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# coding=utf-8
# Copyright 2022 The IDEA Authors. 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.
# ------------------------------------------------------------------------------------------------
# Modified from:
# https://github.com/Atten4Vis/ConditionalDETR/blob/GroupDETR/models/conditional_detr.py
# ------------------------------------------------------------------------------------------------
import math
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from detrex.layers.box_ops import box_cxcywh_to_xyxy, box_xyxy_to_cxcywh
from detrex.layers.mlp import MLP
from detrex.utils.misc import inverse_sigmoid
from detectron2.modeling import detector_postprocess
from detectron2.structures import Boxes, ImageList, Instances
class GroupDETR(nn.Module):
"""Implement Group-DETR upon Conditional-DETR in
`Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment
<https://arxiv.org/abs/2207.13085>`_
Args:
backbone (nn.Module): Backbone module for feature extraction.
in_features (List[str]): Selected backbone output features for transformer module.
in_channels (int): Dimension of the last feature in `in_features`.
position_embedding (nn.Module): Position encoding layer for generating position embeddings.
transformer (nn.Module): Transformer module used for further processing features and input queries.
embed_dim (int): Hidden dimension for transformer module.
num_classes (int): Number of total categories.
num_queries (int): Number of proposal dynamic anchor boxes in Transformer
criterion (nn.Module): Criterion for calculating the total losses.
aux_loss (bool): Whether to calculate auxiliary loss in criterion. Default: True.
group_num (int): The number of query groups used in GroupDETR. Default: 11.
pixel_mean (List[float]): Pixel mean value for image normalization.
Default: [123.675, 116.280, 103.530].
pixel_std (List[float]): Pixel std value for image normalization.
Default: [58.395, 57.120, 57.375].
select_box_nums_for_evaluation (int): Select the top-k confidence predicted boxes for inference.
Default: 300.
device (str): Training device. Default: "cuda".
"""
def __init__(
self,
backbone: nn.Module,
in_features: List[str],
in_channels: int,
position_embedding: nn.Module,
transformer: nn.Module,
embed_dim: int,
num_classes: int,
num_queries: int,
criterion: nn.Module,
aux_loss: bool = True,
group_nums: int = 11,
pixel_mean: List[float] = [123.675, 116.280, 103.530],
pixel_std: List[float] = [58.395, 57.120, 57.375],
select_box_nums_for_evaluation: int = 300,
device: str = "cuda",
):
super(GroupDETR, self).__init__()
# define backbone and position embedding module
self.backbone = backbone
self.in_features = in_features
self.position_embedding = position_embedding
# project the backbone output feature
# into the required dim for transformer block
self.input_proj = nn.Conv2d(in_channels, embed_dim, kernel_size=1)
# define leanable object query embed and transformer module
self.transformer = transformer
self.query_embed = nn.Embedding(num_queries * group_nums, embed_dim)
self.num_queries = num_queries
# define classification head and box head
self.class_embed = nn.Linear(embed_dim, num_classes)
self.bbox_embed = MLP(input_dim=embed_dim, hidden_dim=embed_dim, output_dim=4, num_layers=3)
self.num_classes = num_classes
# where to calculate auxiliary loss in criterion
self.aux_loss = aux_loss
self.criterion = criterion
# normalizer for input raw images
self.device = device
pixel_mean = torch.Tensor(pixel_mean).to(self.device).view(3, 1, 1)
pixel_std = torch.Tensor(pixel_std).to(self.device).view(3, 1, 1)
self.normalizer = lambda x: (x - pixel_mean) / pixel_std
# The total nums of selected boxes for evaluation
self.select_box_nums_for_evaluation = select_box_nums_for_evaluation
self.init_weights()
def init_weights(self):
"""Initialize weights for Conditioanl-DETR."""
prior_prob = 0.01
bias_value = -math.log((1 - prior_prob) / prior_prob)
self.class_embed.bias.data = torch.ones(self.num_classes) * bias_value
nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)
nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0)
def forward(self, batched_inputs):
"""Forward function of `DAB-DETR` which excepts a list of dict as inputs.
Args:
batched_inputs (List[dict]): A list of instance dict, and each instance dict must consists of:
- dict["image"] (torch.Tensor): The unnormalized image tensor.
- dict["height"] (int): The original image height.
- dict["width"] (int): The original image width.
- dict["instance"] (detectron2.structures.Instances):
Image meta informations and ground truth boxes and labels during training.
Please refer to
https://detectron2.readthedocs.io/en/latest/modules/structures.html#detectron2.structures.Instances
for the basic usage of Instances.
Returns:
dict: Returns a dict with the following elements:
- dict["pred_logits"]: the classification logits for all queries.
with shape ``[batch_size, num_queries, num_classes]``
- dict["pred_boxes"]: The normalized boxes coordinates for all queries in format
``(x, y, w, h)``. These values are normalized in [0, 1] relative to the size of
each individual image (disregarding possible padding). See PostProcess for information
on how to retrieve the unnormalized bounding box.
