def eval(args):
frames = args.frames
caption_length = args.caption_length
glove_file = args.glove_file
if args.cuda:
ctx = mx.gpu()
else:
ctx = mx.cpu()
if args.load_pretrain:
pretrain_model = vision.vgg16_bn(pretrained=True,ctx=ctx)
transform = utils.Compose([utils.ToTensor(ctx),
utils.normalize(ctx),
utils.extractFeature(ctx,pretrain_model)
])
else:
pretrain_model = None
transform = utils.Compose([utils.ToTensor(ctx),
utils.normalize(ctx),
])
target_transform = utils.targetCompose([utils.WordToTensor(ctx)])
val_dataset = videoFolder(args.val_folder,args.val_dict, frames, glove_file, caption_length, ctx, transform=transform, target_transform=target_transform)
val_loader = gluon.data.DataLoader(val_dataset, batch_size=args.batch_size,last_batch='discard',shuffle=True)
def loadData(self):
trainset = SimulationDataset("train", transforms=transforms.Compose([
utils.RandomCoose(['center']),
utils.Preprocess(self.input_shape),
# utils.RandomResizedCrop(self.input_shape),
# utils.RandomNoise(),
utils.RandomTranslate(10, 10),
# utils.RandomBrightness(),
# utils.RandomContrast(),
# utils.RandomHue(),
utils.RandomHorizontalFlip(),
utils.ToTensor(),
utils.Normalize([0.1, 0.4, 0.4], [0.9, 0.6, 0.5])
]))
# weights = utils.get_weights(trainset)
# sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights), replacement=False)
# self.trainloader = torch.utils.data.DataLoader(trainset, batch_size=self.cfg.batch_size, sampler=sampler, num_workers=0, pin_memory=True)
self.trainloader = torch.utils.data.DataLoader(trainset, shuffle=True, batch_size=self.cfg.batch_size, num_workers=0, pin_memory=True)
testset = SimulationDataset("test", transforms=transforms.Compose([
utils.RandomCoose(['center']),
utils.Preprocess(self.input_shape),
utils.ToTensor(),
utils.Normalize([0.1, 0.4, 0.4], [0.9, 0.6, 0.5])
]))
self.testloader = torch.utils.data.DataLoader(testset, batch_size=self.cfg.batch_size, shuffle=False, num_workers=0, pin_memory=True)
def main(args):
utils.seedme(args.seed)
cudnn.benchmark = True
os.system('mkdir -p {}'.format(args.outf))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
images_train, images_test, masks_train, masks_test = utils.load_seismic_data(
args.root_dir, test_size=.2, random_state=args.seed)
images_train, masks_train = utils.concatenate_hflips(
images_train, masks_train, shuffle=True, random_state=args.seed)
images_test, masks_test = utils.concatenate_hflips(images_test,
masks_test,
shuffle=True,
random_state=args.seed)
# transform = transforms.Compose([utils.augment(), utils.ToTensor()])
transform = transforms.Compose([utils.ToTensor()])
dataset_train = utils.SegmentationDataset(images_train,
masks_train,
transform=transform)
dataloader = torch.utils.data.DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
num_workers=1)
dataiter = utils.dataiterator(dataloader)
netF = models.choiceF[args.archF](num_features=args.num_features_F,
num_residuals=args.num_residuals,
gated=args.gated,
gate_param=args.gate_param).to(device)
optimizerF = optim.Adam(netF.parameters(), lr=args.lr, amsgrad=True)
loss_func = torch.nn.BCELoss()
log = logger.LoggerBCE(args.outf,
netF,
torch.from_numpy(images_train),
torch.from_numpy(masks_train),
torch.from_numpy(images_test),
torch.from_numpy(masks_test),
bcefunc=loss_func,
device=device)
for i in range(args.niter):
optimizerF.zero_grad()
images, masks = next(dataiter)
images, masks = images.to(device), masks.to(device)
masks_pred = netF(images)
loss = loss_func(masks_pred, masks)
loss.backward()
optimizerF.step()
if (i + 1) % args.nprint == 0:
print "[{}/{}] | loss: {:.3f}".format(i + 1, args.niter,
loss.item())
log.flush(i + 1)
if (i + 1) > 5000:
torch.save(netF.state_dict(),
'{}/netF_iter_{}.pth'.format(args.outf, i + 1))
def predict(self, image, preloaded=False):
# set test mode
self.net.eval()
if (not preloaded):
loadModel()
print('Loaded Model')
print('Starting Prediction')
composed = transforms.Compose([
utils.Preprocess(self.input_shape),
utils.ToTensor(),
utils.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Target gets discareded
sample = {'image': image, 'target': 0}
sample = composed(sample)
inputs = sample['image']
# Add single batch diemension
inputs = inputs.unsqueeze(0)
if (self.cfg.cuda):
inputs = Variable(inputs.cuda(async=True))
else:
inputs = Variable(inputs)
if (self.cfg.cuda):
outputs = self.net(inputs).cuda(async=True)
else:
outputs = self.net(inputs)
print('Finished Prediction')
print('Control tensor: %.6f ' % (outputs.item()))
# set train mode
self.net.train()
return outputs.item()
def loadData(self):
trainset = SimulationDataset(
"train",
transforms=transforms.Compose([
utils.RandomCoose(['centre', 'left', 'right']),
utils.Preprocess(self.input_shape),
utils.RandomTranslate(100, 10),
utils.RandomBrightness(),
utils.RandomContrast(),
utils.RandomHue(),
utils.RandomHorizontalFlip(),
utils.ToTensor(),
utils.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]))
weights = utils.get_weights(trainset)
sampler = torch.utils.data.sampler.WeightedRandomSampler(
weights, len(weights), replacement=True)
