1033 lines
42 KiB
Python
1033 lines
42 KiB
Python
# coding=utf-8
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# Copyright 2023 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 FocalNet 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 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 ...utils import (
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ModelOutput,
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from ...utils.backbone_utils import BackboneMixin
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from .configuration_focalnet import FocalNetConfig
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "FocalNetConfig"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "microsoft/focalnet-tiny"
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_EXPECTED_OUTPUT_SHAPE = [1, 49, 768]
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# Image classification docstring
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_IMAGE_CLASS_CHECKPOINT = "microsoft/focalnet-tiny"
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_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
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from ..deprecated._archive_maps import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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@dataclass
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class FocalNetEncoderOutput(ModelOutput):
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"""
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FocalNet encoder's outputs, with potential 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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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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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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reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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@dataclass
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class FocalNetModelOutput(ModelOutput):
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"""
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FocalNet 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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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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reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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@dataclass
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class FocalNetMaskedImageModelingOutput(ModelOutput):
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"""
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FocalNet masked image model outputs.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
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Masked image modeling (MLM) loss.
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reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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Reconstructed pixel values.
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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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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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reconstruction: torch.FloatTensor = None
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hidden_states: 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 FocalNetImageClassifierOutput(ModelOutput):
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"""
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FocalNet 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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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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reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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class FocalNetEmbeddings(nn.Module):
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"""
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Construct the patch embeddings and layernorm. Optionally, also the mask token.
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"""
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def __init__(self, config, use_mask_token=False):
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super().__init__()
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self.patch_embeddings = FocalNetPatchEmbeddings(
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config=config,
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image_size=config.image_size,
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patch_size=config.patch_size,
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num_channels=config.num_channels,
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embed_dim=config.embed_dim,
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use_conv_embed=config.use_conv_embed,
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is_stem=True,
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)
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self.patch_grid = self.patch_embeddings.grid_size
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self.mask_token = nn.Parameter(torch.zeros(1, 1, config.embed_dim)) if use_mask_token else None
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self.norm = nn.LayerNorm(config.embed_dim, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(
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self, pixel_values: Optional[torch.FloatTensor], bool_masked_pos: Optional[torch.BoolTensor] = None
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) -> Tuple[torch.Tensor]:
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embeddings, output_dimensions = self.patch_embeddings(pixel_values)
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embeddings = self.norm(embeddings)
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batch_size, seq_len, _ = embeddings.size()
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if bool_masked_pos is not None:
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mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
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# replace the masked visual tokens by mask_tokens
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mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
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embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
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embeddings = self.dropout(embeddings)
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return embeddings, output_dimensions
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class FocalNetPatchEmbeddings(nn.Module):
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def __init__(
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self,
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config,
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image_size,
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patch_size,
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num_channels,
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embed_dim,
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add_norm=False,
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use_conv_embed=False,
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is_stem=False,
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):
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super().__init__()
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image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
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patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_patches = num_patches
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self.grid_size = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
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if use_conv_embed:
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# if we choose to use conv embedding, then we treat the stem and non-stem differently
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if is_stem:
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kernel_size = 7
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padding = 2
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stride = 4
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else:
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kernel_size = 3
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padding = 1
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stride = 2
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self.projection = nn.Conv2d(
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num_channels, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
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)
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else:
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self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
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if add_norm:
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self.norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
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else:
