def test_splits_are_mutually_exclusive(self):
data = [5, 2, 3, 4, 1, 6]
splits = random_split(data, [2, 4])
all_values = []
all_values.extend(list(splits[0]))
all_values.extend(list(splits[1]))
data.sort()
all_values.sort()
self.assertListEqual(data, all_values)
def criterion_seg(pred, label):
return nn.MSELoss()(pred, label)
train_dataset = UnpairedDataset('../',
path_a='ct_sag_kr/train',
path_b=None,
corrupt=True,
augment=True)
test_dataset = UnpairedDataset('../',
path_a='ct_sag_kr/test',
path_b=None,
corrupt=True)
num_test = len(test_dataset) - num_val
train_dataset, _ = random_split(
train_dataset, [num_train, len(train_dataset) - num_train])
val_dataset, test_dataset = random_split(test_dataset, [num_val, num_test])
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
num_workers=5,
pin_memory=True)
val_loader = DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=5,
pin_memory=True)
test_loader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=5,
])
else:
transformed_dataset = ConcatDataset([
transformed_dataset_cw_1, transformed_dataset_cw_2,
transformed_dataset_cw_3, transformed_dataset_cw_4,
transformed_dataset_cw_5, transformed_dataset_cw_6,
transformed_dataset_cw_7, transformed_dataset_cw_8,
transformed_dataset_cw_9, transformed_dataset_cw_10,
transformed_dataset_cw_11
])
train_len = np.int(args.train_percent * len(transformed_dataset))
train_data, test_data = random_split(
transformed_dataset,
[train_len, len(transformed_dataset) - train_len])
# load trainig data loader
kwargs = {'num_workers': 8, 'pin_memory': True} if args.cuda else {}
train_loader = DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
drop_last=False,
**kwargs)
# load testing data loader
test_loader = DataLoader(test_data,
batch_size=args.test_batch_size,
shuffle=True,
drop_last=False,
# preprocess data and feed them into data loadesr -----------
x = np.array(df['data'].values, dtype=np.uint8).reshape((len(df), 1))
x = np.unpackbits(x, axis=1) # change decimal to binary
y = np.array(df['label'].values)
print(x.shape)
print(y.shape)
# create data_loaders
x_tensor = torch.from_numpy(x).float()
y_tensor = torch.from_numpy(y).float()
dataset = TensorDataset(x_tensor, y_tensor)
train_size = int(len(dataset) / 5 * 4) # 20% used for validation
train_dataset, val_dataset = random_split(
dataset, [train_size, len(dataset) - train_size])
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size)
val_loader = DataLoader(dataset=val_dataset, batch_size=len(val_dataset))
# model -----------------------------------------------------
# model with one hidden layer and one output layer
class my_model(nn.Module):
def __init__(self, n_in=8, n_hidden=10, n_out=2):
super(my_model, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.layer1 = nn.Linear(self.n_in, self.n_out,
bias=True) # hidden layer
])
test_transform = A.Compose([
A.SmallestMaxSize(args.max_size),
#A.CenterCrop(args.image_size, args.image_size, p=1.),
A.Normalize(mean=[0.4452, 0.4457, 0.4464], std=[0.2592, 0.2596, 0.2600]),
ToTensorV2(),
])
# Dataset, Dataloader 정의
dataset = TrainDataset(args, transform=train_transform)
# Add remained last one data
train_size = int(len(dataset) * 0.8) + 1
val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
val_dataset.transform = test_transform
test_dataset = TestDataset(args, transform=test_transform)
train_sampler = RandomSampler(train_dataset)
val_sampler = SequentialSampler(val_dataset)
test_sampler = SequentialSampler(test_dataset)
train_loader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=False,
drop_last=True)
val_loader = DataLoader(val_dataset,
sampler=val_sampler,
# return none for the time being
return mean_dice_a, mean_dice_b, mean_loss_a, mean_loss_b
# loader from siegen dataset for validation
train_scan_dataset = UnpairedDataset('../',
'ct_sag_kr/train',
'mr_sag_kr/train',
contours=True)
test_scan_dataset = UnpairedDataset('../',
'ct_sag_kr/test',
'mr_sag_kr/test',
contours=True)
scan_dataset, _ = random_split(
train_scan_dataset,
[num_train, len(train_scan_dataset) - num_train])
scan_dataset_test, scan_dataset_val, _ = random_split(
test_scan_dataset,
[num_val, num_test,
len(test_scan_dataset) - (num_val + num_test)])
train_loader = DataLoader(scan_dataset, batch_size=batch_size, num_workers=5)
val_loader = DataLoader(scan_dataset_val,
batch_size=1,
shuffle=False,
num_workers=5)
