Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/detr
/modeling_detr.py
# coding=utf-8 | |
# Copyright 2021 Facebook AI Research The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""PyTorch DETR model.""" | |
import math | |
from dataclasses import dataclass | |
from typing import Dict, List, Optional, Tuple, Union | |
import torch | |
from torch import Tensor, nn | |
from ...activations import ACT2FN | |
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask | |
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput | |
from ...modeling_utils import PreTrainedModel | |
from ...utils import ( | |
ModelOutput, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
is_accelerate_available, | |
is_scipy_available, | |
is_timm_available, | |
is_vision_available, | |
logging, | |
replace_return_docstrings, | |
requires_backends, | |
) | |
from ...utils.backbone_utils import load_backbone | |
from .configuration_detr import DetrConfig | |
if is_accelerate_available(): | |
from accelerate import PartialState | |
from accelerate.utils import reduce | |
if is_scipy_available(): | |
from scipy.optimize import linear_sum_assignment | |
if is_timm_available(): | |
from timm import create_model | |
if is_vision_available(): | |
from transformers.image_transforms import center_to_corners_format | |
logger = logging.get_logger(__name__) | |
_CONFIG_FOR_DOC = "DetrConfig" | |
_CHECKPOINT_FOR_DOC = "facebook/detr-resnet-50" | |
class DetrDecoderOutput(BaseModelOutputWithCrossAttentions): | |
""" | |
Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions, | |
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them | |
gone through a layernorm. This is useful when training the model with auxiliary decoding losses. | |
Args: | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Sequence of hidden-states at the output of the last layer of the model. | |
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of | |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer | |
plus the initial embedding outputs. | |
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in | |
the self-attention heads. | |
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, | |
used to compute the weighted average in the cross-attention heads. | |
intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): | |
Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a | |
layernorm. | |
""" | |
intermediate_hidden_states: Optional[torch.FloatTensor] = None | |
class DetrModelOutput(Seq2SeqModelOutput): | |
""" | |
Base class for outputs of the DETR encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput, | |
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them | |
gone through a layernorm. This is useful when training the model with auxiliary decoding losses. | |
Args: | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Sequence of hidden-states at the output of the last layer of the decoder of the model. | |
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of | |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each | |
layer plus the initial embedding outputs. | |
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the | |
weighted average in the self-attention heads. | |
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, | |
used to compute the weighted average in the cross-attention heads. | |
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the encoder of the model. | |
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of | |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each | |
layer plus the initial embedding outputs. | |
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the | |
weighted average in the self-attention heads. | |
intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): | |
Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a | |
layernorm. | |
""" | |
intermediate_hidden_states: Optional[torch.FloatTensor] = None | |
class DetrObjectDetectionOutput(ModelOutput): | |
""" | |
Output type of [`DetrForObjectDetection`]. | |
Args: | |
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): | |
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a | |
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized | |
scale-invariant IoU loss. | |
loss_dict (`Dict`, *optional*): | |
A dictionary containing the individual losses. Useful for logging. | |
logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): | |
Classification logits (including no-object) for all queries. | |
pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): | |
Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These | |
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding | |
possible padding). You can use [`~DetrImageProcessor.post_process_object_detection`] to retrieve the | |
unnormalized bounding boxes. | |
auxiliary_outputs (`list[Dict]`, *optional*): | |
Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) | |
and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and | |
`pred_boxes`) for each decoder layer. | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the decoder of the model. | |
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of | |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each | |
layer plus the initial embedding outputs. | |
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the | |
weighted average in the self-attention heads. | |
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, | |
used to compute the weighted average in the cross-attention heads. | |
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the encoder of the model. | |
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of | |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each | |
layer plus the initial embedding outputs. | |
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the | |
weighted average in the self-attention heads. | |
""" | |
loss: Optional[torch.FloatTensor] = None | |
loss_dict: Optional[Dict] = None | |
logits: torch.FloatTensor = None | |
pred_boxes: torch.FloatTensor = None | |
auxiliary_outputs: Optional[List[Dict]] = None | |
last_hidden_state: Optional[torch.FloatTensor] = None | |
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None | |
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None | |
encoder_last_hidden_state: Optional[torch.FloatTensor] = None | |
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None | |
class DetrSegmentationOutput(ModelOutput): | |
""" | |
Output type of [`DetrForSegmentation`]. | |
Args: | |
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): | |
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a | |
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized | |
scale-invariant IoU loss. | |
loss_dict (`Dict`, *optional*): | |
A dictionary containing the individual losses. Useful for logging. | |
logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): | |
Classification logits (including no-object) for all queries. | |
pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): | |
Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These | |
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding | |
possible padding). You can use [`~DetrImageProcessor.post_process_object_detection`] to retrieve the | |
unnormalized bounding boxes. | |
pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height/4, width/4)`): | |
Segmentation masks logits for all queries. See also | |
[`~DetrImageProcessor.post_process_semantic_segmentation`] or | |
[`~DetrImageProcessor.post_process_instance_segmentation`] | |
[`~DetrImageProcessor.post_process_panoptic_segmentation`] to evaluate semantic, instance and panoptic | |
