1322 lines
57 KiB
Python
1322 lines
57 KiB
Python
# coding=utf-8
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# Copyright 2022 School of EIC, Huazhong University of Science & Technology 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 YOLOS 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 Dict, List, Optional, Set, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import Tensor, nn
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from ...activations import ACT2FN
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from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
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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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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_accelerate_available,
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is_scipy_available,
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is_vision_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 .configuration_yolos import YolosConfig
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if is_scipy_available():
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from scipy.optimize import linear_sum_assignment
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if is_vision_available():
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from transformers.image_transforms import center_to_corners_format
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if is_accelerate_available():
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from accelerate import PartialState
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from accelerate.utils import reduce
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "YolosConfig"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "hustvl/yolos-small"
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_EXPECTED_OUTPUT_SHAPE = [1, 3401, 384]
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from ..deprecated._archive_maps import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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@dataclass
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class YolosObjectDetectionOutput(ModelOutput):
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"""
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Output type of [`YolosForObjectDetection`].
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
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Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
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bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
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scale-invariant IoU loss.
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loss_dict (`Dict`, *optional*):
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A dictionary containing the individual losses. Useful for logging.
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logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
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Classification logits (including no-object) for all queries.
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pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
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Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
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values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
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possible padding). You can use [`~YolosImageProcessor.post_process`] to retrieve the unnormalized bounding
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boxes.
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auxiliary_outputs (`list[Dict]`, *optional*):
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Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
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and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
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`pred_boxes`) for each decoder layer.
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
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Sequence of hidden-states at the output of the last layer of the decoder 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, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
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the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
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the self-attention heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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loss_dict: Optional[Dict] = None
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logits: torch.FloatTensor = None
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pred_boxes: torch.FloatTensor = None
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auxiliary_outputs: Optional[List[Dict]] = None
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last_hidden_state: 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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class YolosEmbeddings(nn.Module):
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"""
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Construct the CLS token, detection tokens, position and patch embeddings.
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"""
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def __init__(self, config: YolosConfig) -> None:
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super().__init__()
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self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.detection_tokens = nn.Parameter(torch.zeros(1, config.num_detection_tokens, config.hidden_size))
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self.patch_embeddings = YolosPatchEmbeddings(config)
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num_patches = self.patch_embeddings.num_patches
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self.position_embeddings = nn.Parameter(
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torch.zeros(1, num_patches + config.num_detection_tokens + 1, config.hidden_size)
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)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.interpolation = InterpolateInitialPositionEmbeddings(config)
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self.config = config
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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batch_size, num_channels, height, width = pixel_values.shape
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embeddings = self.patch_embeddings(pixel_values)
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batch_size, seq_len, _ = embeddings.size()
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# add the [CLS] and detection tokens to the embedded patch tokens
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cls_tokens = self.cls_token.expand(batch_size, -1, -1)
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detection_tokens = self.detection_tokens.expand(batch_size, -1, -1)
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embeddings = torch.cat((cls_tokens, embeddings, detection_tokens), dim=1)
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# add positional encoding to each token
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# this might require interpolation of the existing position embeddings
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position_embeddings = self.interpolation(self.position_embeddings, (height, width))
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embeddings = embeddings + position_embeddings
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embeddings = self.dropout(embeddings)
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return embeddings
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class InterpolateInitialPositionEmbeddings(nn.Module):
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def __init__(self, config) -> None:
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super().__init__()
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self.config = config
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def forward(self, pos_embed, img_size=(800, 1344)) -> torch.Tensor:
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cls_pos_embed = pos_embed[:, 0, :]
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cls_pos_embed = cls_pos_embed[:, None]
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det_pos_embed = pos_embed[:, -self.config.num_detection_tokens :, :]
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patch_pos_embed = pos_embed[:, 1 : -self.config.num_detection_tokens, :]
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patch_pos_embed = patch_pos_embed.transpose(1, 2)
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batch_size, hidden_size, seq_len = patch_pos_embed.shape
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patch_height, patch_width = (
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self.config.image_size[0] // self.config.patch_size,
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self.config.image_size[1] // self.config.patch_size,
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)
