1752 lines
70 KiB
Python
1752 lines
70 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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from dataclasses import dataclass
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from typing import List, Optional, Tuple, Union
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import einops
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from torch.nn import functional as F
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from transformers.activations import ACT2FN
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from transformers.configuration_utils import PretrainedConfig
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from transformers.modeling_outputs import (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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from transformers.modeling_utils import PreTrainedModel
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from transformers.pytorch_utils import (find_pruneable_heads_and_indices,
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prune_linear_layer)
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from transformers.utils import (add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward, logging,
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replace_return_docstrings)
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from models.utils import interpolate_temporal_pos_embed
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from ...modules.temporal_modelling import (X_CLIP, STAdapter, TemporalAttention,
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TemporalS4, WindowTemporalAttention)
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "BeitConfig"
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_FEAT_EXTRACTOR_FOR_DOC = "BeitFeatureExtractor"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "microsoft/beit-base-patch16-224-pt22k"
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_EXPECTED_OUTPUT_SHAPE = [1, 197, 768]
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# Image classification docstring
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_IMAGE_CLASS_CHECKPOINT = "microsoft/beit-base-patch16-224"
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_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
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BEIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"microsoft/beit-base-patch16-224",
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# See all BEiT models at https://huggingface.co/models?filter=beit
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]
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class BeitConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BeitModel`]. It is used to instantiate an BEiT
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the BEiT
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[microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) architecture.
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Args:
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vocab_size (`int`, *optional*, defaults to 8092):
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Vocabulary size of the BEiT model. Defines the number of different image tokens that can be used during
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pre-training.
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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use_mask_token (`bool`, *optional*, defaults to `False`):
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Whether to use a mask token for masked image modeling.
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use_absolute_position_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to use BERT-style absolute position embeddings.
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use_relative_position_bias (`bool`, *optional*, defaults to `False`):
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Whether to use T5-style relative position embeddings in the self-attention layers.
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use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`):
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Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
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layer_scale_init_value (`float`, *optional*, defaults to 0.1):
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Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
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drop_path_rate (`float`, *optional*, defaults to 0.1):
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Stochastic depth rate per sample (when applied in the main path of residual layers).
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use_mean_pooling (`bool`, *optional*, defaults to `True`):
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Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
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CLS token, before applying the classification head.
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out_indices (`List[int]`, *optional*, defaults to `[3, 5, 7, 11]`):
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Indices of the feature maps to use for semantic segmentation.
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pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
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Pooling scales used in Pooling Pyramid Module applied on the last feature map.
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use_auxiliary_head (`bool`, *optional*, defaults to `True`):
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Whether to use an auxiliary head during training.
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auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
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Weight of the cross-entropy loss of the auxiliary head.
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auxiliary_channels (`int`, *optional*, defaults to 256):
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Number of channels to use in the auxiliary head.
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auxiliary_num_convs (`int`, *optional*, defaults to 1):
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Number of convolutional layers to use in the auxiliary head.
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auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
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Whether to concatenate the output of the auxiliary head with the input before the classification layer.
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semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
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The index that is ignored by the loss function of the semantic segmentation model.
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Example:
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```python
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>>> from transformers import BeitModel, BeitConfig
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>>> # Initializing a BEiT beit-base-patch16-224-pt22k style configuration
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>>> configuration = BeitConfig()
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>>> # Initializing a model from the beit-base-patch16-224-pt22k style configuration
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>>> model = BeitModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "beit"
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def __init__(
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self,
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vocab_size=8192,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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is_encoder_decoder=False,
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image_size=224,
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num_frames=1,
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patch_size=16,
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num_channels=3,
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use_mask_token=False,
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use_absolute_position_embeddings=False,
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use_relative_position_bias=False,
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use_shared_relative_position_bias=False,
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layer_scale_init_value=0.1,
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drop_path_rate=0.1,
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use_mean_pooling=True,
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out_indices=[3, 5, 7, 11],
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pool_scales=[1, 2, 3, 6],
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use_auxiliary_head=True,
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auxiliary_loss_weight=0.4,
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auxiliary_channels=256,
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auxiliary_num_convs=1,
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auxiliary_concat_input=False,
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semantic_loss_ignore_index=255,
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temporal_model_block="none",
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temporal_model_position="last",
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temporal_model_init_value=0.0,
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temporal_model_config={},
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use_temporal_position_embedding=False,
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add_k_prompts=0,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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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.use_mask_token = use_mask_token
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self.use_absolute_position_embeddings = use_absolute_position_embeddings
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self.use_relative_position_bias = use_relative_position_bias
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self.use_shared_relative_position_bias = use_shared_relative_position_bias
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self.layer_scale_init_value = layer_scale_init_value
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self.drop_path_rate = drop_path_rate
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self.use_mean_pooling = use_mean_pooling
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# decode head attributes (semantic segmentation)
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self.out_indices = out_indices
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self.pool_scales = pool_scales
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# auxiliary head attributes (semantic segmentation)
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self.use_auxiliary_head = use_auxiliary_head
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self.auxiliary_loss_weight = auxiliary_loss_weight
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self.auxiliary_channels = auxiliary_channels
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self.auxiliary_num_convs = auxiliary_num_convs
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self.auxiliary_concat_input = auxiliary_concat_input
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self.semantic_loss_ignore_index = semantic_loss_ignore_index
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self.temporal_model_block = temporal_model_block
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self.temporal_model_config = temporal_model_config
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self.temporal_model_position = temporal_model_position
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self.temporal_model_init_value = temporal_model_init_value
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self.use_temporal_position_embedding = use_temporal_position_embedding
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self.add_k_prompts = add_k_prompts
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self.num_frames = num_frames
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@dataclass
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class BeitModelOutputWithPooling(BaseModelOutputWithPooling):
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"""
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Class for outputs of [`BeitModel`].
