ImageGPT

Overview

The ImageGPT model was proposed in Generative Pretraining from Pixels by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. ImageGPT (iGPT) is a GPT-2-like model trained to predict the next pixel value, allowing for both unconditional and conditional image generation.

The abstract from the paper is the following:

Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full fine-tuning, matching the top supervised pre-trained models. We are also competitive with self-supervised benchmarks on ImageNet when substituting pixels for a VQVAE encoding, achieving 69.0% top-1 accuracy on a linear probe of our features.

The figure below summarizes the approach (taken from the original paper):

https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/imagegpt_architecture.png

Tips:

  • ImageGPT is almost exactly the same as GPT-2, with the exception that a different activation function is used (namely “quick gelu”), and the layer normalization layers don’t mean center the inputs. ImageGPT also doesn’t have tied input- and output embeddings.

  • As the time- and memory requirements of the attention mechanism of Transformers scales quadratically in the sequence length, the authors pre-trained ImageGPT on smaller input resolutions, such as 32x32 and 64x64. However, feeding a sequence of 32x32x3=3072 tokens from 0..255 into a Transformer is still prohibitively large. Therefore, the authors applied k-means clustering to the (R,G,B) pixel values with k=512. This way, we only have a 32*32 = 1024-long sequence, but now of integers in the range 0..511. So we are shrinking the sequence length at the cost of a bigger embedding matrix. In other words, the vocabulary size of ImageGPT is 512, + 1 for a special “start of sentence” (SOS) token, used at the beginning of every sequence. One can use ImageGPTFeatureExtractor to prepare images for the model.

  • Despite being pre-trained entirely unsupervised (i.e. without the use of any labels), ImageGPT produces fairly performant image features useful for downstream tasks, such as image classification. The authors showed that the features in the middle of the network are the most performant, and can be used as-is to train a linear model (such as a sklearn logistic regression model for example). This is also referred to as “linear probing”. Features can be easily obtained by first forwarding the image through the model, then specifying output_hidden_states=True, and then average-pool the hidden states at whatever layer you like.

  • Alternatively, one can further fine-tune the entire model on a downstream dataset, similar to BERT. For this, you can use ImageGPTForImageClassification.

  • ImageGPT comes in different sizes: there’s ImageGPT-small, ImageGPT-medium and ImageGPT-large. The authors did also train an XL variant, which they didn’t release. The differences in size are summarized in the following table:

Model variant

Number of layers

Hidden size

Number of heads

# params

iGPT-small

24

512

8

76 million

iGPT-medium

36

1024

8

455 million

iGPT-large

48

1536

16

1.4 million

iGPT-XL

60

3072

not specified

6.8 billion

This model was contributed by nielsr, based on this issue. The original code can be found here.

ImageGPTConfig

class transformers.ImageGPTConfig(vocab_size=513, n_positions=1024, n_embd=512, n_layer=24, n_head=8, n_inner=None, activation_function='quick_gelu', resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-05, initializer_range=0.02, scale_attn_weights=True, use_cache=True, tie_word_embeddings=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False, **kwargs)[source]

This is the configuration class to store the configuration of a ImageGPTModel or a TFImageGPTModel. It is used to instantiate a GPT-2 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 ImageGPT small architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Parameters
  • vocab_size (int, optional, defaults to 512) – Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling ImageGPTModel or TFImageGPTModel.

  • n_positions (int, optional, defaults to 32*32) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).

  • n_embd (int, optional, defaults to 512) – Dimensionality of the embeddings and hidden states.

  • n_layer (int, optional, defaults to 24) – Number of hidden layers in the Transformer encoder.

  • n_head (int, optional, defaults to 8) – Number of attention heads for each attention layer in the Transformer encoder.

  • n_inner (int, optional, defaults to None) – Dimensionality of the inner feed-forward layers. None will set it to 4 times n_embd

  • activation_function (str, optional, defaults to "quick_gelu") – Activation function (can be one of the activation functions defined in src/transformers/activations.py). Defaults to “quick_gelu”.

  • resid_pdrop (float, optional, defaults to 0.1) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

  • embd_pdrop (int, optional, defaults to 0.1) – The dropout ratio for the embeddings.

  • attn_pdrop (float, optional, defaults to 0.1) – The dropout ratio for the attention.

  • layer_norm_epsilon (float, optional, defaults to 1e-5) – The epsilon to use in the layer normalization layers.

  • initializer_range (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

  • scale_attn_weights (bool, optional, defaults to True) – Scale attention weights by dividing by sqrt(hidden_size)..

  • use_cache (bool, optional, defaults to True) – Whether or not the model should return the last key/values attentions (not used by all models).

  • scale_attn_by_inverse_layer_idx (bool, optional, defaults to False) – Whether to additionally scale attention weights by 1 / layer_idx + 1.

  • reorder_and_upcast_attn (bool, optional, defaults to False) – Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention dot-product/softmax to float() when training with mixed precision.

