def train():
args = setup_run_arguments()
# args = parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"[INFO] Initializing UNet-model using: {device}")
net = UNet(n_channels=args.n_channels, n_classes=args.n_classes, bilinear=True)
if args.from_pretrained:
net.load_state_dict(torch.load(args.from_pretrained, map_location=device))
net.to(device=device)
training_loop.run(network=net,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.learning_rate,
device=device,
n_classes=args.n_classes,
val_percent=args.val_percent,
image_dir=args.image_dir,
mask_dir=args.mask_dir,
checkpoint_path=args.checkpoint_path,
loss=args.loss,
num_workers=args.num_workers
)
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
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 load_finetuned_model(args, baseline_model):
"""
:param args:
:param baseline_model:
:return:
"""
# augment_net = Net(0, 0.0, 32, 3, 0.0, num_classes=32**2 * 3, do_res=True)
augment_net = UNet(in_channels=3, n_classes=3, depth=1, wf=2, padding=True, batch_norm=False,
do_noise_channel=True,
up_mode='upsample', use_identity_residual=True) # TODO(PV): Initialize UNet properly
# TODO (JON): DEPTH 1 WORKED WELL. Changed upconv to upsample. Use a wf of 2.
# This ResNet outputs scalar weights to be applied element-wise to the per-example losses
from models.simple_models import CNN, Net
imsize, in_channel, num_classes = 32, 3, 10
reweighting_net = Net(0, 0.0, imsize, in_channel, 0.0, num_classes=1)
#resnet_cifar.resnet20(num_classes=1)
if args.load_finetune_checkpoint:
checkpoint = torch.load(args.load_finetune_checkpoint)
baseline_model.load_state_dict(checkpoint['elementary_model_state_dict'])
augment_net.load_state_dict(checkpoint['augment_model_state_dict'])
try:
reweighting_net.load_state_dict(checkpoint['reweighting_model_state_dict'])
except KeyError:
pass
augment_net, reweighting_net, baseline_model = augment_net.cuda(), reweighting_net.cuda(), baseline_model.cuda()
augment_net.train(), reweighting_net.train(), baseline_model.train()
return augment_net, reweighting_net, baseline_model
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
def prediction_to_json(image_path, chkp_path, net=None) -> dict:
"""
Convert mask prediction to json. The format matches the format in the training annotation data:
{'filename':file_name, 'labels':
[{'name': label_name, 'annotations': [{'id':some_unique_integer_id, 'segmentation':[x,y,x,y,x,y....]}
....] }
....]
}
"""
file_name = os.path.basename(image_path)
annotation = {'filename': file_name, 'labels': []}
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if not net:
net = UNet(n_channels=3, n_classes=4)
net.to(device=device)
net.load_state_dict(torch.load(chkp_path, map_location=device))
img = Image.open(image_path)
msk = predict_on_image(net=net, device=device, src_img=img)
msk = msk.transpose((1, 2, 0))
h, w, n_labels = msk.shape
rgb_mask = np.ones((h, w, 3), dtype=np.uint8)
annotation['height'] = h
annotation['width'] = w
for label in range(1, n_labels):
color = hex_labels[str(label)]
category = category_labels[str(label)]
c_label = {'color': color, 'name': category, 'annotations': []}
label_mask = msk[:, :, label].astype(int).astype(np.uint8)
contours, hierarchy = cv2.findContours(label_mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
vector_points = []
for x, y in contour.reshape((len(contour), 2)):
vector_points += [float(x), float(y)]
c_label['annotations'].append({'segmentation': vector_points})
idx = np.where(msk[:, :, label].astype(int) == 1)
rgb_mask[idx] = colors_from_hex[str(label)]
annotation['labels'].append(c_label)
return annotation
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 load_finetuned_model(self, baseline_model):
"""
Loads the augmentation net, sample reweighting net, and baseline model
Note: sets all these models to train mode
"""
# augment_net = Net(0, 0.0, 32, 3, 0.0, num_classes=32**2 * 3, do_res=True)
if self.args.dataset == DATASET_MNIST:
imsize, in_channel, num_classes = 28, 1, 10
else:
imsize, in_channel, num_classes = 32, 3, 10
augment_net = UNet(
in_channels=in_channel,
n_classes=in_channel,
depth=2,
wf=3,
padding=True,
batch_norm=False,
do_noise_channel=True,
up_mode='upconv',
use_identity_residual=True) # TODO(PV): Initialize UNet properly
# TODO (JON): DEPTH 1 WORKED WELL. Changed upconv to upsample. Use a wf of 2.
# This ResNet outputs scalar weights to be applied element-wise to the per-example losses
reweighting_net = Net(1, 0.0, imsize, in_channel, 0.0, num_classes=1)
# resnet_cifar.resnet20(num_classes=1)
if self.args.load_finetune_checkpoint:
checkpoint = torch.load(self.args.load_finetune_checkpoint)
# temp_baseline_model = baseline_model
# baseline_model.load_state_dict(checkpoint['elementary_model_state_dict'])
if 'weight_decay' in checkpoint:
baseline_model.weight_decay = checkpoint['weight_decay']
# baseline_model.weight_decay = temp_baseline_model.weight_decay
# baseline_model.load_state_dict(checkpoint['elementary_model_state_dict'])
augment_net.load_state_dict(checkpoint['augment_model_state_dict'])
try:
reweighting_net.load_state_dict(
checkpoint['reweighting_model_state_dict'])
except KeyError:
pass
augment_net, reweighting_net, baseline_model = augment_net.to(
self.device), reweighting_net.to(self.device), baseline_model.to(
self.device)
augment_net.train(), reweighting_net.train(), baseline_model.train()
return augment_net, reweighting_net, baseline_model
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)
num_workers=5,
pin_memory=True)
test_b_loader = DataLoader(test_b_dataset,
batch_size=1,
shuffle=False,
num_workers=5,
pin_memory=True)
# net and optimizer
ds_unet = UNet(1, 1, domain_specific=True)
ds_unet.cuda()
labeller = UNet(1, 1)
