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263
projects/SparseRCNN/sparsercnn/test_time_augmentation.py
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# Modified by Rufeng Zhang, Peize Sun | ||
# Contact: {sunpeize, cxrfzhang}@foxmail.com | ||
# | ||
# Copyright (c) Megvii, Inc. and its affiliates. All Rights Reserved | ||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | ||
# | ||
from itertools import count | ||
import torch | ||
import torch.nn as nn | ||
from torch.nn.parallel import DistributedDataParallel | ||
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from detectron2.modeling import GeneralizedRCNNWithTTA, DatasetMapperTTA | ||
from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference_single_image | ||
from detectron2.structures import Instances, Boxes | ||
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class SparseRCNNWithTTA(GeneralizedRCNNWithTTA): | ||
""" | ||
A SparseRCNN with test-time augmentation enabled. | ||
Its :meth:`__call__` method has the same interface as :meth:`SparseRCNN.forward`. | ||
""" | ||
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def __init__(self, cfg, model, tta_mapper=None, batch_size=3): | ||
""" | ||
Args: | ||
cfg (CfgNode): | ||
model (SparseRCNN): a SparseRCNN to apply TTA on. | ||
tta_mapper (callable): takes a dataset dict and returns a list of | ||
augmented versions of the dataset dict. Defaults to | ||
`DatasetMapperTTA(cfg)`. | ||
batch_size (int): batch the augmented images into this batch size for inference. | ||
""" | ||
# fix the issue: cannot assign module before Module.__init__() call | ||
nn.Module.__init__(self) | ||
if isinstance(model, DistributedDataParallel): | ||
model = model.module | ||
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self.cfg = cfg.clone() | ||
self.model = model | ||
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if tta_mapper is None: | ||
tta_mapper = DatasetMapperTTA(cfg) | ||
self.tta_mapper = tta_mapper | ||
self.batch_size = batch_size | ||
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# cvpods tta. | ||
self.enable_cvpods_tta = cfg.TEST.AUG.CVPODS_TTA | ||
self.enable_scale_filter = cfg.TEST.AUG.SCALE_FILTER | ||
self.scale_ranges = cfg.TEST.AUG.SCALE_RANGES | ||
self.max_detection = cfg.MODEL.SparseRCNN.NUM_PROPOSALS | ||
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def _batch_inference(self, batched_inputs, detected_instances=None): | ||
""" | ||
Execute inference on a list of inputs, | ||
using batch size = self.batch_size, instead of the length of the list. | ||
Inputs & outputs have the same format as :meth:`SparseRCNN.forward` | ||
""" | ||
if detected_instances is None: | ||
detected_instances = [None] * len(batched_inputs) | ||
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factors = 2 if self.tta_mapper.flip else 1 | ||
if self.enable_scale_filter: | ||
assert len(batched_inputs) == len(self.scale_ranges) * factors | ||
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outputs = [] | ||
inputs, instances = [], [] | ||
for idx, input, instance in zip(count(), batched_inputs, detected_instances): | ||
inputs.append(input) | ||
instances.append(instance) | ||
if self.enable_cvpods_tta: | ||
output = self.model.forward(inputs, do_postprocess=False)[0] | ||
if self.enable_scale_filter: | ||
pred_boxes = output.get("pred_boxes") | ||
keep = self.filter_boxes(pred_boxes.tensor, *self.scale_ranges[idx // factors]) | ||
output = Instances( | ||
image_size=output.image_size, | ||
pred_boxes=Boxes(pred_boxes.tensor[keep]), | ||
pred_classes=output.pred_classes[keep], | ||
scores=output.scores[keep]) | ||
outputs.extend([output]) | ||
else: | ||
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if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1: | ||
outputs.extend( | ||
self.model.forward( | ||
inputs, | ||
do_postprocess=False, | ||
) | ||
) | ||
inputs, instances = [], [] | ||
return outputs | ||
