def __init__(self):
super().__init__()
model = smp.FPN(encoder_name='se_resnet50',
encoder_weights='imagenet', classes=10, activation=None)
self.model = model
## seg model
parser.add_argument('--gpu_ids',
type=str,
default='0',
help="device id to run")
parser.add_argument(
'--seg_model_path',
type=str,
default='results/random_take_8obj_1e-4_ok/checkpoint_3.pth')
args = parser.parse_args()
if args.use_typeVector:
args.network = 'cnn_type'
print('Load RL model.')
model, env = train(args)
print('Load Segmentation model.')
seg_models = []
test_loaders = []
gpus = args.gpu_ids.split(',')
ckpt = torch.load(args.seg_model_path)
for i in range(4):
seg_model = smp.FPN('resnet50', in_channels=4, classes=3).cuda()
seg_model = nn.DataParallel(seg_model,
device_ids=[int(_) for _ in gpus])
seg_model.load_state_dict(ckpt['FPN_' + str(i)])
seg_models.append(seg_model)
make_video(model, env, seg_models, args)
def main():
# Training settings
parser = argparse.ArgumentParser(
description='Preston\'s MRI-segmentation Arguments')
parser.add_argument(
'--mode',
type=str,
default="train",
metavar='M', # need to wrap the argument in double quotation
help='Select either train or test (default: "train")')
parser.add_argument('--checkpoint',
type=str,
default=None,
help="Path to checkpoint")
parser.add_argument(
'--num_files',
type=int,
default=4000,
metavar='N',
help='Number of files in training; split using tv_ratio (default: 4000)'
),
parser.add_argument(
'--tv_ratio',
type=list,
default=[0.8, 0.2],
help=
'Train-validate ratio in a list, must add up to 1, (default: [0.8, 0.2])'
)
parser.add_argument('--train_batch_size',
type=int,
default=4,
metavar='N',
help='input batch size for training (default: 4)')
parser.add_argument('--valid_batch_size',
type=int,
default=4,
metavar='N',
help='input batch size for validating (default: 4)')
parser.add_argument('--epochs',
type=int,
default=40,
metavar='N',
help='number of epochs to train (default: 40)')
parser.add_argument('--lr',
type=float,
default=3e-3,
metavar='LR',
help='learning rate (default: 3e-3)')
parser.add_argument('--sch_step',
type=int,
default=10,
metavar='sch_step',
help='Scheduler step size (default: 10)')
parser.add_argument('--loss',
type=str,
default="dl",
metavar='LOSS',
help='Type of loss - dl or gdl (default: dl)')
parser.add_argument('--beta',
type=float,
default=1,
metavar='B',
help='Beta in dice loss (default: 1)')
parser.add_argument('--pw',
type=float,
default=1000,
metavar='PW',
help='Positive weight in the bce function')
parser.add_argument('--gamma',
type=float,
default=0.7,
metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument(
'--no-cuda',
action='store_true',
default=
False, # if I specify "--no-cuda," I don't need an argument and this assumes that I am using my CPU. Otherwise, don't specify anything and use GPU by default
help='disables CUDA training')
parser.add_argument(
'--just_show',
type=int,
default=None,
help='set this flag when you want to skip the test metrics')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
start_time = time.time()
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
# Model Definition
ENCODER = 'se_resnext50_32x4d'
# ENCODER = 'vgg16'
ENCODER_WEIGHTS = 'imagenet' # None or 'imagenet' (if None, then weights are randomly initialized)
CLASSES = ['tumor'] # only 1 class in this example
ACTIVATION = 'sigmoid'
# could be None for logits or 'softmax2d' for multicalss segmentation; sigmoid for binary classification
DEVICE = "cuda" if use_cuda else "cpu"
# create segmentation model
model = smp.FPN(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
classes=len(CLASSES), # only 1 class in this case
activation=ACTIVATION,
)
preprocessing_fn = smp.encoders.get_preprocessing_fn(ENCODER,
pretrained='imagenet')
if args.loss == "dl":
loss = smp.utils.losses.DiceLoss(beta=args.beta)
elif args.loss == "gdl":
loss = smp.utils.losses.GeneralizedDiceLoss()
elif args.loss == "dl+bce":
pw = torch.FloatTensor(
[args.pw]
) # gonna use 1000 here because we of the pos_thresh we set in this pruned dataset
pw = torch.reshape(pw, (1, 1, 1, 1))
loss = smp.utils.losses.DiceLoss(
beta=args.beta) + smp.utils.losses.BCEWithLogitsLoss(pos_weight=pw)
elif args.loss == "dl+log(bce)": # doesn't work yet
pw = torch.FloatTensor([args.pw])
pw = torch.reshape(pw, (1, 1, 1, 1))
loss = smp.utils.losses.DiceLoss(beta=args.beta) + torch.log(
smp.utils.losses.BCEWithLogitsLoss(pos_weight=pw))
else:
raise ValueError("Loss can only be dl or gdl for now")
metrics = [
smp.utils.metrics.IoU(threshold=0.5),
]
data_dir = 'D:\\MRI Segmentation\\data'
model_dir = r"C:\Users\prestonpan\PycharmProjects\Segmentation_example\runs"
if args.mode == "train":
comment = f'lr={args.lr}, loss={args.loss}, epochs={args.epochs}, ' \
f'num_files={args.num_files}, sch_step={args.sch_step}, train_batch_size={args.train_batch_size}, ' \
f'beta={args.beta}, pw={args.pw}'
tb = SummaryWriter(comment=comment)
print(comment
) # to verify that the hyperparameter values are set correctly
dfTrain, dfVal = pp.prepare_csv(data_dir,
args.tv_ratio,
num_files=args.num_files,
mode=args.mode)
# Gonna modify the loader so that it looks more similar to the Dataset class used in smp's example
train_dataset = MRIDataset(
dfTrain, # might just remove this kwarg later on
classes=CLASSES,
augmentation=pp.get_training_augmentation(),
preprocessing=pp.get_preprocessing(preprocessing_fn))
valid_dataset = MRIDataset(
dfVal,
classes=CLASSES,
augmentation=pp.get_training_augmentation(),
preprocessing=pp.get_preprocessing(preprocessing_fn))
train_loader = DataLoader(train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
num_workers=12)
valid_loader = DataLoader(valid_dataset,
batch_size=args.valid_batch_size,
shuffle=False,
num_workers=4)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.90,
weight_decay=1e-6,
nesterov=True)
# optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-6) # this works very poorly
start_epoch = 0
max_score = 0
scheduler = optim.lr_scheduler.StepLR(
optimizer, step_size=args.sch_step, gamma=args.gamma
) # every step_size number of epoch, the lr is multiplied by args.gamma - reduces learning rate due to subgradient method
# Loads the checkpoint and update some parameters
if args.checkpoint is not None:
root = tk.Tk()
checkpoint_path = filedialog.askopenfilename(
parent=root,
initialdir=model_dir,
title='Please select a model (.pth)')
model, optimizer, scheduler, max_score, start_epoch = \
pp.load_checkpoint(model, optimizer, scheduler, filename=checkpoint_path)
train_epoch = smp.utils.train.TrainEpoch(
model,
loss=loss,
metrics=metrics,
optimizer=optimizer,
device=DEVICE,
verbose=True,
)
valid_epoch = smp.utils.train.ValidEpoch(
model,
loss=loss,
metrics=metrics,
device=DEVICE,
verbose=True,
)
stagnant_epoch = 0
for i in range(start_epoch, args.epochs):
print('\nEpoch: {}'.format(i + 1)) # 1-index the epoch number
train_logs = train_epoch.run(train_loader)
valid_logs = valid_epoch.run(valid_loader)
tb.add_scalar('Loss', train_logs[loss.__name__], i)
tb.add_scalar('IOU', train_logs['iou_score'], i)
scheduler.step()
if max_score < valid_logs[
'iou_score']: # we really care more about the validation metric because
stagnant_epoch = 0
# training metric can be overfit, which is unrepresentative
max_score = valid_logs['iou_score']
print('New best IOU: %.5f' % max_score)
print('Saving checkpoint...')