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self.norm = None
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def maybe_pad(self, pixel_values, height, width):
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if width % self.patch_size[1] != 0:
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pad_values = (0, self.patch_size[1] - width % self.patch_size[1])
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pixel_values = nn.functional.pad(pixel_values, pad_values)
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if height % self.patch_size[0] != 0:
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pad_values = (0, 0, 0, self.patch_size[0] - height % self.patch_size[0])
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pixel_values = nn.functional.pad(pixel_values, pad_values)
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return pixel_values
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def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
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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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# pad the input to be divisible by self.patch_size, if needed
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pixel_values = self.maybe_pad(pixel_values, height, width)
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embeddings = self.projection(pixel_values)
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_, _, height, width = embeddings.shape
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output_dimensions = (height, width)
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embeddings = embeddings.flatten(2).transpose(1, 2)
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if self.norm is not None:
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embeddings = self.norm(embeddings)
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return embeddings, output_dimensions
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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->FocalNet
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class FocalNetDropPath(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 FocalNetModulation(nn.Module):
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def __init__(self, config, index, dim, focal_factor=2, bias=True, projection_dropout=0.0):
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super().__init__()
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self.dim = dim
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self.focal_window = config.focal_windows[index]
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self.focal_level = config.focal_levels[index]
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self.focal_factor = focal_factor
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self.use_post_layernorm_in_modulation = config.use_post_layernorm_in_modulation
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self.normalize_modulator = config.normalize_modulator
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self.projection_in = nn.Linear(dim, 2 * dim + (self.focal_level + 1), bias=bias)
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self.projection_context = nn.Conv2d(dim, dim, kernel_size=1, stride=1, bias=bias)
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self.activation = nn.GELU()
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self.projection_out = nn.Linear(dim, dim)
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self.projection_dropout = nn.Dropout(projection_dropout)
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self.focal_layers = nn.ModuleList()
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self.kernel_sizes = []
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for k in range(self.focal_level):
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kernel_size = self.focal_factor * k + self.focal_window
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self.focal_layers.append(
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nn.Sequential(
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nn.Conv2d(
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dim, dim, kernel_size=kernel_size, stride=1, groups=dim, padding=kernel_size // 2, bias=False
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),
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nn.GELU(),
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)
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)
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self.kernel_sizes.append(kernel_size)
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if self.use_post_layernorm_in_modulation:
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self.layernorm = nn.LayerNorm(dim, eps=config.layer_norm_eps)
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def forward(self, hidden_state):
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"""
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Args:
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hidden_state:
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Input features with shape of (batch_size, height, width, num_channels)
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"""
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num_channels = hidden_state.shape[-1]
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# pre linear projection
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x = self.projection_in(hidden_state).permute(0, 3, 1, 2).contiguous()
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q, ctx, self.gates = torch.split(x, (num_channels, num_channels, self.focal_level + 1), 1)
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# context aggreation
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ctx_all = 0
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for level in range(self.focal_level):
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ctx = self.focal_layers[level](ctx)
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ctx_all = ctx_all + ctx * self.gates[:, level : level + 1]
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ctx_global = self.activation(ctx.mean(2, keepdim=True).mean(3, keepdim=True))
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ctx_all = ctx_all + ctx_global * self.gates[:, self.focal_level :]
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# normalize context
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if self.normalize_modulator:
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ctx_all = ctx_all / (self.focal_level + 1)
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# focal modulation
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self.modulator = self.projection_context(ctx_all)
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x_out = q * self.modulator
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x_out = x_out.permute(0, 2, 3, 1).contiguous()
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if self.use_post_layernorm_in_modulation:
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x_out = self.layernorm(x_out)
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# post linear porjection
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x_out = self.projection_out(x_out)
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x_out = self.projection_dropout(x_out)
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return x_out
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class FocalNetMlp(nn.Module):
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def __init__(self, config, in_features, hidden_features=None, out_features=None, drop=0.0):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.activation = ACT2FN[config.hidden_act]
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, hidden_state):
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hidden_state = self.fc1(hidden_state)
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hidden_state = self.activation(hidden_state)
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hidden_state = self.drop(hidden_state)
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hidden_state = self.fc2(hidden_state)
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hidden_state = self.drop(hidden_state)
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return hidden_state
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class FocalNetLayer(nn.Module):
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r"""Focal Modulation Network layer (block).
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Args:
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config (`FocalNetConfig`):
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Model config.
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index (`int`):
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Layer index.
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dim (`int`):
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Number of input channels.
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input_resolution (`Tuple[int]`):
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Input resulotion.
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drop_path (`float`, *optional*, defaults to 0.0):
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Stochastic depth rate.