# self.trainloader = torch.utils.data.DataLoader(trainset, batch_size=self.cfg.batch_size, sampler=sampler, num_workers=4)
self.trainloader = torch.utils.data.DataLoader(
trainset, batch_size=self.cfg.batch_size, num_workers=4)
testset = SimulationDataset("test",
transforms=transforms.Compose([
utils.RandomCoose(['center']),
utils.Preprocess(self.input_shape),
utils.ToTensor(),
utils.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]))
self.testloader = torch.utils.data.DataLoader(
testset,
batch_size=self.cfg.batch_size,
shuffle=False,
num_workers=4)
def create_operators(self):
size = 224
img_mean = [0.485, 0.456, 0.406]
img_std = [0.229, 0.224, 0.225]
img_scale = 1.0 / 255.0
decode_op = utils.DecodeImage()
resize_op = utils.ResizeImage(resize_short=256)
crop_op = utils.CropImage(size=(size, size))
normalize_op = utils.NormalizeImage(scale=img_scale,
mean=img_mean,
std=img_std)
totensor_op = utils.ToTensor()
return [decode_op, resize_op, crop_op, normalize_op, totensor_op]
def create_operators(interpolation=1):
size = 224
img_mean = [0.485, 0.456, 0.406]
img_std = [0.229, 0.224, 0.225]
img_scale = 1.0 / 255.0
resize_op = utils.ResizeImage(resize_short=256,
interpolation=interpolation)
crop_op = utils.CropImage(size=(size, size))
normalize_op = utils.NormalizeImage(scale=img_scale,
mean=img_mean,
std=img_std)
totensor_op = utils.ToTensor()
return [resize_op, crop_op, normalize_op, totensor_op]
def test(args):
if args.gpu:
ctx = [mx.gpu(0)]
else:
ctx = [mx.cpu(0)]
if args.dataset == "Sony":
out_channels = 12
scale = 2
else:
out_channels = 27
scale = 3
# load data
test_transform = utils.Compose([utils.ToTensor()])
test_dataset = data.MyDataset(args.dataset,
"test",
transform=test_transform)
test_loader = gluon.data.DataLoader(test_dataset,
batch_size=1,
last_batch='discard')
unet = net.UNet(out_channels, scale)
unet.load_params(args.model, ctx=ctx)
batches = 0
avg_psnr = 0.
for img, gt in test_loader:
batches += 1
imgs = gluon.utils.split_and_load(img[0], ctx)
label = gluon.utils.split_and_load(gt[0], ctx)
outputs = []
for x in imgs:
outputs.append(unet(x))
metric.update(label, outputs)
avg_psnr += 10 * math.log10(1 / metric.get()[1])
metric.reset()
avg_psnr /= batches
print('Test avg psnr: {:.3f}'.format(avg_psnr))
def train(args):
frames = args.frames
caption_length = args.caption_length
glove_file = args.glove_file
#CPU_COUNT = multiprocessing.cpu_count()
if args.cuda:
ctx = mx.gpu()
else:
ctx = mx.cpu()
if args.load_pretrain:
pretrain_model = vision.vgg16_bn(pretrained=True, ctx=ctx)
transform = utils.Compose([
utils.ToTensor(ctx),
utils.normalize(ctx),
utils.extractFeature(ctx, pretrain_model)
])
else:
pretrain_model = None
transform = utils.Compose([
utils.ToTensor(ctx),
utils.normalize(ctx),
])
target_transform = utils.targetCompose([utils.WordToTensor(ctx)])
train_dataset = videoFolder(args.train_folder,
args.train_dict,
frames,
glove_file,
caption_length,
ctx,
img_size=args.img_size,
transform=transform,
target_transform=target_transform)
test_dataset = videoFolder(args.test_folder,
args.test_dict,
frames,
glove_file,
caption_length,
ctx,
img_size=args.img_size,
transform=transform,
target_transform=target_transform)
train_loader = gluon.data.DataLoader(train_dataset,
batch_size=args.batch_size,
last_batch='discard',
shuffle=True)
test_loader = gluon.data.DataLoader(test_dataset,
batch_size=args.batch_size,
last_batch='discard',
shuffle=False)
loss = L2Loss()
#net = lstm_net(caption_length,ctx,pretrained=args.load_pretrain)
net = resnet18_v2(caption_length=caption_length, ctx=ctx)
net.collect_params().initialize(init=mx.initializer.MSRAPrelu(), ctx=ctx)
trainer = gluon.Trainer(net.collect_params(), 'adam',
{'learning_rate': args.lr})
train_loss = []
test_loss = []
train_loss_batch = []
test_loss_batch = []
smoothing_constant = 0.01
for e in range(args.epochs):
epoch_loss = 0.
for batch_id, (x, _) in enumerate(train_loader):
with autograd.record():
pred = net(x)
batch_loss = loss(pred, _)
trainer.step(x.shape[0], ignore_stale_grad=True)
batch_loss.backward()
mx.nd.waitall()
#print(batch_loss.shape)
batch_loss = nd.mean(batch_loss).asscalar()
#print(batch_id,batch_loss)
if ((batch_id == 0) or (e == 0)):
epoch_loss = batch_loss
else:
epoch_loss = (1 - smoothing_constant
) * epoch_loss + smoothing_constant * batch_loss
train_loss_batch.append(batch_loss)
if (batch_id + 1) % 200 == 0:
print("Train Batch:{}, batch_loss:{}".format(
batch_id + 1, batch_loss))
if ((e + 1) * (batch_id + 1)) % (2 * args.log_interval) == 0:
# save model
save_model_filename = "Epoch_" + str(e) + "_iters_" + str(
batch_id + 1) + '_' + str(time.ctime()).replace(
' ', '_') + "_" + ".params"
save_model_path = os.path.join(args.model_path,
save_model_filename)
net.save_parameters(save_model_path)
print("\nCheckpoint, trained model saved at", save_model_path)
train_loss_filename = "Epoch_" + str(e) + "_iters_" + str(
batch_id + 1) + str(time.ctime()).replace(
' ', '_') + "_train_loss" + ".txt"
train_loss_path = os.path.join(args.log_path,
train_loss_filename)
np.savetxt(train_loss_path, np.array(train_loss_batch))
epoch_loss_1 = 0.
