def test():
model = Unet(3, 1)
model.load_state_dict(torch.load(Model_path, map_location='cpu'))
#card_dataset = CardDataset("data/val", transform=x_transforms,target_transform=y_transforms)
#dataloaders = DataLoader(card_dataset, batch_size=1)
model.eval()
with torch.no_grad():
for name in names:
img = cv2.imread(name, 1)
x = cv2img_process(img)
y = model(x)
img_y = (torch.squeeze(y).numpy() * -0.4 * 40 / 255.0 - 0.3) / 0.7
img_y = np.where(img_y < 0.3, 0, img_y)
img_y = np.where(img_y > 0.3, 1, img_y)
cv2.imshow("x", img)
cv2.imshow("predict", img_y)
#print(img.shape)
#print(img_y.shape)
#print("max ",img_y.max())
#print("min ",img_y.min())
print(img_y[250][250])
cv2.waitKey(10)
def test(args):
model = Unet(3, 1)
model.load_state_dict(torch.load(args.ckpt,map_location='cpu'))
liver_dataset = LiverDataset("val/healthysick_2", transform=x_transforms,target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
test_loss = 0
correct = 0
import matplotlib.pyplot as plt
import torchvision.utils as vutils
plt.ion()
with torch.no_grad():
i = 0
for x, y, target in dataloaders:
output1, output2 = model(x)
img_y=torch.squeeze(output2).numpy()
plt.imshow(img_y)
plt.show()
plt.pause(0.01)
test_loss += F.nll_loss(output1, target, reduction='sum').item()
print("-----------")
print(output1)
pred = output1.argmax(dim=1, keepdim=True)
print("pretend: {}".format(pred.view_as(target)))
print('target: {}'.format(target))
correct += pred.eq(target.view_as(pred)).sum().item()
print("-----------")
vutils.save_image(x, 'save3/iter%d-data.jpg' % i, padding=0)
vutils.save_image(y, 'save3/iter%d-mask.jpg' % i, padding=0)
vutils.save_image(output2, 'save3/iter%d-target.jpg' % i, padding=0)
i = i+1
test_loss /= len(liver_dataset)
print('Average loss is: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(test_loss, correct, len(liver_dataset), 100.*correct/len(liver_dataset)))
def test():
model = Unet(3, 1)
model.load_state_dict(
torch.load('weight/weights_{}.pth'.format(str(num_epochs - 1)),
map_location='cpu'))
liver_dataset = LiverDataset("data/img",
"data/label",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
i = 0
with torch.no_grad():
for x, _ in dataloaders:
#print (x.shape)
y = model(x)
img_y = torch.squeeze(y).numpy()
print(img_y)
img = cv2.normalize(img_y, None, 0, 255, cv2.NORM_MINMAX,
cv2.CV_8U)
cv2.imwrite('data/pred/{}.png'.format(str(i)), img)
i = i + 1
print(i)
def test():
model = Unet(3, 1).to(device) #unet输入是三通道，输出是一通道，因为不算上背景只有肝脏一个类别
model.load_state_dict(torch.load(args.ckp, map_location='cpu')) #载入训练好的模型
liver_dataset = LiverDataset(
r"H:\BaiduNetdisk\BaiduDownload\u_net_liver-master\data\val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion() #开启动态模式
with torch.no_grad():
i = 0 #验证集中第i张图
miou_total = 0
num = len(dataloaders) #验证集图片的总数
for x, _ in dataloaders:
x = x.to(device)
y = model(x)
img_y = torch.squeeze(y).cpu().numpy(
) #输入损失函数之前要把预测图变成numpy格式，且为了跟训练图对应，要额外加多一维表示batchsize
mask = get_data(i)[1] #得到当前mask的路径
miou_total += get_iou(mask, img_y) #获取当前预测图的miou，并加到总miou中
plt.subplot(121)
plt.imshow(Image.open(get_data(i)[0]))
plt.subplot(122)
plt.imshow(img_y)
plt.pause(0.01)
if i < num: i += 1 #处理验证集下一张图
