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# coding=utf-8 | |
# Copyright 2022 SenseTime and 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 Deformable DETR model.""" | |
import copy | |
import math | |
import warnings | |
from dataclasses import dataclass | |
from typing import Dict, List, Optional, Tuple, Union | |
import torch | |
import torch.nn.functional as F | |
from torch import Tensor, nn | |
from torch.autograd import Function | |
from torch.autograd.function import once_differentiable | |
from ...activations import ACT2FN | |
from ...file_utils import ( | |
ModelOutput, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
is_scipy_available, | |
is_timm_available, | |
is_torch_cuda_available, | |
is_vision_available, | |
replace_return_docstrings, | |
requires_backends, | |
) | |
from ...modeling_outputs import BaseModelOutput | |
from ...modeling_utils import PreTrainedModel | |
from ...pytorch_utils import meshgrid | |
from ...utils import is_ninja_available, logging | |
from ..auto import AutoBackbone | |
from .configuration_deformable_detr import DeformableDetrConfig | |
from .load_custom import load_cuda_kernels | |
logger = logging.get_logger(__name__) | |
# Move this to not compile only when importing, this needs to happen later, like in __init__. | |
if is_torch_cuda_available() and is_ninja_available(): | |
logger.info("Loading custom CUDA kernels...") | |
try: | |
MultiScaleDeformableAttention = load_cuda_kernels() | |
except Exception as e: | |
logger.warning(f"Could not load the custom kernel for multi-scale deformable attention: {e}") | |
MultiScaleDeformableAttention = None | |
else: | |
MultiScaleDeformableAttention = None | |
if is_vision_available(): | |
from transformers.image_transforms import center_to_corners_format | |
class MultiScaleDeformableAttentionFunction(Function): | |
def forward( | |
context, | |
value, | |
value_spatial_shapes, | |
value_level_start_index, | |
sampling_locations, | |
attention_weights, | |
im2col_step, | |
): | |
context.im2col_step = im2col_step | |
output = MultiScaleDeformableAttention.ms_deform_attn_forward( | |
value, | |
value_spatial_shapes, | |
value_level_start_index, | |
sampling_locations, | |
attention_weights, | |
context.im2col_step, | |
) | |
context.save_for_backward( | |
value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights | |
) | |
return output | |
def backward(context, grad_output): | |
( | |
value, | |
value_spatial_shapes, | |
value_level_start_index, | |
sampling_locations, | |
attention_weights, | |
) = context.saved_tensors | |
grad_value, grad_sampling_loc, grad_attn_weight = MultiScaleDeformableAttention.ms_deform_attn_backward( | |
value, | |
value_spatial_shapes, | |
value_level_start_index, | |
sampling_locations, | |
attention_weights, | |
grad_output, | |
context.im2col_step, | |
) | |
return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None | |
if is_scipy_available(): | |
from scipy.optimize import linear_sum_assignment | |
if is_timm_available(): | |
from timm import create_model | |
logger = logging.get_logger(__name__) | |
_CONFIG_FOR_DOC = "DeformableDetrConfig" | |
_CHECKPOINT_FOR_DOC = "sensetime/deformable-detr" | |
DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"sensetime/deformable-detr", | |
# See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr | |
] | |
class DeformableDetrDecoderOutput(ModelOutput): | |
""" | |
Base class for outputs of the DeformableDetrDecoder. This class adds two attributes to | |
BaseModelOutputWithCrossAttentions, namely: | |
- a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer) | |
- a stacked tensor of intermediate reference points. | |
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. | |
intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): | |
Stacked intermediate hidden states (output of each layer of the decoder). | |
intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): | |
Stacked intermediate reference points (reference points of each layer of the 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 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. | |
""" | |
last_hidden_state: torch.FloatTensor = None | |
intermediate_hidden_states: torch.FloatTensor = None | |
intermediate_reference_points: torch.FloatTensor = None | |
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
attentions: Optional[Tuple[torch.FloatTensor]] = None | |
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None | |
class DeformableDetrModelOutput(ModelOutput): | |
""" | |
Base class for outputs of the Deformable DETR encoder-decoder model. | |
Args: | |
init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): | |
Initial reference points sent through the Transformer decoder. | |
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): | |
Sequence of hidden-states at the output of the last layer of the decoder of the model. | |
intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): | |
Stacked intermediate hidden states (output of each layer of the decoder). | |
intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): | |
Stacked intermediate reference points (reference points of each layer of the decoder). | |
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, num_queries, 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, num_queries, | |
num_queries)`. 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_queries, num_heads, 4, 4)`. | |
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_queries, num_heads, 4, 4)`. | |
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the | |
self-attention heads. | |
enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): | |
Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are | |
picked as region proposals in the first stage. Output of bounding box binary classification (i.e. | |
foreground and background). | |
enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): | |
Logits of predicted bounding boxes coordinates in the first stage. | |
""" | |
init_reference_points: torch.FloatTensor = None | |
last_hidden_state: torch.FloatTensor = None | |
intermediate_hidden_states: torch.FloatTensor = None | |
intermediate_reference_points: 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 | |
enc_outputs_class: Optional[torch.FloatTensor] = None | |
enc_outputs_coord_logits: Optional[torch.FloatTensor] = None | |
class DeformableDetrObjectDetectionOutput(ModelOutput): | |
""" | |
Output type of [`DeformableDetrForObjectDetection`]. | |
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 [`~DeformableDetrProcessor.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, num_queries, 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, num_queries, 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, num_queries, | |
num_queries)`. 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_queries, num_heads, 4, 4)`. | |
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, sequence_length, num_heads, 4, | |
4)`. 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 `(batch_size, config.decoder_layers, num_queries, hidden_size)`): | |
Stacked intermediate hidden states (output of each layer of the decoder). | |
intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): | |
Stacked intermediate reference points (reference points of each layer of the decoder). | |
init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): | |
Initial reference points sent through the Transformer decoder. | |
enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): | |
Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are | |
picked as region proposals in the first stage. Output of bounding box binary classification (i.e. | |
