1489 lines
63 KiB
Python
1489 lines
63 KiB
Python
# coding=utf-8
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# Copyright 2022 NAVER AI Labs and 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 ViLT model."""
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import collections.abc
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import math
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from dataclasses import dataclass
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from typing import List, Optional, 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 CrossEntropyLoss
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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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MaskedLMOutput,
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ModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import (
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find_pruneable_heads_and_indices,
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meshgrid,
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prune_linear_layer,
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)
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from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
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from .configuration_vilt import ViltConfig
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "ViltConfig"
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_CHECKPOINT_FOR_DOC = "dandelin/vilt-b32-mlm"
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from ..deprecated._archive_maps import VILT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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@dataclass
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class ViltForImagesAndTextClassificationOutput(ModelOutput):
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"""
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Class for outputs of [`ViltForImagesAndTextClassification`].
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Classification (or regression if config.num_labels==1) loss.
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logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
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Classification (or regression if config.num_labels==1) scores (before SoftMax).
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hidden_states (`List[tuple(torch.FloatTensor)]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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List of tuples of `torch.FloatTensor` (one for each image-text pair, each tuple containing the output of
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the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (`List[tuple(torch.FloatTensor)]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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List of tuples of `torch.FloatTensor` (one for each image-text pair, each tuple containing the attention
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weights of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the
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attention softmax, used to compute the weighted average in the self-attention heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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hidden_states: Optional[List[Tuple[torch.FloatTensor]]] = None
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attentions: Optional[List[Tuple[torch.FloatTensor]]] = None
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class ViltEmbeddings(nn.Module):
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"""
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Construct the text and patch embeddings.
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Text embeddings are equivalent to BERT embeddings.
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Patch embeddings are equivalent to ViT embeddings.
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"""
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def __init__(self, config):
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super().__init__()
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# text embeddings
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self.text_embeddings = TextEmbeddings(config)
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# patch embeddings
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self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.patch_embeddings = ViltPatchEmbeddings(config)
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num_patches = self.patch_embeddings.num_patches
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self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
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# modality type (text/patch) embeddings
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self.token_type_embeddings = nn.Embedding(config.modality_type_vocab_size, 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 visual_embed(self, pixel_values, pixel_mask, max_image_length=200):
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_, _, ph, pw = self.patch_embeddings.projection.weight.shape
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x = self.patch_embeddings(pixel_values)
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x_mask = pixel_mask[:, None, :, :].float()
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x_mask = nn.functional.interpolate(x_mask, size=(x.shape[2], x.shape[3])).long()
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x_h = x_mask[:, 0].sum(dim=1)[:, 0]
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x_w = x_mask[:, 0].sum(dim=2)[:, 0]
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batch_size, num_channels, height, width = x.shape
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patch_dim = self.config.image_size // self.config.patch_size
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spatial_pos = self.position_embeddings[:, 1:, :].transpose(1, 2).view(1, num_channels, patch_dim, patch_dim)
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pos_embed = torch.cat(
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[
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nn.functional.pad(
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nn.functional.interpolate(
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spatial_pos,
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size=(h, w),
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mode="bilinear",
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align_corners=True,
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),
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(0, width - w, 0, height - h),
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)
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for h, w in zip(x_h, x_w)
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],
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dim=0,
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)
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pos_embed = pos_embed.flatten(2).transpose(1, 2)
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x = x.flatten(2).transpose(1, 2)
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# Set `device` here, otherwise `patch_index` will always be on `CPU` and will fail near the end for torch>=1.13
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patch_index = torch.stack(
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meshgrid(torch.arange(x_mask.shape[-2]), torch.arange(x_mask.shape[-1]), indexing="ij"), dim=-1
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).to(device=x_mask.device)
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patch_index = patch_index[None, None, :, :, :]
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patch_index = patch_index.expand(x_mask.shape[0], x_mask.shape[1], -1, -1, -1)
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patch_index = patch_index.flatten(1, 3)
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x_mask = x_mask.flatten(1)
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if max_image_length < 0 or max_image_length is None or not isinstance(max_image_length, int):
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# suppose aug is 800 x 1333, then, maximum effective res is 800 x 1333 (if one side gets bigger, the other will be constrained and be shrinked)
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# (800 // self.patch_size) * (1333 // self.patch_size) is the maximum number of patches that single image can get.
