\n positions = tf.expand_dims(\n tf.experimental.numpy.arange(start=0, stop=self.num_patches, step=1), axis=0\n )\n projected_patches = self.projection(patch)\n encoded = projected_patches + self.position_embedding(positions)\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e encoded\n\ndef create_vit_object_detector(\n input_shape,\n patch_size,\n num_patches,\n projection_dim,\n num_heads,\n transformer_units,\n transformer_layers,\n mlp_head_units,\n hidden_dimension\n):\n \n inputs = keras.Input(shape=input_shape)\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create patches\u003c/span\u003e\n patches = Patches(patch_size)(inputs)\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Encode patches\u003c/span\u003e\n encoded_patches = PatchEncoder(num_patches, hidden_dimension)(patches)\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create multiple layers of the Transformer block.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e_\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e range(transformer_layers):\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Layer normalization 1.\u003c/span\u003e\n x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a multi-head attention layer.\u003c/span\u003e\n attention_output = layers.MultiHeadAttention(\n num_heads=num_heads, key_dim=projection_dim, dropout=0.1\n )(x1, x1)\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Skip connection 1.\u003c/span\u003e\n x2 = \u003cspan class=\"pl-en\"\u003elayers.Add\u003c/span\u003e()([attention_output, encoded_patches])\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Layer normalization 2.\u003c/span\u003e\n x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e MLP\u003c/span\u003e\n x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Skip connection 2.\u003c/span\u003e\n encoded_patches = \u003cspan class=\"pl-en\"\u003elayers.Add\u003c/span\u003e()([x3, x2])\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create a [batch_size, projection_dim] tensor.\u003c/span\u003e\n representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n representation = \u003cspan class=\"pl-en\"\u003elayers.GlobalAveragePooling1D\u003c/span\u003e()(representation)\n representation = layers.Dropout(0.3)(representation)\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add MLP.\u003c/span\u003e\n features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.3)\n\n bounding_box = layers.Dense(1024)(\n features\n ) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Final four neurons that output bounding box\u003c/span\u003e\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e return Keras model.\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e keras.Model(inputs=inputs, outputs=bounding_box)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e%% model parameters\u003c/span\u003e\nimage_size = 224\npatch_size = 16\ninput_shape = (image_size, image_size, 3) \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e input image shape\u003c/span\u003e\nnum_patches = (image_size // patch_size) \u003cspan class=\"pl-k\"\u003e**\u003c/span\u003e 2\nprojection_dim = 64\nnum_heads = 12\nhidden_dimension = projection_dim \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e num_heads\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Size of the transformer layers\u003c/span\u003e\ntransformer_units = [\n hidden_dimension \u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e 3,\n hidden_dimension,\n]\ntransformer_layers = 12\nmlp_head_units = [3072, 3072] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Size of the dense layers\u003c/span\u003e\n\nvit_object_detector = create_vit_object_detector(\n input_shape,\n patch_size,\n num_patches,\n projection_dim,\n num_heads,\n transformer_units,\n transformer_layers,\n mlp_head_units,\n hidden_dimension,\n)\n\n\u003cspan class=\"pl-en\"\u003evit_object_detector.summary\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003ch3 dir=\"auto\"\u003eRelevant log output\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eNo 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markdown-body NewMarkdownViewer-module__safe-html-box--cRsz0"><h3 dir="auto">Issue Type</h3> <p dir="auto">Documentation Bug</p> <h3 dir="auto">Source</h3> <p dir="auto">source</p> <h3 dir="auto">Keras Version</h3> <p dir="auto">2.14</p> <h3 dir="auto">Custom Code</h3> <p dir="auto">Yes</p> <h3 dir="auto">OS Platform and Distribution</h3> <p dir="auto">Ubuntu 22.04</p> <h3 dir="auto">Python version</h3> <p dir="auto">3.10</p> <h3 dir="auto">GPU model and memory</h3> <p dir="auto">Nvidia RTX4070 (12GB)</p> <h3 dir="auto">Current Behavior?</h3> <p dir="auto">Hi,<br> I've spotted a mistake in the Vision Transformer examples in Keras.io [3,4,5,6,7].