def test_moving(opt, log_dir, generator=None):
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
if generator == None:
generator = UNet(opt.sample_num, opt.channels, opt.batch_size,
opt.alpha)
checkpoint = torch.load(opt.load_model, map_location=device)
generator.load_state_dict(checkpoint['g_state_dict'])
del checkpoint
torch.cuda.empty_cache()
generator.to(device)
generator.eval()
dataloader = torch.utils.data.DataLoader(
MyDataset_test_moving(opt),
opt.batch_size,
shuffle=True,
num_workers=opt.num_workers_dataloader)
for i, (imgs, filename) in enumerate(dataloader):
with torch.no_grad():
filename = filename[0].split('/')[-1]
for k in range(len(imgs)):
test_img = generator(imgs[k].to(device))
folder_path = os.path.join(log_dir, "test/%s" % filename)
os.makedirs(folder_path, exist_ok=True)
filename_ = filename + '_' + str(k) + '.png'
test_img = convert_im(test_img,
os.path.join(folder_path, filename_),
nrow=5,
normalize=True,
save_im=True)
def run_pipeline(root_dir, model_path, img_size, batch_size, use_gpu):
images_path = os.path.join(root_dir, "images")
masks_path = os.path.join(root_dir, "masks")
outputs_path = os.path.join(root_dir, "outputs")
sizes = []
file_names = []
for f in os.listdir(images_path):
im = Image.open(os.path.join(images_path, f))
sizes.append(im.size)
file_names.append(f)
model = UNet(num_channels=1, num_classes=2)
use_gpu = use_gpu and torch.cuda.is_available()
if use_gpu:
model.cuda()
model.load_state_dict(torch.load(model_path, map_location='cpu'))
test_dataset = TestDataset(images_path,
im_size=[img_size, img_size],
transform=tr.ToTensor())
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=1)
print("Test Data : ", len(test_loader.dataset))
start = timer()
predict(model, test_loader, batch_size, sizes, file_names, masks_path,
use_gpu)
end = timer()
print("Prediction completed in {:0.2f}s".format(end - start))
generate_bbox(images_path, masks_path, outputs_path)
end2 = timer()
print("Bbox generation completed in {:0.2f}s".format(end2 - end))
def start():
model = UNet(num_channels=1, num_classes=2)
criterion = DICELossMultiClass()
dir_path = './Data/images/'
sizes = []
file_names = []
for f in os.listdir(dir_path):
im = Image.open(os.path.join(dir_path, f))
print(im.size)
sizes.append(im.size)
file_names.append(f)
print(sizes)
print(file_names)
test_dataset = TestDataset('./Data/',
im_size=[256, 256],
transform=tr.ToTensor())
test_loader = DataLoader(test_dataset,
batch_size=4,
shuffle=False,
num_workers=1)
print("Test Data : ", len(test_loader.dataset))
model.load_state_dict(
torch.load('unet-model-16-100-0.001', map_location='cpu'))
test_only(model, test_loader, criterion, sizes, file_names)
def train(device, model_path, dataset_path):
"""
Trains the network according on the dataset_path
"""
network = UNet(1, 3).to(device)
optimizer = torch.optim.Adam(network.parameters())
criteria = torch.nn.MSELoss()
dataset = GrayColorDataset(dataset_path, transform=train_transform)
loader = torch.utils.data.DataLoader(dataset,
batch_size=16,
shuffle=True,
num_workers=cpu_count())
if os.path.exists(model_path):
network.load_state_dict(torch.load(model_path))
for _ in tqdm.trange(10, desc="Epoch"):
network.train()
for gray, color in tqdm.tqdm(loader, desc="Training", leave=False):
gray, color = gray.to(device), color.to(device)
optimizer.zero_grad()
pred_color = network(gray)
loss = criteria(pred_color, color)
loss.backward()
optimizer.step()
torch.save(network.state_dict(), model_path)
def __init__(self, proxy_map, opts,startup_check=False):
super(SpecificWorker, self).__init__(proxy_map)
self.Period = 50
if startup_check:
self.startup_check()
else:
self.timer.timeout.connect(self.compute)
self.timer.start(self.Period)
self.opts = opts # Copy user options
self.wait_frames = 5 # Wait for these many frames to get stored before calling gaitset (see gaitSet/pretreatment.py)
# Dictonary to store mapping between tracking id and segmentation mask
self.id2mask = {}
# Segmentation module
segmentation_model = UNet(backbone="resnet18", num_classes=2)
segmentation_model.load_state_dict(torch.load("./src/PretrainedModels/UNet_ResNet18.pth", map_location="cpu")['state_dict'], strict=False)
self.segmentation_inference = ImageListInference(segmentation_model,opts)
# Gait feature extraction
self.gait_model = SetNet(256)
self.gait_model.load_state_dict(torch.load("./src/PretrainedModels/GaitSet_CASIA-B_73_False_256_0.2_128_full_30-80000-encoder.ptm",map_location="cpu"),strict=False)
if self.opts.gpu >= 0:
self.gait_model.cuda()
self.gait_model.eval()