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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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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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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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 (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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def drop_path(
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input: torch.Tensor, drop_prob: float = 0.0, training: bool = False
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) -> 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,) * (
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input.ndim - 1
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) # 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, x: torch.Tensor) -> torch.Tensor:
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return drop_path(x, self.drop_prob, self.training)
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def extra_repr(self) -> str:
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return "p={}".format(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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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(
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torch.zeros(1, num_patches + 1, config.hidden_size)
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)
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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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if config.use_temporal_position_embedding:
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self.temporal_position_embeddings = nn.parameter.Parameter(
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torch.zeros(1, config.num_frames, 1, config.hidden_size)
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)
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else:
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self.temporal_position_embeddings = None
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if config.add_k_prompts > 0:
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self.prompt_tokens = nn.parameter.Parameter(
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torch.zeros(1, config.add_k_prompts, config.hidden_size)
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)
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else:
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self.prompt_tokens = None
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def forward(
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self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None
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) -> torch.Tensor:
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"""
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Args:
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pixel_values (torch.Tensor): The input image patches. Shape: [B, T, C, H, W].
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"""
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t = pixel_values.shape[1]
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pixel_values = einops.rearrange(pixel_values, "b t c h w -> (b t) c h w")
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embeddings = self.patch_embeddings(pixel_values)
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batch_size, seq_len, _ = embeddings.size() # [(b t) l c]
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cls_tokens = self.cls_token.expand(batch_size, -1, -1)
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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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if self.prompt_tokens is not None:
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prompt_tokens = self.prompt_tokens.expand(batch_size, -1, -1)
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embeddings = torch.cat((cls_tokens, embeddings, prompt_tokens), dim=1)
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else:
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embeddings = torch.cat((cls_tokens, embeddings), dim=1) # [B*T, L, C]
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if self.position_embeddings is not None:
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embeddings = embeddings + self.position_embeddings
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embeddings = einops.rearrange(embeddings, "(b t) l c -> b t l c", t=t)
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if self.temporal_position_embeddings is not None:
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if t <= self.temporal_position_embeddings.shape[1]:
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embeddings = embeddings + self.temporal_position_embeddings[:, :t]
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else:
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tpe = interpolate_temporal_pos_embed(self.temporal_position_embeddings, t)
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embeddings = embeddings + tpe
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embeddings = self.dropout(embeddings)
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return embeddings
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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 = (
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image_size
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if isinstance(image_size, collections.abc.Iterable)
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else (image_size, image_size)
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)
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patch_size = (
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patch_size
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if isinstance(patch_size, collections.abc.Iterable)
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else (patch_size, patch_size)
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)
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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(
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num_channels, hidden_size, kernel_size=patch_size, stride=patch_size
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)
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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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if height != self.image_size[0] or width != self.image_size[1]:
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raise ValueError(
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f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
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)
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embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
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return embeddings
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class 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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|
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
|
|
config, "embedding_size"
|
|
):
|
|
raise ValueError(
|
|
f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
|
|
f"heads {config.num_attention_heads}."
