def validate(state_dict_path, use_gpu, device):
model = UNet(n_channels=1, n_classes=2)
model.load_state_dict(torch.load(state_dict_path, map_location='cpu' if not use_gpu else device))
model.to(device)
val_transforms = transforms.Compose([
ToTensor(),
NormalizeBRATS()])
BraTS_val_ds = BRATS2018('./BRATS2018',\
data_set='val',\
seg_type='et',\
scan_type='t1ce',\
transform=val_transforms)
data_loader = DataLoader(BraTS_val_ds, batch_size=2, shuffle=False, num_workers=0)
running_dice_score = 0.
for batch_ind, batch in enumerate(data_loader):
imgs, targets = batch
imgs = imgs.to(device)
targets = targets.to(device)
model.eval()
with torch.no_grad():
outputs = model(imgs)
preds = torch.argmax(F.softmax(outputs, dim=1), dim=1)
running_dice_score += dice_score(preds, targets) * targets.size(0)
print('running dice score: {:.6f}'.format(running_dice_score))
dice = running_dice_score / len(BraTS_val_ds)
print('mean dice score of the validating set: {:.6f}'.format(dice))
def detect_noise_regions(image, args):
# load noise segmentation network (U-Net)
unet_model_path = os.path.join(args.checkpoints, 'unet', 'UNet.pth')
net = UNet(n_channels=3, n_classes=1).to(device)
net.load_state_dict(torch.load(unet_model_path))
net.eval()
# predict noise regions
predict = predict_img(net, device, image)
# search inpaint patches
patches, labels, _, absolute_position, relative_position = search_inpaint_area(np.array(image),
np.array(predict.convert('RGB')))
# save inpaint patches
patches_dir = os.path.join(args.output, 'patches')
labels_dir = os.path.join(args.output, 'labels')
os.makedirs(patches_dir, exist_ok=True)
os.makedirs(labels_dir, exist_ok=True)
filename = os.path.basename(args.input).split('.')[0]
counter = 0
for patch, label in zip(patches, labels):
Image.fromarray(patch).save(os.path.join(patches_dir, '{}-{:0>3d}.png'.format(filename, counter)))
Image.fromarray(label).save(os.path.join(labels_dir, '{}-{:0>3d}.png'.format(filename, counter)))
counter += 1
return patches_dir, labels_dir, absolute_position, relative_position
class EventGANBase(object):
def __init__(self, options):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.generator = UNet(num_input_channels=2*options.n_image_channels,
num_output_channels=options.n_time_bins * 2,
skip_type='concat',
activation='relu',
num_encoders=4,
base_num_channels=32,
num_residual_blocks=2,
norm='BN',
use_upsample_conv=True,
with_activation=True,
sn=options.sn,
multi=False)
latest_checkpoint = get_latest_checkpoint(options.checkpoint_dir)
checkpoint = torch.load(latest_checkpoint)
self.generator.load_state_dict(checkpoint["gen"])
self.generator.to(self.device)
def forward(self, images, is_train=False):
if len(images.shape) == 3:
images = images[None, ...]
assert len(images.shape) == 4 and images.shape[1] == 2, \
"Input images must be either 2xHxW or Bx2xHxW."
if not is_train:
with torch.no_grad():
self.generator.eval()
event_volume = self.generator(images)
self.generator.train()
else:
event_volume = self.generator(images)
return event_volume
class Visualizer(object):
def __init__(self, input_topic, output_topic, resize_width, resize_height,
model_path, force_cpu):
self.bridge = CvBridge()
self.graph = UNet([3, resize_width, resize_height], 3)
self.graph.load_state_dict(torch.load(model_path))
self.force_cpu = force_cpu and torch.cuda.is_available()
self.resize_width, self.resize_height = resize_width, resize_height
if not self.force_cpu:
self.graph.cuda()
self.graph.eval()
self.to_tensor = transforms.Compose([transforms.ToTensor()])
self.publisher = rospy.Publisher(output_topic, ImMsg, queue_size=1)
self.raw_subscriber = rospy.Subscriber(input_topic,
CompressedImage,
self.image_cb,
queue_size=1,
buff_size=10**8)
def convert_to_tensor(self, image):
np_arr = np.fromstring(image.data, np.uint8)
image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
image_np = cv2.resize(image_np,
dsize=(self.resize_width, self.resize_height))
img_to_tensor = PIL.Image.fromarray(image_np)
img_tensor = self.to_tensor(img_to_tensor)
if not self.force_cpu:
return Variable(img_tensor.unsqueeze(0)).cuda()
else:
return Variable(img_tensor.unsqueeze(0))
def image_cb(self, image):
img_tensor = self.convert_to_tensor(image)
# Inference
output = self.graph(img_tensor)
output_data = output.cpu().data.numpy()[0][0]
# # Convert from 32fc1 (0 - 1) to 8uc1 (0 - 255)
cv_output = np.uint8(255 * output_data)
cv_output = cv2.applyColorMap(cv_output, cv2.COLORMAP_JET)
# Convert to ROS message to publish
msg_out = self.bridge.cv2_to_imgmsg(cv_output, 'bgr8')
msg_out.header.stamp = image.header.stamp
self.publisher.publish(msg_out)
def train():
# Init data
train_dataset, val_dataset = prepare_datasets()
train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=10, shuffle=True)
loaders = dict(train=train_loader, val=val_loader)
# Init Model
model = UNet().cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, amsgrad=True)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer,
gamma=0.984)
loss_fn = nn.BCELoss()
epochs = 500
for epoch in range(epochs):
for phase in 'train val'.split():
if phase == 'train':
model = model.train()
torch.set_grad_enabled(True)
else:
model = model.eval()
torch.set_grad_enabled(False)
loader = loaders[phase]
epoch_losses = dict(train=[], val=[])
running_loss = []
for batch in loader:
imgs, masks = batch
imgs = imgs.cuda()
masks = masks.cuda()
outputs = model(imgs)
loss = loss_fn(outputs, masks)
running_loss.append(loss.item())
if phase == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
# End of Epoch
print(f'{epoch}) {phase} loss: {np.mean(running_loss)}')
visualize_results(loader, model, epoch, phase)
epoch_losses[phase].append(np.mean(running_loss))
tensorboard(epoch_losses[phase], phase)
if phase == 'train':
scheduler.step()
target = target.reshape((batch_size, -1))
loss = criterion(outputs, target)
loss.backward()
optimizer.step()
running_loss += loss.item()
train_info = 'epoch:%d train_loss: %.3f' % (epoch + 1, running_loss /
(i + 1))
print(train_info)
write_log('weight/train.log', str(datetime.datetime.now()))
write_log('weight/train.log', train_info)
torch.save(net.state_dict(), 'weight/epoch_{}_{}'.format(epoch, i))
# test phase
with torch.no_grad():
net.eval()
test_loss = 0.0
accuracy = 0
count = 0
for i, batch in enumerate(test_data_loader):
inputs = batch['image'].to(cuda0)
target = batch['target'].to(cuda0)
outputs = net(inputs)
# loss
batch_size = outputs.size(0)
loss = criterion(outputs.reshape((batch_size, -1)),
target.reshape((batch_size, -1)))
test_loss += loss.item()
# accuracy
target, outputs = target.cpu(), torch.squeeze(outputs.cpu(), dim=1)
for tar, out in zip(target, outputs):
def train(input_data_type,
grade,
seg_type,
num_classes,
batch_size,
epochs,
use_gpu,
learning_rate,
w_decay,
pre_trained=False):
logger.info('Start training using {} modal.'.format(input_data_type))
model = UNet(4, 4, residual=True, expansion=2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(params=model.parameters(),
lr=learning_rate,
weight_decay=w_decay)
if pre_trained:
checkpoint = torch.load(pre_trained_path, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
if use_gpu:
ts = time.time()
model.to(device)
print("Finish cuda loading, time elapsed {}".format(time.time() - ts))
scheduler = lr_scheduler.StepLR(
optimizer, step_size=step_size,
gamma=gamma) # decay LR by a factor of 0.5 every 5 epochs
data_set, data_loader = get_dataset_dataloader(input_data_type,
seg_type,
batch_size,
grade=grade)
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_iou = 0.0
epoch_loss = np.zeros((2, epochs))
epoch_acc = np.zeros((2, epochs))
epoch_class_acc = np.zeros((2, epochs))
epoch_mean_iou = np.zeros((2, epochs))
evaluator = Evaluator(num_classes)
def term_int_handler(signal_num, frame):
np.save(os.path.join(score_dir, 'epoch_accuracy'), epoch_acc)
np.save(os.path.join(score_dir, 'epoch_mean_iou'), epoch_mean_iou)
np.save(os.path.join(score_dir, 'epoch_loss'), epoch_loss)
model.load_state_dict(best_model_wts)
logger.info('Got terminated and saved model.state_dict')
torch.save(model.state_dict(),
os.path.join(score_dir, 'terminated_model.pt'))
torch.save(
{
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, os.path.join(score_dir, 'terminated_model.tar'))
quit()
signal.signal(signal.SIGINT, term_int_handler)
signal.signal(signal.SIGTERM, term_int_handler)
for epoch in range(epochs):
logger.info('Epoch {}/{}'.format(epoch + 1, epochs))
logger.info('-' * 28)
for phase_ind, phase in enumerate(['train', 'val']):
if phase == 'train':
model.train()
logger.info(phase)
else:
model.eval()
logger.info(phase)
evaluator.reset()
running_loss = 0.0
running_dice = 0.0
for batch_ind, batch in enumerate(data_loader[phase]):
imgs, targets = batch
imgs = imgs.to(device)
targets = targets.to(device)
# zero the learnable parameters gradients
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(imgs)
loss = criterion(outputs, targets)
if phase == 'train':
loss.backward()
optimizer.step()
preds = torch.argmax(F.softmax(outputs, dim=1),
dim=1,
keepdim=True)
running_loss += loss * imgs.size(0)
logger.debug('Batch {} running loss: {:.4f}'.format(batch_ind,\
running_loss))
# test the iou and pixelwise accuracy using evaluator
preds = torch.squeeze(preds, dim=1)
preds = preds.cpu().numpy()
targets = targets.cpu().numpy()
evaluator.add_batch(targets, preds)
epoch_loss[phase_ind, epoch] = running_loss / len(data_set[phase])
epoch_acc[phase_ind, epoch] = evaluator.Pixel_Accuracy()
epoch_class_acc[phase_ind,
epoch] = evaluator.Pixel_Accuracy_Class()
epoch_mean_iou[phase_ind,
epoch] = evaluator.Mean_Intersection_over_Union()
logger.info('{} loss: {:.4f}, acc: {:.4f}, class acc: {:.4f}, mean iou: {:.6f}'.format(phase,\
epoch_loss[phase_ind, epoch],\
epoch_acc[phase_ind, epoch],\
epoch_class_acc[phase_ind, epoch],\
epoch_mean_iou[phase_ind, epoch]))
if phase == 'val' and epoch_mean_iou[phase_ind, epoch] > best_iou:
best_iou = epoch_mean_iou[phase_ind, epoch]
best_model_wts = copy.deepcopy(model.state_dict())
if phase == 'val' and (epoch + 1) % 10 == 0:
logger.info('Saved model.state_dict in epoch {}'.format(epoch +
1))
torch.save(
model.state_dict(),
os.path.join(score_dir,
'epoch{}_model.pt'.format(epoch + 1)))
print()
time_elapsed = time.time() - since
logger.info('Training completed in {}m {}s'.format(int(time_elapsed / 60),\
int(time_elapsed) % 60))
# load best model weights
model.load_state_dict(best_model_wts)
# save numpy results
np.save(os.path.join(score_dir, 'epoch_accuracy'), epoch_acc)
np.save(os.path.join(score_dir, 'epoch_mean_iou'), epoch_mean_iou)
np.save(os.path.join(score_dir, 'epoch_loss'), epoch_loss)
return model, optimizer
net_is_3d = False
if torch.cuda.device_count() > 1:
print("Using", torch.cuda.device_count(), "GPUs.")