Example:

>>> from transformers import ImageGPTModel, ImageGPTConfig

>>> # Initializing a ImageGPT configuration
>>> configuration = ImageGPTConfig()

>>> # Initializing a model from the configuration
>>> model = ImageGPTModel(configuration)

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

ImageGPTFeatureExtractor

class transformers.ImageGPTFeatureExtractor(clusters, do_resize=True, size=32, resample=2, do_normalize=True, **kwargs)[source]

Constructs an ImageGPT feature extractor. This feature extractor can be used to resize images to a smaller resolution (such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of “pixel values” (color clusters).

This feature extractor inherits from FeatureExtractionMixin which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

Parameters
  • clusters (np.ndarray) – The color clusters to use, as a np.ndarray of shape (n_clusters, 3).

  • do_resize (bool, optional, defaults to True) – Whether to resize the input to a certain size.

  • size (int or Tuple(int), optional, defaults to 32) – Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an integer is provided, then the input will be resized to (size, size). Only has an effect if do_resize is set to True.

  • resample (int, optional, defaults to PIL.Image.BILINEAR) – An optional resampling filter. This can be one of PIL.Image.NEAREST, PIL.Image.BOX, PIL.Image.BILINEAR, PIL.Image.HAMMING, PIL.Image.BICUBIC or PIL.Image.LANCZOS. Only has an effect if do_resize is set to True.

  • do_normalize (bool, optional, defaults to True) – Whether or not to normalize the input to the range between -1 and +1.

__call__(images: Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, List[PIL.Image.Image], List[numpy.ndarray], List[torch.Tensor]], return_tensors: Optional[Union[str, transformers.file_utils.TensorType]] = None, **kwargs) → transformers.feature_extraction_utils.BatchFeature[source]

Main method to prepare for the model one or several image(s).

Warning

NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass PIL images.

Parameters
  • images (PIL.Image.Image, np.ndarray, torch.Tensor, List[PIL.Image.Image], List[np.ndarray], List[torch.Tensor]) – The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width.

  • return_tensors (str or TensorType, optional, defaults to 'np') –

    If set, will return tensors of a particular framework. Acceptable values are:

    • 'tf': Return TensorFlow tf.constant objects.

    • 'pt': Return PyTorch torch.Tensor objects.

    • 'np': Return NumPy np.ndarray objects.

    • 'jax': Return JAX jnp.ndarray objects.

Returns

A BatchFeature with the following fields:

  • pixel_values – Pixel values to be fed to a model, of shape (batch_size, num_channels, height, width).

Return type

BatchFeature

ImageGPTModel

class transformers.ImageGPTModel(config)[source]

The bare ImageGPT Model transformer outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. 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 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.

Parameters

config (ImageGPTConfig) – 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.

forward(pixel_values=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The ImageGPTModel forward method, overrides the __call__() special method.

Note

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.

Parameters
  • pixel_values (torch.LongTensor of shape (batch_size, pixel_values_length)) –

    pixel_values_length = sequence_length if past_key_values is None else past_key_values[0][0].shape[-2] (sequence_length of input past key value states). Indices of input sequence tokens in the vocabulary.

    If past_key_values is used, only pixel_values that do not have their past calculated should be passed as pixel_values.

    Indices can be obtained using ImageGPTFeatureExtractor. See transformers.ImageGPTFeatureExtractor.__call__() for details.

  • past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) – Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see past_key_values output below). Can be used to speed up sequential decoding. The pixel_values which have their past given to this model should not be passed as pixel_values as they have already been computed.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) –

    Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

    What are attention masks?

  • token_type_ids (torch.LongTensor of shape (batch_size, pixel_values_length), optional) –

    Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

    What are token type IDs?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) –

    Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

    What are position IDs?

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) –

    Optionally, instead of passing pixel_values you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert pixel_values indices into associated vectors than the model’s internal embedding lookup matrix.

    If past_key_values is used, optionally only the last inputs_embeds have to be input (see past_key_values).

  • use_cache (bool, optional) – If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

  • output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • 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, sequence_length), optional) – Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set labels = pixel_values Indices are selected in [-100, 0, ..., config.vocab_size] All labels set to -100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size]

Returns

A BaseModelOutputWithPastAndCrossAttentions 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 (ImageGPTConfig) 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.

    If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

  • past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).

    Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • 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.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

Examples:

>>> from transformers import ImageGPTFeatureExtractor, ImageGPTModel
>>> from PIL import Image
>>> import requests

>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> feature_extractor = ImageGPTFeatureExtractor.from_pretrained('openai/imagegpt-small')
>>> model = ImageGPTModel.from_pretrained('openai/imagegpt-small')

>>> inputs = feature_extractor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state

Return type

BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

ImageGPTForCausalImageModeling

class transformers.ImageGPTForCausalImageModeling(config)[source]

The ImageGPT Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).

This model inherits from PreTrainedModel. 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 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.

Parameters

config (ImageGPTConfig) – 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.

forward(pixel_values=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The ImageGPTForCausalImageModeling forward method, overrides the __call__() special method.

Note

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.