foreground and background). | |
enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): | |
Logits of predicted bounding boxes coordinates in the first stage. | |
""" | |
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 | |
init_reference_points: Optional[torch.FloatTensor] = None | |
last_hidden_state: Optional[torch.FloatTensor] = None | |
intermediate_hidden_states: Optional[torch.FloatTensor] = None | |
intermediate_reference_points: 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 | |
enc_outputs_class: Optional = None | |
enc_outputs_coord_logits: Optional = None | |
def _get_clones(module, N): | |
return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) | |
def inverse_sigmoid(x, eps=1e-5): | |
x = x.clamp(min=0, max=1) | |
x1 = x.clamp(min=eps) | |
x2 = (1 - x).clamp(min=eps) | |
return torch.log(x1 / x2) | |
# Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->DeformableDetr | |
class DeformableDetrFrozenBatchNorm2d(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 | |
# Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->DeformableDetr | |
def replace_batch_norm(model): | |
r""" | |
Recursively replace all `torch.nn.BatchNorm2d` with `DeformableDetrFrozenBatchNorm2d`. | |
Args: | |
model (torch.nn.Module): | |
input model | |
""" | |
for name, module in model.named_children(): | |
if isinstance(module, nn.BatchNorm2d): | |
new_module = DeformableDetrFrozenBatchNorm2d(module.num_features) | |
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 DeformableDetrConvEncoder(nn.Module): | |
""" | |
Convolutional backbone, using either the AutoBackbone API or one from the timm library. | |
nn.BatchNorm2d layers are replaced by DeformableDetrFrozenBatchNorm2d as defined above. | |
""" | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
if config.use_timm_backbone: | |
requires_backends(self, ["timm"]) | |
kwargs = {} | |
if config.dilation: | |
kwargs["output_stride"] = 16 | |
backbone = create_model( | |
config.backbone, | |
pretrained=config.use_pretrained_backbone, | |
features_only=True, | |
out_indices=(2, 3, 4) if config.num_feature_levels > 1 else (4,), | |
in_chans=config.num_channels, | |
**kwargs, | |
) | |
else: | |
backbone = AutoBackbone.from_config(config.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 = config.backbone if config.use_timm_backbone else config.backbone_config.model_type | |
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) | |
# Copied from transformers.models.detr.modeling_detr.DetrConvEncoder.forward with Detr->DeformableDetr | |
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 | |
# Copied from transformers.models.detr.modeling_detr.DetrConvModel with Detr->DeformableDetr | |
class DeformableDetrConvModel(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 | |
# Copied from transformers.models.detr.modeling_detr._expand_mask | |
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, target_len: Optional[int] = None): | |
""" | |
Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, target_seq_len, source_seq_len]`. | |
""" | |
batch_size, source_len = mask.size() | |
target_len = target_len if target_len is not None else source_len | |
expanded_mask = mask[:, None, None, :].expand(batch_size, 1, target_len, source_len).to(dtype) | |
inverted_mask = 1.0 - expanded_mask | |
return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min) | |
class DeformableDetrSinePositionEmbedding(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: | |
eps = 1e-6 | |
y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale | |
x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale | |
dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device) | |
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 | |
# Copied from transformers.models.detr.modeling_detr.DetrLearnedPositionEmbedding | |
class DeformableDetrLearnedPositionEmbedding(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 | |
# Copied from transformers.models.detr.modeling_detr.build_position_encoding with Detr->DeformableDetr | |
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 = DeformableDetrSinePositionEmbedding(n_steps, normalize=True) | |
elif config.position_embedding_type == "learned": | |
position_embedding = DeformableDetrLearnedPositionEmbedding(n_steps) | |
else: | |
raise ValueError(f"Not supported {config.position_embedding_type}") | |
return position_embedding | |
def multi_scale_deformable_attention( | |
value: Tensor, value_spatial_shapes: Tensor, sampling_locations: Tensor, attention_weights: Tensor | |
) -> Tensor: | |
batch_size, _, num_heads, hidden_dim = value.shape | |
_, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape | |
value_list = value.split([height.item() * width.item() for height, width in value_spatial_shapes], dim=1) | |
sampling_grids = 2 * sampling_locations - 1 | |
sampling_value_list = [] | |
for level_id, (height, width) in enumerate(value_spatial_shapes): | |
# batch_size, height*width, num_heads, hidden_dim | |
# -> batch_size, height*width, num_heads*hidden_dim | |
# -> batch_size, num_heads*hidden_dim, height*width | |
# -> batch_size*num_heads, hidden_dim, height, width | |
value_l_ = ( | |
value_list[level_id].flatten(2).transpose(1, 2).reshape(batch_size * num_heads, hidden_dim, height, width) | |
) | |
# batch_size, num_queries, num_heads, num_points, 2 | |
# -> batch_size, num_heads, num_queries, num_points, 2 | |
# -> batch_size*num_heads, num_queries, num_points, 2 | |
sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1) | |
# batch_size*num_heads, hidden_dim, num_queries, num_points | |
sampling_value_l_ = nn.functional.grid_sample( | |
value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False | |
) | |
sampling_value_list.append(sampling_value_l_) | |
# (batch_size, num_queries, num_heads, num_levels, num_points) | |
# -> (batch_size, num_heads, num_queries, num_levels, num_points) | |
# -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) | |
attention_weights = attention_weights.transpose(1, 2).reshape( | |
batch_size * num_heads, 1, num_queries, num_levels * num_points | |
) | |
output = ( | |
(torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) | |
.sum(-1) | |
.view(batch_size, num_heads * hidden_dim, num_queries) | |
) | |
return output.transpose(1, 2).contiguous() | |
class DeformableDetrMultiscaleDeformableAttention(nn.Module): | |
""" | |
Multiscale deformable attention as proposed in Deformable DETR. | |
""" | |
def __init__(self, config: DeformableDetrConfig, num_heads: int, n_points: int): | |
super().__init__() | |
if config.d_model % num_heads != 0: | |
raise ValueError( | |
f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}" | |
) | |
dim_per_head = config.d_model // num_heads | |
# check if dim_per_head is power of 2 | |
if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): | |
warnings.warn( | |
"You'd better set embed_dim (d_model) in DeformableDetrMultiscaleDeformableAttention to make the" | |
" dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" | |
" implementation." | |
) | |
self.im2col_step = 64 | |
self.d_model = config.d_model | |
self.n_levels = config.num_feature_levels | |
self.n_heads = num_heads | |
self.n_points = n_points | |
self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2) | |
self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points) | |
self.value_proj = nn.Linear(config.d_model, config.d_model) | |
self.output_proj = nn.Linear(config.d_model, config.d_model) | |
self.disable_custom_kernels = config.disable_custom_kernels | |
self._reset_parameters() | |
def _reset_parameters(self): | |
nn.init.constant_(self.sampling_offsets.weight.data, 0.0) | |
thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) | |