# import weights here...
labeller_path = './results/unet_sobel_eadan_in/net'
labeller.load_state_dict(
torch.load(os.path.join(labeller_path),
map_location=lambda storage, loc: storage))
labeller.cuda()
optimiser = optim.Adam(ds_unet.parameters(), lr=learning_rate)
print('Project name ', project_name)
train_dices = []
train_losses = []
val_a_dices = []
val_a_losses = []
val_b_dices = []
val_b_losses = []
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save):
os.makedirs(args.save)
model = UNet(3, n_classes=args.num_classes)
saved_state_dict = torch.load(args.restore_from)
model.load_state_dict(saved_state_dict)
model.cuda(gpu0)
model.train()
testloader = data.DataLoader(REFUGE(False,
domain='REFUGE_TEST',
is_transform=True),
batch_size=args.batch_size,
shuffle=False,
pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(460, 460),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(460, 460), mode='bilinear')
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd' % index)
image, label, _, _, name = batch
if args.model == 'Unet':
_, _, _, _, output2 = model(
Variable(image, volatile=True).cuda(gpu0))
output = interp(output2).cpu().data.numpy()
for idx, one_name in enumerate(name):
pred = output[idx]
pred = pred.transpose(1, 2, 0)
pred = np.asarray(np.argmax(pred, axis=2), dtype=np.uint8)
output_col = colorize_mask(pred)
if is_polar:
# plt.imshow(output_col)
# plt.show()
output_col = np.array(output_col)
output_col[output_col == 0] = 0
output_col[output_col == 1] = 128
output_col[output_col == 2] = 255
# plt.imshow(output_col)
# plt.show()
output_col = cv2.linearPolar(
rotate(output_col, 90),
(args.ROI_size / 2, args.ROI_size / 2), args.ROI_size / 2,
cv2.WARP_FILL_OUTLIERS + cv2.WARP_INVERSE_MAP)
# plt.imshow(output_col)
# plt.show()
output_col = np.array(output_col * 255, dtype=np.uint8)
output_col[output_col > 200] = 210
output_col[output_col == 0] = 255
output_col[output_col == 210] = 0
output_col[(output_col > 0) & (output_col < 255)] = 128
output_col = Image.fromarray(output_col)
# plt.imshow(output_col)
# plt.show()
one_name = one_name.split('/')[-1]
if len(one_name.split('_')) > 0:
one_name = one_name[:-4]
#pred.save('%s/%s.bmp' % (args.save, one_name))
output_col = output_col.convert('L')
print(output_col.size)
output_col.save('%s/%s.bmp' % (args.save, one_name.split('.')[0]))
}, {
'cmap': 'jet',
'vmin': 0,
'vmax': eval_label.max()
})
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)
def evaluate(frame_num,
layer_nums,
input_channels,
output_channels,
model_path,
evaluate_name,
bn=False):
'''
:param frame_num:
:param layer_nums:
:param input_channels:
:param output_channels:
:param model_path:
:param evaluate_name: compute_auc
:param bn:
:return:
'''
generator = UNet(n_channels=input_channels,
layer_nums=layer_nums,
output_channel=output_channels,
bn=bn).cuda().eval()
video_dirs = os.listdir(testing_data_folder)
video_dirs.sort()
num_videos = len(video_dirs)
time_stamp = time.time()
psnr_records = []
total = 0
generator.load_state_dict(torch.load(model_path))
for dir in video_dirs:
_temp_test_folder = os.path.join(testing_data_folder, dir)
dataset = img_dataset.test_dataset(_temp_test_folder,
clip_length=frame_num)
len_dataset = dataset.pics_len
test_iters = len_dataset - frame_num + 1
test_counter = 0
data_loader = DataLoader(dataset=dataset,
batch_size=1,
shuffle=False,
num_workers=1)
psnrs = np.empty(shape=(len_dataset, ), dtype=np.float32)
for test_input, _ in data_loader:
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()
g_output = generator(test_input)
test_psnr = psnr_error(g_output, test_target)
test_psnr = test_psnr.tolist()
psnrs[test_counter + frame_num - 1] = test_psnr
test_counter += 1
total += 1
if test_counter >= test_iters:
psnrs[:frame_num - 1] = psnrs[frame_num - 1]
psnr_records.append(psnrs)
print('finish test video set {}'.format(_temp_test_folder))
break
result_dict = {
'dataset': dataset_name,
'psnr': psnr_records,
'flow': [],
'names': [],
'diff_mask': []
}
used_time = time.time() - time_stamp
print('total time = {}, fps = {}'.format(used_time, total / used_time))
pickle_path = os.path.join(psnr_dir, os.path.split(model_path)[-1])
with open(pickle_path, 'wb') as writer:
pickle.dump(result_dict, writer, pickle.HIGHEST_PROTOCOL)
results = eval_metric.evaluate(evaluate_name, pickle_path)
print(results)
def train_eval_model(opts):
# parse model configuration
num_epochs = opts["num_epochs"]
train_batch_size = opts["train_batch_size"]
val_batch_size = opts["eval_batch_size"]
dataset_type = opts["dataset_type"]
opti_mode = opts["optimizer"]
loss_criterion = opts["loss_criterion"]
lr = opts["lr"]
lr_decay = opts["lr_decay"]
wd = opts["weight_decay"]
gpus = opts["gpu_list"].split(',')
os.environ['CUDA_VISIBLE_DEVICE'] = opts["gpu_list"]
train_dir = opts["log_dir"]
train_data_dir = opts["train_data_dir"]
eval_data_dir = opts["eval_data_dir"]
pretrained = opts["pretrained_model"]
resume = opts["resume"]
display_iter = opts["display_iter"]
