1569 lines
64 KiB
Python
1569 lines
64 KiB
Python
# coding=utf-8
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# Copyright 2021 Microsoft Research 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 BEiT model."""
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import collections.abc
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import math
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import warnings
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from dataclasses import dataclass
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from typing import Optional, Union
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import torch
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import torch.utils.checkpoint
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from torch import Tensor, 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_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BackboneOutput,
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BaseModelOutput,
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BaseModelOutputWithPooling,
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ImageClassifierOutput,
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MaskedLMOutput,
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SemanticSegmenterOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import compile_compatible_method_lru_cache, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import auto_docstring, logging, torch_int
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from ...utils.backbone_utils import BackboneMixin
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from .configuration_beit import BeitConfig
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logger = logging.get_logger(__name__)
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@dataclass
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@auto_docstring(
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custom_intro="""
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Class for outputs of [`BeitModel`].
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"""
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)
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class BeitModelOutputWithPooling(BaseModelOutputWithPooling):
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r"""
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pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
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Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if
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*config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token
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will be returned.
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"""
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def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
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however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
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layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
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argument.
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"""
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if drop_prob == 0.0 or not training:
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return input
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keep_prob = 1 - drop_prob
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shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
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random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
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random_tensor.floor_() # binarize
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output = input.div(keep_prob) * random_tensor
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return output
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class BeitDropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
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def __init__(self, drop_prob: Optional[float] = None) -> None:
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super().__init__()
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self.drop_prob = drop_prob
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return drop_path(hidden_states, self.drop_prob, self.training)
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def extra_repr(self) -> str:
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return f"p={self.drop_prob}"
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# Based on timm implementation, which can be found here:
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# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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class BeitEmbeddings(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: BeitConfig) -> None:
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super().__init__()
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self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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if config.use_mask_token:
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self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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else:
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self.mask_token = None
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self.patch_embeddings = BeitPatchEmbeddings(config)
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self.patch_size = config.patch_size
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self.image_size = (
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config.image_size
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if isinstance(config.image_size, collections.abc.Iterable)
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else (config.image_size, config.image_size)
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)
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num_patches = self.patch_embeddings.num_patches
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if config.use_absolute_position_embeddings:
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self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
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else:
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self.position_embeddings = None
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
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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 resolution
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images. This method is also adapted to support torch.jit tracing.
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Adapted from:
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- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
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- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
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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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# always interpolate when tracing to ensure the exported model works for dynamic input shapes
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if not torch.jit.is_tracing() and 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[:, :1]
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patch_pos_embed = self.position_embeddings[:, 1:]
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dim = embeddings.shape[-1]
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new_height = height // self.patch_size
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new_width = width // self.patch_size
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sqrt_num_positions = torch_int(num_positions**0.5)
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patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, 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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size=(new_height, new_width),
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mode="bicubic",
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align_corners=False,
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)
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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, 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: Optional[bool] = None,
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) -> torch.Tensor:
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if self.position_embeddings is not None and interpolate_pos_encoding is not None:
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warnings.warn(
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"`interpolate_pos_encoding` argument has no effect for BEiTEmbeddings, embeddings are always "
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"interpolated to the input image size. The argument will be removed in transformers v4.51.0."
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)
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_, _, height, width = pixel_values.shape
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embeddings, (patch_height, patch_width) = self.patch_embeddings(pixel_values)
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batch_size, seq_len, _ = embeddings.size()
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if bool_masked_pos is not None:
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mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
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# replace the masked visual tokens by mask_tokens
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w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
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embeddings = embeddings * (1 - w) + mask_tokens * w
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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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if self.position_embeddings is not None:
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embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
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embeddings = self.dropout(embeddings)
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return embeddings, (patch_height, patch_width)
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class BeitPatchEmbeddings(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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patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
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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.patch_shape = patch_shape
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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) -> 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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)
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embeddings = self.projection(pixel_values)
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patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
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embeddings = embeddings.flatten(2).transpose(1, 2)
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return embeddings, (patch_height, patch_width)
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class BeitSelfAttention(nn.Module):
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def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
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super().__init__()
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self.config = config
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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)
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self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.has_relative_position_bias = bool(window_size)
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if self.has_relative_position_bias:
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self.relative_position_bias = BeitRelativePositionBias(config, window_size=window_size)
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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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relative_position_bias: Optional[torch.Tensor] = None,
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interpolate_pos_encoding: bool = False,
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resolution: Optional[tuple[int]] = None,
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) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
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batch_size, seq_length, _ = hidden_states.shape
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query_layer = (
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self.query(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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key_layer = (
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self.key(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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value_layer = (
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self.value(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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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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# Add relative position bias if present.