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"""
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def __init__(self, config, index, dim, input_resolution, drop_path=0.0):
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super().__init__()
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self.config = config
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# layer-specific attributes
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self.dim = dim
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self.input_resolution = input_resolution
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# general attributes
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self.drop = config.hidden_dropout_prob
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self.use_post_layernorm = config.use_post_layernorm
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self.norm1 = nn.LayerNorm(dim, eps=config.layer_norm_eps)
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self.modulation = FocalNetModulation(
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config=config,
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index=index,
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dim=dim,
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|
projection_dropout=self.drop,
|
|
)
|
|
|
|
self.drop_path = FocalNetDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
|
self.norm2 = nn.LayerNorm(dim, eps=config.layer_norm_eps)
|
|
mlp_hidden_dim = int(dim * config.mlp_ratio)
|
|
self.mlp = FocalNetMlp(config=config, in_features=dim, hidden_features=mlp_hidden_dim, drop=self.drop)
|
|
|
|
self.gamma_1 = 1.0
|
|
self.gamma_2 = 1.0
|
|
if config.use_layerscale:
|
|
self.gamma_1 = nn.Parameter(config.layerscale_value * torch.ones((dim)), requires_grad=True)
|
|
self.gamma_2 = nn.Parameter(config.layerscale_value * torch.ones((dim)), requires_grad=True)
|
|
|
|
def forward(self, hidden_state, input_dimensions):
|
|
height, width = input_dimensions
|
|
batch_size, _, num_channels = hidden_state.shape
|
|
shortcut = hidden_state
|
|
|
|
# Focal Modulation
|
|
hidden_state = hidden_state if self.use_post_layernorm else self.norm1(hidden_state)
|
|
hidden_state = hidden_state.view(batch_size, height, width, num_channels)
|
|
hidden_state = self.modulation(hidden_state).view(batch_size, height * width, num_channels)
|
|
hidden_state = hidden_state if not self.use_post_layernorm else self.norm1(hidden_state)
|
|
|
|
# FFN
|
|
hidden_state = shortcut + self.drop_path(self.gamma_1 * hidden_state)
|
|
hidden_state = hidden_state + self.drop_path(
|
|
self.gamma_2
|
|
* (self.norm2(self.mlp(hidden_state)) if self.use_post_layernorm else self.mlp(self.norm2(hidden_state)))
|
|
)
|
|
|
|
return hidden_state
|
|
|
|
|
|
class FocalNetStage(nn.Module):
|
|
def __init__(self, config, index, input_resolution):
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
self.num_stages = len(config.depths)
|
|
|
|
embed_dim = [config.embed_dim * (2**i) for i in range(self.num_stages)]
|
|
dim = embed_dim[index]
|
|
out_dim = embed_dim[index + 1] if (index < self.num_stages - 1) else None
|
|
downsample = FocalNetPatchEmbeddings if (index < self.num_stages - 1) else None
|
|
|
|
# stochastic depth decay rule
|
|
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
|
|
drop_path = dpr[sum(config.depths[:index]) : sum(config.depths[: index + 1])]
|
|
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
FocalNetLayer(
|
|
config=config,
|
|
index=index,
|
|
dim=dim,
|
|
input_resolution=input_resolution,
|
|
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
|
)
|
|
for i in range(config.depths[index])
|
|
]
|
|
)
|
|
|
|
if downsample is not None:
|
|
self.downsample = downsample(
|
|
config=config,
|
|
image_size=input_resolution,
|
|
patch_size=2,
|
|
num_channels=dim,
|
|
embed_dim=out_dim,
|
|
add_norm=True,
|
|
use_conv_embed=config.use_conv_embed,
|
|
is_stem=False,
|
|
)
|
|
else:
|
|
self.downsample = None
|
|
|
|
self.pointing = False
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_dimensions: Tuple[int, int]) -> Tuple[torch.Tensor]:
|
|
height, width = input_dimensions
|
|
for layer_module in self.layers:
|
|
hidden_states = layer_module(hidden_states, input_dimensions)
|
|
|
|
hidden_states_before_downsampling = hidden_states
|
|
if self.downsample is not None:
|
|
height, width = input_dimensions
|
|
hidden_states = hidden_states.transpose(1, 2).reshape(
|
|
hidden_states_before_downsampling.shape[0], -1, height, width
|
|
)
|
|
hidden_states, output_dimensions = self.downsample(hidden_states)
|
|
|
|
else:
|
|
output_dimensions = (height, width, height, width)
|
|
|
|
stage_outputs = (hidden_states, hidden_states_before_downsampling, output_dimensions)
|
|
|
|
return stage_outputs
|
|
|
|
|
|
class FocalNetEncoder(nn.Module):
|
|
def __init__(self, config, grid_size):
|
|
super().__init__()
|
|
self.num_stages = len(config.depths)
|
|