segmentation masks respectively. | |
auxiliary_outputs (`list[Dict]`, *optional*): | |
Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) | |
and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and | |
`pred_boxes`) for each decoder layer. | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the decoder of the model. | |
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of | |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each | |
layer plus the initial embedding outputs. | |
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the | |
weighted average in the self-attention heads. | |
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, | |
used to compute the weighted average in the cross-attention heads. | |
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the encoder of the model. | |
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of | |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each | |
layer plus the initial embedding outputs. | |
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | |
sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the | |
weighted average in the self-attention heads. | |
""" | |
loss: Optional[torch.FloatTensor] = None | |
loss_dict: Optional[Dict] = None | |
logits: torch.FloatTensor = None | |
pred_boxes: torch.FloatTensor = None | |
pred_masks: torch.FloatTensor = None | |
auxiliary_outputs: Optional[List[Dict]] = None | |
last_hidden_state: Optional[torch.FloatTensor] = None | |
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None | |
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None | |
encoder_last_hidden_state: Optional[torch.FloatTensor] = None | |
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None | |
# BELOW: utilities copied from | |
# https://github.com/facebookresearch/detr/blob/master/backbone.py | |
class DetrFrozenBatchNorm2d(nn.Module): | |
""" | |
BatchNorm2d where the batch statistics and the affine parameters are fixed. | |
Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than | |
torchvision.models.resnet[18,34,50,101] produce nans. | |
""" | |
def __init__(self, n): | |
super().__init__() | |
self.register_buffer("weight", torch.ones(n)) | |
self.register_buffer("bias", torch.zeros(n)) | |
self.register_buffer("running_mean", torch.zeros(n)) | |
self.register_buffer("running_var", torch.ones(n)) | |
def _load_from_state_dict( | |
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs | |
): | |
num_batches_tracked_key = prefix + "num_batches_tracked" | |
if num_batches_tracked_key in state_dict: | |
del state_dict[num_batches_tracked_key] | |
super()._load_from_state_dict( | |
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs | |
) | |
def forward(self, x): | |
# move reshapes to the beginning | |
# to make it user-friendly | |
weight = self.weight.reshape(1, -1, 1, 1) | |
bias = self.bias.reshape(1, -1, 1, 1) | |
running_var = self.running_var.reshape(1, -1, 1, 1) | |
running_mean = self.running_mean.reshape(1, -1, 1, 1) | |
epsilon = 1e-5 | |
scale = weight * (running_var + epsilon).rsqrt() | |
bias = bias - running_mean * scale | |
return x * scale + bias | |
def replace_batch_norm(model): | |
r""" | |
Recursively replace all `torch.nn.BatchNorm2d` with `DetrFrozenBatchNorm2d`. | |
Args: | |
model (torch.nn.Module): | |
input model | |
""" | |
for name, module in model.named_children(): | |
if isinstance(module, nn.BatchNorm2d): | |
new_module = DetrFrozenBatchNorm2d(module.num_features) | |
if not module.weight.device == torch.device("meta"): | |
new_module.weight.data.copy_(module.weight) | |
new_module.bias.data.copy_(module.bias) | |
new_module.running_mean.data.copy_(module.running_mean) | |
new_module.running_var.data.copy_(module.running_var) | |
model._modules[name] = new_module | |
if len(list(module.children())) > 0: | |
replace_batch_norm(module) | |
class DetrConvEncoder(nn.Module): | |
""" | |
Convolutional backbone, using either the AutoBackbone API or one from the timm library. | |
nn.BatchNorm2d layers are replaced by DetrFrozenBatchNorm2d as defined above. | |
""" | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
# For backwards compatibility we have to use the timm library directly instead of the AutoBackbone API | |
if config.use_timm_backbone: | |
# We default to values which were previously hard-coded. This enables configurability from the config | |
# using backbone arguments, while keeping the default behavior the same. | |
requires_backends(self, ["timm"]) | |
kwargs = getattr(config, "backbone_kwargs", {}) | |
kwargs = {} if kwargs is None else kwargs.copy() | |
out_indices = kwargs.pop("out_indices", (1, 2, 3, 4)) | |
num_channels = kwargs.pop("in_chans", config.num_channels) | |
if config.dilation: | |
kwargs["output_stride"] = kwargs.get("output_stride", 16) | |
backbone = create_model( | |
config.backbone, | |
pretrained=config.use_pretrained_backbone, | |
features_only=True, | |
out_indices=out_indices, | |
in_chans=num_channels, | |
**kwargs, | |
) | |
else: | |
backbone = load_backbone(config) | |
# replace batch norm by frozen batch norm | |
with torch.no_grad(): | |
replace_batch_norm(backbone) | |
self.model = backbone | |
self.intermediate_channel_sizes = ( | |
self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels | |
) | |
backbone_model_type = None | |
if config.backbone is not None: | |
backbone_model_type = config.backbone | |
elif config.backbone_config is not None: | |
backbone_model_type = config.backbone_config.model_type | |
else: | |
raise ValueError("Either `backbone` or `backbone_config` should be provided in the config") | |
if "resnet" in backbone_model_type: | |
for name, parameter in self.model.named_parameters(): | |
if config.use_timm_backbone: | |
if "layer2" not in name and "layer3" not in name and "layer4" not in name: | |
parameter.requires_grad_(False) | |
else: | |
if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name: | |
parameter.requires_grad_(False) | |
def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): | |
# send pixel_values through the model to get list of feature maps | |
features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps | |
out = [] | |
for feature_map in features: | |
# downsample pixel_mask to match shape of corresponding feature_map | |
mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] | |
out.append((feature_map, mask)) | |
return out | |
class DetrConvModel(nn.Module): | |
""" | |
This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder. | |
""" | |
def __init__(self, conv_encoder, position_embedding): | |
super().__init__() | |
self.conv_encoder = conv_encoder | |
self.position_embedding = position_embedding | |
def forward(self, pixel_values, pixel_mask): | |
# send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples | |
out = self.conv_encoder(pixel_values, pixel_mask) | |
pos = [] | |
for feature_map, mask in out: | |
# position encoding | |
pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype)) | |
return out, pos | |
class DetrSinePositionEmbedding(nn.Module): | |
""" | |
This is a more standard version of the position embedding, very similar to the one used by the Attention is all you | |
need paper, generalized to work on images. | |
""" | |
def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None): | |
super().__init__() | |
self.embedding_dim = embedding_dim | |
self.temperature = temperature | |
self.normalize = normalize | |
if scale is not None and normalize is False: | |
raise ValueError("normalize should be True if scale is passed") | |
if scale is None: | |
scale = 2 * math.pi | |
self.scale = scale | |
def forward(self, pixel_values, pixel_mask): | |