for batch_id, (x, _) in enumerate(test_loader):
with autograd.predict_mode():
predict = net(x)
batch_loss_1 = loss(predict, _)
#batch_loss_1 = F.mean(batch_loss_1.asscalar())
batch_loss_1 = nd.mean(batch_loss_1).asscalar()
if ((batch_id == 0) or (e == 0)):
epoch_loss_1 = batch_loss_1
else:
epoch_loss_1 = (
1 - smoothing_constant
) * epoch_loss_1 + smoothing_constant * batch_loss_1
test_loss_batch.append(batch_loss_1)
if ((e + 1) * (batch_id + 1)) % (args.log_interval) == 0:
test_loss_file_name = "Epoch_" + str(e) + "_iters_" + str(
batch_id + 1) + str(time.ctime()).replace(
' ', '_') + "_test_loss" + ".txt"
test_loss_path = os.path.join(args.log_path,
test_loss_file_name)
np.savetxt(test_loss_path, np.array(test_loss_batch))
train_loss.append(epoch_loss)
test_loss.append(epoch_loss_1)
print("Epoch {}, train_loss:{}, test_loss:{}".format(
e + 1, epoch_loss, epoch_loss_1))
# save model
save_model_filename = "Final_epoch_" + str(args.epochs) + "_" + str(
time.ctime()).replace(' ', '_') + "_" + ".params"
save_model_path = os.path.join(args.model_path, save_model_filename)
net.save_parameters(save_model_path)
print("\nDone, trained model saved at", save_model_path)
train_epoch_loss_file_name = 'train_epoch_loss.txt'
test_epoch_loss_file_name = 'test_epoch_loss.txt'
train_epoch_loss_path = os.path.join(args.log_path,
train_epoch_loss_file_name)
test_epoch_loss_path = os.path.join(args.log_path,
test_epoch_loss_file_name)
np.savetxt(train_epoch_loss_path, train_loss)
np.savetxt(test_epoch_loss_path, test_loss)
import os
import torch
from torchvision import transforms
from torch.utils.data import DataLoader
from datasets import *
import utils
from model import Net
transform = transforms.Compose([utils.ToTensor()])
mnist_dataset = MNIST_Datasets('training', './data', transform)
test_dataset = MNIST_Datasets('testing', './data', transform)
dataloader = DataLoader(mnist_dataset,
batch_size=4,
shuffle=True,
num_workers=4)
testloader = DataLoader(test_dataset,
batch_size=4,
shuffle=False,
num_workers=4)
print 'Dataset size:', len(mnist_dataset)
n_epoches = 5
model = Net()
# How to init weights in model
# grad = True, False
# Normalize
# GPU
# Save model
# Transfer learning
def main(args):
# log hyperparameter
print(args)
# select device
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda: 0" if args.cuda else "cpu")
# set random seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# data loader
transform = transforms.Compose([utils.Normalize(), utils.ToTensor()])
infer_dataset = InferTVDataset(root=args.root,
sub_size=args.block_size,
volume_list="volume_test_list.txt",
max_k=args.infering_step,
transform=transform)
kwargs = {"num_workers": 4, "pin_memory": True} if args.cuda else {}
infer_loader = DataLoader(infer_dataset,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
# model
def generator_weights_init(m):
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
if m.bias is not None:
nn.init.zeros_(m.bias)
g_model = Generator(args.upsample_mode, args.forward, args.backward,
args.gen_sn)
g_model.apply(generator_weights_init)
if args.data_parallel and torch.cuda.device_count() > 1:
g_model = nn.DataParallel(g_model)
g_model.to(device)
mse_loss = nn.MSELoss()
adversarial_loss = nn.MSELoss()
train_losses, test_losses = [], []
d_losses, g_losses = [], []
# load checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint {}".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint["epoch"]
g_model.load_state_dict(checkpoint["g_model_state_dict"])
# g_optimizer.load_state_dict(checkpoint["g_optimizer_state_dict"])
if args.gan_loss != "none":
# d_model.load_state_dict(checkpoint["d_model_state_dict"])
# d_optimizer.load_state_dict(checkpoint["d_optimizer_state_dict"])
d_losses = checkpoint["d_losses"]
g_losses = checkpoint["g_losses"]
train_losses = checkpoint["train_losses"]
test_losses = checkpoint["test_losses"]
print("=> load chekcpoint {} (epoch {})".format(
args.resume, checkpoint["epoch"]))
g_model.eval()
inferRes = []
zSize, ySize, xSize = 120, 720, 480
for i in range(args.infering_step):
inferRes.append(np.zeros((zSize, ySize, xSize)))
inferScale = np.zeros((zSize, ySize, xSize))
time_start = 0
volume_type = ''
with torch.no_grad():
for i, sample in tqdm(enumerate(infer_loader)):
v_f = sample["v_f"].to(device)
v_b = sample["v_b"].to(device)
fake_volumes = g_model(v_f, v_b, args.infering_step,
args.wo_ori_volume, args.norm)
volume_type, time_start, x_start, y_start, z_start = utils.Parse(
sample["vf_name"][0])
for j in range(fake_volumes.shape[1]):
volume = fake_volumes[0, j, 0]
min_value = -0.015 # -0.012058
max_value = 1.01 # 1.009666
mean = (min_value + max_value) / 2
std = mean - min_value
volume = volume.to("cpu").numpy() * std + mean
inferRes[j][z_start:z_start + args.block_size,
y_start:y_start + args.block_size,
x_start:x_start + args.block_size] += volume