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# if self.patch_size = 32, 25 * 41 = 1025
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# if res is 384 x 640, 12 * 20 = 240
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effective_resolution = x_h * x_w
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max_image_length = effective_resolution.max()
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else:
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effective_resolution = x_h * x_w
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max_image_length = min(effective_resolution.max(), max_image_length)
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valid_idx = x_mask.nonzero(as_tuple=False)
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non_valid_idx = (1 - x_mask).nonzero(as_tuple=False)
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unique_rows = valid_idx[:, 0].unique()
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valid_row_idx = [valid_idx[valid_idx[:, 0] == u] for u in unique_rows]
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non_valid_row_idx = [non_valid_idx[non_valid_idx[:, 0] == u] for u in unique_rows]
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valid_nums = [v.size(0) for v in valid_row_idx]
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non_valid_nums = [v.size(0) for v in non_valid_row_idx]
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pad_nums = [max_image_length - v for v in valid_nums]
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select = []
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for i, (v, nv, p) in enumerate(zip(valid_nums, non_valid_nums, pad_nums)):
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if p <= 0:
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valid_choice = torch.multinomial(torch.ones(v).float(), max_image_length)
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select.append(valid_row_idx[i][valid_choice])
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else:
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pad_choice = torch.multinomial(torch.ones(nv).float(), p, replacement=True)
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select.append(torch.cat([valid_row_idx[i], non_valid_row_idx[i][pad_choice]], dim=0))
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select = torch.cat(select, dim=0)
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x = x[select[:, 0], select[:, 1]].view(batch_size, -1, num_channels)
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x_mask = x_mask[select[:, 0], select[:, 1]].view(batch_size, -1)
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# `patch_index` should be on the same device as `select` (for torch>=1.13), which is ensured at definition time.
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patch_index = patch_index[select[:, 0], select[:, 1]].view(batch_size, -1, 2)
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pos_embed = pos_embed[select[:, 0], select[:, 1]].view(batch_size, -1, num_channels)
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cls_tokens = self.cls_token.expand(batch_size, -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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pos_embed = torch.cat(
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(self.position_embeddings[:, 0, :][:, None, :].expand(batch_size, -1, -1), pos_embed), dim=1
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)
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x = x + pos_embed
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x = self.dropout(x)
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x_mask = torch.cat([torch.ones(x_mask.shape[0], 1).to(x_mask), x_mask], dim=1)
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return x, x_mask, (patch_index, (height, width))
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def forward(
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self,
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input_ids,
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attention_mask,
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token_type_ids,
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pixel_values,
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pixel_mask,
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inputs_embeds,
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image_embeds,
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image_token_type_idx=1,
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):
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# PART 1: text embeddings
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text_embeds = self.text_embeddings(
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input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
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)
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# PART 2: patch embeddings (with interpolated position encodings)
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if image_embeds is None:
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image_embeds, image_masks, patch_index = self.visual_embed(
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pixel_values, pixel_mask, max_image_length=self.config.max_image_length
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)
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else:
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image_masks = pixel_mask.flatten(1)
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# PART 3: add modality type embeddings
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# 0 indicates text, 1 indicates image, 2 is optionally used when a second image is provided (NLVR2)
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if image_token_type_idx is None:
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image_token_type_idx = 1
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text_embeds = text_embeds + self.token_type_embeddings(
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torch.zeros_like(attention_mask, dtype=torch.long, device=text_embeds.device)
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)
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image_embeds = image_embeds + self.token_type_embeddings(
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torch.full_like(image_masks, image_token_type_idx, dtype=torch.long, device=text_embeds.device)
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)
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# PART 4: concatenate
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embeddings = torch.cat([text_embeds, image_embeds], dim=1)
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masks = torch.cat([attention_mask, image_masks], dim=1)
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return embeddings, masks
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class TextEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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self.register_buffer(
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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self.register_buffer(
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"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
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)
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def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
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if input_ids is not None:
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input_shape = input_ids.size()
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else:
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input_shape = inputs_embeds.size()[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:, :seq_length]
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# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
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# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
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# issue #5664
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if token_type_ids is None:
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if hasattr(self, "token_type_ids"):
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buffered_token_type_ids = self.token_type_ids[:, :seq_length]
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buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
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token_type_ids = buffered_token_type_ids_expanded
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else:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings
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if self.position_embedding_type == "absolute":
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class ViltPatchEmbeddings(nn.Module):
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"""
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Image to Patch Embedding.