test_loader = DataLoader(scan_dataset_test,
batch_size=1,
shuffle=False,
num_workers=5)
def test_splits_have_correct_size(self):
splits = random_split([1, 2, 3, 4, 5, 6], [2, 4])
self.assertEqual(len(splits), 2)
self.assertEqual(len(splits[0]), 2)
self.assertEqual(len(splits[1]), 4)
def model_train(config, train_dataset, checkpoint_dir=None):
# print(config)
test_abs = int(len(train_dataset) * 0.8)
train_subset, val_subset = random_split(
train_dataset, [test_abs, len(train_dataset) - test_abs])
train_loader = DataLoader(dataset=train_subset,
batch_size=int(config["batch_size"]),
shuffle=True)
val_loader = DataLoader(dataset=val_subset,
batch_size=int(config["batch_size"]),
shuffle=True)
'''Define the CNN model'''
model = CNN()
# print(model.state_dict())
loss_function = nn.MSELoss() #(reduction='mean')
optimizer = torch.optim.Adam(model.parameters(),
lr=3 * pow(10, -3)) # 3 * pow(10, -5)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
if checkpoint_dir:
model_state, optimizer_state = torch.load(
os.path.join(checkpoint_dir, "checkpoint"))
model.load_state_dict(model_state)
optimizer.load_state_dict(optimizer_state)
'''Model training'''
model.train()
for epoch in range(config["n_epoch"]):
running_loss = []
for i, data in enumerate(train_loader, 0):
x_batch, y_batch = data
predictions = model(x_batch)
# print(predictions.size(), y_batch.size())
loss = loss_function(predictions, y_batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss.append(loss.item())
# if i % 100 == 99: # print every 200 mini-batches
# print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1, np.mean(np.array(running_loss))))
# running_loss = []
val_loss = 0.0
val_steps = 0
mean_error = 0
model.eval()
with torch.no_grad():
predictions_list = []
targets_list = []
for i, data in enumerate(val_loader, 0):
x_batch, y_batch = data
predictions = model(x_batch)
# print(predictions.size(), y_batch.size())
loss = loss_function(predictions, y_batch)
predictions = predictions.numpy().reshape(-1)
y_batch = y_batch.numpy().reshape(-1)
predictions_list.extend(predictions.tolist())
targets_list.extend(y_batch.tolist())
mean_error += np.mean(abs(predictions - y_batch))
val_loss += loss.item()
val_steps += 1
scheduler.step(val_loss)
with tune.checkpoint_dir(epoch) as checkpoint_dir:
path = os.path.join(checkpoint_dir, "checkpoint")
torch.save((model.state_dict(), optimizer.state_dict()), path)
tune.report(train_loss=np.mean(np.array(running_loss)),
loss=(val_loss / val_steps),
mean_error=mean_error / val_steps)
#calib_posterior.set_to(calib_prior)
#calib_posterior.row_cov.parameter.detach()
calib_posterior.stddev = .15
calib_posterior.loc.data = torch.randn(1)
model = AdditiveDiscrepancy(computer_model,
discrepancy,
calib_prior,
calib_posterior,
true_calib=true_calib)
### Initialization of the computer model
init_batchsize_run = run_size # all data are taken for initialization
init_data_run, _ = random_split(
train_data_loader.loaders[1].dataset,
[init_batchsize_run, run_size - init_batchsize_run])
dataloader_run_for_init = SingleSpaceBatchLoader(DataLoader(
init_data_run, batch_size=init_batchsize_run),
cat_inputs=True)
computer_model_initializer = IBLMInitializer(computer_model,
dataloader_run_for_init,
noise_var=noise_std_run**2)
computer_model_initializer.initialize()
lr = .02
iterations_free_noise = 100
device = None
verbose = False
lr_calib = 0.1
outdir = vcal.vardl_utils.next_path('workspace/minimalist_example/%s/' %
VGG_loss = VGGContentLossMultiLayer([27]).to(device)
L1_loss = nn.L1Loss().to(device)
L2_loss = nn.MSELoss().to(device)
def critereon(input, output, target):
return VGG_loss(output, target) + L2_loss(output, target) + L1_loss(
output, input)
black_flags = open("./data/black_flags.txt",
encoding="utf-8").read().splitlines()
dataset = BallFlagDatasetBSM("./data",
exclude_countries=black_flags,
use_augmentation=True)
test_len = int(len(dataset) * test_percentage)
train_len = len(dataset) - test_len
train_dataset, test_dataset = random_split(dataset, [train_len, test_len])
train_dataloader = DataLoader(train_dataset,
bs,
shuffle=True,
pin_memory=True,
drop_last=True,
num_workers=1)
test_dataloader = DataLoader(test_dataset,
bs,
shuffle=False,
pin_memory=True,
drop_last=True,
num_workers=0)
assert len(test_dataloader) > 0, "too few samples"
# Weird code but this is how it has to be done
seed_torch()