- dict["aux_outputs"]: Optional, only returned when auxilary losses are activated. It is a list of
dictionnaries containing the two above keys for each decoder layer.
"""
images = self.preprocess_image(batched_inputs)
if self.training:
batch_size, _, H, W = images.tensor.shape
img_masks = images.tensor.new_ones(batch_size, H, W)
for img_id in range(batch_size):
img_h, img_w = batched_inputs[img_id]["instances"].image_size
img_masks[img_id, :img_h, :img_w] = 0
else:
batch_size, _, H, W = images.tensor.shape
img_masks = images.tensor.new_zeros(batch_size, H, W)
# only use last level feature in Conditional-DETR
features = self.backbone(images.tensor)[self.in_features[-1]]
features = self.input_proj(features)
img_masks = F.interpolate(img_masks[None], size=features.shape[-2:]).to(torch.bool)[0]
pos_embed = self.position_embedding(img_masks)
# training with multi-groups and inference in one group
if self.training:
query_embed_weight = self.query_embed.weight
else:
query_embed_weight = self.query_embed.weight[: self.num_queries]
# hidden_states: transformer output hidden feature
# reference: reference points in format (x, y) with normalized coordinates in range of [0, 1].
hidden_states, reference = self.transformer(
features, img_masks, query_embed_weight, pos_embed
)
reference_before_sigmoid = inverse_sigmoid(reference)
outputs_coords = []
for lvl in range(hidden_states.shape[0]):
tmp = self.bbox_embed(hidden_states[lvl])
tmp[..., :2] += reference_before_sigmoid
outputs_coord = tmp.sigmoid()
outputs_coords.append(outputs_coord)
outputs_coord = torch.stack(outputs_coords)
outputs_class = self.class_embed(hidden_states)
output = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord[-1]}
if self.aux_loss:
output["aux_outputs"] = self._set_aux_loss(outputs_class, outputs_coord)
if self.training:
gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
targets = self.prepare_targets(gt_instances)
loss_dict = self.criterion(output, targets)
weight_dict = self.criterion.weight_dict
for k in loss_dict.keys():
if k in weight_dict:
loss_dict[k] *= weight_dict[k]
return loss_dict
else:
box_cls = output["pred_logits"]
box_pred = output["pred_boxes"]
results = self.inference(box_cls, box_pred, images.image_sizes)
processed_results = []
for results_per_image, input_per_image, image_size in zip(
results, batched_inputs, images.image_sizes
):
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
r = detector_postprocess(results_per_image, height, width)
processed_results.append({"instances": r})
return processed_results
@torch.jit.unused
def _set_aux_loss(self, outputs_class, outputs_coord):
# this is a workaround to make torchscript happy, as torchscript
# doesn't support dictionary with non-homogeneous values, such
# as a dict having both a Tensor and a list.
return [
{"pred_logits": a, "pred_boxes": b}
for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
]
def inference(self, box_cls, box_pred, image_sizes):
"""Inference function for DAB-DETR
Args:
box_cls (torch.Tensor): tensor of shape ``(batch_size, num_queries, K)``.
The tensor predicts the classification probability for each query.
box_pred (torch.Tensor): tensors of shape ``(batch_size, num_queries, 4)``.
The tensor predicts 4-vector ``(x, y, w, h)`` box
regression values for every queryx
image_sizes (List[torch.Size]): the input image sizes
Returns:
results (List[Instances]): a list of #images elements.
"""
assert len(box_cls) == len(image_sizes)
results = []
prob = box_cls.sigmoid()
topk_values, topk_indexes = torch.topk(
prob.view(box_cls.shape[0], -1),
self.select_box_nums_for_evaluation,
dim=1,
)
scores = topk_values
topk_boxes = torch.div(topk_indexes, box_cls.shape[2], rounding_mode="floor")
labels = topk_indexes % box_cls.shape[2]
boxes = torch.gather(box_pred, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
for i, (scores_per_image, labels_per_image, box_pred_per_image, image_size) in enumerate(
zip(scores, labels, boxes, image_sizes)
):
result = Instances(image_size)
result.pred_boxes = Boxes(box_cxcywh_to_xyxy(box_pred_per_image))
result.pred_boxes.scale(scale_x=image_size[1], scale_y=image_size[0])
result.scores = scores_per_image
result.pred_classes = labels_per_image
results.append(result)
return results
def prepare_targets(self, targets):
new_targets = []
for targets_per_image in targets:
h, w = targets_per_image.image_size
image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float, device=self.device)
gt_classes = targets_per_image.gt_classes
gt_boxes = targets_per_image.gt_boxes.tensor / image_size_xyxy
gt_boxes = box_xyxy_to_cxcywh(gt_boxes)
new_targets.append({"labels": gt_classes, "boxes": gt_boxes})
return new_targets
def preprocess_image(self, batched_inputs):
images = [self.normalizer(x["image"].to(self.device)) for x in batched_inputs]
images = ImageList.from_tensors(images)
return images
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