839 lines
35 KiB
Python
839 lines
35 KiB
Python
# coding=utf-8
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# Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch ViT model."""
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import collections.abc
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import math
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from typing import Dict, List, Optional, Set, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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ImageClassifierOutput,
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MaskedImageModelingOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from .configuration_vit import ViTConfig
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "ViTConfig"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "google/vit-base-patch16-224-in21k"
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_EXPECTED_OUTPUT_SHAPE = [1, 197, 768]
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# Image classification docstring
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_IMAGE_CLASS_CHECKPOINT = "google/vit-base-patch16-224"
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_IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat"
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from ..deprecated._archive_maps import VIT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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class ViTEmbeddings(nn.Module):
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"""
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Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
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"""
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def __init__(self, config: ViTConfig, use_mask_token: bool = False) -> None:
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super().__init__()
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self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
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self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
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self.patch_embeddings = ViTPatchEmbeddings(config)
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num_patches = self.patch_embeddings.num_patches
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self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size))
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.config = config
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def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
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"""
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This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
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resolution images.
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Source:
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https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
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"""
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num_patches = embeddings.shape[1] - 1
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num_positions = self.position_embeddings.shape[1] - 1
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if num_patches == num_positions and height == width:
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return self.position_embeddings
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class_pos_embed = self.position_embeddings[:, 0]
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patch_pos_embed = self.position_embeddings[:, 1:]
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dim = embeddings.shape[-1]
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h0 = height // self.config.patch_size
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w0 = width // self.config.patch_size
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# we add a small number to avoid floating point error in the interpolation
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# see discussion at https://github.com/facebookresearch/dino/issues/8
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h0, w0 = h0 + 0.1, w0 + 0.1
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patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
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patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed,
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scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
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mode="bicubic",
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align_corners=False,
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)
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assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1]
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patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
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return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
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def forward(
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self,
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pixel_values: torch.Tensor,
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bool_masked_pos: Optional[torch.BoolTensor] = None,
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interpolate_pos_encoding: bool = False,
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) -> torch.Tensor:
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batch_size, num_channels, height, width = pixel_values.shape
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embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
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if bool_masked_pos is not None:
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seq_length = embeddings.shape[1]
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mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
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# replace the masked visual tokens by mask_tokens
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mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
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embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
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# add the [CLS] token to the embedded patch tokens
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cls_tokens = self.cls_token.expand(batch_size, -1, -1)
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embeddings = torch.cat((cls_tokens, embeddings), dim=1)
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# add positional encoding to each token
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if interpolate_pos_encoding:
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embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
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else:
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embeddings = embeddings + self.position_embeddings
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embeddings = self.dropout(embeddings)
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return embeddings
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class ViTPatchEmbeddings(nn.Module):
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"""
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This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
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`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
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Transformer.
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"""
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def __init__(self, config):
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super().__init__()
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image_size, patch_size = config.image_size, config.patch_size
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num_channels, hidden_size = config.num_channels, config.hidden_size
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image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
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patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_patches = num_patches
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self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
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def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
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batch_size, num_channels, height, width = pixel_values.shape
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if num_channels != self.num_channels:
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raise ValueError(
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"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
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f" Expected {self.num_channels} but got {num_channels}."
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)
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if not interpolate_pos_encoding:
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if height != self.image_size[0] or width != self.image_size[1]:
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raise ValueError(
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f"Input image size ({height}*{width}) doesn't match model"
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f" ({self.image_size[0]}*{self.image_size[1]})."
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)
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embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
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return embeddings
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class ViTSelfAttention(nn.Module):
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def __init__(self, config: ViTConfig) -> None:
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
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f"heads {config.num_attention_heads}."
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
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) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
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mixed_query_layer = self.query(hidden_states)
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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return outputs
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class ViTSelfOutput(nn.Module):
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"""
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The residual connection is defined in ViTLayer instead of here (as is the case with other models), due to the
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layernorm applied before each block.