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) | |
grid_init = ( | |
(grid_init / grid_init.abs().max(-1, keepdim=True)[0]) | |
.view(self.n_heads, 1, 1, 2) | |
.repeat(1, self.n_levels, self.n_points, 1) | |
) | |
for i in range(self.n_points): | |
grid_init[:, :, i, :] *= i + 1 | |
with torch.no_grad(): | |
self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) | |
nn.init.constant_(self.attention_weights.weight.data, 0.0) | |
nn.init.constant_(self.attention_weights.bias.data, 0.0) | |
nn.init.xavier_uniform_(self.value_proj.weight.data) | |
nn.init.constant_(self.value_proj.bias.data, 0.0) | |
nn.init.xavier_uniform_(self.output_proj.weight.data) | |
nn.init.constant_(self.output_proj.bias.data, 0.0) | |
def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]): | |
return tensor if position_embeddings is None else tensor + position_embeddings | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.Tensor] = None, | |
encoder_hidden_states=None, | |
encoder_attention_mask=None, | |
position_embeddings: Optional[torch.Tensor] = None, | |
reference_points=None, | |
spatial_shapes=None, | |
level_start_index=None, | |
output_attentions: bool = False, | |
): | |
# add position embeddings to the hidden states before projecting to queries and keys | |
if position_embeddings is not None: | |
hidden_states = self.with_pos_embed(hidden_states, position_embeddings) | |
batch_size, num_queries, _ = hidden_states.shape | |
batch_size, sequence_length, _ = encoder_hidden_states.shape | |
if (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() != sequence_length: | |
raise ValueError( | |
"Make sure to align the spatial shapes with the sequence length of the encoder hidden states" | |
) | |
value = self.value_proj(encoder_hidden_states) | |
if attention_mask is not None: | |
# we invert the attention_mask | |
value = value.masked_fill(~attention_mask[..., None], float(0)) | |
value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) | |
sampling_offsets = self.sampling_offsets(hidden_states).view( | |
batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2 | |
) | |
attention_weights = self.attention_weights(hidden_states).view( | |
batch_size, num_queries, self.n_heads, self.n_levels * self.n_points | |
) | |
attention_weights = F.softmax(attention_weights, -1).view( | |
batch_size, num_queries, self.n_heads, self.n_levels, self.n_points | |
) | |
# batch_size, num_queries, n_heads, n_levels, n_points, 2 | |
if reference_points.shape[-1] == 2: | |
offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) | |
sampling_locations = ( | |
reference_points[:, :, None, :, None, :] | |
+ sampling_offsets / offset_normalizer[None, None, None, :, None, :] | |
) | |
elif reference_points.shape[-1] == 4: | |
sampling_locations = ( | |
reference_points[:, :, None, :, None, :2] | |
+ sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 | |
) | |
else: | |
raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") | |
if self.disable_custom_kernels: | |
# PyTorch implementation | |
output = multi_scale_deformable_attention(value, spatial_shapes, sampling_locations, attention_weights) | |
else: | |
try: | |
# custom kernel | |
output = MultiScaleDeformableAttentionFunction.apply( | |
value, | |
spatial_shapes, | |
level_start_index, | |
sampling_locations, | |
attention_weights, | |
self.im2col_step, | |
) | |
except Exception: | |
# PyTorch implementation | |
output = multi_scale_deformable_attention(value, spatial_shapes, sampling_locations, attention_weights) | |
output = self.output_proj(output) | |
return output, attention_weights | |
class DeformableDetrMultiheadAttention(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 Deformable 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, position_embeddings: Optional[Tensor]): | |
return tensor if position_embeddings is None else tensor + position_embeddings | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.Tensor] = None, | |
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""" | |
batch_size, target_len, embed_dim = hidden_states.size() | |
# add position embeddings to the hidden states before projecting to queries and keys | |
if position_embeddings is not None: | |
hidden_states_original = hidden_states | |
hidden_states = self.with_pos_embed(hidden_states, position_embeddings) | |
# get queries, keys and values | |
query_states = self.q_proj(hidden_states) * self.scaling | |
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()}" | |
) | |
# expand attention_mask | |
if attention_mask is not None: | |
# [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] | |
attention_mask = _expand_mask(attention_mask, hidden_states.dtype) | |
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 DeformableDetrEncoderLayer(nn.Module): | |
def __init__(self, config: DeformableDetrConfig): | |
super().__init__() | |
self.embed_dim = config.d_model | |
self.self_attn = DeformableDetrMultiscaleDeformableAttention( | |
config, num_heads=config.encoder_attention_heads, n_points=config.encoder_n_points | |
) | |
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, | |
position_embeddings: torch.Tensor = None, | |
reference_points=None, | |
spatial_shapes=None, | |
level_start_index=None, | |
output_attentions: bool = False, | |
): | |
""" | |
Args: | |
hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Input to the layer. | |
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): | |
Attention mask. | |
position_embeddings (`torch.FloatTensor`, *optional*): | |
Position embeddings, to be added to `hidden_states`. | |
reference_points (`torch.FloatTensor`, *optional*): | |
Reference points. | |
spatial_shapes (`torch.LongTensor`, *optional*): | |
Spatial shapes of the backbone feature maps. | |
level_start_index (`torch.LongTensor`, *optional*): | |
Level start index. | |
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 | |
# Apply Multi-scale Deformable Attention Module on the multi-scale feature maps. | |
hidden_states, attn_weights = self.self_attn( | |
hidden_states=hidden_states, | |
attention_mask=attention_mask, | |
encoder_hidden_states=hidden_states, | |
encoder_attention_mask=attention_mask, | |
position_embeddings=position_embeddings, | |
reference_points=reference_points, | |
spatial_shapes=spatial_shapes, | |
level_start_index=level_start_index, | |
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 DeformableDetrDecoderLayer(nn.Module): | |
def __init__(self, config: DeformableDetrConfig): | |
super().__init__() | |
self.embed_dim = config.d_model | |
# self-attention | |
self.self_attn = DeformableDetrMultiheadAttention( | |
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) | |
# cross-attention | |
self.encoder_attn = DeformableDetrMultiscaleDeformableAttention( | |
config, | |
num_heads=config.decoder_attention_heads, | |
n_points=config.decoder_n_points, | |
) | |
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) | |
# feedforward neural networks | |
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, | |
position_embeddings: Optional[torch.Tensor] = None, | |
reference_points=None, | |
spatial_shapes=None, | |
level_start_index=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 `(seq_len, batch, embed_dim)`. | |
position_embeddings (`torch.FloatTensor`, *optional*): | |
Position embeddings that are added to the queries and keys in the self-attention layer. | |
reference_points (`torch.FloatTensor`, *optional*): | |
Reference points. | |
spatial_shapes (`torch.LongTensor`, *optional*): | |
Spatial shapes. | |
level_start_index (`torch.LongTensor`, *optional*): | |
Level start index. | |
encoder_hidden_states (`torch.FloatTensor`): | |