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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):
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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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)
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target_dtype = self.projection.weight.dtype
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x = self.projection(pixel_values.to(dtype=target_dtype))
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return x
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class ViltSelfAttention(nn.Module):
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def __init__(self, config):
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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):
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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(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
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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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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.Softmax(dim=-1)(attention_scores)
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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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# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Vilt
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class ViltSelfOutput(nn.Module):
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"""
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The residual connection is defined in ViltLayer 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: ViltConfig) -> 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 ViltAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.attention = ViltSelfAttention(config)
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self.output = ViltSelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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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)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
|
|
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
|
|
self_outputs = self.attention(hidden_states, attention_mask, head_mask, output_attentions)
|
|
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->Vilt
|
|
class ViltIntermediate(nn.Module):
|
|
def __init__(self, config: ViltConfig) -> None:
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->Vilt
|
|
class ViltOutput(nn.Module):
|
|
def __init__(self, config: ViltConfig) -> None:
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
hidden_states = hidden_states + input_tensor
|
|
|
|
return hidden_states
|
|
|
|
|
|
class ViltLayer(nn.Module):
|
|
"""This corresponds to the Block class in the timm implementation."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = ViltAttention(config)
|
|
self.intermediate = ViltIntermediate(config)
|
|
self.output = ViltOutput(config)
|
|
self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
|
|
self_attention_outputs = self.attention(
|
|
self.layernorm_before(hidden_states), # in ViLT, layernorm is applied before self-attention
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
# first residual connection
|
|
hidden_states = attention_output + hidden_states.to(attention_output.device)
|
|
|
|
# in ViLT, layernorm is also applied after self-attention
|
|
layer_output = self.layernorm_after(hidden_states)
|
|
layer_output = self.intermediate(layer_output)
|
|
|
|
# second residual connection is done here
|
|
layer_output = self.output(layer_output, hidden_states)
|
|
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
|
|
class ViltEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([ViltLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
output_hidden_states=False,
|
|
return_dict=True,
|
|
):
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
class ViltPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = ViltConfig
|
|
base_model_prefix = "vilt"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["ViltEmbeddings", "ViltSelfAttention"]
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
VILT_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 ([`ViltConfig`]): 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.
|
|
"""
|
|
|
|
VILT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `({0})`):
|
|
Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
|
|
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
|
|
IDs?](../glossary#input-ids)
|
|
|
|
attention_mask (`torch.FloatTensor` of shape `({0})`, *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?](../glossary#attention-mask)
|
|
|
|
token_type_ids (`torch.LongTensor` of shape `({0})`, *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?](../glossary#token-type-ids)
|
|
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
|
|
[`ViltImageProcessor.__call__`] for details.
|
|
|
|
pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
|
|
Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for pixels that are real (i.e. **not masked**),
|
|
- 0 for pixels that are padding (i.e. **masked**).
|
|
`What are attention masks? <../glossary.html#attention-mask>`__
|
|
|
|
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 `({0}, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
|
|
image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, 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` into patch embeddings.
|
|
|
|
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 [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
VILT_IMAGES_AND_TEXT_CLASSIFICATION_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `({0})`):
|
|
Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
|
|
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
|
|
IDs?](../glossary#input-ids)
|
|
|
|
attention_mask (`torch.FloatTensor` of shape `({0})`, *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?](../glossary#attention-mask)
|
|
|
|
token_type_ids (`torch.LongTensor` of shape `({0})`, *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?](../glossary#token-type-ids)
|
|
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_images, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
|
|
[`ViltImageProcessor.__call__`] for details.
|
|
|
|
pixel_mask (`torch.LongTensor` of shape `(batch_size, num_images, height, width)`, *optional*):
|
|
Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for pixels that are real (i.e. **not masked**),
|
|
- 0 for pixels that are padding (i.e. **masked**).
|
|
`What are attention masks? <../glossary.html#attention-mask>`__
|
|
|
|
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 `({0}, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
|
|
image_embeds (`torch.FloatTensor` of shape `(batch_size, num_images, num_patches, 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` into patch embeddings.