if j == 0:
inferScale[z_start:z_start + args.block_size,
y_start:y_start + args.block_size,
x_start:x_start + args.block_size] += 1
# pdb.set_trace()
for j in range(args.infering_step):
inferRes[j] = inferRes[j] / inferScale
inferRes[j] = inferRes[j].astype(np.float32)
volume_name = volume_type + '_' + ("%04d" %
(time_start + j + 1)) + '.raw'
inferRes[j].tofile(os.path.join(args.save_pred, volume_name))
def train(args):
frames = args.frames
caption_length = args.caption_length
glove_file = args.glove_file
#CPU_COUNT = multiprocessing.cpu_count()
if args.cuda:
ctx = mx.gpu()
else:
ctx = mx.cpu()
if args.load_pretrain:
pretrain_model = vision.vgg16_bn(pretrained=True,ctx=ctx)
transform = utils.Compose([utils.ToTensor(ctx),
utils.normalize(ctx),
utils.extractFeature(ctx,pretrain_model)
])
else:
pretrain_model = None
transform = utils.Compose([utils.ToTensor(ctx),
utils.normalize(ctx),
])
target_transform = utils.targetCompose([utils.WordToTensor(ctx)])
train_dataset = videoFolder(args.train_folder,args.train_dict, frames, glove_file,
caption_length, ctx, transform=transform, target_transform=target_transform)
test_dataset = videoFolder(args.test_folder,args.test_dict, frames, glove_file,
caption_length, ctx, transform=transform, target_transform=target_transform)
train_loader = gluon.data.DataLoader(train_dataset,batch_size=args.batch_size,
last_batch='discard',shuffle=True)
test_loader = gluon.data.DataLoader(test_dataset,batch_size=args.batch_size,
last_batch='discard',shuffle=False)
#loss = L2Loss_cos()
loss = L2Loss_2()
net = lstm_net(frames,caption_length,ctx,pretrained=args.load_pretrain)
#net = resnet18_v2(caption_length=caption_length,ctx=ctx)
net.collect_params().initialize(init=mx.initializer.MSRAPrelu(), ctx=ctx)
trainer = gluon.Trainer(net.collect_params(), 'adam',
{'learning_rate': args.lr})
smoothing_constant = 0.01
for e in range(args.epochs):
epoch_loss = 0
for batch_id, (x,_) in enumerate(train_loader):
with autograd.record():
pred = net(x)
batch_loss = loss(pred,_)
trainer.step(x.shape[0],ignore_stale_grad=True)
batch_loss.backward()
mx.nd.waitall()
batch_loss = F.mean(batch_loss).asscalar()
if batch_id % 100 == 0:
print("Train Batch:{}, batch_loss:{}".format(batch_id+1, batch_loss))
epoch_loss = (batch_loss if ((batch_id == 0) and (e == 0))
else (1 - smoothing_constant)*epoch_loss + smoothing_constant*batch_loss)
epoch_loss_1 = 0
for batch_id, (x,_) in enumerate(test_loader):
with autograd.predict_mode():
predict = net(x)
batch_loss_1 = loss(pred,_)
batch_loss_1 = F.mean(batch_loss_1).asscalar()
if batch_id % 100 == 0:
print("Test Batch:{}, batch_loss:{}".format(batch_id+1, batch_loss_1))
epoch_loss_1 = (batch_loss_1 if ((batch_id == 0) and (e == 0))
else (1 - smoothing_constant)*epoch_loss_1 + smoothing_constant*batch_loss_1)
print("Epoch {}, train_loss:{}, test_loss:{}".format(e+1, epoch_loss, epoch_loss_1))
if args.save_model == True:
file_name = "./saved_model/" + "lstm_pretrain.params"
net.save_parameters(file_name)
if __name__ == "__main__":
# load config
args = args().parse_args()
syn_world(args)
if world.verbose:
print("loaded configs")
if world.useSigmoid:
world.filename = "checkpoint_sigmoid.pth.tar"
args.tag = time.strftime("%m-%d-%H:%M") + args.opt + str(
args.lr) + "-" + world.comment + "_"
world.filename = args.tag + world.filename
# load data
tran = transforms.Compose([utils.Scale(), utils.ToTensor()])
data = dataloader(transform=tran)
if len(data) == 0:
print("Didn't find dataset.")
raise ValueError("empty dataset")
data_train = DataLoader(data,
batch_size=world.batch_size,
shuffle=True,
num_workers=world.workers)
world.n_batch = len(data_train)
if args.eval == False:
data_test = dataloader(mode="test", transform=tran)
data_test = DataLoader(data_test,
batch_size=world.batch_size,
shuffle=True,
num_workers=world.workers)
def train(args):
np.random.seed(args.seed)
if args.gpu:
ctx = [mx.gpu(0)]
else:
ctx = [mx.cpu(0)]
if args.dataset == "Sony":
out_channels = 12
scale = 2
else:
out_channels = 27
scale = 3
# load data
train_transform = utils.Compose([
utils.RandomCrop(args.patch_size, scale),
utils.RandomFlipLeftRight(),
utils.RandomFlipTopBottom(),
utils.RandomTranspose(),
utils.ToTensor(),
])
train_dataset = data.MyDataset(args.dataset,
"train",
transform=train_transform)
val_transform = utils.Compose([utils.ToTensor()])
val_dataset = data.MyDataset(args.dataset, "val", transform=val_transform)
train_loader = gluon.data.DataLoader(train_dataset,
shuffle=True,
batch_size=args.batch_size,
last_batch='rollover')
val_loader = gluon.data.DataLoader(val_dataset,
batch_size=1,
last_batch='discard')
unet = net.UNet(out_channels, scale)
unet.initialize(init=initializer.Xavier(), ctx=ctx)
# optimizer and loss
trainer = gluon.Trainer(unet.collect_params(), 'adam',
{'learning_rate': args.lr})
l1_loss = gluon.loss.L1Loss()
print "Start training now.."