plt.show()
print('Miou=%f' % (miou_total / 20))
def test():
model = Unet(3, 1).to(device) # unet输入是三通道，输出是一通道，因为不算上背景只有肝脏一个类别
weight_pre = r"./results/weights4_18_35.pth"
model.load_state_dict(torch.load(weight_pre)) # 载入训练好的模型
liver_dataset = LiverDataset(r"D:\project\data_sets\data_sci\val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion() # 开启动态模式
with torch.no_grad():
i = 0 # 验证集中第i张图
miou_total = 0
num = len(dataloaders) # 验证集图片的总数
for x, _ in dataloaders:
x = x.to(device)
y = model(x)
img_y = torch.squeeze(y).cpu().numpy(
) # 输入损失函数之前要把预测图变成numpy格式，且为了跟训练图对应，要额外加多一维表示batchsize
mask = get_data(i)[1] # 得到当前mask的路径
miou_total += get_iou(mask, img_y) # 获取当前预测图的miou，并加到总miou中
plt.subplot(121)
plt.imshow(Image.open(get_data(i)[0]))
plt.subplot(122)
plt.imshow(img_y)
plt.pause(0.01)
if i < num: i += 1 # 处理验证集下一张图
plt.show()
print('Miou=%f' % (miou_total / 10))
res_record("weights4_13_40.pth Miou=%f \n" % (miou_total / 10))
def test(args):
model = Unet(1, 1)
model.load_state_dict(torch.load(args.ckpt, map_location='cuda'))
liver_dataset = LiverDataset("data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
save_root = './data/predict'
model.eval()
plt.ion()
index = 0
with torch.no_grad():
for x, ground in dataloaders:
x = x.type(torch.FloatTensor)
y = model(x)
x = torch.squeeze(x)
x = x.unsqueeze(0)
ground = torch.squeeze(ground)
ground = ground.unsqueeze(0)
img_ground = transform_invert(ground, y_transforms)
img_x = transform_invert(x, x_transforms)
img_y = torch.squeeze(y).numpy()
# cv2.imshow('img', img_y)
src_path = os.path.join(save_root, "predict_%d_s.png" % index)
save_path = os.path.join(save_root, "predict_%d_o.png" % index)
ground_path = os.path.join(save_root, "predict_%d_g.png" % index)
img_ground.save(ground_path)
# img_x.save(src_path)
cv2.imwrite(save_path, img_y * 255)
index = index + 1
def test(args):
model = Unet(1, 1)
model.load_state_dict(torch.load(args.ckpt, map_location='cpu'))
liver_dataset = LiverDataset("data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for x, yy in dataloaders:
y = model(x)
l1loss = nn.L1Loss()
loss = l1loss(y, yy)
print(loss.item())
img_y = torch.squeeze(y).numpy()
img_yy = torch.squeeze(yy).numpy()
# img_y = (img_y + 1) * 127.5
plt.figure()
plt.subplot(121)
plt.imshow(img_y.transpose(),
aspect='auto',
interpolation='none',
cmap=plt.get_cmap('gray'))
plt.subplot(122)
plt.imshow(img_yy.transpose(),
aspect='auto',
interpolation='none',
cmap=plt.get_cmap('gray'))
plt.pause(0.01)
# plt.waitforbuttonpress()
plt.show()
def test_1():
model = Unet(3, 1)
model.load_state_dict(torch.load(args.ckp, map_location='cpu'))
liver_dataset = LiverDataset("data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
imgs = []
root = "data/val"
n = len(os.listdir(root)) // 2
for i in range(n):
img = os.path.join(root, "%03d.png" % i)
# mask = os.path.join(root, "%03d_mask.png" % i)
imgs.append(img)
i = 0
with torch.no_grad():
for x, _ in dataloaders:
y = model(x)
img_x = torch.squeeze(_).numpy()
img_y = torch.squeeze(y).numpy()
img_input = cv2.imread(imgs[i], cv2.IMREAD_GRAYSCALE)
im_color = cv2.applyColorMap(img_input, cv2.COLORMAP_JET)
img_x = img_as_ubyte(img_x)