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patch_pos_embed = patch_pos_embed.view(batch_size, hidden_size, patch_height, patch_width)
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height, width = img_size
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new_patch_heigth, new_patch_width = height // self.config.patch_size, width // self.config.patch_size
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed, size=(new_patch_heigth, new_patch_width), mode="bicubic", align_corners=False
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)
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patch_pos_embed = patch_pos_embed.flatten(2).transpose(1, 2)
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scale_pos_embed = torch.cat((cls_pos_embed, patch_pos_embed, det_pos_embed), dim=1)
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return scale_pos_embed
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class InterpolateMidPositionEmbeddings(nn.Module):
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def __init__(self, config) -> None:
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super().__init__()
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self.config = config
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def forward(self, pos_embed, img_size=(800, 1344)) -> torch.Tensor:
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cls_pos_embed = pos_embed[:, :, 0, :]
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cls_pos_embed = cls_pos_embed[:, None]
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det_pos_embed = pos_embed[:, :, -self.config.num_detection_tokens :, :]
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patch_pos_embed = pos_embed[:, :, 1 : -self.config.num_detection_tokens, :]
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patch_pos_embed = patch_pos_embed.transpose(2, 3)
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depth, batch_size, hidden_size, seq_len = patch_pos_embed.shape
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patch_height, patch_width = (
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self.config.image_size[0] // self.config.patch_size,
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self.config.image_size[1] // self.config.patch_size,
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)
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patch_pos_embed = patch_pos_embed.view(depth * batch_size, hidden_size, patch_height, patch_width)
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height, width = img_size
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new_patch_height, new_patch_width = height // self.config.patch_size, width // self.config.patch_size
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed, size=(new_patch_height, new_patch_width), mode="bicubic", align_corners=False
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)
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patch_pos_embed = (
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patch_pos_embed.flatten(2)
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.transpose(1, 2)
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.contiguous()
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.view(depth, batch_size, new_patch_height * new_patch_width, hidden_size)
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)
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scale_pos_embed = torch.cat((cls_pos_embed, patch_pos_embed, det_pos_embed), dim=2)
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return scale_pos_embed
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class YolosPatchEmbeddings(nn.Module):
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"""
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This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
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`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
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Transformer.
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"""
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def __init__(self, config):
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super().__init__()
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image_size, patch_size = config.image_size, config.patch_size
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num_channels, hidden_size = config.num_channels, config.hidden_size
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image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
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patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_patches = num_patches
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self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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batch_size, num_channels, height, width = pixel_values.shape
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if num_channels != self.num_channels:
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raise ValueError(
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"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
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)
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embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
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return embeddings
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# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Yolos
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class YolosSelfAttention(nn.Module):
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def __init__(self, config: YolosConfig) -> None:
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
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f"heads {config.num_attention_heads}."
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
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) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
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mixed_query_layer = self.query(hidden_states)
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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return outputs
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# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Yolos
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class YolosSelfOutput(nn.Module):
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"""
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The residual connection is defined in YolosLayer instead of here (as is the case with other models), due to the
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layernorm applied before each block.
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"""
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def __init__(self, config: YolosConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Yolos
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class YolosAttention(nn.Module):
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def __init__(self, config: YolosConfig) -> None:
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super().__init__()
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self.attention = YolosSelfAttention(config)
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self.output = YolosSelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads: Set[int]) -> None:
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.attention.query = prune_linear_layer(self.attention.query, index)
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self.attention.key = prune_linear_layer(self.attention.key, index)
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self.attention.value = prune_linear_layer(self.attention.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
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self.attention.all_head_size = self.attention.attention_head_size * self.attention.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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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
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self_outputs = self.attention(hidden_states, head_mask, 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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# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->Yolos
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class YolosIntermediate(nn.Module):
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def __init__(self, config: YolosConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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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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# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->Yolos
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class YolosOutput(nn.Module):
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def __init__(self, config: YolosConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states + input_tensor
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->Yolos
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class YolosLayer(nn.Module):
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"""This corresponds to the Block class in the timm implementation."""