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if self.has_relative_position_bias:
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height, width = resolution
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window_size = (height // self.config.patch_size, width // self.config.patch_size)
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attention_scores = attention_scores + self.relative_position_bias(
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window_size, interpolate_pos_encoding, dim_size=hidden_states.shape[1]
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)
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# Add shared relative position bias if provided.
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if relative_position_bias is not None:
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attention_scores = attention_scores + relative_position_bias
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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 BeitSdpaSelfAttention(BeitSelfAttention):
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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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relative_position_bias: Optional[torch.Tensor] = None,
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interpolate_pos_encoding: bool = False,
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resolution: Optional[tuple[int]] = None,
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) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
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if output_attentions or head_mask is not None:
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logger.warning_once(
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"`BeitSdpaSelfAttention` is used but `torch.nn.functional.scaled_dot_product_attention` does not "
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"support `output_attentions=True` or `head_mask`. Falling back to the manual attention implementation, "
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"but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. "
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'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
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)
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return super().forward(
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hidden_states=hidden_states,
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head_mask=head_mask,
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output_attentions=output_attentions,
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relative_position_bias=relative_position_bias,
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interpolate_pos_encoding=interpolate_pos_encoding,
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resolution=resolution,
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)
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batch_size, seq_length, _ = hidden_states.shape
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query_layer = (
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self.query(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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key_layer = (
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self.key(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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value_layer = (
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self.value(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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attn_bias = None
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if self.has_relative_position_bias:
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height, width = resolution
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window_size = (height // self.config.patch_size, width // self.config.patch_size)
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attn_bias = self.relative_position_bias(
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window_size, interpolate_pos_encoding, dim_size=hidden_states.shape[1]
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)
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# Add shared relative position bias if provided.
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if relative_position_bias is not None:
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if attn_bias is None:
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attn_bias = relative_position_bias
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else:
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attn_bias += relative_position_bias
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scaling = 1 / math.sqrt(self.attention_head_size)
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context_layer = torch.nn.functional.scaled_dot_product_attention(
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query_layer,
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key_layer,
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value_layer,
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attn_mask=attn_bias,
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dropout_p=self.config.attention_probs_dropout_prob if self.training else 0.0,
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is_causal=False,
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scale=scaling,
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)
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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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return context_layer, None
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class BeitSelfOutput(nn.Module):
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"""
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The residual connection is defined in BeitLayer 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: BeitConfig) -> 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, gamma=None) -> 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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BEIT_SELF_ATTENTION_CLASSES = {
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"eager": BeitSelfAttention,
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"sdpa": BeitSdpaSelfAttention,
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}
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class BeitAttention(nn.Module):
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def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
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super().__init__()
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self.attention = BEIT_SELF_ATTENTION_CLASSES[config._attn_implementation](config, window_size=window_size)
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self.output = BeitSelfOutput(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)
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|
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: torch.Tensor,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
relative_position_bias: Optional[torch.Tensor] = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
resolution: Optional[tuple[int]] = None,
|
|
) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
|
|
self_outputs = self.attention(
|
|
hidden_states, head_mask, output_attentions, relative_position_bias, interpolate_pos_encoding, resolution
|
|
)
|
|
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class BeitIntermediate(nn.Module):
|
|
def __init__(self, config: BeitConfig) -> 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
|
|
|
|
|
|
class BeitOutput(nn.Module):
|
|
def __init__(self, config: BeitConfig) -> 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) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class BeitLayer(GradientCheckpointingLayer):
|
|
"""This corresponds to the Block class in the timm implementation."""