|
|
|
|
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 [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare ViLT Model transformer outputting raw hidden-states without any specific head on top.",
|
|
VILT_START_DOCSTRING,
|
|
)
|
|
class ViltModel(ViltPreTrainedModel):
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = ViltEmbeddings(config)
|
|
self.encoder = ViltEncoder(config)
|
|
|
|
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.pooler = ViltPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.text_embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.text_embeddings.word_embeddings = value
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
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(VILT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
pixel_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
image_embeds: Optional[torch.FloatTensor] = None,
|
|
image_token_type_idx: Optional[int] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[BaseModelOutputWithPooling, Tuple[torch.FloatTensor]]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import ViltProcessor, ViltModel
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
|
|
>>> # prepare image and text
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> text = "hello world"
|
|
|
|
>>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
|
|
>>> model = ViltModel.from_pretrained("dandelin/vilt-b32-mlm")
|
|
|
|
>>> inputs = processor(image, text, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
```"""
|
|
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 input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
text_batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((text_batch_size, seq_length)), device=device)
|
|
|
|
if pixel_values is not None and image_embeds is not None:
|
|
raise ValueError("You cannot specify both pixel_values and image_embeds at the same time")
|
|
elif pixel_values is None and image_embeds is None:
|
|
raise ValueError("You have to specify either pixel_values or image_embeds")
|
|
|
|
image_batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeds.shape[0]
|
|
if image_batch_size != text_batch_size:
|
|
raise ValueError("The text inputs and image inputs need to have the same batch size")
|
|
if pixel_mask is None:
|
|
pixel_mask = torch.ones((image_batch_size, self.config.image_size, self.config.image_size), device=device)
|
|
|
|
# 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)
|
|
|
|
embedding_output, attention_mask = self.embeddings(
|
|
input_ids,
|
|
attention_mask,
|
|
token_type_ids,
|
|
pixel_values,
|
|
pixel_mask,
|
|
inputs_embeds,
|
|
image_embeds,
|
|
image_token_type_idx=image_token_type_idx,
|
|
)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
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:
|
|
return (sequence_output, pooled_output) + 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 ViltPooler(nn.Module):
|
|
def __init__(self, config):
|
|
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(
|
|
"""
|
|
ViLT Model with a language modeling head on top as done during pretraining.
|
|
""",
|
|
VILT_START_DOCSTRING,
|
|
)
|
|
class ViltForMaskedLM(ViltPreTrainedModel):
|
|
_tied_weights_keys = ["mlm_score.decoder.weight", "mlm_score.decoder.bias"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.vilt = ViltModel(config)
|
|
self.mlm_score = ViltMLMHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.mlm_score.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.mlm_score.decoder = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
pixel_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
image_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]:
|
|
r"""
|
|
labels (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should be in *[-100, 0, ...,
|
|
config.vocab_size]* (see *input_ids* docstring) Tokens with indices set to *-100* are ignored (masked), the
|
|
loss is only computed for the tokens with labels in *[0, ..., config.vocab_size]*
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import ViltProcessor, ViltForMaskedLM
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
>>> import re
|
|
>>> import torch
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> text = "a bunch of [MASK] laying on a [MASK]."
|
|
|
|
>>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
|
|
>>> model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm")
|
|
|
|
>>> # prepare inputs
|
|
>>> encoding = processor(image, text, return_tensors="pt")
|
|
|
|
>>> # forward pass
|
|
>>> outputs = model(**encoding)
|
|
|
|
>>> tl = len(re.findall("\[MASK\]", text))
|
|
>>> inferred_token = [text]
|
|
|
|
>>> # gradually fill in the MASK tokens, one by one
|
|
>>> with torch.no_grad():
|
|
... for i in range(tl):
|
|
... encoded = processor.tokenizer(inferred_token)
|
|
... input_ids = torch.tensor(encoded.input_ids)
|
|
... encoded = encoded["input_ids"][0][1:-1]
|
|
... outputs = model(input_ids=input_ids, pixel_values=encoding.pixel_values)
|
|
... mlm_logits = outputs.logits[0] # shape (seq_len, vocab_size)
|
|
... # only take into account text features (minus CLS and SEP token)
|
|
... mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :]
|
|
... mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1)
|
|
... # only take into account text
|
|
... mlm_values[torch.tensor(encoded) != 103] = 0
|
|
... select = mlm_values.argmax().item()
|
|
... encoded[select] = mlm_ids[select].item()
|
|
... inferred_token = [processor.decode(encoded)]
|
|
|
|
>>> selected_token = ""
|
|
>>> encoded = processor.tokenizer(inferred_token)
|
|
>>> output = processor.decode(encoded.input_ids[0], skip_special_tokens=True)
|
|
>>> print(output)
|
|
a bunch of cats laying on a couch.