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def __init__(self, config: YolosConfig) -> None:
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = YolosAttention(config)
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self.intermediate = YolosIntermediate(config)
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self.output = YolosOutput(config)
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self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
|
|
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
|
|
self_attention_outputs = self.attention(
|
|
self.layernorm_before(hidden_states), # in Yolos, layernorm is applied before self-attention
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
# first residual connection
|
|
hidden_states = attention_output + hidden_states
|
|
|
|
# in Yolos, layernorm is also applied after self-attention
|
|
layer_output = self.layernorm_after(hidden_states)
|
|
layer_output = self.intermediate(layer_output)
|
|
|
|
# second residual connection is done here
|
|
layer_output = self.output(layer_output, hidden_states)
|
|
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
|
|
class YolosEncoder(nn.Module):
|
|
def __init__(self, config: YolosConfig) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([YolosLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
seq_length = (
|
|
1 + (config.image_size[0] * config.image_size[1] // config.patch_size**2) + config.num_detection_tokens
|
|
)
|
|
self.mid_position_embeddings = (
|
|
nn.Parameter(
|
|
torch.zeros(
|
|
config.num_hidden_layers - 1,
|
|
1,
|
|
seq_length,
|
|
config.hidden_size,
|
|
)
|
|
)
|
|
if config.use_mid_position_embeddings
|
|
else None
|
|
)
|
|
|
|
self.interpolation = InterpolateMidPositionEmbeddings(config) if config.use_mid_position_embeddings else None
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
height,
|
|
width,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
output_hidden_states: bool = False,
|
|
return_dict: bool = True,
|
|
) -> Union[tuple, BaseModelOutput]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
if self.config.use_mid_position_embeddings:
|
|
interpolated_mid_position_embeddings = self.interpolation(self.mid_position_embeddings, (height, width))
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
layer_head_mask,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if self.config.use_mid_position_embeddings:
|
|
if i < (self.config.num_hidden_layers - 1):
|
|
hidden_states = hidden_states + interpolated_mid_position_embeddings[i]
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
class YolosPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = YolosConfig
|
|
base_model_prefix = "vit"
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
|
|
"""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)
|
|
|
|
|
|
YOLOS_START_DOCSTRING = r"""
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
|
|
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
|
|
behavior.
|
|
|
|
Parameters:
|
|
config ([`YolosConfig`]): 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.
|
|
"""
|
|
|
|
YOLOS_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
|
|
[`YolosImageProcessor.__call__`] for details.
|
|
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare YOLOS Model transformer outputting raw hidden-states without any specific head on top.",
|
|
YOLOS_START_DOCSTRING,
|
|
)
|
|
class YolosModel(YolosPreTrainedModel):
|
|
def __init__(self, config: YolosConfig, add_pooling_layer: bool = True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = YolosEmbeddings(config)
|
|
self.encoder = YolosEncoder(config)
|
|
|
|
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.pooler = YolosPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> YolosPatchEmbeddings:
|
|
return self.embeddings.patch_embeddings
|
|
|
|
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
|
|
"""
|
|
Prunes heads of the model.
|
|
|
|
Args:
|
|
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(YOLOS_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPooling,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
modality="vision",
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(pixel_values)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
height=pixel_values.shape[-2],
|
|
width=pixel_values.shape[-1],
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
sequence_output = self.layernorm(sequence_output)
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
|
|
return head_outputs + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class YolosPooler(nn.Module):
|
|
def __init__(self, config: YolosConfig):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states):
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
YOLOS Model (consisting of a ViT encoder) with object detection heads on top, for tasks such as COCO detection.
|
|
""",
|
|
YOLOS_START_DOCSTRING,
|
|
)
|
|
class YolosForObjectDetection(YolosPreTrainedModel):
|
|
def __init__(self, config: YolosConfig):
|
|
super().__init__(config)
|
|
|
|
# YOLOS (ViT) encoder model
|
|
self.vit = YolosModel(config, add_pooling_layer=False)
|
|
|
|
# Object detection heads
|
|
# We add one for the "no object" class
|
|
self.class_labels_classifier = YolosMLPPredictionHead(
|
|
input_dim=config.hidden_size, hidden_dim=config.hidden_size, output_dim=config.num_labels + 1, num_layers=3
|
|
)
|
|
self.bbox_predictor = YolosMLPPredictionHead(
|
|
input_dim=config.hidden_size, hidden_dim=config.hidden_size, output_dim=4, num_layers=3
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
# taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py
|
|
@torch.jit.unused
|
|
def _set_aux_loss(self, outputs_class, outputs_coord):