</p> <p dir="auto">In all five of the examples below, to build the ViT architecture, the authors use a single hyper-parameter named projection_dim, which is used both as the model's hidden dimension, and as the dimension for queries, keys, and values, in the multi-head attention layer. These two hyper-parameters they shouldn't be the same. However, according to [1], they are connected:</p> <p dir="auto">hidden dimension = number of heads * qkv dimension</p> <p dir="auto">One simple way to verify this issue, is to calculate the total number of trainable parameters of the model.<br> Using the architecture from the examples in Keras.io, and setting the same hyper-parameters with vision transformer base, the model has only 15 million parameters (while the Vision Transformer Base has 86 million [2]).</p> <p dir="auto">To fix this issue:</p> <ol dir="auto"> <li> <p dir="auto">a hidden dimension parameter can be defined as:<br> hidden_dim = projection_dim * num_heads</p> </li> <li> <p dir="auto">The encoded patches should be projected in the hidden dimension, instead of the projection_dim:<br> encoded_patches = PatchEncoder(num_patches, hidden_dim)(patches)</p> </li> <li> <p dir="auto">The transformer_units should also use the hidden dimension:<br> transformer_units = [hidden_dim * 2, hidden_dim, ]</p> </li> </ol> <p dir="auto">Then, if the same hyper-parameters used as in the original paper, the number of trainable parameters will be the same, as in the ViT base.</p> <p dir="auto">I understand that the authors may have used alternative versions of the original model, but this particular modification, can change significantly the behaviour of the model.</p> <p dir="auto">If you'll need any further information, please let me know.</p> <p dir="auto">Best wishes,<br> Angelos</p> <p dir="auto">[1] see table 3 in the original paper: <a href="https://arxiv.org/pdf/1706.03762" rel="nofollow">https://arxiv.org/pdf/1706.03762</a><br> [2] <a href="https://arxiv.org/pdf/2010.11929" rel="nofollow">https://arxiv.org/pdf/2010.11929</a><br> [3] <a href="https://keras.io/examples/vision/image_classification_with_vision_transformer/" rel="nofollow">https://keras.io/examples/vision/image_classification_with_vision_transformer/</a><br> [4] <a href="https://keras.io/examples/vision/vit_small_ds/" rel="nofollow">https://keras.io/examples/vision/vit_small_ds/</a><br> [5] <a href="https://keras.io/examples/vision/object_detection_using_vision_transformer/" rel="nofollow">https://keras.io/examples/vision/object_detection_using_vision_transformer/</a><br> [6] <a href="https://keras.io/examples/vision/token_learner/" rel="nofollow">https://keras.io/examples/vision/token_learner/</a><br> [7] <a href="https://keras.io/examples/vision/vit_small_ds/" rel="nofollow">https://keras.io/examples/vision/vit_small_ds/</a></p> <h3 dir="auto">Standalone code to reproduce the issue or tutorial link</h3> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Below is the ViT class (create_vit_object_detector, indentical to [3]), # with the same hyper-parameters as ViT-base, including the hidden dimension hyper-parameter. # If the hidden_dimension is set equal to projection_dim (as implied in the Keras.io examples) # the model will have 15M parameters. # If set to 768 (=projection_dim*num_heads), it will have 86M parameters, as the original model. # The code uses tensorflow 2.14 #%% Import libraries import keras from keras import layers import tensorflow as tf #%% define required functions and classes def mlp(x, hidden_units, dropout_rate): for units in hidden_units: x = layers.Dense(units, activation=keras.activations.gelu)(x) x = layers.Dropout(dropout_rate)(x) return x class Patches(layers.Layer): def __init__(self, patch_size): super().__init__() self.patch_size = patch_size def call(self, images): input_shape = tf.shape(images) batch_size = input_shape[0] height = input_shape[1] width = input_shape[2] channels = input_shape[3] num_patches_h = height // self.patch_size num_patches_w = width // self.patch_size patches = tf.image.extract_patches(images, [1,self.patch_size,self.patch_size,1], [1,self.patch_size,self.patch_size,1], rates=[1, 1, 1, 1], padding="SAME" ) patches = tf.reshape( patches, ( batch_size, num_patches_h * num_patches_w, self.patch_size * self.patch_size * channels, ), ) return patches def get_config(self): config = super().get_config() config.update({"patch_size": self.patch_size}) return config class PatchEncoder(layers.Layer): def __init__(self, num_patches, projection_dim): super().