def run(self, trial=None):
params = self.params
is_ps = params["params_search"]
term_size = shutil.get_terminal_size().columns
# Show Hyper Params
if trial is not None:
sequence_length = params["sequence_length"]
hyper_params = get_hyper_params(trial, sequence_length)
self.params.update(hyper_params)
0 < trial.number and print("\n")
out_str = " Trial: {} ".format(trial.number + 1)
print(out_str.center(term_size, "="))
print("\n" + " Current Hyper Params ".center(term_size, "-"))
print([i for i in sorted(hyper_params.items())])
print("-" * shutil.get_terminal_size().columns + "\n")
# Generate Batch Iterators
train_loader = data.Iterator(
self.train,
batch_size=params["batch_size"],
device=params["device"],
train=True,
)
valid_loader = data.Iterator(
self.valid,
batch_size=params["batch_size"],
device=params["device"],
train=False,
sort=False,
)
if not is_ps:
test_loader = data.Iterator(
self.test,
batch_size=params["batch_size"],
device=params["device"],
train=False,
sort=False,
)
# Calc Batch Size
params["train_batch_total"] = math.ceil(
len(self.train) / params["batch_size"])
params["valid_batch_total"] = math.ceil(
len(self.valid) / params["batch_size"])
if not is_ps:
params["test_batch_total"] = math.ceil(
len(self.test) / params["batch_size"])
# Define xml-cnn model
model = xml_cnn(params, self.TEXT.vocab.vectors)
model = model.to(params["device"])
epochs = params["epochs"]
learning_rate = params["learning_rate"]
# Define Optimizer
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
if not is_ps:
ms = [int(epochs * 0.5), int(epochs * 0.75)]
scheduler = MultiStepLR(optimizer, milestones=ms, gamma=0.1)
best_epoch = 1
num_of_unchanged = 1
measure = params["measure"]
measure = "f1" in measure and measure[:-3] or measure
if not is_ps:
save_best_model_path = params["model_cache_path"] + "best_model.pkl"
# 学習
for epoch in range(1, epochs + 1):
if self.params["params_search"]:
out_str = " Epoch: {} ".format(epoch)
else:
lr = scheduler.get_last_lr()[0]
term_size = shutil.get_terminal_size().columns
out_str = " Epoch: {} (lr={:.20f}) ".format(epoch, lr)
# out_str = " Epoch: {} ".format(epoch)
print(out_str.center(term_size, "-"))
# 学習
training(params, model, train_loader, optimizer)
# 検証
val_measure_epoch_i = validating_testing(params, model,
valid_loader)
# 最良モデルの記録と保存
if epoch < 2:
best_val_measure = val_measure_epoch_i
(not is_ps) and torch.save(model, save_best_model_path)
elif best_val_measure < val_measure_epoch_i:
best_epoch = epoch
best_val_measure = val_measure_epoch_i
num_of_unchanged = 1
(not is_ps) and torch.save(model, save_best_model_path)
else:
num_of_unchanged += 1
# Show Best Epoch
out_str = " Best Epoch: {} (" + measure + ": {:.10f}, "
out_str = out_str.format(best_epoch, best_val_measure)
if bool(params["early_stopping"]):
remaining = params["early_stopping"] - num_of_unchanged
out_str += "ES Remaining: {}) "
out_str = out_str.format(remaining)
else:
out_str += "ES: False) "
print("\n" + out_str.center(term_size, "-") + "\n")
# Early Stopping
if early_stopping(num_of_unchanged, params["early_stopping"]):
break
(not is_ps) and scheduler.step()
if is_ps:
# Show Best Trials
if self.best_trial_measure < best_val_measure:
self.best_trial_measure = best_val_measure
self.num_of_trial = trial.number + 1
out_str = " Best Trial: {} (" + measure + ": {:.20f}) "
out_str = out_str.format(self.num_of_trial,
self.best_trial_measure)
print(out_str.center(term_size, "="))
else:
# Testing on Best Epoch Model
model = torch.load(save_best_model_path)
test_measure = validating_testing(params,
model,
test_loader,
is_valid=False)
out_str = " Finished "
print("\n\n" + out_str.center(term_size, "=") + "\n")
out_str = " Best Epoch: {} (" + measure + ": {:.20f}) "
out_str = out_str.format(best_epoch, test_measure)
print("\n" + out_str.center(term_size, "-") + "\n")
return 1 - best_val_measure
def train_model_multistage_lowlight():
device = DEVICE
#准备数据
train_set = HsiCubicTrainDataset('./data/train_lowlight_patchsize32/')
#print('trainset32 training example:', len(train_set32))
#train_set_64 = HsiCubicTrainDataset('./data/train_lowlight_patchsize64/')
#train_set_list = [train_set32, train_set_64]
#train_set = ConcatDataset(train_set_list) #里面的样本大小必须是一致的，否则会连接失败
print('total training example:', len(train_set))
train_loader = DataLoader(dataset=train_set,
batch_size=BATCH_SIZE,
shuffle=True)
#加载测试label数据
mat_src_path = './data/test_lowlight/origin/soup_bigcorn_orange_1ms.mat'
test_label_hsi = scio.loadmat(mat_src_path)['label']
#加载测试数据
batch_size = 1
test_data_dir = './data/test_lowlight/cubic/'
test_set = HsiCubicLowlightTestDataset(test_data_dir)
test_dataloader = DataLoader(dataset=test_set,
batch_size=batch_size,
shuffle=False)
batch_size, channel, width, height = next(iter(test_dataloader))[0].shape
band_num = len(test_dataloader)
denoised_hsi = np.zeros((width, height, band_num))
#创建模型
net = MultiStageHSID(K)
init_params(net)
#net = nn.DataParallel(net).to(device)
net = net.to(device)
#创建优化器
#hsid_optimizer = optim.Adam(net.parameters(), lr=INIT_LEARNING_RATE, betas=(0.9, 0,999))
hsid_optimizer = optim.Adam(net.parameters(), lr=INIT_LEARNING_RATE)
scheduler = MultiStepLR(hsid_optimizer, milestones=[40, 60, 80], gamma=0.1)
#定义loss 函数
#criterion = nn.MSELoss()
global tb_writer
tb_writer = get_summary_writer(log_dir='logs')
gen_epoch_loss_list = []
cur_step = 0
first_batch = next(iter(train_loader))
best_psnr = 0
best_epoch = 0
best_iter = 0
start_epoch = 1
num_epoch = 100
for epoch in range(start_epoch, num_epoch + 1):
epoch_start_time = time.time()
scheduler.step()
print(epoch, 'lr={:.6f}'.format(scheduler.get_last_lr()[0]))
print(scheduler.get_lr())
gen_epoch_loss = 0
net.train()
#for batch_idx, (noisy, label) in enumerate([first_batch] * 300):
for batch_idx, (noisy, cubic, label) in enumerate(train_loader):
#print('batch_idx=', batch_idx)
noisy = noisy.to(device)
label = label.to(device)
cubic = cubic.to(device)
hsid_optimizer.zero_grad()
#denoised_img = net(noisy, cubic)
#loss = loss_fuction(denoised_img, label)
residual = net(noisy, cubic)
#loss = loss_fuction(residual, label-noisy)
loss = np.sum([
loss_fuction(residual[j], label) for j in range(len(residual))
])
loss.backward() # calcu gradient
hsid_optimizer.step() # update parameter
gen_epoch_loss += loss.item()
if cur_step % display_step == 0:
if cur_step > 0:
print(
f"Epoch {epoch}: Step {cur_step}: Batch_idx {batch_idx}: MSE loss: {loss.item()}"
)
else:
print("Pretrained initial state")
tb_writer.add_scalar("MSE loss", loss.item(), cur_step)
#step ++,每一次循环，每一个batch的处理，叫做一个step
cur_step += 1
gen_epoch_loss_list.append(gen_epoch_loss)
tb_writer.add_scalar("mse epoch loss", gen_epoch_loss, epoch)
#scheduler.step()
#print("Decaying learning rate to %g" % scheduler.get_last_lr()[0])
torch.save(
{
'gen': net.state_dict(),
'gen_opt': hsid_optimizer.state_dict(),
}, f"checkpoints/hsid_multistage_patchsize64_{epoch}.pth")
#测试代码
net.eval()
for batch_idx, (noisy_test, cubic_test,
label_test) in enumerate(test_dataloader):
noisy_test = noisy_test.type(torch.FloatTensor)
label_test = label_test.type(torch.FloatTensor)
cubic_test = cubic_test.type(torch.FloatTensor)
noisy_test = noisy_test.to(DEVICE)
label_test = label_test.to(DEVICE)
cubic_test = cubic_test.to(DEVICE)
with torch.no_grad():
residual = net(noisy_test, cubic_test)
denoised_band = noisy_test + residual[0]
denoised_band_numpy = denoised_band.cpu().numpy().astype(
np.float32)
denoised_band_numpy = np.squeeze(denoised_band_numpy)
denoised_hsi[:, :, batch_idx] = denoised_band_numpy
if batch_idx == 49:
residual_squeezed = torch.squeeze(residual[0], axis=0)
denoised_band_squeezed = torch.squeeze(denoised_band,
axis=0)
label_test_squeezed = torch.squeeze(label_test, axis=0)
noisy_test_squeezed = torch.squeeze(noisy_test, axis=0)
tb_writer.add_image(f"images/{epoch}_restored",
denoised_band_squeezed,
1,
dataformats='CHW')
tb_writer.add_image(f"images/{epoch}_residual",
residual_squeezed,
1,
dataformats='CHW')
tb_writer.add_image(f"images/{epoch}_label",
label_test_squeezed,
1,
dataformats='CHW')
tb_writer.add_image(f"images/{epoch}_noisy",
noisy_test_squeezed,
1,
dataformats='CHW')
psnr = PSNR(denoised_hsi, test_label_hsi)
ssim = SSIM(denoised_hsi, test_label_hsi)
sam = SAM(denoised_hsi, test_label_hsi)
#计算pnsr和ssim
print("=====averPSNR:{:.3f}=====averSSIM:{:.4f}=====averSAM:{:.3f}".