img_y = img_as_ubyte(img_y)
imgStack = stackImages(0.8, [[img_input, img_x, img_y]])
# 转为伪彩色，视情况可以加上
# imgStack = cv2.applyColorMap(imgStack, cv2.COLORMAP_JET)
cv2.imwrite(f'train_img/{i}.png', imgStack)
plt.imshow(imgStack)
i = i + 1
plt.pause(0.1)
plt.show()
def train():
x_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
y_transforms = transforms.ToTensor()
model = Unet(3, 1).to(device)
model.load_state_dict(torch.load(r"./results/weights.pth"))
batch_size = 1
num_epochs = 2
criterion = torch.nn.BCELoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset(r'D:\project\data_sets\liver\train',
transform=x_transforms,
target_transform=y_transforms)
data_loaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
print("Start training at ", strftime("%Y-%m-%d %H:%M:%S", localtime()))
for epoch in range(num_epochs):
prev_time = datetime.now()
print('Epoch{}/{}'.format(epoch, num_epochs))
print('-' * 10)
dt_size = len(data_loaders.dataset)
epoch_loss = 0
step = 0
for x, y in data_loaders:
step += 1
inputs = x.to(device)
labels = y.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
if (step % 10) == 0:
print("%d/%d, train_loss:%0.3f" %
(step, (dt_size - 1) // data_loaders.batch_size + 1,
loss.item()))
# print the results of the current training
cur_time = datetime.now()
h, remainder = divmod((cur_time - prev_time).seconds, 3600)
m, s = divmod(remainder, 60)
time_str = 'Time:{:.0f}:{:.0f}:{:.0f}'.format(h, m, s)
epoch_str = "epoch {} loss:{:.4f} ".format(epoch, epoch_loss / 400)
print(epoch_str + time_str)
res_record("Time:" + strftime("%Y-%m-%d %H:%M:%S ", localtime()))
res_record(epoch_str + '\n')
print("End training at ", strftime("%Y-%m-%d %H:%M:%S", localtime()))
# 记录数据
torch.save(
model.state_dict(),
'./results/weights{}_{}_{}.pth'.format(localtime().tm_mday,
localtime().tm_hour,
localtime().tm_sec))
def main(argv):
with CytomineJob.from_cli(argv) as job:
model_path = os.path.join(str(Path.home()), "models", "thyroid-unet")
model_filepath = pick_model(model_path, job.parameters.tile_size,
job.parameters.cytomine_zoom_level)
device = torch.device(job.parameters.device)
unet = Unet(job.parameters.init_fmaps, n_classes=1)
unet.load_state_dict(torch.load(model_filepath, map_location=device))
unet.to(device)
unet.eval()
segmenter = UNetSegmenter(device=job.parameters.device,
unet=unet,
classes=[0, 1],
threshold=job.parameters.threshold)
working_path = os.path.join(str(Path.home()), "tmp")
tile_builder = CytomineTileBuilder(working_path)
builder = SSLWorkflowBuilder()
builder.set_n_jobs(1)
builder.set_overlap(job.parameters.tile_overlap)
builder.set_tile_size(job.parameters.tile_size,
job.parameters.tile_size)
builder.set_tile_builder(tile_builder)
builder.set_border_tiles(Workflow.BORDER_TILES_EXTEND)
builder.set_background_class(0)
builder.set_distance_tolerance(1)
builder.set_seg_batch_size(job.parameters.batch_size)
builder.set_segmenter(segmenter)
workflow = builder.get()
slide = CytomineSlide(img_instance=ImageInstance().fetch(
job.parameters.cytomine_id_image),
zoom_level=job.parameters.cytomine_zoom_level)
results = workflow.process(slide)
print("-------------------------")
print(len(results))
print("-------------------------")