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.vilt(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
pixel_values=pixel_values,
|
|
pixel_mask=pixel_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
image_embeds=image_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output, pooled_output = outputs[:2]
|
|
# split up final hidden states into text and image features
|
|
text_seq_len = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
text_features, _ = (sequence_output[:, :text_seq_len], sequence_output[:, text_seq_len:])
|
|
|
|
mlm_logits = self.mlm_score(text_features)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss() # -100 index = padding token
|
|
# move labels to correct device to enable PP
|
|
labels = labels.to(mlm_logits.device)
|
|
masked_lm_loss = loss_fct(mlm_logits.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (mlm_logits,) + outputs[2:]
|
|
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=mlm_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class ViltPredictionHeadTransform(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.transform_act_fn = config.hidden_act
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class ViltMLMHead(nn.Module):
|
|
def __init__(self, config, weight=None):
|
|
super().__init__()
|
|
self.config = config
|
|
self.transform = ViltPredictionHeadTransform(config)
|
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
if weight is not None:
|
|
self.decoder.weight = weight
|
|
|
|
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
|
self.decoder.bias = self.bias
|
|
|
|
def forward(self, x):
|
|
x = self.transform(x)
|
|
x = self.decoder(x)
|
|
return x
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Vilt Model transformer with a classifier head on top (a linear layer on top of the final hidden state of the [CLS]
|
|
token) for visual question answering, e.g. for VQAv2.
|
|
""",
|
|
VILT_START_DOCSTRING,
|
|
)
|
|
class ViltForQuestionAnswering(ViltPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.vilt = ViltModel(config)
|
|
|
|
# Classifier head
|
|
self.classifier = nn.Sequential(
|
|
nn.Linear(config.hidden_size, config.hidden_size * 2),
|
|
nn.LayerNorm(config.hidden_size * 2),
|
|
nn.GELU(),
|
|
nn.Linear(config.hidden_size * 2, config.num_labels),
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
pixel_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
image_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
|
|
r"""
|
|
labels (`torch.FloatTensor` of shape `(batch_size, num_labels)`, *optional*):
|
|
Labels for computing the visual question answering loss. This tensor must be either a one-hot encoding of
|
|
all answers that are applicable for a given example in the batch, or a soft encoding indicating which
|
|
answers are applicable, where 1.0 is the highest score.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import ViltProcessor, ViltForQuestionAnswering
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> text = "How many cats are there?"
|
|
|
|
>>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
|
|
>>> model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
|
|
|
|
>>> # prepare inputs
|
|
>>> encoding = processor(image, text, return_tensors="pt")
|
|
|
|
>>> # forward pass
|
|
>>> outputs = model(**encoding)
|
|
>>> logits = outputs.logits
|
|
>>> idx = logits.argmax(-1).item()
|
|
>>> print("Predicted answer:", model.config.id2label[idx])
|
|
Predicted answer: 2
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.vilt(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
pixel_values=pixel_values,
|
|
pixel_mask=pixel_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
image_embeds=image_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooler_output = outputs.pooler_output if return_dict else outputs[1]
|
|
|
|
logits = self.classifier(pooler_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable PP
|
|
labels = labels.to(logits.device)
|
|
loss = nn.functional.binary_cross_entropy_with_logits(logits, labels) * labels.shape[1]
|
|
# see https://github.com/jnhwkim/ban-vqa/blob/master/train.py#L19
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Vilt Model transformer with a classifier head on top (a linear layer on top of the final hidden state of the [CLS]
|
|
token) for image-to-text or text-to-image retrieval, e.g. MSCOCO and F30K.
|
|
""",
|
|
VILT_START_DOCSTRING,
|
|
)
|
|
class ViltForImageAndTextRetrieval(ViltPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.vilt = ViltModel(config)
|
|
|
|
# Classifier head
|
|
self.rank_output = nn.Linear(config.hidden_size, 1)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
pixel_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
image_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels are currently not supported.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import ViltProcessor, ViltForImageAndTextRetrieval
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]
|
|
|
|
>>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-coco")
|
|
>>> model = ViltForImageAndTextRetrieval.from_pretrained("dandelin/vilt-b32-finetuned-coco")
|
|
|
|
>>> # forward pass
|
|
>>> scores = dict()
|
|
>>> for text in texts:
|
|
... # prepare inputs
|
|
... encoding = processor(image, text, return_tensors="pt")
|
|
... outputs = model(**encoding)
|
|
... scores[text] = outputs.logits[0, :].item()
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.vilt(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
pixel_values=pixel_values,
|
|
pixel_mask=pixel_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
image_embeds=image_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooler_output = outputs.pooler_output if return_dict else outputs[1]
|
|
|
|
logits = self.rank_output(pooler_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable PP
|
|
labels = labels.to(logits.device)
|
|
raise NotImplementedError("Training is not yet supported.")