state = {
'model': model,
'epoch': args.epochs,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'iou_score': max_score
}
torch.save(state, './best MRI models/{}.pth'.format(comment))
else:
stagnant_epoch = stagnant_epoch + 1
if stagnant_epoch > 8:
print(
'max iou remained stagnant for the past 8 epochs, returning early'
)
return
total_time = time.time() - start_time
print('Training took %.2f seconds' % total_time)
elif args.mode == "test":
dfTest = pp.prepare_csv(
data_dir, args.tv_ratio, num_files=-1,
mode=args.mode) # number of testing images is fixed
root = tk.Tk()
model_path = filedialog.askopenfilename(
parent=root,
initialdir=model_dir,
title='Please select a model (.pth)')
root.destroy()
# best_model = torch.load(model_path) # This line doesn't work anymore because the model is now saved in a dict
best_model_dict = torch.load(model_path)
best_model = best_model_dict['model']
print('model loaded!')
test_dataset = MRIDataset(
dfTest,
classes=CLASSES,
preprocessing=pp.get_preprocessing(preprocessing_fn))
# We have pre-processing here (to make sure the prediction works properly), but no need augmentation
# because we're not training anymore
test_dataloader = DataLoader(test_dataset)
test_epoch = smp.utils.train.ValidEpoch(
model=best_model,
loss=loss,
metrics=metrics,
device=DEVICE,
)
if args.just_show is not None:
num_to_display = args.just_show
else:
logs = test_epoch.run(test_dataloader)
num_to_display = 5
# # dataset for visualization without augmentation
test_dataset_vis = MRIDataset(
dfTest,
classes=CLASSES) # no preprocessing; this is the native image
for i in range(num_to_display):
n = np.random.choice(len(test_dataset_vis))
image_vis = test_dataset_vis[n][0].astype(
'int16') # arranged in H * W * 3 (3 = RGB channels)
image, gt_masks = test_dataset[n]
# image has 3 channels (RGB) -> shape(image) = 3 * H * W, different from image_vis because of preprocessing
# gt_masks has C classes -> shape(gt_mask) = C * H * W (C = Classes)
x_tensor = torch.from_numpy(image).to(DEVICE).unsqueeze(
0) # shape = 1 * H * W * 3 (3 = RGB channels)
pr_masks = best_model.predict(
x_tensor) # shape = 1 * H * W * C (C = number of classes)
pr_masks = (pr_masks.squeeze(0).cpu().numpy().round()
) # shape = C * H * W (squeeze the 0th dimensions)
# Index through the classes and look at them one at a time
for j in range(len(CLASSES)):
gt_mask = gt_masks[j].squeeze()
pr_mask = pr_masks[j].squeeze()
pp.visualize_2(image=image_vis,
gt=gt_mask,
pr=pr_mask,
iou=1 - loss.forward(torch.from_numpy(gt_mask),
torch.from_numpy(pr_mask)))
def get_model(
model_type: str = "Unet",
encoder: str = "Resnet18",
encoder_weights: str = "imagenet",
activation: str = None,
n_classes: int = 4,
task: str = "segmentation",
source: str = "pretrainedmodels",
head: str = "simple",
):
"""
Get model for training or inference.
Returns loaded models, which is ready to be used.
Args:
model_type: segmentation model architecture
encoder: encoder of the model
encoder_weights: pre-trained weights to use
activation: activation function for the output layer
n_classes: number of classes in the output layer
task: segmentation or classification
source: source of model for classification
head: simply change number of outputs or use better output head
Returns:
"""
if task == "segmentation":
if model_type == "Unet":
model = smp.Unet(
# attention_type='scse',
encoder_name=encoder,
encoder_weights=encoder_weights,
classes=n_classes,
activation=activation,
)
elif model_type == "Linknet":
model = smp.Linknet(
encoder_name=encoder,
encoder_weights=encoder_weights,
classes=n_classes,
activation=activation,
)
elif model_type == "FPN":
model = smp.FPN(
encoder_name=encoder,
encoder_weights=encoder_weights,
classes=n_classes,
activation=activation,
)
elif model_type == "resnet34_fpn":
model = resnet34_fpn(num_classes=n_classes, fpn_features=128)
elif model_type == "effnetB4_fpn":
model = effnetB4_fpn(num_classes=n_classes, fpn_features=128)
else:
model = None
elif task == "classification":
if source == "pretrainedmodels":
model_fn = pretrainedmodels.__dict__[encoder]
model = model_fn(num_classes=1000, pretrained=encoder_weights)
elif source == "torchvision":
model = torchvision.models.__dict__[encoder](
pretrained=encoder_weights)
if head == "simple":
model.last_linear = nn.Linear(model.last_linear.in_features,
n_classes)
else:
model = Net(net=model)
return model
def main():
parser = ArgumentParser()
parser.add_argument('-d',
'--data_path',
dest='data_path',
type=str,
default=None,
help='path to the data')
parser.add_argument('-e',
'--epochs',
dest='epochs',
default=20,
type=int,
help='number of epochs')
parser.add_argument('-b',
'--batch_size',
dest='batch_size',
default=40,
type=int,
help='batch size')
parser.add_argument('-s',
'--image_size',
dest='image_size',
default=256,
type=int,
help='input image size')
parser.add_argument('-lr',
'--learning_rate',
dest='lr',
default=0.0001,
type=float,
help='learning rate')
parser.add_argument('-wd',
'--weight_decay',
dest='weight_decay',
default=5e-4,
type=float,
help='weight decay')
parser.add_argument('-lrs',
'--learning_rate_step',
dest='lr_step',
default=10,
type=int,
help='learning rate step')
parser.add_argument('-lrg',
'--learning_rate_gamma',
dest='lr_gamma',
default=0.5,
type=float,
help='learning rate gamma')
parser.add_argument(
'-m',
'--model',
dest='model',
default='fpn',
)
parser.add_argument('-w',
'--weight_bce',
default=0.5,
type=float,
help='weight BCE loss')
parser.add_argument('-l',
'--load',
dest='load',
default=False,
help='load file model')
parser.add_argument('-v',
'--val_split',
dest='val_split',
default=0.7,
help='train/val split')
parser.add_argument('-o',
'--output_dir',
dest='output_dir',
default='./output',
help='dir to save log and models')
args = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
logger = get_logger(os.path.join(args.output_dir, 'train.log'))
logger.info('Start training with params:')
for arg, value in sorted(vars(args).items()):
logger.info("Argument %s: %r", arg, value)
# net = UNet() # TODO: to use move novel arch or/and more lightweight blocks (mobilenet) to enlarge the batch_size
# net = smp.FPN('mobilenet_v2', encoder_weights='imagenet', classes=2)
net = smp.FPN('se_resnet50', encoder_weights='imagenet', classes=2)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if args.load:
net.load_state_dict(torch.load(args.load))
logger.info('Model type: {}'.format(net.__class__.__name__))
net.to(device)
optimizer = optim.Adam(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
criterion = lambda x, y: (args.weight_bce * nn.BCELoss()(x, y),
(1. - args.weight_bce) * dice_loss(x, y))
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step, gamma=args.lr_gamma) \
if args.lr_step > 0 else None
train_transforms = Compose([
Crop(min_size=1 - 1 / 3., min_ratio=1.0, max_ratio=1.0, p=0.5),
Flip(p=0.05),
RandomRotate(),
Pad(max_size=0.6, p=0.25),
Resize(size=(args.image_size, args.image_size), keep_aspect=True),
ScaleToZeroOne(),
])
val_transforms = Compose([
Resize(size=(args.image_size, args.image_size)),
ScaleToZeroOne(),
])
train_dataset = DetectionDataset(args.data_path,
os.path.join(args.data_path,
'train_mask.json'),
transforms=train_transforms)
val_dataset = DetectionDataset(args.data_path,
None,
transforms=val_transforms)
train_size = int(len(train_dataset) * args.val_split)
val_dataset.image_names = train_dataset.image_names[train_size:]
val_dataset.mask_names = train_dataset.mask_names[train_size:]
train_dataset.image_names = train_dataset.image_names[:train_size]
train_dataset.mask_names = train_dataset.mask_names[:train_size]
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=8,
shuffle=True,
drop_last=True)
val_dataloader = DataLoader(val_dataset,
batch_size=args.batch_size,
num_workers=4,
shuffle=False,
drop_last=False)
logger.info('Number of batches of train/val=%d/%d', len(train_dataloader),
len(val_dataloader))
try:
train(net,
optimizer,
criterion,
scheduler,
train_dataloader,
val_dataloader,
logger=logger,
args=args,
device=device)
except KeyboardInterrupt:
torch.save(
net.state_dict(),
os.path.join(args.output_dir, f'{args.model}_INTERRUPTED.pth'))
logger.info('Saved interrupt')
sys.exit(0)
def create_model_and_train():
# create segmentation model with pretrained encoder
model = smp.FPN(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
classes=len(CarlaLanesDataset.CLASSES),
activation=ACTIVATION,
# encoder_depth = 4
)
train_dataset = CarlaLanesDataset(
x_train_dir,
y_train_dir,
augmentation=get_training_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn),
classes=CarlaLanesDataset.CLASSES,
)
valid_dataset = CarlaLanesDataset(
x_valid_dir,
y_valid_dir,
augmentation=get_validation_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn),
classes=CarlaLanesDataset.CLASSES,
)
bs_train = 2
bs_valid = 2
train_loader = DataLoader(train_dataset, batch_size=bs_train, shuffle=True)
valid_loader = DataLoader(valid_dataset,
batch_size=bs_valid,
shuffle=False)
optimizer = torch.optim.Adam([
dict(params=model.parameters(), lr=1e-4),
# dict(params=model.parameters(), lr=1e-3)
])
# create epoch runners
# it is a simple loop of iterating over dataloader`s samples
train_epoch = smp.utils.train.TrainEpoch(
model,
loss=loss,
metrics=metrics,
optimizer=optimizer,
device=DEVICE,
verbose=True,
)
valid_epoch = smp.utils.train.ValidEpoch(
model,
loss=loss,
metrics=metrics,
device=DEVICE,
verbose=True,
)
# train model
best_loss = 1e10
for i in range(0, 5):
print('\nEpoch: {}'.format(i))
train_logs = train_epoch.run(train_loader)
valid_logs = valid_epoch.run(valid_loader)
