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coco_eval.py
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253 lines (185 loc) · 8.42 KB
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from pycocotools.cocoeval import COCOeval
import json
import torch
from tqdm import tqdm
import numpy as np
def evaluate_coco_RGB(generator, model, threshold=0.05):
"""
add img_rgb, img_ir
"""
with torch.no_grad():
# start collecting results
results = []
image_ids = []
for index in tqdm(range(len(generator))):
data = generator[index]
scale = data['scale']
# run network
if torch.cuda.is_available(): #[C, H, W] --> [1, C, H, W]
scores, labels, boxes = model(data['img_rgb'].unsqueeze(dim=0).cuda().float(), data['boxes'].cuda(), data['classes'].cuda())
else:
scores, labels, boxes = model(data['img_rgb'].unsqueeze(dim=0).float(), data['boxes'], data['classes'])
scores = scores.cuda()#.cpu()
labels = labels.cuda()#.cpu()
boxes = boxes.cuda()#.cpu()
boxes /= scale
# correct boxes for image scale
# change to (x, y, w, h) (MS COCO standard)
boxes[:, :, 2] -= boxes[:, :, 0]
boxes[:, :, 3] -= boxes[:, :, 1]
# compute predicted labels and scores
for box, score, label in zip(boxes[0], scores[0], labels[0]):
#lyf:
label = int(label)
# scores are sorted, so we can break
if score < threshold:
break
# append detection for each positively labeled class
image_result = {
'image_id' : generator.ids[index],
'category_id' : generator.id2category[label], #lyf
'score' : float(score),
'bbox' : box.tolist(),
}
# append detection to results
results.append(image_result)
# append image to list of processed images
image_ids.append(generator.ids[index])
# print progress
#print('{}/{}'.format(index, len(dataset)), end='\r')
if not len(results):
return
# write output
json.dump(results, open('coco_bbox_results.json', 'w'), indent=4)
# json.dump(image_ids, open('{}_processed_image_ids.json'.format(generator.set_name), 'w'), indent=4)
# load results in COCO evaluation tool
coco_true = generator.coco
coco_pred = coco_true.loadRes('coco_bbox_results.json')
# run COCO evaluation
coco_eval = COCOeval(coco_true, coco_pred, 'bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
coco_info = coco_eval.stats.tolist() # numpy to list
return coco_info
def evaluate_coco_IR(generator, model, threshold=0.05):
"""
add img_rgb, img_ir
"""
with torch.no_grad():
# start collecting results
results = []
image_ids = []
for index in tqdm(range(len(generator))):
data = generator[index]
scale = data['scale']
# run network
if torch.cuda.is_available(): #[C, H, W] --> [1, C, H, W]
scores, labels, boxes = model(data['img_ir'].unsqueeze(dim=0).cuda().float(), data['boxes'].cuda(), data['classes'].cuda())
else:
scores, labels, boxes = model(data['img_ir'].unsqueeze(dim=0).float(), data['boxes'], data['classes'])
scores = scores.cuda()#.cpu()
labels = labels.cuda()#.cpu()
boxes = boxes.cuda()#.cpu()
boxes /= scale
# correct boxes for image scale
# change to (x, y, w, h) (MS COCO standard)
boxes[:, :, 2] -= boxes[:, :, 0]
boxes[:, :, 3] -= boxes[:, :, 1]
# compute predicted labels and scores
for box, score, label in zip(boxes[0], scores[0], labels[0]):
#lyf:
label = int(label)
# scores are sorted, so we can break
if score < threshold:
break
# append detection for each positively labeled class
image_result = {
'image_id' : generator.ids[index],
'category_id' : generator.id2category[label], #lyf
'score' : float(score),
'bbox' : box.tolist(),
}
# append detection to results
results.append(image_result)
# append image to list of processed images
image_ids.append(generator.ids[index])
# print progress
#print('{}/{}'.format(index, len(dataset)), end='\r')
if not len(results):
return
# write output
json.dump(results, open('coco_bbox_results.json', 'w'), indent=4)
# json.dump(image_ids, open('{}_processed_image_ids.json'.format(generator.set_name), 'w'), indent=4)
# load results in COCO evaluation tool
coco_true = generator.coco
coco_pred = coco_true.loadRes('coco_bbox_results.json')
# run COCO evaluation
coco_eval = COCOeval(coco_true, coco_pred, 'bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
coco_info = coco_eval.stats.tolist() # numpy to list
return coco_info
def evaluate_coco_multispectral(generator, model, threshold=0.05):
"""
add img_rgb, img_ir
"""
with torch.no_grad():
# start collecting results
results = []
image_ids = []
for index in tqdm(range(len(generator))):
data = generator[index]
scale = data['scale']
# run network
if torch.cuda.is_available(): #[C, H, W] --> [1, C, H, W]
scores, labels, boxes = model(data['img_rgb'].unsqueeze(dim=0).cuda().float(), data['img_ir'].unsqueeze(dim=0).cuda().float(), data['boxes'].cuda(), data['classes'].cuda())
else:
scores, labels, boxes = model(data['img_rgb'].unsqueeze(dim=0).float(), data['img_ir'].unsqueeze(dim=0).float(), data['boxes'], data['classes'])
scores = scores.cuda()#.cpu()
labels = labels.cuda()#.cpu()
boxes = boxes.cuda()#.cpu()
boxes /= scale
# correct boxes for image scale
# change to (x, y, w, h) (MS COCO standard)
boxes[:, :, 2] -= boxes[:, :, 0]
boxes[:, :, 3] -= boxes[:, :, 1]
# compute predicted labels and scores
for box, score, label in zip(boxes[0], scores[0], labels[0]):
#lyf:
label = int(label)
# scores are sorted, so we can break
if score < threshold:
break
# append detection for each positively labeled class
image_result = {
'image_id' : generator.ids[index],
'category_id' : generator.id2category[label], #lyf
'score' : float(score),
'bbox' : box.tolist(),
}
# append detection to results
results.append(image_result)
# append image to list of processed images
image_ids.append(generator.ids[index])
# print progress
#print('{}/{}'.format(index, len(dataset)), end='\r')
if not len(results):
return
# write output
json.dump(results, open('{}_coco_bbox_results.json'.format(generator.name), 'w'), indent=4)
# json.dump(image_ids, open('{}_processed_image_ids.json'.format(generator.set_name), 'w'), indent=4)
# load results in COCO evaluation tool
coco_true = generator.coco
coco_pred = coco_true.loadRes('{}_coco_bbox_results.json'.format(generator.name))
# run COCO evaluation
coco_eval = COCOeval(coco_true, coco_pred, 'bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
coco_info = coco_eval.stats.tolist() # numpy to list
return coco_info