__init__() self.num_patches = num_patches self.projection = layers.Dense(units=projection_dim) self.position_embedding = layers.Embedding( input_dim=num_patches, output_dim=projection_dim ) # Override function to avoid error while saving model def get_config(self): config = super().get_config().copy() config.update( { "input_shape": input_shape, "patch_size": patch_size, "num_patches": num_patches, "projection_dim": projection_dim, "num_heads": num_heads, "transformer_units": transformer_units, "transformer_layers": transformer_layers, "mlp_head_units": mlp_head_units, } ) return config def call(self, patch): positions = tf.expand_dims( tf.experimental.numpy.arange(start=0, stop=self.num_patches, step=1), axis=0 ) projected_patches = self.projection(patch) encoded = projected_patches + self.position_embedding(positions) return encoded def create_vit_object_detector( input_shape, patch_size, num_patches, projection_dim, num_heads, transformer_units, transformer_layers, mlp_head_units, hidden_dimension ): inputs = keras.Input(shape=input_shape) # Create patches patches = Patches(patch_size)(inputs) # Encode patches encoded_patches = PatchEncoder(num_patches, hidden_dimension)(patches) # Create multiple layers of the Transformer block. for _ in range(transformer_layers): # Layer normalization 1. x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) # Create a multi-head attention layer. attention_output = layers.MultiHeadAttention( num_heads=num_heads, key_dim=projection_dim, dropout=0.1 )(x1, x1) # Skip connection 1. x2 = layers.Add()([attention_output, encoded_patches]) # Layer normalization 2. x3 = layers.LayerNormalization(epsilon=1e-6)(x2) # MLP x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1) # Skip connection 2. encoded_patches = layers.Add()([x3, x2]) # Create a [batch_size, projection_dim] tensor. representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) representation = layers.GlobalAveragePooling1D()(representation) representation = layers.Dropout(0.3)(representation) # Add MLP. features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.3) bounding_box = layers.Dense(1024)( features ) # Final four neurons that output bounding box # return Keras model. return keras.Model(inputs=inputs, outputs=bounding_box) #%% model parameters image_size = 224 patch_size = 16 input_shape = (image_size, image_size, 3) # input image shape num_patches = (image_size // patch_size) ** 2 projection_dim = 64 num_heads = 12 hidden_dimension = projection_dim * num_heads # Size of the transformer layers transformer_units = [ hidden_dimension * 3, hidden_dimension, ] transformer_layers = 12 mlp_head_units = [3072, 3072] # Size of the dense layers vit_object_detector = create_vit_object_detector( input_shape, patch_size, num_patches, projection_dim, num_heads, transformer_units, transformer_layers, mlp_head_units, hidden_dimension, ) vit_object_detector.summary()"><pre class="notranslate"><span class="pl-c"><span class="pl-c">#</span> Below is the ViT class (create_vit_object_detector, indentical to [3]),</span> <span class="pl-c"><span class="pl-c">#</span> with the same hyper-parameters as ViT-base, including the hidden dimension hyper-parameter.</span> <span class="pl-c"><span class="pl-c">#</span> If the hidden_dimension is set equal to projection_dim (as implied in the Keras.io examples)</span> <span class="pl-c"><span class="pl-c">#</span> the model will have 15M parameters. </span> <span class="pl-c"><span class="pl-c">#</span> If set to 768 (=projection_dim*num_heads), it will have 86M parameters, as the original model.</span> <span class="pl-c"><span class="pl-c">#</span> The code uses tensorflow 2.14 </span> <span class="pl-c"><span class="pl-c">#</span>%% Import libraries</span> import keras from keras import layers import tensorflow as tf <span class="pl-c"><span class="pl-c">#</span>%% define required functions and classes</span> def mlp(x, hidden_units, dropout_rate): <span class="pl-k">for</span> <span class="pl-smi">units</span> <span class="pl-k">in</span> hidden_units: x = layers.Dense(units, activation=keras.activations.gelu)(x) x = layers.Dropout(dropout_rate)(x) <span class="pl-k">return</span> x class Patches(layers.Layer): def __init__(self, patch_size): <span class="pl-en">super().__init__</span>() self.patch_size = patch_size def call(self, images): input_shape = tf.shape(images) batch_size = input_shape[0] height = input_shape[1] width = input_shape[2] channels = input_shape[3] num_patches_h = height // self.patch_size num_patches_w = width // self.patch_size patches = tf.image.extract_patches(images, [1,self.patch_size,self.patch_size,1], [1,self.patch_size,self.patch_size,1], rates=[1, 1, 1, 1], padding=<span class="pl-s"><span class="pl-pds">"</span>SAME<span class="pl-pds">"</span></span> ) patches = tf.reshape( patches, ( batch_size, num_patches_h <span class="pl-k">*</span> num_patches_w, self.patch_size <span class="pl-k">*</span> self.patch_size <span class="pl-k">*</span> channels, ), ) <span class="pl-k">return</span> patches def get_config(self): config = <span class="pl-en">super().get_config</span>() config.update({<span class="pl-s"><span class="pl-pds">"</span>patch_size<span class="pl-pds">"</span></span>: self.patch_size}) <span class="pl-k">return</span> config class PatchEncoder(layers.Layer): def __init__(self, num_patches, projection_dim): <span class="pl-en">super().