save_epoch = opts["save_every_epoch"]
show = opts["vis"]
# backup train configs
log_file = os.path.join(train_dir, "log_file.txt")
os.makedirs(train_dir, exist_ok=True)
model_dir = os.path.join(train_dir, "code_backup")
os.makedirs(model_dir, exist_ok=True)
if resume is None and os.path.exists(log_file): os.remove(log_file)
shutil.copy("./models/unet.py", os.path.join(model_dir, "unet.py"))
shutil.copy("./trainer_unet.py", os.path.join(model_dir,
"trainer_unet.py"))
shutil.copy("./datasets/dataset.py", os.path.join(model_dir, "dataset.py"))
ckt_dir = os.path.join(train_dir, "checkpoints")
os.makedirs(ckt_dir, exist_ok=True)
# format printing configs
print("*" * 50)
table_key = []
table_value = []
n = 0
for key, value in opts.items():
table_key.append(key)
table_value.append(str(value))
n += 1
print_table([table_key, ["="] * n, table_value])
# format gpu list
gpu_list = []
for str_id in gpus:
id = int(str_id)
gpu_list.append(id)
# dataloader
print("==> Create dataloader")
dataloaders_dict = {
"train":
er_data_loader(train_data_dir,
train_batch_size,
dataset_type,
is_train=True),
"eval":
er_data_loader(eval_data_dir,
val_batch_size,
dataset_type,
is_train=False)
}
# define parameters of two networks
print("==> Create network")
num_channels = 1
num_classes = 1
model = UNet(num_channels, num_classes)
init_weights(model)
# loss layer
criterion = create_criterion(criterion=loss_criterion)
best_acc = 0.0
start_epoch = 0
# load pretrained model
if pretrained is not None and os.path.isfile(pretrained):
print("==> Train from model '{}'".format(pretrained))
checkpoint_gan = torch.load(pretrained)
model.load_state_dict(checkpoint_gan['model_state_dict'])
print("==> Loaded checkpoint '{}')".format(pretrained))
for param in model.parameters():
param.requires_grad = False
# resume training
elif resume is not None and os.path.isfile(resume):
print("==> Resume from checkpoint '{}'".format(resume))
checkpoint = torch.load(resume)
start_epoch = checkpoint['epoch'] + 1
best_acc = checkpoint['best_acc']
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in checkpoint['model_state_dict'].items()
if k in model_dict and v.size() == model_dict[k].size()
}
model_dict.update(pretrained_dict)
model.load_state_dict(pretrained_dict)
print("==> Loaded checkpoint '{}' (epoch {})".format(
resume, checkpoint['epoch'] + 1))
# train from scratch
else:
print("==> Train from initial or random state.")
# define mutiple-gpu mode
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.cuda()
model = nn.DataParallel(model)
# print learnable parameters
print("==> List learnable parameters")
for name, param in model.named_parameters():
if param.requires_grad == True:
print("\t{}, size {}".format(name, param.size()))
params_to_update = [{'params': model.parameters()}]
# define optimizer
print("==> Create optimizer")
optimizer = create_optimizer(params_to_update,
opti_mode,
lr=lr,
momentum=0.9,
wd=wd)
if resume is not None and os.path.isfile(resume):
optimizer.load_state_dict(checkpoint['optimizer'])
# start training
since = time.time()
# Each epoch has a training and validation phase
print("==> Start training")
total_steps = 0
for epoch in range(start_epoch, num_epochs):
print('-' * 50)
print("==> Epoch {}/{}".format(epoch + 1, num_epochs))
total_steps = train_one_epoch(epoch, total_steps,
dataloaders_dict['train'], model, device,
criterion, optimizer, lr, lr_decay,
display_iter, log_file, show)
epoch_acc, epoch_iou, epoch_f1 = eval_one_epoch(
epoch, dataloaders_dict['eval'], model, device, log_file)
if best_acc < epoch_acc and epoch >= 5:
best_acc = epoch_acc
torch.save(
{
'epoch': epoch,
'model_state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_acc': best_acc
}, os.path.join(ckt_dir, "best.pth"))
if (epoch + 1) % save_epoch == 0 and (epoch + 1) >= 20:
torch.save(
{
'epoch': epoch,
'model_state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_iou': epoch_iou
},
os.path.join(ckt_dir,
"checkpoints_" + str(epoch + 1) + ".pth"))
time_elapsed = time.time() - since
time_message = 'Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60)
print(time_message)
with open(log_file, "a+") as fid:
fid.write('%s\n' % time_message)
print('==> Best val Acc: {:4f}'.format(best_acc))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'{torch.cuda.device_count()} cuda device available.')
print(f'Using {device} device.')
batch_size = args.batch_size
if torch.cuda.device_count() > 1:
batch_size *= torch.cuda.device_count()
testset = MaskFolder(args.dataset,
transform=Compose([Resize((512, 512)),
ToTensor()]))
testloader = DataLoader(testset,
batch_size=batch_size,
shuffle=False,
num_workers=batch_size,
pin_memory=True,
drop_last=False)
model = UNet(3, 1)
model.load_state_dict(torch.load(args.weights))
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.to(device)
prec, reca, f1 = test(model,
tqdm(testloader,
desc=f'Testing threshold {args.threshold}'),
device,
threshold=args.threshold)
print(f'Precision {prec}. Recall {reca}. F1 {f1}.')