# Variables to store data for computation
self.lock = False # Lock from taking any more input until features are calculated
self.input_image_list = [] # Store images in this list before performing segmentation
self.input_tracking_id = [] # Store respective tracking id here
def get_model(model_path, model_type):
"""
:param model_path:
:param model_type: 'UNet', 'UNet11', 'UNet16', 'AlbuNet34'
:return:
"""
num_classes = 1
if model_type == 'UNet11':
model = UNet11(num_classes=num_classes)
elif model_type == 'UNet16':
model = UNet16(num_classes=num_classes)
elif model_type == 'AlbuNet34':
model = AlbuNet34(num_classes=num_classes)
elif model_type == 'UNet':
model = UNet(num_classes=num_classes)
else:
model = UNet(num_classes=num_classes)
state = torch.load(str(model_path))
state = {
key.replace('module.', ''): value
for key, value in state['model'].items()
}
model.load_state_dict(state)
if torch.cuda.is_available():
return model.cuda()
model.eval()
return model
def get_model(model_path, model_type, num_classes):
"""
:param model_path:
:param model_type: 'UNet', 'UNet16', 'UNet11', 'LinkNet34',
:param problem_type: 'binary', 'parts', 'instruments'
:return:
"""
if model_type == 'UNet':
model = UNet(num_classes=num_classes)
else:
model_name = model_list[model_type]
model = model_name(num_classes=num_classes)
# print(model)
state = torch.load(str(model_path))
state = {
key.replace('module.', ''): value
for key, value in state['model'].items()
}
model.load_state_dict(state)
if torch.cuda.is_available():
return model.cuda()
model.eval()
return model
def test(opt, log_dir, generator=None):
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
if generator == None:
generator = UNet(opt.sample_num, opt.channels, opt.batch_size,
opt.alpha)
checkpoint = torch.load(opt.load_model, map_location=device)
generator.load_state_dict(checkpoint['g_state_dict'])
del checkpoint
torch.cuda.empty_cache()
generator.to(device)
generator.eval()
dataloader = torch.utils.data.DataLoader(MyDataset_test(opt),
opt.batch_size,
shuffle=True,
num_workers=0)
for i, (imgs, filename) in enumerate(dataloader):
with torch.no_grad():
test_img = generator(imgs.to(device))
filename = filename[0].split('/')[-1]
filename = "test/" + filename + '.png'
test_img = convert_im(test_img,
os.path.join(log_dir, filename),
nrow=5,
normalize=True,
save_im=True)
def export2caffe(weights, num_classes, img_size):
model = UNet(num_classes)
weights = torch.load(weights, map_location='cpu')
model.load_state_dict(weights['model'])
model.eval()
fuse(model)
name = 'DeepLabV3Plus'
dummy_input = torch.ones([1, 3, img_size[1], img_size[0]])
pytorch2caffe.trans_net(model, dummy_input, name)
pytorch2caffe.save_prototxt('{}.prototxt'.format(name))
pytorch2caffe.save_caffemodel('{}.caffemodel'.format(name))
def setup(opts):
model = UNet(backbone=opts["backbone"], num_classes=2)
if torch.cuda.is_available():
print("Using CUDA")
trained_dict = torch.load(opts["checkpoint"])['state_dict']
model.load_state_dict(trained_dict, strict=False)
model.cuda()
else:
print("Using CPU")
trained_dict = torch.load(opts["checkpoint"], map_location="cpu")['state_dict']
model.load_state_dict(trained_dict, strict=False)
return model
def denoise_3d(mat, model=None, filepath='/home/jupyter/notebooks/checkpoints/3d_denoise.pt', return_detached=True,
batch_size=5000, device=torch.device('cuda')):
if model is None:
model = UNet(in_channels=1, num_classes=1, out_channels=[4, 8, 16], num_conv=2, n_dim=3,
kernel_size=[3, 3, 3], same_shape=True).to(device)
model.load_state_dict(torch.load(filepath))
with torch.no_grad():
num_batches = (mat.size(0) + batch_size - 1)//batch_size
mat = torch.cat([model(mat[batch_size*i:batch_size*(i+1)]) for i in range(num_batches)], dim=0)
if return_detached:
mat = mat.detach()
for k in [k for k in locals().keys() if k!='mat']:
del locals()[k]
torch.cuda.empty_cache()
return mat
def denoise_trace(trace, model=None, filepath='/home/jupyter/notebooks/checkpoints/denoise_trace.pt', return_detached=True,
device=torch.device('cuda')):
if model is None:
model = UNet(in_channels=1, num_classes=1, out_channels=[8, 16, 32], num_conv=2,
n_dim=1, kernel_size=3).to(device)
model.load_state_dict(torch.load(filepath))
with torch.no_grad():
mean = trace.mean()
std = trace.std()
pred = model((trace-mean)/std)
pred = model(pred)
pred = pred * std + mean
if return_detached:
pred = pred.detach()
for k in [k for k in locals().keys() if k!='pred']:
del locals()[k]
torch.cuda.empty_cache()
return pred