if pixel_mask is None: | |
raise ValueError("No pixel mask provided") | |
y_embed = pixel_mask.cumsum(1, dtype=torch.float32) | |
x_embed = pixel_mask.cumsum(2, dtype=torch.float32) | |
if self.normalize: | |
y_embed = y_embed / (y_embed[:, -1:, :] + 1e-6) * self.scale | |
x_embed = x_embed / (x_embed[:, :, -1:] + 1e-6) * self.scale | |
dim_t = torch.arange(self.embedding_dim, dtype=torch.int64, device=pixel_values.device).float() | |
dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim) | |
pos_x = x_embed[:, :, :, None] / dim_t | |
pos_y = y_embed[:, :, :, None] / dim_t | |
pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) | |
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) | |
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) | |
return pos | |
class DetrLearnedPositionEmbedding(nn.Module): | |
""" | |
This module learns positional embeddings up to a fixed maximum size. | |
""" | |
def __init__(self, embedding_dim=256): | |
super().__init__() | |
self.row_embeddings = nn.Embedding(50, embedding_dim) | |
self.column_embeddings = nn.Embedding(50, embedding_dim) | |
def forward(self, pixel_values, pixel_mask=None): | |
height, width = pixel_values.shape[-2:] | |
width_values = torch.arange(width, device=pixel_values.device) | |
height_values = torch.arange(height, device=pixel_values.device) | |
x_emb = self.column_embeddings(width_values) | |
y_emb = self.row_embeddings(height_values) | |
pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1) | |
pos = pos.permute(2, 0, 1) | |
pos = pos.unsqueeze(0) | |
pos = pos.repeat(pixel_values.shape[0], 1, 1, 1) | |
return pos | |
def build_position_encoding(config): | |
n_steps = config.d_model // 2 | |
if config.position_embedding_type == "sine": | |
# TODO find a better way of exposing other arguments | |
position_embedding = DetrSinePositionEmbedding(n_steps, normalize=True) | |
elif config.position_embedding_type == "learned": | |
position_embedding = DetrLearnedPositionEmbedding(n_steps) | |
else: | |
raise ValueError(f"Not supported {config.position_embedding_type}") | |
return position_embedding | |
class DetrAttention(nn.Module): | |
""" | |
Multi-headed attention from 'Attention Is All You Need' paper. | |
Here, we add position embeddings to the queries and keys (as explained in the DETR paper). | |
""" | |
def __init__( | |
self, | |
embed_dim: int, | |
num_heads: int, | |
dropout: float = 0.0, | |
bias: bool = True, | |
): | |
super().__init__() | |
self.embed_dim = embed_dim | |
self.num_heads = num_heads | |
self.dropout = dropout | |
self.head_dim = embed_dim // num_heads | |
if self.head_dim * num_heads != self.embed_dim: | |
raise ValueError( | |
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" | |
f" {num_heads})." | |
) | |
self.scaling = self.head_dim**-0.5 | |
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) | |
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) | |
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) | |
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) | |
def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): | |
return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() | |
def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]): | |
return tensor if object_queries is None else tensor + object_queries | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.Tensor] = None, | |
object_queries: Optional[torch.Tensor] = None, | |
key_value_states: Optional[torch.Tensor] = None, | |
spatial_position_embeddings: Optional[torch.Tensor] = None, | |
output_attentions: bool = False, | |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | |
"""Input shape: Batch x Time x Channel""" | |
# if key_value_states are provided this layer is used as a cross-attention layer | |
# for the decoder | |
is_cross_attention = key_value_states is not None | |
batch_size, target_len, embed_dim = hidden_states.size() | |
# add position embeddings to the hidden states before projecting to queries and keys | |
if object_queries is not None: | |
hidden_states_original = hidden_states | |
hidden_states = self.with_pos_embed(hidden_states, object_queries) | |
# add key-value position embeddings to the key value states | |
if spatial_position_embeddings is not None: | |
key_value_states_original = key_value_states | |
key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings) | |
# get query proj | |
query_states = self.q_proj(hidden_states) * self.scaling | |
# get key, value proj | |
if is_cross_attention: | |
# cross_attentions | |
key_states = self._shape(self.k_proj(key_value_states), -1, batch_size) | |
value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size) | |
else: | |
# self_attention | |
key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) | |
value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) | |
proj_shape = (batch_size * self.num_heads, -1, self.head_dim) | |
query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) | |
key_states = key_states.view(*proj_shape) | |
value_states = value_states.view(*proj_shape) | |
source_len = key_states.size(1) | |
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) | |
if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): | |
raise ValueError( | |
f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" | |
f" {attn_weights.size()}" | |
) | |
if attention_mask is not None: | |
if attention_mask.size() != (batch_size, 1, target_len, source_len): | |
raise ValueError( | |
f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" | |
f" {attention_mask.size()}" | |
) | |
attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask | |
attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) | |
attn_weights = nn.functional.softmax(attn_weights, dim=-1) | |
if output_attentions: | |
# this operation is a bit awkward, but it's required to | |
# make sure that attn_weights keeps its gradient. | |
# In order to do so, attn_weights have to reshaped | |
# twice and have to be reused in the following | |
attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) | |
attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) | |
else: | |
attn_weights_reshaped = None | |
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) | |
attn_output = torch.bmm(attn_probs, value_states) | |
if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim): | |
raise ValueError( | |
f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" | |
f" {attn_output.size()}" | |
) | |
attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) | |
attn_output = attn_output.transpose(1, 2) | |
attn_output = attn_output.reshape(batch_size, target_len, embed_dim) | |
attn_output = self.out_proj(attn_output) | |
return attn_output, attn_weights_reshaped | |
class DetrEncoderLayer(nn.Module): | |
def __init__(self, config: DetrConfig): | |
super().__init__() | |
self.embed_dim = config.d_model | |
self.self_attn = DetrAttention( | |
embed_dim=self.embed_dim, | |
num_heads=config.encoder_attention_heads, | |
dropout=config.attention_dropout, | |
) | |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) | |
self.dropout = config.dropout | |
self.activation_fn = ACT2FN[config.activation_function] | |
self.activation_dropout = config.activation_dropout | |
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) | |
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) | |
self.final_layer_norm = nn.LayerNorm(self.embed_dim) | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: torch.Tensor, | |
object_queries: torch.Tensor = None, | |
output_attentions: bool = False, | |
): | |
""" | |
Args: | |
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` | |
