print(len(index))
mean_vals = [0.485, 0.456, 0.406]
std_vals = [0.229, 0.224, 0.225]
cam_thr_list = [0.0, 0.1, 0.12, 0.14]
# thr = [100, 0, 20, 40, 60, 80, 120, 140, 160, 180, 200]
thr = [100]
for aa, thr in enumerate(thr):
# for step, threshold in enumerate(cam_thr_list):
threshold = 0.12
params = list(net.parameters())
net.train(False)
net.eval()
with torch.no_grad():
top1 = Metric.AverageEpochMeter('Top-1 Classification Acc')
top5 = Metric.AverageEpochMeter('Top-5 Classification Acc')
GT_loc = Metric.AverageEpochMeter('Top-1 GT-Known Localization Acc')
top1_loc = Metric.AverageEpochMeter('Top-1 Localization Acc')
top5_loc = Metric.AverageEpochMeter('Top-5 Localization Acc')
progress = Metric.ProgressEpochMeter(
len(test_loader),
[top1, top5, top1_loc, top5_loc, GT_loc],
prefix="\nValidation Phase: ")
gt_file = './CUB_datalist/test_bounding_box.txt'
f = open(gt_file, 'r')
net.eval()
for i, (img_path, image_tensor, image_level) in enumerate(test_loader):
image_tensor = image_tensor.cuda(non_blocking=True)
image_level = image_level.cuda(non_blocking=True)
logit, _ = net(image_tensor)