def attention_map(mat, model=None, filepath='/home/jupyter/notebooks/checkpoints/segmentation_count_hardmask.pt',
batch_size=5000, return_detached=True, device=torch.device('cuda')):
if model is None:
model = UNet(in_channels=1, num_classes=1, out_channels=[4, 8, 16], num_conv=2, n_dim=3,
kernel_size=[3, 3, 3], same_shape=True).to(device)
model.load_state_dict(torch.load(filepath))
nrow, ncol = mat.shape[1:]
if batch_size*nrow*ncol > 1e7:
batch_size = int(1e7 / (nrow*ncol))
with torch.no_grad():
num_batches = (mat.size(0) + batch_size - 1)//batch_size
mat = torch.cat([model(mat[batch_size*i:batch_size*(i+1)]) for i in range(num_batches)], dim=0).mean(0)
if return_detached:
mat = mat.detach()
for k in [k for k in locals().keys() if k!='mat']:
del locals()[k]
torch.cuda.empty_cache()
return mat
def test(weights_path):
# Get all images in train set
image_names = os.listdir('dataset/train/images/')
image_names = [name for name in image_names if name.endswith(('.jpg', '.JPG', '.png'))]
# Initialize model and transfer to device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = UNet()
model = model.to(device)
model.eval()
# Load weights
model.load_state_dict(torch.load(weights_path, map_location=device))
# Misc info
img_size = 512
# Predict on images
for image_name in tqdm(image_names):
# Load image, prepare for inference
img = cv2.imread(os.path.join('dataset/train/images/', image_name))
img_torch = prepare_image(img, img_size)
with torch.no_grad():
# Get predictions for image
pred_egg_mask, pred_pan_mask = model(img_torch)
# Threshold by 0.5
pred_egg_mask = (torch.sigmoid(pred_egg_mask) >= 0.5).type(pred_egg_mask.dtype)
pred_pan_mask = (torch.sigmoid(pred_pan_mask) >= 0.5).type(pred_pan_mask.dtype)
pred_egg_mask, pred_pan_mask = pred_egg_mask.cpu().detach().numpy(), pred_pan_mask.cpu().detach().numpy()
# Resize masks back to original shape
pred_egg_mask, pred_pan_mask = pred_egg_mask[0][0] * 256, pred_pan_mask[0][0] * 256
pred_egg_mask, pred_pan_mask = postprocess_masks(img, pred_egg_mask, pred_pan_mask)
cv2.imwrite('test_vis/' + image_name[:-4] + '_egg' + image_name[-4:], pred_egg_mask)
cv2.imwrite('test_vis/' + image_name[:-4] + '_pan' + image_name[-4:], pred_pan_mask)
cv2.imwrite('test_vis/' + image_name, img)
def test(device, model_path, dataset_path, out_path):
"""
Tests the network on the dataset_path
"""
network = UNet(1, 3).to(device)
if os.path.exists(model_path):
network.load_state_dict(torch.load(model_path))
dataset = GrayDataset(dataset_path, transform=val_transform)
loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=cpu_count())
with torch.no_grad():
network.eval()
for i, gray in enumerate(tqdm.tqdm(loader, desc="Testing",
leave=False)):
gray = gray.to(device)
pred_color = network(gray)
result = F.to_pil_image((pred_color.cpu().squeeze() * 0.5) + 0.5)
result.save(os.path.join(out_path, "{:06d}.png".format(i)))
def test(device, gen_model, fake_dataset_path, out_dir):
"""tests a gan"""
print("Test a gan")
val_transform = tv.transforms.Compose([
tv.transforms.Resize((224, 224)),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, ), (0.5, ))
])
fakedataset = GrayDataset(fake_dataset_path, transform=val_transform)
fakeloader = torch.utils.data.DataLoader(fakedataset,
batch_size=1,
shuffle=False,
num_workers=cpu_count())
generator = UNet(1, 3).to(device)
if os.path.exists(gen_model):
generator.load_state_dict(torch.load(gen_model))
with torch.no_grad():
generator.eval()
for i, fake_data in enumerate(tqdm.tqdm(fakeloader)):
fake_data = fake_data.to(device)
fake = generator(fake_data)
fake_im = fake.squeeze().cpu() * 0.5 + 0.5
fake_im = tv.transforms.functional.to_pil_image(fake_im)
fake_im.save(os.path.join(out_dir, "{:06d}.png".format(i)))
def inference(img_dir='data/samples',
img_size=256,
output_dir='outputs',
weights='weights/best_miou.pt',
unet=False):
os.makedirs(output_dir, exist_ok=True)
if unet:
model = UNet(30)
else:
model = DeepLabV3Plus(30)
model = model.to(device)
state_dict = torch.load(weights, map_location=device)
model.load_state_dict(state_dict['model'])
model.eval()
names = [
n for n in os.listdir(img_dir)
if os.path.splitext(n)[1] in ['.jpg', '.jpeg', '.png', '.tiff']
]
with torch.no_grad():
for name in tqdm(names):
path = os.path.join(img_dir, name)
img = cv2.imread(path)
img_shape = img.shape
h = (img.shape[0] / max(img.shape[:2]) * img_size) // 32
w = (img.shape[1] / max(img.shape[:2]) * img_size) // 32
img = cv2.resize(img, (int(w * 32), int(h * 32)))
img = img[:, :, ::-1]
img = img.transpose(2, 0, 1)
img = torch.FloatTensor([img], device=device) / 255.