# do something (save model, change lr, etc.)
if best_loss > valid_logs[loss_string]:
best_loss = valid_logs[loss_string]
torch.save(model, './best_model_{}.pth'.format(loss_string))
print('Model saved!')
if i == 3:
optimizer.param_groups[0]['lr'] = 1e-5
print('Decrease decoder learning rate to 1e-5!')
collate_fn=collate_fn)
train_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
collate_fn=collate_fn,
drop_last=True)
val_loader = DataLoader(dataset=val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=4,
collate_fn=collate_fn)
model = smp.FPN(encoder_name="efficientnet-b3", encoder_weights="imagenet", in_channels=3, classes=12)
model = model.to(device)
train(num_epochs, model, train_loader, val_loader, val_every, device, 'test.pt')
checkpoint = torch.load('saved/test.pt', map_location=device)
model = model.to(device)
model.load_state_dict(checkpoint)
pseudo_labeling(num_epochs, model, train_loader, val_loader, test_loader, device, val_every, 'test_sudo.pt')
submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)
checkpoint = torch.load('saved/test_sudo.pt', map_location=device)
model = model.to(device)
model.load_state_dict(checkpoint)
file_names, preds = test(model, test_loader, device)
############################################
# Seg_model = smp.FPN('efficientnet-b5',encoder_weights=None,classes=5).cuda()
#
# weights_name = ['f0/Heng-efficient_f0_1076.pth',
# 'f1/Heng-efficient_f1_10719.pth',
# 'f2/Heng-efficient_f2_1085.pth',
# 'f3/Heng-efficient_f3_1086.pth',
# 'f4/Heng-efficient_f4_10818.pth',]
# seg_path = '../kaggle-steel-0911/weights/'
#
# load_weights(Seg_model,os.path.join(seg_path,'Heng-efficient',weights_name[FOLD]))#resnet34_f4_10611
# Seg_model = Model([seg4])
# for net in Seg_model.models:
# net.eval()
Seg_model = smp.FPN('resnet34',
encoder_weights=None,
classes=4,
activation='sigmoid').cuda()
load_weights(
Seg_model,
"../kaggle-256crop-4cls-seg/weights/res34FPN/f0/res34FPN_f0_10126.pth")
print('load weights done!!')
model_trainer = Trainer(Seg_model, Cls_model)
dice_grid = model_trainer.start()
print('grid search done!!')
np.save('grid_search.npy', dice_grid)
seg_probs = []
cloud_mask = 0
with torch.no_grad():
for fold in range(K):
print('Fold{}:'.format(fold))
if MODEL == 'UNET':
seg_model = smp.Unet(encoder_name=ENCODER,
classes=4,
encoder_weights=None,
ED_drop=ED_drop)
elif MODEL == 'FPN':
seg_model = smp.FPN(encoder_name=ENCODER,
classes=4,
encoder_weights=None,
ED_drop=ED_drop,
dropout=dropout)
else:
seg_model = None
seg_model.load_state_dict(
torch.load(os.path.join(save_dir, 'model_{}.pth'.format(fold))))
seg_model.cuda()
seg_model.eval()
preprocessing_fn = smp.encoders.get_preprocessing_fn(
ENCODER, encoder_weights)
# validate
seg_probs_fold = 0
for tt in range(4):
f"({train_parameters['downsample_mask_factor']})"
)
if not train_parameters["width_crop_size"] / train_parameters["downsample_mask_factor"]:
raise ValueError(
f"Width crop size ({train_parameters['width_crop_size']}) "
f"should be divisible by the downsample_mask_factor"
f"({train_parameters['downsample_mask_factor']})"
)
final_upsampling = None
else:
final_upsampling = 4
model = smp.FPN(
encoder_type, encoder_weights="imagenet", classes=num_classes, activation=None, final_upsampling=final_upsampling
)
pad_factor = 64
imread_library = "cv2" # can be cv2 or jpeg4py
optimizer = RAdam(
[
{"params": model.decoder.parameters(), "lr": train_parameters["lr"]},
# decrease lr for encoder in order not to permute
# pre-trained weights with large gradients on training start
{"params": model.encoder.parameters(), "lr": train_parameters["lr"] / 100},
],
weight_decay=1e-2,
)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
#segmodel = smp.FPN('efficientnet-b5',encoder_weights=None,classes=5).cuda()
# segmodel = smp.Unet('resnet34',encoder_weights=None,classes=4).cuda()
# seg_path = '../kaggle-256crop-4cls-seg/weights/resnet34/'
# fold_mdoel = [
# 'f0/resnet34_f0_10422.pth',
# 'f1/resnet34_f1_10512.pth',
# 'f2/resnet34_f2_1060.pth',
# 'f3/resnet34_f3_1063.pth',
# 'f4/resnet34_f4_10611.pth'
# ]
segmodel = smp.FPN('efficientnet-b5', encoder_weights=None,
classes=4).cuda() #se_resnet50
seg_path = '../kaggle-256crop-4cls-seg/weights/effb5FPN/'
fold_mdoel = [
'f0/effb5FPN_f0_101312.pth', 'f1/effb5FPN_f1_10148.pth',
'f2/effb5FPN_f2_10148.pth', 'f3/effb5FPN_f3_10153.pth',
'f4/effb5FPN_f4_10164.pth'
]
load_weights(segmodel, seg_path + fold_mdoel[FOLD]) #<<<<<<<<< set fold
segmodel.eval()
clsmodel = Net().cuda()
#print(model)
model_trainer = Trainer(segmodel, clsmodel)
model_trainer.start()
# n_channels=3 for RGB images
# n_classes is the number of probabilities you want to get per pixel
# - For 1 class and background, use n_classes=1
# - For 2 classes, use n_classes=1
# - For N > 2 classes, use n_classes=N
if config.MODEL_SELECTION == 'og_unet':
net = UNet(n_channels=config.NUM_CHANNELS,
n_classes=config.NUM_CLASSES,
bilinear=True)
elif config.MODEL_SELECTION == 'smp_unet':
net = smp.Unet(config.BACKBONE,
encoder_weights=config.ENCODER_WEIGHTS,
classes=config.NUM_CLASSES)
elif config.MODEL_SELECTION == 'smp_fpn':
net = smp.FPN(config.BACKBONE,
encoder_weights=config.ENCODER_WEIGHTS,
classes=config.NUM_CLASSES)
elif config.MODEL_SELECTION == 'pytorch_deeplab':
net = DeepLabv3_plus(nInputChannels=config.NUM_CHANNELS,
n_classes=config.NUM_CLASSES,
os=16,
pretrained=True,
_print=False)
elif config.MODEL_SELECTION == 'og_deeplab':
net = Res_Deeplab(num_classes=config.NUM_CLASSES)
else:
raise NotImplementedError
upsample = nn.Upsample(size=(config.CROP_H, config.CROP_W),
mode='bilinear',
align_corners=True)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
collate_fn=collate_fn)
val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
collate_fn=collate_fn)
# model 불러오기
# 출력 레이블 수 정의 (classes = 12)
model = smp.FPN(encoder_name=encoder_name,
classes=12,
encoder_weights="noisy-student",
activation=None)
model = model.to(device)
wandb.watch(model, log_freq=100)
def train(num_epochs, model, data_loader, val_loader, criterion, optimizer,
scheduler, saved_dir, val_every, device):
print(f'Start training..fold{fold+1}')
