def main():
global args, num_batches, use_gpu
if not args.zeroTrain:
model = models.sigmoid_ResnetModel(args.arch, 224, args.num_classs,
args.Conv_Type, args.Branch,
args.Conv_Num)
if args.resume:
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint, strict=True)
if use_gpu:
model = model.cuda()
print("Use GPU!")
else:
print("Use CPU!")
train_loader = dataset.train_loader(args.train_path,
args.relative_path,
batch_size=args.batch_size,
num_workers=10,
pin_memory=True)
#test_train_loader = dataset.test_loader(args.test_path, args.relative_path, batch_size=1, num_workers=5, pin_memory=True)
#val_loader = dataset.test_loader(args.val_path, args.relative_path, batch_size=1, num_workers=5, pin_memory=True)
# test_loader = dataset.test_loader(rgs.test_path, args.relative_path, batch_size=1, num_workers=5, pin_memory=True)
train_val(model,
train_loader,
test_train_loader=None,
val_loader=None,
print_freq=args.print_freq,
optimizer=None,
epoches=args.epochs,
Branch=args.Branch)
def main():
global args, num_batches, use_gpu
model = models.sigmoid_ResnetModel('Resnet152', 224, args.num_classs, 2,
17, 5)
if args.resume:
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint, strict=True)
if use_gpu:
model = model.cuda()
print("Use GPU!")
else:
print("Use CPU!")
csvfile = open('./Consumer_loss.csv', 'wb')
fieldnames = ['img path', 'total loss']
writer = csv.DictWriter(csvfile, delimiter=',', fieldnames=fieldnames) #
writer.writeheader()
data_loader = dataset.train_loader(args.data_path,
args.relative_path,
batch_size=16,
num_workers=10,
pin_memory=True)
get_data_loss(csvfile, writer, model, data_loader)
def main():
time1 = time()
global args, use_gpu
input_size = args.input_size
resume1 = './checkpoint/BBX/NewAnnoCleaned/Resnet152_64_{}_Epoch.pth'
# resume2 = './checkpoint/BBX/sigmoid_model/Lr_0_001/Five_Branch/Bottleneck/Five_Depth/Resnet152_64_{}_Epoch.pth'
test_loader = dataset.test_loader(args.test_path,
args.relative_path,
batch_size=args.batch_size,
num_workers=10,
pin_memory=True)
model = models.sigmoid_ResnetModel(args.arch, 224, args.num_classs, 2, 17,
5)
for i in range(49, 48, -1):
f = open(
'./result/NewAnnoCleaned/LR_0_001_FiveDepth_epoch_{}_top3_top5.txt'
.format(str(i)), 'w')
print("{} epoch".format(str(i)))
checkpoint = torch.load(resume1.format(str(i)))
model.load_state_dict(checkpoint, strict=True)
f.write("{} epoch\n".format(str(i)))
if use_gpu:
model = model.cuda()
else:
pass
if args.Branch == 1:
# _, _, overall_top3_recall, overall_top5_recall = top3_5_recall(model, test_loader, topk=(3,5), epoch=i)
pass
elif args.Branch == 17:
_, _, overall_top3_recall, overall_top5_recall = recall_precision(
model, test_loader, topk=(3, 5), epoch=i)
f.write("top-3 ")
for key in overall_top3_recall.keys():
f.write("{} ".format(key))
f.write("\n")
for _, value in overall_top3_recall.items():
f.write("{} ".format(str(round(value * 100, 3))))
f.write("\n")
f.write("top-5 ")
for key in overall_top5_recall.keys():
f.write("{} ".format(key))
f.write("\n")
for _, value in overall_top5_recall.items():
f.write("{} ".format(str(round(value * 100, 3))))
f.write("\n")
f.close()
print("total time is: {} seconds".format(round(time() - time1, 2)))
def __init__(self, modelPath=attrModelPath,\
namePath=attrNamePath, savePath=attrSavePath, inputSize=224):
super(attributesForward, self).__init__()
self.modelPath = modelPath
self.namePath = namePath
self.numClasses, self.idx2class = attributesNameParser(self.namePath)
self.savePath = savePath
self.inputSize = inputSize
self.model = models.sigmoid_ResnetModel('Resnet152', self.inputSize, \
self.numClasses, 2, 17, 5)
self.model.load_state_dict(torch.load(modelPath), strict=True)
if useGpu:
self.model = self.model.cuda()
self.model.eval()
else:
self.model.eval()