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plot.py
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plot.py
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import matplotlib.pyplot as plt
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
import argparse
import os
from datasets import KidneyDataset
from models import UNet
def main(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Using {device} device.')
transform = transforms.Compose([
transforms.Resize(192),
transforms.ToTensor(),
])
test_set = KidneyDataset('test', transform=transform)
test_loader = DataLoader(test_set, batch_size=5, shuffle=False, num_workers=0)
net = UNet(args.num_classes).to(device)
if args.model_path and os.path.exists(args.model_path):
# Load model weights.
net.load_state_dict(torch.load(args.model_path, map_location=device))
net.eval()
for index, (images, masks) in enumerate(test_loader, 1):
images = images.to(device)
with torch.no_grad():
outputs = net(images)
plt.figure(figsize=(2.5, 2.5))
plt.imshow(images[4].cpu().numpy().transpose(1, 2, 0))
plt.tight_layout()
plt.savefig(f'{args.figure_path}/source.png', dpi=100)
plt.figure(figsize=(2.5, 2.5))
plt.imshow(masks[4].numpy().transpose(1, 2, 0))
plt.tight_layout()
plt.savefig(f'{args.figure_path}/mask.png', dpi=100)
plt.figure(figsize=(2.5, 2.5))
plt.imshow(outputs[4].detach().cpu().numpy().transpose(1, 2, 0))
plt.tight_layout()
plt.savefig(f'{args.figure_path}/predicted.png', dpi=100)
if index == 3:
break
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str)
parser.add_argument('--num_classes', default=1, type=int)
parser.add_argument('--figure_path', default='figure', type=str)
args = parser.parse_args()
print(vars(args))
os.makedirs(args.figure_path, exist_ok=True)
main(args)