def __init__(self, params):
assert isinstance(params, (tuple, list))
self.list_of_transforms = []
for param in params:
self.list_of_transforms.append(
TransformRandomBolding.SingleBoldingTransform(**param))
self.transform = RandomChoice(self.list_of_transforms)
def transform_image_only(image):
if random.random() > 0.5:
# random change the colors of the picture
image = RandomChoice(
[Grayscale(num_output_channels=3),
ColorJitter(1, 1, 1)])(image)
return image
def __init__(self, sub_policies, interpolation=None):
if interpolation is not None:
resample = interpolate_any2int[interpolation]
for sub_policy in sub_policies:
for operator, _ in sub_policy:
if isinstance(operator, op.ResampleOp):
operator.set_resample_mode(resample)
self.transform = RandomChoice([
Compose(
[RandomApply(operator, prob) for operator, prob in sub_policy])
for sub_policy in sub_policies
])
def build_input_transform(rng, h, w, upscale_factor: int):
return Compose([
ColorJitter(rng),
RandomApply([AddNoise()], 0.3),
RandomChoice([
RandomApply([MotionBlur()], 0.3),
RandomApply([DefocusBlur()], 0.3)
]),
RandomApply([JpegCompress()], 0.3),
Resize((h // upscale_factor, w // upscale_factor),
interpolation=Image.BICUBIC),
ToTensor(),
])
def __getitem__(self, index):
# Anything could go here, e.g. image loading from file or a different structure
datapoint = self.data[index]
datapoint = Image.fromarray(datapoint)
target = self.target[index]
if self.transform:
transform = Compose([
RandomHorizontalFlip(),
ColorJitter(brightness=0.5, contrast=0.5),
RandomChoice([
Resize((34, 34)),
Resize((32, 32)),
Resize((39, 39)),
Resize((32, 32))
]),
RandomCrop(32, 32),
ToTensor()
])
else:
transform = Compose([ToTensor()])
return transform(datapoint), torch.tensor(target)
def get_train_transforms(jitter, dataset):
if dataset == STL10:
crop_size = 88
else:
crop_size = 32
if not jitter:
return Compose([
ToTensor(),
Pad(2),
RandomCrop(crop_size),
RandomHorizontalFlip(p=0.5)
])
else:
return Compose([
RandomChoice([
ColorJitter(brightness=(0.3, 1.0)),
ColorJitter(contrast=(0.3, 1.0))
]),
ToTensor(),
Pad(2),
RandomCrop(crop_size),
RandomHorizontalFlip(p=0.5)
])
def train(args, log_dir, writer, logger):
with open(log_dir + '/args.json', 'w') as out:
json.dump(vars(args), out, indent=4)
# Augmentation setup
if args.scale:
aug = Compose([
RandomChoice([
RandomCropToSizeTorch(data_format='dict',
size=(args.image_size, args.image_size)),
ResizePaddedTorch((0, 0),
data_format='dict',
size=(args.image_size, args.image_size))
]),
RandomRotations(format='cubi'),
DictToTensor(),
ColorJitterTorch()
])
else:
aug = Compose([
RandomCropToSizeTorch(data_format='dict',
size=(args.image_size, args.image_size)),
RandomRotations(format='cubi'),
DictToTensor(),
ColorJitterTorch()
])
# Setup Dataloader
writer.add_text('parameters', str(vars(args)))
logging.info('Loading data...')
train_set = FloorplanSVG(args.data_path,
'train.txt',
format='lmdb',
augmentations=aug)
val_set = FloorplanSVG(args.data_path,
'val.txt',
format='lmdb',
augmentations=DictToTensor())
if args.debug:
num_workers = 0
print("In debug mode.")
logger.info('In debug mode.')
else:
num_workers = 8
trainloader = data.DataLoader(train_set,
batch_size=args.batch_size,
num_workers=num_workers,
shuffle=True,
pin_memory=True)
valloader = data.DataLoader(val_set,
batch_size=1,
num_workers=num_workers,
pin_memory=True)
# Setup Model
logging.info('Loading model...')