for i in range(args.epochs):
total_loss = 0
count = 0
profiler.set_state('run')
for batch_id, (img, gt) in enumerate(train_loader):
batch_size = img.shape[0]
count += batch_size
img_list = gluon.utils.split_and_load(img[0], ctx)
gt_list = gluon.utils.split_and_load(gt[0], ctx)
with autograd.record():
preds = [unet(x) for x in img_list]
losses = []
for ii in range(len(preds)):
loss = l1_loss(gt_list[ii], preds[ii])
losses.append(loss)
for loss in losses:
loss.backward()
total_loss += sum([l.sum().asscalar() for l in losses])
avg_loss = total_loss / count
trainer.step(batch_size)
metric.update(gt_list, preds)
F.waitall()
profiler.set_state('stop')
print profiler.dumps()
break
gt_save = gt_list[0]
output_save = preds[0]
if (batch_id + 1) % 100 == 0:
message = "Epoch {}: [{}/{}]: l1_loss: {:.4f}".format(
i + 1, count, len(train_dataset), avg_loss)
print message
temp = F.concat(gt_save, output_save, dim=3)
temp = temp.asnumpy().reshape(temp.shape[2], temp.shape[3], 3)
scipy.misc.toimage(temp * 255,
high=255,
low=0,
cmin=0,
cmax=255,
mode='RGB').save(args.save_model_dir +
'%04d_%05d_00_train.jpg' %
(i + 1, count))
# evaluate
batches = 0
avg_psnr = 0.
for img, gt in val_loader:
batches += 1
imgs = gluon.utils.split_and_load(img[0], ctx)
label = gluon.utils.split_and_load(gt[0], ctx)
outputs = []
for x in imgs:
outputs.append(unet(x))
metric.update(label, outputs)
avg_psnr += 10 * math.log10(1 / metric.get()[1])
metric.reset()
avg_psnr /= batches
print('Epoch {}: validation avg psnr: {:.3f}'.format(i + 1, avg_psnr))
# save model
if (i + 1) % args.save_freq == 0:
save_model_filename = "Epoch_" + str(i + 1) + ".params"
save_model_path = os.path.join(args.save_model_dir,
save_model_filename)
unet.save_params(save_model_path)
print("\nCheckpoint, trained model saved at", save_model_path)
# save model
save_model_filename = "Final_Epoch_" + str(i + 1) + ".params"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
unet.save_params(save_model_path)
print("\nCheckpoint, trained model saved at", save_model_path)
def train(args):
train_logger = None
if args.log_dir is not None:
train_logger = tb.SummaryWriter(
os.path.join(args.log_dir, "train"), flush_secs=1
)
transform = utils.Compose(
[
utils.Rotation(),
utils.Crop(),
utils.Resize((32, 32)),
utils.IntensityNormalize(),
utils.ToTensor(),
]
)
train_loader = dataset.load_data(
args.data_dir, batch_size=args.batch_size, transform=transform
)
print("Train length:", len(train_loader))
model = FacialModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-3)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
print_every = 10
num_train = len(train_loader)
for epoch in range(args.epochs):
model.train()
lr = adjust_learning_rate_poly(optimizer, 1e-3, epoch, args.epochs)
running_print_loss = 0
running_print_accuracy = 0
running_accuracy = 0
for i, data in enumerate(train_loader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
acc = accuracy(labels, outputs)
running_print_accuracy += acc.item()
running_accuracy += acc.item()
running_print_loss += loss.item()
running_loss = loss.item()
if (i + 1) % print_every == 0: # print every 2000 mini-batches
print(
"[%d, %5d] loss: %.3f accuracy: %.3f lr: %.8f"
% (
epoch + 1,
i + 1,
running_print_loss / print_every,
running_print_accuracy / print_every,
lr,
)
)
running_print_loss = 0
running_print_accuracy = 0
# write train loss summaries
train_logger.add_scalar("loss", running_loss, epoch * num_train + i + 1)
train_logger.add_scalar("accuracy", running_accuracy / num_train, epoch + 1)
# plt.imshow(F.to_pil_image(sample['image']))
# plt.title(str(sample['target']))
# plt.show()
return sample['image'], sample['target']
def __len__(self):
return len(self.image_paths)
if __name__ == '__main__':
input_shape = (utils.IMAGE_HEIGHT, utils.IMAGE_WIDTH)
dataset = SimulationDataset("train",
transforms=transforms.Compose([
utils.RandomCoose(['center']),
utils.Preprocess(input_shape),
utils.RandomHorizontalFlip(),
utils.ToTensor(),
utils.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]))
print(dataset.__len__())
print(dataset.__getitem__(0)[0].size())
for c in range(3):
for i in range(dataset.__len__()):
print(dataset.__getitem__(i)[c].mean())
print(dataset.__getitem__(i)[c].std())
# print(dataset.__getitem__(0))
# print(len(dataset.__get_annotations__()))
def main(args):
# log hyperparameter
print(args)
# select device
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda: 0" if args.cuda else "cpu")
# set random seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# data loader
transform = transforms.Compose([
utils.Normalize(),
utils.ToTensor()
])
train_dataset = TVDataset(
root=args.root,
sub_size=args.block_size,
volume_list=args.volume_train_list,
max_k=args.training_step,
train=True,
transform=transform
)
test_dataset = TVDataset(
root=args.root,
sub_size=args.block_size,
volume_list=args.volume_test_list,
max_k=args.training_step,
train=False,
transform=transform
)
kwargs = {"num_workers": 4, "pin_memory": True} if args.cuda else {}
train_loader = DataLoader(train_dataset, batch_size=args.batch_size,
shuffle=True, **kwargs)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size,
shuffle=False, **kwargs)
# model
def generator_weights_init(m):
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.zeros_(m.bias)
def discriminator_weights_init(m):
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
if m.bias is not None:
nn.init.zeros_(m.bias)
g_model = Generator(args.upsample_mode, args.forward, args.backward, args.gen_sn, args.residual)
g_model.apply(generator_weights_init)
if args.data_parallel and torch.cuda.device_count() > 1:
g_model = nn.DataParallel(g_model)
g_model.to(device)
if args.gan_loss != "none":
d_model = Discriminator(args.dis_sn)
d_model.apply(discriminator_weights_init)
# if args.dis_sn:
# d_model = add_sn(d_model)
if args.data_parallel and torch.cuda.device_count() > 1:
d_model = nn.DataParallel(d_model)
d_model.to(device)
mse_loss = nn.MSELoss()
adversarial_loss = nn.MSELoss()
train_losses, test_losses = [], []
d_losses, g_losses = [], []
# optimizer
g_optimizer = optim.Adam(g_model.parameters(), lr=args.lr,
betas=(args.beta1, args.beta2))
if args.gan_loss != "none":
d_optimizer = optim.Adam(d_model.parameters(), lr=args.d_lr,
betas=(args.beta1, args.beta2))
Tensor = torch.cuda.FloatTensor if args.cuda else torch.FloatTensor
# load checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint {}".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint["epoch"]
g_model.load_state_dict(checkpoint["g_model_state_dict"])
# g_optimizer.load_state_dict(checkpoint["g_optimizer_state_dict"])
if args.gan_loss != "none":
d_model.load_state_dict(checkpoint["d_model_state_dict"])
# d_optimizer.load_state_dict(checkpoint["d_optimizer_state_dict"])
d_losses = checkpoint["d_losses"]
g_losses = checkpoint["g_losses"]
train_losses = checkpoint["train_losses"]
test_losses = checkpoint["test_losses"]
print("=> load chekcpoint {} (epoch {})"
.format(args.resume, checkpoint["epoch"]))
# main loop
for epoch in tqdm(range(args.start_epoch, args.epochs)):