|
|
# this is a workaround to make torchscript happy, as torchscript
|
|
# doesn't support dictionary with non-homogeneous values, such
|
|
# as a dict having both a Tensor and a list.
|
|
return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]
|
|
|
|
@add_start_docstrings_to_model_forward(YOLOS_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=YolosObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
labels: Optional[List[Dict]] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, YolosObjectDetectionOutput]:
|
|
r"""
|
|
labels (`List[Dict]` of len `(batch_size,)`, *optional*):
|
|
Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
|
|
following 2 keys: `'class_labels'` and `'boxes'` (the class labels and bounding boxes of an image in the
|
|
batch respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding
|
|
boxes in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image,
|
|
4)`.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, AutoModelForObjectDetection
|
|
>>> 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("hustvl/yolos-tiny")
|
|
>>> model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-tiny")
|
|
|
|
>>> inputs = image_processor(images=image, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
|
|
>>> target_sizes = torch.tensor([image.size[::-1]])
|
|
>>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
|
|
... 0
|
|
... ]
|
|
|
|
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
|
|
... box = [round(i, 2) for i in box.tolist()]
|
|
... print(
|
|
... f"Detected {model.config.id2label[label.item()]} with confidence "
|
|
... f"{round(score.item(), 3)} at location {box}"
|
|
... )
|
|
Detected remote with confidence 0.991 at location [46.48, 72.78, 178.98, 119.3]
|
|
Detected remote with confidence 0.908 at location [336.48, 79.27, 368.23, 192.36]
|
|
Detected cat with confidence 0.934 at location [337.18, 18.06, 638.14, 373.09]
|
|
Detected cat with confidence 0.979 at location [10.93, 53.74, 313.41, 470.67]
|
|
Detected remote with confidence 0.974 at location [41.63, 72.23, 178.09, 119.99]
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# First, sent images through YOLOS base model to obtain hidden states
|
|
outputs = self.vit(
|
|
pixel_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
# Take the final hidden states of the detection tokens
|
|
sequence_output = sequence_output[:, -self.config.num_detection_tokens :, :]
|
|
|
|
# Class logits + predicted bounding boxes
|
|
logits = self.class_labels_classifier(sequence_output)
|
|
pred_boxes = self.bbox_predictor(sequence_output).sigmoid()
|
|
|
|
loss, loss_dict, auxiliary_outputs = None, None, None
|
|
if labels is not None:
|
|
# First: create the matcher
|
|
matcher = YolosHungarianMatcher(
|
|
class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost
|
|
)
|
|
# Second: create the criterion
|
|
losses = ["labels", "boxes", "cardinality"]
|
|
criterion = YolosLoss(
|
|
matcher=matcher,
|
|
num_classes=self.config.num_labels,
|
|
eos_coef=self.config.eos_coefficient,
|
|
losses=losses,
|
|
)
|
|
criterion.to(self.device)
|
|
# Third: compute the losses, based on outputs and labels
|
|
outputs_loss = {}
|
|
outputs_loss["logits"] = logits
|
|
outputs_loss["pred_boxes"] = pred_boxes
|
|
if self.config.auxiliary_loss:
|
|
intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4]
|
|
outputs_class = self.class_labels_classifier(intermediate)
|
|
outputs_coord = self.bbox_predictor(intermediate).sigmoid()
|
|
auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord)
|
|
outputs_loss["auxiliary_outputs"] = auxiliary_outputs
|
|
|
|
loss_dict = criterion(outputs_loss, labels)
|
|
# Fourth: compute total loss, as a weighted sum of the various losses
|
|
weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient}
|
|
weight_dict["loss_giou"] = self.config.giou_loss_coefficient
|
|
if self.config.auxiliary_loss:
|
|
aux_weight_dict = {}
|
|
for i in range(self.config.decoder_layers - 1):
|
|
aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
|
|
weight_dict.update(aux_weight_dict)
|
|
loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
|
|
|
|
if not return_dict:
|
|
if auxiliary_outputs is not None:
|
|
output = (logits, pred_boxes) + auxiliary_outputs + outputs
|
|
else:
|
|
output = (logits, pred_boxes) + outputs
|
|
return ((loss, loss_dict) + output) if loss is not None else output
|
|
|
|
return YolosObjectDetectionOutput(
|
|
loss=loss,
|
|
loss_dict=loss_dict,
|
|
logits=logits,
|
|
pred_boxes=pred_boxes,
|
|
auxiliary_outputs=auxiliary_outputs,
|
|
last_hidden_state=outputs.last_hidden_state,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr.dice_loss
|
|
def dice_loss(inputs, targets, num_boxes):
|
|
"""
|
|
Compute the DICE loss, similar to generalized IOU for masks
|
|
|
|
Args:
|
|
inputs: A float tensor of arbitrary shape.