# Paths to the files with training, and validation sets.
# Each file contains pairs (path to image, output vector)
#pathFileTrain = ''
pathFileTrain = '1kmultiViewCSV.csv'
# pathFileTrain = 'CheXpert-v1.0-small/multiViewCSV.csv'
pathFileValid = 'CheXpert-v1.0-small/valid.csv'
#LOAD DATASET
#dataset = CheXpertDataSet(pathFileTrain ,transformSequence, policy=policy)
dataset = CheXpertDataSet(pathFileTrain, transformSequence, policy=policy)
datasetTest, datasetTrain = random_split(dataset, [500, len(dataset) - 500])
# datasetTest = torch.load("test_frontal_pa.txt")
datasetValid = CheXpertDataSet(pathFileValid, transformSequence)
dataLoaderTrain = DataLoader(dataset=datasetTrain,
batch_size=trBatchSize,
shuffle=True,
num_workers=24,
pin_memory=True)
dataLoaderVal = DataLoader(dataset=datasetValid,
batch_size=trBatchSize,
shuffle=False,
num_workers=24,
pin_memory=True)
dataLoaderTest = DataLoader(dataset=datasetTest,
def main():
num_args = len(sys.argv)
# Checking if filename input is specified
if num_args < 2:
sys.exit("Please specify an input file")
filename = str(sys.argv[1])
p = Path(filename)
# Checking if filepath is valid and/or file exists
if not (p.exists()):
sys.exit("File not found")
# Prepare data processing pipelines
tokenizer = get_tokenizer('basic_english')
train_iter = AG_NEWS(split='train')
vocab = build_vocab_from_iterator(yield_tokens(train_iter, tokenizer),
specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])
text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1
# Generate data batch and iterator
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def collate_batch(batch):
label_list, text_list, offsets = [], [], [0]
for (_label, _text) in batch:
label_list.append(label_pipeline(_label))
processed_text = torch.tensor(text_pipeline(_text),
dtype=torch.int64)
text_list.append(processed_text)
offsets.append(processed_text.size(0))
label_list = torch.tensor(label_list, dtype=torch.int64)
offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
text_list = torch.cat(text_list)
return label_list.to(device), text_list.to(device), offsets.to(device)
# This variable needs to be initialized twice or else an IndexError occurs
train_iter = AG_NEWS(split='train')
dataloader = DataLoader(train_iter,
batch_size=8,
shuffle=False,
collate_fn=collate_batch)
# Build an instance
num_class = len(set([label for (label, text) in train_iter]))
vocab_size = len(vocab)
emsize = 64
model = TextClassificationModel(vocab_size, emsize, num_class).to(device)
# Split the dataset and run the model
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
total_accu = None
train_iter, test_iter = AG_NEWS()
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = \
random_split(train_dataset,
[num_train, len(train_dataset) - num_train])
train_dataloader = DataLoader(split_train_,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=collate_batch)
# Run epochs
for epoch in range(1, EPOCHS + 1):
epoch_start_time = time.time()
train(train_dataloader, model, optimizer, criterion, epoch)
accu_val = evaluate(valid_dataloader, model, criterion)
if total_accu is not None and total_accu > accu_val:
scheduler.step()
else:
total_accu = accu_val
print('-' * 59)
print('| end of epoch {:3d} | time: {:5.2f}s | '
'valid accuracy {:8.3f} '.format(epoch,
time.time() - epoch_start_time,
accu_val))
print('-' * 59)
print('Checking the results of test dataset.')
accu_test = evaluate(test_dataloader, model, criterion)
print('test accuracy {:8.3f}'.format(accu_test))
# Run article prediction
ag_news_label = {1: "World", 2: "Sports", 3: "Business", 4: "Sci/Tec"}
with p.open() as readfile:
ex_text_str = readfile.read()
model = model.to("cpu")
print("This is a %s news" %
ag_news_label[predict(ex_text_str, text_pipeline, model)])
def test2():
Ms = [GRU, LSTM]
name = ["MoE", "GRU", "LSTM"]
X = torch.randn((100, 8, 30))
X2 = torch.randn((100, 8, 40))
Y = X
Y2 = X2
feature_dims = {
"phase_dim": 5,
"pose_dim": 15,
"cost_dim": 10,
"g_input_dim": config["k"] + config["cost_hidden_dim"],
"g_output_dim": 5 + config["k"] + 10
}
feature_dims2 = {
"phase_dim": 5,
"pose_dim": 25,
"cost_dim": 10,
"g_input_dim": config["k"] + config["cost_hidden_dim"],
"g_output_dim": 5 + config["k"] + 10
}
in_slice = [5, 15, 10]
in_slice2 = [5, 25, 10]
out_slice = [5, config["k"], 10]
out_slice2 = [5, config["k"], 10]
dataset = TensorDataset(X, Y)
dataset2 = TensorDataset(X2, Y2)
train_set, val_set, test_set = random_split(dataset, [80, 10, 10])
train_set2, val_set2, test_set2 = random_split(dataset2, [80, 10, 10])
train_loader = DataLoader(train_set, batch_size=10)
train_loader2 = DataLoader(train_set2, batch_size=10)
val_loader = DataLoader(val_set, batch_size=10)
val_loader2 = DataLoader(val_set2, batch_size=10)
test_loader = DataLoader(test_set, batch_size=10)
test_loader2 = DataLoader(test_set2, batch_size=10)
for nam, M in zip(name, Ms):
pose_encoder = MLP(config=config, dimensions=[15])
pose_encoder2 = MLP(config=config,
dimensions=[15],
pose_labels=torch.arange(63).unsqueeze(0))