format(psnr, ssim, sam))
tb_writer.add_scalars("validation metrics", {
'average PSNR': psnr,
'average SSIM': ssim,
'avarage SAM': sam
}, epoch) #通过这个我就可以看到，那个epoch的性能是最好的
#保存best模型
if psnr > best_psnr:
best_psnr = psnr
best_epoch = epoch
best_iter = cur_step
torch.save(
{
'epoch': epoch,
'gen': net.state_dict(),
'gen_opt': hsid_optimizer.state_dict(),
}, f"checkpoints/hsid_multistage_patchsize64_best.pth")
print(
"[epoch %d it %d PSNR: %.4f --- best_epoch %d best_iter %d Best_PSNR %.4f]"
% (epoch, cur_step, psnr, best_epoch, best_iter, best_psnr))
print(
"------------------------------------------------------------------"
)
print("Epoch: {}\tTime: {:.4f}\tLoss: {:.4f}\tLearningRate {:.6f}".
format(epoch,
time.time() - epoch_start_time, gen_epoch_loss,
scheduler.get_lr()[0]))
print(
"------------------------------------------------------------------"
)
#保存当前模型
torch.save(
{
'epoch': epoch,
'gen': net.state_dict(),
'gen_opt': hsid_optimizer.state_dict()
}, os.path.join('./checkpoints', "model_latest.pth"))
tb_writer.close()
class RunnerFSL(object):
def __init__(self, data_train, classes):
# all data
self.data_train = data_train
self.task_train = FSLDataset(self.data_train, classes,
Config.fsl_num_way, Config.fsl_num_shot)
self.task_train_loader = DataLoader(self.task_train,
Config.fsl_batch_size,
True,
num_workers=Config.num_workers)
# model
self.matching_net = RunnerTool.to_cuda(Config.fsl_matching_net)
self.matching_net = RunnerTool.to_cuda(
nn.DataParallel(self.matching_net))
cudnn.benchmark = True
RunnerTool.to_cuda(self.matching_net.apply(RunnerTool.weights_init))
self.norm = Normalize(2)
# optim
self.matching_net_optim = torch.optim.Adam(
self.matching_net.parameters(), lr=Config.fsl_learning_rate)
self.matching_net_scheduler = MultiStepLR(self.matching_net_optim,
Config.fsl_lr_schedule,
gamma=1 / 3)
# loss
self.fsl_loss = RunnerTool.to_cuda(nn.MSELoss())
# Eval
self.test_tool_fsl = FSLTestTool(
self.matching_test,
data_root=Config.data_root,
num_way=Config.fsl_num_way,
num_shot=Config.fsl_num_shot,
episode_size=Config.fsl_episode_size,
test_episode=Config.fsl_test_episode,
transform=self.task_train.transform_test)
pass
def load_model(self):
if os.path.exists(Config.mn_dir):
self.matching_net.load_state_dict(torch.load(Config.mn_dir))
Tools.print("load matching net success from {}".format(
Config.mn_dir))
pass
def matching(self, task_data):
data_batch_size, data_image_num, data_num_channel, data_width, data_weight = task_data.shape
data_x = task_data.view(-1, data_num_channel, data_width, data_weight)
net_out = self.matching_net(data_x)
z = net_out.view(data_batch_size, data_image_num, -1)
# 特征
z_support, z_query = z.split(Config.fsl_num_shot * Config.fsl_num_way,
dim=1)
z_batch_size, z_num, z_dim = z_support.shape
z_support = z_support.view(z_batch_size,
Config.fsl_num_way * Config.fsl_num_shot,
z_dim)
z_query_expand = z_query.expand(
z_batch_size, Config.fsl_num_way * Config.fsl_num_shot, z_dim)
# 相似性
z_support = self.norm(z_support)
similarities = torch.sum(z_support * z_query_expand, -1)
similarities = torch.softmax(similarities, dim=1)
similarities = similarities.view(z_batch_size, Config.fsl_num_way,
Config.fsl_num_shot)
predicts = torch.mean(similarities, dim=-1)
return predicts
def matching_test(self, samples, batches):
batch_num, _, _, _ = batches.shape
sample_z = self.matching_net(samples) # 5x64*5*5
batch_z = self.matching_net(batches) # 75x64*5*5
z_support = sample_z.view(Config.fsl_num_way * Config.fsl_num_shot, -1)
z_query = batch_z.view(batch_num, -1)
_, z_dim = z_query.shape
z_support_expand = z_support.unsqueeze(0).expand(
batch_num, Config.fsl_num_way * Config.fsl_num_shot, z_dim)
z_query_expand = z_query.unsqueeze(1).expand(
batch_num, Config.fsl_num_way * Config.fsl_num_shot, z_dim)
# 相似性
z_support_expand = self.norm(z_support_expand)
similarities = torch.sum(z_support_expand * z_query_expand, -1)
similarities = torch.softmax(similarities, dim=1)
similarities = similarities.view(batch_num, Config.fsl_num_way,
Config.fsl_num_shot)
predicts = torch.mean(similarities, dim=-1)
return predicts
def train(self):
Tools.print()
Tools.print("Training...")
best_accuracy = 0.0
for epoch in range(1, 1 + Config.fsl_train_epoch):
self.matching_net.train()
Tools.print()
all_loss, is_ok_total, is_ok_acc = 0.0, 0, 0
for task_data, task_labels, task_index, task_ok in tqdm(
self.task_train_loader):
task_data, task_labels = RunnerTool.to_cuda(
task_data), RunnerTool.to_cuda(task_labels)
###########################################################################
# 1 calculate features
relations = self.matching(task_data)
# 3 loss
loss = self.fsl_loss(relations, task_labels)
all_loss += loss.item()
# 4 backward
self.matching_net.zero_grad()
loss.backward()
self.matching_net_optim.step()
# is ok
is_ok_acc += torch.sum(torch.cat(task_ok))
is_ok_total += torch.prod(
torch.tensor(torch.cat(task_ok).shape))
###########################################################################
pass
###########################################################################
# print
Tools.print("{:6} loss:{:.3f} ok:{:.3f}({}/{}) lr:{}".format(
epoch, all_loss / len(self.task_train_loader),
int(is_ok_acc) / int(is_ok_total), is_ok_acc, is_ok_total,
self.matching_net_scheduler.get_last_lr()))
self.matching_net_scheduler.step()
###########################################################################
###########################################################################
# Val
if epoch % Config.fsl_val_freq == 0:
self.matching_net.eval()
val_accuracy = self.test_tool_fsl.val(episode=epoch,
is_print=True,
has_test=False)
if val_accuracy > best_accuracy:
best_accuracy = val_accuracy
torch.save(self.matching_net.state_dict(), Config.mn_dir)
Tools.print("Save networks for epoch: {}".format(epoch))
pass
pass
###########################################################################
pass
pass
pass
def main_train(args):
# 获取命令参数
if args.resume_training is not None:
if not os.path.isfile(args.resume_training):
print(f"{args.resume_training} 不是一个合法的文件!")
return
else:
print(f"加载检查点:{args.resume_training}")
cuda = args.cuda
resume = args.resume_training
batch_size = args.batch_size
milestones = args.milestones
lr = args.lr
total_epoch = args.epochs
resume_checkpoint_filename = args.resume_training
best_model_name = args.best_model_name
checkpoint_name = args.best_model_name
data_path = args.data_path
start_epoch = 1
print("加载数据....")
dataset = ISONetData(data_path=data_path)
dataset_test = ISONetData(data_path=data_path, train=False)
data_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=6,
pin_memory=True)
data_loader_test = DataLoader(dataset=dataset_test,
batch_size=batch_size,
shuffle=False)
print("成功加载数据...")
print(f"训练集数量: {len(dataset)}")
print(f"验证集数量: {len(dataset_test)}")
model_path = Path("models")
checkpoint_path = model_path.joinpath("checkpoint")
if not model_path.exists():
model_path.mkdir()
if not checkpoint_path.exists():
checkpoint_path.mkdir()
if torch.cuda.is_available():
device = torch.cuda.current_device()
else:
print("cuda 无效!")
