def cross_session(data, label, subject_id, category_number, batch_size, iteration, lr, momentum, log_interval):
target_data, target_label = copy.deepcopy(data[2][subject_id]), copy.deepcopy(label[2][subject_id])
source_data, source_label = [copy.deepcopy(data[0][subject_id]), copy.deepcopy(data[1][subject_id])], [copy.deepcopy(label[0][subject_id]), copy.deepcopy(label[1][subject_id])]
# one_sub_data, one_sub_label = data[i], label[i]
# target_data, target_label = one_session_data.pop(), one_session_label.pop()
# source_data, source_label = one_session_data.copy(), one_session_label.copy()
# print(len(source_data))
source_data_comb = np.vstack((source_data[0], source_data[1]))
source_label_comb = np.vstack((source_label[0], source_label[1]))
for j in range(1, len(source_data)):
source_data_comb = np.vstack((source_data_comb, source_data[j]))
source_label_comb = np.vstack((source_label_comb, source_label[j]))
source_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data_comb, source_label_comb),
batch_size=batch_size,
shuffle=True,
drop_last=True)
target_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_data, target_label),
batch_size=batch_size,
shuffle=True,
drop_last=True)
model = DANNet(model=models.DAN(pretrained=False, number_of_category=category_number),
source_loader=source_loader,
target_loader=target_loader,
batch_size=batch_size,
iteration=iteration,
lr=lr,
momentum=momentum,
log_interval=log_interval)
# print(model.__getModel__())
acc = model.train()
return acc
def __init__(self, model=models.DAN(), source_loader=0, target_loader=0, batch_size=64, iteration=10000, lr=0.001, momentum=0.9, log_interval=10):
self.model = model
self.model.to(device)
self.source_loader = source_loader
self.target_loader = target_loader
self.batch_size = batch_size
self.iteration = iteration
self.lr = lr
self.momentum = momentum
self.log_interval = log_interval
def cross_subject(data, label, session_id, category_number, batch_size, iteration, lr, momentum, log_interval):
# cross-subject, for 3 sessions, 1-14 as sources, 15 as target
one_session_data, one_session_label = copy.deepcopy(data[session_id]), copy.deepcopy(label[session_id])
target_data, target_label = one_session_data.pop(), one_session_label.pop()
source_data, source_label = copy.deepcopy(one_session_data), copy.deepcopy(one_session_label.copy())
# print(len(source_data))
source_data_comb = source_data[0]
source_label_comb = source_label[0]
for j in range(1, len(source_data)):
source_data_comb = np.vstack((source_data_comb, source_data[j]))
source_label_comb = np.vstack((source_label_comb, source_label[j]))
if bn == 'ele':
source_data_comb = utils.norminy(source_data_comb)
target_data = utils.norminy(target_data)
elif bn == 'sample':
source_data_comb = utils.norminx(source_data_comb)
target_data = utils.norminx(target_data)
elif bn == 'global':
source_data_comb = utils.normalization(source_data_comb)
target_data = utils.normalization(target_data)
elif bn == 'none':
pass
else:
pass
# source_data_comb = utils.norminy(source_data_comb)
# target_data = utils.norminy(target_data)
source_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data_comb, source_label_comb),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# source_loaders = []
# for j in range(len(source_data)):
# source_loaders.append(torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data[j], source_label[j]),
# batch_size=batch_size,
# shuffle=True,
# drop_last=True))
target_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(target_data, target_label),
batch_size=batch_size,
shuffle=True,
drop_last=True)
model = DANNet(model=models.DAN(pretrained=False, number_of_category=category_number),
source_loader=source_loader,
target_loader=target_loader,
batch_size=batch_size,
iteration=iteration,
lr=lr,
momentum=momentum,
log_interval=log_interval)
# print(model.__getModel__())
acc = model.train()
return acc
def cross_subject(data, label, session_id, subject_id, category_number,
batch_size, iteration, lr, momentum, log_interval):
## LOSO
one_session_data, one_session_label = copy.deepcopy(
data[session_id]), copy.deepcopy(label[session_id])
train_idxs = list(range(15))
del train_idxs[subject_id]
test_idx = subject_id
target_data, target_label = one_session_data[test_idx], one_session_label[
test_idx]
source_data, source_label = copy.deepcopy(
one_session_data[train_idxs]), copy.deepcopy(
one_session_label[train_idxs])
del one_session_label
del one_session_data
# print(len(source_data))
source_data_comb = source_data[0]