attention_mask (`torch.FloatTensor`): attention mask of size | |
`(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative | |
values. | |
object_queries (`torch.FloatTensor`, *optional*): | |
Object queries (also called content embeddings), to be added to the hidden states. | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under | |
returned tensors for more detail. | |
""" | |
residual = hidden_states | |
hidden_states, attn_weights = self.self_attn( | |
hidden_states=hidden_states, | |
attention_mask=attention_mask, | |
object_queries=object_queries, | |
output_attentions=output_attentions, | |
) | |
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.self_attn_layer_norm(hidden_states) | |
residual = hidden_states | |
hidden_states = self.activation_fn(self.fc1(hidden_states)) | |
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) | |
hidden_states = self.fc2(hidden_states) | |
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.final_layer_norm(hidden_states) | |
if self.training: | |
if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any(): | |
clamp_value = torch.finfo(hidden_states.dtype).max - 1000 | |
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (attn_weights,) | |
return outputs | |
class DetrDecoderLayer(nn.Module): | |
def __init__(self, config: DetrConfig): | |
super().__init__() | |
self.embed_dim = config.d_model | |
self.self_attn = DetrAttention( | |
embed_dim=self.embed_dim, | |
num_heads=config.decoder_attention_heads, | |
dropout=config.attention_dropout, | |
) | |
self.dropout = config.dropout | |
self.activation_fn = ACT2FN[config.activation_function] | |
self.activation_dropout = config.activation_dropout | |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) | |
self.encoder_attn = DetrAttention( | |
self.embed_dim, | |
config.decoder_attention_heads, | |
dropout=config.attention_dropout, | |
) | |
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) | |
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) | |
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) | |
self.final_layer_norm = nn.LayerNorm(self.embed_dim) | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.Tensor] = None, | |
object_queries: Optional[torch.Tensor] = None, | |
query_position_embeddings: Optional[torch.Tensor] = None, | |
encoder_hidden_states: Optional[torch.Tensor] = None, | |
encoder_attention_mask: Optional[torch.Tensor] = None, | |
output_attentions: Optional[bool] = False, | |
): | |
""" | |
Args: | |
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` | |
attention_mask (`torch.FloatTensor`): attention mask of size | |
`(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative | |
values. | |
object_queries (`torch.FloatTensor`, *optional*): | |
object_queries that are added to the hidden states | |
in the cross-attention layer. | |
query_position_embeddings (`torch.FloatTensor`, *optional*): | |
position embeddings that are added to the queries and keys | |
in the self-attention layer. | |
encoder_hidden_states (`torch.FloatTensor`): | |
cross attention input to the layer of shape `(batch, seq_len, embed_dim)` | |
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size | |
`(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative | |
values. | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under | |
returned tensors for more detail. | |
""" | |
residual = hidden_states | |
# Self Attention | |
hidden_states, self_attn_weights = self.self_attn( | |
hidden_states=hidden_states, | |
object_queries=query_position_embeddings, | |
attention_mask=attention_mask, | |
output_attentions=output_attentions, | |
) | |
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.self_attn_layer_norm(hidden_states) | |
# Cross-Attention Block | |
cross_attn_weights = None | |
if encoder_hidden_states is not None: | |
residual = hidden_states | |
hidden_states, cross_attn_weights = self.encoder_attn( | |
hidden_states=hidden_states, | |
object_queries=query_position_embeddings, | |
key_value_states=encoder_hidden_states, | |
attention_mask=encoder_attention_mask, | |
spatial_position_embeddings=object_queries, | |
output_attentions=output_attentions, | |
) | |
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.encoder_attn_layer_norm(hidden_states) | |
# Fully Connected | |
residual = hidden_states | |
hidden_states = self.activation_fn(self.fc1(hidden_states)) | |
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) | |
hidden_states = self.fc2(hidden_states) | |
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.final_layer_norm(hidden_states) | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (self_attn_weights, cross_attn_weights) | |
return outputs | |
class DetrPreTrainedModel(PreTrainedModel): | |
config_class = DetrConfig | |
base_model_prefix = "model" | |
main_input_name = "pixel_values" | |
_no_split_modules = [r"DetrConvEncoder", r"DetrEncoderLayer", r"DetrDecoderLayer"] | |
def _init_weights(self, module): | |
std = self.config.init_std | |
xavier_std = self.config.init_xavier_std | |
if isinstance(module, DetrMHAttentionMap): | |
nn.init.zeros_(module.k_linear.bias) | |
nn.init.zeros_(module.q_linear.bias) | |
nn.init.xavier_uniform_(module.k_linear.weight, gain=xavier_std) | |
nn.init.xavier_uniform_(module.q_linear.weight, gain=xavier_std) | |
elif isinstance(module, DetrLearnedPositionEmbedding): | |
nn.init.uniform_(module.row_embeddings.weight) | |
nn.init.uniform_(module.column_embeddings.weight) | |
if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): | |
# 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=std) | |
if module.bias is not None: | |
module.bias.data.zero_() | |
elif isinstance(module, nn.Embedding): | |
module.weight.data.normal_(mean=0.0, std=std) | |
if module.padding_idx is not None: | |
module.weight.data[module.padding_idx].zero_() | |
DETR_START_DOCSTRING = r""" | |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the | |
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads | |
etc.) | |
This model is also 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 ([`DetrConfig`]): | |
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. | |
""" | |
DETR_INPUTS_DOCSTRING = r""" | |
Args: | |
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): | |
Pixel values. Padding will be ignored by default should you provide it. | |
Pixel values can be obtained using [`AutoImageProcessor`]. See [`DetrImageProcessor.__call__`] for details. | |
pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): | |
Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: | |
- 1 for pixels that are real (i.e. **not masked**), | |
- 0 for pixels that are padding (i.e. **masked**). | |
[What are attention masks?](../glossary#attention-mask) | |
decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*): | |
Not used by default. Can be used to mask object queries. | |
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): | |
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) | |
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of | |
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. | |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you | |
can choose to directly pass a flattened representation of an image. | |
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): | |
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an | |
embedded representation. | |
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. | |
""" | |
class DetrEncoder(DetrPreTrainedModel): | |
""" | |
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a | |
[`DetrEncoderLayer`]. | |
The encoder updates the flattened feature map through multiple self-attention layers. | |