output = model(img)[0].cpu().numpy().transpose(1, 2, 0)
output = cv2.resize(output, (img_shape[1], img_shape[0]))
output = output.argmax(2)
seg = np.zeros(img_shape, dtype=np.uint8)
for ci, color in enumerate(VOC_COLORMAP):
seg[output == ci] = color
cv2.imwrite(os.path.join(output_dir, name), seg)
def get_model(model_path, model_type='unet11', problem_type='binary'):
"""
:param model_path:
:param model_type: 'UNet', 'UNet16', 'UNet11', 'LinkNet34'
:param problem_type: 'binary', 'parts', 'instruments'
:return:
"""
if problem_type == 'binary':
num_classes = 1
elif problem_type == 'parts':
num_classes = 4
elif problem_type == 'instruments':
num_classes = 8
if model_type == 'UNet16':
model = UNet16(num_classes=num_classes)
elif model_type == 'UNet11':
model = UNet11(num_classes=num_classes)
elif model_type == 'LinkNet34':
model = LinkNet34(num_classes=num_classes)
elif model_type == 'UNet':
model = UNet(num_classes=num_classes)
elif model_type == 'DLinkNet':
model = D_LinkNet34(num_classes=num_classes, pretrained=True)
state = torch.load(str(model_path))
state = {key.replace('module.', ''): value for key, value in state['model'].items()}
model.load_state_dict(state)
if torch.cuda.is_available():
return model.cuda()
model.eval()
return model
def predict():
net = UNet(n_channels=1, n_classes=1)
net.eval()
# 将多GPU模型加载为CPU模型
if opt.load_model_path:
checkpoint = t.load(opt.load_model_path)
state_dict = checkpoint['net']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
net.load_state_dict(new_state_dict) # 加载模型
print('加载预训练模型{}'.format(opt.load_model_path))
if opt.use_gpu:
net.cuda()
test_data = NodeDataSet(test=True)
test_dataloader = DataLoader(test_data,
opt.test_batch_size,
shuffle=False,
num_workers=opt.num_workers)
for ii, full_img in enumerate(test_dataloader):
img_test = full_img[0][0].unsqueeze(
0) # 第一个[0] 取 原图像的一个batch，第二个[0]指batch为1
if opt.use_gpu:
img_test = img_test.cuda()
with t.no_grad(): # pytorch0.4版本写法
output = net(img_test)
probs = t.sigmoid(output).squeeze(0)
full_mask = probs.squeeze().cpu().numpy()
# ===========================================下面方法可能未考虑 一通道图像
# if opt.use_dense_crf:
# full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
mask = full_mask > opt.out_threshold # 预测mask值都太小,最大0.01
# # 可视化1
# plt.imsave(opt.save_test_dir+str(10000+ii)+'full_img.jpg', full_img[0][0].squeeze(0),cmap = cm.gray) #保存原图
# plt.imsave(opt.save_test_dir+str(10000+ii)+'mask.jpg', mask,cmap = cm.gray) #保存mask
# plt.imsave(opt.save_test_dir+str(10000+ii)+'full_mask.jpg', full_img[0][0].squeeze(0).squeeze(0).numpy() * mask,cmap = cm.gray) #保存mask之后的原图
# 可视化2
# # 多子图显示原图和mask
# plt.subplot(1,3,1)
# plt.title('origin')
# plt.imshow(full_img[0][0].squeeze(0),cmap='Greys_r')
#
# plt.subplot(1, 3, 2)
# plt.title('mask')
# plt.imshow(mask,cmap='Greys_r')
#
# plt.subplot(1, 3, 3)
# plt.title('origin_after_mask')
# plt.imshow( full_img[0][0].squeeze(0).squeeze(0).numpy() * mask,cmap='Greys_r')
#
# plt.show()
# 保存mask为npy
np.save('/home/bobo/data/test/test8/' + str(10000 + ii) + '_mask.npy',
mask)
print('测试完毕')
def train():
t.cuda.set_device(1)
# n_channels：医学影像为一通道灰度图 n_classes：二分类
net = UNet(n_channels=1, n_classes=1)
optimizer = t.optim.SGD(net.parameters(),
lr=opt.learning_rate,
momentum=0.9,
weight_decay=0.0005)
criterion = t.nn.BCELoss() # 二进制交叉熵(适合mask占据图像面积较大的场景)
start_epoch = 0
if opt.load_model_path:
checkpoint = t.load(opt.load_model_path)
# 加载多GPU模型参数到 单模型上
state_dict = checkpoint['net']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
net.load_state_dict(new_state_dict) # 加载模型
optimizer.load_state_dict(checkpoint['optimizer']) # 加载优化器
start_epoch = checkpoint['epoch'] # 加载训练批次
# 学习率每当到达milestones值则更新参数
if start_epoch == 0:
scheduler = t.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=opt.milestones,
gamma=0.1,
last_epoch=-1) # 默认为-1
print('从头训练 ，学习率为{}'.format(optimizer.param_groups[0]['lr']))
else:
scheduler = t.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=opt.milestones,
gamma=0.1,
last_epoch=start_epoch)
print('加载预训练模型{}并从{}轮开始训练,学习率为{}'.format(
opt.load_model_path, start_epoch, optimizer.param_groups[0]['lr']))
# 网络转移到GPU上
if opt.use_gpu:
net = t.nn.DataParallel(net, device_ids=opt.device_ids) # 模型转为GPU并行
net.cuda()
cudnn.benchmark = True
# 定义可视化对象
vis = Visualizer(opt.env)
train_data = NodeDataSet(train=True)
val_data = NodeDataSet(val=True)
test_data = NodeDataSet(test=True)
# 数据集加载器
train_dataloader = DataLoader(train_data,
opt.batch_size,
shuffle=True,
num_workers=opt.num_workers)
val_dataloader = DataLoader(val_data,
opt.batch_size,
shuffle=True,
num_workers=opt.num_workers)
test_dataloader = DataLoader(test_data,
opt.test_batch_size,
shuffle=False,
num_workers=opt.num_workers)
for epoch in range(opt.max_epoch - start_epoch):
print('开始 epoch {}/{}.'.format(start_epoch + epoch + 1, opt.max_epoch))
epoch_loss = 0