pose_encoder3 = MLP(config=config,
dimensions=[
25, config["hidden_dim"], config["hidden_dim"],
config["k"]
],
pose_labels=torch.arange(63).unsqueeze(0))
m1 = MotionGenerationModelRNN(
config=config,
Model=M,
pose_autoencoder=pose_encoder,
feature_dims=feature_dims,
input_slicers=in_slice,
output_slicers=out_slice,
train_set=train_set,
val_set=val_set,
test_set=test_set,
)
m2 = MotionGenerationModelRNN(
config=config,
Model=M,
pose_autoencoder=pose_encoder2,
feature_dims=feature_dims,
input_slicers=in_slice,
output_slicers=out_slice,
train_set=train_set,
val_set=val_set,
test_set=test_set,
)
print("-" * 50, nam, "-" * 50)
trainer = pl.Trainer(max_epochs=5)
print(nam, "-TEST RESULTS BEFORE", "-" * 50)
res1 = trainer.test(m1, test_loader)
trainer.fit(
m1,
train_loader,
val_loader,
)
print(nam, "-TEST RESULTS AFTER", "-" * 50)
res2 = trainer.test(m1, test_loader)
print("IMPROVEMENT: ", res1[0]["test_loss"] - res2[0]["test_loss"])
print("-" * 50, nam, "-" * 50)
trainer = pl.Trainer(max_epochs=5)
print(nam, "-TEST RESULTS BEFORE", "-" * 50)
res1 = trainer.test(m2, test_loader)
trainer.fit(
m2,
train_loader,
val_loader,
)
print(nam, "-TEST RESULTS AFTER", "-" * 50)
res2 = trainer.test(m2, test_loader)
print("IMPROVEMENT: ", res1[0]["test_loss"] - res2[0]["test_loss"])
m2.swap_pose_encoder(pose_encoder=pose_encoder3,
input_dim=in_slice2,
output_dim=out_slice2,
feature_dims=feature_dims2,
freeze=True)
print("-" * 50, nam, "-" * 50)
trainer = pl.Trainer(max_epochs=5)
print(nam, "-TEST RESULTS BEFORE", "-" * 50)
res1 = trainer.test(m2, test_loader2)
trainer.fit(m2, train_loader2, val_loader2)
print(nam, "-TEST RESULTS AFTER", "-" * 50)
res2 = trainer.test(m2, test_loader2)
print("IMPROVEMENT: ", res1[0]["test_loss"] - res2[0]["test_loss"])
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
epochs = 5
lr = 0.01
momentum = 0.5
log_interval = 200
seed = 1
torch.manual_seed(seed)
# create training and validation dataset
dataset_train, dataset_valid = random_split(
Dataset('./pca1_data/'), [149, 30],
generator=torch.Generator().manual_seed(42))
dataset_train, dataset_test = random_split(
dataset_train, [121, 28], generator=torch.Generator().manual_seed(42))
NWORKERS = 24
device = 'cuda' if torch.cuda.is_available() else 'cpu'
TRAIN = DataLoader(dataset=dataset_train,
batch_size=3,
shuffle=True,
drop_last=False,
num_workers=NWORKERS)
TEST = DataLoader(dataset=dataset_test,
batch_size=3,
shuffle=True,
def test_splits_have_correct_size(self):
splits = random_split([1, 2, 3, 4, 5, 6], [2, 4])
self.assertEqual(len(splits), 2)
self.assertEqual(len(splits[0]), 2)
self.assertEqual(len(splits[1]), 4)
class CustomDataset(Dataset):
def __init__(self, x_tensor, y_tensor):
self.x = x_tensor
self.y = y_tensor
def __getitem__(self, index):
return (self.x[index], self.y[index])
def __len__(self):
return len(self.x)
dataset = TensorDataset(x_tensor, y_tensor)
(train_dataset, val_dataset) = random_split(dataset, [80, 20])
hvd_sampler_train_loader = torch.utils.data.distributed.DistributedSampler(
dataset=train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
train_loader = DataLoader(dataset=train_dataset,
batch_size=16,
sampler=hvd_sampler_train_loader)
hvd_sampler_val_loader = torch.utils.data.distributed.DistributedSampler(
dataset=val_dataset, num_replicas=hvd.size(), rank=hvd.rank())
val_loader = DataLoader(dataset=val_dataset,
batch_size=20,
sampler=hvd_sampler_val_loader)
class ManualLinearRegression(nn.Module):
def __init__(self):
super().__init__()
def train_model(model, x, y):
if len(np.shape(x)) == 1:
x = x[:, None]
if len(np.shape(y)) == 1:
y = y[:, None]
D_in = np.shape(x)[1]
D_out = np.shape(y)[1]
N = 50
dataset = TensorDataset(x, y)
N_train = int(3 * len(y) / 5)
train_dataset, val_dataset = random_split(
dataset, [N_train, len(y) - N_train])
train_loader = DataLoader(dataset=train_dataset, batch_size=N)
val_loader = DataLoader(dataset=val_dataset, batch_size=N)
loss_fn = torch.nn.MSELoss(reduction='sum')
val_loss_fn = lambda target, output: loss_fn(target[:, :2], output[:, :2])
# Use the optim package to define an Optimizer that will update the weights of
# the model for us. Here we will use Adam; the optim package contains many other
# optimization algorithms. The first argument to the Adam constructor tells the
# optimizer which Tensors it should update.
learning_rate = .0001
n_epochs = 10000
training_losses = []
validation_losses = []
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for t in range(n_epochs):
batch_losses = []
with torch.no_grad():
val_losses = []
for x_val, y_val in val_loader:
x_val = x_val.to(device)
y_val = y_val.to(device)
yhat = model(x_val)
val_loss = val_loss_fn(y_val, yhat).item()
val_losses.append(val_loss)
validation_loss = np.mean(val_losses)
validation_losses.append(validation_loss)
for x_batch, y_batch in train_loader:
x_batch = x_batch.to(device)
y_batch = y_batch.to(device)
# Forward pass: compute predicted y by passing x to the model.
y_pred = model(x_batch)