Small tweak for DETR: | |
- object_queries are added to the forward pass. | |
Args: | |
config: DetrConfig | |
""" | |
def __init__(self, config: DetrConfig): | |
super().__init__(config) | |
self.dropout = config.dropout | |
self.layerdrop = config.encoder_layerdrop | |
self.layers = nn.ModuleList([DetrEncoderLayer(config) for _ in range(config.encoder_layers)]) | |
# in the original DETR, no layernorm is used at the end of the encoder, as "normalize_before" is set to False by default | |
# Initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
inputs_embeds=None, | |
attention_mask=None, | |
object_queries=None, | |
output_attentions=None, | |
output_hidden_states=None, | |
return_dict=None, | |
): | |
r""" | |
Args: | |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. | |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`: | |
- 1 for pixel features that are real (i.e. **not masked**), | |
- 0 for pixel features that are padding (i.e. **masked**). | |
[What are attention masks?](../glossary#attention-mask) | |
object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Object queries that are added to the queries in each self-attention layer. | |
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. | |
""" | |
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 | |
hidden_states = inputs_embeds | |
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) | |
# expand attention_mask | |
if attention_mask is not None: | |
# [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] | |
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype) | |
encoder_states = () if output_hidden_states else None | |
all_attentions = () if output_attentions else None | |
for i, encoder_layer in enumerate(self.layers): | |
if output_hidden_states: | |
encoder_states = encoder_states + (hidden_states,) | |
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) | |
to_drop = False | |
if self.training: | |
dropout_probability = torch.rand([]) | |
if dropout_probability < self.layerdrop: # skip the layer | |
to_drop = True | |
if to_drop: | |
layer_outputs = (None, None) | |
else: | |
# we add object_queries as extra input to the encoder_layer | |
layer_outputs = encoder_layer( | |
hidden_states, | |
attention_mask, | |
object_queries=object_queries, | |
output_attentions=output_attentions, | |
) | |
hidden_states = layer_outputs[0] | |
if output_attentions: | |
all_attentions = all_attentions + (layer_outputs[1],) | |
if output_hidden_states: | |
encoder_states = encoder_states + (hidden_states,) | |
if not return_dict: | |
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) | |
return BaseModelOutput( | |
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions | |
) | |
class DetrDecoder(DetrPreTrainedModel): | |
""" | |
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`]. | |
The decoder updates the query embeddings through multiple self-attention and cross-attention layers. | |
Some small tweaks for DETR: | |
- object_queries and query_position_embeddings are added to the forward pass. | |
- if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers. | |
Args: | |
config: DetrConfig | |
""" | |
def __init__(self, config: DetrConfig): | |
super().__init__(config) | |
self.dropout = config.dropout | |
self.layerdrop = config.decoder_layerdrop | |
self.layers = nn.ModuleList([DetrDecoderLayer(config) for _ in range(config.decoder_layers)]) | |
# in DETR, the decoder uses layernorm after the last decoder layer output | |
self.layernorm = nn.LayerNorm(config.d_model) | |
self.gradient_checkpointing = False | |
# Initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
inputs_embeds=None, | |
attention_mask=None, | |
encoder_hidden_states=None, | |
encoder_attention_mask=None, | |
object_queries=None, | |
query_position_embeddings=None, | |
output_attentions=None, | |
output_hidden_states=None, | |
return_dict=None, | |
): | |
r""" | |
Args: | |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
The query embeddings that are passed into the decoder. | |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`: | |
- 1 for queries that are **not masked**, | |
- 0 for queries that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention | |
of the decoder. | |
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): | |
Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected | |
in `[0, 1]`: | |
- 1 for pixels that are real (i.e. **not masked**), | |
- 0 for pixels that are padding (i.e. **masked**). | |
object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Object queries that are added to the queries and keys in each cross-attention layer. | |
query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): | |
, *optional*): Position embeddings that are added to the values and keys in each self-attention layer. | |
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. | |
""" | |
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 inputs_embeds is not None: | |
hidden_states = inputs_embeds | |
input_shape = inputs_embeds.size()[:-1] | |
combined_attention_mask = None | |
if attention_mask is not None and combined_attention_mask is not None: | |
# [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] | |
combined_attention_mask = combined_attention_mask + _prepare_4d_attention_mask( | |
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] | |
) | |
# expand encoder attention mask | |
if encoder_hidden_states is not None and encoder_attention_mask is not None: | |
# [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] | |
encoder_attention_mask = _prepare_4d_attention_mask( | |
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] | |
) | |
# optional intermediate hidden states | |
intermediate = () if self.config.auxiliary_loss else None | |
# decoder layers | |
all_hidden_states = () if output_hidden_states else None | |
all_self_attns = () if output_attentions else None | |
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None | |
for idx, decoder_layer in enumerate(self.layers): | |
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
if self.training: | |
dropout_probability = torch.rand([]) | |
if dropout_probability < self.layerdrop: | |
continue | |
if self.gradient_checkpointing and self.training: | |
layer_outputs = self._gradient_checkpointing_func( | |
decoder_layer.__call__, | |
hidden_states, | |
combined_attention_mask, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
None, | |
) | |
else: | |
layer_outputs = decoder_layer( | |
hidden_states, | |
attention_mask=combined_attention_mask, | |
object_queries=object_queries, | |
query_position_embeddings=query_position_embeddings, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
output_attentions=output_attentions, | |
) | |
hidden_states = layer_outputs[0] | |
if self.config.auxiliary_loss: | |
hidden_states = self.layernorm(hidden_states) | |
intermediate += (hidden_states,) | |
if output_attentions: | |
all_self_attns += (layer_outputs[1],) | |
if encoder_hidden_states is not None: | |
all_cross_attentions += (layer_outputs[2],) | |
# finally, apply layernorm | |
hidden_states = self.layernorm(hidden_states) | |
# add hidden states from the last decoder layer | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
# stack intermediate decoder activations | |
if self.config.auxiliary_loss: | |
intermediate = torch.stack(intermediate) | |
if not return_dict: | |
return tuple( | |
v | |
for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate] | |
if v is not None | |
) | |
return DetrDecoderOutput( | |
last_hidden_state=hidden_states, | |
hidden_states=all_hidden_states, | |
attentions=all_self_attns, | |