device_ids = [i for i in range(torch.cuda.device_count())]
model = nn.DataParallel(model, device_ids=device_ids)
model = model.to(device)
if experiment == "Unet":
model.load_state_dict(torch.load("best_weights.pth"))
elif experiment == "DeepLab":
model.load_state_dict(torch.load(f"best_weights_{backbone}_deeplab.pth"))
model.eval()
eval_images, eval_labels, eval_label_corners = batch_generator(
eval_image, eval_label, **windowing_params, return_corners=True)
eval_dataset = PlateletDataset(eval_images, eval_labels, train=False)
prob_maps = stitch(model, eval_images, eval_labels, eval_label.shape,
eval_label_corners, windowing_params, net_is_3d, n_classes,
device, channels)
stitched_classes = np.argmax(prob_maps, axis=0)
# A few plots for sanity check
for i in [0, 10, 20]:
def inference():
"""Support two mode: evaluation (on valid set) or inference mode (on test-set for submission)
"""
parser = argparse.ArgumentParser(description="Inference mode")
parser.add_argument('-testf', "--test-filepath", type=str, default=None, required=True,
help="testing dataset filepath.")
parser.add_argument("-eval", "--evaluate", action="store_true", default=False,
help="Evaluation mode")
parser.add_argument("--load-weights", type=str, default=None,
help="Load pretrained weights, torch state_dict() (filepath, default: None)")
parser.add_argument("--load-model", type=str, default=None,
help="Load pretrained model, entire model (filepath, default: None)")
parser.add_argument("--save2dir", type=str, default=None,
help="save the prediction labels to the directory (default: None)")
parser.add_argument("--debug", action="store_true", default=False)
parser.add_argument("--batch-size", type=int, default=32,
help="Batch size")
parser.add_argument("--num-cpu", type=int, default=10,
help="Number of CPUs to use in parallel for dataloader.")
parser.add_argument('--cuda', type=int, default=0,
help='CUDA visible device (use CPU if -1, default: 0)')
args = parser.parse_args()
printYellow("="*10 + " Inference mode. "+"="*10)
if args.save2dir:
os.makedirs(args.save2dir, exist_ok=True)
device = torch.device("cuda:{}".format(args.cuda) if torch.cuda.is_available()
and (args.cuda >= 0) else "cpu")
transform_normalize = transforms.Normalize(mean=[0.5],
std=[0.5])
data_transform = transforms.Compose([
transforms.ToTensor(),
transform_normalize
])
data_loader_params = {'batch_size': args.batch_size,
'shuffle': False,
'num_workers': args.num_cpu,
'drop_last': False,
'pin_memory': False
}
test_set = LiTSDataset(args.test_filepath,
dtype=np.float32,
pixelwise_transform=data_transform,
inference_mode=(not args.evaluate),
)
dataloader_test = torch.utils.data.DataLoader(test_set, **data_loader_params)
# =================== Build model ===================
if args.load_weights:
model = UNet(in_ch=1,
out_ch=3, # there are 3 classes: 0: background, 1: liver, 2: tumor
depth=4,
start_ch=64,
inc_rate=2,
kernel_size=3,
padding=True,
batch_norm=True,
spec_norm=False,
dropout=0.5,
up_mode='upconv',
include_top=True,
include_last_act=False,
)
model.load_state_dict(torch.load(args.load_weights))
printYellow("Successfully loaded pretrained weights.")
elif args.load_model:
# load entire model
model = torch.load(args.load_model)
printYellow("Successfully loaded pretrained model.")
model.eval()
model.to(device)
# n_batch_per_epoch = len(dataloader_test)
sigmoid_act = torch.nn.Sigmoid()
st = time.time()
volume_start_index = test_set.volume_start_index
spacing = test_set.spacing
direction = test_set.direction # use it for the submission
offset = test_set.offset
msk_pred_buffer = []
if args.evaluate:
msk_gt_buffer = []
for data_batch in tqdm(dataloader_test):
# import ipdb
# ipdb.set_trace()
if args.evaluate:
img, msk_gt = data_batch
msk_gt_buffer.append(msk_gt.cpu().detach().numpy())
else:
img = data_batch
img = img.to(device)
with torch.no_grad():
msk_pred = model(img) # shape (N, 3, H, W)
msk_pred = sigmoid_act(msk_pred)
msk_pred_buffer.append(msk_pred.cpu().detach().numpy())
msk_pred_buffer = np.vstack(msk_pred_buffer) # shape (N, 3, H, W)
if args.evaluate:
msk_gt_buffer = np.vstack(msk_gt_buffer)
results = []
for vol_ind, vol_start_ind in enumerate(volume_start_index):
if vol_ind == len(volume_start_index) - 1:
volume_msk = msk_pred_buffer[vol_start_ind:] # shape (N, 3, H, W)
if args.evaluate:
volume_msk_gt = msk_gt_buffer[vol_start_ind:]
else:
vol_end_ind = volume_start_index[vol_ind+1]
volume_msk = msk_pred_buffer[vol_start_ind:vol_end_ind] # shape (N, 3, H, W)
if args.evaluate:
volume_msk_gt = msk_gt_buffer[vol_start_ind:vol_end_ind]
if args.evaluate:
# liver
liver_scores = get_scores(volume_msk[:, 1] >= 0.5, volume_msk_gt >= 1, spacing[vol_ind])
# tumor
lesion_scores = get_scores(volume_msk[:, 2] >= 0.5, volume_msk_gt == 2, spacing[vol_ind])
print("Liver dice", liver_scores['dice'], "Lesion dice", lesion_scores['dice'])
results.append([vol_ind, liver_scores, lesion_scores])
# ===========================
else:
# import ipdb; ipdb.set_trace()
if args.save2dir:
# reverse the order, because we prioritize tumor, liver then background.
msk_pred = (volume_msk >= 0.5)[:, ::-1, ...] # shape (N, 3, H, W)
msk_pred = np.argmax(msk_pred, axis=1) # shape (N, H, W) = (z, x, y)
msk_pred = np.transpose(msk_pred, axes=(1, 2, 0)) # shape (x, y, z)
# remember to correct 'direction' and np.transpose before the submission !!!
if direction[vol_ind][0] == -1:
# x-axis
msk_pred = msk_pred[::-1, ...]
