print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
return cmc[0]
#######################################################################################################
print("Model is being initialized")
model = ResNet.ResNet50().to(device)
#SAVED_MODEL_PATH = 'saved_models/p1.pth.tar'
#checkpoint = torch.load(SAVED_MODEL_PATH)
#model.load_state_dict(checkpoint['state_dict'])
#start_epoch = checkpoint['epoch']
print("Model size: {:.5f}M".format(
sum(p.numel() for p in model.parameters()) / 1000000.0))
optim = torch.optim.Adam(model.parameters())
if stepsize > 0:
scheduler = lr_scheduler.StepLR(optim, step_size=stepsize, gamma=0.1)
num_epochs = 242
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
return cmc[0]
#######################################################################################################
print("Training of model has been started")
print("Model is being initialized")
model = ResNet.ResNet50(num_classes=dataset.num_train_pids,
num_fcs=2).to(device)
print("Model size: {:.5f}M".format(
sum(p.numel() for p in model.parameters()) / 1000000.0))
optim = torch.optim.Adam(model.parameters())
if stepsize > 0:
scheduler = lr_scheduler.StepLR(optim, step_size=stepsize, gamma=0.1)
start_epoch = 0
num_epochs = 250
if args.resume:
print("Loading checkpoint from '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
start_epoch = checkpoint['epoch']