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@staticmethod | ||
def filter_boxes(boxes, min_scale, max_scale): | ||
""" | ||
boxes: (N, 4) shape | ||
""" | ||
# assert boxes.mode == "xyxy" | ||
w = boxes[:, 2] - boxes[:, 0] | ||
h = boxes[:, 3] - boxes[:, 1] | ||
keep = (w * h > min_scale * min_scale) & (w * h < max_scale * max_scale) | ||
return keep | ||
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def _inference_one_image(self, input): | ||
""" | ||
Args: | ||
input (dict): one dataset dict with "image" field being a CHW tensor | ||
Returns: | ||
dict: one output dict | ||
""" | ||
orig_shape = (input["height"], input["width"]) | ||
augmented_inputs, tfms = self._get_augmented_inputs(input) | ||
# Detect boxes from all augmented versions | ||
all_boxes, all_scores, all_classes = self._get_augmented_boxes(augmented_inputs, tfms) | ||
# merge all detected boxes to obtain final predictions for boxes | ||
if self.enable_cvpods_tta: | ||
merged_instances = self._merge_detections_cvpods_tta(all_boxes, all_scores, all_classes, orig_shape) | ||
else: | ||
merged_instances = self._merge_detections(all_boxes, all_scores, all_classes, orig_shape) | ||
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return {"instances": merged_instances} | ||
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def _merge_detections(self, all_boxes, all_scores, all_classes, shape_hw): | ||
# select from the union of all results | ||
num_boxes = len(all_boxes) | ||
num_classes = self.cfg.MODEL.SparseRCNN.NUM_CLASSES | ||
# +1 because fast_rcnn_inference expects background scores as well | ||
all_scores_2d = torch.zeros(num_boxes, num_classes + 1, device=all_boxes.device) | ||
for idx, cls, score in zip(count(), all_classes, all_scores): | ||
all_scores_2d[idx, cls] = score | ||
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merged_instances, _ = fast_rcnn_inference_single_image( | ||
all_boxes, | ||
all_scores_2d, | ||
shape_hw, | ||
1e-8, | ||
self.cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST, | ||
self.cfg.TEST.DETECTIONS_PER_IMAGE, | ||
) | ||
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return merged_instances | ||
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def _merge_detections_cvpods_tta(self, all_boxes, all_scores, all_classes, shape_hw): | ||
all_scores = torch.tensor(all_scores).to(all_boxes.device) | ||
all_classes = torch.tensor(all_classes).to(all_boxes.device) | ||
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all_boxes, all_scores, all_classes = self.merge_result_from_multi_scales( | ||
all_boxes, all_scores, all_classes, | ||
nms_type="soft_vote", vote_thresh=0.65, | ||
max_detection=self.max_detection | ||
) | ||
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all_boxes = Boxes(all_boxes) | ||
all_boxes.clip(shape_hw) | ||
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result = Instances(shape_hw) | ||
result.pred_boxes = all_boxes | ||
result.scores = all_scores | ||
result.pred_classes = all_classes.long() | ||
return result | ||
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def merge_result_from_multi_scales( | ||
self, boxes, scores, labels, nms_type="soft-vote", vote_thresh=0.65, max_detection=100 | ||
): | ||
boxes, scores, labels = self.batched_vote_nms( | ||
boxes, scores, labels, nms_type, vote_thresh | ||
) | ||
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number_of_detections = boxes.shape[0] | ||
# Limit to max_per_image detections **over all classes** | ||
if number_of_detections > max_detection > 0: | ||
boxes = boxes[:max_detection] | ||
scores = scores[:max_detection] | ||
labels = labels[:max_detection] | ||
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return boxes, scores, labels | ||
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def batched_vote_nms(self, boxes, scores, labels, vote_type, vote_thresh=0.65): | ||
# apply per class level nms, add max_coordinates on boxes first, then remove it. | ||
labels = labels.float() | ||