self.config = config
|
|
|
|
self.stages = nn.ModuleList(
|
|
[
|
|
FocalNetStage(
|
|
config=config,
|
|
index=i_layer,
|
|
input_resolution=(grid_size[0] // (2**i_layer), grid_size[1] // (2**i_layer)),
|
|
)
|
|
for i_layer in range(self.num_stages)
|
|
]
|
|
)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
input_dimensions: Tuple[int, int],
|
|
output_hidden_states: Optional[bool] = False,
|
|
output_hidden_states_before_downsampling: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> Union[Tuple, FocalNetEncoderOutput]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_reshaped_hidden_states = () if output_hidden_states else None
|
|
|
|
if output_hidden_states:
|
|
batch_size, _, hidden_size = hidden_states.shape
|
|
# rearrange b (h w) c -> b c h w
|
|
reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
|
|
reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
|
|
all_hidden_states += (hidden_states,)
|
|
all_reshaped_hidden_states += (reshaped_hidden_state,)
|
|
|
|
for i, stage_module in enumerate(self.stages):
|
|
if self.gradient_checkpointing and self.training:
|
|
stage_outputs = self._gradient_checkpointing_func(
|
|
stage_module.__call__,
|
|
hidden_states,
|
|
input_dimensions,
|
|
)
|
|
else:
|
|
stage_outputs = stage_module(hidden_states, input_dimensions)
|
|
|
|
hidden_states = stage_outputs[0]
|
|
hidden_states_before_downsampling = stage_outputs[1]
|
|
output_dimensions = stage_outputs[2]
|
|
|
|
input_dimensions = (output_dimensions[-2], output_dimensions[-1])
|
|
|
|
if output_hidden_states and output_hidden_states_before_downsampling:
|
|
batch_size, _, hidden_size = hidden_states_before_downsampling.shape
|
|
# rearrange b (h w) c -> b c h w
|
|
# here we use the original (not downsampled) height and width
|
|
reshaped_hidden_state = hidden_states_before_downsampling.view(
|
|
batch_size, *(output_dimensions[0], output_dimensions[1]), hidden_size
|
|
)
|
|
reshaped_hidden_state = reshaped_hidden_state.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:
|
|
batch_size, _, hidden_size = hidden_states.shape
|
|
# rearrange b (h w) c -> b c h w
|
|
reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
|
|
reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
|
|
all_hidden_states += (hidden_states,)
|
|
all_reshaped_hidden_states += (reshaped_hidden_state,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
|
|
|
|
return FocalNetEncoderOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
reshaped_hidden_states=all_reshaped_hidden_states,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.swin.modeling_swin.SwinPreTrainedModel with Swin->FocalNet,swin->focalnet
|
|
class FocalNetPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = FocalNetConfig
|
|
base_model_prefix = "focalnet"
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = True
|
|
|
|
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)
|
|
|
|
|
|
FOCALNET_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 ([`FocalNetConfig`]): 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.
|
|
"""
|
|
|
|
FOCALNET_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
|
|
[`AutoImageProcessor.__call__`] for details.
|
|
|
|
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 FocalNet Model outputting raw hidden-states without any specific head on top.",
|
|
FOCALNET_START_DOCSTRING,
|
|
)
|
|
class FocalNetModel(FocalNetPreTrainedModel):
|
|
def __init__(self, config, add_pooling_layer=True, use_mask_token=False):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.num_stages = len(config.depths)
|
|
self.num_features = int(config.embed_dim * 2 ** (self.num_stages - 1))
|
|
|
|
self.embeddings = FocalNetEmbeddings(config, use_mask_token=use_mask_token)
|
|
self.encoder = FocalNetEncoder(config, self.embeddings.patch_grid)
|
|
|
|
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
|
|
|
|
@add_start_docstrings_to_model_forward(FOCALNET_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=FocalNetModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
modality="vision",
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
bool_masked_pos: Optional[torch.BoolTensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, FocalNetModelOutput]:
|
|
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).
|
|
"""
|
|
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, input_dimensions = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
input_dimensions,
|
|
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.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 FocalNetModelOutput(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""FocalNet Model with a decoder on top for masked image modeling.
|
|
|
|
This follows the same implementation as in [SimMIM](https://arxiv.org/abs/2111.09886).
|
|
|
|
<Tip>
|
|
|
|
Note that we provide a script to pre-train this model on custom data in our [examples
|
|
directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).