|
|
|
|
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None, drop_path_rate: float = 0.0) -> None:
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = BeitAttention(config, window_size=window_size)
|
|
self.intermediate = BeitIntermediate(config)
|
|
self.output = BeitOutput(config)
|
|
self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.drop_path = BeitDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
|
|
self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
init_values = config.layer_scale_init_value
|
|
if init_values > 0:
|
|
self.lambda_1 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
|
|
self.lambda_2 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
|
|
else:
|
|
self.lambda_1, self.lambda_2 = None, None
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
relative_position_bias: Optional[torch.Tensor] = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
resolution: Optional[tuple[int, int]] = None,
|
|
) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
|
|
self_attention_outputs = self.attention(
|
|
self.layernorm_before(hidden_states), # in BEiT, layernorm is applied before self-attention
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
relative_position_bias=relative_position_bias,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
resolution=resolution,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
# apply lambda_1 if present
|
|
if self.lambda_1 is not None:
|
|
attention_output = self.lambda_1 * attention_output
|
|
|
|
# first residual connection
|
|
hidden_states = self.drop_path(attention_output) + hidden_states
|
|
|
|
# in BEiT, layernorm is also applied after self-attention
|
|
layer_output = self.layernorm_after(hidden_states)
|
|
|
|
layer_output = self.intermediate(layer_output)
|
|
layer_output = self.output(layer_output)
|
|
|
|
if self.lambda_2 is not None:
|
|
layer_output = self.lambda_2 * layer_output
|
|
|
|
# second residual connection
|
|
layer_output = self.drop_path(layer_output) + hidden_states
|
|
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
|
|
class BeitRelativePositionBias(nn.Module):
|
|
def __init__(self, config: BeitConfig, window_size: tuple) -> None:
|
|
super().__init__()
|
|
self.window_size = window_size
|
|
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
|
|
self.relative_position_bias_table = nn.Parameter(
|
|
torch.zeros(self.num_relative_distance, config.num_attention_heads)
|
|
) # 2*Wh-1 * 2*Ww-1, nH
|
|
# cls to token & token 2 cls & cls to cls
|
|
|
|
@compile_compatible_method_lru_cache(maxsize=10)
|
|
def generate_relative_position_index(self, window_size: tuple[int, int]) -> torch.Tensor:
|
|
"""
|
|
This method creates the relative position index, modified to support arbitrary window sizes,
|
|
as introduced in [MiDaS v3.1](https://huggingface.co/papers/2307.14460).
|
|
"""
|
|
num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
|
|
# cls to token & token 2 cls & cls to cls
|
|
# get pair-wise relative position index for each token inside the window
|
|
window_area = window_size[0] * window_size[1]
|
|
grid = torch.meshgrid(torch.arange(window_size[0]), torch.arange(window_size[1]), indexing="ij")
|
|
coords = torch.stack(grid) # 2, Wh, Ww
|
|
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
|
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
|
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
|
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
|
|
relative_coords[:, :, 1] += window_size[1] - 1
|
|
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
|
|
relative_position_index = torch.zeros(size=(window_area + 1,) * 2, dtype=relative_coords.dtype)
|
|
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
|
relative_position_index[0, 0:] = num_relative_distance - 3
|
|
relative_position_index[0:, 0] = num_relative_distance - 2
|
|
relative_position_index[0, 0] = num_relative_distance - 1
|
|
return relative_position_index
|
|
|
|
def forward(self, window_size, interpolate_pos_encoding: bool = False, dim_size=None) -> torch.Tensor:
|
|
"""
|
|
Modification of timm.models.beit.py: Attention._get_rel_pos_bias to support arbitrary window sizes.
|
|
"""
|
|
old_height = 2 * self.window_size[0] - 1
|
|
old_width = 2 * self.window_size[1] - 1
|
|
|
|
new_height = 2 * window_size[0] - 1
|
|
new_width = 2 * window_size[1] - 1
|
|
|
|
old_relative_position_bias_table = self.relative_position_bias_table
|
|
|
|
old_num_relative_distance = self.num_relative_distance
|
|
new_num_relative_distance = new_height * new_width + 3
|
|
|
|
old_sub_table = old_relative_position_bias_table[: old_num_relative_distance - 3]
|
|
|
|
old_sub_table = old_sub_table.reshape(1, old_width, old_height, -1).permute(0, 3, 1, 2)
|
|
new_sub_table = nn.functional.interpolate(
|
|
old_sub_table, size=(torch_int(new_height), torch_int(new_width)), mode="bilinear"
|
|
)
|
|
new_sub_table = new_sub_table.permute(0, 2, 3, 1).reshape(new_num_relative_distance - 3, -1)
|
|
|
|
new_relative_position_bias_table = torch.cat(
|
|
[new_sub_table, old_relative_position_bias_table[old_num_relative_distance - 3 :]]
|
|
)
|
|
|
|
relative_position_index = self.generate_relative_position_index(window_size)
|
|
relative_position_bias = new_relative_position_bias_table[relative_position_index.view(-1)]
|
|
|
|
# patch_size*num_patches_height, patch_size*num_patches_width, num_attention_heads
|
|
relative_position_bias = relative_position_bias.view(
|
|
window_size[0] * window_size[1] + 1, window_size[0] * window_size[1] + 1, -1