collection = AnnotationCollection()
for obj in results:
wkt = shift_poly(obj.polygon,
slide,
zoom_level=job.parameters.cytomine_zoom_level).wkt
collection.append(
Annotation(location=wkt,
id_image=job.parameters.cytomine_id_image,
id_terms=[154005477],
id_project=job.project.id))
collection.save(n_workers=job.parameters.n_jobs)
return {}
def train():
model = Unet(3, 1).to(device)
model.load_state_dict(torch.load(r"./results/weights4_13_40.pth"))
batch_size = 5
criterion = torch.nn.BCELoss()
optimizer = optim.Adam(model.parameters())
liver_dataset = LiverDataset(r"D:\project\data_sets\data_sci\train",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
train_model(model, criterion, optimizer, dataloaders)
def test(args):
model = Unet(3, 1)
model.load_state_dict(torch.load(args.ckpt,map_location='cpu'))
liver_dataset = LiverDataset("data/val", transform=x_transforms,target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for x, _ in dataloaders:
y=model(x).sigmoid()
img_y=torch.squeeze(y).numpy()
plt.imshow(img_y)
plt.pause(0.01)
plt.show()
def infer():
model = Unet(3, 1)
model.load_state_dict(torch.load('weights_19.pth', map_location='cpu'))
liver_dataset = LiverDataset("./../data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for x, _ in dataloaders:
y = model(x)
img_y = torch.squeeze(y).numpy()
plt.imshow(img_y)
plt.pause(0.01)
plt.show()
def test(args):
model = Unet(3, 1)
model.load_state_dict(torch.load(args.ckpt, map_location='cpu'))
liver_dataset = LiverDataset("data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
plt.ion()
with torch.no_grad():
for x, y in dataloaders:
y = model(x).sigmoid()
img_y = torch.squeeze(y).numpy()
plt.imshow(img_y, cmap='gray', interpolation='nearest')
plt.pause(0.5)
plt.ioff()
plt.show()
def test(args):
model = Unet(3, 1).to(device) # 构建模型
model.load_state_dict(torch.load(args.ckpt, map_location='cpu')) # 加载参数
liver_dataset = LiverDataset("data/val",
transform=x_transforms,
target_transform=y_transforms
) # 加载数据，这里懒了用的train的函数，所以也要加载mask，不过识别时没用到
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for x, _ in dataloaders:
y = model(x.to(device))
img_y = torch.squeeze(y.cpu()).numpy() > 0
plt.imshow(img_y)
plt.pause(0.01)
plt.show()
def test():
model = Unet(3, 1).to(device)
model.load_state_dict(torch.load(args.ckp))
liver_dataset = LiverDataset("data/test",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
with torch.no_grad():
for x, _, x_path in tqdm(dataloaders):
x_path = str(x_path).split("/")
x = x.to(device)
y = model(x)
img_numpy = y[0].cpu().float().numpy()
img_numpy = (np.transpose(img_numpy, (1, 2, 0)))
img_numpy = (img_numpy >= 0.5) * 255
img_out = img_numpy.astype(np.uint8)
imgs = transforms.ToPILImage()(img_out)
imgs.save('result/' + x_path[2][:-3])
def test(args):
model = Unet(3, 1)
model.load_state_dict(torch.load(args.ckpt))
liver_dataset = LiverDataset(
"/home/ices/work/tzh/predrnn/results/my_data_predrnn/1050/1",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for x, name in dataloaders:
y = model(x)
img_y = torch.squeeze(y).numpy()
img_y = np.asarray(img_y)
copy = img_y
# print(len(copy[copy<0.5]))
copy[copy > 0.5] = int(255)
