def eval_list(cfgfile, namefile, weightfile, testfile):
m = Darknet(cfgfile)
m.load_weights(weightfile)
use_cuda = 1
if use_cuda:
m.cuda()
class_names = load_class_names(namefile)
file_list = []
with open(testfile, "r") as fin:
for f in fin:
file_list.append(f.strip())
for imgfile in file_list:
img = Image.open(imgfile).convert('RGB')
sized = img.resize((m.width, m.height))
filename = os.path.basename(imgfile)
filename = os.path.splitext(filename)[0]
#print(filename, img.width, img.height, sized_width, sized_height)
if m.width * m.height > 1024 * 2560:
print('omit %s' % filename)
continue
if False:
boxes = do_detect(m, sized, conf_thresh, nms_thresh, use_cuda)
else:
m.eval()
sized = image2torch(sized).cuda();
#output = m(Variable(sized, volatile=True)).data
output = m(sized)
#boxes = get_region_boxes(output, conf_thresh, m.num_classes, m.anchors, m.num_anchors, 0, 1)[0]
boxes = get_all_boxes(output, conf_thresh, m.num_classes)[0]
boxes = np.array(nms(boxes, nms_thresh))
if False:
savename = get_det_image_name(imgfile)
print('img: save to %s' % savename)
plot_boxes(img, boxes, savename, class_names)
if False:
savename = get_det_result_name(imgfile)
print('det: save to %s' % savename)
save_boxes(imgfile, img, boxes, savename)
print(model.models[0][1].bias)
print("--- bn running_mean ---")
print(model.models[0][1].running_mean)
print("--- bn running_var ---")
print(model.models[0][1].running_var)
model.train()
m = model.cuda()
optimizer = optim.SGD(model.parameters(),
lr=1e-2,
momentum=0.9,
weight_decay=0.1)
img = Image.open(imgpath)
img = image2torch(img)
img = Variable(img.cuda())
target = Variable(label)
print("----- img ---------------------")
print(img.data.storage()[0:100])
print("----- target -----------------")
print(target.data.storage()[0:100])
optimizer.zero_grad()
output = m(img)
print("----- output ------------------")
print(output.data.storage()[0:100])
exit()
print('--- bn weight ---')
print(m.models[0][1].weight)
print('--- bn bias ---')
print(m.models[0][1].bias)
print('--- bn running_mean ---')
print(m.models[0][1].running_mean)
print('--- bn running_var ---')
print(m.models[0][1].running_var)
m.train()
m = m.cuda()
optimizer = optim.SGD(m.parameters(), lr=1e-2, momentum=0.9, weight_decay=0.1)
img = Image.open(imgpath)
img = image2torch(img).cuda()
target = label
print('----- img ---------------------')
print(img.data.storage()[0:100])
print('----- target -----------------')
print(target.data.storage()[0:100])
optimizer.zero_grad()
output = m(img)
print('----- output ------------------')
print(output.data.storage()[0:100])
exit()
loss = region_loss(output, target)
def detect(model, img, conf_thresh, nms_thresh, use_cuda):
img = image2torch(img)
img = img.to(torch.device('cuda' if use_cuda else 'cpu'))
out_boxes = model(img)
boxes = get_all_boxes(out_boxes, conf_thresh, model.num_classes, use_cuda=use_cuda)[0]
return nms(boxes, nms_thresh)