cuda = False
net = ISONet()
criterion = nn.MSELoss(reduction="mean")
optimizer = optim.Adam(net.parameters(), lr=lr)
if cuda:
net = net.to(device=device)
criterion = criterion.to(device=device)
scheduler = MultiStepLR(optimizer=optimizer,
milestones=milestones,
gamma=0.1)
writer = SummaryWriter()
# 恢复训练
if resume:
print("恢复训练中...")
checkpoint = torch.load(
checkpoint_path.joinpath(resume_checkpoint_filename))
net.load_state_dict(checkpoint["net"])
optimizer.load_state_dict((checkpoint["optimizer"]))
scheduler.load_state_dict(checkpoint["scheduler"])
resume_epoch = checkpoint["epoch"]
best_test_loss = checkpoint["best_test_loss"]
start_epoch = resume_epoch + 1
print(f"从第[{start_epoch}]轮开始训练...")
print(f"上一次的损失为: [{best_test_loss}]...")
else:
# 初始化权重
for m in net.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.bias, 0)
if not locals().get("best_test_loss"):
best_test_loss = 0
record = 0
for epoch in range(start_epoch, total_epoch):
print(f"开始第 [{epoch}] 轮训练...")
net.train()
writer.add_scalar("Train/Learning Rate",
scheduler.get_last_lr()[0], epoch)
for i, (data, label) in enumerate(data_loader, 0):
if i == 0:
start_time = int(time.time())
if cuda:
data = data.to(device=device)
label = label.to(device=device)
label = label.unsqueeze(1)
optimizer.zero_grad()
output = net(data)
loss = criterion(output, label)
loss.backward()
optimizer.step()
if i % 500 == 499:
end_time = int(time.time())
use_time = end_time - start_time
print(
f">>> epoch[{epoch}] loss[{loss:.4f}] {i * batch_size}/{len(dataset)} lr{scheduler.get_last_lr()} ",
end="")
left_time = ((len(dataset) - i * batch_size) / 500 /
batch_size) * (end_time - start_time)
print(
f"耗费时间：[{end_time - start_time:.2f}]秒,估计剩余时间: [{left_time:.2f}]秒"
)
start_time = end_time
# 记录到 tensorboard
if i % 128 == 127:
writer.add_scalar("Train/loss", loss, record)
record += 1
# validate
print("测试模型...")
net.eval()
test_loss = 0
with torch.no_grad():
loss_t = nn.MSELoss(reduction="mean")
if cuda:
loss_t = loss_t.to(device)
for data, label in data_loader_test:
if cuda:
data = data.to(device)
label = label.to(device)
# expand dim
label = label.unsqueeze_(1)
predict = net(data)
# sum up batch loss
test_loss += loss_t(predict, label).item()
test_loss /= len(dataset_test)
test_loss *= batch_size
print(
f'\nTest Data: Average batch[{batch_size}] loss: {test_loss:.4f}\n'
)
scheduler.step()
writer.add_scalar("Test/Loss", test_loss, epoch)
checkpoint = {
"net": net.state_dict(),
"optimizer": optimizer.state_dict(),
"epoch": epoch,
"scheduler": scheduler.state_dict(),
"best_test_loss": best_test_loss
}
if best_test_loss == 0:
print("保存模型中...")
torch.save(net.state_dict(), model_path.joinpath(best_model_name))
best_test_loss = test_loss
else:
# 保存更好的模型
if test_loss < best_test_loss:
print("获取到更好的模型,保存中...")
torch.save(net.state_dict(),
model_path.joinpath(best_model_name))
best_test_loss = test_loss
# 保存检查点
if epoch % args.save_every_epochs == 0:
c_time = time2str()
torch.save(
checkpoint,
checkpoint_path.joinpath(
f"{checkpoint_name}_{epoch}_{c_time}.cpth"))
print(f"保存检查点: [{checkpoint_name}_{epoch}_{c_time}.cpth]...\n")
class Runner(object):
def __init__(self):
self.best_accuracy = 0.0
# all data
self.data_train = CIFARDataset.get_data_all(Config.data_root)
self.task_train = CIFARDataset(self.data_train, Config.num_way,
Config.num_shot)
self.task_train_loader = DataLoader(self.task_train,
Config.batch_size,
True,
num_workers=Config.num_workers)
# model
self.matching_net = RunnerTool.to_cuda(Config.matching_net)
RunnerTool.to_cuda(self.matching_net.apply(RunnerTool.weights_init))
self.norm = Normalize(2)
# loss
self.loss = RunnerTool.to_cuda(nn.MSELoss())
# optim
self.matching_net_optim = torch.optim.Adam(
self.matching_net.parameters(), lr=Config.learning_rate)
self.matching_net_scheduler = MultiStepLR(self.matching_net_optim,
Config.train_epoch_lr,
gamma=0.5)
self.test_tool = FSLTestTool(self.matching_test,
data_root=Config.data_root,
num_way=Config.num_way_test,
num_shot=Config.num_shot,
episode_size=Config.episode_size,
test_episode=Config.test_episode,
transform=self.task_train.transform_test)
pass
def load_model(self):
if os.path.exists(Config.mn_dir):
self.matching_net.load_state_dict(torch.load(Config.mn_dir))
Tools.print("load proto net success from {}".format(Config.mn_dir),
txt_path=Config.log_file)
pass
def matching(self, task_data):
data_batch_size, data_image_num, data_num_channel, data_width, data_weight = task_data.shape
data_x = task_data.view(-1, data_num_channel, data_width, data_weight)
net_out = self.matching_net(data_x)
z = net_out.view(data_batch_size, data_image_num, -1)
# 特征
z_support, z_query = z.split(Config.num_shot * Config.num_way, dim=1)
z_batch_size, z_num, z_dim = z_support.shape
z_support = z_support.view(z_batch_size,
Config.num_way * Config.num_shot, z_dim)
z_query_expand = z_query.expand(z_batch_size,
Config.num_way * Config.num_shot,
z_dim)
# 相似性
z_support = self.norm(z_support)
similarities = torch.sum(z_support * z_query_expand, -1)
similarities = torch.softmax(similarities, dim=1)
similarities = similarities.view(z_batch_size, Config.num_way,
Config.num_shot)
predicts = torch.mean(similarities, dim=-1)
return predicts
def matching_test(self, samples, batches):
batch_num, _, _, _ = batches.shape
sample_z = self.matching_net(samples) # 5x64*5*5
batch_z = self.matching_net(batches) # 75x64*5*5
z_support = sample_z.view(Config.num_way_test * Config.num_shot, -1)
z_query = batch_z.view(batch_num, -1)
_, z_dim = z_query.shape
z_support_expand = z_support.unsqueeze(0).expand(
batch_num, Config.num_way_test * Config.num_shot, z_dim)
z_query_expand = z_query.unsqueeze(1).expand(
batch_num, Config.num_way_test * Config.num_shot, z_dim)
# 相似性
z_support_expand = self.norm(z_support_expand)
similarities = torch.sum(z_support_expand * z_query_expand, -1)
similarities = torch.softmax(similarities, dim=1)
similarities = similarities.view(batch_num, Config.num_way_test,
Config.num_shot)
predicts = torch.mean(similarities, dim=-1)
return predicts
def train(self):
Tools.print()
Tools.print("Training...", txt_path=Config.log_file)
for epoch in range(1, 1 + Config.train_epoch):
self.matching_net.train()
Tools.print()
all_loss = 0.0
for task_data, task_labels, task_index in tqdm(
self.task_train_loader):
task_data, task_labels = RunnerTool.to_cuda(
task_data), RunnerTool.to_cuda(task_labels)
# 1 calculate features
predicts = self.matching(task_data)
# 2 loss
loss = self.loss(predicts, task_labels)
all_loss += loss.item()
# 3 backward
self.matching_net.zero_grad()
loss.backward()
self.matching_net_optim.step()
###########################################################################
pass
###########################################################################
# print
Tools.print("{:6} loss:{:.3f} lr:{}".format(
epoch, all_loss / len(self.task_train_loader),
self.matching_net_scheduler.get_last_lr()),
txt_path=Config.log_file)
self.matching_net_scheduler.step()
###########################################################################
###########################################################################
# Val
if epoch % Config.val_freq == 0:
Tools.print()
Tools.print("Test {} {} .......".format(
epoch, Config.model_name),
txt_path=Config.log_file)
self.matching_net.eval()
val_accuracy = self.test_tool.val(episode=epoch,
is_print=True,
has_test=False)
if val_accuracy > self.best_accuracy:
self.best_accuracy = val_accuracy
torch.save(self.matching_net.state_dict(), Config.mn_dir)
Tools.print("Save networks for epoch: {}".format(epoch),
txt_path=Config.log_file)
pass
pass
###########################################################################
pass
pass
pass
class Trainer:
def __init__(self, cfg):
self.cfg = cfg
self.paths = cfg['paths']
self.net_params = cfg['net']
self.train_params = cfg['train']