|
|
)
|
|
# num_attention_heads, patch_size*num_patches_width, patch_size*num_patches_height
|
|
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
|
|
|
|
if interpolate_pos_encoding:
|
|
relative_position_bias = nn.functional.interpolate(
|
|
relative_position_bias.unsqueeze(1),
|
|
size=(dim_size, dim_size),
|
|
mode="bilinear",
|
|
align_corners=False,
|
|
).squeeze(1)
|
|
|
|
return relative_position_bias.unsqueeze(0)
|
|
|
|
|
|
class BeitEncoder(nn.Module):
|
|
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.has_relative_position_bias = config.use_shared_relative_position_bias
|
|
if self.has_relative_position_bias:
|
|
self.relative_position_bias = BeitRelativePositionBias(config, window_size=window_size)
|
|
|
|
# stochastic depth decay rule
|
|
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers, device="cpu")]
|
|
self.layer = nn.ModuleList(
|
|
[
|
|
BeitLayer(
|
|
config,
|
|
window_size=window_size if config.use_relative_position_bias else None,
|
|
drop_path_rate=dpr[i],
|
|
)
|
|
for i in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
output_hidden_states: bool = False,
|
|
interpolate_pos_encoding: bool = False,
|
|
resolution: Optional[tuple[int, int]] = None,
|
|
return_dict: bool = True,
|
|
) -> Union[tuple, BaseModelOutput]:
|
|
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,)
|
|
|
|
if self.has_relative_position_bias:
|
|
height, width = resolution
|
|
window_size = (height // self.config.patch_size, width // self.config.patch_size)
|
|
relative_position_bias = self.relative_position_bias(
|
|
window_size, interpolate_pos_encoding=interpolate_pos_encoding, dim_size=hidden_states.shape[1]
|
|
)
|
|
else:
|
|
relative_position_bias = None
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
head_mask=layer_head_mask,
|
|
output_attentions=output_attentions,
|
|
relative_position_bias=relative_position_bias,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
resolution=resolution,
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class BeitPreTrainedModel(PreTrainedModel):
|
|
config: BeitConfig
|
|
base_model_prefix = "beit"
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["BeitLayer"]
|
|
_keys_to_ignore_on_load_unexpected = [r".*relative_position_index.*"]
|
|
_supports_sdpa = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
|
|
# 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)
|
|
elif isinstance(module, BeitEmbeddings):
|
|
module.cls_token.data.zero_()
|
|
if module.mask_token is not None:
|
|
module.mask_token.data.zero_()
|
|
if module.position_embeddings is not None:
|
|
module.position_embeddings.data.zero_()
|
|
elif isinstance(module, BeitRelativePositionBias):
|
|
module.relative_position_bias_table.data.zero_()
|
|
elif isinstance(module, BeitLayer):
|
|
if module.lambda_1 is not None:
|
|
module.lambda_1.data.fill_(self.config.layer_scale_init_value)
|
|
module.lambda_2.data.fill_(self.config.layer_scale_init_value)
|
|
|
|
|
|
@auto_docstring
|
|
class BeitModel(BeitPreTrainedModel):
|
|
def __init__(self, config: BeitConfig, add_pooling_layer: bool = True) -> None:
|
|
r"""
|
|
add_pooling_layer (bool, *optional*, defaults to `True`):
|
|
Whether to add a pooling layer
|
|
"""
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = BeitEmbeddings(config)
|
|
self.encoder = BeitEncoder(config, window_size=self.embeddings.patch_embeddings.patch_shape)
|
|
|
|
self.layernorm = (
|
|
nn.Identity() if config.use_mean_pooling else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
)
|
|
self.pooler = BeitPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.patch_embeddings
|
|
|
|
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)
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor,
|
|
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: bool = False,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BeitModelOutputWithPooling]:
|
|
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
|
|
|
|
# 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, _ = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
|
|
resolution = pixel_values.shape[2:]
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
resolution=resolution,
|
|
return_dict=return_dict,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
)
|
|
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 BeitModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class BeitPooler(nn.Module):
|
|
def __init__(self, config: BeitConfig) -> None:
|
|
super().__init__()
|
|
self.layernorm = (
|
|
nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) if config.use_mean_pooling else None
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
if self.layernorm is not None:
|
|
# Mean pool the final hidden states of the patch tokens
|
|
patch_tokens = hidden_states[:, 1:, :]
|
|
pooled_output = self.layernorm(patch_tokens.mean(1))
|
|
else:
|
|
# Pool by simply taking the final hidden state of the [CLS] token
|
|
pooled_output = hidden_states[:, 0]
|
|
|
|
return pooled_output
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Beit Model transformer with a 'language' modeling head on top. BEiT does masked image modeling by predicting
|
|
visual tokens of a Vector-Quantize Variational Autoencoder (VQ-VAE), whereas other vision models like ViT and DeiT
|
|
predict RGB pixel values. As a result, this class is incompatible with [`AutoModelForMaskedImageModeling`], so you
|
|
will need to use [`BeitForMaskedImageModeling`] directly if you wish to do masked image modeling with BEiT.