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
datasets = torchvision.datasets.VOCSegmentation(
dataroot,
year='2012',
image_set='train',
download=False,
transform=original_transform,
target_transform=teacher_transform)
train_loader = torch.utils.data.DataLoader(datasets,
batch_size=1,
shuffle=False)
model = UNet(n_channels=3, n_classes=21) #.cuda()
model.load_state_dict(torch.load(checkpoint_path))
model.eval()
with torch.no_grad():
# data, target = iter(train_loader).next()
abab = 0
for data, target in train_loader:
# output = model(data).data
abab += 1
# data = data.cuda()
# output = model(data).data # [4, 21, 512, 512]
img = Image.fromarray(data[0].detach().cpu().transpose(0, 1).transpose(
1, 2).numpy().astype(np.uint8))
y = output[0].detach().cpu()
anno_class_img = Image.fromarray(np.uint8(np.argmax(y.numpy(), axis=0)),
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)
class Noise2Noise(object):
"""Implementation of Noise2Noise from Lehtinen et al. (2018)."""
def __init__(self, params, trainable):
"""Initializes model."""
self.p = params
self.trainable = trainable
self._compile() #初始化模型
def _compile(self):
"""
Compiles model (architecture, loss function, optimizers, etc.).
初始化 网络、损失函数、优化器等
"""
print('Noise2Noise: Learning Image Restoration without Clean Data (Lethinen et al., 2018)')
# Model (3x3=9 channels for Monte Carlo since it uses 3 HDR buffers) 已删除蒙特卡洛相关代码
if self.p.noise_type == 'mc':
self.is_mc = True
self.model = UNet(in_channels=9)
else:
self.is_mc = False
self.model = UNet(in_channels=3)
# Set optimizer and loss, if in training mode
# 如果 为训练，则初始化优化器和损失
if self.trainable:
self.optim = Adam(self.model.parameters(),
lr=self.p.learning_rate,
betas=self.p.adam[:2],
eps=self.p.adam[2])
# Learning rate adjustment
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optim,
patience=self.p.nb_epochs/4, factor=0.5, verbose=True)
# Loss function
if self.p.loss == 'hdr':
assert self.is_mc, 'Using HDR loss on non Monte Carlo images'
self.loss = HDRLoss()
elif self.p.loss == 'l2':
self.loss = nn.MSELoss()
else:
self.loss = nn.L1Loss()
# CUDA support
self.use_cuda = torch.cuda.is_available() and self.p.cuda
if self.use_cuda:
self.model = self.model.cuda()
if self.trainable:
self.loss = self.loss.cuda()
def _print_params(self):
"""Formats parameters to print when training."""
print('Training parameters: ')
self.p.cuda = self.use_cuda
param_dict = vars(self.p)
pretty = lambda x: x.replace('_', ' ').capitalize()
print('\n'.join(' {} = {}'.format(pretty(k), str(v)) for k, v in param_dict.items()))
print()
def save_model(self, epoch, stats, first=False):
"""Saves model to files; can be overwritten at every epoch to save disk space."""
# Create directory for model checkpoints, if nonexistent
if first:
if self.p.clean_targets:
ckpt_dir_name = f'{datetime.now():{self.p.noise_type}-clean-%H%M}'
else:
ckpt_dir_name = f'{datetime.now():{self.p.noise_type}-%H%M}'
if self.p.ckpt_overwrite:
if self.p.clean_targets:
ckpt_dir_name = f'{self.p.noise_type}-clean'
else:
ckpt_dir_name = self.p.noise_type
self.ckpt_dir = os.path.join(self.p.ckpt_save_path, ckpt_dir_name)
if not os.path.isdir(self.p.ckpt_save_path):
os.mkdir(self.p.ckpt_save_path)
if not os.path.isdir(self.ckpt_dir):
os.mkdir(self.ckpt_dir)
# Save checkpoint dictionary
if self.p.ckpt_overwrite:
fname_unet = '{}/n2n-{}.pt'.format(self.ckpt_dir, self.p.noise_type)
else:
valid_loss = stats['valid_loss'][epoch]
fname_unet = '{}/n2n-epoch{}-{:>1.5f}.pt'.format(self.ckpt_dir, epoch + 1, valid_loss)
print('Saving checkpoint to: {}\n'.format(fname_unet))
torch.save(self.model.state_dict(), fname_unet)
# Save stats to JSON
fname_dict = '{}/n2n-stats.json'.format(self.ckpt_dir)
with open(fname_dict, 'w') as fp:
json.dump(stats, fp, indent=2)
def load_model(self, ckpt_fname):
"""Loads model from checkpoint file."""
print('Loading checkpoint from: {}'.format(ckpt_fname))
if self.use_cuda:
self.model.load_state_dict(torch.load(ckpt_fname))
else:
self.model.load_state_dict(torch.load(ckpt_fname, map_location='cpu'))
def _on_epoch_end(self, stats, train_loss, epoch, epoch_start, valid_loader):
"""Tracks and saves starts after each epoch."""
# Evaluate model on validation set
print('\rTesting model on validation set... ', end='')
epoch_time = time_elapsed_since(epoch_start)[0]
valid_loss, valid_time, valid_psnr = self.eval(valid_loader)
show_on_epoch_end(epoch_time, valid_time, valid_loss, valid_psnr)
# Decrease learning rate if plateau
self.scheduler.step(valid_loss)
# Save checkpoint
stats['train_loss'].append(train_loss)
stats['valid_loss'].append(valid_loss)
stats['valid_psnr'].append(valid_psnr)
self.save_model(epoch, stats, epoch == 0)
def test(self, test_loader, show=1):
"""Evaluates denoiser on test set."""
