Transformers documentation

Convolutional Vision Transformer (CvT)

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Convolutional Vision Transformer (CvT)

Overview

The CvT model was proposed in CvT: Introducing Convolutions to Vision Transformers by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang. The Convolutional vision Transformer (CvT) improves the Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs.

The abstract from the paper is the following:

We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) to the ViT architecture (\ie shift, scale, and distortion invariance) while maintaining the merits of Transformers (\ie dynamic attention, global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. In addition, performance gains are maintained when pretrained on larger datasets (\eg ImageNet-22k) and fine-tuned to downstream tasks. Pre-trained on ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7\% on the ImageNet-1k val set. Finally, our results show that the positional encoding, a crucial component in existing Vision Transformers, can be safely removed in our model, simplifying the design for higher resolution vision tasks.

Tips:

This model was contributed by anugunj. The original code can be found here.

CvtConfig

class transformers.CvtConfig

< >

( 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 CvtModel, CvtConfig

>>> # Initializing a Cvt msft/cvt style configuration
>>> configuration = CvtConfig()

>>> # Initializing a model from the msft/cvt style configuration
>>> model = CvtModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

CvtModel

class transformers.CvtModel

< >

( 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

< >

( pixel_values = None output_hidden_states = None return_dict = None ) transformers.models.cvt.modeling_cvt.BaseModelOutputWithCLSToken or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using CvtFeatureExtractor. See CvtFeatureExtractor.__call__ for details.
  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under 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.FloatTensor of 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.FloatTensor of 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 when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.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 AutoFeatureExtractor, CvtModel
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/cvt-13")
>>> model = CvtModel.from_pretrained("microsoft/cvt-13")

>>> inputs = feature_extractor(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

< >

( 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

< >

( pixel_values = None labels = None output_hidden_states = None return_dict = None ) transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using CvtFeatureExtractor. See CvtFeatureExtractor.__call__ for details.
  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
  • return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
  • labels (torch.LongTensor of shape (batch_size,), optional) — Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

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.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
  • logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.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 AutoFeatureExtractor, CvtForImageClassification
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/cvt-13")
>>> model = CvtForImageClassification.from_pretrained("microsoft/cvt-13")

>>> inputs = feature_extractor(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 cat