|
|
The predictions for each example.
|
|
targets: A float tensor with the same shape as inputs. Stores the binary
|
|
classification label for each element in inputs (0 for the negative class and 1 for the positive
|
|
class).
|
|
"""
|
|
inputs = inputs.sigmoid()
|
|
inputs = inputs.flatten(1)
|
|
numerator = 2 * (inputs * targets).sum(1)
|
|
denominator = inputs.sum(-1) + targets.sum(-1)
|
|
loss = 1 - (numerator + 1) / (denominator + 1)
|
|
return loss.sum() / num_boxes
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr.sigmoid_focal_loss
|
|
def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
|
|
"""
|
|
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
|
|
|
|
Args:
|
|
inputs (`torch.FloatTensor` of arbitrary shape):
|
|
The predictions for each example.
|
|
targets (`torch.FloatTensor` with the same shape as `inputs`)
|
|
A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class
|
|
and 1 for the positive class).
|
|
alpha (`float`, *optional*, defaults to `0.25`):
|
|
Optional weighting factor in the range (0,1) to balance positive vs. negative examples.
|
|
gamma (`int`, *optional*, defaults to `2`):
|
|
Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples.
|
|
|
|
Returns:
|
|
Loss tensor
|
|
"""
|
|
prob = inputs.sigmoid()
|
|
ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
|
|
# add modulating factor
|
|
p_t = prob * targets + (1 - prob) * (1 - targets)
|
|
loss = ce_loss * ((1 - p_t) ** gamma)
|
|
|
|
if alpha >= 0:
|
|
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
|
|
loss = alpha_t * loss
|
|
|
|
return loss.mean(1).sum() / num_boxes
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr.DetrLoss with Detr->Yolos
|
|
class YolosLoss(nn.Module):
|
|
"""
|
|
This class computes the losses for YolosForObjectDetection/YolosForSegmentation. The process happens in two steps: 1)
|
|
we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair
|
|
of matched ground-truth / prediction (supervise class and box).
|
|
|
|
A note on the `num_classes` argument (copied from original repo in detr.py): "the naming of the `num_classes`
|
|
parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where `max_obj_id` is
|
|
the maximum id for a class in your dataset. For example, COCO has a `max_obj_id` of 90, so we pass `num_classes` to
|
|
be 91. As another example, for a dataset that has a single class with `id` 1, you should pass `num_classes` to be 2
|
|
(`max_obj_id` + 1). For more details on this, check the following discussion
|
|
https://github.com/facebookresearch/detr/issues/108#issuecomment-650269223"
|
|
|
|
|
|
Args:
|
|
matcher (`YolosHungarianMatcher`):
|
|
Module able to compute a matching between targets and proposals.
|
|
num_classes (`int`):
|
|
Number of object categories, omitting the special no-object category.
|
|
eos_coef (`float`):
|
|
Relative classification weight applied to the no-object category.
|
|
losses (`List[str]`):
|
|
List of all the losses to be applied. See `get_loss` for a list of all available losses.
|
|
"""
|
|
|
|
def __init__(self, matcher, num_classes, eos_coef, losses):
|
|
super().__init__()
|
|
self.matcher = matcher
|
|
self.num_classes = num_classes
|
|
self.eos_coef = eos_coef
|
|
self.losses = losses
|
|
empty_weight = torch.ones(self.num_classes + 1)
|
|
empty_weight[-1] = self.eos_coef
|
|
self.register_buffer("empty_weight", empty_weight)
|
|
|
|
# removed logging parameter, which was part of the original implementation
|
|
def loss_labels(self, outputs, targets, indices, num_boxes):
|
|
"""
|
|
Classification loss (NLL) targets dicts must contain the key "class_labels" containing a tensor of dim
|
|
[nb_target_boxes]
|
|
"""
|
|
if "logits" not in outputs:
|
|
raise KeyError("No logits were found in the outputs")
|
|
source_logits = outputs["logits"]
|
|
|
|
idx = self._get_source_permutation_idx(indices)
|
|
target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)])
|
|
target_classes = torch.full(
|
|
source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device
|
|
)
|
|
target_classes[idx] = target_classes_o
|
|
|
|
loss_ce = nn.functional.cross_entropy(source_logits.transpose(1, 2), target_classes, self.empty_weight)
|
|
losses = {"loss_ce": loss_ce}
|
|
|
|
return losses
|
|
|
|
@torch.no_grad()
|
|
def loss_cardinality(self, outputs, targets, indices, num_boxes):
|
|
"""
|
|
Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes.