copy[copy <= 0.5] = int(0)
copy = copy.astype(np.int16)
copy = cv.resize(copy, (64, 64))
cv.imwrite(os.path.join("./data/my_result", name[0] + ".png"),
copy)
print(name[0])
copy = cv.imread(
os.path.join("./data/my_result", name[0] + ".png"), 0)
# raw_img = cv.imread(os.path.join("../Unet/raw_images",name[0]+".png"))
# cv.imwrite(os.path.join("./data/train",name[0]+".png"),raw_img)
# kernel = np.ones((7, 7), np.uint8)
# copy = cv.morphologyEx(copy, cv.MORPH_CLOSE, kernel)
# copy = remove_small(copy, 100)
# copy = cv.GaussianBlur(copy,(3,3),0)
# cv.imwrite(os.path.join("./data/result",name+"hihi.png"),file)
# cv.imwrite(os.path.join("./data/result",name+"_mask.png"),file)
edges = cv.Canny(copy, 50, 150)
# print(len(edges[edges!=0]))
cv.imwrite(os.path.join("./data/my_result", name[0] + ".png"),
edges)
draw_edge(name[0])
def test_image():
#model = Unet(3, 3)
model = Unet(1, 1)
model.load_state_dict(
torch.load('ckp_xin/fd3model.pth', map_location='cpu'))
model.eval()
'''
img = Image.open("data/aug/24.bmp")
img = x_transforms(img)
img = torch.unsqueeze(img,0)
'''
#img_x = pydicom.dcmread("data/aug/32.dcm")
#img_x = WL(img_x,150,300)
#img_x = Image.fromarray(img_x)
img_x = Image.open("data/aug/76.bmp")
img_x = img_x.convert('L')
#img_x.save('data/aug/32dtp.bmp')
#img_x.show()
img_x = x_transforms(img_x)
img_x = torch.unsqueeze(img_x, 0)
labels = Image.open("data/aug/76_mask.bmp")
labels = labels.convert('L')
labels = y_transforms(labels)
labels = torch.unsqueeze(labels, 0)
out = model(img_x)
print(IOU(out.to("cpu"), labels.to("cpu")).item())
'''
img_mask = Image.open("data/aug/166_mask.png")
img_mask = y_transforms(img_mask)
img_mask = torch.unsqueeze(img_mask,0)
out = model(img)
dice = dice_coeff(out,img_mask)
print(dice.detach().numpy())
'''
trann = transforms.ToPILImage()
out = torch.squeeze(out)
out = trann(out)
out.save("data/aug/76_maskfd3.bmp")
def test(args):
model = Unet(3, 1) #.to(device)
model.load_state_dict(torch.load(args.ckpt, map_location='cpu'))
liver_dataset = LiverDataset("data/train",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for x, _ in dataloaders:
y = model(x)
print("y的shape", y.shape())
img_y = torch.squeeze(y).numpy()
plt.imshow(img_y)
plt.savefig("./results/output_%d.jpg" % random.randint(0, 100))
plt.pause(0.01)
plt.show()
def test():
model = Unet(1, 1)
model.load_state_dict(torch.load(PATH))
test_dataset = TestDataset("dataset/test",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(test_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for index, x in enumerate(dataloaders):
y = model(x)
img_y = torch.squeeze(y).numpy()
img_y = img_y[:, :, np.newaxis]
img = labelVisualize(2, COLOR_DICT,
img_y) if False else img_y[:, :, 0]
io.imsave("./dataset/test/" + str(index) + "_predict.png", img)
plt.pause(0.01)
plt.show()
def test_video():
cap = cv2.VideoCapture(2)
model = Unet(3, 1)
model.load_state_dict(torch.load(Model_path))
model.to(device)
#card_dataset = CardDataset("data/val", transform=x_transforms,target_transform=y_transforms)
#dataloaders = DataLoader(card_dataset, batch_size=1)
model.eval()
with torch.no_grad():
while True:
ret, frame = cap.read()
if ret is None:
print("camera is not ready")
exit(0)
frame = frame[0:480, 0:480]
img = cv2.resize(frame, (512, 512))
#img = cv2.imread(name,1)
x = cv2img_process(img)
x_cuda = x.cuda()
y = model(x_cuda)
y_cpu = y.cpu()
img_y = (torch.squeeze(y_cpu).numpy() * -0.4 * 40 / 255.0 -
0.3) / 0.7
img_y = np.where(img_y < 0.3, 0, img_y)
img_y = np.where(img_y > 0.3, 1, img_y)
cv2.imshow("x", img)
cv2.imshow("predict", img_y)
#print(img.shape)
#print(img_y.shape)
#print("max ",img_y.max())
#print("min ",img_y.min())
#print(img_y[250][250])
cv2.waitKey(1)
def test(args):
model = Unet(3, 1)
model.load_state_dict(torch.load(args.ckpt, map_location='cpu'))
card_dataset = CardDataset("data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(card_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
for x, _ in dataloaders:
org = x
y = model(x)
img_y = torch.squeeze(y).numpy()
plt.subplot(1, 2, 1)
plt.imshow(org.numpy()[0].reshape(512, 512, 3))
plt.subplot(1, 2, 2)
plt.imshow(img_y)
plt.pause(0.01)
plt.show()
def test(args):
model = Unet(3, 1)
model.load_state_dict(torch.load(args.ckpt, map_location='cpu')) #加载模型
liver_dataset = LiverDataset("data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1) #batch_size默认为1
model.eval()
import matplotlib.pyplot as plt
plt.ion()
with torch.no_grad():
n = 0
for x, _ in dataloaders:
y = model(x)
img_y = torch.squeeze(y).numpy() #对数据的维度进行压缩或者解压。Tensor转化为PIL图片
from PIL import Image
# image_array是归一化的二维浮点数矩阵
img_y *= 255 # 变换为0-255的灰度值
im = Image.fromarray(img_y)
im = im.convert('L') # 这样才能转为灰度图，如果是彩色图则改L为‘RGB’
matplotlib.image.imsave('%03d_predict.png' % n, im)
threshold = 180
table = []
for i in range(256):
if i < threshold:
table.append(0)
else:
table.append(1)
photo = im.point(table, '1')
matplotlib.image.imsave('%03d_predict1.png' % n, photo)
#plt.imshow(img_y)
n = n + 1
# plt.pause(5)
# plt.show()
print("hello")
def test(args):
model = Unet(
3, 1
) #The unet input is three channels, and the output is one channel, because there is only one category of fingerprints on the background
model.load_state_dict(torch.load(
args.ckpt, map_location='cpu')) #Load the trained model
liver_dataset = LiverDataset("data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion() #Turn on dynamic mode
with torch.no_grad():
i = 0
miou_total = 0
num = len(dataloaders)
for x, _ in dataloaders:
x = x.to(device)
y = model(x)
img_y = torch.squeeze(y).cpu().numpy(
) #Before inputting the loss function, the prediction graph must be converted into numpy format, and in order to correspond to the training graph, an additional one-dimensional representation of the batch size must be added
mask = get_data(i)[1] #Get the current mask path
miou_total += get_iou(
mask, img_y
) #Get the miou of the current prediction graph and add it to the total miou
plt.subplot(121)
plt.imshow(Image.open(get_data(i)[0]))
plt.subplot(122)
img_y = img_y * 255
img_y = Image.fromarray(img_y)
plt.imshow(img_y.convert('L'))
plt.pause(2)
if i < num: i += 1 # Processing the next set of validation sets
plt.show()
print('Miou=%f' % (miou_total / 106))
def test():
model = Unet(3, 1).to(device)
weight_pre = r"./results/weights18_14_41.pth"