# Compute and print loss.
loss = loss_fn(y_pred, y_batch)
optimizer.zero_grad()
# Backward pass: compute gradient of the loss with respect to model
# parameters
loss.backward()
# Calling the step function on an Optimizer makes an update to its
# parameters
optimizer.step()
batch_losses.append(loss.item())
training_loss = np.mean(batch_losses)
training_losses.append(training_loss)
print(
f"[{t+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}"
)
if t > 100 and validation_losses[-2] <= validation_losses[-1]:
break
plt.figure()
plt.semilogy(range(len(training_losses)),
training_losses,
label='Training Loss')
plt.semilogy(range(len(training_losses)),
validation_losses,
label='Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()
model.eval()
return model
def test_lengths_must_equal_datset_size(self):
with self.assertRaises(ValueError):
random_split([1, 2, 3, 4], [1, 2])
return loss / len(data_), acc / len(data_)
# ## Split the dataset and run the model
import time
from torch.utils.data.dataset import random_split
N_EPOCHS = 5
min_valid_loss = float('inf')
criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=4.0)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.9)
train_len = int(len(train_dataset) * 0.95)
sub_train_, sub_valid_ = random_split(
train_dataset, [train_len, len(train_dataset) - train_len])
for epoch in range(N_EPOCHS):
start_time = time.time()
train_loss, train_acc = train_func(sub_train_)
valid_loss, valid_acc = test(sub_valid_)
secs = int(time.time() - start_time)
mins = secs / 60
secs = secs % 60
print('Epoch: %d' % (epoch + 1),
" | time in %d minutes, %d seconds" % (mins, secs))
print(
f'\tLoss: {train_loss:.4f}(train)\t|\tAcc: {train_acc * 100:.1f}%(train)'
y_tensors = torch.stack([
torch.from_numpy(clip[1:][:, :-(cost_dim + extra_feature_len)]).float()
for clip in data_tensors
])
print(len(x_tensors), x_tensors[0].shape)
print(len(y_tensors), y_tensors[0].shape)
dataset = TensorDataset(x_tensors, y_tensors)
N = len(x_tensors)
train_ratio = int(.7 * N)
val_ratio = int((N - train_ratio) / 2.0)
test_ratio = N - train_ratio - val_ratio
train_set, val_set, test_set = random_split(
dataset, [train_ratio, val_ratio, test_ratio],
generator=torch.Generator().manual_seed(2021))
print(len(train_set), len(val_set), len(test_set))
input_dim = phase_dim + pose_dim + cost_dim
output_dim = phase_dim + pose_dim - extra_feature_len
print(input_dim)
print(output_dim)
config = {
"k_experts": tune.choice([1, 2, 4, 8, 10]),
"gate_size": tune.choice([16, 32, 64, 128]),
"keep_prob": tune.choice([.2, .25, .3]),
"hidden_dim": tune.choice([16, 32, 64, 128, 256, 512]),
"cost_hidden_dim": tune.choice([16, 32, 64, 128]),
"batch_size": tune.choice([1]),
def train_model(x, y):
if len(np.shape(y)) == 1:
y = y[:, None]
D_in = np.shape(x)[1]
D_out = np.shape(y)[1]
x_train_tensor = torch.from_numpy(x).float()
y_train_tensor = torch.from_numpy(y).float()
dataset = TensorDataset(x_train_tensor, y_train_tensor)
N_train = int(4 * len(y) / 5)
train_dataset, val_dataset = random_split(
dataset, [N_train, len(y) - N_train])
train_loader = DataLoader(dataset=train_dataset, batch_size=N)
val_loader = DataLoader(dataset=val_dataset, batch_size=N)
# Use the nn package to define our model and loss function.
model = torch.nn.Sequential(
torch.nn.Linear(D_in, H),
torch.nn.ReLU(),
torch.nn.ReLU(),
torch.nn.ReLU(),
torch.nn.Linear(H, D_out),
)
loss_fn = torch.nn.MSELoss(reduction='sum')
# Use the optim package to define an Optimizer that will update the weights of
# the model for us. Here we will use Adam; the optim package contains many other
# optimization algorithms. The first argument to the Adam constructor tells the
# optimizer which Tensors it should update.
learning_rate = .0001
n_epochs = 5000
training_losses = []
validation_losses = []
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for t in range(n_epochs):
with torch.no_grad():
val_losses = []
for x_val, y_val in val_loader:
x_val = x_val.to(device)
y_val = y_val.to(device)
model.eval()
yhat = model(x_val)
val_loss = loss_fn(y_val, yhat).item()
val_losses.append(val_loss)
validation_loss = np.mean(val_losses)
validation_losses.append(validation_loss)
batch_losses = []
for x_batch, y_batch in train_loader:
x_batch = x_batch.to(device)
y_batch = y_batch.to(device)
model.train()
# Forward pass: compute predicted y by passing x to the model.
y_pred = model(x_batch)
# Compute and print loss.
loss = loss_fn(y_pred, y_batch)