|
|
|
|
This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
|
|
"""
|
|
logits = outputs["logits"]
|
|
device = logits.device
|
|
target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device)
|
|
# Count the number of predictions that are NOT "no-object" (which is the last class)
|
|
card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1)
|
|
card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float())
|
|
losses = {"cardinality_error": card_err}
|
|
return losses
|
|
|
|
def loss_boxes(self, outputs, targets, indices, num_boxes):
|
|
"""
|
|
Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss.
|
|
|
|
Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes
|
|
are expected in format (center_x, center_y, w, h), normalized by the image size.
|
|
"""
|
|
if "pred_boxes" not in outputs:
|
|
raise KeyError("No predicted boxes found in outputs")
|
|
idx = self._get_source_permutation_idx(indices)
|
|
source_boxes = outputs["pred_boxes"][idx]
|
|
target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0)
|
|
|
|
loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none")
|
|
|
|
losses = {}
|
|
losses["loss_bbox"] = loss_bbox.sum() / num_boxes
|
|
|
|
loss_giou = 1 - torch.diag(
|
|
generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes))
|
|
)
|
|
losses["loss_giou"] = loss_giou.sum() / num_boxes
|
|
return losses
|
|
|
|
def loss_masks(self, outputs, targets, indices, num_boxes):
|
|
"""
|
|
Compute the losses related to the masks: the focal loss and the dice loss.
|
|
|
|
Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w].
|
|
"""
|
|
if "pred_masks" not in outputs:
|
|
raise KeyError("No predicted masks found in outputs")
|
|
|
|
source_idx = self._get_source_permutation_idx(indices)
|
|
target_idx = self._get_target_permutation_idx(indices)
|
|
source_masks = outputs["pred_masks"]
|
|
source_masks = source_masks[source_idx]
|
|
masks = [t["masks"] for t in targets]
|
|
# TODO use valid to mask invalid areas due to padding in loss
|
|
target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
|
|
target_masks = target_masks.to(source_masks)
|
|
target_masks = target_masks[target_idx]
|
|
|
|
# upsample predictions to the target size
|
|
source_masks = nn.functional.interpolate(
|
|
source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False
|
|
)
|
|
source_masks = source_masks[:, 0].flatten(1)
|
|
|
|
target_masks = target_masks.flatten(1)
|
|
target_masks = target_masks.view(source_masks.shape)
|
|
losses = {
|
|
"loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes),
|
|
"loss_dice": dice_loss(source_masks, target_masks, num_boxes),
|
|
}
|
|
return losses
|
|
|
|
def _get_source_permutation_idx(self, indices):
|
|
# permute predictions following indices
|
|
batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)])
|
|
source_idx = torch.cat([source for (source, _) in indices])
|
|
return batch_idx, source_idx
|
|
|
|
def _get_target_permutation_idx(self, indices):
|
|
# permute targets following indices
|
|
batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)])
|
|
target_idx = torch.cat([target for (_, target) in indices])
|
|
return batch_idx, target_idx
|
|
|
|
def get_loss(self, loss, outputs, targets, indices, num_boxes):
|
|
loss_map = {
|
|
"labels": self.loss_labels,
|
|
"cardinality": self.loss_cardinality,
|
|
"boxes": self.loss_boxes,
|
|
"masks": self.loss_masks,
|
|
}
|
|
if loss not in loss_map:
|
|
raise ValueError(f"Loss {loss} not supported")
|
|
return loss_map[loss](outputs, targets, indices, num_boxes)
|
|
|
|
def forward(self, outputs, targets):
|
|
"""
|
|
This performs the loss computation.