best_mIoU = 0
best_epoch = 0
for epoch in tqdm(range(num_epochs)):
model.train()
for step, (images, masks, _) in enumerate(data_loader):
images = torch.stack(images) # (batch, channel, height, width)
masks = torch.stack(
masks).long() # (batch, channel, height, width)
def main():
args = parse_args()
torch.backends.cudnn.benchmark = True
args.distributed = False
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
args.world_size = 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
args.world_size = torch.distributed.get_world_size()
# print(args.world_size, args.local_rank, args.distributed)
cfg.merge_from_file(args.cfg)
cfg.DIR = os.path.join(
cfg.DIR,
args.cfg.split('/')[-1].split('.')[0] +
datetime.now().strftime('-%Y-%m-%d-%a-%H:%M:%S:%f'))
# Output directory
# if not os.path.isdir(cfg.DIR):
if args.local_rank == 0:
os.makedirs(cfg.DIR, exist_ok=True)
os.makedirs(os.path.join(cfg.DIR, 'weight'), exist_ok=True)
os.makedirs(os.path.join(cfg.DIR, 'history'), exist_ok=True)
shutil.copy(args.cfg, cfg.DIR)
if os.path.exists(os.path.join(cfg.DIR, 'log.txt')):
os.remove(os.path.join(cfg.DIR, 'log.txt'))
logger = setup_logger(distributed_rank=args.local_rank,
filename=os.path.join(cfg.DIR, 'log.txt'))
logger.info("Loaded configuration file {}".format(args.cfg))
logger.info("Running with config:\n{}".format(cfg))
if cfg.MODEL.arch == 'deeplab':
model = DeepLab(
num_classes=cfg.DATASET.num_class,
backbone=cfg.MODEL.backbone, # resnet101
output_stride=cfg.MODEL.os,
ibn_mode=cfg.MODEL.ibn_mode,
freeze_bn=False)
elif cfg.MODEL.arch == 'smp-deeplab':
model = smp.DeepLabV3(encoder_name='resnet101', classes=7)
elif cfg.MODEL.arch == 'FPN':
model = smp.FPN(encoder_name='resnet101', classes=7)
elif cfg.MODEL.arch == 'Unet':
model = smp.Unet(encoder_name='resnet101', classes=7)
if cfg.DATASET.val_channels[0] == 'rgbn':
convert_model(model, 4)
model = apex.parallel.convert_syncbn_model(model)
model = model.cuda()
model = amp.initialize(model, opt_level="O1")
if args.distributed:
model = DDP(model, delay_allreduce=True)
if cfg.VAL.checkpoint != "":
if args.local_rank == 0:
logger.info("Loading weight from {}".format(cfg.VAL.checkpoint))
weight = torch.load(
cfg.VAL.checkpoint,
map_location=lambda storage, loc: storage.cuda(args.local_rank))
if not args.distributed:
weight = {k[7:]: v for k, v in weight.items()}
model.load_state_dict(weight)
dataset_val = AgriValDataset(cfg.DATASET.root_dataset,
cfg.DATASET.list_val,
cfg.DATASET,
channels=cfg.DATASET.val_channels[0])
val_sampler = None
if args.distributed:
val_sampler = torch.utils.data.distributed.DistributedSampler(
dataset_val, num_replicas=args.world_size, rank=args.local_rank)
loader_val = torch.utils.data.DataLoader(
dataset_val,
batch_size=cfg.VAL.batch_size_per_gpu,
shuffle=False, # we do not use this param
num_workers=cfg.VAL.batch_size_per_gpu,
drop_last=True,
pin_memory=True,
sampler=val_sampler)
cfg.VAL.epoch_iters = len(loader_val)
cfg.VAL.log_fmt = 'Mean IoU: {:.4f}\n'
logger.info("World Size: {}".format(args.world_size))
logger.info("VAL.epoch_iters: {}".format(cfg.VAL.epoch_iters))
logger.info("VAL.sum_bs: {}".format(cfg.VAL.batch_size_per_gpu *
args.world_size))
os.makedirs(cfg.VAL.visualized_pred, exist_ok=True)
os.makedirs(cfg.VAL.visualized_label, exist_ok=True)
val(loader_val, model, args, logger)
in_channels (int): bnumber of input channels
num_classes (int): number of classes + 1 for background
activation (srt): output activation function, default is None
"""
model = smp.Unet(
encoder_name=encoder,
encoder_weights='imagenet',
classes=num_classes,
in_channels=in_channels,
activation=activation,
)
return model
model = smp.FPN('resnet34', in_channels=1)
def get_fpn(encoder: str = 'resnet50',
in_channels: int = 4,
num_classes: int = 1,
activation: str = None):
"""
Get FPN model from qubvel libruary
create segmentation model with pretrained encoder
Args:
encoder (str): encoder basenet 'resnext101_32x8d', 'resnet18', 'resnet50', 'resnet101'...
in_channels (int): bnumber of input channels
num_classes (int): number of classes + 1 for background
activation (srt): output activation function, default is None
"""
def get_model(config):
"""
"""
arch = config.MODEL.ARCHITECTURE
backbone = config.MODEL.BACKBONE
encoder_weights = config.MODEL.ENCODER_PRETRAINED_FROM
in_channels = config.MODEL.IN_CHANNELS
n_classes = len(config.INPUT.CLASSES)
activation = config.MODEL.ACTIVATION
# unet specific
decoder_attention_type = 'scse' if config.MODEL.UNET_ENABLE_DECODER_SCSE else None
if arch == 'unet':
model = smp.Unet(
encoder_name=backbone,
encoder_weights=encoder_weights,
decoder_channels=config.MODEL.UNET_DECODER_CHANNELS,
decoder_attention_type=decoder_attention_type,
in_channels=in_channels,
classes=n_classes,
activation=activation
)
elif arch == 'fpn':
model = smp.FPN(
encoder_name=backbone,
encoder_weights=encoder_weights,
decoder_dropout=config.MODEL.FPN_DECODER_DROPOUT,
in_channels=in_channels,
classes=n_classes,
activation=activation
)
elif arch == 'pan':
model = smp.PAN(
encoder_name=backbone,
encoder_weights=encoder_weights,
in_channels=in_channels,
classes=n_classes,
activation=activation
)
elif arch == 'pspnet':
model = smp.PSPNet(
encoder_name=backbone,
encoder_weights=encoder_weights,
psp_dropout=config.MODEL.PSPNET_DROPOUT,
in_channels=in_channels,
classes=n_classes,
activation=activation
)
elif arch == 'deeplabv3':
model = smp.DeepLabV3(
encoder_name=backbone,
encoder_weights=encoder_weights,
in_channels=in_channels,
classes=n_classes,
activation=activation
)
elif arch == 'linknet':
model = smp.Linknet(
encoder_name=backbone,
encoder_weights=encoder_weights,
in_channels=in_channels,
classes=n_classes,
activation=activation
)
else:
raise ValueError()
model = torch.nn.DataParallel(model)
if config.MODEL.WEIGHT and config.MODEL.WEIGHT != 'none':
# load weight from file
model.load_state_dict(
torch.load(
config.MODEL.WEIGHT,
map_location=torch.device('cpu')
)
)
model = model.to(config.MODEL.DEVICE)
return model
# > You can read more about them in [our blog post](https://github.com/catalyst-team/catalyst-info#catalyst-info-1-segmentation-models).
