957 lines
39 KiB
Python
957 lines
39 KiB
Python
# coding=utf-8
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# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch Neighborhood Attention Transformer model."""
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import math
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_outputs import BackboneOutput
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import (
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ModelOutput,
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OptionalDependencyNotAvailable,
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_natten_available,
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logging,
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replace_return_docstrings,
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requires_backends,
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)
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from ...utils.backbone_utils import BackboneMixin
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from .configuration_nat import NatConfig
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if is_natten_available():
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from natten.functional import natten2dav, natten2dqkrpb
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else:
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def natten2dqkrpb(*args, **kwargs):
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raise OptionalDependencyNotAvailable()
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def natten2dav(*args, **kwargs):
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raise OptionalDependencyNotAvailable()
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "NatConfig"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "shi-labs/nat-mini-in1k-224"
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_EXPECTED_OUTPUT_SHAPE = [1, 7, 7, 512]
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# Image classification docstring
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_IMAGE_CLASS_CHECKPOINT = "shi-labs/nat-mini-in1k-224"
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_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat"
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from ..deprecated._archive_maps import NAT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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# drop_path and NatDropPath are from the timm library.
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@dataclass
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class NatEncoderOutput(ModelOutput):
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"""
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Nat encoder's outputs, with potential hidden states and attentions.
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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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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 stage) 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 stage) 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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reshaped_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 stage) of
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shape `(batch_size, hidden_size, height, width)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
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include the spatial dimensions.
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"""
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class NatModelOutput(ModelOutput):
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"""
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Nat model's outputs that also contains a pooling of the last hidden states.
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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)`, *optional*, returned when `add_pooling_layer=True` is passed):
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Average pooling of the last layer hidden-state.
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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 stage) 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 stage) 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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reshaped_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 stage) of
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shape `(batch_size, hidden_size, height, width)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
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include the spatial dimensions.
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"""
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last_hidden_state: torch.FloatTensor = None
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pooler_output: Optional[torch.FloatTensor] = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class NatImageClassifierOutput(ModelOutput):
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"""
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Nat outputs for image classification.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Classification (or regression if config.num_labels==1) loss.
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logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
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Classification (or regression if config.num_labels==1) scores (before SoftMax).
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hidden_states (`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 stage) 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 stage) 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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reshaped_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 stage) of
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shape `(batch_size, hidden_size, height, width)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
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include the spatial dimensions.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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class NatEmbeddings(nn.Module):
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"""
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Construct the patch and position embeddings.
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"""
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def __init__(self, config):
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super().__init__()
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self.patch_embeddings = NatPatchEmbeddings(config)
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self.norm = nn.LayerNorm(config.embed_dim)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor]:
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embeddings = self.patch_embeddings(pixel_values)
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embeddings = self.norm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class NatPatchEmbeddings(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, height, width, 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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patch_size = config.patch_size
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num_channels, hidden_size = config.num_channels, config.embed_dim
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self.num_channels = num_channels
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if patch_size == 4:
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pass
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else:
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# TODO: Support arbitrary patch sizes.
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raise ValueError("Dinat only supports patch size of 4 at the moment.")
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self.projection = nn.Sequential(
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nn.Conv2d(self.num_channels, hidden_size // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
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nn.Conv2d(hidden_size // 2, hidden_size, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
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)
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def forward(self, pixel_values: Optional[torch.FloatTensor]) -> torch.Tensor:
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_, 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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embeddings = embeddings.permute(0, 2, 3, 1)
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return embeddings
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class NatDownsampler(nn.Module):
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"""
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Convolutional Downsampling Layer.
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Args:
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dim (`int`):
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Number of input channels.
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norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
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Normalization layer class.
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"""
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def __init__(self, dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
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super().__init__()
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self.dim = dim
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self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
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self.norm = norm_layer(2 * dim)
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def forward(self, input_feature: torch.Tensor) -> torch.Tensor:
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input_feature = self.reduction(input_feature.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
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input_feature = self.norm(input_feature)
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return input_feature
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# Copied from transformers.models.beit.modeling_beit.drop_path
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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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# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Nat
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class NatDropPath(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 "p={}".format(self.drop_prob)
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class NeighborhoodAttention(nn.Module):
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def __init__(self, config, dim, num_heads, kernel_size):
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super().__init__()
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if dim % num_heads != 0:
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raise ValueError(
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f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
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)
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self.num_attention_heads = num_heads
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self.attention_head_size = int(dim / num_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.kernel_size = kernel_size
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# rpb is learnable relative positional biases; same concept is used Swin.