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"""
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def __init__(self, config: ViTConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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class ViTAttention(nn.Module):
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def __init__(self, config: ViTConfig) -> None:
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super().__init__()
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self.attention = ViTSelfAttention(config)
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self.output = ViTSelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads: Set[int]) -> None:
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.attention.query = prune_linear_layer(self.attention.query, index)
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self.attention.key = prune_linear_layer(self.attention.key, index)
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self.attention.value = prune_linear_layer(self.attention.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
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self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states: torch.Tensor,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
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self_outputs = self.attention(hidden_states, head_mask, output_attentions)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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class ViTIntermediate(nn.Module):
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def __init__(self, config: ViTConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class ViTOutput(nn.Module):
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def __init__(self, config: ViTConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states + input_tensor
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return hidden_states
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class ViTLayer(nn.Module):
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"""This corresponds to the Block class in the timm implementation."""
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def __init__(self, config: ViTConfig) -> None:
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = ViTAttention(config)
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self.intermediate = ViTIntermediate(config)
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self.output = ViTOutput(config)
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self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
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self_attention_outputs = self.attention(
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self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention
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head_mask,
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output_attentions=output_attentions,
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)
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attention_output = self_attention_outputs[0]
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outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
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# first residual connection
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hidden_states = attention_output + hidden_states
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# in ViT, layernorm is also applied after self-attention
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layer_output = self.layernorm_after(hidden_states)
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layer_output = self.intermediate(layer_output)
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# second residual connection is done here
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layer_output = self.output(layer_output, hidden_states)
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outputs = (layer_output,) + outputs
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return outputs
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class ViTEncoder(nn.Module):
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def __init__(self, config: ViTConfig) -> None:
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([ViTLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states: torch.Tensor,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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output_hidden_states: bool = False,
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return_dict: bool = True,
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) -> Union[tuple, BaseModelOutput]:
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all_hidden_states = () if output_hidden_states else None
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all_self_attentions = () if output_attentions else None
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for i, layer_module in enumerate(self.layer):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer_head_mask = head_mask[i] if head_mask is not None else None
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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layer_module.__call__,
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hidden_states,
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layer_head_mask,
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output_attentions,
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)
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else:
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layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_self_attentions = all_self_attentions + (layer_outputs[1],)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if not return_dict:
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return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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)
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class ViTPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = ViTConfig
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base_model_prefix = "vit"
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main_input_name = "pixel_values"
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supports_gradient_checkpointing = True
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_no_split_modules = ["ViTEmbeddings", "ViTLayer"]
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def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
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"""Initialize the weights"""
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if isinstance(module, (nn.Linear, nn.Conv2d)):
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# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
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# `trunc_normal_cpu` not implemented in `half` issues
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module.weight.data = nn.init.trunc_normal_(
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module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
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).to(module.weight.dtype)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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elif isinstance(module, ViTEmbeddings):
|
|
module.position_embeddings.data = nn.init.trunc_normal_(
|
|
module.position_embeddings.data.to(torch.float32),
|
|
mean=0.0,
|
|
std=self.config.initializer_range,
|
|
).to(module.position_embeddings.dtype)
|
|
|
|
module.cls_token.data = nn.init.trunc_normal_(
|
|
module.cls_token.data.to(torch.float32),
|
|
mean=0.0,
|
|
std=self.config.initializer_range,
|
|
).to(module.cls_token.dtype)
|
|
|
|
|
|
VIT_START_DOCSTRING = r"""
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#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 ([`ViTConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
VIT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
|
|
for details.
|
|
|
|
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**.
|
|
|
|
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.
|
|
interpolate_pos_encoding (`bool`, *optional*):
|
|
Whether to interpolate the pre-trained position encodings.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare ViT Model transformer outputting raw hidden-states without any specific head on top.",
|
|
VIT_START_DOCSTRING,
|
|
)
|
|
class ViTModel(ViTPreTrainedModel):
|
|
def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = ViTEmbeddings(config, use_mask_token=use_mask_token)
|
|
self.encoder = ViTEncoder(config)
|
|
|
|
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.pooler = ViTPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> ViTPatchEmbeddings:
|
|
return self.embeddings.patch_embeddings
|
|
|
|
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPooling,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
modality="vision",
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
bool_masked_pos: Optional[torch.BoolTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
interpolate_pos_encoding: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
|
|
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
# TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?)
|
|
expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype
|
|
if pixel_values.dtype != expected_dtype:
|
|
pixel_values = pixel_values.to(expected_dtype)
|
|
|
|
embedding_output = self.embeddings(
|
|
pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
|
|
)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
sequence_output = self.layernorm(sequence_output)
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
|
|
return head_outputs + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class ViTPooler(nn.Module):
|
|
def __init__(self, config: ViTConfig):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states):
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""ViT Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://arxiv.org/abs/2111.09886).