if direction[vol_ind][1] == -1:
# y-axis
msk_pred = msk_pred[:, ::-1, :]
if direction[vol_ind][2] == -1:
# z-axis
msk_pred = msk_pred[..., ::-1]
# save medical image header as well
# see: http://loli.github.io/medpy/generated/medpy.io.header.Header.html
file_header = med_header(spacing=tuple(spacing[vol_ind]),
offset=tuple(offset[vol_ind]),
direction=np.diag(direction[vol_ind]))
# submission guide:
# see: https://github.com/PatrickChrist/LITS-CHALLENGE/blob/master/submission-guide.md
# test-segmentation-X.nii
filepath = os.path.join(args.save2dir, f"test-segmentation-{vol_ind}.nii")
med_save(msk_pred, filepath, hdr=file_header)
if args.save2dir:
# outpath = os.path.join(args.save2dir, "results.csv")
outpath = os.path.join(args.save2dir, "results.pkl")
with open(outpath, "wb") as file:
final_result = {}
final_result['liver'] = defaultdict(list)
final_result['tumor'] = defaultdict(list)
for vol_ind, liver_scores, lesion_scores in results:
# [OTC] assuming vol_ind is continuous
for key in liver_scores:
final_result['liver'][key].append(liver_scores[key])
for key in lesion_scores:
final_result['tumor'][key].append(lesion_scores[key])
pickle.dump(final_result, file, protocol=3)
# ======== code from official metric ========
# create line for csv file
# outstr = str(vol_ind) + ','
# for l in [liver_scores, lesion_scores]:
# for k, v in l.items():
# outstr += str(v) + ','
# outstr += '\n'
# # create header for csv file if necessary
# if not os.path.isfile(outpath):
# headerstr = 'Volume,'
# for k, v in liver_scores.items():
# headerstr += 'Liver_' + k + ','
# for k, v in liver_scores.items():
# headerstr += 'Lesion_' + k + ','
# headerstr += '\n'
# outstr = headerstr + outstr
# # write to file
# f = open(outpath, 'a+')
# f.write(outstr)
# f.close()
# ===========================
printGreen(f"Total elapsed time: {time.time()-st}")
return results
def train(args):
'''
-------------------------Hyperparameters--------------------------
'''
EPOCHS = args.epochs
START = 0 # could enter a checkpoint start epoch
ITER = args.iterations # per epoch
LR = args.lr
MOM = args.momentum
# LOGInterval = args.log_interval
BATCHSIZE = args.batch_size
TEST_BATCHSIZE = args.test_batch_size
NUMBER_OF_WORKERS = args.workers
DATA_FOLDER = args.data
TESTSET_FOLDER = args.testset
ROOT = args.run
WEIGHT_DIR = os.path.join(ROOT, "weights")
CUSTOM_LOG_DIR = os.path.join(ROOT, "additionalLOGS")
CHECKPOINT = os.path.join(WEIGHT_DIR,
str(args.model) + str(args.name) + ".pt")
useTensorboard = args.tb
# check existance of data
if not os.path.isdir(DATA_FOLDER):
print("data folder not existant or in wrong layout.\n\t", DATA_FOLDER)
exit(0)
# check existance of testset
if TESTSET_FOLDER is not None and not os.path.isdir(TESTSET_FOLDER):
print("testset folder not existant or in wrong layout.\n\t",
DATA_FOLDER)
exit(0)
'''
---------------------------preparations---------------------------
'''
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
print("using device: ", str(device))
# loading the validation samples to make online evaluations
path_to_valX = args.valX
path_to_valY = args.valY
valX = None
valY = None
if path_to_valX is not None and path_to_valY is not None \
and os.path.exists(path_to_valX) and os.path.exists(path_to_valY) \
and os.path.isfile(path_to_valX) and os.path.isfile(path_to_valY):
with torch.no_grad():
valX, valY = torch.load(path_to_valX, map_location='cpu'), \
torch.load(path_to_valY, map_location='cpu')
'''
---------------------------loading dataset and normalizing---------------------------
'''
# Dataloader Parameters
train_params = {
'batch_size': BATCHSIZE,
'shuffle': True,
'num_workers': NUMBER_OF_WORKERS
}
test_params = {
'batch_size': TEST_BATCHSIZE,
'shuffle': False,
'num_workers': NUMBER_OF_WORKERS
}
# create a folder for the weights and custom logs
if not os.path.isdir(WEIGHT_DIR):
os.makedirs(WEIGHT_DIR)
if not os.path.isdir(CUSTOM_LOG_DIR):
os.makedirs(CUSTOM_LOG_DIR)
labelsNorm = None
# NORMLABEL
# normalizing on a trainingset wide mean and std
mean = None
std = None
if args.norm:
print('computing mean and std over trainingset')
# computes mean and std over all ground truths in dataset to tackle the problem of numerical insignificance
mean, std = computeMeanStdOverDataset('CONRADataset', DATA_FOLDER,
train_params, device)
print('\niodine (mean/std): {}\t{}'.format(mean[0], std[0]))
print('water (mean/std): {}\t{}\n'.format(mean[1], std[1]))
labelsNorm = transforms.Normalize(mean=[0, 0], std=std)
m2, s2 = computeMeanStdOverDataset('CONRADataset',
DATA_FOLDER,
train_params,
device,
transform=labelsNorm)
print("new mean and std are:")
print('\nnew iodine (mean/std): {}\t{}'.format(m2[0], s2[0]))
print('new water (mean/std): {}\t{}\n'.format(m2[1], s2[1]))
traindata = CONRADataset(DATA_FOLDER,
True,
device=device,
precompute=True,
transform=labelsNorm)
testdata = None
if TESTSET_FOLDER is not None:
testdata = CONRADataset(TESTSET_FOLDER,
False,
device=device,
precompute=True,
transform=labelsNorm)
else:
testdata = CONRADataset(DATA_FOLDER,
False,
device=device,
precompute=True,
transform=labelsNorm)
trainingset = DataLoader(traindata, **train_params)
testset = DataLoader(testdata, **test_params)
'''
----------------loading model and checkpoints---------------------
'''
if args.model == "unet":
m = UNet(2, 2).to(device)
print(
"using the U-Net architecture with {} trainable params; Good Luck!"
.format(count_trainables(m)))
else:
m = simpleConvNet(2, 2).to(device)
o = optim.SGD(m.parameters(), lr=LR, momentum=MOM)
loss_fn = nn.MSELoss()
test_loss = None
train_loss = None
if len(os.listdir(WEIGHT_DIR)) != 0:
checkpoints = os.listdir(WEIGHT_DIR)
checkDir = {}
latestCheckpoint = 0
for i, checkpoint in enumerate(checkpoints):
stepOfCheckpoint = int(
checkpoint.split(str(args.model) +
str(args.name))[-1].split('.pt')[0])
checkDir[stepOfCheckpoint] = checkpoint
latestCheckpoint = max(latestCheckpoint, stepOfCheckpoint)
print("[{}] {}".format(stepOfCheckpoint, checkpoint))
# if on development machine, prompt for input, else just take the most recent one
if 'faui' in os.uname()[1]:
toUse = int(input("select checkpoint to use: "))
else:
toUse = latestCheckpoint
checkpoint = torch.load(os.path.join(WEIGHT_DIR, checkDir[toUse]))
m.load_state_dict(checkpoint['model_state_dict'])
m.to(device) # pushing weights to gpu
o.load_state_dict(checkpoint['optimizer_state_dict'])
train_loss = checkpoint['train_loss']
test_loss = checkpoint['test_loss']
START = checkpoint['epoch']
print("using checkpoint {}:\n\tloss(train/test): {}/{}".format(
toUse, train_loss, test_loss))
else:
print("starting from scratch")
'''
-----------------------------training-----------------------------
'''
global_step = 0
# calculating initial loss
if test_loss is None or train_loss is None:
print("calculating initial loss")
m.eval()
print("testset...")
test_loss = calculate_loss(set=testset,
loss_fn=loss_fn,
length_set=len(testdata),
dev=device,
model=m)
print("trainset...")
train_loss = calculate_loss(set=trainingset,
loss_fn=loss_fn,
length_set=len(traindata),
dev=device,
model=m)
## SSIM and R value
R = []
SSIM = []
performanceFLE = os.path.join(CUSTOM_LOG_DIR, "performance.csv")
with open(performanceFLE, 'w+') as f:
f.write(
"step, SSIMiodine, SSIMwater, Riodine, Rwater, train_loss, test_loss\n"
)
print("computing ssim and r coefficents to: {}".format(performanceFLE))
# printing runtime information
print(
"starting training at {} for {} epochs {} iterations each\n\t{} total".
format(START, EPOCHS, ITER, EPOCHS * ITER))
print("\tbatchsize: {}\n\tloss: {}\n\twill save results to \"{}\"".format(
BATCHSIZE, train_loss, CHECKPOINT))
print(
"\tmodel: {}\n\tlearningrate: {}\n\tmomentum: {}\n\tnorming output space: {}"
.format(args.model, LR, MOM, args.norm))
#start actual training loops
for e in range(START, START + EPOCHS):
# iterations will not be interupted with validation and metrics
for i in range(ITER):
global_step = (e * ITER) + i
# training
m.train()
iteration_loss = 0
for x, y in tqdm(trainingset):
x, y = x.to(device=device,
dtype=torch.float), y.to(device=device,
dtype=torch.float)
pred = m(x)
loss = loss_fn(pred, y)
iteration_loss += loss.item()
o.zero_grad()
loss.backward()
o.step()
print("\niteration {}: --accumulated loss {}".format(
global_step, iteration_loss))
# validation, saving and logging
print("\nvalidating")
m.eval() # disable dropout batchnorm etc
print("testset...")
test_loss = calculate_loss(set=testset,
loss_fn=loss_fn,
length_set=len(testdata),
dev=device,
model=m)
print("trainset...")
train_loss = calculate_loss(set=trainingset,
loss_fn=loss_fn,
length_set=len(traindata),
dev=device,
model=m)
print("calculating SSIM and R coefficients")
currSSIM, currR = performance(set=testset,
dev=device,
model=m,
bs=TEST_BATCHSIZE)
print("SSIM (iod/water): {}/{}\nR (iod/water): {}/{}".format(
currSSIM[0], currSSIM[1], currR[0], currR[1]))
with open(performanceFLE, 'a') as f:
newCSVline = "{}, {}, {}, {}, {}, {}, {}\n".format(
global_step, currSSIM[0], currSSIM[1], currR[0], currR[1],
train_loss, test_loss)
f.write(newCSVline)
print("wrote new line to csv:\n\t{}".format(newCSVline))
'''
if valX and valY were set in preparations, use them to perform analytics.
if not, use the first sample from the testset to perform analytics
'''
with torch.no_grad():
truth, pred = None, None
IMAGE_LOG_DIR = os.path.join(CUSTOM_LOG_DIR, str(global_step))
if not os.path.isdir(IMAGE_LOG_DIR):
os.makedirs(IMAGE_LOG_DIR)
if valX is not None and valY is not None:
batched = np.zeros((BATCHSIZE, *valX.numpy().shape))
batched[0] = valX.numpy()
batched = torch.from_numpy(batched).to(device=device,
dtype=torch.float)
pred = m(batched)
pred = pred.cpu().numpy()[0]
truth = valY.numpy() # still on cpu
assert pred.shape == truth.shape
else:
for x, y in testset:
# x, y in shape[2,2,480,620] [b,c,h,w]
x, y = x.to(device=device,
dtype=torch.float), y.to(device=device,
dtype=torch.float)
pred = m(x)
pred = pred.cpu().numpy()[
0] # taking only the first sample of batch
truth = y.cpu().numpy()[
0] # first projection for evaluation
advanvedMetrics(truth, pred, mean, std, global_step, args.norm,
IMAGE_LOG_DIR)
print("logging")
CHECKPOINT = os.path.join(
WEIGHT_DIR,
str(args.model) + str(args.name) + str(global_step) + ".pt")
torch.save(
{
'epoch': e + 1, # end of this epoch; so resume at next.