input_slice = [21, 12, 11]
if args.arch == 'hg_furukawa_original':
model = get_model(args.arch, 51)
criterion = UncertaintyLoss(input_slice=input_slice)
if args.furukawa_weights:
logger.info(
"Loading furukawa model weights from checkpoint '{}'".format(
args.furukawa_weights))
checkpoint = torch.load(args.furukawa_weights)
model.load_state_dict(checkpoint['model_state'])
criterion.load_state_dict(checkpoint['criterion_state'])
model.conv4_ = torch.nn.Conv2d(256,
args.n_classes,
bias=True,
kernel_size=1)
model.upsample = torch.nn.ConvTranspose2d(args.n_classes,
args.n_classes,
kernel_size=4,
stride=4)
for m in [model.conv4_, model.upsample]:
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
nn.init.constant_(m.bias, 0)
else:
model = get_model(args.arch, args.n_classes)
criterion = UncertaintyLoss(input_slice=input_slice)
model.cuda()
# Drawing graph for TensorBoard
dummy = torch.zeros((2, 3, args.image_size, args.image_size)).cuda()
model(dummy)
writer.add_graph(model, dummy)
params = [{
'params': model.parameters(),
'lr': args.l_rate
}, {
'params': criterion.parameters(),
'lr': args.l_rate
}]
if args.optimizer == 'adam-patience':
optimizer = torch.optim.Adam(params, eps=1e-8, betas=(0.9, 0.999))
scheduler = ReduceLROnPlateau(optimizer,
'min',
patience=args.patience,
factor=0.5)
elif args.optimizer == 'adam-patience-previous-best':
optimizer = torch.optim.Adam(params, eps=1e-8, betas=(0.9, 0.999))
elif args.optimizer == 'sgd':
def lr_drop(epoch):
return (1 - epoch / args.n_epoch)**0.9
optimizer = torch.optim.SGD(params,
momentum=0.9,
weight_decay=10**-4,
nesterov=True)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,
lr_lambda=lr_drop)
elif args.optimizer == 'adam-scheduler':
def lr_drop(epoch):
return 0.5**np.floor(epoch / args.l_rate_drop)
optimizer = torch.optim.Adam(params, eps=1e-8, betas=(0.9, 0.999))
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,
lr_lambda=lr_drop)
first_best = True
best_loss = np.inf
best_loss_var = np.inf
best_train_loss = np.inf
best_acc = 0
start_epoch = 0
running_metrics_room_val = runningScore(input_slice[1])
running_metrics_icon_val = runningScore(input_slice[2])
best_val_loss_variance = np.inf
no_improvement = 0
if args.weights is not None:
if os.path.exists(args.weights):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(
args.weights))
checkpoint = torch.load(args.weights)
model.load_state_dict(checkpoint['model_state'])
criterion.load_state_dict(checkpoint['criterion_state'])
if not args.new_hyperparams:
optimizer.load_state_dict(checkpoint['optimizer_state'])
logger.info("Using old optimizer state.")
logger.info("Loaded checkpoint '{}' (epoch {})".format(
args.weights, checkpoint['epoch']))
else:
logger.info("No checkpoint found at '{}'".format(args.weights))
for epoch in range(start_epoch, args.n_epoch):
model.train()
lossess = []
losses = pd.DataFrame()
variances = pd.DataFrame()
ss = pd.DataFrame()
# Training
for i, samples in tqdm(enumerate(trainloader),
total=len(trainloader),
ncols=80,
leave=False):
images = samples['image'].cuda(non_blocking=True)
labels = samples['label'].cuda(non_blocking=True)
outputs = model(images)
loss = criterion(outputs, labels)
lossess.append(loss.item())
losses = losses.append(criterion.get_loss(), ignore_index=True)
variances = variances.append(criterion.get_var(),
ignore_index=True)
ss = ss.append(criterion.get_s(), ignore_index=True)
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_loss = np.mean(lossess)
avg_loss = np.inf
loss = losses.mean()
variance = variances.mean()
s = ss.mean()
logging.info("Epoch [%d/%d] Loss: %.4f" %
(epoch + 1, args.n_epoch, avg_loss))
writer.add_scalars('training/loss', loss, global_step=1 + epoch)
writer.add_scalars('training/variance',
variance,
global_step=1 + epoch)
writer.add_scalars('training/s', s, global_step=1 + epoch)