self.trans_params = cfg['train']['transforms']
self.checkpoints = self.paths['checkpoints']
Path(self.checkpoints).mkdir(parents=True, exist_ok=True)
shutil.copyfile('config.yaml', f'{self.checkpoints}/config.yaml')
self.update_interval = self.paths['update_interval']
# amp training
self.use_amp = self.train_params['mixed_precision']
self.scaler = GradScaler() if self.use_amp else None
# data setup
dataset_name = self.train_params['dataset']
self.use_multi = dataset_name == 'multi'
print(f'Using dataset: {dataset_name}')
self.train_dataset = get_pedestrian_dataset(
dataset_name,
self.paths,
augment=get_train_transforms(self.trans_params),
mode='train',
multi_datasets=self.train_params['multi_datasets']
if self.use_multi else None)
print(f'Train dataset: {len(self.train_dataset)} samples')
self.val_dataset = get_pedestrian_dataset(
dataset_name,
self.paths,
augment=get_val_transforms(self.trans_params),
mode='val',
multi_datasets=self.train_params['multi_datasets']
if self.use_multi else None)
print(f'Val dataset: {len(self.val_dataset)} samples')
tests_data = self.train_params['test_datasets']
self.test_datasets = [
get_pedestrian_dataset(d_name,
self.paths,
augment=get_test_transforms(
self.trans_params),
mode='test') for d_name in tests_data
]
self.criterion = AnchorFreeLoss(self.train_params)
self.writer = Writer(self.paths['log_dir'])
print('Tensorboard logs are saved to: {}'.format(
self.paths['log_dir']))
self.sched_type = self.train_params['scheduler']
self.scheduler = None
self.optimizer = None
def save_checkpoints(self, epoch, net):
path = osp.join(self.checkpoints, f'Epoch_{epoch}.pth')
torch.save(
{
'epoch': epoch,
'net_state_dict': net.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict()
}, path)
def train(self):
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
batch_size = self.train_params['batch_size']
self.batch_size = batch_size
num_workers = self.train_params['num_workers']
pin_memory = self.train_params['pin_memory']
print('Batch-size = {}'.format(batch_size))
train_loader = DataLoader(self.train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=pin_memory,
drop_last=True)
val_loader = DataLoader(self.val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory,
drop_last=False)
# net setup
print('Preparing net: ')
net = get_fpn_net(self.net_params)
# train setup
lr = self.train_params['lr']
epochs = self.train_params['epochs']
weight_decay = self.train_params['weight_decay']
self.optimizer = optim.Adam(net.parameters(),
lr=lr,
weight_decay=weight_decay,
eps=1e-4)
if self.net_params['pretrained']:
checkpoint = torch.load(self.net_params['pretrained_model'],
map_location="cuda")
net.load_state_dict(checkpoint['net_state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
for p in self.optimizer.param_groups:
p['lr'] = lr
for state in self.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
print('CHECKPOINT LOADED')
net.cuda()
first_epoch = 0
# scheduler
if self.sched_type == 'ocp':
last_epoch = -1 if first_epoch == 0 else first_epoch * len(
train_loader)
self.scheduler = OneCycleLR(
self.optimizer,
max_lr=lr,
epochs=epochs,
last_epoch=last_epoch,
steps_per_epoch=len(train_loader),
pct_start=self.train_params['ocp_params']['max_lr_pct'])
elif self.sched_type == 'multi_step':
last_epoch = -1 if first_epoch == 0 else first_epoch
self.scheduler = MultiStepLR(
self.optimizer,
milestones=self.train_params['multi_params']['milestones'],
gamma=self.train_params['multi_params']['gamma'],
last_epoch=last_epoch)
#start training
net.train()
val_rate = self.train_params['val_rate']
test_rate = self.train_params['test_rate']
for epoch in range(first_epoch, epochs):
self.train_epoch(net, train_loader, epoch)
if self.sched_type != 'ocp':
self.writer.log_lr(epoch, self.scheduler.get_last_lr()[0])
self.scheduler.step()
if (epoch + 1) % val_rate == 0 or epoch == epochs - 1:
self.eval(net, val_loader, epoch * len(train_loader))
if (epoch + 1) % (val_rate *
test_rate) == 0 or epoch == epochs - 1:
self.test_ap(net, epoch)
self.save_checkpoints(epoch, net)
def train_epoch(self, net, loader, epoch):
net.train()
loss_metric = LossMetric(self.cfg)
probs = ProbsAverageMeter()
for mini_batch_i, read_mini_batch in tqdm(enumerate(loader),
desc=f'Epoch {epoch}:',
ascii=True,
total=len(loader)):
data, labels = read_mini_batch
data = data.cuda()
labels = [label.cuda() for label in labels]
with amp.autocast():
out = net(data)
loss_dict, hm_probs = self.criterion(out, labels)
loss = loss_metric.calculate_loss(loss_dict)
self.optimizer.zero_grad()
self.scaler.scale(loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
probs.update(hm_probs)
if self.sched_type == 'ocp':
self.scheduler.step()
loss_metric.add_sample(loss_dict)
if mini_batch_i % self.update_interval == 0:
if self.sched_type == 'ocp':
# TODO write average lr
self.writer.log_lr(epoch * len(loader) + mini_batch_i,
self.scheduler.get_last_lr()[0])
self.writer.log_training(epoch * len(loader) + mini_batch_i,
loss_metric)
self.writer.log_probs(epoch, probs.get_average())
def eval(self, net, loader, step):
net.eval()
loss_metric = LossMetric(self.cfg)
with torch.no_grad():
for _, read_mini_batch in tqdm(enumerate(loader),
desc=f'Val:',
ascii=True,
total=len(loader)):
data, labels = read_mini_batch
data = data.cuda()
labels = [label.cuda() for label in labels]
with amp.autocast():
out = net(data)
loss_dict, _ = self.criterion(out, labels)
loss_metric.add_sample(loss_dict)
self.writer.log_eval(step, loss_metric)
def test_ap(self, net, epoch):
for dataset in self.test_datasets:
ap, _ = test(net, dataset, batch_size=self.batch_size)
self.writer.log_ap(epoch, ap, dataset.name())
def train_model(config):
# Define hyper-parameters.
depth = int(config["DnCNN"]["depth"])
n_channels = int(config["DnCNN"]["n_channels"])
img_channel = int(config["DnCNN"]["img_channel"])
kernel_size = int(config["DnCNN"]["kernel_size"])
use_bnorm = config.getboolean("DnCNN", "use_bnorm")
epochs = int(config["DnCNN"]["epoch"])
batch_size = int(config["DnCNN"]["batch_size"])
train_data_dir = config["DnCNN"]["train_data_dir"]
test_data_dir = config["DnCNN"]["test_data_dir"]
eta_min = float(config["DnCNN"]["eta_min"])
eta_max = float(config["DnCNN"]["eta_max"])
dose = float(config["DnCNN"]["dose"])
model_save_dir = config["DnCNN"]["model_save_dir"]
train_mode = config["DnCNN"]["train_mode"]
log_file_name = config["DnCNN"]["log_file_name"]
# Save logs to txt file.
log_dir = config["DnCNN"]["log_dir"]
if train_mode == "residual":
log_dir = Path(log_dir) / "res_learning_smaller_lr_dose{}".format(str(int(dose * 100)))
elif train_mode == "direct":
log_dir = Path(log_dir) / "direct_predict_smaller_lr_dose{}".format(str(int(dose * 100)))
log_file = log_dir / log_file_name
# Define device.
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Initiate a DnCNN instance.
# Load the model to device and set the model to training.
model = DnCNN(depth=depth, n_channels=n_channels,
img_channel=img_channel,
use_bnorm=use_bnorm,
kernel_size=kernel_size)
model = model.to(device)
model.train()
# Define loss criterion and optimizer
optimizer = optim.Adam(model.parameters(), lr=5e-5)
scheduler = MultiStepLR(optimizer, milestones=[20, 40, 60], gamma=0.2)
criterion = LossFunc(reduction="mean", weight_mse=1, weight_nll=0, total_dose=dose * 100)
# Get a validation test set and corrupt with noise for validation performance.
# For every epoch, use this pre-determined noisy images.
test_file_list = glob.glob(test_data_dir + "/*.png")
xs_test = []
# Can't directly convert the xs_test from list to ndarray because some images are 512*512
# while the rest are 256*256.
for i in range(len(test_file_list)):
img = cv2.imread(test_file_list[i], 0)
img = np.array(img, dtype="float32") / 255.0
img = np.expand_dims(img, axis=0)
img_noisy, _ = nm(img, eta_min, eta_max, dose, t=100)
xs_test.append((img_noisy, img))