'model_state_dict': m.state_dict(),
'optimizer_state_dict': o.state_dict(),
'train_loss': train_loss,
'test_loss': test_loss
},
CHECKPOINT)
print('\tsaved weigths to: ', CHECKPOINT)
if logger is not None and train_loss is not None:
logger.add_scalar('test_loss', test_loss, global_step=global_step)
logger.add_scalar('train_loss',
train_loss,
global_step=global_step)
logger.add_image("iodine-prediction",
pred[0].reshape(1, 480, 620),
global_step=global_step)
logger.add_image("water-prediction",
pred[1].reshape(1, 480, 620),
global_step=global_step)
# logger.add_image("water-prediction", wat)
print(
"\ttensorboard updated with test/train loss and a sample image"
)
elif train_loss is not None:
print("\tloss of global-step {}: {}".format(
global_step, train_loss))
elif not useTensorboard:
print("\t(tb-logging disabled) test/train loss: {}/{} ".format(
test_loss, train_loss))
else:
print("\tno loss accumulated yet")
# saving final results
print("saving upon exit")
torch.save(
{
'epoch': EPOCHS,
'model_state_dict': m.state_dict(),
'optimizer_state_dict': o.state_dict(),
'train_loss': train_loss,
'test_loss': test_loss
}, CHECKPOINT)
print('\tsaved progress to: ', CHECKPOINT)
if logger is not None and train_loss is not None:
logger.add_scalar('test_loss', test_loss, global_step=global_step)
logger.add_scalar('train_loss', train_loss, global_step=global_step)
def main(args):
def log_string(str):
# logger.info(str)
print(str)
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
'''CREATE DIR'''
timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M'))
experiment_dir = Path('./log/')
experiment_dir.mkdir(exist_ok=True)
experiment_dir = experiment_dir.joinpath('part_seg')
experiment_dir.mkdir(exist_ok=True)
if args.log_dir is None:
experiment_dir = experiment_dir.joinpath(timestr)
else:
experiment_dir = experiment_dir.joinpath(args.log_dir)
experiment_dir.mkdir(exist_ok=True)
checkpoints_dir = experiment_dir.joinpath('checkpoints/')
checkpoints_dir.mkdir(exist_ok=True)
log_dir = experiment_dir.joinpath('logs/')
log_dir.mkdir(exist_ok=True)
'''LOG'''
args = parse_args()
logger = logging.getLogger("Model")
logger.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model))
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
log_string('PARAMETER ...')
log_string(args)
root = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/'
# file_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/train2.list'
val_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/val2.list'
# TRAIN_DATASET = KittiDataset(root = root, file_list=file_list, npoints=args.npoint, training=True, augment=True)
# trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=2)
TEST_DATASET = KittiDataset(root=root,
file_list=val_list,
npoints=args.npoint,
training=False,
augment=False)
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=2)
# log_string("The number of training data is: %d" % len(TRAIN_DATASET))
log_string("The number of test data is: %d" % len(TEST_DATASET))
# num_classes = 16
num_devices = args.num_gpus #torch.cuda.device_count()
# assert num_devices > 1, "Cannot detect more than 1 GPU."
# print(num_devices)
devices = list(range(num_devices))
target_device = devices[0]
# MODEL = importlib.import_module(args.model)
net = UNet(4, 20, nPlanes)
# net = MODEL.get_model(num_classes, normal_channel=args.normal)
net = net.to(target_device)
try:
checkpoint = torch.load(
str(experiment_dir) + '/checkpoints/best_model.pth')
start_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['model_state_dict'])
log_string('Use pretrain model')
except:
log_string('No existing model, starting training from scratch...')
quit()
if 1:
with torch.no_grad():
net.eval()
evaluator = iouEval(num_classes, ignore)
evaluator.reset()
# for iteration, (points, target, ins, mask) in tqdm(enumerate(testDataLoader), total=len(testDataLoader), smoothing=0.9):
for iteration, (points, target, ins,
mask) in enumerate(testDataLoader):
evaone = iouEval(num_classes, ignore)
evaone.reset()
cur_batch_size, NUM_POINT, _ = points.size()
if iteration > 128:
break
inputs, targets, masks = [], [], []
coords = []
for i in range(num_devices):
start = int(i * (cur_batch_size / num_devices))
end = int((i + 1) * (cur_batch_size / num_devices))
with torch.cuda.device(devices[i]):
pc = points[start:end, :, :].to(devices[i])
#feas = points[start:end,:,3:].to(devices[i])
targeti = target[start:end, :].to(devices[i])
maski = mask[start:end, :].to(devices[i])
locs, feas, label, maski, offsets = input_layer(
pc, targeti, maski, scale.to(devices[i]),
spatialSize.to(devices[i]), True)
# print(locs.size(), feas.size(), label.size(), maski.size(), offsets.size())
org_coords = locs[1]
label = Variable(label, requires_grad=False)
inputi = ME.SparseTensor(feas.cpu(), locs[0].cpu())
inputs.append([inputi.to(devices[i]), org_coords])
targets.append(label)
masks.append(maski)
replicas = parallel.replicate(net, devices)
outputs = parallel.parallel_apply(replicas,
inputs,
devices=devices)
seg_pred = outputs[0].cpu()
mask = masks[0].cpu()
target = targets[0].cpu()
loc = locs[0].cpu()
for i in range(1, num_devices):
seg_pred = torch.cat((seg_pred, outputs[i].cpu()), 0)
mask = torch.cat((mask, masks[i].cpu()), 0)
target = torch.cat((target, targets[i].cpu()), 0)
seg_pred = seg_pred[target > 0, :]
target = target[target > 0]
_, seg_pred = seg_pred.data.max(1) #[1]
target = target.data.numpy()
evaluator.addBatch(seg_pred, target)
evaone.addBatch(seg_pred, target)
cur_accuracy = evaone.getacc()
cur_jaccard, class_jaccard = evaone.getIoU()
print('%.4f %.4f' % (cur_accuracy, cur_jaccard))
m_accuracy = evaluator.getacc()
m_jaccard, class_jaccard = evaluator.getIoU()
log_string('Validation set:\n'
'Acc avg {m_accuracy:.3f}\n'
'IoU avg {m_jaccard:.3f}'.format(m_accuracy=m_accuracy,
m_jaccard=m_jaccard))
# print also classwise
for i, jacc in enumerate(class_jaccard):
if i not in ignore:
log_string(
'IoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
i=i,
class_str=class_strings[class_inv_remap[i]],
jacc=jacc))
def main():
parser = argparse.ArgumentParser(description="Train the model")
parser.add_argument('-trainf', "--train-filepath", type=str, default=None, required=True,
help="training dataset filepath.")
parser.add_argument('-validf', "--val-filepath", type=str, default=None,
help="validation dataset filepath.")
parser.add_argument("--shuffle", action="store_true", default=False,
help="Shuffle the dataset")
parser.add_argument("--load-weights", type=str, default=None,
help="load pretrained weights")
parser.add_argument("--load-model", type=str, default=None,
help="load pretrained model, entire model (filepath, default: None)")
parser.add_argument("--debug", action="store_true", default=False)
parser.add_argument('--epochs', type=int, default=30,
help='number of epochs to train (default: 30)')
parser.add_argument("--batch-size", type=int, default=32,
help="Batch size")
parser.add_argument('--img-shape', type=str, default="(1,512,512)",
help='Image shape (default "(1,512,512)"')
parser.add_argument("--num-cpu", type=int, default=10,
help="Number of CPUs to use in parallel for dataloader.")
parser.add_argument('--cuda', type=int, default=0,
help='CUDA visible device (use CPU if -1, default: 0)')
parser.add_argument('--cuda-non-deterministic', action='store_true', default=False,
help="sets flags for non-determinism when using CUDA (potentially fast)")
parser.add_argument('-lr', type=float, default=0.0005,
help='Learning rate')
parser.add_argument('--seed', type=int, default=0,
help='Seed (numpy and cuda if GPU is used.).')
parser.add_argument('--log-dir', type=str, default=None,
help='Save the results/model weights/logs under the directory.')
args = parser.parse_args()
# TODO: support image reshape
img_shape = tuple(map(int, args.img_shape.strip()[1:-1].split(",")))
if args.log_dir:
os.makedirs(args.log_dir, exist_ok=True)
best_model_path = os.path.join(args.log_dir, "model_weights.pth")
else:
best_model_path = None
if args.seed is not None:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda >= 0:
if args.cuda_non_deterministic:
printBlue("Warning: using CUDA non-deterministc. Could be faster but results might not be reproducible.")
else:
printBlue("Using CUDA deterministc. Use --cuda-non-deterministic might accelerate the training a bit.")
# Make CuDNN Determinist
torch.backends.cudnn.deterministic = not args.cuda_non_deterministic
# torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# TODO [OPT] enable multi-GPUs ?
# https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html
device = torch.device("cuda:{}".format(args.cuda) if torch.cuda.is_available()
and (args.cuda >= 0) else "cpu")
# ================= Build dataloader =================
# DataLoader
# transform_normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
# std=[0.5, 0.5, 0.5])
transform_normalize = transforms.Normalize(mean=[0.5],
std=[0.5])
# Warning: DO NOT use geometry transform (do it in the dataloader instead)
data_transform = transforms.Compose([
# transforms.ToPILImage(mode='F'), # mode='F' for one-channel image
# transforms.Resize((256, 256)) # NO
# transforms.RandomResizedCrop(256), # NO
# transforms.RandomHorizontalFlip(p=0.5), # NO
# WARNING, ISSUE: transforms.ColorJitter doesn't work with ToPILImage(mode='F').
# Need custom data augmentation functions: TODO: DONE.
# transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
# Use OpenCVRotation, OpenCVXXX, ... (our implementation)
# OpenCVRotation((-10, 10)), # angles (in degree)
transforms.ToTensor(), # already done in the dataloader
transform_normalize
])
geo_transform = GeoCompose([
OpenCVRotation(angles=(-10, 10),
scales=(0.9, 1.1),
centers=(-0.05, 0.05)),
# TODO add more data augmentation here
])
def worker_init_fn(worker_id):
# WARNING spawn start method is used,
# worker_init_fn cannot be an unpicklable object, e.g., a lambda function.
# A work-around for issue #5059: https://github.com/pytorch/pytorch/issues/5059
np.random.seed()
data_loader_train = {'batch_size': args.batch_size,
'shuffle': args.shuffle,
'num_workers': args.num_cpu,
# 'sampler': balanced_sampler,
'drop_last': True, # for GAN-like
'pin_memory': False,
'worker_init_fn': worker_init_fn,
}
data_loader_valid = {'batch_size': args.batch_size,
'shuffle': False,
'num_workers': args.num_cpu,
'drop_last': False,
'pin_memory': False,
}
train_set = LiTSDataset(args.train_filepath,
dtype=np.float32,
geometry_transform=geo_transform, # TODO enable data augmentation
pixelwise_transform=data_transform,
)
valid_set = LiTSDataset(args.val_filepath,
dtype=np.float32,
pixelwise_transform=data_transform,
)
dataloader_train = torch.utils.data.DataLoader(train_set, **data_loader_train)
dataloader_valid = torch.utils.data.DataLoader(valid_set, **data_loader_valid)
# =================== Build model ===================
# TODO: control the model by bash command
if args.load_weights:
model = UNet(in_ch=1,
out_ch=3, # there are 3 classes: 0: background, 1: liver, 2: tumor
depth=4,
start_ch=32, # 64
inc_rate=2,
kernel_size=5, # 3
padding=True,
batch_norm=True,
spec_norm=False,
dropout=0.5,
up_mode='upconv',
include_top=True,
include_last_act=False,
)
printYellow(f"Loading pretrained weights from: {args.load_weights}...")
model.load_state_dict(torch.load(args.load_weights))
printYellow("+ Done.")
elif args.load_model:
# load entire model
model = torch.load(args.load_model)
printYellow("Successfully loaded pretrained model.")