# Before the backward pass, use the optimizer object to zero all of the
# gradients for the variables it will update (which are the learnable
# weights of the model). This is because by default, gradients are
# accumulated in buffers( i.e, not overwritten) whenever .backward()
# is called. Checkout docs of torch.autograd.backward for more details.
optimizer.zero_grad()
# Backward pass: compute gradient of the loss with respect to model
# parameters
loss.backward()
# Calling the step function on an Optimizer makes an update to its
# parameters
optimizer.step()
batch_losses.append(loss.item())
training_loss = np.mean(batch_losses)
training_losses.append(training_loss)
print(
f"[{t+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}"
)
if t > 1000 and validation_losses[-1] < 0.05 and np.mean(
validation_losses[-20:-10]) < np.mean(
validation_losses[-9:-1]):
break
model.eval()
# plt.figure()
# plt.semilogy(range(len(training_losses)), training_losses, label='Training Loss')
# plt.semilogy(range(len(training_losses)), validation_losses, label='Validation Loss')
# plt.xlabel('Epoch')
# plt.ylabel('Loss')
# plt.legend()
# plt.show()
return model
def get_data_loader(dataset_name, batch_size):
# retrieve dataset constructor
if dataset_name == "svhn":
dataset = torchvision.datasets.SVHN
elif dataset_name == "cifar10":
dataset = torchvision.datasets.CIFAR10
elif dataset_name == "cifar100":
dataset = torchvision.datasets.CIFAR100
elif dataset_name == "stl10":
dataset = torchvision.datasets.STL10
elif dataset_name == "imagenet":
dataset = torchvision.datasets.ImageNet
# data normalization
image_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((.5, .5, .5), (.5, .5, .5))
])
# training and validation data
try:
trainvalid = dataset(dataset_name,
split='train+unlabeled',
download=True,
transform=image_transform)
except:
try:
trainvalid = dataset(dataset_name,
split='train',
download=True,
transform=image_transform)
except:
trainvalid = dataset(dataset_name,
train=True,
download=True,
transform=image_transform)
trainset_size = int(len(trainvalid) * 0.9)
trainset, validset = random_split(
trainvalid,
[trainset_size, len(trainvalid) - trainset_size])
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True)
validloader = torch.utils.data.DataLoader(validset, batch_size=batch_size)
# test data
try:
testdata = dataset(dataset_name,
split='test',
download=True,
transform=image_transform)
except:
testdata = dataset(dataset_name,
train=False,
download=True,
transform=image_transform)
testloader = torch.utils.data.DataLoader(testdata, batch_size=batch_size)
try:
n_classes = int(np.max(trainvalid.labels) + 1)
except:
n_classes = int(np.max(trainvalid.targets) + 1)
return trainloader, validloader, testloader, n_classes
torch.reshape(encode_sent.get('attention_mask'), (-1, )))
#%%
train_x = torch.stack(outputs)
train_x_mask = torch.stack(outputs_masks)
train_y = torch.tensor(train_df['polarity'], dtype=torch.long)
test_x = torch.stack(test_outputs)
test_x_mask = torch.stack(test_outputs_masks)
train_data = TensorDataset(train_x, train_x_mask, train_y)
test_data = TensorDataset(test_x, test_x_mask)
TrainData, ValidationData = random_split(
train_data,
[int(0.9 * len(train_data)),
len(train_data) - int(0.9 * len(train_data))])
HIDDEN_DIM = 256
EMB_DIM = 768
BATCH_SIZE = 64
OUTPUT_DIM = 2
N_LAYERS = 2
BIDIRECTIONAL = True
DROPOUT = 0.5
EPOCHS = 3
batch_size = 64
#Device = False
# loss and optimization functions
learningrate = 0.0001
loss += loss.item()
acc += (output.argmax(1) == cls).sum().item()
return loss / len(data_), acc / len(data_)
N_EPOCHS = 5
min_valid_loss = float('inf')
criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=4.0)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.9)
train_len = int(len(train_dataset) * 0.95)
sub_train_, sub_valid_ = \
random_split(train_dataset, [train_len, len(train_dataset) - train_len])
for epoch in range(args.epochs):
start_time = time.time()
train_loss, train_acc = train_func(sub_train_)
valid_loss, valid_acc = test(sub_valid_)
secs = int(time.time() - start_time)
mins = secs / 60
secs = secs % 60
print('Epoch: %d' % (epoch + 1),
" | time in %d minutes, %d seconds" % (mins, secs))
print(
f'\tLoss: {train_loss:.4f}(train)\t|\tAcc: {train_acc * 100:.1f}%(train)'
# Disable annoying PyTorch warnings
warnings.filterwarnings('ignore')
# Train the neural network
# Performs k-fold cross validation
for run in range(runs):
if cv:
# Split dataset into k folds for k-fold cross validation
k = 10 # Number of data folds
f_sizes = (k - 1) * [int(k/100. * len(dataset))]
f_sizes.append(len(dataset) - sum(f_sizes))
# Set seed
torch.manual_seed(run)
folds = random_split(dataset, f_sizes)
else:
# If not cv, devide dataset into training and test sets at random
nb_train_data = int(0.9 * len(dataset))
train_idx = np.random.choice(len(dataset), size=nb_train_data, replace=False)
test_idx = np.array([i for i in range(len(dataset)) if i not in train_idx])
# Training
ntimes = k if cv else 1
for i in range(ntimes):
net = spi.ClassificationModule(d, f, out)
net.double()
criterion = F.cross_entropy if out > 1 else F.binary_cross_entropy
optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=wd)
if cv:
test_dataloader = torch.utils.data.DataLoader(folds[i], batch_size=len(folds[i]))
# return none for the time being
return mean_dice, mean_loss
# 3 pairs of dataset, CT (Train / test), MRI (Train/test), Siegen public MRI (Train/test)
train_a_dataset = UnpairedDataset('../', path_a='ct_sag_kr/train', path_b=None)
test_a_dataset = UnpairedDataset('../', path_a='ct_sag_kr/test', path_b=None)
train_b_dataset = UnpairedDataset('../', path_a='mr_sag_kr/train', path_b=None)
test_b_dataset = UnpairedDataset('../', path_a='mr_sag_kr/test', path_b=None)
train_c_dataset = UnpairedDataset('../', path_a='siegen/train', path_b=None)
test_c_dataset = UnpairedDataset('../', path_a='siegen/test', path_b=None)
train_a_dataset, _ = random_split(train_a_dataset, [num_a_train, len(train_a_dataset) - num_a_train])
val_a_dataset, test_a_dataset, _ = random_split(test_a_dataset,
[num_a_val, num_a_test, len(test_a_dataset) - (num_a_val + num_a_test)])
train_b_dataset, _ = random_split(train_b_dataset, [num_b_train, len(train_b_dataset) - num_b_train])
val_b_dataset, test_b_dataset, _ = random_split(test_b_dataset,
[num_b_val, num_b_test, len(test_b_dataset) - (num_b_val + num_b_test)])
train_c_dataset, _ = random_split(train_c_dataset, [num_c_train, len(train_c_dataset) - num_c_train])
val_c_dataset, test_c_dataset, _ = random_split(test_c_dataset,
[num_c_val, num_c_test, len(test_c_dataset) - (num_c_val + num_c_test)])
train_a_loader = DataLoader(train_a_dataset, batch_size=batch_a_size, num_workers=5, pin_memory=True)
val_a_loader = DataLoader(val_a_dataset, batch_size=1, shuffle=False, num_workers=5, pin_memory=True)
test_a_loader = DataLoader(test_a_dataset, batch_size=1, shuffle=False, num_workers=5, pin_memory=True)
# lr_steps = 5
# lr_test = np.linspace(lr_min,lr_max,lr_steps)
#lr_test = [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5] #optimal was 0.0005 during first run
#lr_test = [0.0005]
#batch_size_test = [512, 256, 128, 64, 32, 16, 8]
#momentum_test = [0.3, 0.6, 0.8, 0.9, 1, 1.1]
#####################################################
print('Using augmentation: ',transform_name)
trainset = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform1)
trainset, validset = random_split(trainset,[45000,5000])
trainset_transformed2 = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform2)
trainset_transformed2, valid_transformed2 = random_split(trainset_transformed2,[45000,5000])
trainset_transformed3 = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform3)
trainset_transformed3, valid_transformed3 = random_split(trainset_transformed3,[45000,5000])
trainset = ConcatDataset([trainset, trainset_transformed2, trainset_transformed3])
#trainset = ConcatDataset([trainset, trainset_transformed2,])
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)
validloader = torch.utils.data.DataLoader(validset, batch_size=batch_size, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=transform1)
def forward(self, x):
x = x.view(x.size(0), -1)
x = self.layer1(x)
x = F.relu(x)
x = self.layer2(x)
x = self.log_smx(x)
return x
# download data
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transform)
mnist_test = MNIST(os.getcwd(), train=False, download=True, transform=transform)
mnist_train, mnist_val = random_split(mnist_train, [55000, 5000])
# train dataloader
mnist_train = DataLoader(mnist_train, batch_size=64)
# val dataloader
mnist_val = DataLoader(mnist_val, batch_size=64)
# test dataloader
mnist_test = DataLoader(mnist_test, batch_size=64)
# optimizer + scheduler
net = Net()
optimizer = torch.optim.Adam(net.parameters(), lr=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
def create_train_and_test(model_data, split_percent=0.9):
train_len = int(len(model_data) * split_percent)
sub_train, sub_valid = random_split(
model_data, [train_len, len(model_data) - train_len])
return sub_train, sub_valid
def test_lengths_must_equal_datset_size(self):
with self.assertRaises(ValueError):
random_split([1, 2, 3, 4], [1, 2])
from torch.utils.data.dataset import random_split
# Hyperparameters
EPOCHS = 10 # epoch
LR = 5 # learning rate
BATCH_SIZE = 64 # batch size for training
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
total_accu = None
train_iter, test_iter = AG_NEWS()
train_dataset = list(train_iter)
test_dataset = list(test_iter)
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = \
random_split(train_dataset, [num_train, len(train_dataset) - num_train])
train_dataloader = DataLoader(split_train_, batch_size=BATCH_SIZE,
shuffle=True, collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_, batch_size=BATCH_SIZE,
shuffle=True, collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE,
shuffle=True, collate_fn=collate_batch)
for epoch in range(1, EPOCHS + 1):
epoch_start_time = time.time()
train(train_dataloader)
accu_val = evaluate(valid_dataloader)
if total_accu is not None and total_accu > accu_val:
scheduler.step()
else:
#
# max_pwr_idx = np.argmax(valid_pwr)
# predictions.append(valid_freqs[max_pwr_idx] * 60.0)
# targets.append(data[1].numpy().tolist()[0][0])
# print(predictions)
# print(targets)
# cor = np.corrcoef(np.array(predictions), np.array(targets))[0][1]
# print("Correlation between input data and targets:", cor)
# from scipy import signal
# cross_corr = signal.correlate(predictions, targets, mode='same')
# print(len(cross_corr), cross_corr)
# plt.plot(cross_corr, label='cross-correlation')
# plt.show()
train_num = int(train_test_ratio * (len(dataset)))