#
# But for now let's take the model from [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch) (SMP for short). The same segmentation architectures have been implemented in this repository, but there are many more pre-trained encoders.
#
# [](https://github.com/qubvel/segmentation_models.pytorch)
# In[22]:
aux_params = dict(
dropout=0.5, # dropout ratio, default is None
classes=6, # define number of output labels
)
# We will use Feature Pyramid Network with pre-trained ResNeXt50 backbone
model = smp.FPN(encoder_name="resnext50_32x4d",
classes=6,
aux_params=aux_params)
# ### Model training
#
# We will optimize loss as the sum of IoU, Dice and BCE, specifically this function: $IoU + Dice + 0.8*BCE$.
#
# In[23]:
# we have multiple criterions
criterion = {
"dice": DiceLoss(),
"iou": IoULoss(),
#"bce": nn.BCEWithLogitsLoss()
"ce": nn.CrossEntropyLoss()
def cutHorizontal(x):
return torch.cat(list(x[...,i*400:(i+1)*400] for i in range(4)), dim=0)
def to416(x):
size = list(x.size())
size[-1]=416
new = torch.zeros(size)
new[...,8:-8] = x
return new
# In[9]:
model = smp.FPN(MODEL_NAME, encoder_weights="imagenet", classes=4, activation=None)
# ### Training and Validation
# In[10]:
class Trainer(object):
'''This class takes care of training and validation of our model'''
def __init__(self, model):
self.num_workers = 6
self.batch_size = {"train": BS, "val": BS}
self.accumulation_steps = 32 // self.batch_size['train']
self.lr = LR
self.num_epochs = EPOCHS
def get_model(num_classes, model_name):
if model_name == "UNet":
print("using UNet")
model = smp.Unet(encoder_name='resnet50',
classes=num_classes,
activation='softmax')
if args.num_channels > 3:
weight = model.encoder.conv1.weight.clone()
model.encoder.conv1 = torch.nn.Conv2d(4,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
with torch.no_grad():
print("using 4c")
model.encoder.conv1.weight[:, :3] = weight
model.encoder.conv1.weight[:,
3] = model.encoder.conv1.weight[:,
0]
return model
elif model_name == "PSPNet":
print("using PSPNet")
model = smp.PSPNet(encoder_name="resnet50",
classes=num_classes,
activation='softmax')
if args.num_channels > 3:
weight = model.encoder.conv1.weight.clone()
model.encoder.conv1 = torch.nn.Conv2d(4,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
with torch.no_grad():
print("using 4c")
model.encoder.conv1.weight[:, :3] = weight
model.encoder.conv1.weight[:,
3] = model.encoder.conv1.weight[:,
0]
return model
elif model_name == "FPN":
print("using FPN")
model = smp.FPN(encoder_name='resnet50', classes=num_classes)
if args.num_channels > 3:
weight = model.encoder.conv1.weight.clone()
model.encoder.conv1 = torch.nn.Conv2d(4,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
with torch.no_grad():
print("using 4c")
model.encoder.conv1.weight[:, :3] = weight
model.encoder.conv1.weight[:,
3] = model.encoder.conv1.weight[:,
0]
return model
elif model_name == "AlbuNet":
print("using AlbuNet")
model = AlbuNet(pretrained=True, num_classes=num_classes)
return model
elif model_name == "YpUnet":
print("using YpUnet")
model = UNet(pretrained=True, num_classes=num_classes)
return model
else:
print("error in model")
return None
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--encoder', type=str, default='efficientnet-b0')
parser.add_argument('--model', type=str, default='unet')
parser.add_argument('--loc', type=str)
parser.add_argument('--data_folder', type=str, default='../input/')
parser.add_argument('--batch_size', type=int, default=2)
parser.add_argument('--optimize', type=bool, default=False)
parser.add_argument('--tta_pre', type=bool, default=False)
parser.add_argument('--tta_post', type=bool, default=False)
parser.add_argument('--merge', type=str, default='mean')
parser.add_argument('--min_size', type=int, default=10000)
parser.add_argument('--thresh', type=float, default=0.5)
parser.add_argument('--name', type=str)
args = parser.parse_args()
encoder = args.encoder
model = args.model
loc = args.loc
data_folder = args.data_folder
bs = args.batch_size
optimize = args.optimize
tta_pre = args.tta_pre
tta_post = args.tta_post
merge = args.merge
min_size = args.min_size
thresh = args.thresh
name = args.name
if model == 'unet':
model = smp.Unet(encoder_name=encoder,
encoder_weights='imagenet',
classes=4,
activation=None)
if model == 'fpn':
model = smp.FPN(
encoder_name=encoder,
encoder_weights='imagenet',
classes=4,
activation=None,
)
if model == 'pspnet':
model = smp.PSPNet(
encoder_name=encoder,
encoder_weights='imagenet',
classes=4,
activation=None,
)
if model == 'linknet':
model = smp.Linknet(
encoder_name=encoder,
encoder_weights='imagenet',
classes=4,
activation=None,
)
preprocessing_fn = smp.encoders.get_preprocessing_fn(encoder, 'imagenet')
test_df = get_dataset(train=False)
test_df = prepare_dataset(test_df)
test_ids = test_df['Image_Label'].apply(
lambda x: x.split('_')[0]).drop_duplicates().values
test_dataset = CloudDataset(
df=test_df,
datatype='test',
img_ids=test_ids,
transforms=valid1(),
preprocessing=get_preprocessing(preprocessing_fn))
test_loader = DataLoader(test_dataset, batch_size=bs, shuffle=False)
val_df = get_dataset(train=True)
val_df = prepare_dataset(val_df)
_, val_ids = get_train_test(val_df)
valid_dataset = CloudDataset(
df=val_df,
datatype='train',
img_ids=val_ids,
transforms=valid1(),
preprocessing=get_preprocessing(preprocessing_fn))
valid_loader = DataLoader(valid_dataset, batch_size=bs, shuffle=False)
model.load_state_dict(torch.load(loc)['model_state_dict'])
class_params = {
0: (thresh, min_size),
1: (thresh, min_size),
2: (thresh, min_size),
3: (thresh, min_size)
}
if optimize:
print("OPTIMIZING")
print(tta_pre)
if tta_pre:
opt_model = tta.SegmentationTTAWrapper(
model,
tta.Compose([
tta.HorizontalFlip(),
tta.VerticalFlip(),
tta.Rotate90(angles=[0, 180])
]),
merge_mode=merge)
else:
opt_model = model
tta_runner = SupervisedRunner()
print("INFERRING ON VALID")
tta_runner.infer(
model=opt_model,
loaders={'valid': valid_loader},
callbacks=[InferCallback()],
verbose=True,
)
valid_masks = []
probabilities = np.zeros((4 * len(valid_dataset), 350, 525))
for i, (batch, output) in enumerate(
tqdm(
zip(valid_dataset,
tta_runner.callbacks[0].predictions["logits"]))):
_, mask = batch
for m in mask:
if m.shape != (350, 525):
m = cv2.resize(m,
dsize=(525, 350),
interpolation=cv2.INTER_LINEAR)
valid_masks.append(m)
for j, probability in enumerate(output):
if probability.shape != (350, 525):
probability = cv2.resize(probability,
dsize=(525, 350),
interpolation=cv2.INTER_LINEAR)
probabilities[(i * 4) + j, :, :] = probability
print("RUNNING GRID SEARCH")
for class_id in range(4):
print(class_id)
attempts = []
for t in range(30, 70, 5):
t /= 100
for ms in [7500, 10000, 12500, 15000, 175000]:
masks = []
for i in range(class_id, len(probabilities), 4):
probability = probabilities[i]
predict, num_predict = post_process(
sigmoid(probability), t, ms)
masks.append(predict)
d = []
for i, j in zip(masks, valid_masks[class_id::4]):
if (i.sum() == 0) & (j.sum() == 0):
d.append(1)
else:
d.append(dice(i, j))
attempts.append((t, ms, np.mean(d)))
attempts_df = pd.DataFrame(attempts,
columns=['threshold', 'size', 'dice'])
attempts_df = attempts_df.sort_values('dice', ascending=False)
print(attempts_df.head())
best_threshold = attempts_df['threshold'].values[0]
best_size = attempts_df['size'].values[0]
class_params[class_id] = (best_threshold, best_size)
del opt_model
del tta_runner
del valid_masks
del probabilities
gc.collect()
if tta_post:
model = tta.SegmentationTTAWrapper(model,
tta.Compose([
tta.HorizontalFlip(),
tta.VerticalFlip(),
tta.Rotate90(angles=[0, 180])
]),
merge_mode=merge)
else:
model = model
print(tta_post)
runner = SupervisedRunner()
runner.infer(
model=model,
loaders={'test': test_loader},
callbacks=[InferCallback()],
verbose=True,
)
encoded_pixels = []
image_id = 0
for i, image in enumerate(tqdm(runner.callbacks[0].predictions['logits'])):
for i, prob in enumerate(image):
if prob.shape != (350, 525):
prob = cv2.resize(prob,
dsize=(525, 350),
interpolation=cv2.INTER_LINEAR)
predict, num_predict = post_process(sigmoid(prob),
class_params[image_id % 4][0],
class_params[image_id % 4][1])
if num_predict == 0:
encoded_pixels.append('')
else:
r = mask2rle(predict)