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self.rpb = nn.Parameter(torch.zeros(num_heads, (2 * self.kernel_size - 1), (2 * self.kernel_size - 1)))
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self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
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self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
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self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(new_x_shape)
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return x.permute(0, 3, 1, 2, 4)
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def forward(
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self,
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hidden_states: torch.Tensor,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor]:
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query_layer = self.transpose_for_scores(self.query(hidden_states))
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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# Apply the scale factor before computing attention weights. It's usually more efficient because
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# attention weights are typically a bigger tensor compared to query.
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# It gives identical results because scalars are commutable in matrix multiplication.
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query_layer = query_layer / math.sqrt(self.attention_head_size)
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# Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
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attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.kernel_size, 1)
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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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context_layer = natten2dav(attention_probs, value_layer, self.kernel_size, 1)
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context_layer = context_layer.permute(0, 2, 3, 1, 4).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 NeighborhoodAttentionOutput(nn.Module):
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def __init__(self, config, dim):
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super().__init__()
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self.dense = nn.Linear(dim, dim)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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class NeighborhoodAttentionModule(nn.Module):
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def __init__(self, config, dim, num_heads, kernel_size):
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super().__init__()
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self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size)
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self.output = NeighborhoodAttentionOutput(config, dim)
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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.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.self.query = prune_linear_layer(self.self.query, index)
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self.self.key = prune_linear_layer(self.self.key, index)
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self.self.value = prune_linear_layer(self.self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states: torch.Tensor,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor]:
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self_outputs = self.self(hidden_states, output_attentions)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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class NatIntermediate(nn.Module):
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def __init__(self, config, dim):
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super().__init__()
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self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class NatOutput(nn.Module):
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def __init__(self, config, dim):
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super().__init__()
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self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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class NatLayer(nn.Module):
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def __init__(self, config, dim, num_heads, drop_path_rate=0.0):
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.kernel_size = config.kernel_size
|
|
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
|
|
self.attention = NeighborhoodAttentionModule(config, dim, num_heads, kernel_size=self.kernel_size)
|
|
self.drop_path = NatDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
|
|
self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
|
|
self.intermediate = NatIntermediate(config, dim)
|
|
self.output = NatOutput(config, dim)
|
|
self.layer_scale_parameters = (
|
|
nn.Parameter(config.layer_scale_init_value * torch.ones((2, dim)), requires_grad=True)
|
|
if config.layer_scale_init_value > 0
|
|
else None
|
|
)
|
|
|
|
def maybe_pad(self, hidden_states, height, width):
|
|
window_size = self.kernel_size
|
|
pad_values = (0, 0, 0, 0, 0, 0)
|
|
if height < window_size or width < window_size:
|
|
pad_l = pad_t = 0
|
|
pad_r = max(0, window_size - width)
|
|
pad_b = max(0, window_size - height)
|
|
pad_values = (0, 0, pad_l, pad_r, pad_t, pad_b)
|
|
hidden_states = nn.functional.pad(hidden_states, pad_values)
|
|
return hidden_states, pad_values
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
batch_size, height, width, channels = hidden_states.size()
|
|
shortcut = hidden_states
|
|
|
|
hidden_states = self.layernorm_before(hidden_states)
|
|
# pad hidden_states if they are smaller than kernel size
|
|
hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
|
|
|
|
_, height_pad, width_pad, _ = hidden_states.shape
|
|
|
|
attention_outputs = self.attention(hidden_states, output_attentions=output_attentions)
|
|
|
|
attention_output = attention_outputs[0]
|
|
|
|
was_padded = pad_values[3] > 0 or pad_values[5] > 0
|
|
if was_padded:
|
|
attention_output = attention_output[:, :height, :width, :].contiguous()
|
|
|
|
if self.layer_scale_parameters is not None:
|
|
attention_output = self.layer_scale_parameters[0] * attention_output
|
|
|
|
hidden_states = shortcut + self.drop_path(attention_output)
|
|
|
|
layer_output = self.layernorm_after(hidden_states)
|
|
layer_output = self.output(self.intermediate(layer_output))
|
|
|
|
if self.layer_scale_parameters is not None:
|
|
layer_output = self.layer_scale_parameters[1] * layer_output
|
|
|
|
layer_output = hidden_states + self.drop_path(layer_output)
|
|
|
|
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
|
|
return layer_outputs
|
|
|
|
|
|
class NatStage(nn.Module):
|
|
def __init__(self, config, dim, depth, num_heads, drop_path_rate, downsample):
|
|
super().__init__()
|
|
self.config = config
|
|
self.dim = dim
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
NatLayer(
|
|
config=config,
|
|
dim=dim,
|
|
num_heads=num_heads,
|
|
drop_path_rate=drop_path_rate[i],
|
|
)
|
|
for i in range(depth)
|
|
]
|
|
)
|
|
|
|
# patch merging layer
|
|
if downsample is not None:
|
|
self.downsample = downsample(dim=dim, norm_layer=nn.LayerNorm)
|
|
else:
|
|
self.downsample = None
|
|
|
|
self.pointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
_, height, width, _ = hidden_states.size()
|
|
for i, layer_module in enumerate(self.layers):
|
|
layer_outputs = layer_module(hidden_states, output_attentions)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
hidden_states_before_downsampling = hidden_states
|
|
if self.downsample is not None:
|
|
hidden_states = self.downsample(hidden_states_before_downsampling)
|
|
|
|
stage_outputs = (hidden_states, hidden_states_before_downsampling)
|
|
|
|
if output_attentions:
|
|
stage_outputs += layer_outputs[1:]
|
|
return stage_outputs
|
|
|
|
|
|
class NatEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.num_levels = len(config.depths)
|
|
self.config = config
|
|
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
|
|
self.levels = nn.ModuleList(
|
|
[
|
|
NatStage(
|
|
config=config,
|
|
dim=int(config.embed_dim * 2**i_layer),
|
|
depth=config.depths[i_layer],
|
|
num_heads=config.num_heads[i_layer],
|
|
drop_path_rate=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
|
|
downsample=NatDownsampler if (i_layer < self.num_levels - 1) else None,
|
|
)
|
|
for i_layer in range(self.num_levels)
|
|
]
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
output_hidden_states_before_downsampling: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> Union[Tuple, NatEncoderOutput]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_reshaped_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
if output_hidden_states:
|
|
# rearrange b h w c -> b c h w
|
|
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
|
|
all_hidden_states += (hidden_states,)
|
|
all_reshaped_hidden_states += (reshaped_hidden_state,)
|
|
|
|
for i, layer_module in enumerate(self.levels):
|
|
layer_outputs = layer_module(hidden_states, output_attentions)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
hidden_states_before_downsampling = layer_outputs[1]
|
|
|
|
if output_hidden_states and output_hidden_states_before_downsampling:
|
|
# rearrange b h w c -> b c h w
|
|
reshaped_hidden_state = hidden_states_before_downsampling.permute(0, 3, 1, 2)
|
|
all_hidden_states += (hidden_states_before_downsampling,)
|
|
all_reshaped_hidden_states += (reshaped_hidden_state,)
|
|
elif output_hidden_states and not output_hidden_states_before_downsampling:
|
|
# rearrange b h w c -> b c h w
|
|
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
|
|
all_hidden_states += (hidden_states,)
|
|
all_reshaped_hidden_states += (reshaped_hidden_state,)
|
|
|
|
if output_attentions:
|
|
all_self_attentions += layer_outputs[2:]
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
|
|
|
|
return NatEncoderOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
reshaped_hidden_states=all_reshaped_hidden_states,
|
|
)
|
|
|
|
|
|
class NatPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = NatConfig
|
|
base_model_prefix = "nat"
|
|
main_input_name = "pixel_values"
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
NAT_START_DOCSTRING = r"""
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
|
|
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
|
|
behavior.
|
|
|
|
Parameters:
|
|
config ([`NatConfig`]): 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.
|
|
"""
|
|
|
|
|
|
NAT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
|
|
for details.