# Get a validation train set and corrupt with noise.
# For every epoch, use this pre-determined noisy images to see the training performance.
train_file_list = glob.glob(train_data_dir + "/*png")
xs_train = []
for i in range(len(train_file_list)):
img = cv2.imread(train_file_list[i], 0)
img = np.array(img, dtype="float32") / 255.0
img = np.expand_dims(img, axis=0)
img_noisy, _ = nm(img, eta_min, eta_max, dose, t=100)
xs_train.append((img_noisy, img))
# Train the model.
loss_store = []
epoch_loss_store = []
psnr_store = []
ssim_store = []
psnr_tr_store = []
ssim_tr_store = []
for epoch in range(epochs):
# For each epoch, generate clean augmented patches from the training directory.
# Convert the data from uint8 to float32 then scale them to make it in [0, 1].
# Then make the patches to be of shape [N, C, H, W],
# where N is the batch size, C is the number of color channels.
# H and W are height and width of image patches.
xs = dg.datagenerator(data_dir=train_data_dir)
xs = xs.astype("float32") / 255.0
xs = torch.from_numpy(xs.transpose((0, 3, 1, 2)))
train_set = dg.DenoisingDatatset(xs, eta_min, eta_max, dose)
train_loader = DataLoader(dataset=train_set, num_workers=4,
drop_last=True, batch_size=batch_size,
shuffle=True) # TODO: if drop_last=True, the dropping in the
# TODO: data_generator is not necessary?
# train_loader_test = next(iter(train_loader))
t_start = timer()
epoch_loss = 0
for idx, data in enumerate(train_loader):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs, mode=train_mode)
loss = criterion(outputs, labels)
loss_store.append(loss.item())
epoch_loss += loss.item()
loss.backward()
optimizer.step()
if idx % 100 == 0:
print("Epoch [{} / {}], step [{} / {}], loss = {:.5f}, lr = {:.8f}, elapsed time = {:.2f}s".format(
epoch + 1, epochs, idx, len(train_loader), loss.item(), *scheduler.get_last_lr(), timer()-t_start))
epoch_loss_store.append(epoch_loss / len(train_loader))
# At each epoch validate the result.
model = model.eval()
# Firstly validate on training sets. This takes a long time so I commented.
tr_psnr = []
tr_ssim = []
# t_start = timer()
with torch.no_grad():
for idx, test_data in enumerate(xs_train):
inputs, labels = test_data
inputs = np.expand_dims(inputs, axis=0)
inputs = torch.from_numpy(inputs).to(device)
labels = labels.squeeze()
outputs = model(inputs, mode=train_mode)
outputs = outputs.squeeze().cpu().detach().numpy()
tr_psnr.append(peak_signal_noise_ratio(labels, outputs))
tr_ssim.append(structural_similarity(outputs, labels))
psnr_tr_store.append(sum(tr_psnr) / len(tr_psnr))
ssim_tr_store.append(sum(tr_ssim) / len(tr_ssim))
# print("Elapsed time = {}".format(timer() - t_start))
print("Validation on train set: epoch [{} / {}], aver PSNR = {:.2f}, aver SSIM = {:.4f}".format(
epoch + 1, epochs, psnr_tr_store[-1], ssim_tr_store[-1]))
# Validate on test set
val_psnr = []
val_ssim = []
with torch.no_grad():
for idx, test_data in enumerate(xs_test):
inputs, labels = test_data
inputs = np.expand_dims(inputs, axis=0)
inputs = torch.from_numpy(inputs).to(device)
labels = labels.squeeze()
outputs = model(inputs, mode=train_mode)
outputs = outputs.squeeze().cpu().detach().numpy()
val_psnr.append(peak_signal_noise_ratio(labels, outputs))
val_ssim.append(structural_similarity(outputs, labels))
psnr_store.append(sum(val_psnr) / len(val_psnr))
ssim_store.append(sum(val_ssim) / len(val_ssim))
print("Validation on test set: epoch [{} / {}], aver PSNR = {:.2f}, aver SSIM = {:.4f}".format(
epoch + 1, epochs, psnr_store[-1], ssim_store[-1]))
# Set model to train mode again.
model = model.train()
scheduler.step()
# Save model
save_model(model, model_save_dir, epoch, dose * 100, train_mode)
# Save the loss and validation PSNR, SSIM.
if not log_dir.exists():
Path.mkdir(log_dir)
with open(log_file, "a+") as fh:
fh.write("{} Epoch [{} / {}], loss = {:.6f}, train PSNR = {:.2f}dB, train SSIM = {:.4f}, "
"validation PSNR = {:.2f}dB, validation SSIM = {:.4f}\n".format(
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S:"),
epoch + 1, epochs, epoch_loss_store[-1],
psnr_tr_store[-1], ssim_tr_store[-1],
psnr_store[-1], ssim_store[-1]))
class ParameterServer(object):
def __init__(self, i, args, model, bounded_delay):
self.ps_index = i
self.net = model.cuda()
self.optimizer = optim.SGD(self.net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
self.sched = MultiStepLR(self.optimizer, [70], gamma=0.1) # TODO
self.criterion = nn.CrossEntropyLoss()
self.args = args
self.num_of_workers = args.world_size
self.num_of_groups = 4
self.worker_per_group = 4
self.push_num = [0] * self.worker_per_group
self.pss = []
self.bounded_delay = bounded_delay
self.local_iter_list = self.worker_per_group * [0]
self.sum_gradients = []
sys.stdout = open(f'{self.args.stdout}/ps{self.ps_index:02}_stdout.log', 'a+', 1)
sys.stderr = open(f'{self.args.stdout}/ps{self.ps_index:02}_stdout.log', 'a+', 1)
self.epoch_timelist = [[-1] * self.num_of_groups for i in
range(self.worker_per_group)] # timelist of all workers
self.epoch_grouplist = [-1] * self.num_of_groups # mean iteration time of the group
self.initial_ratio = 0.4
self.comp_ratio = [0.4, 0.4, 0.4, 0, 4]
def init_pss(self, ps0, ps1, ps2):
self.pss = [ps0, ps1, ps2]
def desparsify(self, values, indices, ctx):
# values, indices = tensors
numel, shape = ctx
tensor_decompressed = torch.zeros(numel, dtype=values.dtype, layout=values.layout, device=values.device)
tensor_decompressed.scatter_(0, indices.long(), values)
# print("after shape,",indices.long().dtype())
return tensor_decompressed.view(shape)
def apply_gradients(self, *gradients):
total = 0
for ele in range(0, len(self.push_num)):
total = total + self.push_num[ele]
degradients = []
for tensors in gradients:
for values, indices, ctx in tensors:
afterdesparsify = self.desparsify(values, indices, ctx)
degradients.append(afterdesparsify)
summed_gradients = [
torch.stack(gradient_zip).sum(dim=0) / self.args.world_size
for gradient_zip in zip(degradients)
]
self.optimizer.zero_grad()
self.net.set_gradients(summed_gradients)
self.optimizer.step()
if total == 0:
self.sum_gradients = summed_gradients
if total % 4 == 0 and total != 0:
self.sum_gradients = [
torch.stack(gradient_zip).sum(dim=0)
for gradient_zip in zip(self.sum_gradients, summed_gradients)
]
for i in range(3):
self.pss[i].ps_syn.remote(*(self.sum_gradients))
self.sum_gradients.clear()
elif total % 4 == 1:
# self.sum_gradients.append(gradients)
self.sum_gradients = summed_gradients
# print("sumgradi",summed_gradients)
else:
self.sum_gradients = [
torch.stack(gradient_zip).sum(dim=0)
for gradient_zip in zip(self.sum_gradients, summed_gradients)
]
def apply_allgradients(self, *gradients):
summed_gradients = [
torch.stack(gradient_zip).sum(dim=0) / self.args.world_size
for gradient_zip in zip(*gradients)
]
self.optimizer.zero_grad()
self.net.set_gradients(summed_gradients)
self.optimizer.step()
for i in range(3):
self.pss[i].ps_allsyn.remote(*gradients)
def ps_allsyn(self, *gradients):
summed_gradients = [
torch.stack(gradient_zip).sum(dim=0) / self.args.world_size
for gradient_zip in zip(*gradients)
]
self.optimizer.zero_grad()
self.net.set_gradients(summed_gradients)
self.optimizer.step()
# return self.net.get_weights()
def ps_syn(self, summed_gradients):
self.optimizer.zero_grad()
self.net.set_gradients(summed_gradients)
self.optimizer.step()
def get_weights(self):
return {k: v.cuda() for k, v in self.net.state_dict().items() if 'weight' in k or 'bias' in k}
def lr_sched(self):
self.sched.step()
print(self.sched.get_last_lr()[0])
def blocked(self, worker_index, local_iter):
self.local_iter_list[int(worker_index / 4)] = local_iter
min_iter = min(self.local_iter_list)
return local_iter > min_iter + self.bounded_delay
def get_time(self, worker_index, epoch_time): # decide the fastest or the slowest
self.epoch_timelist[worker_index % 4][int(worker_index / 4)] = epoch_time
workercount = 0
sumtime = 0
for i in range(4):
# some workers haven't finish 1 epoch
if self.epoch_timelist[i].count(-1) != 0:
return -1
for i in range(4):
workercount += 1
sumtime += self.epoch_timelist[worker_index % 4][i]
self.epoch_grouplist[worker_index % 4] = sumtime / workercount
if self.epoch_grouplist[worker_index % 4] == max(self.epoch_grouplist):
return 1
elif self.epoch_grouplist[worker_index % 4] == min(self.epoch_grouplist):
return 0
else:
return 2
def train_model(config):