|
|
"""
|
|
)
|
|
class BeitForMaskedImageModeling(BeitPreTrainedModel):
|
|
def __init__(self, config: BeitConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.beit = BeitModel(config, add_pooling_layer=False)
|
|
|
|
# Classifier head
|
|
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
# NOTE: get_output_embeddings() must return None to prevent accidental weight tying.
|
|
# Vision models like BEiT use a Conv2d patch embed layer (no `.weight`) so calling tie_weights() would fail.
|
|
# See e.g. https://github.com/huggingface/transformers/pull/39339#discussion_r2219126400
|
|
return None
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
bool_masked_pos: Optional[torch.BoolTensor] = 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: bool = False,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, MaskedLMOutput]:
|
|
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).
|
|
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).
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, BeitForMaskedImageModeling
|
|
>>> 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("microsoft/beit-base-patch16-224-pt22k")
|
|
>>> model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
|
|
|
|
>>> 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, logits = outputs.loss, outputs.logits
|
|
>>> list(logits.shape)
|
|
[1, 196, 8192]
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.beit(
|
|
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]
|
|
sequence_output = self.layernorm(sequence_output)
|
|
prediction_scores = self.lm_head(sequence_output[:, 1:])
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss() # -100 index = padding token
|
|
masked_lm_loss = loss_fct(prediction_scores[bool_masked_pos], labels)
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[1:]
|
|
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final
|
|
hidden states of the patch tokens) e.g. for ImageNet.
|
|
"""
|
|
)
|
|
class BeitForImageClassification(BeitPreTrainedModel):
|
|
def __init__(self, config: BeitConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.beit = BeitModel(config, add_pooling_layer=True)
|
|
|
|
# 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()
|
|
|
|
@auto_docstring
|
|
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: bool = False,
|
|
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.beit(
|
|
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,
|
|
)
|
|
|
|
pooled_output = outputs.pooler_output if return_dict else outputs[1]
|
|
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
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[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return ImageClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class BeitConvModule(nn.Module):
|
|
"""
|
|
A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
|
|
layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
|
|
|
|
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
kernel_size: Union[int, tuple[int, int]],
|
|
padding: Union[int, tuple[int, int], str] = 0,
|
|
bias: bool = False,
|
|
dilation: Union[int, tuple[int, int]] = 1,
|
|
) -> None:
|
|
super().__init__()
|
|
self.conv = nn.Conv2d(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=kernel_size,
|
|
padding=padding,
|
|
bias=bias,
|
|
dilation=dilation,
|
|
)
|
|
self.bn = nn.BatchNorm2d(out_channels)
|
|
self.activation = nn.ReLU()
|
|
|
|
def forward(self, input: torch.Tensor) -> torch.Tensor:
|
|
output = self.conv(input)
|
|
output = self.bn(output)
|
|
output = self.activation(output)
|
|
|
|
return output
|
|
|
|
|
|
class BeitPyramidPoolingBlock(nn.Module):
|
|
def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None:
|
|
super().__init__()
|
|
self.layers = [
|
|
nn.AdaptiveAvgPool2d(pool_scale),
|
|
BeitConvModule(in_channels, channels, kernel_size=1),
|
|
]
|
|
for i, layer in enumerate(self.layers):
|
|
self.add_module(str(i), layer)
|
|
|
|
def forward(self, input: torch.Tensor) -> torch.Tensor:
|
|
hidden_state = input
|
|
for layer in self.layers:
|
|
hidden_state = layer(hidden_state)
|
|
return hidden_state
|
|
|
|
|
|
class BeitPyramidPoolingModule(nn.Module):
|
|
"""
|
|
Pyramid Pooling Module (PPM) used in PSPNet.
|
|
|
|
Args:
|
|
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
|
|
Module.
|
|
in_channels (int): Input channels.
|
|
channels (int): Channels after modules, before conv_seg.
|
|
align_corners (bool): align_corners argument of F.interpolate.