encoded_pixels.append(r)
image_id += 1
test_df['EncodedPixels'] = encoded_pixels
test_df.to_csv(name, columns=['Image_Label', 'EncodedPixels'], index=False)
def main():
fold_path = args.fold_path
fold_num = args.fold_num
model_name = args.model_name
train_csv = args.train_csv
sub_csv = args.sub_csv
encoder = args.encoder
num_workers = args.num_workers
batch_size = args.batch_size
num_epochs = args.num_epochs
learn_late = args.learn_late
attention_type = args.attention_type
train = pd.read_csv(train_csv)
sub = pd.read_csv(sub_csv)
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[-1])
train['im_id'] = train['Image_Label'].apply(
lambda x: x.replace('_' + x.split('_')[-1], ''))
sub['label'] = sub['Image_Label'].apply(lambda x: x.split('_')[-1])
sub['im_id'] = sub['Image_Label'].apply(
lambda x: x.replace('_' + x.split('_')[-1], ''))
train_fold = pd.read_csv(f'{fold_path}/train_file_fold_{fold_num}.csv')
val_fold = pd.read_csv(f'{fold_path}/valid_file_fold_{fold_num}.csv')
train_ids = np.array(train_fold.file_name)
valid_ids = np.array(val_fold.file_name)
encoder_weights = 'imagenet'
attention_type = None if attention_type == 'None' else attention_type
if model_name == 'Unet':
model = smp.Unet(
encoder_name=encoder,
encoder_weights=encoder_weights,
classes=4,
activation='softmax',
attention_type=attention_type,
)
if model_name == 'Linknet':
model = smp.Linknet(
encoder_name=encoder,
encoder_weights=encoder_weights,
classes=4,
activation='softmax',
)
if model_name == 'FPN':
model = smp.FPN(
encoder_name=encoder,
encoder_weights=encoder_weights,
classes=4,
activation='softmax',
)
if model_name == 'ORG':
model = Linknet_resnet18_ASPP()
preprocessing_fn = smp.encoders.get_preprocessing_fn(
encoder, encoder_weights)
train_dataset = CloudDataset(
df=train,
datatype='train',
img_ids=train_ids,
transforms=get_training_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn))
valid_dataset = CloudDataset(
df=train,
datatype='valid',
img_ids=valid_ids,
transforms=get_validation_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn))
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
drop_last=True,
pin_memory=True,
)
valid_loader = DataLoader(valid_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
loaders = {"train": train_loader, "valid": valid_loader}
logdir = f"./log/logs_{model_name}_fold_{fold_num}_{encoder}/segmentation"
#for batch_idx, (data, target) in enumerate(loaders['train']):
# print(batch_idx)
print(logdir)
if model_name == 'ORG':
optimizer = NAdam([
{
'params': model.parameters(),
'lr': learn_late
},
])
else:
optimizer = NAdam([
{
'params': model.decoder.parameters(),
'lr': learn_late
},
{
'params': model.encoder.parameters(),
'lr': learn_late
},
])
scheduler = ReduceLROnPlateau(optimizer, factor=0.5, patience=0)
criterion = smp.utils.losses.BCEDiceLoss()
runner = SupervisedRunner()
runner.train(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
callbacks=[
DiceCallback(),
EarlyStoppingCallback(patience=5, min_delta=1e-7)
],
logdir=logdir,
num_epochs=num_epochs,
verbose=1)
def generate_class_params(i_dont_know_how_to_return_values_without_map):
preprocessing_fn = smp.encoders.get_preprocessing_fn(ENCODER, ENCODER_WEIGHTS)
valid_dataset = CloudDataset(df=train, datatype='valid', img_ids=valid_ids, transforms=get_validation_augmentation(), preprocessing=get_preprocessing(preprocessing_fn))
valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=0)
model = smp.FPN(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
classes=4,
activation=ACTIVATION,
)
runner = SupervisedRunner()
# Generate validation predictions
encoded_pixels = []
loaders = {"infer": valid_loader}
runner.infer(
model=model,
loaders=loaders,
callbacks=[
CheckpointCallback(
resume=f"{logdir}/checkpoints/best.pth"),
InferCallback()
],
)
valid_masks = []
probabilities = np.zeros((2220, 350, 525))
for i, (batch, output) in enumerate(tqdm.tqdm(zip(
valid_dataset, runner.callbacks[0].predictions["logits"]))):
image, mask = batch
for m in mask:
if m.shape != (350, 525):
m = cv2.resize(m, dsize=(525, 350), interpolation=cv2.INTER_LINEAR)
valid_masks.append(m)
for j, probability in enumerate(output):
if probability.shape != (350, 525):
probability = cv2.resize(probability, dsize=(525, 350), interpolation=cv2.INTER_LINEAR)
probabilities[i * 4 + j, :, :] = probability
class_params = {}
for class_id in range(4):
print(class_id)
attempts = []
for t in range(30, 100, 5):
t /= 100
for ms in [1200, 5000, 10000]:
masks = []
for i in range(class_id, len(probabilities), 4):
probability = probabilities[i]
predict, num_predict = post_process(sigmoid(probability), t, ms)
masks.append(predict)
d = []
for i, j in zip(masks, valid_masks[class_id::4]):
if (i.sum() == 0) & (j.sum() == 0):
d.append(1)
else:
d.append(dice(i, j))
attempts.append((t, ms, np.mean(d)))
attempts_df = pd.DataFrame(attempts, columns=['threshold', 'size', 'dice'])
attempts_df = attempts_df.sort_values('dice', ascending=False)
print(attempts_df.head())
best_threshold = attempts_df['threshold'].values[0]
best_size = attempts_df['size'].values[0]
class_params[class_id] = (best_threshold, best_size)
return class_params
# visualize(image=image, mask=mask)
import torch
import numpy as np
import segmentation_models_pytorch as smp
ENCODER = 'efficientnet-b0'
ENCODER_WEIGHTS = 'imagenet'
CLASSES = CarlaDataset.CLASSES
ACTIVATION = 'softmax2d'
DEVICE = 'cuda'
# create segmentation model with pretrained encoder
model = smp.FPN(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
classes=len(CLASSES),
activation=ACTIVATION,
)
preprocessing_fn = smp.encoders.get_preprocessing_fn(ENCODER, ENCODER_WEIGHTS)
train_dataset = CarlaDataset(
x_train_dir,
y_train_dir,
augmentation=get_training_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn),
classes=CarlaDataset.CLASSES,
)
valid_dataset = CarlaDataset(
x_valid_dir,
def validation(valid_ids, num_split, encoder, decoder):
"""
模型验证，并选择后处理参数
"""
train = "./data/Clouds_Classify/train.csv"
# Data overview
train = pd.read_csv(open(train))
train.head()
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
train['im_id'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
ENCODER = encoder
ENCODER_WEIGHTS = 'imagenet'
if decoder == 'unet':
model = smp.Unet(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
classes=4,
activation=None,
)
else:
model = smp.FPN(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
classes=4,
activation=None,
)
preprocessing_fn = smp.encoders.get_preprocessing_fn(
ENCODER, ENCODER_WEIGHTS)
num_workers = 4
valid_bs = 32
valid_dataset = CloudDataset(
df=train,
transforms=get_validation_augmentation(),
datatype='valid',
img_ids=valid_ids,
preprocessing=get_preprocessing(preprocessing_fn))
valid_loader = DataLoader(valid_dataset,
batch_size=valid_bs,
shuffle=False,
num_workers=num_workers)
loaders = {"valid": valid_loader}
logdir = "./logs/log_{}_{}/log_{}".format(encoder, decoder, num_split)
valid_masks = []
probabilities = np.zeros((len(valid_ids) * 4, 350, 525))
############### TTA预测 ####################
use_TTA = True
checkpoint_path = logdir + '/checkpoints/best.pth'
runner_out = []
model.load_state_dict(torch.load(checkpoint_path)['model_state_dict'])
if use_TTA:
transforms = tta.Compose([
tta.HorizontalFlip(),
tta.VerticalFlip(),
tta.Scale(scales=[5 / 6, 1, 7 / 6]),
])
tta_model = tta.SegmentationTTAWrapper(model,
transforms,
merge_mode='mean')
else:
tta_model = model
tta_model = tta_model.cuda()
tta_model.eval()
with torch.no_grad():
for i, data in enumerate(tqdm.tqdm(loaders['valid'])):
img, _ = data
img = img.cuda()
batch_preds = tta_model(img).cpu().numpy()
runner_out.extend(batch_preds)
runner_out = np.array(runner_out)
######################END##########################
for i, ((_, mask),
output) in enumerate(tqdm.tqdm(zip(valid_dataset, runner_out))):
for m in mask:
if m.shape != (350, 525):
m = cv2.resize(m,
dsize=(525, 350),
interpolation=cv2.INTER_LINEAR)
valid_masks.append(m)
for j, probability in enumerate(output):
if probability.shape != (350, 525):
probability = cv2.resize(probability,
dsize=(525, 350),
interpolation=cv2.INTER_LINEAR)
probabilities[i * 4 + j, :, :] = probability
# Find optimal values
print('searching for optimal param...')