# Define hyper-parameters.
depth = int(config["DnCNN"]["depth"])
n_channels = int(config["DnCNN"]["n_channels"])
img_channel = int(config["DnCNN"]["img_channel"])
kernel_size = int(config["DnCNN"]["kernel_size"])
use_bnorm = config.getboolean("DnCNN", "use_bnorm")
epochs = int(config["DnCNN"]["epoch"])
batch_size = int(config["DnCNN"]["batch_size"])
train_data_dir = config["DnCNN"]["train_data_dir"]
test_data_dir = config["DnCNN"]["test_data_dir"]
eta_min = float(config["DnCNN"]["eta_min"])
eta_max = float(config["DnCNN"]["eta_max"])
dose = float(config["DnCNN"]["dose"])
model_save_dir = config["DnCNN"]["model_save_dir"]
# Save logs to txt file.
log_dir = config["DnCNN"]["log_dir"]
log_dir = Path(log_dir) / "dose{}".format(str(int(dose * 100)))
log_file = log_dir / "train_result.txt"
# Define device.
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Initiate a DnCNN instance.
# Load the model to device and set the model to training.
model = DnCNN(depth=depth, n_channels=n_channels,
img_channel=img_channel,
use_bnorm=use_bnorm,
kernel_size=kernel_size)
model = model.to(device)
model.train()
# Define loss criterion and optimizer
optimizer = optim.Adam(model.parameters(), lr=1e-3)
scheduler = MultiStepLR(optimizer, milestones=[30, 60, 90], gamma=0.2)
criterion = LossFunc(reduction="mean")
# Get a validation test set and corrupt with noise for validation performance.
# For every epoch, use this pre-determined noisy images.
test_file_list = glob.glob(test_data_dir + "/*.png")
xs_test = []
# Can't directly convert the xs_test from list to ndarray because some images are 512*512
# while the rest are 256*256.
for i in range(len(test_file_list)):
img = cv2.imread(test_file_list[i], 0)
img = np.array(img, dtype="float32") / 255.0
img = np.expand_dims(img, axis=0)
img_noisy, _ = nm(img, eta_min, eta_max, dose, t=100)
xs_test.append((img_noisy, img))
# Train the model.
loss_store = []
epoch_loss_store = []
psnr_store = []
ssim_store = []
psnr_tr_store = []
ssim_tr_store = []
loss_mse = torch.nn.MSELoss()
dtype = torch.cuda.FloatTensor
# load vgg network
vgg = Vgg16().type(dtype)
for epoch in range(epochs):
# For each epoch, generate clean augmented patches from the training directory.
# Convert the data from uint8 to float32 then scale them to make it in [0, 1].
# Then make the patches to be of shape [N, C, H, W],
# where N is the batch size, C is the number of color channels.
# H and W are height and width of image patches.
xs = dg.datagenerator(data_dir=train_data_dir)
xs = xs.astype("float32") / 255.0
xs = torch.from_numpy(xs.transpose((0, 3, 1, 2)))
train_set = dg.DenoisingDatatset(xs, eta_min, eta_max, dose)
train_loader = DataLoader(dataset=train_set, num_workers=4,
drop_last=True, batch_size=batch_size,
shuffle=True) # TODO: if drop_last=True, the dropping in the
# TODO: data_generator is not necessary?
# train_loader_test = next(iter(train_loader))
t_start = timer()
epoch_loss = 0
for idx, data in enumerate(train_loader):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
img_batch_read = len(inputs)
optimizer.zero_grad()
outputs = model(inputs)
# We can use labels for both style and content image
# style image
# style_transform = transforms.Compose([
# normalize_tensor_transform() # normalize with ImageNet values
# ])
# labels_t = style_transform(labels)
labels_t = labels.repeat(1, 3, 1, 1)
outputs_t = outputs.repeat(1, 3, 1, 1)
y_c_features = vgg(labels_t)
style_gram = [gram(fmap) for fmap in y_c_features]
y_hat_features = vgg(outputs_t)
y_hat_gram = [gram(fmap) for fmap in y_hat_features]
# calculate style loss
style_loss = 0.0
for j in range(4):
style_loss += loss_mse(y_hat_gram[j], style_gram[j][:img_batch_read])
style_loss = STYLE_WEIGHT*style_loss
aggregate_style_loss = style_loss
# calculate content loss (h_relu_2_2)
recon = y_c_features[1]
recon_hat = y_hat_features[1]
content_loss = CONTENT_WEIGHT*loss_mse(recon_hat, recon)
aggregate_content_loss = content_loss
loss = aggregate_content_loss + aggregate_style_loss
# loss = criterion(outputs, labels)
loss_store.append(loss.item())
epoch_loss += loss.item()
loss.backward()
optimizer.step()
if idx % 100 == 0:
print("Epoch [{} / {}], step [{} / {}], loss = {:.5f}, lr = {:.6f}, elapsed time = {:.2f}s".format(
epoch + 1, epochs, idx, len(train_loader), loss.item(), *scheduler.get_last_lr(), timer()-t_start))
epoch_loss_store.append(epoch_loss / len(train_loader))
# At each epoch validate the result.
model = model.eval()
# # Firstly validate on training sets. This takes a long time so I commented.
# tr_psnr = []
# tr_ssim = []
# # t_start = timer()
# with torch.no_grad():
# for idx, train_data in enumerate(train_loader):
# inputs, labels = train_data
# # print(inputs.shape)
# # inputs = np.expand_dims(inputs, axis=0)
# # inputs = torch.from_numpy(inputs).to(device)
# inputs = inputs.to(device)
# labels = labels.squeeze().numpy()
#
# outputs = model(inputs)
# outputs = outputs.squeeze().cpu().detach().numpy()
#
# tr_psnr.append(peak_signal_noise_ratio(labels, outputs))
# tr_ssim.append(structural_similarity(outputs, labels))
# psnr_tr_store.append(sum(tr_psnr) / len(tr_psnr))
# ssim_tr_store.append(sum(tr_ssim) / len(tr_ssim))
# # print("Elapsed time = {}".format(timer() - t_start))
#
# print("Validation on train set: epoch [{} / {}], aver PSNR = {:.2f}, aver SSIM = {:.4f}".format(
# epoch + 1, epochs, psnr_tr_store[-1], ssim_tr_store[-1]))
# Validate on test set
val_psnr = []
val_ssim = []
with torch.no_grad():
for idx, test_data in enumerate(xs_test):
inputs, labels = test_data
inputs = np.expand_dims(inputs, axis=0)
inputs = torch.from_numpy(inputs).to(device)
labels = labels.squeeze()
outputs = model(inputs)
outputs = outputs.squeeze().cpu().detach().numpy()
val_psnr.append(peak_signal_noise_ratio(labels, outputs))
val_ssim.append(structural_similarity(outputs, labels))
psnr_store.append(sum(val_psnr) / len(val_psnr))
ssim_store.append(sum(val_ssim) / len(val_ssim))
print("Validation on test set: epoch [{} / {}], aver PSNR = {:.2f}, aver SSIM = {:.4f}".format(
epoch + 1, epochs, psnr_store[-1], ssim_store[-1]))
# Set model to train mode again.
model = model.train()
scheduler.step()
# Save model
save_model(model, model_save_dir, epoch, dose * 100)
