def val300_coco(model_path):
print("loading model at {}".format(model_path))
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
trans = SSDTransformer(dboxes, (300, 300), val=True)
#annotate = "../../coco_ssd/instances_minival2014.json"
#coco_root = "../../coco_data/val2014"
#annotate = "../../coco_ssd/image_info_test-dev2015.json"
#coco_root = "../../coco_data/test2015"
annotate = "../../coco_ssd/instances_val2017.json"
coco_root = "../../coco_data/val2017"
cocoGt = COCO(annotation_file=annotate)
coco = COCODetection(coco_root, annotate, trans)
model = SSD300(coco.labelnum)
od = torch.load(model_path)
model.load_state_dict(od["model"])
model.eval()
model.cuda()
ret = []
inv_map = {v: k for k, v in coco.label_map.items()}
start = time.time()
for idx, image_id in enumerate(coco.img_keys):
img, (htot, wtot), _, _ = coco[idx]
with torch.no_grad():
print("Parsing image: {}/{}".format(idx + 1, len(coco)), end="\r")
ploc, plabel = model(img.unsqueeze(0).cuda())
try:
result = encoder.decode_batch(ploc, plabel, 0.50, 200)[0]
except:
#raise
print("")
print("No object detected in idx: {}".format(idx), end="\r")
continue
loc, label, prob = [r.cpu().numpy() for r in result]
for loc_, label_, prob_ in zip(loc, label, prob):
ret.append([image_id, loc_[0]*wtot, \
loc_[1]*htot,
(loc_[2] - loc_[0])*wtot,
(loc_[3] - loc_[1])*htot,
prob_,
inv_map[label_]])
print("")
print("Predicting Ended, totoal time: {:.2f} s".format(time.time() -
start))
cocoDt = cocoGt.loadRes(np.array(ret))
E = COCOeval(cocoGt, cocoDt, iouType='bbox')
#E.params.useSegm = 0
#E.params.recThrs = [0.5]
#E.params.maxDets = [10, 100, 200]
E.evaluate()
E.accumulate()
E.summarize()
def dboxes300_coco():
figsize = 300
feat_size = [38, 19, 10, 5, 3, 1]
steps = [8, 16, 32, 64, 100, 300]
# use the scales here: https://github.com/amdegroot/ssd.pytorch/blob/master/data/config.py
scales = [21, 45, 99, 153, 207, 261, 315]
aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
dboxes = DefaultBoxes(figsize, feat_size, steps, scales, aspect_ratios)
return dboxes
if __name__ == "__main__":
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
saved_inputs = torch.load('inputs.pth')
bboxes = saved_inputs['bbox'].float()
scores = saved_inputs['scores'].float()
criteria = float(saved_inputs['criteria'])
max_num = int(saved_inputs['max_output'])
print('bboxes: {}, scores: {}'.format(bboxes.shape, scores.shape))
for i in range(bboxes.shape[0]):
box, label, score = encoder.decode_batch(bboxes[i, :, :].unsqueeze(0),
scores[i, :, :].unsqueeze(0),
criteria, max_num)[0]
print('r: {}'.format(label))