|
|
|
|
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 Nat Model transformer outputting raw hidden-states without any specific head on top.",
|
|
NAT_START_DOCSTRING,
|
|
)
|
|
class NatModel(NatPreTrainedModel):
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
super().__init__(config)
|
|
|
|
requires_backends(self, ["natten"])
|
|
|
|
self.config = config
|
|
self.num_levels = len(config.depths)
|
|
self.num_features = int(config.embed_dim * 2 ** (self.num_levels - 1))
|
|
|
|
self.embeddings = NatEmbeddings(config)
|
|
self.encoder = NatEncoder(config)
|
|
|
|
self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps)
|
|
self.pooler = nn.AdaptiveAvgPool1d(1) 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(NAT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=NatModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
modality="vision",
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, NatModelOutput]:
|
|
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")
|
|
|
|
embedding_output = self.embeddings(pixel_values)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
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 = None
|
|
if self.pooler is not None:
|
|
pooled_output = self.pooler(sequence_output.flatten(1, 2).transpose(1, 2))
|
|
pooled_output = torch.flatten(pooled_output, 1)
|
|
|
|
if not return_dict:
|
|
output = (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return output
|
|
|
|
return NatModelOutput(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Nat Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
|
|
the [CLS] token) e.g. for ImageNet.
|
|
""",
|
|
NAT_START_DOCSTRING,
|
|
)
|
|
class NatForImageClassification(NatPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
requires_backends(self, ["natten"])
|
|
|
|
self.num_labels = config.num_labels
|
|
self.nat = NatModel(config)
|
|
|
|
# Classifier head
|
|
self.classifier = (
|
|
nn.Linear(self.nat.num_features, 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(NAT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_IMAGE_CLASS_CHECKPOINT,
|
|
output_type=NatImageClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, NatImageClassifierOutput]:
|
|
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.nat(
|
|
pixel_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = 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 NatImageClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
reshaped_hidden_states=outputs.reshaped_hidden_states,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"NAT backbone, to be used with frameworks like DETR and MaskFormer.",
|
|
NAT_START_DOCSTRING,
|
|
)
|
|
class NatBackbone(NatPreTrainedModel, BackboneMixin):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
super()._init_backbone(config)
|
|
|
|
requires_backends(self, ["natten"])
|
|
|
|
self.embeddings = NatEmbeddings(config)
|
|
self.encoder = NatEncoder(config)
|
|
self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
|
|
|
|
# Add layer norms to hidden states of out_features
|
|
hidden_states_norms = {}
|
|
for stage, num_channels in zip(self.out_features, self.channels):
|
|
hidden_states_norms[stage] = nn.LayerNorm(num_channels)
|
|
self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.patch_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> BackboneOutput:
|
|
"""
|
|
Returns:
|
|
|
|
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("shi-labs/nat-mini-in1k-224")
|
|
>>> model = AutoBackbone.from_pretrained(
|
|
... "shi-labs/nat-mini-in1k-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, 512, 7, 7]
|
|
```"""
|
|
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
|
|
|
|
embedding_output = self.embeddings(pixel_values)
|
|
|
|
outputs = self.encoder(
|
|
embedding_output,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=True,
|
|
output_hidden_states_before_downsampling=True,
|
|
return_dict=True,
|
|
)
|
|
|
|
hidden_states = outputs.reshaped_hidden_states
|
|
|
|
feature_maps = ()
|
|
for stage, hidden_state in zip(self.stage_names, hidden_states):
|
|
if stage in self.out_features:
|
|
# TODO can we simplify this?
|
|
batch_size, num_channels, height, width = hidden_state.shape
|
|
hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
|
|
hidden_state = hidden_state.view(batch_size, height * width, num_channels)
|
|
hidden_state = self.hidden_states_norms[stage](hidden_state)
|
|
hidden_state = hidden_state.view(batch_size, height, width, num_channels)
|
|
hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
|
|
feature_maps += (hidden_state,)
|
|
|
|
if not return_dict:
|
|
output = (feature_maps,)
|
|
if output_hidden_states:
|
|
output += (outputs.hidden_states,)
|
|
return output
|
|
|
|
return BackboneOutput(
|
|
feature_maps=feature_maps,
|
|
hidden_states=outputs.hidden_states if output_hidden_states else None,
|
|
attentions=outputs.attentions,
|
|
)
|