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.95)) # TODO
best_valid_loss = float('inf')
# TODO TODO: add learning decay
for epoch in range(args.epochs):
for valid_mode, dataloader in enumerate([dataloader_train, dataloader_valid]):
n_batch_per_epoch = len(dataloader)
if args.debug:
n_batch_per_epoch = 1
# infinite dataloader allows several update per iteration (for special models e.g. GAN)
dataloader = infinite_dataloader(dataloader)
if valid_mode:
printYellow("Switch to validation mode.")
model.eval()
prev_grad_mode = torch.is_grad_enabled()
torch.set_grad_enabled(False)
else:
model.train()
st = time.time()
cum_loss = 0
for iter_ind in range(n_batch_per_epoch):
supplement_logs = ""
# reset cumulated losses at the begining of each batch
# loss_manager.reset_losses() # TODO: use torch.utils.tensorboard !!
optimizer.zero_grad()
img, msk = next(dataloader)
img, msk = img.to(device), msk.to(device)
# TODO this is ugly: convert dtype and convert the shape from (N, 1, 512, 512) to (N, 512, 512)
msk = msk.to(torch.long).squeeze(1)
msk_pred = model(img) # shape (N, 3, 512, 512)
# label_weights is determined according the liver_ratio & tumor_ratio
# loss = CrossEntropyLoss(msk_pred, msk, label_weights=[1., 10., 100.], device=device)
loss = DiceLoss(msk_pred, msk, label_weights=[1., 20., 50.], device=device)
# loss = DiceLoss(msk_pred, msk, label_weights=[1., 20., 500.], device=device)
if valid_mode:
pass
else:
loss.backward()
optimizer.step()
loss = loss.item() # release
cum_loss += loss
if valid_mode:
print("\r--------(valid) {:.2%} Loss: {:.3f} (time: {:.1f}s) |supp: {}".format(
(iter_ind+1)/n_batch_per_epoch, cum_loss/(iter_ind+1), time.time()-st, supplement_logs), end="")
else:
print("\rEpoch: {:3}/{} {:.2%} Loss: {:.3f} (time: {:.1f}s) |supp: {}".format(
(epoch+1), args.epochs, (iter_ind+1)/n_batch_per_epoch, cum_loss/(iter_ind+1), time.time()-st, supplement_logs), end="")
print()
if valid_mode:
torch.set_grad_enabled(prev_grad_mode)
valid_mean_loss = cum_loss/(iter_ind+1) # validation (mean) loss of the current epoch
if best_model_path and (valid_mean_loss < best_valid_loss):
printGreen("Valid loss decreases from {:.5f} to {:.5f}, saving best model.".format(
best_valid_loss, valid_mean_loss))
best_valid_loss = valid_mean_loss
# Only need to save the weights
# torch.save(model.state_dict(), best_model_path)
# save the entire model
torch.save(model, best_model_path)
return best_valid_loss
# either train pseudolabeller or the net
# first 10 epochs train the pseudo labeller on edges
if e < epochs_pseudo:
edges_a = data['A'][2].cuda()
target_a = data['A'][1].cuda()
res_pseudo = pseudo.downsample(edges_a)
pred_seg_a = pseudo.upsample(*res_pseudo)
# pred_seg_a = pseudo(edges_a)
loss_seg_a = criterion(pred_seg_a, target_a)
loss_seg_a.backward()
optimiser_ps.step()
else:
pseudo.eval()
image_a = data['A'][0].cuda()
target_a = data['A'][1].cuda()
image_b = data['B'][0].cuda()
edges_b = data['B'][2].cuda()
pseudo_b = pseudo.downsample(edges_b)
pred_pseudo_b = pseudo.upsample(*pseudo_b)
# pred_pseudo_b = pseudo(edges_b)
target_b = torch.round(pred_pseudo_b).detach().cuda()
net.set_domain(DOMAIN_A)
res_a = net.downsample(image_a)
pred_seg_a = net.upsample(*res_a)
net.set_domain(DOMAIN_B)
def train_UNet():
cfg = UnetConfig()
train_transform = transforms.Compose([
GrayscaleNormalization(mean=0.5, std=0.5),
RandomRotation(),
RandomFlip(),
ToTensor(),
])
val_transform = transforms.Compose([
GrayscaleNormalization(mean=0.5, std=0.5),
ToTensor(),
])
# Set Dataset
train_dataset = Dataset(imgs_dir=TRAIN_IMGS_DIR,
labels_dir=TRAIN_LABELS_DIR,
transform=train_transform)
train_loader = DataLoader(train_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=True,
num_workers=0)
val_dataset = Dataset(imgs_dir=VAL_IMGS_DIR,
labels_dir=VAL_LABELS_DIR,
transform=val_transform)
val_loader = DataLoader(val_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=False,
num_workers=0)
train_data_num = len(train_dataset)
val_data_num = len(val_dataset)
train_batch_num = int(np.ceil(train_data_num / cfg.BATCH_SIZE)) # np.ceil
val_batch_num = int(np.ceil(val_data_num / cfg.BATCH_SIZE))
# Network
net = UNet().to(device)
print(count_parameters(net))
# Loss Function
loss_fn = nn.BCEWithLogitsLoss().to(device)
# Optimizer
optim = torch.optim.Adam(params=net.parameters(), lr=cfg.LEARNING_RATE)
# Tensorboard
# train_writer = SummaryWriter(log_dir=TRAIN_LOG_DIR)
# val_writer = SummaryWriter(log_dir=VAL_LOG_DIR)
# Training
start_epoch = 0
# Load Checkpoint File
if os.listdir(os.path.join(CKPT_DIR, 'unet')):
net, optim, start_epoch = load_net(ckpt_dir=os.path.join(
CKPT_DIR, 'unet'),
net=net,
optim=optim)
else:
print('* Training from scratch')
num_epochs = cfg.NUM_EPOCHS
for epoch in range(start_epoch + 1, num_epochs + 1):
net.train()
train_loss_arr = list()
for batch_idx, data in enumerate(train_loader, 1):
# Forward Propagation
img = data['img'].to(device)
label = data['label'].to(device)
output = net(img)
# Backward Propagation
optim.zero_grad()
loss = loss_fn(output, label)
loss.backward()
optim.step()
# Calc Loss Function
train_loss_arr.append(loss.item())
print_form = '[Train] | Epoch: {:0>4d} / {:0>4d} | Batch: {:0>4d} / {:0>4d} | Loss: {:.4f}'
print(
print_form.format(epoch, num_epochs, batch_idx,
train_batch_num, train_loss_arr[-1]))
train_loss_avg = np.mean(train_loss_arr)
# train_writer.add_scalar(tag='loss', scalar_value=train_loss_avg, global_step=epoch)
# Validation (No Back Propagation)
with torch.no_grad():
net.eval() # Evaluation Mode
val_loss_arr = list()
for batch_idx, data in enumerate(val_loader, 1):
# Forward Propagation
img = data['img'].to(device)
label = data['label'].to(device)
output = net(img)
# Calc Loss Function
loss = loss_fn(output, label)
val_loss_arr.append(loss.item())
print_form = '[Validation] | Epoch: {:0>4d} / {:0>4d} | Batch: {:0>4d} / {:0>4d} | Loss: {:.4f}'
print(
print_form.format(epoch, num_epochs, batch_idx,
val_batch_num, val_loss_arr[-1]))
val_loss_avg = np.mean(val_loss_arr)
# val_writer.add_scalar(tag='loss', scalar_value=val_loss_avg, global_step=epoch)
print_form = '[Epoch {:0>4d}] Training Avg Loss: {:.4f} | Validation Avg Loss: {:.4f}'
print(print_form.format(epoch, train_loss_avg, val_loss_avg))
if epoch % 10 == 0:
save_net(ckpt_dir=os.path.join(CKPT_DIR, 'unet'),
net=net,
optim=optim,
epoch=epoch)
def test_UNet():
cfg = UnetConfig()
transform = transforms.Compose([
GrayscaleNormalization(mean=0.5, std=0.5),
ToTensor(),
])
RESULTS_DIR = os.path.join(ROOT_DIR, 'test_results/unet')
if not os.path.exists(RESULTS_DIR):
os.makedirs(RESULTS_DIR)
label_save_path = os.path.join(RESULTS_DIR, 'label')
output_save_path = os.path.join(RESULTS_DIR, 'output')
if not os.path.exists(label_save_path):
os.makedirs(label_save_path, exist_ok=True)
if not os.path.exists(output_save_path):
os.makedirs(output_save_path, exist_ok=True)
test_dataset = Dataset(imgs_dir=TEST_IMGS_DIR,
labels_dir=TEST_LABELS_DIR,
transform=transform)
test_loader = DataLoader(test_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=False,
num_workers=0)
test_data_num = len(test_dataset)
test_batch_num = int(np.ceil(test_data_num / cfg.BATCH_SIZE))
# Network
net = UNet().to(device)
# Loss Function
loss_fn = nn.BCEWithLogitsLoss().to(device)
# Optimizer
optim = torch.optim.Adam(params=net.parameters(), lr=cfg.LEARNING_RATE)
start_epoch = 0
# Load Checkpoint File
if os.listdir(CKPT_DIR):
net, optim, _ = load_net(ckpt_dir=os.path.join(CKPT_DIR, 'unet'),
net=net,
optim=optim)
# Evaluation
with torch.no_grad():
net.eval()
loss_arr = list()
for batch_idx, data in enumerate(test_loader, 1):
# Forward Propagation
img = data['img'].to(device)
label = data['label'].to(device)
output = net(img)
# Calc Loss Function
loss = loss_fn(output, label)
loss_arr.append(loss.item())
print_form = '[Test] | Batch: {:0>4d} / {:0>4d} | Loss: {:.4f}'
print(print_form.format(batch_idx, test_batch_num, loss_arr[-1]))
label = to_numpy(label)
output = to_numpy(classify_class(output))
for j in range(label.shape[0]):
crt_id = int(test_batch_num * (batch_idx - 1) + j)
plt.imsave(os.path.join(label_save_path, f'{crt_id:04}.png'),
label[j].squeeze(),
cmap='gray')
plt.imsave(os.path.join(output_save_path, f'{crt_id:04}.png'),
output[j].squeeze(),
cmap='gray')
unet_acc(output_save_path, label_save_path)
def train(model_name=''):
# Init data
train_dataset, val_dataset = prepare_datasets()
train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=10, shuffle=True)
loaders = dict(train=train_loader, val=val_loader)
# Init Model
if model_name == '':
model = UNet().cuda()
else:
model = data_utils.load_model(model_name).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, amsgrad=True)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer,
gamma=0.984)
loss_fn = nn.BCELoss()
epochs = 500
epoch_losses = dict(train=[], val=[])
for epoch in range(epochs):
for phase in 'train val'.split():
if phase == 'train':
model = model.train()
torch.set_grad_enabled(True)
else:
model = model.eval()
torch.set_grad_enabled(False)
loader = loaders[phase]
running_loss = []
for batch in loader:
imgs, masks = batch
imgs = imgs.cuda()
masks = masks.cuda()
outputs = model(imgs)
loss = loss_fn(outputs, masks)
running_loss.append(loss.item())
if phase == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
# End of Epoch
print(f'{epoch}) {phase} loss: {np.mean(running_loss)}')
visualize_results(loader, model, epoch, phase)
if epoch % 10 == 0:
results_dir = 'weight/'
if not os.path.isdir(results_dir):
os.makedirs(results_dir)
data_utils.save_model(model, results_dir + f'model_{epoch}.pt')
epoch_losses[phase].append(np.mean(running_loss))
if phase == 'val':
df = pd.DataFrame(data=epoch_losses)
df.to_csv('loss.csv')