__init__</span>() self.num_patches = num_patches self.projection = layers.Dense(units=projection_dim) self.position_embedding = layers.Embedding( input_dim=num_patches, output_dim=projection_dim ) <span class="pl-c"><span class="pl-c">#</span> Override function to avoid error while saving model</span> def get_config(self): config = <span class="pl-en">super().get_config().copy</span>() config.update( { <span class="pl-s"><span class="pl-pds">"</span>input_shape<span class="pl-pds">"</span></span>: input_shape, <span class="pl-s"><span class="pl-pds">"</span>patch_size<span class="pl-pds">"</span></span>: patch_size, <span class="pl-s"><span class="pl-pds">"</span>num_patches<span class="pl-pds">"</span></span>: num_patches, <span class="pl-s"><span class="pl-pds">"</span>projection_dim<span class="pl-pds">"</span></span>: projection_dim, <span class="pl-s"><span class="pl-pds">"</span>num_heads<span class="pl-pds">"</span></span>: num_heads, <span class="pl-s"><span class="pl-pds">"</span>transformer_units<span class="pl-pds">"</span></span>: transformer_units, <span class="pl-s"><span class="pl-pds">"</span>transformer_layers<span class="pl-pds">"</span></span>: transformer_layers, <span class="pl-s"><span class="pl-pds">"</span>mlp_head_units<span class="pl-pds">"</span></span>: mlp_head_units, } ) <span class="pl-k">return</span> config def call(self, patch): positions = tf.expand_dims( tf.experimental.numpy.arange(start=0, stop=self.num_patches, step=1), axis=0 ) projected_patches = self.projection(patch) encoded = projected_patches + self.position_embedding(positions) <span class="pl-k">return</span> encoded def create_vit_object_detector( input_shape, patch_size, num_patches, projection_dim, num_heads, transformer_units, transformer_layers, mlp_head_units, hidden_dimension ): inputs = keras.Input(shape=input_shape) <span class="pl-c"><span class="pl-c">#</span> Create patches</span> patches = Patches(patch_size)(inputs) <span class="pl-c"><span class="pl-c">#</span> Encode patches</span> encoded_patches = PatchEncoder(num_patches, hidden_dimension)(patches) <span class="pl-c"><span class="pl-c">#</span> Create multiple layers of the Transformer block.</span> <span class="pl-k">for</span> <span class="pl-smi">_</span> <span class="pl-k">in</span> range(transformer_layers): <span class="pl-c"><span class="pl-c">#</span> Layer normalization 1.</span> x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) <span class="pl-c"><span class="pl-c">#</span> Create a multi-head attention layer.</span> attention_output = layers.MultiHeadAttention( num_heads=num_heads, key_dim=projection_dim, dropout=0.1 )(x1, x1) <span class="pl-c"><span class="pl-c">#</span> Skip connection 1.</span> x2 = <span class="pl-en">layers.Add</span>()([attention_output, encoded_patches]) <span class="pl-c"><span class="pl-c">#</span> Layer normalization 2.</span> x3 = layers.LayerNormalization(epsilon=1e-6)(x2) <span class="pl-c"><span class="pl-c">#</span> MLP</span> x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1) <span class="pl-c"><span class="pl-c">#</span> Skip connection 2.</span> encoded_patches = <span class="pl-en">layers.Add</span>()([x3, x2]) <span class="pl-c"><span class="pl-c">#</span> Create a [batch_size, projection_dim] tensor.</span> representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) representation = <span class="pl-en">layers.GlobalAveragePooling1D</span>()(representation) representation = layers.Dropout(0.3)(representation) <span class="pl-c"><span class="pl-c">#</span> Add MLP.</span> features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.3) bounding_box = layers.Dense(1024)( features ) <span class="pl-c"><span class="pl-c">#</span> Final four neurons that output bounding box</span> <span class="pl-c"><span class="pl-c">#</span> return Keras model.</span> <span class="pl-k">return</span> keras.Model(inputs=inputs, outputs=bounding_box) <span class="pl-c"><span class="pl-c">#</span>%% model parameters</span> image_size = 224 patch_size = 16 input_shape = (image_size, image_size, 3) <span class="pl-c"><span class="pl-c">#</span> input image shape</span> num_patches = (image_size // patch_size) <span class="pl-k">**</span> 2 projection_dim = 64 num_heads = 12 hidden_dimension = projection_dim <span class="pl-k">*</span> num_heads <span class="pl-c"><span class="pl-c">#</span> Size of the transformer layers</span> transformer_units = [ hidden_dimension <span class="pl-k">*</span> 3, hidden_dimension, ] transformer_layers = 12 mlp_head_units = [3072, 3072] <span class="pl-c"><span class="pl-c">#</span> Size of the dense layers</span> vit_object_detector = create_vit_object_detector( input_shape, patch_size, num_patches, projection_dim, num_heads, transformer_units, transformer_layers, mlp_head_units, hidden_dimension, ) <span class="pl-en">vit_object_detector.summary</span>()</pre></div> <h3 dir="auto">Relevant log output</h3> <p dir="auto"><em>No response</em></p></div></div><div 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</button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 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6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>