self.model.train(False)
source_imgs = []
denoised_imgs = []
clean_imgs = []
# Create directory for denoised images
denoised_dir = os.path.dirname(self.p.data)
save_path = os.path.join(denoised_dir, 'denoised')
if not os.path.isdir(save_path):
os.mkdir(save_path)
for batch_idx, (source, target) in enumerate(test_loader):
# Only do first <show> images
if show == 0 or batch_idx >= show:
break
source_imgs.append(source)
clean_imgs.append(target)
if self.use_cuda:
source = source.cuda()
# Denoise
denoised_img = self.model(source).detach()
denoised_imgs.append(denoised_img)
# Squeeze tensors
source_imgs = [t.squeeze(0) for t in source_imgs]
denoised_imgs = [t.squeeze(0) for t in denoised_imgs]
clean_imgs = [t.squeeze(0) for t in clean_imgs]
# Create montage and save images
print('Saving images and montages to: {}'.format(save_path))
for i in range(len(source_imgs)):
img_name = test_loader.dataset.imgs[i]
create_montage(img_name, self.p.noise_type, save_path, source_imgs[i], denoised_imgs[i], clean_imgs[i], show)
def eval(self, valid_loader):
"""Evaluates denoiser on validation set."""
self.model.train(False)
valid_start = datetime.now()
loss_meter = AvgMeter()
psnr_meter = AvgMeter()
for batch_idx, (source, target) in enumerate(valid_loader):
if self.use_cuda:
source = source.cuda()
target = target.cuda()
# Denoise
source_denoised = self.model(source)
# Update loss
loss = self.loss(source_denoised, target)
loss_meter.update(loss.item())
# Compute PSRN
if self.is_mc:
source_denoised = reinhard_tonemap(source_denoised)
# TODO: Find a way to offload to GPU, and deal with uneven batch sizes
for i in range(self.p.batch_size):
source_denoised = source_denoised.cpu()
target = target.cpu()
psnr_meter.update(psnr(source_denoised[i], target[i]).item())
valid_loss = loss_meter.avg
valid_time = time_elapsed_since(valid_start)[0]
psnr_avg = psnr_meter.avg
return valid_loss, valid_time, psnr_avg
def train(self, train_loader, valid_loader):
"""Trains denoiser on training set."""
self.model.train(True)
self._print_params()
num_batches = len(train_loader)
assert num_batches % self.p.report_interval == 0, 'Report interval must divide total number of batches'
# Dictionaries of tracked stats
stats = {'noise_type': self.p.noise_type,
'noise_param': self.p.noise_param,
'train_loss': [],
'valid_loss': [],
'valid_psnr': []}
# Main training loop
train_start = datetime.now()
for epoch in range(self.p.nb_epochs):
print('EPOCH {:d} / {:d}'.format(epoch + 1, self.p.nb_epochs))
# Some stats trackers
epoch_start = datetime.now()
train_loss_meter = AvgMeter()
loss_meter = AvgMeter()
time_meter = AvgMeter()
# Minibatch SGD
for batch_idx, (source, target) in enumerate(train_loader):
batch_start = datetime.now()
progress_bar(batch_idx, num_batches, self.p.report_interval, loss_meter.val)
if self.use_cuda:
source = source.cuda()
target = target.cuda()
# Denoise image
source_denoised = self.model(source)
loss = self.loss(source_denoised, target)
loss_meter.update(loss.item())
# Zero gradients, perform a backward pass, and update the weights
self.optim.zero_grad()
loss.backward()
self.optim.step()
# Report/update statistics
time_meter.update(time_elapsed_since(batch_start)[1])
if (batch_idx + 1) % self.p.report_interval == 0 and batch_idx:
show_on_report(batch_idx, num_batches, loss_meter.avg, time_meter.avg)
train_loss_meter.update(loss_meter.avg)
loss_meter.reset()
time_meter.reset()
# Epoch end, save and reset tracker
self._on_epoch_end(stats, train_loss_meter.avg, epoch, epoch_start, valid_loader)
train_loss_meter.reset()
train_elapsed = time_elapsed_since(train_start)[0]
print('Training done! Total elapsed time: {}\n'.format(train_elapsed))
def val(cfg, model=None):
if model:
test_folder = cfg.test_folder
print("The test folder", test_folder)
else:
model_path = '/project/bo/exp_data/FFP/%s_%d/' % (cfg.dataset_type,
cfg.version)
ckpt_path = model_path + "model-%d.pth" % cfg.ckpt_step
if cfg.dataset_augment_test_type != "frames/testing/" and "venue" in cfg.dataset_type:
rain_type = str(
cfg.dataset_augment_test_type.strip().split('_')[0])
brightness = int(
cfg.dataset_augment_test_type.strip().split('_')[-1]) / 10
data_dir = cfg.dataset_path + "Avenue/frames/%s_testing/bright_%.2f/" % (
rain_type, brightness)
if not os.path.exists(data_dir):
aug_data.save_avenue_rain_or_bright(cfg.dataset_path,
rain_type,
True,
"testing",
bright_space=brightness)
else:
data_dir = cfg.dataset_path + '/%s/%s/' % (
"Avenue", cfg.dataset_augment_test_type)
rain_type = "original"
brightness = 1.0
test_folder = data_dir
orig_stdout = sys.stdout
f = open(
os.path.join(
model_path,
'output_rain_%s_bright_%s.txt' % (rain_type, brightness)), 'w')
sys.stdout = f
cfg.gt = np.load('/project/bo/anomaly_data/Avenue/gt_label.npy',
allow_pickle=True)
if model: # This is for testing during training.