# training..
g_model.train()
if args.gan_loss != "none":
d_model.train()
train_loss = 0.
volume_loss_part = np.zeros(args.training_step)
for i, sample in enumerate(train_loader):
params = list(g_model.named_parameters())
# pdb.set_trace()
# params[0][1].register_hook(lambda g: print("{}.grad: {}".format(params[0][0], g)))
# adversarial ground truths
real_label = Variable(Tensor(sample["v_i"].shape[0], sample["v_i"].shape[1], 1, 1, 1, 1).fill_(1.0), requires_grad=False)
fake_label = Variable(Tensor(sample["v_i"].shape[0], sample["v_i"].shape[1], 1, 1, 1, 1).fill_(0.0), requires_grad=False)
v_f = sample["v_f"].to(device)
v_b = sample["v_b"].to(device)
v_i = sample["v_i"].to(device)
g_optimizer.zero_grad()
fake_volumes = g_model(v_f, v_b, args.training_step, args.wo_ori_volume, args.norm)
# adversarial loss
# update discriminator
if args.gan_loss != "none":
avg_d_loss = 0.
avg_d_loss_real = 0.
avg_d_loss_fake = 0.
for k in range(args.n_d):
d_optimizer.zero_grad()
decisions = d_model(v_i)
d_loss_real = adversarial_loss(decisions, real_label)
fake_decisions = d_model(fake_volumes.detach())
d_loss_fake = adversarial_loss(fake_decisions, fake_label)
d_loss = d_loss_real + d_loss_fake
d_loss.backward()
avg_d_loss += d_loss.item() / args.n_d
avg_d_loss_real += d_loss_real / args.n_d
avg_d_loss_fake += d_loss_fake / args.n_d
d_optimizer.step()
# update generator
if args.gan_loss != "none":
avg_g_loss = 0.
avg_loss = 0.
for k in range(args.n_g):
loss = 0.
g_optimizer.zero_grad()
# adversarial loss
if args.gan_loss != "none":
fake_decisions = d_model(fake_volumes)
g_loss = args.gan_loss_weight * adversarial_loss(fake_decisions, real_label)
loss += g_loss
avg_g_loss += g_loss.item() / args.n_g
# volume loss
if args.volume_loss:
volume_loss = args.volume_loss_weight * mse_loss(v_i, fake_volumes)
for j in range(v_i.shape[1]):
volume_loss_part[j] += mse_loss(v_i[:, j, :], fake_volumes[:, j, :]) / args.n_g / args.log_every
loss += volume_loss
# feature loss
if args.feature_loss:
feat_real = d_model.extract_features(v_i)
feat_fake = d_model.extract_features(fake_volumes)
for m in range(len(feat_real)):
loss += args.feature_loss_weight / len(feat_real) * mse_loss(feat_real[m], feat_fake[m])
avg_loss += loss / args.n_g
loss.backward()
g_optimizer.step()
train_loss += avg_loss
# log training status
subEpoch = (i + 1) // args.log_every
if (i+1) % args.log_every == 0:
print("Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
epoch, (i+1) * args.batch_size, len(train_loader.dataset), 100. * (i+1) / len(train_loader),
avg_loss
))
print("Volume Loss: ")
for j in range(volume_loss_part.shape[0]):
print("\tintermediate {}: {:.6f}".format(
j+1, volume_loss_part[j]
))
if args.gan_loss != "none":
print("DLossReal: {:.6f} DLossFake: {:.6f} DLoss: {:.6f}, GLoss: {:.6f}".format(
avg_d_loss_real, avg_d_loss_fake, avg_d_loss, avg_g_loss
))
d_losses.append(avg_d_loss)
g_losses.append(avg_g_loss)
# train_losses.append(avg_loss)
train_losses.append(train_loss.item() / args.log_every)
print("====> SubEpoch: {} Average loss: {:.6f} Time {}".format(
subEpoch, train_loss.item() / args.log_every, time.asctime(time.localtime(time.time()))
))
train_loss = 0.
volume_loss_part = np.zeros(args.training_step)
# testing...
if (i + 1) % args.test_every == 0:
g_model.eval()
if args.gan_loss != "none":
d_model.eval()
test_loss = 0.
