Convolutional Vision Transformer (CvT)
Overview
CvT モデルは、Haping Wu、Bin Xiao、Noel Codella、Mengchen Liu、Xiyang Dai、Lu Yuan、Lei Zhang によって CvT: Introduction Convolutions to Vision Transformers で提案されました。畳み込みビジョン トランスフォーマー (CvT) は、ViT に畳み込みを導入して両方の設計の長所を引き出すことにより、ビジョン トランスフォーマー (ViT) のパフォーマンスと効率を向上させます。
論文の要約は次のとおりです。
この論文では、ビジョン トランスフォーマー (ViT) を改善する、畳み込みビジョン トランスフォーマー (CvT) と呼ばれる新しいアーキテクチャを紹介します。 ViT に畳み込みを導入して両方の設計の長所を引き出すことで、パフォーマンスと効率を向上させます。これは次のようにして実現されます。 2 つの主要な変更: 新しい畳み込みトークンの埋め込みを含むトランスフォーマーの階層と、畳み込みトランスフォーマー 畳み込み射影を利用したブロック。これらの変更により、畳み込みニューラル ネットワーク (CNN) の望ましい特性が導入されます。 トランスフォーマーの利点 (動的な注意力、 グローバルなコンテキストとより良い一般化)。私たちは広範な実験を実施することで CvT を検証し、このアプローチが達成できることを示しています。 ImageNet-1k 上の他のビジョン トランスフォーマーや ResNet よりも、パラメータが少なく、FLOP が低い、最先端のパフォーマンスを実現します。加えて、 より大きなデータセット (例: ImageNet-22k) で事前トレーニングし、下流のタスクに合わせて微調整すると、パフォーマンスの向上が維持されます。事前トレーニング済み ImageNet-22k、当社の CvT-W24 は、ImageNet-1k val set で 87.7\% というトップ 1 の精度を獲得しています。最後に、私たちの結果は、位置エンコーディングが、 既存のビジョン トランスフォーマーの重要なコンポーネントであるこのコンポーネントは、モデルでは安全に削除できるため、高解像度のビジョン タスクの設計が簡素化されます。
このモデルは anugunj によって提供されました。元のコードは ここ にあります。
Usage tips
- CvT モデルは通常の Vision Transformer ですが、畳み込みでトレーニングされています。 ImageNet-1K および CIFAR-100 で微調整すると、オリジナル モデル (ViT) よりも優れたパフォーマンスを発揮します。
- カスタム データの微調整だけでなく推論に関するデモ ノートブックも ここ で確認できます (
ViTFeatureExtractor を置き換えるだけで済みます)による AutoImageProcessor およびViTForImageClassificationによる CvtForImageClassification)。 - 利用可能なチェックポイントは、(1) ImageNet-22k (1,400 万の画像と 22,000 のクラスのコレクション) でのみ事前トレーニングされている、(2) も問題ありません。 ImageNet-22k で調整、または (3) ImageNet-1k (ILSVRC 2012 とも呼ばれるコレクション) でも微調整130万の 画像と 1,000 クラス)。
Resources
CvT を始めるのに役立つ公式 Hugging Face およびコミュニティ (🌎 で示される) リソースのリスト。
- CvtForImageClassification は、この サンプル スクリプト および ノートブック。
- 参照: 画像分類タスク ガイド
ここに含めるリソースの送信に興味がある場合は、お気軽にプル リクエストを開いてください。審査させていただきます。リソースは、既存のリソースを複製するのではなく、何か新しいものを示すことが理想的です。
CvtConfig
class transformers.CvtConfig
< source >( num_channels = 3 patch_sizes = [7, 3, 3] patch_stride = [4, 2, 2] patch_padding = [2, 1, 1] embed_dim = [64, 192, 384] num_heads = [1, 3, 6] depth = [1, 2, 10] mlp_ratio = [4.0, 4.0, 4.0] attention_drop_rate = [0.0, 0.0, 0.0] drop_rate = [0.0, 0.0, 0.0] drop_path_rate = [0.0, 0.0, 0.1] qkv_bias = [True, True, True] cls_token = [False, False, True] qkv_projection_method = ['dw_bn', 'dw_bn', 'dw_bn'] kernel_qkv = [3, 3, 3] padding_kv = [1, 1, 1] stride_kv = [2, 2, 2] padding_q = [1, 1, 1] stride_q = [1, 1, 1] initializer_range = 0.02 layer_norm_eps = 1e-12 **kwargs )
Parameters
- num_channels (
int, optional, defaults to 3) — The number of input channels. - patch_sizes (
List[int], optional, defaults to[7, 3, 3]) — The kernel size of each encoder’s patch embedding. - patch_stride (
List[int], optional, defaults to[4, 2, 2]) — The stride size of each encoder’s patch embedding. - patch_padding (
List[int], optional, defaults to[2, 1, 1]) — The padding size of each encoder’s patch embedding. - embed_dim (
List[int], optional, defaults to[64, 192, 384]) — Dimension of each of the encoder blocks. - num_heads (
List[int], optional, defaults to[1, 3, 6]) — Number of attention heads for each attention layer in each block of the Transformer encoder. - depth (
List[int], optional, defaults to[1, 2, 10]) — The number of layers in each encoder block. - mlp_ratios (
List[float], optional, defaults to[4.0, 4.0, 4.0, 4.0]) — Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the encoder blocks. - attention_drop_rate (