# 每轮判断是否更新学习率
scheduler.step()
# 迭代数据集加载器
for ii, (img, mask) in enumerate(
train_dataloader): # pytorch0.4写法，不再将tensor封装为Variable
# 将数据转到GPU
if opt.use_gpu:
img = img.cuda()
true_masks = mask.cuda()
masks_pred = net(img)
# 经过sigmoid
masks_probs = t.sigmoid(masks_pred)
# 损失 = 二进制交叉熵损失 + dice损失
loss = criterion(masks_probs.view(-1), true_masks.view(-1))
# 加入dice损失
if opt.use_dice_loss:
loss += dice_loss(masks_probs, true_masks)
epoch_loss += loss.item()
if ii % 2 == 0:
vis.plot('训练集loss', loss.item())
# 优化器梯度清零
optimizer.zero_grad()
# 反向传播
loss.backward()
# 更新参数
optimizer.step()
# 当前时刻的一些信息
vis.log("epoch:{epoch},lr:{lr},loss:{loss}".format(
epoch=epoch, loss=loss.item(), lr=optimizer.param_groups[0]['lr']))
vis.plot('每轮epoch的loss均值', epoch_loss / ii)
# 保存模型、优化器、当前轮次等
state = {
'net': net.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch
}
t.save(state, opt.checkpoint_root + '{}_unet.pth'.format(epoch))
# ============验证===================
net.eval()
# 评价函数：Dice系数 Dice距离用于度量两个集合的相似性
tot = 0
for jj, (img_val, mask_val) in enumerate(val_dataloader):
img_val = img_val
true_mask_val = mask_val
if opt.use_gpu:
img_val = img_val.cuda()
true_mask_val = true_mask_val.cuda()
mask_pred = net(img_val)
mask_pred = (t.sigmoid(mask_pred) > 0.5).float() # 阈值为0.5
# 评价函数：Dice系数 Dice距离用于度量两个集合的相似性
tot += dice_loss(mask_pred, true_mask_val).item()
val_dice = tot / jj
vis.plot('验证集 Dice损失', val_dice)
# ============验证召回率===================
# 每10轮验证一次测试集召回率
if epoch % 10 == 0:
result_test = []
for kk, (img_test, mask_test) in enumerate(test_dataloader):
# 测试 unet分割能力，故 不使用真值mask
if opt.use_gpu:
img_test = img_test.cuda()
mask_pred_test = net(img_test) # [1,1,512,512]
probs = t.sigmoid(mask_pred_test).squeeze().squeeze().cpu(
).detach().numpy() # [512,512]
mask = probs > opt.out_threshold
result_test.append(mask)
# 得到 测试集所有预测掩码,计算二维召回率
vis.plot('测试集二维召回率', getRecall(result_test).getResult())
net.train()
loss_list = []
for i in tqdm(range(args.epochs)):
train(i, exp_lr_scheduler, loss_list)
test()
plt.plot(loss_list)
plt.title("UNet bs={}, ep={}, lr={}".format(args.batch_size, args.epochs,
args.lr))
plt.xlabel("Number of iterations")
plt.ylabel("Average DICE loss per batch")
plt.savefig("plots/{}-UNet_Loss_bs={}_ep={}_lr={}.png".format(
args.save, args.batch_size, args.epochs, args.lr))
np.save(
'npy-files/loss-files/{}-UNet_Loss_bs={}_ep={}_lr={}.npy'.format(
args.save, args.batch_size, args.epochs, args.lr),
np.asarray(loss_list))
torch.save(
model.state_dict(),
'{}unetsmall-final-{}-{}-{}'.format(SAVE_MODEL_NAME, args.batch_size,
args.epochs, args.lr))
# elif args.pred:
# predict()
elif args.load is not None:
model.load_state_dict(torch.load(args.load))
#test()
predict()
device = torch.device(CUDA_SELECT if torch.cuda.is_available() else "cpu")
model = UNet(num_classes=2, input_channels=1)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model)
model = model.to(device)
# To handle epoch start number and pretrained weight
epoch_start = '0'
if (use_pretrained):
print("Loading Model {}".format(
os.path.basename(pretrained_model_path)))
model.load_state_dict(torch.load(pretrained_model_path))
epoch_start = os.path.basename(pretrained_model_path).split('.')[0]
print(epoch_start)
trainLoader = DataLoader(DatasetImageMaskLocal(train_file_names,
object_type,
mode='train'),
batch_size=batch_size)
devLoader = DataLoader(
DatasetImageMaskLocal(val_file_names, object_type, mode='valid'))
displayLoader = DataLoader(DatasetImageMaskLocal(val_file_names,
object_type,
mode='valid'),
batch_size=val_batch_size)
optimizer = Adam(model.parameters(), lr=1e-4)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model', required=True)
parser.add_argument('--input', required=True)
parser.add_argument('--output', default='predicted.jpg')
parser.add_argument('--output-dir', default='logs')
opt = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = UNet(3, 1)
if device == torch.device('cuda'):
net = nn.DataParallel(net)
net.to(device=device)
net.load_state_dict(torch.load(os.path.expanduser(opt.model)))
net.eval()
# image
image = Image.open(os.path.expanduser(opt.input))
image = CarvanaDatasetTransforms([256, 512]).transform(image)
image.to(device)
mask = net(image.unsqueeze(0))[0]
mask = torch.sigmoid(mask)
mask = mask.squeeze(0).cpu().detach().numpy()
mask = mask > 0.5
mask = Image.fromarray((mask * 255).astype(np.uint8))
mask.save(os.path.join(opt.output_dir, opt.output))
test_file = config_params['test_images_txt']
input_size = config_params['input_size']
num_channels = config_params['num_channels']
n_classes = config_params['n_classes']
bilinear = config_params['bilinear']
# torch.manual_seed(config_params['seed'])
test_set = NucleusTestDataset(test_file, input_size)
test_loader = DataLoader(test_set,
batch_size=1,
sampler=RandomSampler(test_set))
# Inference device
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Load Model