generator = model
generator.eval()
else:
generator = UNet(input_channels=12, output_channel=3).cuda().eval()
generator.load_state_dict(torch.load(ckpt_path)['net_g'])
# generator.load_state_dict(torch.load('weights/' + cfg.trained_model)['net_g'])
print("The pre-trained generator has been loaded from", ckpt_path)
# print(f'The pre-trained generator has been loaded from \'weights/{cfg.trained_model}\'.\n')
videos = {}
videos, video_string = input_utils.setup(test_folder, videos)
fps = 0
psnr_group = []
if not model:
if cfg.show_curve:
fig = plt.figure("Image")
manager = plt.get_current_fig_manager()
manager.window.setGeometry(550, 200, 600, 500)
# This works for QT backend, for other backends, check this ⬃⬃⬃.
# https://stackoverflow.com/questions/7449585/how-do-you-set-the-absolute-position-of-figure-windows-with-matplotlib
plt.xlabel('frames')
plt.ylabel('psnr')
plt.title('psnr curve')
plt.grid(ls='--')
cv2.namedWindow('target frames', cv2.WINDOW_NORMAL)
cv2.resizeWindow('target frames', 384, 384)
cv2.moveWindow("target frames", 100, 100)
if cfg.show_heatmap:
cv2.namedWindow('difference map', cv2.WINDOW_NORMAL)
cv2.resizeWindow('difference map', 384, 384)
cv2.moveWindow('difference map', 100, 550)
with torch.no_grad():
for i, folder in enumerate(video_string):
if not model:
name = folder.split('/')[-1]
fourcc = cv2.VideoWriter_fourcc('X', 'V', 'I', 'D')
if cfg.show_curve:
video_writer = cv2.VideoWriter(f'results/{name}_video.avi',
fourcc, 30, cfg.img_size)
curve_writer = cv2.VideoWriter(f'results/{name}_curve.avi',
fourcc, 30, (600, 430))
js = []
plt.clf()
ax = plt.axes(xlim=(0, len(dataset)), ylim=(30, 45))
line, = ax.plot([], [], '-b')
if cfg.show_heatmap:
heatmap_writer = cv2.VideoWriter(
f'results/{name}_heatmap.avi', fourcc, 30,
cfg.img_size)
psnrs = []
dataset = input_utils.test_dataset(videos[folder]['frame'],
[imh, imw])
print("Start video %s with %d frames...................." %
(folder, len(dataset)))
psnrs = []
for j, clip in enumerate(dataset):
input_np = clip[0:12, :, :]
target_np = clip[12:15, :, :]
input_frames = torch.from_numpy(input_np).unsqueeze(0).cuda()
target_frame = torch.from_numpy(target_np).unsqueeze(0).cuda()
G_frame = generator(input_frames)
test_psnr = psnr_error(G_frame,
target_frame).cpu().detach().numpy()
psnrs.append(float(test_psnr))
if not model:
if cfg.show_curve:
cv2_frame = ((target_np + 1) * 127.5).transpose(
1, 2, 0).astype('uint8')
js.append(j)
line.set_xdata(
js
) # This keeps the existing figure and updates the X-axis and Y-axis data,
line.set_ydata(
psnrs) # which is faster, but still not perfect.
plt.pause(0.001) # show curve
cv2.imshow('target frames', cv2_frame)
cv2.waitKey(1) # show video
video_writer.write(
cv2_frame) # Write original video frames.
buffer = io.BytesIO(
) # Write curve frames from buffer.
fig.canvas.print_png(buffer)
buffer.write(buffer.getvalue())
curve_img = np.array(Image.open(buffer))[...,
(2, 1, 0)]
curve_writer.write(curve_img)
if cfg.show_heatmap:
diff_map = torch.sum(
torch.abs(G_frame - target_frame).squeeze(), 0)
diff_map -= diff_map.min() # Normalize to 0 ~ 255.
diff_map /= diff_map.max()
diff_map *= 255
diff_map = diff_map.cpu().detach().numpy().astype(
'uint8')
heat_map = cv2.applyColorMap(diff_map,
cv2.COLORMAP_JET)
cv2.imshow('difference map', heat_map)
cv2.waitKey(1)
heatmap_writer.write(heat_map) # Write heatmap frames.
torch.cuda.synchronize()
end = time.time()
if j > 1: # Compute fps by calculating the time used in one completed iteration, this is more accurate.
fps = 1 / (end - temp)
temp = end
# print(f'\rDetecting: [{i + 1:02d}] {j + 1}/{len(dataset)}, {fps:.2f} fps.', end='')
psnr_group.append(np.array(psnrs))
if not model:
if cfg.show_curve:
video_writer.release()
curve_writer.release()
if cfg.show_heatmap:
heatmap_writer.release()
print('\nAll frames were detected, begin to compute AUC.')
auc = give_score(psnr_group, cfg.gt)
if not model:
sys.stdout = orig_stdout
f.close()
return auc
default=1,
type='int',
help='select model (int): (1-Unet, )')
parser.add_option('-c',
'--load',
dest='load',
default=False,
help='load file model')
(options, args) = parser.parse_args()
if (options.model == 1):
net = UNet(3, 1)
if options.load:
net.load_state_dict(torch.load(options.load))
print('Model loaded from {}'.format(options.load))
if options.gpu:
net.cuda()
cudnn.benchmark = True
try:
train_net(net,
options.epochs,
options.batchsize,
options.lr,
gpu=options.gpu)
except KeyboardInterrupt:
torch.save(net.state_dict(), 'INTERRUPTED.pth')
print('Saved interrupt')
# Предсказание и сохранение результата
TEST_DATA_PATH = '/home/kovalexal/Spaces/learning/made/made_cv/competitions/facial_points/data/test/'
test_dataset = ThousandLandmarksDataset(TEST_DATA_PATH,
train_transforms,
split='test')
# Размер батча
TEST_BATCH_SIZE = 2
test_dataloader = DataLoader(test_dataset,
batch_size=TEST_BATCH_SIZE,
num_workers=8,
pin_memory=True,
shuffle=False,
drop_last=False)
with open('{}_best.pth'.format(MODEL_NAME), 'rb') as fp:
best_state_dict = torch.load(fp, map_location="cpu")
model.load_state_dict(best_state_dict)
test_predictions = predict(model, test_dataloader, device)
with open('{}_test_predictions.pkl'.format(MODEL_NAME), 'wb') as fp:
pickle.dump(
{
'image_names': test_dataset.image_names,
'landmarks': test_predictions
}, fp)
create_submission(TEST_DATA_PATH, test_predictions,
'{}_submit.csv'.format(MODEL_NAME))
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
batch_size=cfg.bs,
shuffle=True,
num_workers=8,
pin_memory=True,
drop_last=False)
if cfg.model == 'unet':
model = UNet(input_channels=3).cuda()
model.apply(model.weights_init_normal)
else:
model = DLASeg(cfg).cuda()
model.train()
if cfg.resume:
resume_epoch = int(cfg.resume.split('.')[0].split('_')[1]) + 1
model.load_state_dict(torch.load('weights/' + cfg.resume), strict=True)
print(f'Resume training with \'{cfg.resume}\'.')