tensorboard(epoch_losses[phase], phase)
if phase == 'train':
scheduler.step()
def validate():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup Dataloader
# data_loader = get_loader(cfg["data"]["dataset"])
model_id = "20200404_00_UNet"
checkpoint_path = "../checkpoints/{}/checkpoint.pth.tar".format(model_id)
base_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
dataroot = os.path.join(os.path.dirname(base_path), "datasets")
if not os.path.exists(dataroot):
os.mkdir(dataroot)
n_classes = 21
model = UNet(n_channels=3, n_classes=21).to(device) # .cuda()
datasets = torchvision.datasets.VOCSegmentation(
dataroot,
year='2012',
image_set='train',
download=False,
transform=original_transform,
target_transform=teacher_transform)
# valloader = data.DataLoader(loader, batch_size=cfg["training"]["batch_size"], num_workers=8)
valloader = torch.utils.data.DataLoader(datasets,
batch_size=1,
shuffle=False)
running_metrics = runningScore(n_classes)
# Setup Model
model.load_state_dict(torch.load(checkpoint_path))
model.eval()
# model.to(device)
flag = False
for i, (images, labels) in enumerate(valloader):
start_time = timeit.default_timer()
images = images.to(device)
if flag:
outputs = model(images)
# Flip images in numpy (not support in tensor)
outputs = outputs.data.cpu().numpy()
flipped_images = np.copy(images.data.cpu().numpy()[:, :, :, ::-1])
flipped_images = torch.from_numpy(flipped_images).float().to(
device)
outputs_flipped = model(flipped_images)
outputs_flipped = outputs_flipped.data.cpu().numpy()
outputs = (outputs + outputs_flipped[:, :, :, ::-1]) / 2.0
pred = np.argmax(outputs, axis=1)
else:
outputs = model(images)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels.numpy()
if True:
elapsed_time = timeit.default_timer() - start_time
print("Inference time \
(iter {0:5d}): {1:3.5f} fps".format(
i + 1, pred.shape[0] / elapsed_time))
running_metrics.update(gt, pred)
score, class_iou = running_metrics.get_scores()
for k, v in score.items():
print(k, v)
for i in range(n_classes):
print(i, class_iou[i])
def evaluate_performance(args, gridargs, logger):
'''
-------------------------Hyperparameters--------------------------
'''
EPOCHS = args.epochs
ITER = args.iterations # per epoch
LR = gridargs['lr']
MOM = gridargs['mom']
# LOGInterval = args.log_interval
BATCHSIZE = args.batch_size
NUMBER_OF_WORKERS = args.workers
DATA_FOLDER = args.data
ROOT = gridargs['run']
CUSTOM_LOG_DIR = os.path.join(ROOT, "additionalLOGS")
# check existance of data
if not os.path.isdir(DATA_FOLDER):
print("data folder not existant or in wrong layout.\n\t", DATA_FOLDER)
exit(0)
'''
---------------------------preparations---------------------------
'''
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
print("using device: ", str(device))
'''
---------------------------loading dataset and normalizing---------------------------
'''
# Dataloader Parameters
train_params = {'batch_size': BATCHSIZE,
'shuffle': True,
'num_workers': NUMBER_OF_WORKERS}
test_params = {'batch_size': BATCHSIZE,
'shuffle': False,
'num_workers': NUMBER_OF_WORKERS}
# create a folder for the weights and custom logs
if not os.path.isdir(CUSTOM_LOG_DIR):
os.makedirs(CUSTOM_LOG_DIR)
traindata = CONRADataset(DATA_FOLDER,
True,
device=device,
precompute=True,
transform=None)
testdata = CONRADataset(DATA_FOLDER,
False,
device=device,
precompute=True,
transform=None)
trainingset = DataLoader(traindata, **train_params)
testset = DataLoader(testdata, **test_params)
if args.model == "unet":
m = UNet(2, 2).to(device)
else:
m = simpleConvNet(2, 2).to(device)
o = optim.SGD(m.parameters(),
lr=LR,
momentum=MOM)
loss_fn = nn.MSELoss()
test_loss = None
train_loss = None
'''
-----------------------------training-----------------------------
'''
global_step = 0
# calculating initial loss
if test_loss is None or train_loss is None:
print("calculating initial loss")
m.eval()
print("testset...")
test_loss = calculate_loss(set=testset, loss_fn=loss_fn, length_set=len(testdata), dev=device, model=m)
print("trainset...")
train_loss = calculate_loss(set=trainingset, loss_fn=loss_fn, length_set=len(traindata), dev=device, model=m)
# printing runtime information
print("starting training at {} for {} epochs {} iterations each\n\t{} total".format(0, EPOCHS, ITER, EPOCHS * ITER))
print("\tbatchsize: {}\n\tloss: {}\n".format(BATCHSIZE, train_loss))
print("\tmodel: {}\n\tlearningrate: {}\n\tmomentum: {}\n\tnorming output space: {}".format(args.model, LR, MOM, False))
#start actual training loops
for e in range(0, EPOCHS):
# iterations will not be interupted with validation and metrics
for i in range(ITER):
global_step = (e * ITER) + i
# training
m.train()
iteration_loss = 0
for x, y in tqdm(trainingset):
x, y = x.to(device=device, dtype=torch.float), y.to(device=device, dtype=torch.float)
pred = m(x)
loss = loss_fn(pred, y)
iteration_loss += loss.item()
o.zero_grad()
loss.backward()
o.step()
print("\niteration {}: --accumulated loss {}".format(global_step, iteration_loss))
if not np.isfinite(iteration_loss):
print("EXPLODING OR VANISHING GRADIENT at lr: {} mom: {} step: {}".format(LR, MOM, global_step))
return
# validation, saving and logging
print("\nvalidating")
m.eval() # disable dropout batchnorm etc
print("testset...")
test_loss = calculate_loss(set=testset, loss_fn=loss_fn, length_set=len(testdata), dev=device, model=m)
print("trainset...")
train_loss = calculate_loss(set=trainingset, loss_fn=loss_fn, length_set=len(traindata), dev=device, model=m)
print("calculating performace...")
currSSIM, currR = performance(set=testset, dev=device, model=m, bs=BATCHSIZE)
print("SSIM (iod/water): {}/{}\nR (iod/water): {}/{}".format(currSSIM[0], currSSIM[1], currR[0], currR[1]))
#f.write("num, lr, mom, step, ssimIOD, ssimWAT, rIOD, rWAT, trainLOSS, testLOSS\n")
with open(gridargs['stats'], 'a') as f:
newCSVline = "{}, {}, {}, {}, {}, {}, {}, {}, {}, {}\n".format(gridargs['runnum'], LR,
MOM, global_step,
currSSIM[0], currSSIM[1],
currR[0], currR[1],
train_loss, test_loss)
f.write(newCSVline)
print("wrote new line to csv:\n\t{}".format(newCSVline))
print("advanced metrics")
with torch.no_grad():
for x, y in testset:
# x, y in shape[2,2,480,620] [b,c,h,w]
x, y = x.to(device=device, dtype=torch.float), y.to(device=device, dtype=torch.float)
pred = m(x)
iod = pred.cpu().numpy()[0, 0, :, :]
water = pred.cpu().numpy()[0, 1, :, :]
gtiod = y.cpu().numpy()[0, 0, :, :]
gtwater = y.cpu().numpy()[0, 1, :, :]
IMAGE_LOG_DIR = os.path.join(CUSTOM_LOG_DIR, str(global_step))
if not os.path.isdir(IMAGE_LOG_DIR):
os.makedirs(IMAGE_LOG_DIR)
plt.imsave(os.path.join(IMAGE_LOG_DIR, 'iod' + str(global_step) + '.png'), iod, cmap='gray')
plt.imsave(os.path.join(IMAGE_LOG_DIR, 'water' + str(global_step) + '.png'), water, cmap='gray')
plt.imsave(os.path.join(IMAGE_LOG_DIR, 'gtiod' + str(global_step) + '.png'), gtiod, cmap='gray')
plt.imsave(os.path.join(IMAGE_LOG_DIR, 'gtwater' + str(global_step) + '.png'), gtwater, cmap='gray')
print("creating and saving profile plot at 240")
fig2, (ax1, ax2) = plt.subplots(nrows=2,
ncols=1) # plot water and iodine in one plot
ax1.plot(iod[240])
ax1.plot(gtiod[240])
ax1.title.set_text("iodine horizontal profile")
ax1.set_ylabel("mm iodine")
ax1.set_ylim([np.min(gtiod), np.max(gtiod)])
print("max value in gtiod is {}".format(np.max(gtiod)))
ax2.plot(water[240])
ax2.plot(gtwater[240])
ax2.title.set_text("water horizontal profile")
ax2.set_ylabel("mm water")
ax2.set_ylim([np.min(gtwater), np.max(gtwater)])
plt.subplots_adjust(wspace=0.3)
plt.savefig(os.path.join(IMAGE_LOG_DIR, 'ProfilePlots' + str(global_step) + '.png'))
break
if logger is not None and train_loss is not None:
logger.add_scalar('test_loss', test_loss, global_step=global_step)
logger.add_scalar('train_loss', train_loss, global_step=global_step)
logger.add_image("iodine-prediction", iod.reshape(1, 480, 620), global_step=global_step)
logger.add_image("ground-truth", gtiod.reshape(1, 480, 620), global_step=global_step)
# logger.add_image("water-prediction", wat)
print("\ttensorboard updated with test/train loss and a sample image")
# saving final results
CHECKPOINT = os.path.join(ROOT, "finalWeights.pt")
print("saving upon exit")
torch.save({
'epoch': EPOCHS,
'iterations': ITER,
'model_state_dict': m.state_dict(),
'optimizer_state_dict': o.state_dict(),
'train_loss': train_loss,
'test_loss': test_loss},
CHECKPOINT)
print('\tsaved progress to: ', CHECKPOINT)
if logger is not None and train_loss is not None:
logger.add_scalar('test_loss', test_loss, global_step=global_step)
logger.add_scalar('train_loss', train_loss, global_step=global_step)
def train(frame_num,
layer_nums,
input_channels,
output_channels,
discriminator_num_filters,
bn=False,
pretrain=False,
generator_pretrain_path=None,
discriminator_pretrain_path=None):
generator = UNet(n_channels=input_channels,
layer_nums=layer_nums,
output_channel=output_channels,
bn=bn)
discriminator = PixelDiscriminator(output_channels,
discriminator_num_filters,
use_norm=False)
generator = generator.cuda()
discriminator = discriminator.cuda()
flow_network = Network()
flow_network.load_state_dict(torch.load(lite_flow_model_path))
flow_network.cuda().eval()
adversarial_loss = Adversarial_Loss().cuda()
discriminate_loss = Discriminate_Loss().cuda()
gd_loss = Gradient_Loss(alpha, num_channels).cuda()
op_loss = Flow_Loss().cuda()
int_loss = Intensity_Loss(l_num).cuda()
step = 0
if not pretrain:
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
else:
assert (generator_pretrain_path != None
and discriminator_pretrain_path != None)
generator.load_state_dict(torch.load(generator_pretrain_path))
discriminator.load_state_dict(torch.load(discriminator_pretrain_path))
step = int(generator_pretrain_path.split('-')[-1])
print('pretrained model loaded!')