params_0 = [[35, 76], [12000, 19001]]
params_1 = [[35, 76], [12000, 19001]]
params_2 = [[35, 76], [12000, 19001]]
params_3 = [[35, 76], [8000, 15001]]
param = [params_0, params_1, params_2, params_3]
for class_id in range(4):
par = param[class_id]
attempts = []
for t in range(par[0][0], par[0][1], 5):
t /= 100
for ms in range(par[1][0], par[1][1], 2000):
masks = []
print('==> searching [class_id:%d threshold:%.3f ms:%d]' %
(class_id, t, ms))
for i in tqdm.tqdm(range(class_id, len(probabilities), 4)):
probability = probabilities[i]
predict, _ = post_process(sigmoid(probability), t, ms)
masks.append(predict)
d = []
for i, j in zip(masks, valid_masks[class_id::4]):
if (i.sum() == 0) & (j.sum() == 0):
d.append(1)
else:
d.append(dice(i, j))
attempts.append((t, ms, np.mean(d)))
attempts_df = pd.DataFrame(attempts,
columns=['threshold', 'size', 'dice'])
attempts_df = attempts_df.sort_values('dice', ascending=False)
attempts_df.to_csv(
'./params/{}_{}_par/params_{}/tta_params_{}.csv'.format(
encoder, decoder, num_split, class_id),
columns=['threshold', 'size', 'dice'],
index=False)
def train(args):
logger = Logger(ckpt_path=args.ckpt_path, tsbd_path=args.vis_path)
args.logger = logger
train_transforms = image_train()
test_transforms = image_test()
models = []
optimizers = []
train_loaders = []
test_loaders = []
gpus = args.gpu_ids.split(',')
for i in range(args.type):
model = smp.FPN(args.encoder, in_channels=4, classes=3).cuda()
model = nn.DataParallel(model, device_ids=[int(_) for _ in gpus])
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=0.0005,
nesterov=True)
models.append(model)
optimizers.append(optimizer)
train_list = os.path.join(args.data_list_path,
'label_' + str(i) + '_train.txt')
test_list = os.path.join(args.data_list_path,
'label_' + str(i) + '_test.txt')
train_dset = ImageList(open(train_list).readlines(),
datadir=args.data_path,
transform=train_transforms)
test_dset = ImageList(open(test_list).readlines(),
datadir=args.data_path,
transform=test_transforms)
train_loader = DataLoader(train_dset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
drop_last=False)
test_loader = DataLoader(test_dset,
batch_size=args.test_batch,
shuffle=False,
num_workers=4,
drop_last=False)
train_loaders.append(train_loader)
test_loaders.append(test_loader)
total_epochs = args.total_epochs
total_progress_bar = tqdm.tqdm(desc='Train iter', total=total_epochs)
for epoch in range(total_epochs):
total_progress_bar.update(1)
## train $type$ model
for tp in range(args.type):
train_loader = train_loaders[tp]
test_loader = test_loaders[tp]
model = models[tp]
optimizer = optimizers[tp]
model.train()
temp_progress_bar = tqdm.tqdm(desc='Train iter for type ' +
str(tp),
total=len(train_loader))
for it, (imgs, pixels, obj_types,
rewards) in enumerate(train_loader):
temp_progress_bar.update(1)
imgs = imgs.cuda()
pixels = pixels.cuda()
rewards = rewards.cuda()
masks = model(imgs)
## TODO: better format
loss = CE_pixel(masks, pixels, rewards)
optimizer.zero_grad()
loss.backward()
optimizer.step()
## vis
logger.add_scalar('loss_' + str(tp),
loss.item() / args.batch_size)
logger.step(1)
logger.log('Loss {:d}: {:.3f}'.format(tp, loss.item()))
## test
if epoch % args.test_interval == 1:
accs = test(args, models, test_loaders)
state = {}
for i in range(args.type):
state['FPN_' + str(i)] = models[i].state_dict()
logger.add_scalar_print('acc_' + str(i), accs[i])
logger.log('Acc {:d}: {:.3f}'.format(i, accs[i]))
logger.save_ckpt_iter(state=state, iter=epoch)
## at last
accs = test(args, models, test_loaders)
state = {}
for i in range(args.type):
state['FPN_' + str(i)] = models[i].state_dict()
logger.add_scalar_print('acc_' + str(i), accs[i])
logger.log('Acc {:d}: {:.3f}'.format(i, accs[i]))
logger.save_ckpt_iter(state=state, iter=total_epochs)
def testing(num_split, class_params, encoder, decoder):
"""
测试推理
"""
import gc
torch.cuda.empty_cache()
gc.collect()
sub = "./data/Clouds_Classify/sample_submission.csv"
sub = pd.read_csv(open(sub))
sub.head()
sub['label'] = sub['Image_Label'].apply(lambda x: x.split('_')[1])
sub['im_id'] = sub['Image_Label'].apply(lambda x: x.split('_')[0])
preprocessing_fn = smp.encoders.get_preprocessing_fn(encoder, 'imagenet')
if decoder == 'unet':
model = smp.Unet(
encoder_name=encoder,
encoder_weights='imagenet',
classes=4,
activation=None,
)
else:
model = smp.FPN(
encoder_name=encoder,
encoder_weights='imagenet',
classes=4,
activation=None,
)
test_ids = [id for id in os.listdir(test_imgs_folder)]
test_dataset = CloudDataset(
df=sub,
transforms=get_validation_augmentation(),
datatype='test',
img_ids=test_ids,
preprocessing=get_preprocessing(preprocessing_fn))
test_loader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=2)
loaders = {"test": test_loader}
logdir = "./logs/log_{}_{}/log_{}".format(encoder, decoder, num_split)
encoded_pixels = []
###############使用pytorch TTA预测####################
use_TTA = True
checkpoint_path = logdir + '/checkpoints/best.pth'
runner_out = []
model.load_state_dict(torch.load(checkpoint_path)['model_state_dict'])
#使用tta预测
if use_TTA:
transforms = tta.Compose([
tta.HorizontalFlip(),
tta.VerticalFlip(),
tta.Scale(scales=[5 / 6, 1, 7 / 6]),
])
tta_model = tta.SegmentationTTAWrapper(model,
transforms,
merge_mode='mean')
else:
tta_model = model
tta_model = tta_model.cuda()
tta_model.eval()
with torch.no_grad():
for i, data in enumerate(tqdm.tqdm(loaders['test'])):
img, _ = data
img = img.cuda()
batch_preds = tta_model(img).cpu().numpy()
runner_out.extend(batch_preds)
runner_out = np.array(runner_out)
for i, output in tqdm.tqdm(enumerate(runner_out)):
for j, probability in enumerate(output):
if probability.shape != (350, 525):
probability = cv2.resize(probability,
dsize=(525, 350),
interpolation=cv2.INTER_LINEAR)
logit = sigmoid(probability)
predict, num_predict = post_process(logit, class_params[j][0],
class_params[j][1])
if num_predict == 0:
encoded_pixels.append('')
else:
r = mask2rle(predict)
encoded_pixels.append(r)
sub['EncodedPixels'] = encoded_pixels
sub.to_csv('./sub/{}_{}/tta_submission_{}.csv'.format(
encoder, decoder, num_split),
columns=['Image_Label', 'EncodedPixels'],
index=False)