cross attention input to the layer of shape `(seq_len, batch, 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, | |
position_embeddings=position_embeddings, | |
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) | |
second_residual = hidden_states | |
# Cross-Attention | |
cross_attn_weights = None | |
hidden_states, cross_attn_weights = self.encoder_attn( | |
hidden_states=hidden_states, | |
attention_mask=encoder_attention_mask, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
position_embeddings=position_embeddings, | |
reference_points=reference_points, | |
spatial_shapes=spatial_shapes, | |
level_start_index=level_start_index, | |
output_attentions=output_attentions, | |
) | |
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) | |
hidden_states = second_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 | |
# Copied from transformers.models.detr.modeling_detr.DetrClassificationHead | |
class DeformableDetrClassificationHead(nn.Module): | |
"""Head for sentence-level classification tasks.""" | |
def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float): | |
super().__init__() | |
self.dense = nn.Linear(input_dim, inner_dim) | |
self.dropout = nn.Dropout(p=pooler_dropout) | |
self.out_proj = nn.Linear(inner_dim, num_classes) | |
def forward(self, hidden_states: torch.Tensor): | |
hidden_states = self.dropout(hidden_states) | |
hidden_states = self.dense(hidden_states) | |
hidden_states = torch.tanh(hidden_states) | |
hidden_states = self.dropout(hidden_states) | |
hidden_states = self.out_proj(hidden_states) | |
return hidden_states | |
class DeformableDetrPreTrainedModel(PreTrainedModel): | |
config_class = DeformableDetrConfig | |
base_model_prefix = "model" | |
main_input_name = "pixel_values" | |
def _init_weights(self, module): | |
std = self.config.init_std | |
if isinstance(module, DeformableDetrLearnedPositionEmbedding): | |
nn.init.uniform_(module.row_embeddings.weight) | |
nn.init.uniform_(module.column_embeddings.weight) | |
elif isinstance(module, DeformableDetrMultiscaleDeformableAttention): | |
module._reset_parameters() | |
elif 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_() | |
if hasattr(module, "reference_points") and not self.config.two_stage: | |
nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0) | |
nn.init.constant_(module.reference_points.bias.data, 0.0) | |
if hasattr(module, "level_embed"): | |
nn.init.normal_(module.level_embed) | |
def _set_gradient_checkpointing(self, module, value=False): | |
if isinstance(module, DeformableDetrDecoder): | |
module.gradient_checkpointing = value | |
DEFORMABLE_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 ([`DeformableDetrConfig`]): | |
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. | |
""" | |
DEFORMABLE_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 [`DeformableDetrImageProcessor.__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 [`~file_utils.ModelOutput`] instead of a plain tuple. | |
""" | |
class DeformableDetrEncoder(DeformableDetrPreTrainedModel): | |
""" | |
Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a | |
[`DeformableDetrEncoderLayer`]. | |
The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers. | |
Args: | |
config: DeformableDetrConfig | |
""" | |
def __init__(self, config: DeformableDetrConfig): | |
super().__init__(config) | |
self.dropout = config.dropout | |
self.layers = nn.ModuleList([DeformableDetrEncoderLayer(config) for _ in range(config.encoder_layers)]) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_reference_points(spatial_shapes, valid_ratios, device): | |
""" | |
Get reference points for each feature map. Used in decoder. | |
Args: | |
spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`): | |
Spatial shapes of each feature map. | |
valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`): | |
Valid ratios of each feature map. | |
device (`torch.device`): | |
Device on which to create the tensors. | |
Returns: | |
`torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)` | |
""" | |
reference_points_list = [] | |
for level, (height, width) in enumerate(spatial_shapes): | |
ref_y, ref_x = meshgrid( | |
torch.linspace(0.5, height - 0.5, height, dtype=torch.float32, device=device), | |
torch.linspace(0.5, width - 0.5, width, dtype=torch.float32, device=device), | |
indexing="ij", | |
) | |
# TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36 | |
ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, level, 1] * height) | |
ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, level, 0] * width) | |
ref = torch.stack((ref_x, ref_y), -1) | |
reference_points_list.append(ref) | |
reference_points = torch.cat(reference_points_list, 1) | |
reference_points = reference_points[:, :, None] * valid_ratios[:, None] | |
return reference_points | |
def forward( | |
self, | |
inputs_embeds=None, | |
attention_mask=None, | |
position_embeddings=None, | |
spatial_shapes=None, | |
level_start_index=None, | |
valid_ratios=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) | |
position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Position embeddings that are added to the queries and keys in each self-attention layer. | |
spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`): | |
Spatial shapes of each feature map. | |
level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`): | |
Starting index of each feature map. | |
valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`): | |
Ratio of valid area in each feature level. | |
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 [`~file_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) | |
reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=inputs_embeds.device) | |
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,) | |
layer_outputs = encoder_layer( | |
hidden_states, | |
attention_mask, | |
position_embeddings=position_embeddings, | |
reference_points=reference_points, | |
spatial_shapes=spatial_shapes, | |
level_start_index=level_start_index, | |
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 DeformableDetrDecoder(DeformableDetrPreTrainedModel): | |
""" | |
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DeformableDetrDecoderLayer`]. | |
The decoder updates the query embeddings through multiple self-attention and cross-attention layers. | |
Some tweaks for Deformable DETR: | |
- `position_embeddings`, `reference_points`, `spatial_shapes` and `valid_ratios` are added to the forward pass. | |
- it also returns a stack of intermediate outputs and reference points from all decoding layers. | |
Args: | |
config: DeformableDetrConfig | |
""" | |
def __init__(self, config: DeformableDetrConfig): | |
super().__init__(config) | |
self.dropout = config.dropout | |
self.layers = nn.ModuleList([DeformableDetrDecoderLayer(config) for _ in range(config.decoder_layers)]) | |
self.gradient_checkpointing = False | |
# hack implementation for iterative bounding box refinement and two-stage Deformable DETR | |
self.bbox_embed = None | |
self.class_embed = None | |
# Initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
inputs_embeds=None, | |
encoder_hidden_states=None, | |
encoder_attention_mask=None, | |
position_embeddings=None, | |
reference_points=None, | |
spatial_shapes=None, | |
level_start_index=None, | |
valid_ratios=None, | |
output_attentions=None, | |
output_hidden_states=None, | |
return_dict=None, | |
): | |
r""" | |
Args: | |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): | |
The query embeddings that are passed into the decoder. | |
encoder_hidden_states (`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. Used in the cross-attention | |
of the decoder. | |