print('initializing the model with Generator-Unet {} layers,'
'PixelDiscriminator with filters {} '.format(
layer_nums, discriminator_num_filters))
optimizer_G = torch.optim.Adam(generator.parameters(), lr=g_lr)
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=d_lr)
writer = SummaryWriter(writer_path)
dataset = img_dataset.ano_pred_Dataset(training_data_folder, frame_num)
dataset_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1,
drop_last=True)
test_dataset = img_dataset.ano_pred_Dataset(testing_data_folder, frame_num)
test_dataloader = DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1,
drop_last=True)
for epoch in range(epochs):
for (input, _), (test_input, _) in zip(dataset_loader,
test_dataloader):
# generator = generator.train()
# discriminator = discriminator.train()
target = input[:, -1, :, :, :].cuda()
input = input[:, :-1, ]
input_last = input[:, -1, ].cuda()
input = input.view(input.shape[0], -1, input.shape[-2],
input.shape[-1]).cuda()
test_target = test_input[:, -1, ].cuda()
test_input = test_input[:, :-1].view(test_input.shape[0], -1,
test_input.shape[-2],
test_input.shape[-1]).cuda()
#------- update optim_G --------------
G_output = generator(input)
pred_flow_esti_tensor = torch.cat([input_last, G_output], 1)
gt_flow_esti_tensor = torch.cat([input_last, target], 1)
flow_gt = batch_estimate(gt_flow_esti_tensor, flow_network)
flow_pred = batch_estimate(pred_flow_esti_tensor, flow_network)
g_adv_loss = adversarial_loss(discriminator(G_output))
g_op_loss = op_loss(flow_pred, flow_gt)
g_int_loss = int_loss(G_output, target)
g_gd_loss = gd_loss(G_output, target)
g_loss = lam_adv * g_adv_loss + lam_gd * g_gd_loss + lam_op * g_op_loss + lam_int * g_int_loss
optimizer_G.zero_grad()
g_loss.backward()
optimizer_G.step()
train_psnr = psnr_error(G_output, target)
#----------- update optim_D -------
optimizer_D.zero_grad()
d_loss = discriminate_loss(discriminator(target),
discriminator(G_output.detach()))
#d_loss.requires_grad=True
d_loss.backward()
optimizer_D.step()
#----------- cal psnr --------------
test_generator = generator.eval()
test_output = test_generator(test_input)
test_psnr = psnr_error(test_output, test_target).cuda()
if step % 10 == 0:
print("[{}/{}]: g_loss: {} d_loss {}".format(
step, epoch, g_loss, d_loss))
print('\t gd_loss {}, op_loss {}, int_loss {} ,'.format(
g_gd_loss, g_op_loss, g_int_loss))
print('\t train psnr{}，test_psnr {}'.format(
train_psnr, test_psnr))
writer.add_scalar('psnr/train_psnr',
train_psnr,
global_step=step)
writer.add_scalar('psnr/test_psnr',
test_psnr,
global_step=step)
writer.add_scalar('total_loss/g_loss',
g_loss,
global_step=step)
writer.add_scalar('total_loss/d_loss',
d_loss,
global_step=step)
writer.add_scalar('g_loss/adv_loss',
g_adv_loss,
global_step=step)
writer.add_scalar('g_loss/op_loss',
g_op_loss,
global_step=step)
writer.add_scalar('g_loss/int_loss',
g_int_loss,
global_step=step)
writer.add_scalar('g_loss/gd_loss',
g_gd_loss,
global_step=step)
writer.add_image('image/train_target',
target[0],
global_step=step)
writer.add_image('image/train_output',
G_output[0],
global_step=step)
writer.add_image('image/test_target',
test_target[0],
global_step=step)
writer.add_image('image/test_output',
test_output[0],
global_step=step)
step += 1
if step % 500 == 0:
utils.saver(generator.state_dict(),
model_generator_save_path,
step,
max_to_save=10)
utils.saver(discriminator.state_dict(),
model_discriminator_save_path,
step,
max_to_save=10)
if step >= 2000:
print('==== begin evaluate the model of {} ===='.format(
model_generator_save_path + '-' + str(step)))
auc = evaluate(frame_num=5,
layer_nums=4,
input_channels=12,
output_channels=3,
model_path=model_generator_save_path + '-' +
str(step),
evaluate_name='compute_auc')
writer.add_scalar('results/auc', auc, global_step=step)
class Trainer:
def __init__(self, seq_length, color_channels, unet_path="pretrained/unet.mdl",
discrim_path="pretrained/dicrim.mdl",
facenet_path="pretrained/facenet.mdl",
vgg_path="",
embedding_size=1000,
unet_depth=3,
unet_filts=32,
facenet_filts=32,
resnet=18):
self.color_channels = color_channels
self.margin = 0.5
self.writer = SummaryWriter(log_dir="logs")
self.unet_path = unet_path
self.discrim_path = discrim_path
self.facenet_path = facenet_path
self.unet = UNet(in_channels=color_channels, out_channels=color_channels,
depth=unet_depth,
start_filts=unet_filts,
up_mode="upsample",
merge_mode='concat').to(device)
self.discrim = FaceNetModel(embedding_size=embedding_size, start_filts=facenet_filts,
in_channels=color_channels, resnet=resnet,
pretrained=False).to(device)
self.facenet = FaceNetModel(embedding_size=embedding_size, start_filts=facenet_filts,
in_channels=color_channels, resnet=resnet,
pretrained=False).to(device)
if os.path.isfile(unet_path):
self.unet.load_state_dict(torch.load(unet_path))
print("unet loaded")
if os.path.isfile(discrim_path):
self.discrim.load_state_dict(torch.load(discrim_path))
print("discrim loaded")
if os.path.isfile(facenet_path):
self.facenet.load_state_dict(torch.load(facenet_path))
print("facenet loaded")
if os.path.isfile(vgg_path):
self.vgg_loss_network = LossNetwork(vgg_face_dag(vgg_path)).to(device)
self.vgg_loss_network.eval()
print("vgg loaded")
self.mse_loss_function = nn.MSELoss().to(device)
self.discrim_loss_function = nn.BCELoss().to(device)
self.triplet_loss_function = TripletLoss(margin=self.margin)
self.unet_optimizer = torch.optim.Adam(self.unet.parameters(), betas=(0.9, 0.999))
self.discrim_optimizer = torch.optim.Adam(self.discrim.parameters(), betas=(0.9, 0.999))
self.facenet_optimizer = torch.optim.Adam(self.facenet.parameters(), betas=(0.9, 0.999))
def test(self, test_loader, epoch=0):
X, y = next(iter(test_loader))
B, D, C, W, H = X.shape
# X = X.view(B, C * D, W, H)
self.unet.eval()
self.facenet.eval()
self.discrim.eval()
with torch.no_grad():
y_ = self.unet(X.to(device))
mse = self.mse_loss_function(y_, y.to(device))
loss_G = self.loss_GAN_generator(btch_X=X.to(device))
loss_D = self.loss_GAN_discrimator(btch_X=X.to(device), btch_y=y.to(device))
loss_facenet, _, n_bad = self.loss_facenet(X.to(device), y.to(device))
plt.title(f"epoch {epoch} mse={mse.item():.4} facenet={loss_facenet.item():.4} bad={n_bad / B ** 2}")
i = np.random.randint(0, B)
a = np.hstack((y[i].transpose(0, 1).transpose(1, 2), y_[i].transpose(0, 1).transpose(1, 2).to(cpu)))
b = np.hstack((X[i][0].transpose(0, 1).transpose(1, 2),
X[i][-1].transpose(0, 1).transpose(1, 2)))
plt.imshow(np.vstack((a, b)))
plt.axis('off')
plt.show()
self.writer.add_scalar("test bad_percent", n_bad / B ** 2, global_step=epoch)
self.writer.add_scalar("test loss", mse.item(), global_step=epoch)
# self.writer.add_scalars("test GAN", {"discrim": loss_D.item(),
# "gen": loss_G.item()}, global_step=epoch)
with torch.no_grad():
n_for_show = 10
y_show_ = y_.to(device)
y_show = y.to(device)
embeddings_anc, _ = self.facenet(y_show_)
embeddings_pos, _ = self.facenet(y_show)
embeds = torch.cat((embeddings_anc[:n_for_show], embeddings_pos[:n_for_show]))
imgs = torch.cat((y_show_[:n_for_show], y_show[:n_for_show]))
names = list(range(n_for_show)) * 2
# print(embeds.shape, imgs.shape, len(names))
# self.writer.add_embedding(mat=embeds, metadata=names, label_img=imgs, tag="embeddings", global_step=epoch)
trshs, fprs, tprs = roc_curve(embeddings_anc.detach().to(cpu), embeddings_pos.detach().to(cpu))
rnk1 = rank1(embeddings_anc.detach().to(cpu), embeddings_pos.detach().to(cpu))
plt.step(fprs, tprs)
# plt.xlim((1e-4, 1))
plt.yticks(np.arange(0, 1, 0.05))
plt.xticks(np.arange(min(fprs), max(fprs), 10))
plt.xscale('log')
plt.title(f"ROC auc={auc(fprs, tprs)} rnk1={rnk1}")