model.load_state_dict(torch.load(weight_pre))
x_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
y_transforms = transforms.ToTensor()
liver_dataset = LiverDataset(r"D:\project\data_sets\liver\val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
model.eval()
import matplotlib.pyplot as plt
plt.ion() # 开启动态模式
with torch.no_grad():
i = 0
miou_total = 0
num = len(dataloaders)
for x, y in dataloaders:
x = x.to(device)
y = model(x)
img_y = torch.squeeze(y).cpu().numpy(
) # 输入损失函数之前要把预测图变成numpy格式，且为了跟训练图对应，要额外加多一维表示batchsize
mask = get_data(i)[1] # 得到当前mask的路径
miou_total += get_iou(mask, img_y) # 获取当前预测图的miou，并加到总miou中
plt.subplot(121)
plt.imshow(Image.open(get_data(i)[0]))
plt.subplot(122)
plt.imshow(img_y)
plt.pause(0.01)
if i < num: i += 1 # 处理验证集下一张图
plt.show()
print('Miou=%f' % (miou_total / 20))
res_record("weights18_14_41.pth Miou=%f \n" % (miou_total / 20))
def test(args):
model = Unet(3, 1)
model.load_state_dict(torch.load(args.ckpt, map_location='cpu'))
liver_dataset = LiverDataSet(
"/home/ming/code/u-net-liver-pytorch/data/val",
transform=x_transforms,
target_transform=y_transforms)
dataloaders = DataLoader(liver_dataset, batch_size=1)
#不启用 BatchNormalization 和 Dropout
#训练完 train 样本后，生成的模型 model 要用来测试样本。在 model(test) 之前，
#需要加上model.eval()，否则只要有输入数据，即使不训练，model 也会改变权值。
# 这是model中含有的 batch normalization 层所带来的的性质。
model.eval()
import matplotlib.pyplot as plt
#使matplotlib的显示模式转换为交互（interactive）模式。
# 即使在脚本中遇到plt.show()，代码还是会继续执行
plt.ion()
# 在测试阶段使用with torch.no_grad()可以对整个网络都停止自动求导，
# 可以大大加快速度，也可以使用大的batch_size来测试
# 当然，也可以不使用with torch.no_grad
with torch.no_grad():
for x, _ in dataloaders:
# sigmod把值域在0和1之间，sigmod是为了后面用imgshow方法画热图
y = model(x).sigmoid()
img_y = torch.squeeze(y).numpy()
# get_iou("data/val/000_mask.png",img_y)
# imshow方法首先将二维数组的值标准化为0到1之间的值，
# 然后根据指定的渐变色依次赋予每个单元格对应的颜色，就形成了热图。
plt.imshow(img_y)
plt.pause(0.1)
plt.show()
def predict_image(image_file, model_path):
"""
Introduction
------------
使用MobileNet-UNet预测图像
"""
image = cv2.imread(image_file)
image = cv2.resize(image, (256, 256))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_tensor = transforms.ToTensor()(image).unsqueeze(0)
model = Unet(3, 1)
ckpt = torch.load(model_path, map_location='cpu')
model.load_state_dict(ckpt)
model.eval()
output_mask = model(image_tensor)
output_mask = output_mask.reshape(256, 256)
image_mask = output_mask.detach().numpy() * 255
image_mask = image_mask.astype(np.uint8)
image_mask = cv2.resize(image_mask, (256, 256))
plt.subplot(121)
plt.imshow(image)
plt.subplot(122)
plt.imshow(image_mask)
plt.show()
train_loader = DataLoader(train_data, batch_size=batch_size)
eval_data = KITTIGroundDataset(data_dir, np.arange(10, 11))
eval_loader = DataLoader(eval_data, batch_size=batch_size)
test_data = KITTIGroundDataset(data_dir, np.arange(9, 10))
test_loader = DataLoader(test_data, batch_size=batch_size)
name = 'ground_estimation_net_v2'
net = Unet(32, 1, 5, 32, concat=False)
use_multi_GPU = True
if use_multi_GPU:
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
net = nn.DataParallel(net)
reuse_weights = True
if reuse_weights:
net.load_state_dict(torch.load('./models/model_{}.pth'.format(name)))
try:
best_val_loss = np.load('./models/best_val_loss_{}.npy'.format(name))
except:
best_val_loss = np.finfo(np.float64).max
print("Model reloaded. Previous lowest validation loss =",
str(best_val_loss))
else:
best_val_loss = np.finfo(np.float64).max
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net.to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, weight_decay=1e-4)
best_weights = net.state_dict()
def test():
model = Unet(5, 2)
model.load_state_dict(torch.load(args.ckp, map_location='cpu'))
model.to(device)
test_root_dir = "test"
PAVE_dataset = SSFPTestDataset(root=test_root_dir)
# batch_size has to be divisible by 828 because there are 828 slices per patient
batch_size = 1
dataloaders = DataLoader(PAVE_dataset, batch_size=batch_size)
model.eval()
#import matplotlib.pyplot as plt
#plt.ion()
test_result_dir = "test_result"
if not os.path.exists(test_result_dir):
os.makedirs(test_result_dir)
patients = np.zeros((1, 832, 2, 224, 832))
with torch.no_grad():
for x, slice_num, patient_num, leg in tqdm(dataloaders):
x = x.to(device)
y = model(x)
output = y.cpu().numpy()
if leg[0] == 'left':
patients[patient_num, slice_num + 2, :, :192,
80:400] = output[0, :, :, :]
else:
patients[patient_num, slice_num + 2, :, :192,
480:800] = output[0, :, :, :]
for patient_num in range(10):
image_filename = os.path.join("/home/mng/scratch/PAVE_Challenge/test/",
'case{}'.format(patient_num + 1),
'ssfp.nii.gz')
size_x = nib.load(image_filename).shape[2]
patient_output_vessels = np.transpose(
(patients[patient_num, :, 0, :size_x, :] >= 0.5), axes=(2, 0, 1))
patient_output_arteries = np.transpose(
(patients[patient_num, :, 1, :size_x, :] >= 0.5), axes=(2, 0, 1))
patient_output_veins = np.logical_and(
patient_output_vessels, np.logical_not(patient_output_arteries))
results_file = os.path.join(
test_result_dir,
'case{}_results_vessels.nii.gz'.format(patient_num + 1))
save_nii(patient_output_vessels.astype(np.uint8), results_file)
results_file = os.path.join(
test_result_dir,
'case{}_results_arteries.nii.gz'.format(patient_num + 1))
save_nii(patient_output_arteries.astype(np.uint8), results_file)
results_file = os.path.join(
test_result_dir,
'case{}_results_veins.nii.gz'.format(patient_num + 1))
save_nii(patient_output_veins.astype(np.uint8), results_file)
########################################################################
# optimizer
# if args.optim == 'adam':
optimizer = optim.Adam(net.parameters(), lr=0.001)
########################################################################
# Train the network
seed_everything(seed=args.seed)
if args.load_mod:
history = {
'Train_loss': [],
'Train_dice': [],
'Valid_loss': [],
'Valid_dice': []
}
net.load_state_dict(torch.load(MODEL_FILE))
else:
net_swa, history = train_net(net, optimizer, device, args, LOG_FILE,
MODEL_FILE)
torch.save(net_swa, MODEL_SWA_FILE)
# save the final result
print('Finished Training')
history_df = pd.DataFrame(history)
history_df.to_csv(HISTORY_FILE)
# torch.save(net.state_dict(),MODEL_FILE)
# show the curve
fig, axs = plt.subplots(1, 2, figsize=(16, 4))
axs[0].plot(history['Train_loss'], label='Train Loss')
axs[0].plot(history['Valid_loss'], label='Valid Loss')
axs[0].legend()
axs[0].grid()