cross_attentions=all_cross_attentions, | |
intermediate_hidden_states=intermediate, | |
) | |
class DetrModel(DetrPreTrainedModel): | |
def __init__(self, config: DetrConfig): | |
super().__init__(config) | |
# Create backbone + positional encoding | |
backbone = DetrConvEncoder(config) | |
object_queries = build_position_encoding(config) | |
self.backbone = DetrConvModel(backbone, object_queries) | |
# Create projection layer | |
self.input_projection = nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1) | |
self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model) | |
self.encoder = DetrEncoder(config) | |
self.decoder = DetrDecoder(config) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_encoder(self): | |
return self.encoder | |
def get_decoder(self): | |
return self.decoder | |
def freeze_backbone(self): | |
for name, param in self.backbone.conv_encoder.model.named_parameters(): | |
param.requires_grad_(False) | |
def unfreeze_backbone(self): | |
for name, param in self.backbone.conv_encoder.model.named_parameters(): | |
param.requires_grad_(True) | |
def forward( | |
self, | |
pixel_values: torch.FloatTensor, | |
pixel_mask: Optional[torch.LongTensor] = None, | |
decoder_attention_mask: Optional[torch.FloatTensor] = None, | |
encoder_outputs: Optional[torch.FloatTensor] = None, | |
inputs_embeds: Optional[torch.FloatTensor] = None, | |
decoder_inputs_embeds: Optional[torch.FloatTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple[torch.FloatTensor], DetrModelOutput]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from transformers import AutoImageProcessor, DetrModel | |
>>> 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("facebook/detr-resnet-50") | |
>>> model = DetrModel.from_pretrained("facebook/detr-resnet-50") | |
>>> # prepare image for the model | |
>>> inputs = image_processor(images=image, return_tensors="pt") | |
>>> # forward pass | |
>>> outputs = model(**inputs) | |
>>> # the last hidden states are the final query embeddings of the Transformer decoder | |
>>> # these are of shape (batch_size, num_queries, hidden_size) | |
>>> last_hidden_states = outputs.last_hidden_state | |
>>> list(last_hidden_states.shape) | |
[1, 100, 256] | |
```""" | |
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 | |
batch_size, num_channels, height, width = pixel_values.shape | |
device = pixel_values.device | |
if pixel_mask is None: | |
pixel_mask = torch.ones(((batch_size, height, width)), device=device) | |
# First, sent pixel_values + pixel_mask through Backbone to obtain the features | |
# pixel_values should be of shape (batch_size, num_channels, height, width) | |
# pixel_mask should be of shape (batch_size, height, width) | |
features, object_queries_list = self.backbone(pixel_values, pixel_mask) | |
# get final feature map and downsampled mask | |
feature_map, mask = features[-1] | |
if mask is None: | |
raise ValueError("Backbone does not return downsampled pixel mask") | |
# Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) | |
projected_feature_map = self.input_projection(feature_map) | |
# Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC | |
# In other words, turn their shape into (batch_size, sequence_length, hidden_size) | |
flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) | |
object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1) | |
flattened_mask = mask.flatten(1) | |
# Fourth, sent flattened_features + flattened_mask + position embeddings through encoder | |
# flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) | |
# flattened_mask is a Tensor of shape (batch_size, heigth*width) | |
if encoder_outputs is None: | |
encoder_outputs = self.encoder( | |
inputs_embeds=flattened_features, | |
attention_mask=flattened_mask, | |
object_queries=object_queries, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True | |
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): | |
encoder_outputs = BaseModelOutput( | |
last_hidden_state=encoder_outputs[0], | |
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, | |
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, | |
) | |
# Fifth, sent query embeddings + object_queries through the decoder (which is conditioned on the encoder output) | |
query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1) | |
queries = torch.zeros_like(query_position_embeddings) | |
# decoder outputs consists of (dec_features, dec_hidden, dec_attn) | |
decoder_outputs = self.decoder( | |
inputs_embeds=queries, | |
attention_mask=None, | |
object_queries=object_queries, | |
query_position_embeddings=query_position_embeddings, | |
encoder_hidden_states=encoder_outputs[0], | |
encoder_attention_mask=flattened_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
if not return_dict: | |
return decoder_outputs + encoder_outputs | |
return DetrModelOutput( | |
last_hidden_state=decoder_outputs.last_hidden_state, | |
decoder_hidden_states=decoder_outputs.hidden_states, | |
decoder_attentions=decoder_outputs.attentions, | |
cross_attentions=decoder_outputs.cross_attentions, | |
encoder_last_hidden_state=encoder_outputs.last_hidden_state, | |
encoder_hidden_states=encoder_outputs.hidden_states, | |
encoder_attentions=encoder_outputs.attentions, | |
intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, | |
) | |
class DetrForObjectDetection(DetrPreTrainedModel): | |
def __init__(self, config: DetrConfig): | |
super().__init__(config) | |
# DETR encoder-decoder model | |
self.model = DetrModel(config) | |
# Object detection heads | |
self.class_labels_classifier = nn.Linear( | |
config.d_model, config.num_labels + 1 | |
) # We add one for the "no object" class | |
self.bbox_predictor = DetrMLPPredictionHead( | |
input_dim=config.d_model, hidden_dim=config.d_model, 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 | |
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])] | |
def forward( | |
self, | |
pixel_values: torch.FloatTensor, | |
pixel_mask: Optional[torch.LongTensor] = None, | |
decoder_attention_mask: Optional[torch.FloatTensor] = None, | |
encoder_outputs: Optional[torch.FloatTensor] = None, | |
inputs_embeds: Optional[torch.FloatTensor] = None, | |
decoder_inputs_embeds: Optional[torch.FloatTensor] = None, | |
labels: Optional[List[dict]] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple[torch.FloatTensor], DetrObjectDetectionOutput]: | |
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, DetrForObjectDetection | |
>>> 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("facebook/detr-resnet-50") | |
>>> model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") | |
>>> 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 0.998 at location [40.16, 70.81, 175.55, 117.98] | |
Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66] | |
Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76] | |
Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93] | |
Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72] | |
```""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
# First, sent images through DETR base model to obtain encoder + decoder outputs | |
outputs = self.model( | |
pixel_values, | |
pixel_mask=pixel_mask, | |
decoder_attention_mask=decoder_attention_mask, | |
encoder_outputs=encoder_outputs, | |
inputs_embeds=inputs_embeds, | |
decoder_inputs_embeds=decoder_inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
sequence_output = outputs[0] | |
# 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 = DetrHungarianMatcher( | |
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 = DetrLoss( | |
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 DetrObjectDetectionOutput( | |
loss=loss, | |