train_dataset, test_dataset = random_split(
dataset, [train_num, len(dataset) - train_num])
reporter = CLIReporter(metric_columns=[
"train_loss", "loss", "mean_error", "training_iteration"
])
results = tune.run(
partial(model_train, train_dataset=train_dataset),
# resources_per_trial={"cpu": 2},
config=config,
progress_reporter=reporter)
# Plot loss during training
dfs = results.fetch_trial_dataframes()
[d.train_loss.plot() for d in dfs.values()]
[d.loss.plot() for d in dfs.values()]
plt.xlabel("epoch")
def dal_active_learning(self):
random_seeds = [123, 22, 69, 5, 108]
gen_size = int(
self.active_sample_size /
self.no_classes) ## reconfirm if its okay to keep it out side loop
total_active_cycles = int(
self.labelling_budget / self.active_sample_size) - 1
for i in random_seeds:
print("Executing Random seed " + str(i))
self.save_dir = os.path.join(
self.config.project_root,
f'results/{self.config.model_name}/' + 'random_seed' + str(i))
if not os.path.isdir(self.save_dir):
self.save_dir = os.path.join(
self.config.project_root,
f'results/{self.config.model_name}/' + 'random_seed' +
str(i))
os.makedirs(self.save_dir, exist_ok=True)
_, human_cnn, model, optimizer, scheduler = model_selection(
self.dataset, self.gan_type, self.device, self.active_learning,
i)
model.to(self.device)
for i in range(5):
seed = random_seeds[i]
print("Executing Random seed " + str(i))
active_learning_cycle = 0
results_dir_name = 'results_fashionmnist_accuracy_seed_' + str(
seed)
if not os.path.isdir(results_dir_name):
os.mkdir(results_dir_name)
_, _, model, optimizer, scheduler = model_selection(
self.dataset, self.gan_type, self.device,
self.active_learning, i)
model.to(self.device)
train_dataset = self.data_loader[3]
temp_list_data = [
train_dataset[i][0] for i in range(len(train_dataset))
]
temp_list_data = torch.stack(temp_list_data)
temp_list_labels = [
train_dataset.targets[i] for i in range(len(train_dataset))
]
temp_list_labels = torch.stack(temp_list_labels)
train_dataset = NewDataset(temp_list_data, temp_list_labels)
if self.config.dataset == 'cifar10_2class':
split_data = random_split(train_dataset, [9000, 1000])
else:
split_data = random_split(train_dataset, [50000, 10000])
temp_train_dataset = deepcopy(split_data[0])
validation_dataset = deepcopy(split_data[1])
train_idx = temp_train_dataset.indices
train_dataset.data = train_dataset.data[train_idx]
train_dataset.targets = train_dataset.targets[train_idx]
label_freq_cycle = torch.zeros(total_active_cycles,
self.no_classes)
# Initialisation of training examples
num_samples_class = int(self.intial_samples / self.no_classes)
numpy_labels = np.asarray(train_dataset.train_labels)
sort_labels = np.sort(numpy_labels)
sort_index = np.argsort(numpy_labels)
unique, start_index = np.unique(sort_labels, return_index=True)
training_index = []
for s in start_index:
for i in range(num_samples_class):
training_index.append(sort_index[s + i])
train_dataset.train_data = train_dataset.train_data[
training_index]
train_dataset.train_labels = train_dataset.train_labels[
training_index]
training_data_labels = train_dataset.train_labels.numpy()
self.save_image(train_dataset.data)
num_misclassifications, entropies, properly_classified_data, accuracy_list = (
[] for i in range(4))
val_loader = torch.utils.data.DataLoader(
dataset=validation_dataset,
batch_size=self.batch_size,
shuffle=True)
size = self.intial_samples
while (size <= self.labelling_budget):
model, accuracy = self.get_cnn_accuracy(
train_dataset, val_loader, model, optimizer, scheduler)
accuracy_list.append(accuracy)
print("----Size of training data----", size)
print("-----Accuracy-------", accuracy)
label_freq = torch.Tensor(
np.unique(training_data_labels, return_counts=True)[1]
/ SIZE)
label_freq_cycle[active_learning_cycle] = torch.Tensor(
label_freq)
new_samples, entropy = self.generator.generate_images(
model)
entropies.append(entropy)
if self.intial_samples == size:
self.save_image(new_samples)
outputs = human_cnn(new_samples)
_, latent_code = torch.max(outputs.data, 1)
new_samples = new_samples.data.cpu()
data = NewDataset(new_samples, latent_code.cpu())
train_dataset = torch.utils.data.ConcatDataset(
(train_dataset, data))
training_data_labels = np.append(training_data_labels,
np.array(latent_code))
size = len(train_dataset)
active_learning_cycle = active_learning_cycle + 1
_, _, model, optimizer, scheduler = model_selection(
self.dataset, self.gan_type, self.device,
self.active_learning, i)
model.to(self.device)
if size % 2000 == 0:
path = self.save_dir + '/' + 'intermediate_results' + str(
size)
if not os.path.isdir(path):
os.mkdir(path)
torch.save(accuracy_list, path + '/accuracy_list')
torch.save(entropies, path + '/entropies')
torch.save(label_freq_cycle, path + '/label_frequency')
torch.save(torch.LongTensor(num_misclassifications),
path + '/misclassifications')
torch.save(train_dataset, path + '/train_dataset')
print("--------Random seed " + str(i) + "completed--------")
