def predict_normalized(processed_image: torch.Tensor, model: DenseNet,
topk: int) -> Tuple[List[float], List[str]]:
""" Predict the class (or classes) of an image using a trained deep learning model.
Args:
image_path (str): Location of the image file
model (object): A trained PyTorch model
cat_to_name (dict): Dict which maps category numbers to category names
top_k (int): Number of top classes to return
device (obj): Device to perform inference on
Returns:
prediction_dict (dict): Dictionary of top classes predicted for that image
Example:
>>> result = predict('images/flower.jpg', model, cat_to_name, 5, torch.device('cpu'))
"""
processed_image = processed_image.to(model.device)
model.eval()
with torch.set_grad_enabled(False):
output = model(processed_image)
probs = torch.nn.functional.softmax(output, dim=1)
top_probs, top_classes = probs.topk(topk)
top_probs = top_probs.cpu().numpy().tolist()[0]
top_classes = [
model.class_names[i] for i in top_classes.cpu().numpy().tolist()[0]
]
return top_probs, top_classes
def main(args):
_ = [os.mkdir(args.pretraindir) if not os.path.exists(args.pretraindir) else None]
if not os.path.exists(os.path.join(args.pretraindir,'checkpoints')):
os.mkdir(os.path.join(args.pretraindir,'checkpoints'))
traindata = ImageFolderWithCache(args.traindata, transform=Transform)
trainloader = DataLoader(traindata, 32, shuffle=True)
testdata = ImageFolderWithCache(args.testdata, transform=Transform)
testloader = DataLoader(testdata, 32, shuffle=True)
model = DenseNet(block_config=(6, 12, 48, 32), num_classes=args.classes)
model.cuda()
optim = SGD(
model.parameters(),
lr=1e-1, momentum=0.4, weight_decay=1e-3
)
lch = StepLR(optim, 30, 0.1, -1)
accmeter = AccMeter()
for i in range(args.epoch):
mat, loss = train(trainloader, model, i, optim)
lch.step()
accmeter.compute(mat)
print(f"Epoch{i} finished, avgloss:{loss:.6f}, totalacc:{accmeter.totalacc:.6f}, nacc:{accmeter.nacc}")
print(mat)
writer.add_scalar('Train/acc',accmeter.totalacc,i)
writer.add_scalar('Train/losses',loss,i)
tmat, tloss = test(testloader, model)
accmeter.compute(tmat)
print(f"Test finished, testloss:{tloss:.6f}, totalacc:{accmeter.totalacc:.6f}, nacc:{accmeter.nacc}")
print(tmat)
writer.add_scalar('Test/acc',accmeter.totalacc,i)
pass
def _densenet(arch, growth_rate, block_config, num_init_features, pretrained=False,
progress=True, imagenet_pretrained=False, num_classes=1, lin_features=512,
dropout_prob=0.5, bn_final=False, concat_pool=True, **kwargs):
# Model creation
base_model = DenseNet(growth_rate, block_config, num_init_features, num_classes=num_classes, **kwargs)
# Imagenet pretraining
if imagenet_pretrained:
if pretrained:
raise ValueError('imagenet_pretrained cannot be set to True if pretrained=True')
state_dict = load_state_dict_from_url(imagenet_urls[arch],
progress=progress)
state_dict = _update_state_dict(state_dict)
# Remove FC params from dict
for key in ('classifier.weight', 'classifier.bias'):
state_dict.pop(key, None)
missing, unexpected = base_model.load_state_dict(state_dict, strict=False)
if any(unexpected) or any(not elt.startswith('classifier.') for elt in missing):
raise KeyError(f"Missing parameters: {missing}\nUnexpected parameters: {unexpected}")
# Cut at last conv layers
model = cnn_model(base_model, model_cut, base_model.classifier.in_features, num_classes,
lin_features, dropout_prob, bn_final=bn_final, concat_pool=concat_pool)
# Parameter loading
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch],
progress=progress)
model.load_state_dict(state_dict)
return model
def _densenet(arch, model_path, growth_rate, block_config, num_init_features, pretrained,
**kwargs):
# model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
model = DenseNet(growth_rate, block_config, num_init_features)
if pretrained:
_load_state_dict(model, os.path.join(model_path, model_urls[arch]))
return model
def _densenet(branches, arch, growth_rate, block_config, num_init_features,
pretrained, progress, **kwargs):
model_mtl = MTLDenseNet(branches, growth_rate, block_config,
num_init_features, **kwargs)
model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
if pretrained:
_load_state_dict(model, model_urls[arch], progress)
state_dict = model.state_dict()
own_state = model_mtl.state_dict()
pretrained_dict = {
k: v
for k, v in state_dict.items() if k in own_state
}
# 2. overwrite entries in the existing state dict
own_state.update(pretrained_dict)
# 3. load the new state dict
model_mtl.load_state_dict(own_state)
return model_mtl
def densenet121(pretrained=False, **kwargs):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = Orig_DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), **kwargs)
if pretrained:
# '.'s are no longer allowed in module names, but pervious _DenseLayer
# has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
# They are also in the checkpoints in model_urls. This pattern is used
# to find such keys.
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
state_dict = model_zoo.load_url(model_urls['densenet121'])
for key in list(state_dict.keys()):
res = pattern.match(key)
if res:
new_key = res.group(1) + res.group(2)
state_dict[new_key] = state_dict[key]
del state_dict[key]
model.load_state_dict(state_dict)
return model
correct[sen_idx] = 0.0
print(np.mean(correct))
# counter = 0
# for sen_idx in range(len(pwd_prob)):
# if pwd_prob[sen_idx] < 0.5:
# counter +=1
# print(counter)
if __name__ == '__main__':
args = parse_args()
model = DenseNet(growth_rate=32,
block_config=(6, 12, 24, 16),
num_init_features=64,
bn_size=4,
drop_rate=0.3,
num_classes=args.num_classes)
if args.cuda:
model = model.cuda()
print('cuda_weights')
load_checkpoint(args.load_dir + '/best.pth.tar', model)
images_evaluate(model, args, outputFile='wrongIndex.txt')
images_evaluate_logits(model, args, outputFile='prob.txt')
compare_pwd_others()
print(counter, tot_count)
print(counter/float(tot_count))
mean_metrics = {metric:np.array([x[metric] for x in summ]).mean() for metric in summ[0]}
print(mean_metrics)
return mean_metrics
if __name__ == '__main__':
args = parse_args()
# model = ClassNet(img_col=args.img_col, img_row=args.img_row, num_classes=args.num_classes)
# model = ResNet(Bottleneck, layers=[3, 4, 6, 3], num_classes=args.num_classes)
# model = Inception3(num_classes=10, aux_logits=False)
model = DenseNet(growth_rate=32, block_config=(6, 12, 24, 16),
num_init_features=64, bn_size=4, drop_rate=0.3, num_classes=args.num_classes)
# model = Wide_ResNet(16, 8, 0.3, args.num_classes)
if args.cuda:
model = model.cuda()
print('cuda_weights')
if not os.path.isdir(args.save_dir):
os.mkdir(args.save_dir)
train_dl = torch.utils.data.DataLoader(dataset=Feeder(args.train_folder_path),
batch_size=32,
shuffle=True,
num_workers=4,
def main():
global best_top1, best_top5
start_epoch = args.start_epoch # start from epoch 0 or last checkpoint epoch
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
# Data loading code
traindir = os.path.join(args.data, 'train')
validdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
val_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])
train_data = imagenet_lmdb_dataset(traindir, transform=train_transform)
valid_data = imagenet_lmdb_dataset(validdir, transform=val_transform)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_data)
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=args.train_batch,
shuffle=(train_sampler is None),
pin_memory=True,
num_workers=8,
sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(valid_data,
batch_size=args.test_batch,
shuffle=False,
pin_memory=True,
num_workers=8)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
elif args.arch.startswith('resnext'):
model = models.__dict__[args.arch](
baseWidth=args.base_width,
cardinality=args.cardinality,
)
elif args.arch == 'densenet264':
model = DenseNet(growth_rate=32,
block_config=(6, 12, 64, 48),
num_init_features=64,
bn_size=4,
drop_rate=0,
num_classes=1000,
memory_efficient=False)
elif args.arch == 'resnet200':
model = ResNet(block=Bottleneck,
layers=[3, 24, 36, 3],
num_classes=1000,
zero_init_residual=False,
groups=1,
width_per_group=64,
replace_stride_with_dilation=None,
norm_layer=None)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = DDP(model.features)
model.cuda()
else:
model = model.cuda()
model = DDP(model, delay_allreduce=True)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