loss_dict=loss_dict, | |
logits=logits, | |
pred_boxes=pred_boxes, | |
auxiliary_outputs=auxiliary_outputs, | |
last_hidden_state=outputs.last_hidden_state, | |
decoder_hidden_states=outputs.decoder_hidden_states, | |
decoder_attentions=outputs.decoder_attentions, | |
cross_attentions=outputs.cross_attentions, | |
encoder_last_hidden_state=outputs.encoder_last_hidden_state, | |
encoder_hidden_states=outputs.encoder_hidden_states, | |
encoder_attentions=outputs.encoder_attentions, | |
) | |
class DetrForSegmentation(DetrPreTrainedModel): | |
def __init__(self, config: DetrConfig): | |
super().__init__(config) | |
# object detection model | |
self.detr = DetrForObjectDetection(config) | |
# segmentation head | |
hidden_size, number_of_heads = config.d_model, config.encoder_attention_heads | |
intermediate_channel_sizes = self.detr.model.backbone.conv_encoder.intermediate_channel_sizes | |
self.mask_head = DetrMaskHeadSmallConv( | |
hidden_size + number_of_heads, intermediate_channel_sizes[::-1][-3:], hidden_size | |
) | |
self.bbox_attention = DetrMHAttentionMap( | |
hidden_size, hidden_size, number_of_heads, dropout=0.0, std=config.init_xavier_std | |
) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
pixel_values: torch.FloatTensor, | |
pixel_mask: Optional[torch.LongTensor] = None, | |
decoder_attention_mask: Optional[torch.FloatTensor] = None, | |
encoder_outputs: Optional[torch.FloatTensor] = None, | |
inputs_embeds: Optional[torch.FloatTensor] = None, | |
decoder_inputs_embeds: Optional[torch.FloatTensor] = None, | |
labels: Optional[List[dict]] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple[torch.FloatTensor], DetrSegmentationOutput]: | |
r""" | |
labels (`List[Dict]` of len `(batch_size,)`, *optional*): | |
Labels for computing the bipartite matching loss, DICE/F-1 loss and Focal loss. List of dicts, each | |
dictionary containing at least the following 3 keys: 'class_labels', 'boxes' and 'masks' (the class labels, | |
bounding boxes and segmentation masks 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,)`, the boxes a | |
`torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)` and the masks a | |
`torch.FloatTensor` of shape `(number of bounding boxes in the image, height, width)`. | |
Returns: | |
Examples: | |
```python | |
>>> import io | |
>>> import requests | |
>>> from PIL import Image | |
>>> import torch | |
>>> import numpy | |
>>> from transformers import AutoImageProcessor, DetrForSegmentation | |
>>> from transformers.image_transforms import rgb_to_id | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic") | |
>>> model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic") | |
>>> # prepare image for the model | |
>>> inputs = image_processor(images=image, return_tensors="pt") | |
>>> # forward pass | |
>>> outputs = model(**inputs) | |
>>> # Use the `post_process_panoptic_segmentation` method of the `image_processor` to retrieve post-processed panoptic segmentation maps | |
>>> # Segmentation results are returned as a list of dictionaries | |
>>> result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[(300, 500)]) | |
>>> # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found | |
>>> panoptic_seg = result[0]["segmentation"] | |
>>> # Get prediction score and segment_id to class_id mapping of each segment | |
>>> panoptic_segments_info = result[0]["segments_info"] | |
```""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
batch_size, num_channels, height, width = pixel_values.shape | |
device = pixel_values.device | |
if pixel_mask is None: | |
pixel_mask = torch.ones((batch_size, height, width), device=device) | |
# First, get list of feature maps and position embeddings | |
features, object_queries_list = self.detr.model.backbone(pixel_values, pixel_mask=pixel_mask) | |
# Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) | |
feature_map, mask = features[-1] | |
batch_size, num_channels, height, width = feature_map.shape | |
projected_feature_map = self.detr.model.input_projection(feature_map) | |
# Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC | |
# In other words, turn their shape into (batch_size, sequence_length, hidden_size) | |
flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) | |
object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1) | |
flattened_mask = mask.flatten(1) | |
# Fourth, sent flattened_features + flattened_mask + position embeddings through encoder | |
# flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) | |
# flattened_mask is a Tensor of shape (batch_size, heigth*width) | |
if encoder_outputs is None: | |
encoder_outputs = self.detr.model.encoder( | |
inputs_embeds=flattened_features, | |
attention_mask=flattened_mask, | |
object_queries=object_queries, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True | |
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): | |
encoder_outputs = BaseModelOutput( | |
last_hidden_state=encoder_outputs[0], | |
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, | |
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, | |
) | |
# Fifth, sent query embeddings + position embeddings through the decoder (which is conditioned on the encoder output) | |
query_position_embeddings = self.detr.model.query_position_embeddings.weight.unsqueeze(0).repeat( | |
batch_size, 1, 1 | |
) | |
queries = torch.zeros_like(query_position_embeddings) | |
# decoder outputs consists of (dec_features, dec_hidden, dec_attn) | |
decoder_outputs = self.detr.model.decoder( | |
inputs_embeds=queries, | |
attention_mask=None, | |
object_queries=object_queries, | |
query_position_embeddings=query_position_embeddings, | |
encoder_hidden_states=encoder_outputs[0], | |
encoder_attention_mask=flattened_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
sequence_output = decoder_outputs[0] | |
# Sixth, compute logits, pred_boxes and pred_masks | |
logits = self.detr.class_labels_classifier(sequence_output) | |
pred_boxes = self.detr.bbox_predictor(sequence_output).sigmoid() | |
memory = encoder_outputs[0].permute(0, 2, 1).view(batch_size, self.config.d_model, height, width) | |
mask = flattened_mask.view(batch_size, height, width) | |
# FIXME h_boxes takes the last one computed, keep this in mind | |
# important: we need to reverse the mask, since in the original implementation the mask works reversed | |
# bbox_mask is of shape (batch_size, num_queries, number_of_attention_heads in bbox_attention, height/32, width/32) | |
bbox_mask = self.bbox_attention(sequence_output, memory, mask=~mask) | |
seg_masks = self.mask_head(projected_feature_map, bbox_mask, [features[2][0], features[1][0], features[0][0]]) | |
pred_masks = seg_masks.view(batch_size, self.detr.config.num_queries, seg_masks.shape[-2], seg_masks.shape[-1]) | |
loss, loss_dict, auxiliary_outputs = None, None, None | |
if labels is not None: | |
# First: create the matcher | |
matcher = DetrHungarianMatcher( | |
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", "masks"] | |
criterion = DetrLoss( | |
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 | |
outputs_loss["pred_masks"] = pred_masks | |
if self.config.auxiliary_loss: | |
intermediate = decoder_outputs.intermediate_hidden_states if return_dict else decoder_outputs[-1] | |
outputs_class = self.detr.class_labels_classifier(intermediate) | |
outputs_coord = self.detr.bbox_predictor(intermediate).sigmoid() | |
auxiliary_outputs = self.detr._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 | |
weight_dict["loss_mask"] = self.config.mask_loss_coefficient | |
weight_dict["loss_dice"] = self.config.dice_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, pred_masks) + auxiliary_outputs + decoder_outputs + encoder_outputs | |