|
|
|
|
Args:
|
|
outputs (`dict`, *optional*):
|
|
Dictionary of tensors, see the output specification of the model for the format.
|
|
targets (`List[dict]`, *optional*):
|
|
List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the
|
|
losses applied, see each loss' doc.
|
|
"""
|
|
outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"}
|
|
|
|
# Retrieve the matching between the outputs of the last layer and the targets
|
|
indices = self.matcher(outputs_without_aux, targets)
|
|
|
|
# Compute the average number of target boxes across all nodes, for normalization purposes
|
|
num_boxes = sum(len(t["class_labels"]) for t in targets)
|
|
num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
|
|
world_size = 1
|
|
if is_accelerate_available():
|
|
if PartialState._shared_state != {}:
|
|
num_boxes = reduce(num_boxes)
|
|
world_size = PartialState().num_processes
|
|
num_boxes = torch.clamp(num_boxes / world_size, min=1).item()
|
|
|
|
# Compute all the requested losses
|
|
losses = {}
|
|
for loss in self.losses:
|
|
losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))
|
|
|
|
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
|
|
if "auxiliary_outputs" in outputs:
|
|
for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
|
|
indices = self.matcher(auxiliary_outputs, targets)
|
|
for loss in self.losses:
|
|
if loss == "masks":
|
|
# Intermediate masks losses are too costly to compute, we ignore them.
|
|
continue
|
|
l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes)
|
|
l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
|
|
losses.update(l_dict)
|
|
|
|
return losses
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead with Detr->Yolos
|
|
class YolosMLPPredictionHead(nn.Module):
|
|
"""
|
|
Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
|
|
height and width of a bounding box w.r.t. an image.
|
|
|
|
Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py
|
|
|
|
"""
|
|
|
|
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
|
|
super().__init__()
|
|
self.num_layers = num_layers
|
|
h = [hidden_dim] * (num_layers - 1)
|
|
self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
|
|
|
|
def forward(self, x):
|
|
for i, layer in enumerate(self.layers):
|
|
x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
|
|
return x
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr.DetrHungarianMatcher with Detr->Yolos
|
|
class YolosHungarianMatcher(nn.Module):
|
|
"""
|
|
This class computes an assignment between the targets and the predictions of the network.
|
|
|
|
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
|
|
predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are
|
|
un-matched (and thus treated as non-objects).
|
|
|
|
Args:
|
|
class_cost:
|
|
The relative weight of the classification error in the matching cost.
|
|
bbox_cost:
|
|
The relative weight of the L1 error of the bounding box coordinates in the matching cost.
|
|
giou_cost:
|
|
The relative weight of the giou loss of the bounding box in the matching cost.
|
|
"""
|
|
|
|
def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1):
|
|
super().__init__()
|
|
requires_backends(self, ["scipy"])
|
|
|
|
self.class_cost = class_cost
|
|
self.bbox_cost = bbox_cost
|
|
self.giou_cost = giou_cost
|
|
if class_cost == 0 and bbox_cost == 0 and giou_cost == 0:
|
|
raise ValueError("All costs of the Matcher can't be 0")
|
|
|
|
@torch.no_grad()
|
|
def forward(self, outputs, targets):
|
|
"""
|
|
Args:
|
|
outputs (`dict`):
|
|
A dictionary that contains at least these entries:
|
|
* "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
|
|
* "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates.
|
|
targets (`List[dict]`):
|
|
A list of targets (len(targets) = batch_size), where each target is a dict containing:
|
|
* "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of
|
|
ground-truth
|
|
objects in the target) containing the class labels
|
|
* "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates.
|
|
|
|
Returns:
|
|
`List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where:
|
|
- index_i is the indices of the selected predictions (in order)
|
|
- index_j is the indices of the corresponding selected targets (in order)
|
|
For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
|
|
"""
|
|
batch_size, num_queries = outputs["logits"].shape[:2]
|
|
|
|
# We flatten to compute the cost matrices in a batch
|
|
out_prob = outputs["logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes]
|
|
out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4]
|
|
|
|
# Also concat the target labels and boxes
|
|
target_ids = torch.cat([v["class_labels"] for v in targets])
|
|
target_bbox = torch.cat([v["boxes"] for v in targets])