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, 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**). | |
position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): | |
Position embeddings that are added to the queries and keys in each self-attention layer. | |
reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*): | |
Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area. | |
spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`): | |
Spatial shapes of the feature maps. | |
level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*): | |
Indexes for the start of each feature level. In range `[0, sequence_length]`. | |
valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`, *optional*): | |
Ratio of valid area in each feature level. | |
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 [`~file_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 | |
# 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 | |
intermediate = () | |
intermediate_reference_points = () | |
for idx, decoder_layer in enumerate(self.layers): | |
if reference_points.shape[-1] == 4: | |
reference_points_input = ( | |
reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None] | |
) | |
else: | |
if reference_points.shape[-1] != 2: | |
raise ValueError("Reference points' last dimension must be of size 2") | |
reference_points_input = reference_points[:, :, None] * valid_ratios[:, None] | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
if self.gradient_checkpointing and self.training: | |
def create_custom_forward(module): | |
def custom_forward(*inputs): | |
return module(*inputs, output_attentions) | |
return custom_forward | |
layer_outputs = torch.utils.checkpoint.checkpoint( | |
create_custom_forward(decoder_layer), | |
hidden_states, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
None, | |
) | |
else: | |
layer_outputs = decoder_layer( | |
hidden_states, | |
position_embeddings=position_embeddings, | |
encoder_hidden_states=encoder_hidden_states, | |
reference_points=reference_points_input, | |
spatial_shapes=spatial_shapes, | |
level_start_index=level_start_index, | |
encoder_attention_mask=encoder_attention_mask, | |
output_attentions=output_attentions, | |
) | |
hidden_states = layer_outputs[0] | |
# hack implementation for iterative bounding box refinement | |
if self.bbox_embed is not None: | |
tmp = self.bbox_embed[idx](hidden_states) | |
if reference_points.shape[-1] == 4: | |
new_reference_points = tmp + inverse_sigmoid(reference_points) | |
new_reference_points = new_reference_points.sigmoid() | |
else: | |
if reference_points.shape[-1] != 2: | |
raise ValueError( | |
f"Reference points' last dimension must be of size 2, but is {reference_points.shape[-1]}" | |
) | |
new_reference_points = tmp | |
new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) | |
new_reference_points = new_reference_points.sigmoid() | |
reference_points = new_reference_points.detach() | |
intermediate += (hidden_states,) | |
intermediate_reference_points += (reference_points,) | |
if output_attentions: | |
all_self_attns += (layer_outputs[1],) | |
if encoder_hidden_states is not None: | |
all_cross_attentions += (layer_outputs[2],) | |
# Keep batch_size as first dimension | |
intermediate = torch.stack(intermediate, dim=1) | |
intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1) | |
# add hidden states from the last decoder layer | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
if not return_dict: | |
return tuple( | |
v | |
for v in [ | |
hidden_states, | |
intermediate, | |
intermediate_reference_points, | |
all_hidden_states, | |
all_self_attns, | |
all_cross_attentions, | |
] | |
if v is not None | |
) | |
return DeformableDetrDecoderOutput( | |
last_hidden_state=hidden_states, | |
intermediate_hidden_states=intermediate, | |
intermediate_reference_points=intermediate_reference_points, | |
hidden_states=all_hidden_states, | |
attentions=all_self_attns, | |
cross_attentions=all_cross_attentions, | |
) | |
class DeformableDetrModel(DeformableDetrPreTrainedModel): | |
def __init__(self, config: DeformableDetrConfig): | |
super().__init__(config) | |
# Create backbone + positional encoding | |
backbone = DeformableDetrConvEncoder(config) | |
position_embeddings = build_position_encoding(config) | |
self.backbone = DeformableDetrConvModel(backbone, position_embeddings) | |
# Create input projection layers | |
if config.num_feature_levels > 1: | |
num_backbone_outs = len(backbone.intermediate_channel_sizes) | |
input_proj_list = [] | |
for _ in range(num_backbone_outs): | |
in_channels = backbone.intermediate_channel_sizes[_] | |
input_proj_list.append( | |
nn.Sequential( | |
nn.Conv2d(in_channels, config.d_model, kernel_size=1), | |
nn.GroupNorm(32, config.d_model), | |
) | |
) | |
for _ in range(config.num_feature_levels - num_backbone_outs): | |
input_proj_list.append( | |
nn.Sequential( | |
nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1), | |
nn.GroupNorm(32, config.d_model), | |
) | |
) | |
in_channels = config.d_model | |
self.input_proj = nn.ModuleList(input_proj_list) | |
else: | |
self.input_proj = nn.ModuleList( | |
[ | |
nn.Sequential( | |
nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1), | |
nn.GroupNorm(32, config.d_model), | |
) | |
] | |
) | |
if not config.two_stage: | |
self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model * 2) | |
self.encoder = DeformableDetrEncoder(config) | |
self.decoder = DeformableDetrDecoder(config) | |
self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model)) | |
if config.two_stage: | |
self.enc_output = nn.Linear(config.d_model, config.d_model) | |
self.enc_output_norm = nn.LayerNorm(config.d_model) | |
self.pos_trans = nn.Linear(config.d_model * 2, config.d_model * 2) | |
self.pos_trans_norm = nn.LayerNorm(config.d_model * 2) | |
else: | |
self.reference_points = nn.Linear(config.d_model, 2) | |
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 get_valid_ratio(self, mask): | |
"""Get the valid ratio of all feature maps.""" | |
_, height, width = mask.shape | |
valid_height = torch.sum(mask[:, :, 0], 1) | |
valid_width = torch.sum(mask[:, 0, :], 1) | |
valid_ratio_heigth = valid_height.float() / height | |
valid_ratio_width = valid_width.float() / width | |
valid_ratio = torch.stack([valid_ratio_width, valid_ratio_heigth], -1) | |
return valid_ratio | |
def get_proposal_pos_embed(self, proposals): | |
"""Get the position embedding of the proposals.""" | |
num_pos_feats = self.config.d_model // 2 | |
temperature = 10000 | |
scale = 2 * math.pi | |
dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device) | |
dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats) | |
# batch_size, num_queries, 4 | |
proposals = proposals.sigmoid() * scale | |
# batch_size, num_queries, 4, 128 | |
pos = proposals[:, :, :, None] / dim_t | |
# batch_size, num_queries, 4, 64, 2 -> batch_size, num_queries, 512 | |
pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) | |
return pos | |
def gen_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes): | |
"""Generate the encoder output proposals from encoded enc_output. | |
Args: | |
enc_output (Tensor[batch_size, sequence_length, hidden_size]): Output of the encoder. | |
padding_mask (Tensor[batch_size, sequence_length]): Padding mask for `enc_output`. | |
spatial_shapes (Tensor[num_feature_levels, 2]): Spatial shapes of the feature maps. | |
Returns: | |
`tuple(torch.FloatTensor)`: A tuple of feature map and bbox prediction. | |
- object_query (Tensor[batch_size, sequence_length, hidden_size]): Object query features. Later used to | |
directly predict a bounding box. (without the need of a decoder) | |
- output_proposals (Tensor[batch_size, sequence_length, 4]): Normalized proposals, after an inverse | |