|
|
)
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
|
|
if window_size:
|
|
self.relative_position_bias = BeitRelativePositionBias(
|
|
config, window_size=window_size
|
|
)
|
|
else:
|
|
self.relative_position_bias = None
|
|
|
|
def transpose_for_scores(self, x):
|
|
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
|
x = x.view(*new_x_shape)
|
|
return x.permute(0, 2, 1, 3)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
relative_position_bias: Optional["BeitRelativePositionBias"] = None,
|
|
) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
|
|
mixed_query_layer = self.query(hidden_states)
|
|
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
query_layer = self.transpose_for_scores(mixed_query_layer)
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
|
|
# Add relative position bias if present.
|
|
if self.relative_position_bias is not None:
|
|
attention_scores = attention_scores + self.relative_position_bias().unsqueeze(0)
|
|
|
|
# Add shared relative position bias if provided.
|
|
if relative_position_bias is not None:
|
|
attention_scores = attention_scores + relative_position_bias
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
|
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
attention_probs = self.dropout(attention_probs)
|
|
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(*new_context_layer_shape)
|
|
|
|
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
|
|
|
return outputs
|
|
|
|
|
|
class BeitSelfOutput(nn.Module):
|
|
"""
|
|
The residual connection is defined in BeitLayer instead of here (as is the case with other models), due to the
|
|
layernorm applied before each block.
|
|
"""
|
|
|
|
def __init__(self, config: BeitConfig) -> None:
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(
|
|
self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, gamma=None
|
|
) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class BeitAttention(nn.Module):
|
|
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
|
|
super().__init__()
|
|
self.attention = BeitSelfAttention(config, window_size=window_size)
|
|
self.output = BeitSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads,
|
|
self.attention.num_attention_heads,
|
|
self.attention.attention_head_size,
|
|
self.pruned_heads,
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.attention.query = prune_linear_layer(self.attention.query, index)
|
|
self.attention.key = prune_linear_layer(self.attention.key, index)
|
|
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: torch.Tensor,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
relative_position_bias: Optional["BeitRelativePositionBias"] = None,
|
|
) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
|
|
self_outputs = self.attention(
|
|
hidden_states, head_mask, output_attentions, relative_position_bias
|
|
)
|
|
|
|
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 TemporalAttentionBeit(nn.Module):
|
|
|
|
"""temporal attention using BeitAttention"""
|
|
|
|
def __init__(self, config: BeitConfig):
|
|
"""TODO: to be defined."""
|
|
super().__init__()
|
|
|
|
self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.attention = BeitAttention(config, window_size=None)
|
|
self.scale = nn.Parameter(
|
|
config.temporal_model_init_value * torch.ones((config.hidden_size)),
|
|
requires_grad=True,
|
|
)
|
|
self.drop_path = BeitDropPath(config.drop_path_rate)
|
|
|
|
def forward(self, hidden_states: torch.Tensor):
|
|
"""forward function
|
|
|
|
Args:
|
|
hidden_states (torch.Tensor): The input. Shape: [b,t,l,c]
|
|
|
|
Returns: TODO
|
|
|
|
"""
|
|
b = hidden_states.shape[0]
|
|
output = einops.rearrange(hidden_states, "b t l c -> (b l) t c")
|
|
output = self.layernorm_before(output)
|
|
output = self.attention(output)
|
|
output = einops.rearrange(output[0], "(b l) t c -> b t l c", b=b)
|
|
return hidden_states + self.drop_path(output[0]) * self.scale
|
|
|
|
|
|
class BeitLayer(nn.Module):
|
|
"""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.config = config
|
|
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)
|
|
|
|
self.temporal_model_position = config.temporal_model_position
|
|
|
|
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
|
|
|
|
if config.temporal_model_block == "st_adapter":
|
|
self.temp_model = STAdapter(**config.temporal_model_config)
|
|
elif config.temporal_model_block == "timesformer":
|
|
self.temp_model = TemporalAttention(**config.temporal_model_config)
|
|
elif config.temporal_model_block == "s4":
|
|
self.temp_model = TemporalS4(**config.temporal_model_config)
|
|
elif config.temporal_model_block == "ta_beit":
|
|
self.temp_model = TemporalAttentionBeit(config)
|
|
elif config.temporal_model_block == "window_attention":
|
|
self.temp_model = WindowTemporalAttention(**config.temporal_model_config)
|
|
elif config.temporal_model_block == "xclip":
|
|
self.temp_model = X_CLIP(**config.temporal_model_config)
|
|
elif config.temporal_model_block == "none":
|
|
self.temp_model = None
|
|
else:
|
|
raise ValueError(f"not accepted temporal model: {config.temporal_model_block}")
|
|
|
|
self.temporal_model_block = config.temporal_model_block
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
relative_position_bias: Optional["BeitRelativePositionBias"] = None,
|
|
) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
|
|
|
|
b, t, l, c = hidden_states.shape
|
|
|
|
if self.temporal_model_block == "xclip":
|
|
assert (
|
|
self.temporal_model_position == "first" and self.config.add_k_prompts == 1
|
|
), "xclip must be put before the attention and add_k_prompts must be 1."