with torch.no_grad():
for i, sample in enumerate(test_loader):
v_f = sample["v_f"].to(device)
v_b = sample["v_b"].to(device)
v_i = sample["v_i"].to(device)
fake_volumes = g_model(v_f, v_b, args.training_step, args.wo_ori_volume, args.norm)
test_loss += args.volume_loss_weight * mse_loss(v_i, fake_volumes).item()
test_losses.append(test_loss * args.batch_size / len(test_loader.dataset))
print("====> SubEpoch: {} Test set loss {:4f} Time {}".format(
subEpoch, test_losses[-1], time.asctime(time.localtime(time.time()))
))
# saving...
if (i+1) % args.check_every == 0:
print("=> saving checkpoint at epoch {}".format(epoch))
if args.gan_loss != "none":
torch.save({"epoch": epoch + 1,
"g_model_state_dict": g_model.state_dict(),
"g_optimizer_state_dict": g_optimizer.state_dict(),
"d_model_state_dict": d_model.state_dict(),
"d_optimizer_state_dict": d_optimizer.state_dict(),
"d_losses": d_losses,
"g_losses": g_losses,
"train_losses": train_losses,
"test_losses": test_losses},
os.path.join(args.save_dir, "model_" + str(epoch) + "_" + str(subEpoch) + "_" + "pth.tar")
)
else:
torch.save({"epoch": epoch + 1,
"g_model_state_dict": g_model.state_dict(),
"g_optimizer_state_dict": g_optimizer.state_dict(),
"train_losses": train_losses,
"test_losses": test_losses},
os.path.join(args.save_dir, "model_" + str(epoch) + "_" + str(subEpoch) + "_" + "pth.tar")
)
torch.save(g_model.state_dict(),
os.path.join(args.save_dir, "model_" + str(epoch) + "_" + str(subEpoch) + ".pth"))
num_subEpoch = len(train_loader) // args.log_every
print("====> Epoch: {} Average loss: {:.6f} Time {}".format(
epoch, np.array(train_losses[-num_subEpoch:]).mean(), time.asctime(time.localtime(time.time()))
))
def main(args):
utils.seedme(args.seed)
cudnn.benchmark = True
os.system('mkdir -p {}'.format(args.outf))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print "Using BCE loss: {}".format(not args.no_bce)
images_train, images_test, masks_train, masks_test = utils.load_seismic_data(args.root_dir, test_size=.2, random_state=args.seed)
images_train, masks_train = utils.concatenate_hflips(images_train, masks_train, shuffle=True, random_state=args.seed)
images_test, masks_test = utils.concatenate_hflips(images_test, masks_test, shuffle=True, random_state=args.seed)
# transform = transforms.Compose([utils.augment(), utils.ToTensor()])
transform = transforms.Compose([utils.ToTensor()])
dataset_train = utils.SegmentationDataset(images_train, masks_train, transform=transform)
dataloader = torch.utils.data.DataLoader(dataset_train, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=1)
dataiter = utils.dataiterator(dataloader)
netF = models.choiceF[args.archF](num_features=args.num_features_F, num_residuals=args.num_residuals, gated=args.gated, gate_param=args.gate_param).to(device)
netD = models.choiceD[args.archD](num_features=args.num_features_D, nc=2, dropout=args.dropout).to(device)
if args.netF:
netF.load_state_dict(torch.load(args.netF))
if args.netD:
netD.load_state_dict(torch.load(args.netD))
print netF
print netD
optimizerF = optim.Adam(netF.parameters(), betas=(0.5, 0.999), lr=args.lr, amsgrad=True)
optimizerD = optim.Adam(netD.parameters(), betas=(0.5, 0.999), lr=args.lr, amsgrad=True)
alpha = torch.tensor(args.alpha).to(device)
loss_func = torch.nn.BCELoss()
smooth_binary = utils.SmoothBinary(scale=args.smooth_noise)
# images_test, masks_test = torch.from_numpy(images_test).to(device), torch.from_numpy(masks_test).to(device)
log = logger.LoggerGAN(args.outf, netF, netD, torch.from_numpy(images_train), torch.from_numpy(masks_train), torch.from_numpy(images_test), torch.from_numpy(masks_test), bcefunc=loss_func, device=device)
start_time = time.time()
for i in range(args.niter):
# --- train D
for p in netD.parameters():
p.requires_grad_(True)
for _ in range(args.niterD):
optimizerD.zero_grad()
images_real, masks_real = next(dataiter)
images_real, masks_real = images_real.to(device), masks_real.to(device)
masks_fake = netF(images_real).detach()
x_fake = torch.cat((images_real, masks_fake), dim=1)
# images_real, masks_real = next(dataiter)
# images_real, masks_real = images_real.to(device), masks_real.to(device)
masks_real = smooth_binary(masks_real)
x_real = torch.cat((images_real, masks_real), dim=1)
x_real.requires_grad_() # to compute gradD_real
x_fake.requires_grad_() # to compute gradD_fake
y_real = netD(x_real)
y_fake = netD(x_fake)
lossE = y_real.mean() - y_fake.mean()
# grad() does not broadcast so we compute for the sum, effect is the same
gradD_real = torch.autograd.grad(y_real.sum(), x_real, create_graph=True)[0]
gradD_fake = torch.autograd.grad(y_fake.sum(), x_fake, create_graph=True)[0]
omega = 0.5*(gradD_real.view(gradD_real.size(0), -1).pow(2).sum(dim=1).mean() +
gradD_fake.view(gradD_fake.size(0), -1).pow(2).sum(dim=1).mean())
loss = -lossE - alpha*(1.0 - omega) + 0.5*args.rho*(1.0 - omega).pow(2)
loss.backward()
optimizerD.step()
alpha -= args.rho*(1.0 - omega.item())
# --- train G
for p in netD.parameters():
p.requires_grad_(False)
optimizerF.zero_grad()
images_real, masks_real = next(dataiter)
images_real, masks_real = images_real.to(device), masks_real.to(device)
masks_fake = netF(images_real)
x_fake = torch.cat((images_real, masks_fake), dim=1)
y_fake = netD(x_fake)
loss = -y_fake.mean()
bceloss = loss_func(masks_fake, masks_real)
if not args.no_bce:
loss = loss + bceloss * args.bce_weight
loss.backward()
optimizerF.step()