current_lr = {
'base': optimizer.param_groups[0]['lr'],
'var': optimizer.param_groups[1]['lr']
}
writer.add_scalars('training/lr', current_lr, global_step=1 + epoch)
# Validation
model.eval()
val_losses = pd.DataFrame()
val_variances = pd.DataFrame()
val_ss = pd.DataFrame()
px_rooms = 0
px_icons = 0
total_px = 0
for i_val, samples_val in tqdm(enumerate(valloader),
total=len(valloader),
ncols=80,
leave=False):
with torch.no_grad():
images_val = samples_val['image'].cuda(non_blocking=True)
labels_val = samples_val['label'].cuda(non_blocking=True)
outputs = model(images_val)
labels_val = F.interpolate(labels_val,
size=outputs.shape[2:],
mode='bilinear',
align_corners=False)
loss = criterion(outputs, labels_val)
room_pred = outputs[0, input_slice[0]:input_slice[0] +
input_slice[1]].argmax(
0).data.cpu().numpy()
room_gt = labels_val[0, input_slice[0]].data.cpu().numpy()
running_metrics_room_val.update(room_gt, room_pred)
icon_pred = outputs[0,
input_slice[0] + input_slice[1]:].argmax(
0).data.cpu().numpy()
icon_gt = labels_val[0, input_slice[0] + 1].data.cpu().numpy()
running_metrics_icon_val.update(icon_gt, icon_pred)
total_px += outputs[0, 0].numel()
pr, pi = get_px_acc(outputs[0], labels_val[0], input_slice, 0)
px_rooms += float(pr)
px_icons += float(pi)
val_losses = val_losses.append(criterion.get_loss(),
ignore_index=True)
val_variances = val_variances.append(criterion.get_var(),
ignore_index=True)
val_ss = val_ss.append(criterion.get_s(), ignore_index=True)
val_loss = val_losses.mean()
# print("CNN done", val_mid-val_start)
val_variance = val_variances.mean()
logging.info("val_loss: " + str(val_loss))
writer.add_scalars('validation loss', val_loss, global_step=1 + epoch)
# print(val_variance)
writer.add_scalars('validation variance',
val_variance,
global_step=1 + epoch)
if args.optimizer == 'adam-patience':
scheduler.step(val_loss['total loss with variance'])
elif args.optimizer == 'adam-patience-previous-best':
if best_val_loss_variance > val_loss['total loss with variance']:
best_val_loss_variance = val_loss['total loss with variance']
no_improvement = 0
else:
no_improvement += 1
if no_improvement >= args.patience:
logger.info(
"No no_improvement for " + str(no_improvement) +
" loading last best model and reducing learning rate.")
checkpoint = torch.load(log_dir +
"/model_best_val_loss_var.pkl")
model.load_state_dict(checkpoint['model_state'])
for i, p in enumerate(optimizer.param_groups):
optimizer.param_groups[i]['lr'] = p['lr'] * 0.1
no_improvement = 0
elif args.optimizer == 'sgd' or 'adam-scheduler':
scheduler.step(epoch + 1)
val_variance = val_variances.mean()
val_s = val_ss.mean()
logger.info("val_loss: " + str(val_loss))
room_score, room_class_iou = running_metrics_room_val.get_scores()
writer.add_scalars('validation/room/general',
room_score,
global_step=1 + epoch)
writer.add_scalars('validation/room/IoU',
room_class_iou['Class IoU'],
global_step=1 + epoch)
writer.add_scalars('validation/room/Acc',
room_class_iou['Class Acc'],
global_step=1 + epoch)
running_metrics_room_val.reset()
icon_score, icon_class_iou = running_metrics_icon_val.get_scores()
writer.add_scalars('validation/icon/general',
icon_score,
global_step=1 + epoch)
writer.add_scalars('validation/icon/IoU',
icon_class_iou['Class IoU'],
global_step=1 + epoch)
writer.add_scalars('validation/icon/Acc',
icon_class_iou['Class Acc'],
global_step=1 + epoch)
running_metrics_icon_val.reset()
writer.add_scalars('validation/loss', val_loss, global_step=1 + epoch)
writer.add_scalars('validation/variance',
val_variance,
global_step=1 + epoch)
writer.add_scalars('validation/s', val_s, global_step=1 + epoch)
if val_loss['total loss with variance'] < best_loss_var:
best_loss_var = val_loss['total loss with variance']
logger.info(
"Best validation loss with variance found saving model...")