max_coordinates = boxes.max() + 1 | ||
offsets = labels.reshape(-1, 1) * max_coordinates | ||
boxes = boxes + offsets | ||
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boxes, scores, labels = self.bbox_vote(boxes, scores, labels, vote_thresh, vote_type) | ||
boxes -= labels.reshape(-1, 1) * max_coordinates | ||
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return boxes, scores, labels | ||
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def bbox_vote(self, boxes, scores, labels, vote_thresh, vote_type="softvote"): | ||
assert boxes.shape[0] == scores.shape[0] == labels.shape[0] | ||
det = torch.cat((boxes, scores.reshape(-1, 1), labels.reshape(-1, 1)), dim=1) | ||
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vote_results = torch.zeros(0, 6, device=det.device) | ||
if det.numel() == 0: | ||
return vote_results[:, :4], vote_results[:, 4], vote_results[:, 5] | ||
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order = scores.argsort(descending=True) | ||
det = det[order] | ||
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while det.shape[0] > 0: | ||
# IOU | ||
area = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1]) | ||
xx1 = torch.max(det[0, 0], det[:, 0]) | ||
yy1 = torch.max(det[0, 1], det[:, 1]) | ||
xx2 = torch.min(det[0, 2], det[:, 2]) | ||
yy2 = torch.min(det[0, 3], det[:, 3]) | ||
w = torch.clamp(xx2 - xx1, min=0.) | ||
h = torch.clamp(yy2 - yy1, min=0.) | ||
inter = w * h | ||
iou = inter / (area[0] + area[:] - inter) | ||
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# get needed merge det and delete these det | ||
merge_index = torch.where(iou >= vote_thresh)[0] | ||
vote_det = det[merge_index, :] | ||
det = det[iou < vote_thresh] | ||
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if merge_index.shape[0] <= 1: | ||
vote_results = torch.cat((vote_results, vote_det), dim=0) | ||
else: | ||
if vote_type == "soft_vote": | ||
vote_det_iou = iou[merge_index] | ||
det_accu_sum = self.get_soft_dets_sum(vote_det, vote_det_iou) | ||
elif vote_type == "vote": | ||
det_accu_sum = self.get_dets_sum(vote_det) | ||
vote_results = torch.cat((vote_results, det_accu_sum), dim=0) | ||
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order = vote_results[:, 4].argsort(descending=True) | ||
vote_results = vote_results[order, :] | ||
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return vote_results[:, :4], vote_results[:, 4], vote_results[:, 5] | ||
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@staticmethod | ||
def get_dets_sum(vote_det): | ||
vote_det[:, :4] *= vote_det[:, 4:5].repeat(1, 4) | ||
max_score = vote_det[:, 4].max() | ||
det_accu_sum = torch.zeros((1, 6), device=vote_det.device) | ||
det_accu_sum[:, :4] = torch.sum(vote_det[:, :4], dim=0) / torch.sum(vote_det[:, 4]) | ||
det_accu_sum[:, 4] = max_score | ||
det_accu_sum[:, 5] = vote_det[0, 5] | ||
return det_accu_sum | ||
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@staticmethod | ||
def get_soft_dets_sum(vote_det, vote_det_iou): | ||
soft_vote_det = vote_det.detach().clone() | ||
soft_vote_det[:, 4] *= (1 - vote_det_iou) | ||
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INFERENCE_TH = 0.05 | ||
soft_index = torch.where(soft_vote_det[:, 4] >= INFERENCE_TH)[0] | ||
soft_vote_det = soft_vote_det[soft_index, :] | ||
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vote_det[:, :4] *= vote_det[:, 4:5].repeat(1, 4) | ||
max_score = vote_det[:, 4].max() | ||
det_accu_sum = torch.zeros((1, 6), device=vote_det.device) | ||
det_accu_sum[:, :4] = torch.sum(vote_det[:, :4], dim=0) / torch.sum(vote_det[:, 4]) | ||
det_accu_sum[:, 4] = max_score | ||
det_accu_sum[:, 5] = vote_det[0, 5] | ||
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if soft_vote_det.shape[0] > 0: | ||
det_accu_sum = torch.cat((det_accu_sum, soft_vote_det), dim=0) | ||
return det_accu_sum |
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