log.dump(i+1, lossE.item(), alpha.item(), omega.item())
if (i+1) % args.nprint == 0:
print 'Time per loop: {} sec/loop'.format((time.time() - start_time)/args.nprint)
print "[{}/{}] lossE: {:.3f}, bceloss: {:.3f}, alpha: {:.3f}, omega: {:.3f}".format((i+1), args.niter, lossE.item(), bceloss.item(), alpha.item(), omega.item())
log.flush(i+1)
# if (i+1) > 5000:
torch.save(netF.state_dict(), '{}/netF_iter_{}.pth'.format(args.outf, i+1))
torch.save(netD.state_dict(), '{}/netD_iter_{}.pth'.format(args.outf, i+1))
start_time = time.time()
def train(args):
np.random.seed(args.seed)
if args.cuda:
ctx = mx.gpu(0)
else:
ctx = mx.cpu(0)
# dataloader
transform = utils.Compose([utils.Scale(args.image_size),
utils.CenterCrop(args.image_size),
utils.ToTensor(ctx),
])
train_dataset = data.ImageFolder(args.dataset, transform)
train_loader = gluon.data.DataLoader(train_dataset, batch_size=args.batch_size, last_batch='discard')
style_loader = utils.StyleLoader(args.style_folder, args.style_size, ctx=ctx)
print('len(style_loader):',style_loader.size())
# models
vgg = net.Vgg16()
utils.init_vgg_params(vgg, 'models', ctx=ctx)
style_model = net.Net(ngf=args.ngf)
style_model.initialize(init=mx.initializer.MSRAPrelu(), ctx=ctx)
if args.resume is not None:
print('Resuming, initializing using weight from {}.'.format(args.resume))
style_model.collect_params().load(args.resume, ctx=ctx)
print('style_model:',style_model)
# optimizer and loss
trainer = gluon.Trainer(style_model.collect_params(), 'adam',
{'learning_rate': args.lr})
mse_loss = gluon.loss.L2Loss()
for e in range(args.epochs):
agg_content_loss = 0.
agg_style_loss = 0.
count = 0
for batch_id, (x, _) in enumerate(train_loader):
n_batch = len(x)
count += n_batch
# prepare data
style_image = style_loader.get(batch_id)
style_v = utils.subtract_imagenet_mean_preprocess_batch(style_image.copy())
style_image = utils.preprocess_batch(style_image)
features_style = vgg(style_v)
gram_style = [net.gram_matrix(y) for y in features_style]
xc = utils.subtract_imagenet_mean_preprocess_batch(x.copy())
f_xc_c = vgg(xc)[1]
with autograd.record():
style_model.setTarget(style_image)
y = style_model(x)
y = utils.subtract_imagenet_mean_batch(y)
features_y = vgg(y)
content_loss = 2 * args.content_weight * mse_loss(features_y[1], f_xc_c)
style_loss = 0.
for m in range(len(features_y)):
gram_y = net.gram_matrix(features_y[m])
_, C, _ = gram_style[m].shape
gram_s = F.expand_dims(gram_style[m], 0).broadcast_to((args.batch_size, 1, C, C))
style_loss = style_loss + 2 * args.style_weight * mse_loss(gram_y, gram_s[:n_batch, :, :])
total_loss = content_loss + style_loss
total_loss.backward()
trainer.step(args.batch_size)
mx.nd.waitall()
agg_content_loss += content_loss[0]
agg_style_loss += style_loss[0]
if (batch_id + 1) % args.log_interval == 0:
mesg = "{}\tEpoch {}:\t[{}/{}]\tcontent: {:.3f}\tstyle: {:.3f}\ttotal: {:.3f}".format(
time.ctime(), e + 1, count, len(train_dataset),
agg_content_loss.asnumpy()[0] / (batch_id + 1),
agg_style_loss.asnumpy()[0] / (batch_id + 1),
(agg_content_loss + agg_style_loss).asnumpy()[0] / (batch_id + 1)
)
print(mesg)
if (batch_id + 1) % (4 * args.log_interval) == 0:
# save model
save_model_filename = "Epoch_" + str(e) + "iters_" + str(count) + "_" + str(time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".params"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
style_model.collect_params().save(save_model_path)
print("\nCheckpoint, trained model saved at", save_model_path)
# save model
save_model_filename = "Final_epoch_" + str(args.epochs) + "_" + str(time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".params"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
style_model.collect_params().save(save_model_path)
print("\nDone, trained model saved at", save_model_path)
def train():
"""
Load data, build and train model.
"""
# Get command line arguments.
parser = argparse.ArgumentParser(
description='Behavioral Cloning Training Program')
parser.add_argument('-d',
type=str,
help='data directory',
default='data',
dest='data_dir')
parser.add_argument('-l',
type=float,
help='learning rate',
default=0.001,
dest='learning_rate')
parser.add_argument('-b',
type=int,
help='batch size',
default=40,
dest='batch_size')
parser.add_argument('-e',
type=int,
help='number of epochs',
default=10,
dest='epochs')
args = parser.parse_args()
# Load, pre-process and augment data.
data_set = utils.DataSetGenerator(data_dir=args.data_dir,
transform=transforms.Compose([
utils.AugmentData(),
utils.PreProcessData(),
utils.ToTensor()
]))
# Data loader for batch generation.
data_loader = DataLoader(data_set,
batch_size=args.batch_size,
drop_last=True)
# Build model.
model = CNN(utils.INPUT_SHAPE, args.batch_size)
# Loss and optimizer.
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
# Train model.
best_loss = float('inf')
for epoch in range(args.epochs):
for idx, sample in enumerate(data_loader):
img = Variable(sample['img'])
steering_angle = Variable(sample['steering_angle'])
optimizer.zero_grad()
steering_angle_out = model(img)
loss = criterion(steering_angle_out, steering_angle)
loss.backward()
optimizer.step()
# Save weights.
if loss.data[0] < best_loss:
best_loss = loss.data[0]
torch.save(model.state_dict(), 'train.pth')
if idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, idx * len(img), len(data_loader.dataset),
100. * idx / len(data_loader), loss.data[0]))