|
|
|
|
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
|
|
"""
|
|
|
|
def __init__(self, pool_scales: tuple[int, ...], in_channels: int, channels: int, align_corners: bool) -> None:
|
|
super().__init__()
|
|
self.pool_scales = pool_scales
|
|
self.align_corners = align_corners
|
|
self.in_channels = in_channels
|
|
self.channels = channels
|
|
self.blocks = []
|
|
for i, pool_scale in enumerate(pool_scales):
|
|
block = BeitPyramidPoolingBlock(pool_scale=pool_scale, in_channels=in_channels, channels=channels)
|
|
self.blocks.append(block)
|
|
self.add_module(str(i), block)
|
|
|
|
def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
|
|
ppm_outs = []
|
|
for ppm in self.blocks:
|
|
ppm_out = ppm(x)
|
|
upsampled_ppm_out = nn.functional.interpolate(
|
|
ppm_out, size=x.size()[2:], mode="bilinear", align_corners=self.align_corners
|
|
)
|
|
ppm_outs.append(upsampled_ppm_out)
|
|
return ppm_outs
|
|
|
|
|
|
class BeitUperHead(nn.Module):
|
|
"""
|
|
Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
|
|
[UPerNet](https://huggingface.co/papers/1807.10221).
|
|
|
|
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
|
|
"""
|
|
|
|
def __init__(self, config: BeitConfig) -> None:
|
|
super().__init__()
|
|
|
|
self.pool_scales = config.pool_scales # e.g. (1, 2, 3, 6)
|
|
self.in_channels = [config.hidden_size] * 4 # e.g. [768, 768, 768, 768]
|
|
self.channels = config.hidden_size
|
|
self.align_corners = False
|
|
self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)
|
|
|
|
# PSP Module
|
|
self.psp_modules = BeitPyramidPoolingModule(
|
|
self.pool_scales,
|
|
self.in_channels[-1],
|
|
self.channels,
|
|
align_corners=self.align_corners,
|
|
)
|
|
self.bottleneck = BeitConvModule(
|
|
self.in_channels[-1] + len(self.pool_scales) * self.channels,
|
|
self.channels,
|
|
kernel_size=3,
|
|
padding=1,
|
|
)
|
|
# FPN Module
|
|
self.lateral_convs = nn.ModuleList()
|
|
self.fpn_convs = nn.ModuleList()
|
|
for in_channels in self.in_channels[:-1]: # skip the top layer
|
|
l_conv = BeitConvModule(in_channels, self.channels, kernel_size=1)
|
|
fpn_conv = BeitConvModule(self.channels, self.channels, kernel_size=3, padding=1)
|
|
self.lateral_convs.append(l_conv)
|
|
self.fpn_convs.append(fpn_conv)
|
|
|
|
self.fpn_bottleneck = BeitConvModule(
|
|
len(self.in_channels) * self.channels,
|
|
self.channels,
|
|
kernel_size=3,
|
|
padding=1,
|
|
)
|
|
|
|
def psp_forward(self, inputs):
|
|
x = inputs[-1]
|
|
psp_outs = [x]
|
|
psp_outs.extend(self.psp_modules(x))
|
|
psp_outs = torch.cat(psp_outs, dim=1)
|
|
output = self.bottleneck(psp_outs)
|
|
|
|
return output
|
|
|
|
def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# build laterals
|
|
laterals = [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)]
|
|
|
|
laterals.append(self.psp_forward(encoder_hidden_states))
|
|
|
|
# build top-down path
|
|
used_backbone_levels = len(laterals)
|
|
for i in range(used_backbone_levels - 1, 0, -1):
|
|
prev_shape = laterals[i - 1].shape[2:]
|
|
laterals[i - 1] = laterals[i - 1] + nn.functional.interpolate(
|
|
laterals[i], size=prev_shape, mode="bilinear", align_corners=self.align_corners
|
|
)
|
|
|
|
# build outputs
|
|
fpn_outs = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)]
|
|
# append psp feature
|
|
fpn_outs.append(laterals[-1])
|
|
|
|
for i in range(used_backbone_levels - 1, 0, -1):
|
|
fpn_outs[i] = nn.functional.interpolate(
|
|
fpn_outs[i], size=fpn_outs[0].shape[2:], mode="bilinear", align_corners=self.align_corners
|
|
)
|
|
fpn_outs = torch.cat(fpn_outs, dim=1)
|
|
output = self.fpn_bottleneck(fpn_outs)
|
|
output = self.classifier(output)
|
|
|
|
return output
|
|
|
|
|
|
class BeitFCNHead(nn.Module):
|
|
"""
|
|
Fully Convolution Networks for Semantic Segmentation. This head is implemented of
|
|
[FCNNet](https://huggingface.co/papers/1411.4038>).
|
|
|
|
Args:
|
|
config (BeitConfig): Configuration.
|
|
in_channels
|
|
kernel_size (int): The kernel size for convs in the head. Default: 3.
|
|
dilation (int): The dilation rate for convs in the head. Default: 1.