List[float], optional, defaults to[0.0, 0.0, 0.0]) — The dropout ratio for the attention probabilities. - drop_rate (
List[float], optional, defaults to[0.0, 0.0, 0.0]) — The dropout ratio for the patch embeddings probabilities. - drop_path_rate (
List[float], optional, defaults to[0.0, 0.0, 0.1]) — The dropout probability for stochastic depth, used in the blocks of the Transformer encoder. - qkv_bias (
List[bool], optional, defaults to[True, True, True]) — The bias bool for query, key and value in attentions - cls_token (
List[bool], optional, defaults to[False, False, True]) — Whether or not to add a classification token to the output of each of the last 3 stages. - qkv_projection_method (
List[string], optional, defaults to [“dw_bn”, “dw_bn”, “dw_bn”]`) — The projection method for query, key and value Default is depth-wise convolutions with batch norm. For Linear projection use “avg”. - kernel_qkv (
List[int], optional, defaults to[3, 3, 3]) — The kernel size for query, key and value in attention layer - padding_kv (
List[int], optional, defaults to[1, 1, 1]) — The padding size for key and value in attention layer - stride_kv (
List[int], optional, defaults to[2, 2, 2]) — The stride size for key and value in attention layer - padding_q (
List[int], optional, defaults to[1, 1, 1]) — The padding size for query in attention layer - stride_q (
List[int], optional, defaults to[1, 1, 1]) — The stride size for query in attention layer - initializer_range (
float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - layer_norm_eps (
float, optional, defaults to 1e-6) — The epsilon used by the layer normalization layers.
This is the configuration class to store the configuration of a CvtModel. It is used to instantiate a CvT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the CvT microsoft/cvt-13 architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Example:
>>> from transformers import CvtConfig, CvtModel
>>> # Initializing a Cvt msft/cvt style configuration
>>> configuration = CvtConfig()
>>> # Initializing a model (with random weights) from the msft/cvt style configuration
>>> model = CvtModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configCvtModel
class transformers.CvtModel
< source >( config add_pooling_layer = True )
Parameters
- config (CvtConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
The bare Cvt Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( pixel_values: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.models.cvt.modeling_cvt.BaseModelOutputWithCLSToken or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using AutoImageProcessor. SeeCvtImageProcessor.__call__for details. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.models.cvt.modeling_cvt.BaseModelOutputWithCLSToken or tuple(torch.FloatTensor)
A transformers.models.cvt.modeling_cvt.BaseModelOutputWithCLSToken or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (CvtConfig) and inputs.
- last_hidden_state (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model. - cls_token_value (
torch.FloatTensorof shape(batch_size, 1, hidden_size)) — Classification token at the output of the last layer of the model. - hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size). Hidden-states of the model at the output of each layer plus the initial embedding outputs.
The CvtModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import AutoImageProcessor, CvtModel
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image", trust_remote_code=True)
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/cvt-13")
>>> model = CvtModel.from_pretrained("microsoft/cvt-13")
>>> inputs = image_processor(image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 384, 14, 14]CvtForImageClassification
class transformers.CvtForImageClassification
< source >( config )
Parameters
- config (CvtConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
Cvt Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.
This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( pixel_values: Optional = None labels: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using AutoImageProcessor. SeeCvtImageProcessor.__call__for details. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. - labels (
torch.LongTensorof shape(batch_size,), optional) — Labels for computing the image classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)
A transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (CvtConfig) and inputs.
- loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Classification (or regression if config.num_labels==1) loss. - logits (
torch.FloatTensorof shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax). - hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape(batch_size, num_channels, height, width). Hidden-states (also called feature maps) of the model at the output of each stage.
The CvtForImageClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import AutoImageProcessor, CvtForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image", trust_remote_code=True)
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/cvt-13")
>>> model = CvtForImageClassification.from_pretrained("microsoft/cvt-13")
>>> inputs = image_processor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
tabby, tabby catTFCvtModel
class transformers.TFCvtModel
< source >( config: CvtConfig *inputs **kwargs )
Parameters
- config (CvtConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
The bare Cvt Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using keras.Model.fit method which currently requires having all the
tensors in the first argument of the model call function: model(inputs).
call
< source >( pixel_values: tf.Tensor | None = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: Optional[bool] = False ) → transformers.models.cvt.modeling_tf_cvt.TFBaseModelOutputWithCLSToken or tuple(tf.Tensor)
Parameters
- pixel_values (
np.ndarray,tf.Tensor,List[tf.Tensor]`Dict[str, tf.Tensor]orDict[str, np.ndarray]and each example must have the shape(batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using AutoImageProcessor. SeeCvtImageProcessor.__call__for details. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. - training (
bool, optional, defaults to `False“) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
Returns
transformers.models.cvt.modeling_tf_cvt.TFBaseModelOutputWithCLSToken or tuple(tf.Tensor)
A transformers.models.cvt.modeling_tf_cvt.TFBaseModelOutputWithCLSToken or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (CvtConfig) and inputs.
- last_hidden_state (
tf.Tensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model. - cls_token_value (
tf.Tensorof shape(batch_size, 1, hidden_size)) — Classification token at the output of the last layer of the model. - hidden_states (
tuple(tf.Tensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size). Hidden-states of the model at the output of each layer plus the initial embedding outputs.
The TFCvtModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoImageProcessor, TFCvtModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/cvt-13")
>>> model = TFCvtModel.from_pretrained("microsoft/cvt-13")
>>> inputs = image_processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_stateTFCvtForImageClassification
class transformers.TFCvtForImageClassification
< source >( config: CvtConfig *inputs **kwargs )
Parameters
- config (CvtConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
Cvt Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using keras.Model.fit method which currently requires having all the
tensors in the first argument of the model call function: model(inputs).
call
< source >( pixel_values: tf.Tensor | None = None labels: tf.Tensor | None = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: Optional[bool] = False ) → transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention or tuple(tf.Tensor)
Parameters
- pixel_values (
np.ndarray,tf.Tensor,List[tf.Tensor]`Dict[str, tf.Tensor]orDict[str, np.ndarray]and each example must have the shape(batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using AutoImageProcessor. SeeCvtImageProcessor.__call__for details. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. - training (
bool, optional, defaults to `False“) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). - labels (
tf.Tensorornp.ndarrayof shape(batch_size,), optional) — Labels for computing the image classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (CvtConfig) and inputs.
- loss (
tf.Tensorof shape(1,), optional, returned whenlabelsis provided) — Classification (or regression if config.num_labels==1) loss. - logits (
tf.Tensorof shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax). - hidden_states (
tuple(tf.Tensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape(batch_size, num_channels, height, width). Hidden-states (also called feature maps) of the model at the output of each stage.
The TFCvtForImageClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoImageProcessor, TFCvtForImageClassification
>>> import tensorflow as tf
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/cvt-13")
>>> model = TFCvtForImageClassification.from_pretrained("microsoft/cvt-13")
>>> inputs = image_processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
>>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)])