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Vilt Model transformer with a classifier head on top for natural language visual reasoning, e.g. NLVR2.
|
|
""",
|
|
VILT_IMAGES_AND_TEXT_CLASSIFICATION_INPUTS_DOCSTRING,
|
|
)
|
|
class ViltForImagesAndTextClassification(ViltPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.vilt = ViltModel(config)
|
|
|
|
# Classifier head
|
|
num_images = config.num_images
|
|
self.classifier = nn.Sequential(
|
|
nn.Linear(config.hidden_size * num_images, config.hidden_size * num_images),
|
|
nn.LayerNorm(config.hidden_size * num_images),
|
|
nn.GELU(),
|
|
nn.Linear(config.hidden_size * num_images, config.num_labels),
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=ViltForImagesAndTextClassificationOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
pixel_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
image_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[ViltForImagesAndTextClassificationOutput, Tuple[torch.FloatTensor]]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Binary classification labels.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import ViltProcessor, ViltForImagesAndTextClassification
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
|
|
>>> image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw)
|
|
>>> image2 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_1.jpg", stream=True).raw)
|
|
>>> text = "The left image contains twice the number of dogs as the right image."
|
|
|
|
>>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")
|
|
>>> model = ViltForImagesAndTextClassification.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")
|
|
|
|
>>> # prepare inputs
|
|
>>> encoding = processor([image1, image2], text, return_tensors="pt")
|
|
|
|
>>> # forward pass
|
|
>>> outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(0))
|
|
>>> logits = outputs.logits
|
|
>>> idx = logits.argmax(-1).item()
|
|
>>> print("Predicted answer:", model.config.id2label[idx])
|
|
Predicted answer: True
|
|
```"""
|
|
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 not None and pixel_values.ndim == 4:
|
|
# add dummy num_images dimension
|
|
pixel_values = pixel_values.unsqueeze(1)
|
|
|
|
if image_embeds is not None and image_embeds.ndim == 3:
|
|
# add dummy num_images dimension
|
|
image_embeds = image_embeds.unsqueeze(1)
|
|
|
|
num_images = pixel_values.shape[1] if pixel_values is not None else None
|
|
if num_images is None:
|
|
num_images = image_embeds.shape[1] if image_embeds is not None else None
|
|
if num_images != self.config.num_images:
|
|
raise ValueError(
|
|
"Make sure to match the number of images in the model with the number of images in the input."
|
|
)
|
|
pooler_outputs = []
|
|
hidden_states = [] if output_hidden_states else None
|
|
attentions = [] if output_attentions else None
|
|
for i in range(num_images):
|
|
# forward every image through the model
|
|
outputs = self.vilt(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
pixel_values=pixel_values[:, i, :, :, :] if pixel_values is not None else None,
|
|
pixel_mask=pixel_mask[:, i, :, :] if pixel_mask is not None else None,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
image_embeds=image_embeds[:, i, :, :] if image_embeds is not None else None,
|
|
image_token_type_idx=i + 1,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooler_output = outputs.pooler_output if return_dict else outputs[1]
|
|
pooler_outputs.append(pooler_output)
|
|
if output_hidden_states:
|
|
hidden_states.append(outputs.hidden_states)
|
|
if output_attentions:
|
|
attentions.append(outputs.attentions)
|
|
|
|
pooled_output = torch.cat(pooler_outputs, dim=-1)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
# move labels to correct device to enable PP
|
|
labels = labels.to(logits.device)
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits, hidden_states, attentions)
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return ViltForImagesAndTextClassificationOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=hidden_states,
|
|
attentions=attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
ViLT Model with a token classification head on top (a linear layer on top of the final hidden-states of the text
|
|
tokens) e.g. for Named-Entity-Recognition (NER) tasks.
|
|
""",
|
|
VILT_START_DOCSTRING,
|
|
)
|
|
class ViltForTokenClassification(ViltPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.vilt = ViltModel(config, add_pooling_layer=False)
|
|
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
pixel_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
image_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
|
|
|
Returns:
|
|
"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.vilt(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
pixel_values=pixel_values,
|
|
pixel_mask=pixel_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
image_embeds=image_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
text_input_size = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output[:, :text_input_size])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
# move labels to correct device to enable PP
|
|
labels = labels.to(logits.device)
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|