sigmoid. | |
""" | |
batch_size = enc_output.shape[0] | |
proposals = [] | |
_cur = 0 | |
for level, (height, width) in enumerate(spatial_shapes): | |
mask_flatten_ = padding_mask[:, _cur : (_cur + height * width)].view(batch_size, height, width, 1) | |
valid_height = torch.sum(~mask_flatten_[:, :, 0, 0], 1) | |
valid_width = torch.sum(~mask_flatten_[:, 0, :, 0], 1) | |
grid_y, grid_x = meshgrid( | |
torch.linspace(0, height - 1, height, dtype=torch.float32, device=enc_output.device), | |
torch.linspace(0, width - 1, width, dtype=torch.float32, device=enc_output.device), | |
indexing="ij", | |
) | |
grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) | |
scale = torch.cat([valid_width.unsqueeze(-1), valid_height.unsqueeze(-1)], 1).view(batch_size, 1, 1, 2) | |
grid = (grid.unsqueeze(0).expand(batch_size, -1, -1, -1) + 0.5) / scale | |
width_heigth = torch.ones_like(grid) * 0.05 * (2.0**level) | |
proposal = torch.cat((grid, width_heigth), -1).view(batch_size, -1, 4) | |
proposals.append(proposal) | |
_cur += height * width | |
output_proposals = torch.cat(proposals, 1) | |
output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True) | |
output_proposals = torch.log(output_proposals / (1 - output_proposals)) # inverse sigmoid | |
output_proposals = output_proposals.masked_fill(padding_mask.unsqueeze(-1), float("inf")) | |
output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) | |
# assign each pixel as an object query | |
object_query = enc_output | |
object_query = object_query.masked_fill(padding_mask.unsqueeze(-1), float(0)) | |
object_query = object_query.masked_fill(~output_proposals_valid, float(0)) | |
object_query = self.enc_output_norm(self.enc_output(object_query)) | |
return object_query, output_proposals | |
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], DeformableDetrModelOutput]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from transformers import AutoImageProcessor, DeformableDetrModel | |
>>> 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("SenseTime/deformable-detr") | |
>>> model = DeformableDetrModel.from_pretrained("SenseTime/deformable-detr") | |
>>> inputs = image_processor(images=image, return_tensors="pt") | |
>>> outputs = model(**inputs) | |
>>> last_hidden_states = outputs.last_hidden_state | |
>>> list(last_hidden_states.shape) | |
[1, 300, 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)), dtype=torch.long, device=device) | |
# Extract multi-scale feature maps of same resolution `config.d_model` (cf Figure 4 in paper) | |
# First, sent pixel_values + pixel_mask through Backbone to obtain the features | |
# which is a list of tuples | |
features, position_embeddings_list = self.backbone(pixel_values, pixel_mask) | |
# Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) | |
sources = [] | |
masks = [] | |
for level, (source, mask) in enumerate(features): | |
sources.append(self.input_proj[level](source)) | |
masks.append(mask) | |
if mask is None: | |
raise ValueError("No attention mask was provided") | |
# Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage | |
if self.config.num_feature_levels > len(sources): | |
_len_sources = len(sources) | |
for level in range(_len_sources, self.config.num_feature_levels): | |
if level == _len_sources: | |
source = self.input_proj[level](features[-1][0]) | |
else: | |
source = self.input_proj[level](sources[-1]) | |
mask = nn.functional.interpolate(pixel_mask[None].float(), size=source.shape[-2:]).to(torch.bool)[0] | |
pos_l = self.backbone.position_embedding(source, mask).to(source.dtype) | |
sources.append(source) | |
masks.append(mask) | |
position_embeddings_list.append(pos_l) | |
# Create queries | |
query_embeds = None | |
if not self.config.two_stage: | |
query_embeds = self.query_position_embeddings.weight | |
# Prepare encoder inputs (by flattening) | |
source_flatten = [] | |
mask_flatten = [] | |
lvl_pos_embed_flatten = [] | |
spatial_shapes = [] | |
for level, (source, mask, pos_embed) in enumerate(zip(sources, masks, position_embeddings_list)): | |
batch_size, num_channels, height, width = source.shape | |
spatial_shape = (height, width) | |
spatial_shapes.append(spatial_shape) | |
source = source.flatten(2).transpose(1, 2) | |
mask = mask.flatten(1) | |
pos_embed = pos_embed.flatten(2).transpose(1, 2) | |
lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1) | |
lvl_pos_embed_flatten.append(lvl_pos_embed) | |
source_flatten.append(source) | |
mask_flatten.append(mask) | |
source_flatten = torch.cat(source_flatten, 1) | |
mask_flatten = torch.cat(mask_flatten, 1) | |
lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) | |
spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=source_flatten.device) | |
level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) | |
valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) | |
valid_ratios = valid_ratios.float() | |
# Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder | |
# Also provide spatial_shapes, level_start_index and valid_ratios | |
if encoder_outputs is None: | |
encoder_outputs = self.encoder( | |
inputs_embeds=source_flatten, | |
attention_mask=mask_flatten, | |
position_embeddings=lvl_pos_embed_flatten, | |
spatial_shapes=spatial_shapes, | |
level_start_index=level_start_index, | |
valid_ratios=valid_ratios, | |
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, prepare decoder inputs | |
batch_size, _, num_channels = encoder_outputs[0].shape | |
enc_outputs_class = None | |
enc_outputs_coord_logits = None | |
if self.config.two_stage: | |
object_query_embedding, output_proposals = self.gen_encoder_output_proposals( | |
encoder_outputs[0], ~mask_flatten, spatial_shapes | |
) | |
# hack implementation for two-stage Deformable DETR | |
# apply a detection head to each pixel (A.4 in paper) | |
# linear projection for bounding box binary classification (i.e. foreground and background) | |
enc_outputs_class = self.decoder.class_embed[-1](object_query_embedding) | |
# 3-layer FFN to predict bounding boxes coordinates (bbox regression branch) | |
delta_bbox = self.decoder.bbox_embed[-1](object_query_embedding) | |
enc_outputs_coord_logits = delta_bbox + output_proposals | |
# only keep top scoring `config.two_stage_num_proposals` proposals | |
topk = self.config.two_stage_num_proposals | |
topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1] | |
topk_coords_logits = torch.gather( | |
enc_outputs_coord_logits, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4) | |
) | |
topk_coords_logits = topk_coords_logits.detach() | |
reference_points = topk_coords_logits.sigmoid() | |
init_reference_points = reference_points | |
pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_logits))) | |
query_embed, target = torch.split(pos_trans_out, num_channels, dim=2) | |
else: | |
query_embed, target = torch.split(query_embeds, num_channels, dim=1) | |
query_embed = query_embed.unsqueeze(0).expand(batch_size, -1, -1) | |
target = target.unsqueeze(0).expand(batch_size, -1, -1) | |
reference_points = self.reference_points(query_embed).sigmoid() | |
init_reference_points = reference_points | |
decoder_outputs = self.decoder( | |
inputs_embeds=target, | |
position_embeddings=query_embed, | |
encoder_hidden_states=encoder_outputs[0], | |
encoder_attention_mask=mask_flatten, | |
reference_points=reference_points, | |
spatial_shapes=spatial_shapes, | |
level_start_index=level_start_index, | |
valid_ratios=valid_ratios, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
if not return_dict: | |
enc_outputs = tuple(value for value in [enc_outputs_class, enc_outputs_coord_logits] if value is not None) | |