model = UNet(n_channels=num_channels,
n_classes=n_classes,
bilinear=bilinear).to(device)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
# Threshold for prediction
threshold = float(args.threshold)
# Get test image
img, idx = next(iter(test_loader))
pred = predict_mask(model, img, threshold, device)
# Visualise Prediction
visualize(img, pred)
def train(device, gen_model, disc_model, real_dataset_path, epochs):
"""trains a gan"""
train_transform = tv.transforms.Compose([
tv.transforms.Resize((224, 224)),
tv.transforms.RandomHorizontalFlip(0.5),
tv.transforms.RandomVerticalFlip(0.5),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, ), (0.5, ))
])
realdataset = ColorDataset(real_dataset_path, transform=train_transform)
realloader = torch.utils.data.DataLoader(realdataset,
batch_size=20,
shuffle=True,
num_workers=cpu_count(),
drop_last=True)
realiter = iter(realloader)
discriminator = discriminator_model(3, 1024).to(device)
disc_optimizer = torch.optim.Adam(discriminator.parameters(),
lr=0.0001,
betas=(0, 0.9))
if os.path.exists(disc_model):
discriminator.load_state_dict(torch.load(disc_model))
generator = UNet(1, 3).to(device)
gen_optimizer = torch.optim.Adam(generator.parameters(),
lr=0.0001,
betas=(0, 0.9))
if os.path.exists(gen_model):
generator.load_state_dict(torch.load(gen_model))
one = torch.FloatTensor([1])
mone = one * -1
one = one.to(device).squeeze()
mone = mone.to(device).squeeze()
n_critic = 5
lam = 10
for _ in tqdm.trange(epochs, desc="Epochs"):
for param in discriminator.parameters():
param.requires_grad = True
for _ in range(n_critic):
real_data, realiter = try_iter(realiter, realloader)
real_data = real_data.to(device)
disc_optimizer.zero_grad()
disc_real = discriminator(real_data)
real_cost = torch.mean(disc_real)
real_cost.backward(mone)
# fake_data, fakeiter = try_iter(fakeiter, fakeloader)
fake_data = torch.randn(real_data.shape[0], 1, 224, 224)
fake_data = fake_data.to(device)
disc_fake = discriminator(generator(fake_data))
fake_cost = torch.mean(disc_fake)
fake_cost.backward(one)
gradient_penalty = calc_gp(device, discriminator, real_data,
fake_data, lam)
gradient_penalty.backward()
disc_optimizer.step()
for param in discriminator.parameters():
param.requires_grad = False
gen_optimizer.zero_grad()
# fake_data, fakeiter = try_iter(fakeiter, fakeloader)
fake_data = torch.randn(real_data.shape[0], 1, 224, 224)
fake_data = fake_data.to(device)
disc_g = discriminator(generator(fake_data)).mean()
disc_g.backward(mone)
gen_optimizer.step()
torch.save(generator.state_dict(), gen_model)
torch.save(discriminator.state_dict(), disc_model)
def start():
parser = argparse.ArgumentParser(
description='UNet + BDCLSTM for BraTS Dataset')
parser.add_argument('--batch-size',
type=int,
default=4,
metavar='N',
help='input batch size for training (default: 4)')
parser.add_argument('--test-batch-size',
type=int,
default=4,
metavar='N',
help='input batch size for testing (default: 4)')
parser.add_argument('--train',
action='store_true',
default=False,
help='Argument to train model (default: False)')
parser.add_argument('--epochs',
type=int,
default=2,
metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr',
type=float,
default=0.001,
metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='enables CUDA training (default: False)')
parser.add_argument('--log-interval',
type=int,
default=1,
metavar='N',
help='batches to wait before logging training status')
parser.add_argument('--size',
type=int,
default=128,
metavar='N',
help='imsize')
parser.add_argument('--load',
type=str,
default=None,
metavar='str',
help='weight file to load (default: None)')
parser.add_argument('--data',
type=str,
default='./Data/',
metavar='str',
help='folder that contains data')
parser.add_argument('--save',
type=str,
default='OutMasks',
metavar='str',
help='Identifier to save npy arrays with')
parser.add_argument('--modality',
type=str,
default='flair',
metavar='str',
help='Modality to use for training (default: flair)')
parser.add_argument('--optimizer',
type=str,
default='SGD',
metavar='str',
help='Optimizer (default: SGD)')
args = parser.parse_args()
args.cuda = args.cuda and torch.cuda.is_available()
DATA_FOLDER = args.data
# %% Loading in the model
# Binary
# model = UNet(num_channels=1, num_classes=2)
# Multiclass
model = UNet(num_channels=1, num_classes=3)
if args.cuda:
model.cuda()
if args.optimizer == 'SGD':
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.99)
if args.optimizer == 'ADAM':
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2))
# Defining Loss Function
criterion = DICELossMultiClass()
if args.train:
# %% Loading in the Dataset
full_dataset = BraTSDatasetUnet(DATA_FOLDER,
im_size=[args.size, args.size],
transform=tr.ToTensor())
#dset_test = BraTSDatasetUnet(DATA_FOLDER, train=False,