|
|
|
|
if self.temp_model is not None and self.temporal_model_position == "first":
|
|
hidden_states = self.temp_model(hidden_states)
|
|
|
|
hidden_states = einops.rearrange(hidden_states, "b t l c -> (b t) l c")
|
|
|
|
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,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
# add self attentions if we output attention weights
|
|
outputs = self_attention_outputs[1:]
|
|
|
|
# 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
|
|
|
|
layer_output = einops.rearrange(layer_output, "(b t) l c -> b t l c", b=b)
|
|
|
|
# apply temporal modeling block
|
|
if self.temp_model is not None and self.temporal_model_position == "last":
|
|
layer_output = self.temp_model(layer_output)
|
|
|
|
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
|
|
|
|
# get pair-wise relative position index for each token inside the window
|
|
coords_h = torch.arange(window_size[0])
|
|
coords_w = torch.arange(window_size[1])
|
|
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 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_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype
|
|
)
|
|
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
|
relative_position_index[0, 0:] = self.num_relative_distance - 3
|
|
relative_position_index[0:, 0] = self.num_relative_distance - 2
|
|
relative_position_index[0, 0] = self.num_relative_distance - 1
|
|
|
|
self.register_buffer("relative_position_index", relative_position_index)
|
|
|
|
# add bias for prompts
|
|
k = config.add_k_prompts
|
|
self.k = k
|
|
if k > 0:
|
|
self.prompt_bias_table = nn.parameter.Parameter(
|
|
torch.zeros((2 + k) * k, config.num_attention_heads)
|
|
) # k prompt-to-token, k token-to-prompt, k*k prompt-to-promt
|
|
else:
|
|
self.prompt_bias_table = None
|
|
|
|
def forward(self) -> torch.Tensor:
|
|
relative_position_bias = self.relative_position_bias_table[
|
|
self.relative_position_index.view(-1)
|
|
].view(
|
|
self.window_size[0] * self.window_size[1] + 1,
|
|
self.window_size[0] * self.window_size[1] + 1,
|
|
-1,
|
|
) # Wh*Ww,Wh*Ww,nH
|
|
|
|
k = self.k
|
|
if k > 0:
|
|
l = self.window_size[0] * self.window_size[1] + 1
|
|
bias = torch.zeros(l + k, l + k, relative_position_bias.shape[-1]).to(
|
|
relative_position_bias.device
|
|
)
|
|
bias[:l, :l] = relative_position_bias
|
|
bias[l:, :l] = self.prompt_bias_table[:k].view(k, 1, -1) # prompt to token
|
|
bias[:l, l:] = self.prompt_bias_table[k : 2 * k].view(1, k, -1) # token to prompt
|
|
bias[l:, l:] = self.prompt_bias_table[2 * k, :].view(k, k, -1) # prompt to prompt
|
|
|
|
# bias[k:, k:] = relative_position_bias
|
|
# bias[:k, k:] = self.prompt_bias_table[:k].view(k, 1, -1)
|
|
# bias[k:, :k] = self.prompt_bias_table[k : 2 * k].view(1, k, -1)
|
|
# bias[:k, :k] = self.prompt_bias_table[2 * k :].view(k, k, -1)
|
|
else:
|
|
bias = relative_position_bias
|
|
|
|
return bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
|
|
|
|
|
class BeitEncoder(nn.Module):
|
|
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
if config.use_shared_relative_position_bias:
|
|
self.relative_position_bias = BeitRelativePositionBias(
|
|
config, window_size=window_size
|
|
)
|
|
else:
|
|
self.relative_position_bias = None
|
|
|
|
# stochastic depth decay rule
|
|
dpr = [
|
|
x.item()
|
|
for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)
|
|
]
|
|
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,
|
|
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 + (
|
|
# einops.rearrange(hidden_states, "b t l c -> (b t) l c"),
|
|
# )
|
|
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:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(layer_module),
|
|
hidden_states,
|
|
layer_head_mask,
|
|
use_reentrant=False,
|
|
)
|
|
else:
|
|
relative_position_bias = (
|
|
self.relative_position_bias()
|
|
if self.relative_position_bias is not None
|
|
else None
|
|
)
|
|
layer_outputs = layer_module(
|
|
hidden_states, layer_head_mask, output_attentions, relative_position_bias
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
|
|
# hidden_states = einops.rearrange(hidden_states, "b t l c -> (b t) l c")
|
|
|
|
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 BeitPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = BeitConfig
|
|
base_model_prefix = "beit"
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = 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)
|
|
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
|
if isinstance(module, BeitEncoder):
|
|
module.gradient_checkpointing = value
|
|
|
|
|
|
BEIT_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 ([`BeitConfig`]): 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.