|
|
|
|
|
|
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
|
|
"""
|
|
|
|
def __init__(
|
|
self, config: BeitConfig, in_index: int = 2, kernel_size: int = 3, dilation: Union[int, tuple[int, int]] = 1
|
|
) -> None:
|
|
super().__init__()
|
|
self.in_channels = config.hidden_size
|
|
self.channels = config.auxiliary_channels
|
|
self.num_convs = config.auxiliary_num_convs
|
|
self.concat_input = config.auxiliary_concat_input
|
|
self.in_index = in_index
|
|
|
|
conv_padding = (kernel_size // 2) * dilation
|
|
convs = []
|
|
convs.append(
|
|
BeitConvModule(
|
|
self.in_channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
|
|
)
|
|
)
|
|
for i in range(self.num_convs - 1):
|
|
convs.append(
|
|
BeitConvModule(
|
|
self.channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
|
|
)
|
|
)
|
|
if self.num_convs == 0:
|
|
self.convs = nn.Identity()
|
|
else:
|
|
self.convs = nn.Sequential(*convs)
|
|
if self.concat_input:
|
|
self.conv_cat = BeitConvModule(
|
|
self.in_channels + self.channels, self.channels, kernel_size=kernel_size, padding=kernel_size // 2
|
|
)
|
|
|
|
self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)
|
|
|
|
def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# just take the relevant feature maps
|
|
hidden_states = encoder_hidden_states[self.in_index]
|
|
output = self.convs(hidden_states)
|
|
if self.concat_input:
|
|
output = self.conv_cat(torch.cat([hidden_states, output], dim=1))
|
|
output = self.classifier(output)
|
|
return output
|
|
|
|
|
|
@auto_docstring
|
|
class BeitForSemanticSegmentation(BeitPreTrainedModel):
|
|
def __init__(self, config: BeitConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.beit = BeitModel(config, add_pooling_layer=False)
|
|
|
|
# FPNs
|
|
if len(self.config.out_indices) != 4:
|
|
raise ValueError(
|
|
"BeitForSemanticSegmentation requires config.out_indices to be a list of 4 integers, "
|
|
"specifying which features to use from the backbone. One can use [3, 5, 7, 11] in case of "
|
|
"a base-sized architecture."
|
|
)
|
|
self.fpn1 = nn.Sequential(
|
|
nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
|
|
nn.BatchNorm2d(config.hidden_size),
|
|
nn.GELU(),
|
|
nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
|
|
)
|
|
self.fpn2 = nn.Sequential(
|
|
nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
|
|
)
|
|
self.fpn3 = nn.Identity()
|
|
self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)
|
|
|
|
# Semantic segmentation head(s)
|
|
self.decode_head = BeitUperHead(config)
|
|
self.auxiliary_head = BeitFCNHead(config) if config.use_auxiliary_head else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def compute_loss(self, logits, auxiliary_logits, labels):
|
|
# upsample logits to the images' original size
|
|
upsampled_logits = nn.functional.interpolate(
|
|
logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
|
|
)
|
|
if auxiliary_logits is not None:
|
|
upsampled_auxiliary_logits = nn.functional.interpolate(
|
|
auxiliary_logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
|
|
)
|
|
# compute weighted loss
|
|
loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
|
|
main_loss = loss_fct(upsampled_logits, labels)
|
|
loss = main_loss
|
|
if auxiliary_logits is not None:
|
|
auxiliary_loss = loss_fct(upsampled_auxiliary_logits, labels)
|
|
loss += self.config.auxiliary_loss_weight * auxiliary_loss
|
|
|
|
return loss
|
|
|
|
@auto_docstring
|
|
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: bool = False,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, SemanticSegmenterOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
|
|
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, BeitForSemanticSegmentation
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
|
|
>>> model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
|
|
|
|
>>> inputs = image_processor(images=image, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
>>> # logits are of shape (batch_size, num_labels, height, width)
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
if labels is not None and self.config.num_labels == 1:
|
|
raise ValueError("The number of labels should be greater than one")
|
|
|
|
outputs = self.beit(
|
|
pixel_values,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=True, # we need the intermediate hidden states
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
|
|
|
|
# only keep certain features, and reshape
|
|
# note that we do +1 as the encoder_hidden_states also includes the initial embeddings
|
|
features = [feature for idx, feature in enumerate(encoder_hidden_states) if idx + 1 in self.config.out_indices]
|
|
batch_size = pixel_values.shape[0]
|
|
patch_resolution = self.config.image_size // self.config.patch_size
|
|
features = [
|
|
x[:, 1:, :].permute(0, 2, 1).reshape(batch_size, -1, patch_resolution, patch_resolution) for x in features
|
|
]
|
|
|
|
# apply FPNs
|
|
ops = [self.fpn1, self.fpn2, self.fpn3, self.fpn4]
|
|
for i in range(len(features)):
|
|
features[i] = ops[i](features[i])
|
|
|
|
logits = self.decode_head(features)
|
|
|
|
auxiliary_logits = None
|
|
if self.auxiliary_head is not None:
|
|
auxiliary_logits = self.auxiliary_head(features)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.compute_loss(logits, auxiliary_logits, labels)
|
|
|
|
if not return_dict:
|
|
if output_hidden_states:
|
|
output = (logits,) + outputs[1:]
|
|
else:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SemanticSegmenterOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states if output_hidden_states else None,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
BEiT backbone, to be used with frameworks like DETR and MaskFormer.