|
|
|
|
</Tip>
|
|
""",
|
|
FOCALNET_START_DOCSTRING,
|
|
)
|
|
class FocalNetForMaskedImageModeling(FocalNetPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.focalnet = FocalNetModel(config, add_pooling_layer=False, use_mask_token=True)
|
|
|
|
self.num_stages = len(config.depths)
|
|
num_features = int(config.embed_dim * 2 ** (self.num_stages - 1))
|
|
self.decoder = nn.Sequential(
|
|
nn.Conv2d(
|
|
in_channels=num_features, out_channels=config.encoder_stride**2 * config.num_channels, kernel_size=1
|
|
),
|
|
nn.PixelShuffle(config.encoder_stride),
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(FOCALNET_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=FocalNetMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
bool_masked_pos: Optional[torch.BoolTensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, FocalNetMaskedImageModelingOutput]:
|
|
r"""
|
|
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
|
|
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, FocalNetConfig, FocalNetForMaskedImageModeling
|
|
>>> import torch
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/focalnet-base-simmim-window6-192")
|
|
>>> config = FocalNetConfig()
|
|
>>> model = FocalNetForMaskedImageModeling(config)
|
|
|
|
>>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
|
|
>>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
|
|
>>> # create random boolean mask of shape (batch_size, num_patches)
|
|
>>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()
|
|
|
|
>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
|
|
>>> loss, reconstructed_pixel_values = outputs.loss, outputs.logits
|
|
>>> list(reconstructed_pixel_values.shape)
|
|
[1, 3, 192, 192]
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.focalnet(
|
|
pixel_values,
|
|
bool_masked_pos=bool_masked_pos,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
# Reshape to (batch_size, num_channels, height, width)
|
|
sequence_output = sequence_output.transpose(1, 2)
|
|
batch_size, num_channels, sequence_length = sequence_output.shape
|
|
height = width = math.floor(sequence_length**0.5)
|
|
sequence_output = sequence_output.reshape(batch_size, num_channels, height, width)
|
|
|
|
# Reconstruct pixel values
|
|
reconstructed_pixel_values = self.decoder(sequence_output)
|
|
|
|
masked_im_loss = None
|
|
if bool_masked_pos is not None:
|
|
size = self.config.image_size // self.config.patch_size
|
|
bool_masked_pos = bool_masked_pos.reshape(-1, size, size)
|
|
mask = (
|
|
bool_masked_pos.repeat_interleave(self.config.patch_size, 1)
|
|
.repeat_interleave(self.config.patch_size, 2)
|
|
.unsqueeze(1)
|
|
.contiguous()
|
|
)
|
|
reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none")
|
|
masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels
|
|
|
|
if not return_dict:
|
|
output = (reconstructed_pixel_values,) + outputs[2:]
|
|
return ((masked_im_loss,) + output) if masked_im_loss is not None else output
|
|
|
|
return FocalNetMaskedImageModelingOutput(
|
|
loss=masked_im_loss,
|
|
reconstruction=reconstructed_pixel_values,
|
|
hidden_states=outputs.hidden_states,
|
|
reshaped_hidden_states=outputs.reshaped_hidden_states,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
FocalNet Model with an image classification head on top (a linear layer on top of the pooled output) e.g. for
|
|
ImageNet.
|
|
""",
|
|
FOCALNET_START_DOCSTRING,
|
|
)
|
|
class FocalNetForImageClassification(FocalNetPreTrainedModel):
|
|
# Copied from transformers.models.swin.modeling_swin.SwinForImageClassification.__init__ with Swin->FocalNet, swin->focalnet
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.focalnet = FocalNetModel(config)
|
|
|
|
# Classifier head
|
|
self.classifier = (
|
|
nn.Linear(self.focalnet.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(FOCALNET_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_IMAGE_CLASS_CHECKPOINT,
|
|
output_type=FocalNetImageClassifierOutput,
|
|
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_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, FocalNetImageClassifierOutput]:
|
|
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.focalnet(
|
|
pixel_values,
|
|
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 FocalNetImageClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
reshaped_hidden_states=outputs.reshaped_hidden_states,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
FocalNet backbone, to be used with frameworks like X-Decoder.
|
|
""",
|
|
FOCALNET_START_DOCSTRING,
|
|
)
|
|
class FocalNetBackbone(FocalNetPreTrainedModel, BackboneMixin):
|
|
def __init__(self, config: FocalNetConfig):
|
|
super().__init__(config)
|
|
super()._init_backbone(config)
|
|
|
|
self.num_features = [config.embed_dim] + config.hidden_sizes
|
|
self.focalnet = FocalNetModel(config)
|
|
|
|
# initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(FOCALNET_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,
|
|
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("microsoft/focalnet-tiny-lrf")
|
|
>>> model = AutoBackbone.from_pretrained("microsoft/focalnet-tiny-lrf")
|
|
|
|
>>> inputs = processor(image, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
```"""
|
|
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.focalnet(pixel_values, output_hidden_states=True, return_dict=True)
|
|
|
|
hidden_states = outputs.reshaped_hidden_states
|
|
|
|
feature_maps = ()
|
|
for idx, stage in enumerate(self.stage_names):
|
|
if stage in self.out_features:
|
|
feature_maps += (hidden_states[idx],)
|
|
|
|
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=None,
|
|
)
|