|
|
"""
|
|
|
|
BEIT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`BeitFeatureExtractor`]. See
|
|
[`BeitFeatureExtractor.__call__`] for details.
|
|
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Beit Model transformer outputting raw hidden-states without any specific head on top.",
|
|
BEIT_START_DOCSTRING,
|
|
)
|
|
class BeitModel(BeitPreTrainedModel):
|
|
def __init__(self, config: BeitConfig, add_pooling_layer: bool = True) -> None:
|
|
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)
|
|
|
|
@add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BeitModelOutputWithPooling,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
modality="vision",
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
bool_masked_pos: Optional[torch.BoolTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BeitModelOutputWithPooling]:
|
|
output_attentions = (
|
|
output_attentions
|
|
if output_attentions is not None
|
|
else self.config.output_attentions
|
|
)
|
|
output_hidden_states = (
|
|
output_hidden_states
|
|
if output_hidden_states is not None
|
|
else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
# pixel_values: [bsz, nframes, c, h, w]
|
|
assert pixel_values.ndim == 5, logger.error(
|
|
f"input shape to st_beit: {pixel_values.shape}"
|
|
)
|
|
|
|
embedding_output = self.embeddings(
|
|
pixel_values, bool_masked_pos
|
|
) # [bs, nframes, L, c]
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
sequence_output = self.layernorm(sequence_output)
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
# logger.info(f"sequence_output: {sequence_output.shape}. pooled_output: {pooled_output.shape}")
|
|
|
|
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.num_prompts = config.add_k_prompts
|
|
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:
|
|
"""
|
|
Args:
|
|
hidden_states (torch.Tensor): Shape: [B,T,L,C]
|
|
"""
|
|
if self.layernorm is not None:
|
|
# Mean pool the final hidden states of the patch tokens
|
|
# patch_tokens = hidden_states[:, 1 + self.num_prompts :, :]
|
|
if self.num_prompts > 0:
|
|
patch_tokens = hidden_states[:, :, 1 : -self.num_prompts, :]
|
|
else:
|
|
patch_tokens = hidden_states[:, :, 1:, :]
|
|
pooled_output = self.layernorm(patch_tokens.mean(2))
|
|
else:
|
|
# Pool by simply taking the final hidden state of the [CLS] token
|
|
pooled_output = hidden_states[:, :, 0]
|
|
|
|
return pooled_output
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""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.""",
|
|
BEIT_START_DOCSTRING,
|
|
)
|
|
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()
|
|
|
|
@add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
bool_masked_pos: Optional[torch.BoolTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
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).
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import BeitFeatureExtractor, 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)
|
|
|
|
>>> feature_extractor = BeitFeatureExtractor.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 = feature_extractor(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,
|
|
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,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
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.
|
|
""",
|
|
BEIT_START_DOCSTRING,
|
|
)
|
|
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()
|
|
|
|
@add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
|
|
checkpoint=_IMAGE_CLASS_CHECKPOINT,
|
|
output_type=ImageClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
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,
|
|
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://arxiv.org/abs/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://arxiv.org/abs/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
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Beit Model transformer with a semantic segmentation head on top e.g. for ADE20k, CityScapes.
|
|
""",
|
|
BEIT_START_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
|
|
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)
|
|
auxiliary_loss = loss_fct(upsampled_auxiliary_logits, labels)
|
|
loss = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
|
|
|
|
return loss
|
|
|
|
@add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(
|
|
output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC
|
|
)
|
|
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,
|
|
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).
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoFeatureExtractor, BeitForSemanticSegmentation
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
|
|
>>> model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
|
|
|
|
>>> inputs = feature_extractor(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
|
|
)
|
|
|
|
outputs = self.beit(
|
|
pixel_values,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=True, # we need the intermediate hidden states
|
|
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:
|
|
if self.config.num_labels == 1:
|
|
raise ValueError("The number of labels should be greater than one")
|
|
else:
|
|
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,
|
|
)
|