else:
resume_epoch = 0
print('Training with ImageNet pre-trained weights.')
criterion = nn.CrossEntropyLoss(ignore_index=255).cuda()
if cfg.optim == 'sgd':
optimizer = torch.optim.SGD(model.optim_parameters(),
cfg.lr,
cfg.momentum,
weight_decay=cfg.decay)
elif cfg.optim == 'radam':
optimizer = RAdam(model.optim_parameters(),
lr=cfg.lr,
weight_decay=cfg.decay)
model = UNet(input_channels=NUM_INPUT_CHANNELS,
output_channels=NUM_OUTPUT_CHANNELS)
elif args.model == "segnet":
model = SegNet(input_channels=NUM_INPUT_CHANNELS,
output_hannels=NUM_OUTPUT_CHANNELS)
else:
model = PSPNet(
layers=50,
bins=(1, 2, 3, 6),
dropout=0.1,
classes=NUM_OUTPUT_CHANNELS,
use_ppm=True,
pretrained=True,
)
# class_weights = 1.0 / train_dataset.get_class_probability()
# criterion = torch.nn.CrossEntropyLoss(weight=class_weights)
criterion = torch.nn.CrossEntropyLoss()
if CUDA:
model = model.cuda(device=GPU_ID)
# class_weights = class_weights.cuda(GPU_ID)
criterion = criterion.cuda(device=GPU_ID)
if args.checkpoint:
model.load_state_dict(torch.load(args.checkpoint))
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
train()
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():
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
device = torch.device(
config['device_num'] if torch.cuda.is_available() else 'cpu')
if config['cont_model_path'] is None:
# define the network structure -- UNet
# the output size is not always equal to your input size !!!
model = UNet(**config['model']).to(device)
# load alrady trained model
elif os.path.isdir(config['cont_model_path']):
cont_model_path = config['cont_model_path']
current_optimizer = config['optimizer']
with open(os.path.join(config['cont_model_path'], 'config.json'),
'r') as f:
model_config = json.load(f)
# We overwrite this to be able to configure the optimizer on subsequent runs.
config['model'] = model_config['model']
model = UNet(**config['model']).to(device)
model.load_state_dict(
torch.load(os.path.join(cont_model_path, 'checkpoint.pt'),
map_location=device))
else:
raise Exception(
f"Model to continue training not found: {config['cont_model_path']}."
)
# # need to add the mask parameter when training the partial Unet model
trainNet(model, train_loader, val_loader, val_loader_ttimes, device)
if not (os.path.exists(CHECKPOINT) and os.path.isfile(CHECKPOINT)):
print("weights in wrong format or non-existant: \n\t{}".format(
CHECKPOINT))
exit()
# loading the model
m = None
if args.model == "unet":
m = UNet(2, 2).to(device)
else:
m = simpleConvNet(2, 2).to(device)
print("loading model weights from \"{}\"".format(CHECKPOINT))
checkpoint = torch.load(CHECKPOINT)
m.load_state_dict(checkpoint['model_state_dict'])
m.to(device) # pushing weights to gpu
train_loss = checkpoint['train_loss']
test_loss = checkpoint['test_loss']
START = checkpoint['epoch']
scans = [
os.path.join(root_dir, i)
for i in os.listdir(os.path.abspath(root_dir)) if
os.path.isdir(os.path.join(os.path.abspath(root_dir), i)) and "_" in i
]
if len(scans) == 0:
print(
"no scan data found (folder name must be in format mmddhhmmss_x with x beeing the serialnumber"
)
exit()
def val(cfg, model=None):
if model: # This is for testing during training.
generator = model
generator.eval()
else:
generator = UNet(input_channels=12, output_channel=3).cuda().eval()
generator.load_state_dict(torch.load('weights/' + cfg.trained_model)['net_g'])
print(f'The pre-trained generator has been loaded from \'weights/{cfg.trained_model}\'.\n')
# video_folders = os.listdir(cfg.test_data)
# video_folders.sort()
# video_folders = [os.path.join(cfg.test_data, aa) for aa in video_folders]
with open(os.path.join(cfg.data_root, 'val_split_with_obj.txt')) as f:
all_video_names = f.read().splitlines()
video_folders = [os.path.join(cfg.data_root, 'frames', vid, 'images') for vid in all_video_names]
fps = 0
psnr_group = []
if not model:
if cfg.show_curve:
fig = plt.figure("Image")
manager = plt.get_current_fig_manager()
manager.window.setGeometry(550, 200, 600, 500)
# This works for QT backend, for other backends, check this ⬃⬃⬃.
# https://stackoverflow.com/questions/7449585/how-do-you-set-the-absolute-position-of-figure-windows-with-matplotlib
plt.xlabel('frames')
plt.ylabel('psnr')
plt.title('psnr curve')
plt.grid(ls='--')
cv2.namedWindow('target frames', cv2.WINDOW_NORMAL)
cv2.resizeWindow('target frames', 384, 384)
cv2.moveWindow("target frames", 100, 100)
if cfg.show_heatmap:
cv2.namedWindow('difference map', cv2.WINDOW_NORMAL)
cv2.resizeWindow('difference map', 384, 384)
cv2.moveWindow('difference map', 100, 550)
# load gt labels
gt_loader = Label_loader(cfg, video_folders) # Get gt labels.