# keywords=[args.modality], im_size=[args.size,args.size], transform=tr.ToTensor())
train_size = int(0.9 * len(full_dataset))
test_size = len(full_dataset) - train_size
train_dataset, validation_dataset = torch.utils.data.random_split(
full_dataset, [train_size, test_size])
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
validation_loader = DataLoader(validation_dataset,
batch_size=args.test_batch_size,
shuffle=False,
num_workers=1)
#test_loader = DataLoader(full_dataset, batch_size=args.test_batch_size, shuffle=False, num_workers=1)
print("Training Data : ", len(train_loader.dataset))
print("Validaion Data : ", len(validation_loader.dataset))
#print("Test Data : ", len(test_loader.dataset))
loss_list = []
start = timer()
for i in tqdm(range(args.epochs)):
train(model, i, loss_list, train_loader, optimizer, criterion,
args)
test(model, validation_loader, criterion, args, validation=True)
end = timer()
print("Training completed in {:0.2f}s".format(end - start))
plt.plot(loss_list)
plt.title("UNet bs={}, ep={}, lr={}".format(args.batch_size,
args.epochs, args.lr))
plt.xlabel("Number of iterations")
plt.ylabel("Average DICE loss per batch")
plt.savefig("./plots/{}-UNet_Loss_bs={}_ep={}_lr={}.png".format(
args.save, args.batch_size, args.epochs, args.lr))
np.save(
'./npy-files/loss-files/{}-UNet_Loss_bs={}_ep={}_lr={}.npy'.format(
args.save, args.batch_size, args.epochs, args.lr),
np.asarray(loss_list))
print("Testing Validation")
test(model, validation_loader, criterion, args, save_output=True)
torch.save(
model.state_dict(),
'unet-multiclass-model-{}-{}-{}'.format(args.batch_size,
args.epochs, args.lr))
print("Testing PDF images")
test_dataset = TestDataset('./pdf_data/',
im_size=[args.size, args.size],
transform=tr.ToTensor())
test_loader = DataLoader(test_dataset,
batch_size=args.test_batch_size,
shuffle=False,
num_workers=1)
print("Test Data : ", len(test_loader.dataset))
test_only(model, test_loader, criterion, args)
elif args.load is not None:
test_dataset = TestDataset(DATA_FOLDER,
im_size=[args.size, args.size],
transform=tr.ToTensor())
test_loader = DataLoader(test_dataset,
batch_size=args.test_batch_size,
shuffle=False,
num_workers=1)
print("Test Data : ", len(test_loader.dataset))
model.load_state_dict(torch.load(args.load))
test_only(model, test_loader, criterion, args)
def main(cfg: DictConfig):
# This is here to collapse the code in VS Code
if True:
# Setup
print = logging.getLogger(__name__).info
print(OmegaConf.to_yaml(cfg))
pl.seed_everything(cfg.seed)
# Create validation and test segmentation datasets
# NOTE: The batch size must be 1 for test because the masks are different sizes,
# and evaluation should be done using the mask at the original resolution.
val_dataloaders = []
test_dataloaders = []
for _cfg in cfg.data_seg.data:
kwargs = dict(images_dir=_cfg.images_dir,
labels_dir=_cfg.labels_dir,
image_size=cfg.data_seg.image_size)
val_dataset = SegmentationDataset(**kwargs, crop=True)
test_dataset = SegmentationDataset(**kwargs,
crop=_cfg.crop,
resize_mask=False)
val_dataloaders.append(DataLoader(val_dataset, **cfg.dataloader))
test_dataloaders.append(
DataLoader(test_dataset, **{
**cfg.dataloader, 'batch_size': 1
}))
# Evaluate only
if not cfg.train:
assert cfg.eval_checkpoint is not None
# Print dataset info
for i, dataloader in enumerate(test_dataloaders):
dataset = dataloader.dataset
print(
f'Test dataset / dataloader size [{i}]: {len(dataset)} / {len(dataset)}'
)
# Create trainer
trainer = pl.Trainer(**cfg.trainer)
# Load checkpoint(s)
net = UNet().eval()
checkpoint = torch.load(cfg.eval_checkpoint, map_location='cpu')
state_dict = {
k.replace('net.', ''): v
for k, v in checkpoint["state_dict"].items()
}
net.load_state_dict(state_dict)
print(f'Loaded checkpoint from {cfg.eval_checkpoint}')
# Create module
module = SementationModule(net, cfg).eval()
# Compute test results
trainer.test(module, test_dataloaders=test_dataloaders)
# Pretty print results
table = utils.get_metrics_as_table(trainer.callback_metrics)
print('\n' + str(table.round(decimals=3)))
# Train
else:
# Generated images: load from disk
if cfg.data_gen.load_from_disk:
print('Loading images from disk')
# Transforms
train_transform = val_transform = A.Compose([
A.Resize(cfg.data_gen.image_size, cfg.data_gen.image_size),
A.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
ToTensorV2()
])
# Loaders
gan_train_dataloader, gan_val_dataloader = create_train_and_val_dataloaders(
cfg,
train_transform=train_transform,
val_transform=val_transform)
# Generated images: generate on the fly
else:
print('Loading images on-the-fly')
# Create GAN dataset
gan_train_dataset = create_gan_dataset(cfg.data_gen)
# GAN training dataloader
# NOTE: Only 1 process (num_workers=0) supported
gan_train_dataloader = DataLoader(gan_train_dataset, batch_size=1)
# Load or create GAN validation batches
print('Creating new GAN validation set.')