self.writer.add_figure("ROC test", plt.gcf(), global_step=epoch)
self.writer.add_scalar("auc", auc(fprs, tprs), global_step=epoch)
self.writer.add_scalar("rank1", rnk1, global_step=epoch)
print(f"\n###### {epoch} TEST mse={mse.item():.4} GAN(G/D)={loss_G.item():.4}/{loss_D.item():.4} "
f"facenet={loss_facenet.item():.4} bad={n_bad / B ** 2:.4} auc={auc(fprs, tprs)} rank1={rnk1} #######")
def test_test(self, test_loader):
X, ys = next(iter(test_loader))
true_idx = 0
x = X[true_idx]
D, C, W, H = x.shape
# x = x.view(C * D, W, H)
dists = list()
with torch.no_grad():
y_ = self.unet(x.to(device))
embedding_anc, _ = self.facenet(y_)
embeddings_pos, _ = self.facenet(ys)
for emb_pos_item in embeddings_pos:
dist = l2_dist.forward(embedding_anc, emb_pos_item)
dists.append(dist)
a_sorted = np.argsort(dists)
a = np.hstack((ys[true_idx].transpose(0, 1).transpose(1, 2),
y_.transpose(0, 1).transpose(1, 2).to(cpu).numpy(),
ys[a_sorted[0]].transpose(0, 1).transpose(1, 2)))
b = np.hstack((x[0:3].transpose(0, 1).transpose(1, 2),
x[D // 2 * C:D // 2 * C + 3].transpose(0, 1).transpose(1, 2),
x[-3:].transpose(0, 1).transpose(1, 2)))
b_ = b - np.min(b)
b_ = b_ / np.max(b)
b_ = equalize_func([(b_ * 255).astype(np.uint8)], use_clahe=True)[0]
b = b_.astype(np.float32) / 255
plt.imshow(cv2.cvtColor(np.vstack((a, b)), cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.show()
def loss_facenet(self, X, y, is_detached=False):
B, D, C, W, H = X.shape
y_ = self.unet(X)
embeddings_anc, D_fake = self.facenet(y_ if not is_detached else y_.detach())
embeddings_pos, D_real = self.facenet(y)
target_real = torch.full_like(D_fake, 1)
loss_gen = self.discrim_loss_function(D_fake, target_real)
pos_dist = l2_dist.forward(embeddings_anc, embeddings_pos)
bad_triplets_loss = None
n_bad = 0
for shift in range(1, B):
embeddings_neg = torch.roll(embeddings_pos, shift, 0)
neg_dist = l2_dist.forward(embeddings_anc, embeddings_neg)
bad_triplets_idxs = np.where((neg_dist - pos_dist < self.margin).cpu().numpy().flatten())[0]
if shift == 1:
bad_triplets_loss = self.triplet_loss_function.forward(embeddings_anc[bad_triplets_idxs],
embeddings_pos[bad_triplets_idxs],
embeddings_neg[bad_triplets_idxs]).to(
device)
else:
bad_triplets_loss += self.triplet_loss_function.forward(embeddings_anc[bad_triplets_idxs],
embeddings_pos[bad_triplets_idxs],
embeddings_neg[bad_triplets_idxs]).to(device)
n_bad += len(bad_triplets_idxs)
bad_triplets_loss /= B
return bad_triplets_loss, torch.mean(loss_gen), n_bad
# def loss_mse(self, btch_X, btch_y):
# btch_y_ = self.unet(btch_X)
# loss_unet = self.mse_loss_function(btch_y_, btch_y)
#
# features_target = self.facenet.forward_mse(btch_y)
# features = self.facenet.forward_mse(btch_y_)
#
# loss_first_layer = self.mse_loss_function(features, features_target)
# return loss_unet + loss_first_layer
def loss_mse_vgg(self, btch_X, btch_y, k_mse, k_vgg):
btch_y_ = self.unet(btch_X)
# print(btch_y_.shape,btch_y.shape)
perceptual_btch_y_ = self.vgg_loss_network(btch_y_)
perceptual_btch_y = self.vgg_loss_network(btch_y)
perceptual_loss = 0.0
for a, b in zip(perceptual_btch_y_, perceptual_btch_y):
perceptual_loss += self.mse_loss_function(a, b)
return k_vgg * perceptual_loss + k_mse * self.mse_loss_function(btch_y_, btch_y)
def loss_GAN_discrimator(self, btch_X, btch_y):
btch_y_ = self.unet(btch_X)
_, y_D_fake_ = self.discrim(btch_y_.detach())
_, y_D_real_ = self.discrim(btch_y)
target_fake = torch.full_like(y_D_fake_, 0)
target_real = torch.full_like(y_D_real_, 1)
loss_D_fake_ = self.discrim_loss_function(y_D_fake_, target_fake)
loss_D_real_ = self.discrim_loss_function(y_D_real_, target_real)
loss_discrim = (loss_D_real_ + loss_D_fake_)
return loss_discrim
def loss_GAN_generator(self, btch_X):
btch_y_ = self.unet(btch_X)
_, y_D_fake_ = self.discrim(btch_y_)
target_real = torch.full_like(y_D_fake_, 1)
loss_gen = self.discrim_loss_function(y_D_fake_, target_real)
return loss_gen
def relax_discriminator(self, btch_X, btch_y):
self.discrim.zero_grad()
# train with real
y_discrim_real_ = self.discrim(btch_y)
y_discrim_real_ = y_discrim_real_.mean()
y_discrim_real_.backward(self.mone)
# train with fake
btch_y_ = self.unet(btch_X)
y_discrim_fake_detached_ = self.discrim(btch_y_.detach())
y_discrim_fake_detached_ = y_discrim_fake_detached_.mean()
y_discrim_fake_detached_.backward(self.one)
# gradient_penalty
gradient_penalty = self.discrim_gradient_penalty(btch_y, btch_y_)
gradient_penalty.backward()
self.discrim_optimizer.step()
def relax_generator(self, btch_X):
self.unet.zero_grad()
btch_y_ = self.unet(btch_X)
y_discrim_fake_ = self.discrim(btch_y_)
y_discrim_fake_ = y_discrim_fake_.mean()
y_discrim_fake_.backward(self.mone)
self.unet_optimizer.step()
def discrim_gradient_penalty(self, real_y, fake_y):
lambd = 10
btch_size = real_y.shape[0]
alpha = torch.rand(btch_size, 1, 1, 1).to(device)
# print(alpha.shape, real_y.shape)
alpha = alpha.expand_as(real_y)
interpolates = alpha * real_y + (1 - alpha) * fake_y
interpolates = interpolates.to(device)
interpolates = autograd.Variable(interpolates, requires_grad=True)
interpolates_out = self.discrim(interpolates)
gradients = autograd.grad(outputs=interpolates_out, inputs=interpolates,
grad_outputs=torch.ones(interpolates_out.size()).to(device),
create_graph=True, retain_graph=True, only_inputs=True)[0]
gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * lambd
return gradient_penalty
def train(self, train_loader, test_loader, batch_size=2, epochs=30,
k_gen=1, k_discrim=1, k_mse=1, k_facenet=1, k_facenet_back=1, k_vgg=1):
"""
:param X: np.array shape=(n_videos, n_frames, h, w)
:param y: np.array shape=(n_videos, h, w)
:param epochs: int
"""
print("\nSTART TRAINING\n")
for epoch in range(epochs):
self.test(test_loader, epoch)
self.unet.train()
self.facenet.train()
self.discrim.train()
# train by batches
for idx, (btch_X, btch_y) in enumerate(train_loader):
B, D, C, W, H = btch_X.shape
# btch_X = btch_X.view(B, C * D, W, H)
btch_X = btch_X.to(device)
btch_y = btch_y.to(device)
# Mse loss
self.unet.zero_grad()
mse = self.loss_mse_vgg(btch_X, btch_y, k_mse, k_vgg)
mse.backward()
self.unet_optimizer.step()
# facenet_backup = deepcopy(self.facenet.state_dict())
# for i in range(unrolled_iterations):
self.discrim.zero_grad()
loss_D = self.loss_GAN_discrimator(btch_X, btch_y)
loss_D = k_discrim * loss_D
loss_D.backward()
self.discrim_optimizer.step()
self.discrim.zero_grad()
self.unet.zero_grad()
loss_G = self.loss_GAN_generator(btch_X)
loss_G = k_gen * loss_G
loss_G.backward()
self.unet_optimizer.step()
# Facenet
self.unet.zero_grad()
self.facenet.zero_grad()
facenet_loss, _, n_bad = self.loss_facenet(btch_X, btch_y)
facenet_loss = k_facenet * facenet_loss
facenet_loss.backward()
self.facenet_optimizer.step()
self.unet.zero_grad()
self.facenet.zero_grad()
facenet_back_loss, _, n_bad = self.loss_facenet(btch_X, btch_y)
facenet_back_loss = k_facenet_back * facenet_back_loss
facenet_back_loss.backward()
self.unet_optimizer.step()
print(f"btch {idx * batch_size} mse={mse.item():.4} GAN(G/D)={loss_G.item():.4}/{loss_D.item():.4} "
f"facenet={facenet_loss.item():.4} bad={n_bad / B ** 2:.4}")
global_step = epoch * len(train_loader.dataset) // batch_size + idx
self.writer.add_scalar("train bad_percent", n_bad / B ** 2, global_step=global_step)
self.writer.add_scalar("train loss", mse.item(), global_step=global_step)
# self.writer.add_scalars("train GAN", {"discrim": loss_D.item(),
# "gen": loss_G.item()}, global_step=global_step)
torch.save(self.unet.state_dict(), self.unet_path)
torch.save(self.discrim.state_dict(), self.discrim_path)
torch.save(self.facenet.state_dict(), self.facenet_path)