tuple_outputs = (init_reference_points,) + decoder_outputs + encoder_outputs + enc_outputs | |
return tuple_outputs | |
return DeformableDetrModelOutput( | |
init_reference_points=init_reference_points, | |
last_hidden_state=decoder_outputs.last_hidden_state, | |
intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, | |
intermediate_reference_points=decoder_outputs.intermediate_reference_points, | |
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, | |
enc_outputs_class=enc_outputs_class, | |
enc_outputs_coord_logits=enc_outputs_coord_logits, | |
) | |
class DeformableDetrForObjectDetection(DeformableDetrPreTrainedModel): | |
# When using clones, all layers > 0 will be clones, but layer 0 *is* required | |
_tied_weights_keys = [r"bbox_embed\.[1-9]\d*", r"class_embed\.[1-9]\d*"] | |
def __init__(self, config: DeformableDetrConfig): | |
super().__init__(config) | |
# Deformable DETR encoder-decoder model | |
self.model = DeformableDetrModel(config) | |
# Detection heads on top | |
self.class_embed = nn.Linear(config.d_model, config.num_labels) | |
self.bbox_embed = DeformableDetrMLPPredictionHead( | |
input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3 | |
) | |
prior_prob = 0.01 | |
bias_value = -math.log((1 - prior_prob) / prior_prob) | |
self.class_embed.bias.data = torch.ones(config.num_labels) * bias_value | |
nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0) | |
nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0) | |
# if two-stage, the last class_embed and bbox_embed is for region proposal generation | |
num_pred = (config.decoder_layers + 1) if config.two_stage else config.decoder_layers | |
if config.with_box_refine: | |
self.class_embed = _get_clones(self.class_embed, num_pred) | |
self.bbox_embed = _get_clones(self.bbox_embed, num_pred) | |
nn.init.constant_(self.bbox_embed[0].layers[-1].bias.data[2:], -2.0) | |
# hack implementation for iterative bounding box refinement | |
self.model.decoder.bbox_embed = self.bbox_embed | |
else: | |
nn.init.constant_(self.bbox_embed.layers[-1].bias.data[2:], -2.0) | |
self.class_embed = nn.ModuleList([self.class_embed for _ in range(num_pred)]) | |
self.bbox_embed = nn.ModuleList([self.bbox_embed for _ in range(num_pred)]) | |
self.model.decoder.bbox_embed = None | |
if config.two_stage: | |
# hack implementation for two-stage | |
self.model.decoder.class_embed = self.class_embed | |
for box_embed in self.bbox_embed: | |
nn.init.constant_(box_embed.layers[-1].bias.data[2:], 0.0) | |
# 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], DeformableDetrObjectDetectionOutput]: | |
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, DeformableDetrForObjectDetection | |
>>> 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("SenseTime/deformable-detr") | |
>>> model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr") | |
>>> inputs = image_processor(images=image, return_tensors="pt") | |
>>> outputs = model(**inputs) | |
>>> # convert outputs (bounding boxes and class logits) to COCO API | |
>>> target_sizes = torch.tensor([image.size[::-1]]) | |
>>> results = image_processor.post_process_object_detection(outputs, threshold=0.5, 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 cat with confidence 0.8 at location [16.5, 52.84, 318.25, 470.78] | |
Detected cat with confidence 0.789 at location [342.19, 24.3, 640.02, 372.25] | |
Detected remote with confidence 0.633 at location [40.79, 72.78, 176.76, 117.25] | |
```""" | |
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, | |
) | |
hidden_states = outputs.intermediate_hidden_states if return_dict else outputs[2] | |
init_reference = outputs.init_reference_points if return_dict else outputs[0] | |
inter_references = outputs.intermediate_reference_points if return_dict else outputs[3] | |
# class logits + predicted bounding boxes | |
outputs_classes = [] | |
outputs_coords = [] | |
for level in range(hidden_states.shape[1]): | |
if level == 0: | |
reference = init_reference | |
else: | |
reference = inter_references[:, level - 1] | |
reference = inverse_sigmoid(reference) | |
outputs_class = self.class_embed[level](hidden_states[:, level]) | |
delta_bbox = self.bbox_embed[level](hidden_states[:, level]) | |
if reference.shape[-1] == 4: | |
outputs_coord_logits = delta_bbox + reference | |
elif reference.shape[-1] == 2: | |
delta_bbox[..., :2] += reference | |
outputs_coord_logits = delta_bbox | |
else: | |
raise ValueError(f"reference.shape[-1] should be 4 or 2, but got {reference.shape[-1]}") | |
outputs_coord = outputs_coord_logits.sigmoid() | |
outputs_classes.append(outputs_class) | |
outputs_coords.append(outputs_coord) | |
outputs_class = torch.stack(outputs_classes) | |
outputs_coord = torch.stack(outputs_coords) | |
logits = outputs_class[-1] | |
pred_boxes = outputs_coord[-1] | |
loss, loss_dict, auxiliary_outputs = None, None, None | |
if labels is not None: | |
# First: create the matcher | |
matcher = DeformableDetrHungarianMatcher( | |
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 = DeformableDetrLoss( | |
matcher=matcher, | |
num_classes=self.config.num_labels, | |
focal_alpha=self.config.focal_alpha, | |
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: | |
auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord) | |
outputs_loss["auxiliary_outputs"] = auxiliary_outputs | |
if self.config.two_stage: | |
enc_outputs_coord = outputs.enc_outputs_coord_logits.sigmoid() | |
outputs_loss["enc_outputs"] = {"logits": outputs.enc_outputs_class, "pred_boxes": enc_outputs_coord} | |
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 | |
tuple_outputs = ((loss, loss_dict) + output) if loss is not None else output | |
return tuple_outputs | |
dict_outputs = DeformableDetrObjectDetectionOutput( | |
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, | |
intermediate_hidden_states=outputs.intermediate_hidden_states, | |
intermediate_reference_points=outputs.intermediate_reference_points, | |
init_reference_points=outputs.init_reference_points, | |
enc_outputs_class=outputs.enc_outputs_class, | |
enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, | |
) | |
return dict_outputs | |
# Copied from transformers.models.detr.modeling_detr.dice_loss | |
def dice_loss(inputs, targets, num_boxes): | |
""" | |
Compute the DICE loss, similar to generalized IOU for masks | |
Args: | |
inputs: A float tensor of arbitrary shape. | |
The predictions for each example. | |
targets: A float tensor with the same shape as inputs. Stores the binary | |
classification label for each element in inputs (0 for the negative class and 1 for the positive | |
class). | |
""" | |
inputs = inputs.sigmoid() | |
inputs = inputs.flatten(1) | |
numerator = 2 * (inputs * targets).sum(1) | |
denominator = inputs.sum(-1) + targets.sum(-1) | |
loss = 1 - (numerator + 1) / (denominator + 1) | |
return loss.sum() / num_boxes | |
# Copied from transformers.models.detr.modeling_detr.sigmoid_focal_loss | |
def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): | |
""" | |
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. | |
Args: | |
inputs (`torch.FloatTensor` of arbitrary shape): | |
The predictions for each example. | |
targets (`torch.FloatTensor` with the same shape as `inputs`) | |
A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class | |
and 1 for the positive class). | |
alpha (`float`, *optional*, defaults to `0.25`): | |
Optional weighting factor in the range (0,1) to balance positive vs. negative examples. | |
gamma (`int`, *optional*, defaults to `2`): | |
Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. | |
Returns: | |
Loss tensor | |
""" | |
prob = inputs.sigmoid() | |
ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") | |