Parameters
  • pixel_values (torch.LongTensor of shape (batch_size, pixel_values_length)) –

    pixel_values_length = sequence_length if past_key_values is None else past_key_values[0][0].shape[-2] (sequence_length of input past key value states). Indices of input sequence tokens in the vocabulary.

    If past_key_values is used, only pixel_values that do not have their past calculated should be passed as pixel_values.

    Indices can be obtained using ImageGPTFeatureExtractor. See transformers.ImageGPTFeatureExtractor.__call__() for details.

  • past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) – Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see past_key_values output below). Can be used to speed up sequential decoding. The pixel_values which have their past given to this model should not be passed as pixel_values as they have already been computed.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) –

    Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

    What are attention masks?

  • token_type_ids (torch.LongTensor of shape (batch_size, pixel_values_length), optional) –

    Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

    What are token type IDs?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) –

    Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

    What are position IDs?

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) –

    Optionally, instead of passing pixel_values you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert pixel_values indices into associated vectors than the model’s internal embedding lookup matrix.

    If past_key_values is used, optionally only the last inputs_embeds have to be input (see past_key_values).

  • use_cache (bool, optional) – If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

  • output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • 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, sequence_length), optional) – Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set labels = pixel_values Indices are selected in [-100, 0, ..., config.vocab_size] All labels set to -100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size]

Returns

A CausalLMOutputWithCrossAttentions 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 (ImageGPTConfig) and inputs.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Language modeling loss (for next-token prediction).

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) – Prediction scores of the language modeling head (scores for each vocabulary token 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 + 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.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if config.is_decoder = True.

    Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

Examples:

>>> from transformers import ImageGPTFeatureExtractor, ImageGPTForCausalImageModeling
>>> import torch
>>> import matplotlib.pyplot as plt
>>> import numpy as np

>>> feature_extractor = ImageGPTFeatureExtractor.from_pretrained('openai/imagegpt-small')
>>> model = ImageGPTForCausalImageModeling.from_pretrained('openai/imagegpt-small')
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> model.to(device)

>>> # unconditional generation of 8 images
>>> batch_size = 8
>>> context = torch.full((batch_size, 1), model.config.vocab_size - 1) #initialize with SOS token
>>> context = torch.tensor(context).to(device)
>>> output = model.generate(pixel_values=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40)

>>> clusters = feature_extractor.clusters
>>> n_px = feature_extractor.size

>>> samples = output[:,1:].cpu().detach().numpy()
>>> samples_img = [np.reshape(np.rint(127.5 * (clusters[s] + 1.0)), [n_px, n_px, 3]).astype(np.uint8) for s in samples] # convert color cluster tokens back to pixels
>>> f, axes = plt.subplots(1, batch_size, dpi=300)

>>> for img, ax in zip(samples_img, axes):
...    ax.axis('off')
...    ax.imshow(img)

Return type

CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

ImageGPTForImageClassification

class transformers.ImageGPTForImageClassification(config)[source]

The ImageGPT Model transformer with an image classification head on top (linear layer). ImageGPTForImageClassification average-pools the hidden states in order to do the classification.

This model inherits from PreTrainedModel. 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 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.

Parameters

config (ImageGPTConfig) – 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.

forward(pixel_values=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The ImageGPTForImageClassification forward method, overrides the __call__() special method.

Note

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.

Parameters
  • pixel_values (torch.LongTensor of shape (batch_size, pixel_values_length)) –

    pixel_values_length = sequence_length if past_key_values is None else past_key_values[0][0].shape[-2] (sequence_length of input past key value states). Indices of input sequence tokens in the vocabulary.

    If past_key_values is used, only pixel_values that do not have their past calculated should be passed as pixel_values.

    Indices can be obtained using ImageGPTFeatureExtractor. See transformers.ImageGPTFeatureExtractor.__call__() for details.

  • past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) – Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see past_key_values output below). Can be used to speed up sequential decoding. The pixel_values which have their past given to this model should not be passed as pixel_values as they have already been computed.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) –

    Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

    What are attention masks?

  • token_type_ids (torch.LongTensor of shape (batch_size, pixel_values_length), optional) –

    Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

    What are token type IDs?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) –

    Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

    What are position IDs?

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) –

    Optionally, instead of passing pixel_values you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert pixel_values indices into associated vectors than the model’s internal embedding lookup matrix.

    If past_key_values is used, optionally only the last inputs_embeds have to be input (see past_key_values).

  • use_cache (bool, optional) – If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

  • output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • 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 sequence 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).

Returns

A SequenceClassifierOutputWithPast 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 (ImageGPTConfig) 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).

  • past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head))

    Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • 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.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Examples:

>>> from transformers import ImageGPTFeatureExtractor, ImageGPTForImageClassification
>>> from PIL import Image
>>> import requests

>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> feature_extractor = ImageGPTFeatureExtractor.from_pretrained('openai/imagegpt-small')
>>> model = ImageGPTForImageClassification.from_pretrained('openai/imagegpt-small')

>>> inputs = feature_extractor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits

Return type

SequenceClassifierOutputWithPast or tuple(torch.FloatTensor)