num_batches = max(
1, cfg.data_gen.val_images // cfg.data_gen.kwargs.batch_size)
gan_val_batches = utils.get_subset_of_dataset(
dataset=gan_train_dataset, num_batches=num_batches)
gan_val_dataset = TensorDataset(*gan_val_batches)
# Save example images from GAN validation dataset
fname = 'generated-val-examples.png'
utils.save_overlayed_images(gan_val_batches,
filename=fname,
is_mask=True)
print(f'Saved visualization images to {fname}')
# Validation dataloader
gan_val_dataloader = DataLoader(gan_val_dataset, **cfg.dataloader)
# Summary of dataset/dataloader sizes
print(f'Generated train {utils.get_dl_size(gan_train_dataloader)}')
print(f'Generated val {utils.get_dl_size(gan_val_dataloader)}')
for i, dl in enumerate(val_dataloaders):
print(f'Seg val [{i}] {utils.get_dl_size(dl)}')
# Validation dataloaders
val_dataloaders = [gan_val_dataloader, *val_dataloaders]
# Checkpointer
callbacks = [
pl.callbacks.ModelCheckpoint(monitor='train_loss',
save_top_k=20,
save_last=True,
verbose=True),
pl.callbacks.LearningRateMonitor('step')
]
# Logging
logger = pl.loggers.WandbLogger(name=cfg.name) if cfg.wandb else True
# Trainer
trainer = pl.Trainer(logger=logger, callbacks=callbacks, **cfg.trainer)
# Lightning
net = UNet().train()
module = SementationModule(net, cfg)
# Train
trainer.fit(module,
train_dataloader=gan_train_dataloader,
val_dataloaders=val_dataloaders)
# Test
trainer.test(module, test_dataloaders=test_dataloaders)
# Pretty print results
table = utils.get_metrics_as_table(trainer.callback_metrics)
print('\n' + str(table.round(decimals=3)))
COLOR1 = [255, 0, 0]
COLOR2 = [0, 0, 255]
#------------------------------------------------------------------------------
# Create model and load weights
#------------------------------------------------------------------------------
model = UNet(
backbone="mobilenetv2",
num_classes=2,
pretrained_backbone=None
)
if args.use_cuda:
model = model.cuda()
trained_dict = torch.load(args.checkpoint, map_location="cpu")['state_dict']
model.load_state_dict(trained_dict, strict=False)
model.eval()
#------------------------------------------------------------------------------
# Predict frames
#------------------------------------------------------------------------------
i = 0
while(cap.isOpened()):
# Read frame from camera
start_time = time()
_, frame = cap.read()
image = cv2.transpose(frame[...,::-1])
h, w = image.shape[:2]
read_cam_time = time()
model2 = torchvision.models.segmentation.deeplabv3_resnet101(
pretrained=False, num_classes=1)
model2 = model2.to(device)
modelName2 = model2.__class__.__name__
model3 = UNet(1, 1).to(device)
modelName3 = model3.__class__.__name__
model1_checkpoint = torch.load(
train().checkpointsPath + '/' + modelName1 + '/' +
'2019-08-30 13:21:52.559302_epoch-5_dice-0.4743926368317377.pth')
model2_checkpoint = torch.load(
train().checkpointsPath + '/' + modelName2 + '/' +
'2019-08-22 08:37:06.839794_epoch-1_dice-0.4479589270841744.pth')
model3_checkpoint = torch.load(
train().checkpointsPath + '/' + modelName3 + '/' +
'2019-09-03 03:21:05.647040_epoch-253_dice-0.46157537277322264.pth')
model1.load_state_dict(model1_checkpoint['model_state_dict'])
model2.load_state_dict(model2_checkpoint['model_state_dict'])
model3.load_state_dict(model3_checkpoint['model_state_dict'])
try:
# Create model Directory
train().main(model1, model2, model3, device)
except KeyboardInterrupt:
print('Interrupted')
try:
sys.exit(0)
except SystemExit:
os._exit(0)
def main():
net = UNet(num_classes=num_classes).cuda()
if len(train_args['snapshot']) == 0:
curr_epoch = 0
else:
print 'training resumes from ' + train_args['snapshot']
net.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, train_args['snapshot'])))
split_snapshot = train_args['snapshot'].split('_')
curr_epoch = int(split_snapshot[1])
train_record['best_val_loss'] = float(split_snapshot[3])
train_record['corr_mean_iu'] = float(split_snapshot[6])
train_record['corr_epoch'] = curr_epoch
net.train()
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
train_simul_transform = simul_transforms.Compose([
simul_transforms.Scale(int(train_args['input_size'][0] / 0.875)),
simul_transforms.RandomCrop(train_args['input_size']),
simul_transforms.RandomHorizontallyFlip()
])
val_simul_transform = simul_transforms.Compose([
simul_transforms.Scale(int(train_args['input_size'][0] / 0.875)),
simul_transforms.CenterCrop(train_args['input_size'])
])
img_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = standard_transforms.Compose([
expanded_transforms.MaskToTensor(),
expanded_transforms.ChangeLabel(ignored_label, num_classes - 1)
])
restore_transform = standard_transforms.Compose([
expanded_transforms.DeNormalize(*mean_std),
standard_transforms.ToPILImage()
])
train_set = CityScapes('train', simul_transform=train_simul_transform, transform=img_transform,
target_transform=target_transform)
train_loader = DataLoader(train_set, batch_size=train_args['batch_size'], num_workers=16, shuffle=True)
val_set = CityScapes('val', simul_transform=val_simul_transform, transform=img_transform,
target_transform=target_transform)
val_loader = DataLoader(val_set, batch_size=val_args['batch_size'], num_workers=16, shuffle=False)
weight = torch.ones(num_classes)
weight[num_classes - 1] = 0
criterion = CrossEntropyLoss2d(weight).cuda()
# don't use weight_decay for bias
optimizer = optim.SGD([
{'params': [param for name, param in net.named_parameters() if name[-4:] == 'bias'],
'lr': 2 * train_args['lr']},
{'params': [param for name, param in net.named_parameters() if name[-4:] != 'bias'],
'lr': train_args['lr'], 'weight_decay': train_args['weight_decay']}
], momentum=0.9, nesterov=True)
if len(train_args['snapshot']) > 0:
optimizer.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, 'opt_' + train_args['snapshot'])))
optimizer.param_groups[0]['lr'] = 2 * train_args['lr']
optimizer.param_groups[1]['lr'] = train_args['lr']
if not os.path.exists(ckpt_path):
os.mkdir(ckpt_path)
if not os.path.exists(os.path.join(ckpt_path, exp_name)):
os.mkdir(os.path.join(ckpt_path, exp_name))
for epoch in range(curr_epoch, train_args['epoch_num']):
train(train_loader, net, criterion, optimizer, epoch)
validate(val_loader, net, criterion, optimizer, epoch, restore_transform)