|
|
"""
|
|
)
|
|
class BeitBackbone(BeitPreTrainedModel, BackboneMixin):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
super()._init_backbone(config)
|
|
|
|
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
|
|
self.embeddings = BeitEmbeddings(config)
|
|
self.encoder = BeitEncoder(config, window_size=self.embeddings.patch_embeddings.patch_shape)
|
|
|
|
if config.add_fpn:
|
|
if len(self.config.out_indices) != 4:
|
|
raise ValueError(
|
|
"BeitBackbone requires config.out_indices to be a list of 4 integers, "
|
|
"specifying which features to use from the backbone. One can use [3, 5, 7, 11] in case of "
|
|
"a base-sized architecture."
|
|
)
|
|
hidden_size = config.hidden_size
|
|
self.fpn1 = nn.Sequential(
|
|
nn.ConvTranspose2d(hidden_size, hidden_size, kernel_size=2, stride=2),
|
|
nn.BatchNorm2d(hidden_size, eps=config.batch_norm_eps),
|
|
nn.GELU(),
|
|
nn.ConvTranspose2d(hidden_size, hidden_size, kernel_size=2, stride=2),
|
|
)
|
|
|
|
self.fpn2 = nn.Sequential(nn.ConvTranspose2d(hidden_size, hidden_size, kernel_size=2, stride=2))
|
|
self.fpn3 = nn.Identity()
|
|
self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)
|
|
|
|
# initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.patch_embeddings
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: Tensor,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> BackboneOutput:
|
|
r"""
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, AutoBackbone
|
|
>>> 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)
|
|
|
|
>>> processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224")
|
|
>>> model = AutoBackbone.from_pretrained(
|
|
... "microsoft/beit-base-patch16-224", out_features=["stage1", "stage2", "stage3", "stage4"]
|
|
... )
|
|
|
|
>>> inputs = processor(image, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> feature_maps = outputs.feature_maps
|
|
>>> list(feature_maps[-1].shape)
|
|
[1, 768, 14, 14]
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
|
|
batch_size = pixel_values.shape[0]
|
|
embedding_output, (patch_height, patch_width) = self.embeddings(pixel_values)
|
|
resolution = pixel_values.shape[2:]
|
|
|
|
outputs = self.encoder(
|
|
embedding_output,
|
|
output_hidden_states=True,
|
|
output_attentions=output_attentions,
|
|
resolution=resolution,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = outputs.hidden_states if return_dict else outputs[1]
|
|
|
|
feature_maps = ()
|
|
for stage, hidden_state in zip(self.stage_names, hidden_states):
|
|
if stage in self.out_features:
|
|
if self.config.reshape_hidden_states:
|
|
hidden_state = hidden_state[:, 1:, :]
|
|
hidden_state = hidden_state.permute(0, 2, 1)
|
|
hidden_state = hidden_state.reshape(batch_size, -1, patch_height, patch_width)
|
|
|
|
feature_maps += (hidden_state,)
|
|
|
|
if self.config.add_fpn:
|
|
feature_maps = [
|
|
self.fpn1(feature_maps[0]),
|
|
self.fpn2(feature_maps[1]),
|
|
self.fpn3(feature_maps[2]),
|
|
self.fpn4(feature_maps[3]),
|
|
]
|
|
feature_maps = tuple(feature_maps)
|
|
|
|
if not return_dict:
|
|
if output_hidden_states:
|
|
output = (feature_maps,) + outputs[1:]
|
|
else:
|
|
output = (feature_maps,) + outputs[2:]
|
|
return output
|
|
|
|
return BackboneOutput(
|
|
feature_maps=feature_maps,
|
|
hidden_states=outputs.hidden_states if output_hidden_states else None,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"BeitForImageClassification",
|
|
"BeitForMaskedImageModeling",
|
|
"BeitForSemanticSegmentation",
|
|
"BeitModel",
|
|
"BeitPreTrainedModel",
|
|
"BeitBackbone",
|
|
]
|