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 homework2(test_dir):
test_transform = transforms.Compose([
transforms.Resize((224, 224)),
AddPepperNoise(0.9, p=0.8),
transforms.ToTensor(),
])
test_data = RMBDataset(data_dir=test_dir, transform=test_transform)
test_loader = DataLoader(dataset=test_data, batch_size=1)
for i, data in enumerate(test_loader):
inputs, labels = data # B C H W
img_tensor = inputs[0, ...] # C H W
img = transform_invert(img_tensor, test_transform)
plt.imshow(img)
plt.show()
plt.pause(0.5)
plt.close()
def homework1(test_dir):
test_transform = transforms.Compose([
transforms.Resize((224, 224)),
# 1 裁剪
#transforms.CenterCrop(120),
# 2 翻转
#transforms.RandomHorizontalFlip(p=1),
# 3 旋转
#transforms.RandomRotation(45),
# 4 色相
#transforms.ColorJitter(hue=0.4),
# 5 饱和度
#transforms.ColorJitter(saturation=50),
# 6 灰度图
#transforms.Grayscale(3),
# 7 错切
#transforms.RandomAffine(0,shear=45),
# 8 缩放
#transforms.RandomAffine(0,scale=(0.5,0.5)),
# 9 平移
#transforms.RandomAffine(0,translate=(0.5,0)),
# 10 遮挡
#transforms.ToTensor(),
#transforms.RandomErasing(p=0.5,scale=(0.1,0.4),value=0),
transforms.ToTensor(),
])
# 构建MyDataset实例
test_data = RMBDataset(data_dir=test_dir, transform=test_transform)
test_loader = DataLoader(dataset=test_data, batch_size=1)
for i, data in enumerate(test_loader):
inputs, labels = data # B C H W
img_tensor = inputs[0, ...] # C H W
img = transform_invert(img_tensor, test_transform)
plt.imshow(img)
plt.show()
plt.pause(0.5)
plt.close()
print("像素值:", output_tensor_3)
print("=======================================")
output_tensor_4 = conv_layer4(input_tensor)
print("卷积前尺寸:{}\n卷积后尺寸:{}".format(input_tensor.shape, output_tensor_4.shape))
print("像素值:", output_tensor_4)
# ================================ 2 ================================
import matplotlib.pyplot as plt
from common_tools import transform_invert
from torchvision import transforms
from PIL import Image
lena_img = "lena.png"
img = Image.open(lena_img).convert('RGB')
img_transform = transforms.Compose([transforms.ToTensor()])
img_tensor = img_transform(img)
img_tensor.unsqueeze_(dim=0)
conv_layer = nn.Conv3d(3, 1, (3, 3, 3), padding=(1, 0, 0), bias=False)
nn.init.xavier_normal_(conv_layer.weight.data)
img_tensor.unsqueeze_(dim=2)
img_conv = conv_layer(img_tensor)
#img_conv = transform_invert(img_conv[0, 0:1, ...], img_transform)
img_conv = transform_invert(img_conv[:, :, ...], img_transform)
img_raw = transform_invert(img_tensor.squeeze(), img_transform)
plt.subplot(122).imshow(img_conv, cmap='gray')
plt.subplot(121).imshow(img_raw)
plt.show()