|
|
|
|
<Tip>
|
|
|
|
Note that we provide a script to pre-train this model on custom data in our [examples
|
|
directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).
|
|
|
|
</Tip>
|
|
""",
|
|
VIT_START_DOCSTRING,
|
|
)
|
|
class ViTForMaskedImageModeling(ViTPreTrainedModel):
|
|
def __init__(self, config: ViTConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.vit = ViTModel(config, add_pooling_layer=False, use_mask_token=True)
|
|
|
|
self.decoder = nn.Sequential(
|
|
nn.Conv2d(
|
|
in_channels=config.hidden_size,
|
|
out_channels=config.encoder_stride**2 * config.num_channels,
|
|
kernel_size=1,
|
|
),
|
|
nn.PixelShuffle(config.encoder_stride),
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=MaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
bool_masked_pos: Optional[torch.BoolTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
interpolate_pos_encoding: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, MaskedImageModelingOutput]:
|
|
r"""
|
|
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
|
|
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, ViTForMaskedImageModeling
|
|
>>> import torch
|
|
>>> 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("google/vit-base-patch16-224-in21k")
|
|
>>> model = ViTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k")
|
|
|
|
>>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
|
|
>>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
|
|
>>> # create random boolean mask of shape (batch_size, num_patches)
|
|
>>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()
|
|
|
|
>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
|
|
>>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
|
|
>>> list(reconstructed_pixel_values.shape)
|
|
[1, 3, 224, 224]
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if bool_masked_pos is not None and (self.config.patch_size != self.config.encoder_stride):
|
|
raise ValueError(
|
|
"When `bool_masked_pos` is provided, `patch_size` must be equal to `encoder_stride` to ensure that "
|
|
"the reconstructed image has the same dimensions as the input. "
|
|
f"Got `patch_size` = {self.config.patch_size} and `encoder_stride` = {self.config.encoder_stride}."
|
|
)
|
|
|
|
outputs = self.vit(
|
|
pixel_values,
|
|
bool_masked_pos=bool_masked_pos,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
# Reshape to (batch_size, num_channels, height, width)
|
|
sequence_output = sequence_output[:, 1:]
|
|
batch_size, sequence_length, num_channels = sequence_output.shape
|
|
height = width = math.floor(sequence_length**0.5)
|
|
sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
|
|
|
|
# Reconstruct pixel values
|
|
reconstructed_pixel_values = self.decoder(sequence_output)
|
|
|
|
masked_im_loss = None
|
|
if bool_masked_pos is not None:
|
|
size = self.config.image_size // self.config.patch_size
|
|
bool_masked_pos = bool_masked_pos.reshape(-1, size, size)
|
|
mask = (
|
|
bool_masked_pos.repeat_interleave(self.config.patch_size, 1)
|
|
.repeat_interleave(self.config.patch_size, 2)
|
|
.unsqueeze(1)
|
|
.contiguous()
|
|
)
|
|
reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none")
|
|
masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels
|
|
|
|
if not return_dict:
|
|
output = (reconstructed_pixel_values,) + outputs[1:]
|
|
return ((masked_im_loss,) + output) if masked_im_loss is not None else output
|
|
|
|
return MaskedImageModelingOutput(
|
|
loss=masked_im_loss,
|
|
reconstruction=reconstructed_pixel_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
ViT 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.
|
|
|
|
<Tip>
|
|
|
|
Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by
|
|
setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
|
|
position embeddings to the higher resolution.
|
|
|
|
</Tip>
|
|
""",
|
|
VIT_START_DOCSTRING,
|
|
)
|
|
class ViTForImageClassification(ViTPreTrainedModel):
|
|
def __init__(self, config: ViTConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.vit = ViTModel(config, add_pooling_layer=False)
|
|
|
|
# Classifier head
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_IMAGE_CLASS_CHECKPOINT,
|
|
output_type=ImageClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
interpolate_pos_encoding: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, ImageClassifierOutput]:
|
|
r"""
|
|
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).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.vit(
|
|
pixel_values,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.classifier(sequence_output[:, 0, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return ImageClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|