training_dist = [2, 4, 10, 20, 25, 35]
unseen_dist = [1, 3, 15, 30]
dataset_folder = os.path.join(data_dir, 'dataset')
root_path = partial(os.path.join, saved_models)
regex = re.compile(r'\d+')
selected_testdata = select_dist(dist_list=training_dist,
root_folder=dataset_folder)
selected_unseen = select_dist(dist_list=unseen_dist,
root_folder=dataset_folder)
data_loaders_test, data_lengths_test = define_dataset(
dataset_folder,
batch_size=8,
include_list=selected_testdata,
alldata=False)
print(data_lengths_test)
data_loaders_unseen, data_lengths_unseen = define_dataset(
dataset_folder, batch_size=8, include_list=selected_unseen, alldata=True)
print(data_lengths_unseen)
model_names = os.listdir(saved_models)
def get_fnames(coeff, prefix='Trained'):
idx_coef = 6 if prefix == 'Trained' else 4
idx_epoch = 5 if prefix == 'Trained' else 3
train_transformers = [
RandomCrop(p=1),
Swap(p=0.7),
FlipLR(p=0.7),
FlipUD(p=0.7),
GaussianNoise(p=0.75, mean=noise, sigma=1),
Rescale(0.25),
ChannelsFirst(),
ToTensor()
]
print("Load dataset")
data_loaders, data_length = define_dataset(
root_folder=ROOT_DIR,
base_transformers=base_transformers,
train_transformers=train_transformers,
batch_size=16,
excluded_list=EXCLUDED,
alldata=False,
multi_processing=4)
print("combined loss: {}*dice_loss + {} mse".format(coef, 1.0 - coef))
torch.cuda.empty_cache()
print("Train model")
model = rUNet(out_size=1)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
checkpoint_file = os.path.join(
SRC_DIR, 'saved_models', 'trained_6positions_multi_loss',
'Trained_rUNet_pytorch_6positions_dataset_100epochs_{}coeff_mask.pkl'
.format(coef))
print(torch.load(checkpoint_file).keys())
history = retrain_rUNet_multi_loss(
model=model,
SEED = 8
torch.manual_seed(SEED)
np.random.seed(SEED)
DATA_DIR_DEEPTHOUGHT = "/storage/yw18581/data"
data_dir = DATA_DIR_DEEPTHOUGHT
src_dir = os.path.join("/", "storage", "yw18581", "src", "leaf_reco")
root_dir = os.path.join(data_dir, "dataset")
selected_distances = select_dist(dist_list=[2, 4, 10, 25], root_folder=root_dir, keys_list=['first'])
excluded_distances = select_dist(root_folder=root_dir, keys_list=['second', 'third'])
print(selected_distances)
print("Load dataset")
data_loaders, data_lengths = define_dataset(root_folder=root_dir, batch_size=16, include_list=selected_distances,
excluded_list=excluded_distances, multi_processing=4)
print(data_lengths)
print("Define model")
n_epochs = 100
torch.cuda.empty_cache()
print("Train model")
model = UNet()
optimizer = optim.Adam(model.parameters(), lr=1e-4)
print("here")
history = training_UNet(model=model, optimizer=optimizer, criterion_mask=dice_loss,
src_dir='/storage/yw18581/src/leaf_reco',
task_folder_name="trained_UNet_4positions_firstbatch",
data_loaders=data_loaders, data_lengths=data_lengths,
epochs=n_epochs, batch_size=16, model_checkpoint=5,
SEED = 42
torch.manual_seed(SEED)
np.random.seed(SEED)
DATA_DIR_DEEPTHOUGHT = "/storage/yw18581/data"
DATA_DIR = DATA_DIR_DEEPTHOUGHT
SRC_DIR = os.path.join("/", "storage", "yw18581", "src", "leaf_reco")
ROOT_DIR = os.path.join(DATA_DIR, "dataset")
EXCLUDED = select_dist(dist_list=[1, 3, 15, 30], root_folder=ROOT_DIR)
print(EXCLUDED)
print("Load dataset")
data_loaders, data_lengths = define_dataset(root_folder=ROOT_DIR,
batch_size=16,
add_noise=10000,
excluded_list=EXCLUDED,
multi_processing=4)
print(data_lengths)
print("Define model")
coeffs = [0.40, 0.70]
n_epochs = 25
for coef in coeffs:
print("combined loss: {}*dice_loss + {} mse".format(coef, 1.0 - coef))
torch.cuda.empty_cache()
print("Train model")
model = rUNet(out_size=1)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
train_transformers = [
RandomCrop(p=1),
Swap(p=0.7),
FlipLR(p=0.7),
FlipUD(p=0.7),
GaussianNoise(p=0.75, mean=150, sigma=1),
Rescale(0.25),
ChannelsFirst(),
ToTensor()
]
print("Load dataset")
data_loaders, data_length = define_dataset(
root_folder=root_dir,
base_transformers=base_transformers,
train_transformers=train_transformers,
batch_size=16,
include_list=selected_distances,
alldata=False,
multi_processing=4)
print("combined loss: {}*dice_loss + {} mse".format(coef, 1.0 - coef))
torch.cuda.empty_cache()
print("Train model")
model = rUNet(out_size=1)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
history = training_phase_rUNet_multi_loss(
model=model,
optimizer=optimizer,
loss_coeff=coef,
criterion_dist=nn.MSELoss(),
criterion_mask=dice_loss,
import torch.optim as optim
import torch.nn as nn
SEED = 42
torch.manual_seed(SEED)
np.random.seed(SEED)
DATA_DIR_DEEPTHOUGHT = "/storage/yw18581/data"
data_dir = DATA_DIR_DEEPTHOUGHT
src_dir = os.path.join("/", "storage", "yw18581", "src", "leaf_reco")
root_dir = os.path.join(data_dir, "dataset")
excluded = select_dist(dist_list=[1, 3, 15, 30], root_folder=root_dir)
print(excluded)
print("Load dataset")
data_loaders, data_lengths = define_dataset(root_folder=root_dir, batch_size=16, excluded_list=excluded,
multi_processing=4)
print(data_lengths)
print("Define model")
coeffs = [0.75, 0.70, 0.60, 0.50]
coeffs_2 = [0.25, 0.30, 0.40]
n_epochs = 100
for coef in coeffs_2:
print("combined loss: {}*dice_loss + {} mse".format(coef, 1.0 - coef))
torch.cuda.empty_cache()
print("Train model")
model = rUNet(out_size=1)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
history = training_phase_rUNet(model=model, optimizer=optimizer, loss_coeff=coef,