|
|
|
|
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
|
|
# but approximate it in 1 - proba[target class].
|
|
# The 1 is a constant that doesn't change the matching, it can be ommitted.
|
|
class_cost = -out_prob[:, target_ids]
|
|
|
|
# Compute the L1 cost between boxes
|
|
bbox_cost = torch.cdist(out_bbox, target_bbox, p=1)
|
|
|
|
# Compute the giou cost between boxes
|
|
giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox))
|
|
|
|
# Final cost matrix
|
|
cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost
|
|
cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu()
|
|
|
|
sizes = [len(v["boxes"]) for v in targets]
|
|
indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
|
|
return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr._upcast
|
|
def _upcast(t: Tensor) -> Tensor:
|
|
# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
|
|
if t.is_floating_point():
|
|
return t if t.dtype in (torch.float32, torch.float64) else t.float()
|
|
else:
|
|
return t if t.dtype in (torch.int32, torch.int64) else t.int()
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr.box_area
|
|
def box_area(boxes: Tensor) -> Tensor:
|
|
"""
|
|
Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
|
|
|
|
Args:
|
|
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
|
|
Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
|
|
< x2` and `0 <= y1 < y2`.
|
|
|
|
Returns:
|
|
`torch.FloatTensor`: a tensor containing the area for each box.
|
|
"""
|
|
boxes = _upcast(boxes)
|
|
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr.box_iou
|
|
def box_iou(boxes1, boxes2):
|
|
area1 = box_area(boxes1)
|
|
area2 = box_area(boxes2)
|
|
|
|
left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
|
|
right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
|
|
|
|
width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2]
|
|
inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M]
|
|
|
|
union = area1[:, None] + area2 - inter
|
|
|
|
iou = inter / union
|
|
return iou, union
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr.generalized_box_iou
|
|
def generalized_box_iou(boxes1, boxes2):
|
|
"""
|
|
Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format.
|
|
|
|
Returns:
|
|
`torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2)
|
|
"""
|
|
# degenerate boxes gives inf / nan results
|
|
# so do an early check
|
|
if not (boxes1[:, 2:] >= boxes1[:, :2]).all():
|
|
raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}")
|
|
if not (boxes2[:, 2:] >= boxes2[:, :2]).all():
|
|
raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}")
|
|
iou, union = box_iou(boxes1, boxes2)
|
|
|
|
top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2])
|
|
bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
|
|
|
|
width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2]
|
|
area = width_height[:, :, 0] * width_height[:, :, 1]
|
|
|
|
return iou - (area - union) / area
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr._max_by_axis
|
|
def _max_by_axis(the_list):
|
|
# type: (List[List[int]]) -> List[int]
|
|
maxes = the_list[0]
|
|
for sublist in the_list[1:]:
|
|
for index, item in enumerate(sublist):
|
|
maxes[index] = max(maxes[index], item)
|
|
return maxes
|
|
|
|
|
|
# Copied from transformers.models.detr.modeling_detr.NestedTensor
|
|
class NestedTensor(object):
|
|
def __init__(self, tensors, mask: Optional[Tensor]):
|
|
self.tensors = tensors
|
|
self.mask = mask
|
|
|
|
def to(self, device):
|
|
cast_tensor = self.tensors.to(device)
|
|
mask = self.mask
|
|
if mask is not None:
|
|
cast_mask = mask.to(device)
|
|
else:
|
|
cast_mask = None
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return NestedTensor(cast_tensor, cast_mask)
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|
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def decompose(self):
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return self.tensors, self.mask
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|
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def __repr__(self):
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return str(self.tensors)
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|
|
|
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# Copied from transformers.models.detr.modeling_detr.nested_tensor_from_tensor_list
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def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
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if tensor_list[0].ndim == 3:
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max_size = _max_by_axis([list(img.shape) for img in tensor_list])
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batch_shape = [len(tensor_list)] + max_size
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batch_size, num_channels, height, width = batch_shape
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dtype = tensor_list[0].dtype
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device = tensor_list[0].device
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tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
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mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device)
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for img, pad_img, m in zip(tensor_list, tensor, mask):
|
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pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
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m[: img.shape[1], : img.shape[2]] = False
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else:
|
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raise ValueError("Only 3-dimensional tensors are supported")
|
|
return NestedTensor(tensor, mask)
|