# Save the loss and validation PSNR, SSIM.
if not log_dir.exists():
Path.mkdir(log_dir)
with open(log_file, "a+") as fh:
# fh.write("{} Epoch [{} / {}], loss = {:.6f}, train PSNR = {:.2f}dB, train SSIM = {:.4f}, "
# "validation PSNR = {:.2f}dB, validation SSIM = {:.4f}".format(
# datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S:"),
# epoch + 1, epochs, epoch_loss_store[-1],
# psnr_tr_store[-1], ssim_tr_store[-1],
# psnr_store[-1], ssim_store[-1]))
fh.write("{} Epoch [{} / {}], loss = {:.6f}, "
"validation PSNR = {:.2f}dB, validation SSIM = {:.4f}\n".format(
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S:"),
epoch + 1, epochs, epoch_loss_store[-1],
psnr_store[-1], ssim_store[-1]))
# np.savetxt(log_file, np.hstack((epoch + 1, epoch_loss_store[-1], psnr_store[-1], ssim_store[-1])), fmt="%.6f", delimiter=", ")
fig, ax = plt.subplots()
ax.plot(loss_store[-len(train_loader):])
ax.set_title("Last 1862 losses")
ax.set_xlabel("iteration")
fig.show()
def train_model_residual_lowlight_twostage_unet():
learning_rate = INIT_LEARNING_RATE * 0.5
device = DEVICE
#准备数据
train_set = HsiCubicTrainDataset('./data/train_lowlight/')
print('total training example:', len(train_set))
train_loader = DataLoader(dataset=train_set,
batch_size=BATCH_SIZE,
shuffle=True)
#加载测试label数据
mat_src_path = './data/test_lowlight/origin/soup_bigcorn_orange_1ms.mat'
test_label_hsi = scio.loadmat(mat_src_path)['label']
#加载测试数据
batch_size = 1
test_data_dir = './data/test_lowlight/cubic/'
test_set = HsiCubicLowlightTestDataset(test_data_dir)
test_dataloader = DataLoader(dataset=test_set,
batch_size=batch_size,
shuffle=False)
batch_size, channel, width, height = next(iter(test_dataloader))[0].shape
band_num = len(test_dataloader)
denoised_hsi = np.zeros((width, height, band_num))
#创建模型
net = TwoStageHSIDWithUNet(K)
init_params(net)
#net = nn.DataParallel(net).to(device)
net = net.to(device)
#创建优化器
#hsid_optimizer = optim.Adam(net.parameters(), lr=INIT_LEARNING_RATE, betas=(0.9, 0,999))
hsid_optimizer = optim.Adam(net.parameters(), lr=learning_rate)
scheduler = MultiStepLR(hsid_optimizer, milestones=[40, 60, 80], gamma=0.1)
#定义loss 函数
#criterion = nn.MSELoss()
global tb_writer
tb_writer = get_summary_writer(log_dir='logs')
gen_epoch_loss_list = []
cur_step = 0
first_batch = next(iter(train_loader))
for epoch in range(NUM_EPOCHS):
scheduler.step()
print(epoch, 'lr={:.6f}'.format(scheduler.get_last_lr()[0]))
gen_epoch_loss = 0
net.train()
#for batch_idx, (noisy, label) in enumerate([first_batch] * 300):
for batch_idx, (noisy, cubic, label) in enumerate(train_loader):
#print('batch_idx=', batch_idx)
noisy = noisy.to(device)
label = label.to(device)
cubic = cubic.to(device)
hsid_optimizer.zero_grad()
#denoised_img = net(noisy, cubic)
#loss = loss_fuction(denoised_img, label)
residual, residual_stage2 = net(noisy, cubic)
loss = loss_function_with_tvloss(
residual, label - noisy) + loss_function_with_tvloss(
residual_stage2, label - noisy)
loss.backward() # calcu gradient
hsid_optimizer.step() # update parameter
gen_epoch_loss += loss.item()
if cur_step % display_step == 0:
if cur_step > 0:
print(
f"Epoch {epoch}: Step {cur_step}: Batch_idx {batch_idx}: MSE loss: {loss.item()}"
)
else:
print("Pretrained initial state")
tb_writer.add_scalar("MSE loss", loss.item(), cur_step)
#step ++,每一次循环，每一个batch的处理，叫做一个step
cur_step += 1
gen_epoch_loss_list.append(gen_epoch_loss)
tb_writer.add_scalar("mse epoch loss", gen_epoch_loss, epoch)
#scheduler.step()
#print("Decaying learning rate to %g" % scheduler.get_last_lr()[0])
torch.save(
{
'gen': net.state_dict(),
'gen_opt': hsid_optimizer.state_dict(),
}, f"checkpoints/two_stage_unet_hsid_{epoch}.pth")
#测试代码
net.eval()
for batch_idx, (noisy_test, cubic_test,
label_test) in enumerate(test_dataloader):
noisy_test = noisy_test.type(torch.FloatTensor)
label_test = label_test.type(torch.FloatTensor)
cubic_test = cubic_test.type(torch.FloatTensor)
noisy_test = noisy_test.to(DEVICE)
label_test = label_test.to(DEVICE)
cubic_test = cubic_test.to(DEVICE)
with torch.no_grad():
residual, residual_stage2 = net(noisy_test, cubic_test)
denoised_band = noisy_test + residual_stage2
denoised_band_numpy = denoised_band.cpu().numpy().astype(
np.float32)
denoised_band_numpy = np.squeeze(denoised_band_numpy)
denoised_hsi[:, :, batch_idx] = denoised_band_numpy
psnr = PSNR(denoised_hsi, test_label_hsi)
ssim = SSIM(denoised_hsi, test_label_hsi)
sam = SAM(denoised_hsi, test_label_hsi)
#计算pnsr和ssim
print("=====averPSNR:{:.3f}=====averSSIM:{:.4f}=====averSAM:{:.3f}".
format(psnr, ssim, sam))
tb_writer.add_scalars("validation metrics", {
'average PSNR': psnr,
'average SSIM': ssim,
'avarage SAM': sam
}, epoch) #通过这个我就可以看到，那个epoch的性能是最好的
tb_writer.close()
def train_model():
device = DEVICE
#准备数据
train_set = HsiCubicTrainDataset('./data/train_cubic/')
train_loader = DataLoader(dataset=train_set,
batch_size=BATCH_SIZE,
shuffle=True)
#加载测试label数据
test_label_hsi = np.load('./data/origin/test_washington.npy')
#加载测试数据
test_data_dir = './data/test_level25/'
test_set = HsiTrainDataset(test_data_dir)
test_dataloader = DataLoader(test_set, batch_size=1, shuffle=False)
#创建模型
net = HSID_1x3(K)
init_params(net)
net = nn.DataParallel(net).to(device)
#创建优化器
#hsid_optimizer = optim.Adam(net.parameters(), lr=INIT_LEARNING_RATE, betas=(0.9, 0,999))
hsid_optimizer = optim.Adam(net.parameters(), lr=INIT_LEARNING_RATE)
scheduler = MultiStepLR(hsid_optimizer,
milestones=[15, 30, 45],
gamma=0.25)
#定义loss 函数
#criterion = nn.MSELoss()
global tb_writer
tb_writer = get_summary_writer(log_dir='logs')
gen_epoch_loss_list = []
cur_step = 0
first_batch = next(iter(train_loader))
for epoch in range(NUM_EPOCHS):
gen_epoch_loss = 0
net.train()
#for batch_idx, (noisy, label) in enumerate([first_batch] * 300):
for batch_idx, (noisy, cubic, label) in enumerate(train_loader):
noisy = noisy.to(device)
label = label.to(device)
cubic = cubic.to(device)
hsid_optimizer.zero_grad()
denoised_img = net(noisy, cubic)
loss = loss_fuction(denoised_img, label)
loss.backward() # calcu gradient
hsid_optimizer.step() # update parameter
gen_epoch_loss += loss.item()
if cur_step % display_step == 0:
if cur_step > 0:
print(
f"Epoch {epoch}: Step {cur_step}: MSE loss: {loss.item()}"
)
else:
print("Pretrained initial state")
tb_writer.add_scalar("MSE loss", loss.item(), cur_step)
#step ++,每一次循环，每一个batch的处理，叫做一个step
cur_step += 1
gen_epoch_loss_list.append(gen_epoch_loss)
tb_writer.add_scalar("mse epoch loss", gen_epoch_loss, epoch)
scheduler.step()
print("Decaying learning rate to %g" % scheduler.get_last_lr()[0])
torch.save(
{
'gen': net.state_dict(),
'gen_opt': hsid_optimizer.state_dict(),
}, f"checkpoints/hsid_{epoch}.pth")
#预测代码
net.eval()
for batch_idx, (noisy, label) in enumerate(test_dataloader):
noisy = noisy.type(torch.FloatTensor)
label = label.type(torch.FloatTensor)
batch_size, width, height, band_num = noisy.shape
denoised_hsi = np.zeros((width, height, band_num))
noisy = noisy.to(DEVICE)
label = label.to(DEVICE)
with torch.no_grad():
for i in range(band_num): #遍历每个band去处理
current_noisy_band = noisy[:, :, :, i]
current_noisy_band = current_noisy_band[:, None]
adj_spectral_bands = get_adjacent_spectral_bands(
noisy, K,
i) # shape: batch_size, width, height, band_num
adj_spectral_bands = torch.transpose(
adj_spectral_bands, 3, 1
) #交换第一维和第三维 ，shape: batch_size, band_num, height, width
denoised_band = net(current_noisy_band, adj_spectral_bands)
denoised_band_numpy = denoised_band.cpu().numpy().astype(
np.float32)
denoised_band_numpy = np.squeeze(denoised_band_numpy)
denoised_hsi[:, :, i] = denoised_band_numpy
psnr = PSNR(denoised_hsi, test_label_hsi)
ssim = SSIM(denoised_hsi, test_label_hsi)
sam = SAM(denoised_hsi, test_label_hsi)
#计算pnsr和ssim
print("=====averPSNR:{:.3f}=====averSSIM:{:.4f}=====averSAM:{:.3f}".