# add modulating factor | |
p_t = prob * targets + (1 - prob) * (1 - targets) | |
loss = ce_loss * ((1 - p_t) ** gamma) | |
if alpha >= 0: | |
alpha_t = alpha * targets + (1 - alpha) * (1 - targets) | |
loss = alpha_t * loss | |
return loss.mean(1).sum() / num_boxes | |
class DeformableDetrLoss(nn.Module): | |
""" | |
This class computes the losses for `DeformableDetrForObjectDetection`. 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). | |
Args: | |
matcher (`DeformableDetrHungarianMatcher`): | |
Module able to compute a matching between targets and proposals. | |
num_classes (`int`): | |
Number of object categories, omitting the special no-object category. | |
focal_alpha (`float`): | |
Alpha parameter in focal loss. | |
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, focal_alpha, losses): | |
super().__init__() | |
self.matcher = matcher | |
self.num_classes = num_classes | |
self.focal_alpha = focal_alpha | |
self.losses = losses | |
# removed logging parameter, which was part of the original implementation | |
def loss_labels(self, outputs, targets, indices, num_boxes): | |
""" | |
Classification loss (Binary focal loss) 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 | |
target_classes_onehot = torch.zeros( | |
[source_logits.shape[0], source_logits.shape[1], source_logits.shape[2] + 1], | |
dtype=source_logits.dtype, | |
layout=source_logits.layout, | |
device=source_logits.device, | |
) | |
target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1) | |
target_classes_onehot = target_classes_onehot[:, :, :-1] | |
loss_ce = ( | |
sigmoid_focal_loss(source_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2) | |
* source_logits.shape[1] | |
) | |
losses = {"loss_ce": loss_ce} | |
return losses | |
# Copied from transformers.models.detr.modeling_detr.DetrLoss.loss_cardinality | |
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 | |
# Copied from transformers.models.detr.modeling_detr.DetrLoss.loss_boxes | |
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 | |
# Copied from transformers.models.detr.modeling_detr.DetrLoss._get_source_permutation_idx | |
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 | |
# Copied from transformers.models.detr.modeling_detr.DetrLoss._get_target_permutation_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, | |
} | |
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" and k != "enc_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 accross 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) | |
# (Niels): comment out function below, distributed training to be added | |
# if is_dist_avail_and_initialized(): | |
# torch.distributed.all_reduce(num_boxes) | |
# (Niels) in original implementation, num_boxes is divided by get_world_size() | |
num_boxes = torch.clamp(num_boxes, 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: | |
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) | |
if "enc_outputs" in outputs: | |
enc_outputs = outputs["enc_outputs"] | |
bin_targets = copy.deepcopy(targets) | |
for bt in bin_targets: | |
bt["class_labels"] = torch.zeros_like(bt["class_labels"]) | |
indices = self.matcher(enc_outputs, bin_targets) | |
for loss in self.losses: | |
l_dict = self.get_loss(loss, enc_outputs, bin_targets, indices, num_boxes) | |
l_dict = {k + "_enc": v for k, v in l_dict.items()} | |
losses.update(l_dict) | |
return losses | |
# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead | |
class DeformableDetrMLPPredictionHead(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 | |
class DeformableDetrHungarianMatcher(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).sigmoid() # [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. | |
alpha = 0.25 | |
gamma = 2.0 | |
neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log()) | |
pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log()) | |
class_cost = pos_cost_class[:, target_ids] - neg_cost_class[:, target_ids] | |
# Compute the L1 cost between boxes | |
bbox_cost = torch.cdist(out_bbox, target_bbox, p=1) | |
# Compute the giou cost between boxes | |
giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox)) | |
# Final cost matrix | |
cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost | |
cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu() | |
sizes = [len(v["boxes"]) for v in targets] | |
indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))] | |
return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] | |
# Copied from transformers.models.detr.modeling_detr._upcast | |
def _upcast(t: Tensor) -> Tensor: | |
# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type | |
if t.is_floating_point(): | |
return t if t.dtype in (torch.float32, torch.float64) else t.float() | |
else: | |
return t if t.dtype in (torch.int32, torch.int64) else t.int() | |
# Copied from transformers.models.detr.modeling_detr.box_area | |
def box_area(boxes: Tensor) -> Tensor: | |
""" | |
Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. | |
Args: | |
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): | |
Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 | |
< x2` and `0 <= y1 < y2`. | |
Returns: | |
`torch.FloatTensor`: a tensor containing the area for each box. | |
""" | |
boxes = _upcast(boxes) | |
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) | |
# Copied from transformers.models.detr.modeling_detr.box_iou | |
def box_iou(boxes1, boxes2): | |
area1 = box_area(boxes1) | |
area2 = box_area(boxes2) | |
left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] | |
right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] | |
width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] | |
inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] | |
union = area1[:, None] + area2 - inter | |
iou = inter / union | |
return iou, union | |
# Copied from transformers.models.detr.modeling_detr.generalized_box_iou | |
def generalized_box_iou(boxes1, boxes2): | |
""" | |
Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. | |
Returns: | |
`torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) | |
""" | |
# degenerate boxes gives inf / nan results | |
# so do an early check | |
if not (boxes1[:, 2:] >= boxes1[:, :2]).all(): | |
raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}") | |
if not (boxes2[:, 2:] >= boxes2[:, :2]).all(): | |
raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}") | |
iou, union = box_iou(boxes1, boxes2) | |
top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2]) | |
bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) | |
width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2] | |
area = width_height[:, :, 0] * width_height[:, :, 1] | |
return iou - (area - union) / area | |
# Copied from transformers.models.detr.modeling_detr._max_by_axis | |
def _max_by_axis(the_list): | |
# type: (List[List[int]]) -> List[int] | |
maxes = the_list[0] | |
for sublist in the_list[1:]: | |
for index, item in enumerate(sublist): | |
maxes[index] = max(maxes[index], item) | |
return maxes | |
# Copied from transformers.models.detr.modeling_detr.NestedTensor | |
class NestedTensor(object): | |
def __init__(self, tensors, mask: Optional[Tensor]): | |
self.tensors = tensors | |
self.mask = mask | |
def to(self, device): | |
cast_tensor = self.tensors.to(device) | |
mask = self.mask | |
if mask is not None: | |
cast_mask = mask.to(device) | |
else: | |
cast_mask = None | |
return NestedTensor(cast_tensor, cast_mask) | |
def decompose(self): | |
return self.tensors, self.mask | |
def __repr__(self): | |
return str(self.tensors) | |
# Copied from transformers.models.detr.modeling_detr.nested_tensor_from_tensor_list | |
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) | |