if args.optimizer.lower() == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'adamw':
optimizer = AdamW(model.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2),
weight_decay=args.weight_decay,
warmup=0)
elif args.optimizer.lower() == 'radam':
optimizer = RAdam(model.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2),
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lsadam':
optimizer = LSAdamW(model.parameters(),
lr=args.lr * ((1. + 4. * args.sigma)**(0.25)),
betas=(args.beta1, args.beta2),
weight_decay=args.weight_decay,
sigma=args.sigma)
elif args.optimizer.lower() == 'lsradam':
sigma = 0.1
optimizer = LSRAdam(model.parameters(),
lr=args.lr * ((1. + 4. * args.sigma)**(0.25)),
betas=(args.beta1, args.beta2),
weight_decay=args.weight_decay,
sigma=args.sigma)
elif args.optimizer.lower() == 'srsgd':
iter_count = 1
optimizer = SGD_Adaptive(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
iter_count=iter_count,
restarting_iter=args.restart_schedule[0])
elif args.optimizer.lower() == 'sradam':
iter_count = 1
optimizer = SRNAdam(model.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2),
iter_count=iter_count,
weight_decay=args.weight_decay,
restarting_iter=args.restart_schedule[0])
elif args.optimizer.lower() == 'sradamw':
iter_count = 1
optimizer = SRAdamW(model.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2),
iter_count=iter_count,
weight_decay=args.weight_decay,
warmup=0,
restarting_iter=args.restart_schedule[0])
elif args.optimizer.lower() == 'srradam':
#NOTE: need to double-check this
iter_count = 1
optimizer = SRRAdam(model.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2),
iter_count=iter_count,
weight_decay=args.weight_decay,
warmup=0,
restarting_iter=args.restart_schedule[0])
schedule_index = 1
# Resume
title = 'ImageNet-' + args.arch
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isfile(
args.resume), 'Error: no checkpoint directory found!'
# args.checkpoint = os.path.dirname(args.resume)
# checkpoint = torch.load(args.resume, map_location = lambda storage, loc: storage.cuda(args.local_rank))
checkpoint = torch.load(args.resume, map_location=torch.device('cpu'))
best_top1 = checkpoint['best_top1']
best_top5 = checkpoint['best_top5']
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
if args.optimizer.lower() == 'srsgd' or args.optimizer.lower(
) == 'sradam' or args.optimizer.lower(
) == 'sradamw' or args.optimizer.lower() == 'srradam':
iter_count = optimizer.param_groups[0]['iter_count']
schedule_index = checkpoint['schedule_index']
state['lr'] = optimizer.param_groups[0]['lr']
if args.checkpoint == args.resume:
logger = LoggerDistributed(os.path.join(args.checkpoint,
'log.txt'),
rank=args.local_rank,
title=title,
resume=True)
else:
logger = LoggerDistributed(os.path.join(args.checkpoint,
'log.txt'),
rank=args.local_rank,
title=title)
if args.local_rank == 0:
logger.set_names([
'Learning Rate', 'Train Loss', 'Valid Loss', 'Train Top1',
'Valid Top1', 'Train Top5', 'Valid Top5'
])
else:
logger = LoggerDistributed(os.path.join(args.checkpoint, 'log.txt'),
rank=args.local_rank,
title=title)
if args.local_rank == 0:
logger.set_names([
'Learning Rate', 'Train Loss', 'Valid Loss', 'Train Top1',
'Valid Top1', 'Train Top5', 'Valid Top5'
])
if args.local_rank == 0:
logger.file.write(' Total params: %.2fM' %
(sum(p.numel()
for p in model.parameters()) / 1000000.0))
if args.evaluate:
if args.local_rank == 0:
logger.file.write('\nEvaluation only')
test_loss, test_top1, test_top5 = test(val_loader, model, criterion,
start_epoch, use_cuda, logger)
if args.local_rank == 0:
logger.file.write(
' Test Loss: %.8f, Test Top1: %.2f, Test Top5: %.2f' %
(test_loss, test_top1, test_top5))
return
# Train and val
for epoch in range(start_epoch, args.epochs):