else: | |
output = (logits, pred_boxes, pred_masks) + decoder_outputs + encoder_outputs | |
return ((loss, loss_dict) + output) if loss is not None else output | |
return DetrSegmentationOutput( | |
loss=loss, | |
loss_dict=loss_dict, | |
logits=logits, | |
pred_boxes=pred_boxes, | |
pred_masks=pred_masks, | |
auxiliary_outputs=auxiliary_outputs, | |
last_hidden_state=decoder_outputs.last_hidden_state, | |
decoder_hidden_states=decoder_outputs.hidden_states, | |
decoder_attentions=decoder_outputs.attentions, | |
cross_attentions=decoder_outputs.cross_attentions, | |
encoder_last_hidden_state=encoder_outputs.last_hidden_state, | |
encoder_hidden_states=encoder_outputs.hidden_states, | |
encoder_attentions=encoder_outputs.attentions, | |
) | |
def _expand(tensor, length: int): | |
return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1) | |
# taken from https://github.com/facebookresearch/detr/blob/master/models/segmentation.py | |
class DetrMaskHeadSmallConv(nn.Module): | |
""" | |
Simple convolutional head, using group norm. Upsampling is done using a FPN approach | |
""" | |
def __init__(self, dim, fpn_dims, context_dim): | |
super().__init__() | |
if dim % 8 != 0: | |
raise ValueError( | |
"The hidden_size + number of attention heads must be divisible by 8 as the number of groups in" | |
" GroupNorm is set to 8" | |
) | |
inter_dims = [dim, context_dim // 2, context_dim // 4, context_dim // 8, context_dim // 16, context_dim // 64] | |
self.lay1 = nn.Conv2d(dim, dim, 3, padding=1) | |
self.gn1 = nn.GroupNorm(8, dim) | |
self.lay2 = nn.Conv2d(dim, inter_dims[1], 3, padding=1) | |
self.gn2 = nn.GroupNorm(min(8, inter_dims[1]), inter_dims[1]) | |
self.lay3 = nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1) | |
self.gn3 = nn.GroupNorm(min(8, inter_dims[2]), inter_dims[2]) | |
self.lay4 = nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1) | |
self.gn4 = nn.GroupNorm(min(8, inter_dims[3]), inter_dims[3]) | |
self.lay5 = nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1) | |
self.gn5 = nn.GroupNorm(min(8, inter_dims[4]), inter_dims[4]) | |
self.out_lay = nn.Conv2d(inter_dims[4], 1, 3, padding=1) | |
self.dim = dim | |
self.adapter1 = nn.Conv2d(fpn_dims[0], inter_dims[1], 1) | |
self.adapter2 = nn.Conv2d(fpn_dims[1], inter_dims[2], 1) | |
self.adapter3 = nn.Conv2d(fpn_dims[2], inter_dims[3], 1) | |
for m in self.modules(): | |
if isinstance(m, nn.Conv2d): | |
nn.init.kaiming_uniform_(m.weight, a=1) | |
nn.init.constant_(m.bias, 0) | |
def forward(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor]): | |
# here we concatenate x, the projected feature map, of shape (batch_size, d_model, heigth/32, width/32) with | |
# the bbox_mask = the attention maps of shape (batch_size, n_queries, n_heads, height/32, width/32). | |
# We expand the projected feature map to match the number of heads. | |
x = torch.cat([_expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1) | |
x = self.lay1(x) | |
x = self.gn1(x) | |
x = nn.functional.relu(x) | |
x = self.lay2(x) | |
x = self.gn2(x) | |
x = nn.functional.relu(x) | |
cur_fpn = self.adapter1(fpns[0]) | |
if cur_fpn.size(0) != x.size(0): | |
cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) | |
x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") | |
x = self.lay3(x) | |
x = self.gn3(x) | |
x = nn.functional.relu(x) | |
cur_fpn = self.adapter2(fpns[1]) | |
if cur_fpn.size(0) != x.size(0): | |
cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) | |
x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") | |
x = self.lay4(x) | |
x = self.gn4(x) | |
x = nn.functional.relu(x) | |
cur_fpn = self.adapter3(fpns[2]) | |
if cur_fpn.size(0) != x.size(0): | |
cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) | |
x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") | |
x = self.lay5(x) | |
x = self.gn5(x) | |
x = nn.functional.relu(x) | |
x = self.out_lay(x) | |
return x | |
class DetrMHAttentionMap(nn.Module): | |
"""This is a 2D attention module, which only returns the attention softmax (no multiplication by value)""" | |
def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias=True, std=None): | |
super().__init__() | |
self.num_heads = num_heads | |
self.hidden_dim = hidden_dim | |
self.dropout = nn.Dropout(dropout) | |
self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias) | |
self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias) | |
self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5 | |
def forward(self, q, k, mask: Optional[Tensor] = None): | |
q = self.q_linear(q) | |
k = nn.functional.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias) | |
queries_per_head = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads) | |
keys_per_head = k.view(k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1]) | |
weights = torch.einsum("bqnc,bnchw->bqnhw", queries_per_head * self.normalize_fact, keys_per_head) | |
if mask is not None: | |
weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), torch.finfo(weights.dtype).min) | |
weights = nn.functional.softmax(weights.flatten(2), dim=-1).view(weights.size()) | |
weights = self.dropout(weights) | |
return weights | |
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 | |
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 | |
# taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py | |
class DetrLoss(nn.Module): | |
""" | |
This class computes the losses for DetrForObjectDetection/DetrForSegmentation. 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 (`DetrHungarianMatcher`): | |
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 | |
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 | |
# taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py | |
class DetrMLPPredictionHead(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 | |
# taken from https://github.com/facebookresearch/detr/blob/master/models/matcher.py | |
class DetrHungarianMatcher(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") | |
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] | |
# below: bounding box utilities taken from https://github.com/facebookresearch/detr/blob/master/util/box_ops.py | |
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() | |
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]) | |
# modified from torchvision to also return the union | |
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 | |
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 | |
# below: taken from https://github.com/facebookresearch/detr/blob/master/util/misc.py#L306 | |
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 | |
class NestedTensor: | |
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 | |
return NestedTensor(cast_tensor, cast_mask) | |
def decompose(self): | |
return self.tensors, self.mask | |
def __repr__(self): | |
return str(self.tensors) | |
def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): | |
if tensor_list[0].ndim == 3: | |
max_size = _max_by_axis([list(img.shape) for img in tensor_list]) | |
batch_shape = [len(tensor_list)] + max_size | |
batch_size, num_channels, height, width = batch_shape | |
dtype = tensor_list[0].dtype | |
device = tensor_list[0].device | |
tensor = torch.zeros(batch_shape, dtype=dtype, device=device) | |
mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device) | |
for img, pad_img, m in zip(tensor_list, tensor, mask): | |
pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) | |
m[: img.shape[1], : img.shape[2]] = False | |
else: | |
raise ValueError("Only 3-dimensional tensors are supported") | |
return NestedTensor(tensor, mask) | |