format(psnr, ssim, sam))
tb_writer.close()
class ParameterServer(object):
def __init__(self, i, args, model, bounded_delay):
self.ps_index = i
self.net = model.cuda()
self.optimizer = optim.SGD(self.net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
if args.dataset == "cifar10" and args.model == "resnet18":
self.sched = MultiStepLR(self.optimizer, [50, 70],
gamma=0.1) # TODO
if args.dataset == "cifar10" and args.model == "vgg19":
self.sched = MultiStepLR(self.optimizer, [70, 100],
gamma=0.1) # TODO
self.criterion = nn.CrossEntropyLoss()
self.args = args
self.num_of_workers = args.world_size
self.num_of_groups = 4
self.worker_per_group = 4
self.push_num = [0] * self.worker_per_group
self.pss = []
self.bounded_delay = bounded_delay
self.local_iter_list = self.worker_per_group * [0]
self.sum_gradients = []
sys.stdout = open(
f'{self.args.stdout}/ps{self.ps_index:02}_stdout.log', 'a+', 1)
sys.stderr = open(
f'{self.args.stdout}/ps{self.ps_index:02}_stdout.log', 'a+', 1)
def init_pss(self, ps0, ps1, ps2):
self.pss = [ps0, ps1, ps2]
def apply_gradients(self, worker_index, *gradients):
total = 0
for ele in range(0, len(self.push_num)):
total = total + self.push_num[ele]
itr = self.push_num[int((worker_index - self.ps_index) / 4)]
summed_gradients = [
torch.stack(gradient_zip).sum(dim=0) / self.args.world_size
for gradient_zip in zip(*gradients)
]
self.optimizer.zero_grad()
self.net.set_gradients(summed_gradients)
self.optimizer.step()
if total == 0:
self.sum_gradients = summed_gradients
if total % 4 == 0 and total != 0:
self.sum_gradients = [
torch.stack(gradient_zip).sum(dim=0)
for gradient_zip in zip(self.sum_gradients, summed_gradients)
]
for i in range(3):
self.pss[i].ps_syn.remote(i, worker_index, itr, time.time(),
*(self.sum_gradients))
self.sum_gradients.clear()
elif total % 4 == 1:
# self.sum_gradients.append(gradients)
self.sum_gradients = summed_gradients
# print("sumgradi",summed_gradients)
else:
self.sum_gradients = [
torch.stack(gradient_zip).sum(dim=0)
for gradient_zip in zip(self.sum_gradients, summed_gradients)
]
# return self.net.get_weights()
def ps_syn(self, i, worker_index, summed_gradients):
self.optimizer.zero_grad()
self.net.set_gradients(summed_gradients)
self.optimizer.step()
def lr_sched(self):
self.sched.step()
print("ps_index,", self.ps_index, self.sched.get_last_lr()[0])
def get_weights(self):
return {
k: v.cuda()
for k, v in self.net.state_dict().items()
if 'weight' in k or 'bias' in k
}
def blocked(self, worker_index, local_iter):
self.local_iter_list[int(worker_index / 4)] = local_iter
min_iter = min(self.local_iter_list)
return local_iter > min_iter + self.bounded_delay
def train_model_residual_lowlight():
device = DEVICE
#准备数据
train_set = HsiCubicTrainDataset('../HSID/data/train_lowlight/')
print('total training example:', len(train_set))
train_loader = DataLoader(dataset=train_set,
batch_size=BATCH_SIZE,
shuffle=True)
#加载测试label数据
mat_src_path = '../HSID/data/test_lowlight/origin/soup_bigcorn_orange_1ms.mat'
test_label_hsi = scio.loadmat(mat_src_path)['label']
#加载测试数据
batch_size = 1
test_data_dir = '../HSID/data/test_lowlight/cubic/'
test_set = HsiCubicLowlightTestDataset(test_data_dir)
test_dataloader = DataLoader(dataset=test_set,
batch_size=batch_size,
shuffle=False)
batch_size, channel, width, height = next(iter(test_dataloader))[0].shape
band_num = len(test_dataloader)
denoised_hsi = np.zeros((width, height, band_num))
#创建模型
net = ENCAM()
#init_params(net) #创建encam时，已经通过self._initialize_weights()进行了初始化
net = net.to(device)
#net = nn.DataParallel(net)
#net = net.to(device)
#创建优化器
#hsid_optimizer = optim.Adam(net.parameters(), lr=INIT_LEARNING_RATE, betas=(0.9, 0,999))
hsid_optimizer = optim.Adam(net.parameters(), lr=INIT_LEARNING_RATE)
scheduler = MultiStepLR(hsid_optimizer, milestones=[15, 30, 45], gamma=0.1)
#定义loss 函数
#criterion = nn.MSELoss()
global tb_writer
tb_writer = get_summary_writer(log_dir='logs')
gen_epoch_loss_list = []
cur_step = 0
first_batch = next(iter(train_loader))
best_psnr = 0
best_epoch = 0
best_iter = 0
for epoch in range(NUM_EPOCHS):
scheduler.step()
print(epoch, 'lr={:.6f}'.format(scheduler.get_last_lr()[0]))
gen_epoch_loss = 0
net.train()
#for batch_idx, (noisy, cubic, label) in enumerate([first_batch] * 300):
for batch_idx, (noisy, cubic, label) in enumerate(train_loader):
#noisy, cubic, label = next(iter(train_loader)) #从dataloader中取出一个batch
#print('batch_idx=', batch_idx)
noisy = noisy.to(device)
label = label.to(device)
cubic = cubic.to(device)
hsid_optimizer.zero_grad()
#denoised_img = net(noisy, cubic)
#loss = loss_fuction(denoised_img, label)
residual = net(noisy, cubic)
loss = loss_fuction(residual, label - noisy)
loss.backward() # calcu gradient
hsid_optimizer.step() # update parameter
gen_epoch_loss += loss.item()
if cur_step % display_step == 0:
if cur_step > 0:
print(
f"Epoch {epoch}: Step {cur_step}: Batch_idx {batch_idx}: MSE loss: {loss.item()}"
)
else:
print("Pretrained initial state")
tb_writer.add_scalar("MSE loss", loss.item(), cur_step)
#step ++,每一次循环，每一个batch的处理，叫做一个step
cur_step += 1
gen_epoch_loss_list.append(gen_epoch_loss)
tb_writer.add_scalar("mse epoch loss", gen_epoch_loss, epoch)
#scheduler.step()
#print("Decaying learning rate to %g" % scheduler.get_last_lr()[0])
torch.save(
{
'gen': net.state_dict(),
'gen_opt': hsid_optimizer.state_dict(),
}, f"checkpoints/encam_{epoch}.pth")
#测试代码
net.eval()
for batch_idx, (noisy_test, cubic_test,
label_test) in enumerate(test_dataloader):
noisy_test = noisy_test.type(torch.FloatTensor)
label_test = label_test.type(torch.FloatTensor)
cubic_test = cubic_test.type(torch.FloatTensor)
noisy_test = noisy_test.to(DEVICE)
label_test = label_test.to(DEVICE)
cubic_test = cubic_test.to(DEVICE)
with torch.no_grad():
#对图像下采样
#noisy_permute = noisy.permute(0, 3,1,2)#交换第一维和第三维 ，shape: batch_size, band_num, height, width
#label_permute = label.permute(0, 3, 1, 2)
noisy_test_down = F.interpolate(noisy_test,
scale_factor=0.5,
mode='bilinear')
cubic_test_squeeze = torch.squeeze(cubic_test, 0)
cubic_test_down = F.interpolate(cubic_test_squeeze,
scale_factor=0.5,
mode='bilinear')
cubic_test_down_unsqueeze = torch.unsqueeze(cubic_test_down, 0)
residual = net(noisy_test_down, cubic_test_down_unsqueeze)
denoised_band = noisy_test_down + residual
#图像上采样
denoised_band = F.interpolate(denoised_band,
scale_factor=2,
mode='bilinear')
denoised_band_numpy = denoised_band.cpu().numpy().astype(
np.float32)
denoised_band_numpy = np.squeeze(denoised_band_numpy)
denoised_hsi[:, :, batch_idx] = denoised_band_numpy
psnr = PSNR(denoised_hsi, test_label_hsi)
ssim = SSIM(denoised_hsi, test_label_hsi)
sam = SAM(denoised_hsi, test_label_hsi)
#计算pnsr和ssim
print("=====averPSNR:{:.3f}=====averSSIM:{:.4f}=====averSAM:{:.3f}".
format(psnr, ssim, sam))
tb_writer.add_scalars("validation metrics", {
'average PSNR': psnr,
'average SSIM': ssim,
'avarage SAM': sam
}, epoch) #通过这个我就可以看到，那个epoch的性能是最好的
#保存best模型
if psnr > best_psnr:
best_psnr = psnr
best_epoch = epoch
best_iter = cur_step
torch.save(
{
'gen': net.state_dict(),
'gen_opt': hsid_optimizer.state_dict(),
}, f"checkpoints/encam_best.pth")
print(
"[epoch %d it %d PSNR: %.4f --- best_epoch %d best_iter %d Best_PSNR %.4f]"
% (epoch, cur_step, psnr, best_epoch, best_iter, best_psnr))
tb_writer.close()