# Shuffle the sampler.
train_loader.sampler.set_epoch(epoch + args.manualSeed)
if args.optimizer.lower() == 'srsgd':
if epoch in args.schedule:
optimizer = SGD_Adaptive(
model.parameters(),
lr=args.lr * (args.gamma**schedule_index),
weight_decay=args.weight_decay,
iter_count=iter_count,
restarting_iter=args.restart_schedule[schedule_index])
schedule_index += 1
elif args.optimizer.lower() == 'sradam':
if epoch in args.schedule:
optimizer = SRNAdam(
model.parameters(),
lr=args.lr * (args.gamma**schedule_index),
betas=(args.beta1, args.beta2),
iter_count=iter_count,
weight_decay=args.weight_decay,
restarting_iter=args.restart_schedule[schedule_index])
schedule_index += 1
elif args.optimizer.lower() == 'sradamw':
if epoch in args.schedule:
optimizer = SRAdamW(
model.parameters(),
lr=args.lr * (args.gamma**schedule_index),
betas=(args.beta1, args.beta2),
iter_count=iter_count,
weight_decay=args.weight_decay,
warmup=0,
restarting_iter=args.restart_schedule[schedule_index])
schedule_index += 1
elif args.optimizer.lower() == 'srradam':
if epoch in args.schedule:
optimizer = SRRAdam(
model.parameters(),
lr=args.lr * (args.gamma**schedule_index),
betas=(args.beta1, args.beta2),
iter_count=iter_count,
weight_decay=args.weight_decay,
warmup=0,
restarting_iter=args.restart_schedule[schedule_index])
schedule_index += 1
else:
adjust_learning_rate(optimizer, epoch)
if args.local_rank == 0:
logger.file.write('\nEpoch: [%d | %d] LR: %f' %
(epoch + 1, args.epochs, state['lr']))
if args.optimizer.lower() == 'srsgd' or args.optimizer.lower(
) == 'sradam' or args.optimizer.lower(
) == 'sradamw' or args.optimizer.lower() == 'srradam':
train_loss, train_top1, train_top5, iter_count = train(
train_loader, model, criterion, optimizer, epoch, use_cuda,
logger)
else:
train_loss, train_top1, train_top5 = train(train_loader, model,
criterion, optimizer,
epoch, use_cuda, logger)
test_loss, test_top1, test_top5 = test(val_loader, model, criterion,
epoch, use_cuda, logger)
# append logger file
if args.local_rank == 0:
logger.append([
state['lr'], train_loss, test_loss, train_top1, test_top1,
train_top5, test_top5
])
writer.add_scalars('train_loss', {args.model_name: train_loss},
epoch)
writer.add_scalars('test_loss', {args.model_name: test_loss},
epoch)
writer.add_scalars('train_top1', {args.model_name: train_top1},
epoch)
writer.add_scalars('test_top1', {args.model_name: test_top1},
epoch)
writer.add_scalars('train_top5', {args.model_name: train_top5},
epoch)
writer.add_scalars('test_top5', {args.model_name: test_top5},
epoch)
# save model
is_best = test_top1 > best_top1
best_top1 = max(test_top1, best_top1)
best_top5 = max(test_top5, best_top5)
if args.local_rank == 0:
save_checkpoint(
{
'epoch': epoch + 1,
'schedule_index': schedule_index,
'state_dict': model.state_dict(),
'top1': test_top1,
'top5': test_top5,
'best_top1': best_top1,
'best_top5': best_top5,
'optimizer': optimizer.state_dict(),
},
is_best,
epoch,
checkpoint=args.checkpoint)
if epoch == args.schedule[-1]:
logger.file.write('Best top1: %f at epoch %i' %
(best_top1, epoch))
logger.file.write('Best top5: %f at epoch %i' %
(best_top5, epoch))
print('Best top1: %f at epoch %i' % (best_top1, epoch))
print('Best top5: %f at epoch %i' % (best_top5, epoch))
with open("./all_results_imagenet.txt", "a") as f:
fcntl.flock(f, fcntl.LOCK_EX)
f.write("%s\n" % args.checkpoint)
f.write("best_top1 %f, best_top5 %f at epoch %i\n\n" %
(best_top1, best_top5, epoch))
fcntl.flock(f, fcntl.LOCK_UN)
if args.local_rank == 0:
logger.file.write('Best top1: %f' % best_top1)
logger.file.write('Best top5: %f' % best_top5)
logger.close()
logger.plot()
savefig(os.path.join(args.checkpoint, 'log.eps'))
print('Best top1: %f' % best_top1)
print('Best top5: %f' % best_top5)
with open("./all_results_imagenet.txt", "a") as f:
fcntl.flock(f, fcntl.LOCK_EX)
f.write("%s\n" % args.checkpoint)
f.write("best_top1 %f, best_top5 %f\n\n" % (best_top1, best_top5))
fcntl.flock(f, fcntl.LOCK_UN)
def densenet():
return DenseNet(growth_rate=12,
block_config=(3, 6, 12, 8),
num_classes=1000,
bn_size=4)