state = {
'epoch': epoch + 1,
'model_state': model.state_dict(),
'criterion_state': criterion.state_dict(),
'optimizer_state': optimizer.state_dict(),
'best_loss': best_loss
}
torch.save(state, log_dir + "/model_best_val_loss_var.pkl")
# Making example prediction images to TensorBoard
if args.plot_samples:
for i, samples_val in enumerate(valloader):
with torch.no_grad():
if i == 4:
break
images_val = samples_val['image'].cuda(
non_blocking=True)
labels_val = samples_val['label'].cuda(
non_blocking=True)
if first_best:
# save image and label
writer.add_image("Image " + str(i), images_val[0])
# add_labels_tensorboard(writer, labels_val)
for j, l in enumerate(
labels_val.squeeze().cpu().data.numpy()):
fig = plt.figure(figsize=(18, 12))
plot = fig.add_subplot(111)
if j < 21:
cax = plot.imshow(l, vmin=0, vmax=1)
else:
cax = plot.imshow(l,
vmin=0,
vmax=19,
cmap=plt.cm.tab20)
fig.colorbar(cax)
writer.add_figure(
"Image " + str(i) + " label/Channel " +
str(j), fig)
outputs = model(images_val)
# add_predictions_tensorboard(writer, outputs, epoch)
pred_arr = torch.split(outputs, input_slice, 1)
heatmap_pred, rooms_pred, icons_pred = pred_arr
rooms_pred = softmax(rooms_pred, 1).cpu().data.numpy()
icons_pred = softmax(icons_pred, 1).cpu().data.numpy()
label = "Image " + str(i) + " prediction/Channel "
for j, l in enumerate(np.squeeze(heatmap_pred)):
fig = plt.figure(figsize=(18, 12))
plot = fig.add_subplot(111)
cax = plot.imshow(l, vmin=0, vmax=1)
fig.colorbar(cax)
writer.add_figure(label + str(j),
fig,
global_step=1 + epoch)
fig = plt.figure(figsize=(18, 12))
plot = fig.add_subplot(111)
cax = plot.imshow(np.argmax(np.squeeze(rooms_pred),
axis=0),
vmin=0,
vmax=19,
cmap=plt.cm.tab20)
fig.colorbar(cax)
writer.add_figure(label + str(j + 1),
fig,
global_step=1 + epoch)
fig = plt.figure(figsize=(18, 12))
plot = fig.add_subplot(111)
cax = plot.imshow(np.argmax(np.squeeze(icons_pred),
axis=0),
vmin=0,
vmax=19,
cmap=plt.cm.tab20)
fig.colorbar(cax)
writer.add_figure(label + str(j + 2),
fig,
global_step=1 + epoch)
first_best = False
if val_loss['total loss'] < best_loss:
best_loss = val_loss['total loss']
logger.info("Best validation loss found saving model...")
state = {
'epoch': epoch + 1,
'model_state': model.state_dict(),
'criterion_state': criterion.state_dict(),
'optimizer_state': optimizer.state_dict(),
'best_loss': best_loss
}
torch.save(state, log_dir + "/model_best_val_loss.pkl")
px_acc = room_score["Mean Acc"] + icon_score["Mean Acc"]
if px_acc > best_acc:
best_acc = px_acc
logger.info("Best validation pixel accuracy found saving model...")
state = {
'epoch': epoch + 1,
'model_state': model.state_dict(),
'criterion_state': criterion.state_dict(),
'optimizer_state': optimizer.state_dict()
}
torch.save(state, log_dir + "/model_best_val_acc.pkl")
if avg_loss < best_train_loss:
best_train_loss = avg_loss
logger.info("Best training loss with variance...")
state = {
'epoch': epoch + 1,
'model_state': model.state_dict(),
'criterion_state': criterion.state_dict(),
'optimizer_state': optimizer.state_dict()
}
torch.save(state, log_dir + "/model_best_train_loss_var.pkl")
logger.info("Last epoch done saving final model...")
state = {
'epoch': epoch + 1,
'model_state': model.state_dict(),
'criterion_state': criterion.state_dict(),
'optimizer_state': optimizer.state_dict()
}
torch.save(state, log_dir + "/model_last_epoch.pkl")
from torchvision.transforms import RandomResizedCrop, RandomChoice
from torchvision.transforms import ColorJitter, ToTensor, Normalize
from common.dataset import FilesFromCsvDataset
from common.data_loaders import get_data_loader
SEED = 17
DEBUG = True
DEVICE = 'cuda'
OUTPUT_PATH = "output"
size = 350
TRAIN_TRANSFORMS = [
RandomChoice([
RandomResizedCrop(size, scale=(0.4, 6.0), interpolation=3),
RandomResizedCrop(size, scale=(0.6, 1.0), interpolation=3),
]),
RandomHorizontalFlip(p=0.5),
RandomVerticalFlip(p=0.5),
ColorJitter(hue=0.12, brightness=0.12),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]
VAL_TRANSFORMS = [
RandomResizedCrop(size, scale=(0.7, 1.0), interpolation=3),
RandomHorizontalFlip(p=0.5),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]
def __init__(self, p: float):
self.p = p
self.transform = RandomChoice([
RandomHorizontalFlip(self.p),
RandomVerticalFlip(self.p),
])
# ------- AUGMENTATION ------- #
# choose Resize dimension
dimension = 244 # wide_resnet_50
dimension = 144 # wide_resnet_101
# transformation for training data
train_transform = Compose([
ToPILImage(),
Resize(dimension),
RandomApply([
RandomChoice([
RandomCrop(size=[dimension, dimension], padding=10),
RandomAffine(0, translate=(0.01, 0.01))
])
]), # choose one or 0 transforms that make the image smaller
RandomApply([
RandomChoice([
RandomHorizontalFlip(),
RandomRotation(10)
])
]), # choose one or zero transforms to rotate or flip the image
ToTensor(),
Normalize(mean=train_mean, std=train_std),
])
# define normalization for validation data
test_transform = Compose([
ToPILImage(),