source_label_comb = source_label[0]
for j in range(1, len(source_data)):
source_data_comb = np.vstack((source_data_comb, source_data[j]))
source_label_comb = np.vstack((source_label_comb, source_label[j]))
source_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(
source_data_comb, source_label_comb),
batch_size=batch_size,
shuffle=True,
drop_last=True)
target_loader = torch.utils.data.DataLoader(dataset=utils.CustomDataset(
target_data, target_label),
batch_size=batch_size,
shuffle=True,
drop_last=True)
model = DANNet(model=models.DAN(pretrained=False,
number_of_category=category_number),
source_loader=source_loader,
target_loader=target_loader,
batch_size=batch_size,
iteration=iteration,
lr=lr,
momentum=momentum,
log_interval=log_interval)
# print(model.__getModel__())
acc = model.train()
print('Target_subject_id: {}, current_session_id: {}, acc: {}'.format(
test_idx, session_id, acc))
return acc
source_label_comb),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# source_loaders = []
# for j in range(len(source_data)):
# source_loaders.append(torch.utils.data.DataLoader(dataset=utils.CustomDataset(source_data[j], source_label[j]),
# batch_size=batch_size,
# shuffle=True,
# drop_last=True))
target_loader = torch.utils.data.DataLoader(
dataset=utils.CustomDataset(target_data, target_label),
batch_size=batch_size,
shuffle=True,
drop_last=True)
model = DANNet(model=models.DAN(
pretrained=False, number_of_category=category_number),
source_loader=source_loader,
target_loader=target_loader,
batch_size=batch_size,
iteration=iteration,
lr=lr,
momentum=momentum,
log_interval=log_interval)
# print(model.__getModel__())
acc = model.train()
csub.append(acc)
# cross-session, for 15 subjects, 1-2 as sources, 3 as target
for i in range(15):
target_data, target_label = copy.deepcopy(
data_tmp[2][i]), copy.deepcopy(label[2][i])
def cli_main():
# torch.autograd.set_detect_anomaly(True)
pl.seed_everything(1234)
dataset = customdata.AusData("data/aus_data/D/D230201.mat",
pad=False,
normalise=True)
train_size = int(0.6 * len(dataset))
val_size = int(0.2 * len(dataset))
test_size = int(0.2 * len(dataset))
split_sum = train_size + val_size + test_size
diff = len(dataset) - split_sum
if diff > 0:
while True:
train_size += 1
diff -= 1
if diff == 0:
break
val_size += 1
diff -= 1
if diff == 0:
break
test_size += 1
diff -= 1
if diff == 0:
break
train_set, val_set, test_set = torch.utils.data.random_split(
dataset, [train_size, val_size, test_size])
tb_logger = pl_loggers.TensorBoardLogger('logs/')
trainer = pl.Trainer(logger=tb_logger,
gpus=1,
auto_select_gpus=True,
max_epochs=50)
dataset = customdata.SamsonDataset("sample_data/samson/samson_1.mat",
"sample_data/samson/end3.mat",
transform=transforms.Compose(
[customdata.ToTensor()]))
# shape = (610, 340, 103) 103==bands, 9 pixel types
# dataset = customdata.PaviaUDataset("sample_data/paviaU/PaviaU.mat", transform=transforms.Compose([customdata.ToTensor()]))
# dataset = customdata.ArtificalDataset((128, 128, 18), 5,
# transform=transforms.Compose([customdata.ToTensor()]))
train_loader = DataLoader(dataset,
batch_size=256,
num_workers=32,
shuffle=True)
# test_loader = DataLoader(dataset, batch_size=64, num_workers=32)
val_loader = DataLoader(dataset, batch_size=600, num_workers=32)
# ------------
# model
# ------------
data = dataset.HCI
# data = dataset.images
# noises, cov = hysime.est_noise(data)
# n_endmembers, b = hysime.hysime(data, noises, cov)
data = np.transpose(data, (1, 0))
weights, _, _ = vca.vca(data, 3)
for i in range(3):
weights[:, i] /= np.max(weights[:, i])
model = models.DAN(dataset.shape[1],
3,
noise=0.1,
lr=1e-3,
weights=torch.from_numpy(weights).float())
# ------------
# training
# ------------
trainer.fit(model, train_loader, val_loader)
model.eval()
img = np.array([])
gt = []
for batch in test_loader:
pixels = model.test_custom(batch.float())
fig, axs = plt.subplots(1, 3)
axs = axs.ravel()
pixels = pixels.detach().numpy().reshape((95, 95, 3), order="F")
axs[0].imshow(pixels[:, :, ::-1])
axs[1].imshow(dataset.gt_A)
axs[2].plot(model.decoder[0].weight[0, :].detach().numpy(), color="blue")
axs[2].plot(model.decoder[0].weight[1, :].detach().numpy(), color="red")
axs[2].plot(model.decoder[0].weight[2, :].detach().numpy(), color="orange")
axs[2].plot(dataset.end_members[:, 0], color="orange", marker="+")
axs[2].plot(dataset.end_members[:, 1], color="red", marker="+")
axs[2].plot(dataset.end_members[:, 2], color="blue", marker="+")
plt.show()