gt, gt_bboxes = gt_loader()
with torch.no_grad():
for i, folder in tqdm(enumerate(video_folders)):
dataset = Dataset.test_dataset(cfg, folder)
test_dataloader = DataLoader(dataset=dataset, batch_size=cfg.batch_size,
shuffle=False, num_workers=cfg.batch_size)
vid = folder.split('/')[-2]
if not model:
name = folder.split('/')[-1]
fourcc = cv2.VideoWriter_fourcc('X', 'V', 'I', 'D')
if cfg.show_curve:
video_writer = cv2.VideoWriter(f'results/{name}_video.avi', fourcc, 30, cfg.img_size)
curve_writer = cv2.VideoWriter(f'results/{name}_curve.avi', fourcc, 30, (600, 430))
js = []
plt.clf()
ax = plt.axes(xlim=(0, len(dataset)), ylim=(30, 45))
line, = ax.plot([], [], '-b')
if cfg.show_heatmap:
heatmap_writer = cv2.VideoWriter(f'results/{name}_heatmap.avi', fourcc, 30, cfg.img_size)
psnrs = []
diff_maps = []
# for j, clip in enumerate(dataset):
for clip in test_dataloader:
input_frames = clip[:, 0:12, :, :].cuda()
target_frame = clip[:, 12:15, :, :].cuda()
# input_np = clip[0:12, :, :]
# target_np = clip[12:15, :, :]
# input_frames = torch.from_numpy(input_np).unsqueeze(0).cuda()
# target_frame = torch.from_numpy(target_np).unsqueeze(0).cuda()
G_frame = generator(input_frames)
'''TODO: save predicted frame or difference '''
test_psnr = psnr_error(G_frame, target_frame, reduce_batch=False).cpu().detach().numpy()
# NOTE: Save squred diff so that we could reuse it for differen evaluation
square_diff = (target_frame - G_frame).pow(2).mean(dim=1).cpu().detach().numpy().astype('float16')
diff_maps.append(square_diff)
# psnrs.append(float(test_psnr))
psnrs += list(test_psnr)
if not model:
if cfg.show_curve:
cv2_frame = ((target_np + 1) * 127.5).transpose(1, 2, 0).astype('uint8')
js.append(j)
line.set_xdata(js) # This keeps the existing figure and updates the X-axis and Y-axis data,
line.set_ydata(psnrs) # which is faster, but still not perfect.
plt.pause(0.001) # show curve
cv2.imshow('target frames', cv2_frame)
cv2.waitKey(1) # show video
video_writer.write(cv2_frame) # Write original video frames.
buffer = io.BytesIO() # Write curve frames from buffer.
fig.canvas.print_png(buffer)
buffer.write(buffer.getvalue())
curve_img = np.array(Image.open(buffer))[..., (2, 1, 0)]
curve_writer.write(curve_img)
if cfg.show_heatmap:
diff_map = torch.sum(torch.abs(G_frame - target_frame).squeeze(), 0)
diff_map -= diff_map.min() # Normalize to 0 ~ 255.
diff_map /= diff_map.max()
diff_map *= 255
diff_map = diff_map.cpu().detach().numpy().astype('uint8')
heat_map = cv2.applyColorMap(diff_map, cv2.COLORMAP_JET)
cv2.imshow('difference map', heat_map)
cv2.waitKey(1)
heatmap_writer.write(heat_map) # Write heatmap frames.
torch.cuda.synchronize()
# end = time.time()
# if j > 1: # Compute fps by calculating the time used in one completed iteration, this is more accurate.
# fps = 1 / (end - temp)
# temp = end
# print(f'\rDetecting: [{i + 1:02d}] {j + 1}/{len(dataset)}, {fps:.2f} fps.', end='')
diff_maps = np.concatenate(diff_maps, axis=0)
np.save(os.path.join('saved_difference_map', vid+'.npy'), diff_maps)
if len(psnrs) != len(gt[i]) - 4 or len(psnrs) != len(diff_maps):
pdb.set_trace()
psnr_group.append(np.array(psnrs))
if not model:
if cfg.show_curve:
video_writer.release()
curve_writer.release()
if cfg.show_heatmap:
heatmap_writer.release()
print('\nAll frames were detected, begin to compute AUC.')
assert len(psnr_group) == len(gt), f'Ground truth has {len(gt)} videos, but got {len(psnr_group)} detected videos.'
# save psnr
torch.save(psnr_group, 'results/psnr_group.pth')
scores = np.array([], dtype=np.float32)
labels = np.array([], dtype=np.int8)
for i in range(len(psnr_group)):
distance = psnr_group[i]
distance -= min(distance) # distance = (distance - min) / (max - min)
distance /= max(distance)
scores = np.concatenate((scores, distance), axis=0)
labels = np.concatenate((labels, gt[i][4:]), axis=0) # Exclude the first 4 unpredictable frames in gt.
torch.save(psnr_group, 'results/psnr_normalized.pth')
assert scores.shape == labels.shape, \
f'Ground truth has {labels.shape[0]} frames, but got {scores.shape[0]} detected frames.'
fpr, tpr, thresholds = metrics.roc_curve(labels, scores, pos_label=0)
auc = metrics.auc(fpr, tpr)
print(f'AUC: {auc}\n')
return auc