def __init__(self):
super().__init__()
self.model = smp.FPN('dpn131',
encoder_weights='imagenet',
classes=4,
activation=None)
def training(train_ids, valid_ids, num_split, encoder, decoder):
"""
模型训练
"""
train = "./data/Clouds_Classify/train.csv"
# Data overview
train = pd.read_csv(open(train))
train.head()
train['label'] = train['Image_Label'].apply(lambda x: x.split('_')[1])
train['im_id'] = train['Image_Label'].apply(lambda x: x.split('_')[0])
ENCODER = encoder
ENCODER_WEIGHTS = 'imagenet'
if decoder == 'unet':
model = smp.Unet(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
classes=4,
activation=None,
)
else:
model = smp.FPN(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
classes=4,
activation=None,
)
preprocessing_fn = smp.encoders.get_preprocessing_fn(
ENCODER, ENCODER_WEIGHTS)
num_workers = 4
bs = 12
train_dataset = CloudDataset(
df=train,
transforms=get_training_augmentation(),
datatype='train',
img_ids=train_ids,
preprocessing=get_preprocessing(preprocessing_fn))
valid_dataset = CloudDataset(
df=train,
transforms=get_validation_augmentation(),
datatype='valid',
img_ids=valid_ids,
preprocessing=get_preprocessing(preprocessing_fn))
train_loader = DataLoader(train_dataset,
batch_size=bs,
shuffle=True,
num_workers=num_workers)
valid_loader = DataLoader(valid_dataset,
batch_size=bs,
shuffle=False,
num_workers=num_workers)
loaders = {"train": train_loader, "valid": valid_loader}
num_epochs = 50
logdir = "./logs/log_{}_{}/log_{}".format(encoder, decoder, num_split)
# model, criterion, optimizer
optimizer = torch.optim.Adam([
{
'params': model.decoder.parameters(),
'lr': 1e-2
},
{
'params': model.encoder.parameters(),
'lr': 1e-3
},
])
scheduler = ReduceLROnPlateau(optimizer, factor=0.35, patience=4)
criterion = smp.utils.losses.BCEDiceLoss(eps=1.)
runner = SupervisedRunner()
runner.train(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
callbacks=[DiceCallback()],
logdir=logdir,
num_epochs=num_epochs,
verbose=True)
# Exploring predictions
loaders = {"infer": valid_loader}
runner.infer(
model=model,
loaders=loaders,
callbacks=[
CheckpointCallback(resume=f"{logdir}/checkpoints/best.pth"),
InferCallback()
],
)
if not train_parameters["width_crop_size"] / train_parameters[
"downsample_mask_factor"]:
raise ValueError(
f"Width crop size ({train_parameters['width_crop_size']}) "
f"should be divisible by the downsample_mask_factor"
f"({train_parameters['downsample_mask_factor']})")
final_upsampling = None
else:
final_upsampling = 4
model = smp.FPN(
encoder_type,
encoder_weights="imagenet",
classes=num_classes,
activation=None,
final_upsampling=final_upsampling,
dropout=0.5,
decoder_merge_policy="cat",
)
pad_factor = 64
imread_library = "cv2" # can be cv2 or jpeg4py
optimizer = RAdam(
[
{
"params": model.decoder.parameters(),
"lr": train_parameters["lr"]
},
# decrease lr for encoder in order not to permute
def generate_test_preds(ensemble_info):
test_preds = np.zeros((len(sub), 350, 525), dtype=np.float32)
num_models = len(ensemble_info)
for model_info in ensemble_info:
class_params = model_info['class_params']
encoder = model_info['encoder']
model_type = model_info['model_type']
logdir = model_info['logdir']
preprocessing_fn = smp.encoders.get_preprocessing_fn(
encoder, ENCODER_WEIGHTS)
model = None
if model_type == 'unet':
model = smp.Unet(
encoder_name=encoder,
encoder_weights=ENCODER_WEIGHTS,
classes=4,
activation=ACTIVATION,
)
elif model_type == 'fpn':
model = smp.FPN(
encoder_name=encoder,
encoder_weights=ENCODER_WEIGHTS,
classes=4,
activation=ACTIVATION,
)
else:
raise NotImplementedError("We only support FPN and UNet")
runner = SupervisedRunner(model)
# HACK: We are loading a few examples from our dummy loader so catalyst will properly load the weights
# from our checkpoint
dummy_dataset = CloudDataset(
df=sub,
datatype='test',
img_ids=test_ids[:1],
transforms=get_validation_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn))
dummy_loader = DataLoader(dummy_dataset,
batch_size=1,
shuffle=False,
num_workers=0)
loaders = {"test": dummy_loader}
runner.infer(
model=model,
loaders=loaders,
callbacks=[
CheckpointCallback(resume=f"{logdir}/checkpoints/best.pth"),
InferCallback()
],
)
# Now we do real inference on the full dataset
test_dataset = CloudDataset(
df=sub,
datatype='test',
img_ids=test_ids,
transforms=get_validation_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn))
test_loader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=0)
image_id = 0
for batch_index, test_batch in enumerate(tqdm.tqdm(test_loader)):
runner_out = runner.predict_batch(
{"features":
test_batch[0].cuda()})['logits'].cpu().detach().numpy()
# Applt TTA transforms
v_flip = test_batch[0].flip(dims=(2, ))
h_flip = test_batch[0].flip(dims=(3, ))
v_flip_out = runner.predict_batch({"features": v_flip.cuda()
})['logits'].cpu().detach()
h_flip_out = runner.predict_batch({"features": h_flip.cuda()
})['logits'].cpu().detach()
# Undo transforms
v_flip_out = v_flip_out.flip(dims=(2, )).numpy()
h_flip_out = h_flip_out.flip(dims=(3, )).numpy()
# Get average
tta_avg_out = (v_flip_out + h_flip_out) / 2
# Combine with original predictions
beta = 0.4 # From fastai TTA
runner_out = (beta) * runner_out + (1 - beta) * tta_avg_out
for preds in runner_out:
preds = preds.transpose((1, 2, 0))
preds = cv2.resize(
preds,
(525, 350)) # height and width are backward in cv2...
preds = preds.transpose((2, 0, 1))
idx = batch_index * 4
test_preds[idx + 0] += sigmoid(preds[0]) / num_models # fish
test_preds[idx + 1] += sigmoid(preds[1]) / num_models # flower
test_preds[idx + 2] += sigmoid(preds[2]) / num_models # gravel
test_preds[idx + 3] += sigmoid(preds[3]) / num_models # sugar
# Convert ensembled predictions to RLE predictions
encoded_pixels = []
for image_id, preds in enumerate(test_preds):
predict, num_predict = post_process(preds,
class_params[image_id % 4][0],
class_params[image_id % 4][1])
if num_predict == 0:
encoded_pixels.append('')
else:
r = mask2rle(predict)
encoded_pixels.append(r)
print("Saving submission...")
sub['EncodedPixels'] = encoded_pixels
sub.to_csv('ensembled_submission.csv',
columns=['Image_Label', 'EncodedPixels'],
index=False)
print("Saved.")
