def main():
# frames to infer
files = [
"C:\\AML_seg_proj\\CRC-Segmentation\\data\\test\\frames\\frame#1058.npz",
"C:\\AML_seg_proj\\CRC-Segmentation\\data\\test\\frames\\frame#139.npz",
"C:\\AML_seg_proj\\CRC-Segmentation\\data\\test\\frames\\frame#26.npz"
]
chkpt = "C:\\AML_seg_proj\\CRC-Segmentation\\model_large_drop_batch_wce\\model_chkpt_30.pt"
# transforms to apply
composed = Compose([
transforms.MirrorPad(((6, ), (6, ), (0, ))),
transforms.ToTensor(),
transforms.Normalize(means=(0.7942, 0.6693, 0.7722),
stds=(0.1998, 0.3008, 0.2037))
])
# model
#model = UNet((512, 512), (500, 500), 32, 64, 128, 256, 512, droprate=0.5, Norm=torch.nn.BatchNorm2d)
model = UNet((512, 512), (500, 500),
32,
64,
128,
256,
512,
droprate=0.5,
Norm=torch.nn.BatchNorm2d)
model.load_state_dict(torch.load(chkpt, map_location='cpu'))
model.eval()
# evaluate metrics on the fly
dice_sc, px_acc = Dice_Score(), Pixel_Accuracy((500, 500))
# make predictions and write images and masks to disk as png files
with torch.no_grad():
for file in files:
# load img, mask
img, ground_truth = np.load(file)["arr_0"], np.load(
file.replace("frame", "mask"))["arr_0"]
img_copy = img.copy()
# transform img
img, ground_truth = composed([img, ground_truth])
# prediction shape (1, 3, 500, 500)
pred = model(img.unsqueeze(0))
dice, pp_acc = dice_sc(pred, ground_truth.unsqueeze(0)), px_acc(
pred, ground_truth.unsqueeze(0))
print(f"Dice Score: {dice}, PP-Accuracy: {pp_acc}")
# mask shape (1, 500, 500)
mask = (torch.argmax(F.softmax(pred, dim=1),
dim=1).squeeze(0).numpy() / 2 * 255).astype(
np.uint8)
# prep image for writing, shape (1, 3, 512, 512)
img = (img.squeeze(0).numpy() * 255).astype(np.uint8)
identifier = file.split("\\")[-1].replace(".npz", ".png").replace(
"#", "_")
w_PNG(identifier=identifier, np_img=img_copy)
w_PNG(identifier=identifier.replace("frame", "mask"), np_img=mask)
plot_triple(img_copy, ground_truth, mask,
identifier.replace(".png", "_triple.png"))
def main():
# get test set and test set loader
test_set = CRC_Dataset(root_dir=os.path.join(os.getcwd(), "data\\test"),
transforms=[
MirrorPad(((6, ), (6, ), (0, ))),
ToTensor(),
Normalize(means=(0.7942, 0.6693, 0.7722),
stds=(0.1998, 0.3008, 0.2037))
])
test_loader = torch.utils.data.DataLoader(
test_set,
batch_size=args.batch_size if args.batch_size else 8,
num_workers=args.workers if args.workers else 1,
pin_memory=use_cuda,
)
model = UNet((512, 512), (500, 500),
32,
64,
128,
256,
512,
droprate=0.5,
Norm=torch.nn.BatchNorm2d)
#model = UNet((512, 512), (500, 500), 32, 64, 128, 256, Norm=torch.nn.BatchNorm2d)
model.load_state_dict(torch.load(chkpt))
model.to(device)
dice, acc = compute_metrics_on_test_set(model, test_loader)
print(dice, acc)
from U_Net import UNet
model_chkpt = "C:\\AML_seg_proj\\CRC-Segmentation\\model_large_drop_batch_dice_ce\\model_chkpt_60.pt"
# define input
inp = torch.zeros(size=(1, 3, 32, 32), requires_grad=True)
model = UNet((32, 32), (32, 32),
32,
64,
128,
256,
512,
droprate=0.5,
Norm=torch.nn.BatchNorm2d)
model.load_state_dict(torch.load(model_chkpt, map_location='cpu'))
model.eval()
grad_tensor = grad(model(inp)[0, 0, 15, 15], inp)
# plot sum of grad tensor
np_img = np.abs(grad_tensor[0][0][0].numpy())
np_img = np_img / np.max(np_img)
coords = np.column_stack(np.where(np_img != 0))
rf_size = np.max(coords, axis=0) - np.min(coords, axis=0)
print(f"Receptive field: {rf_size[0]}x{rf_size[1]}")
plt.figure(figsize=(15, 15))
plt.imshow(np_img, cmap="gray")
plt.colorbar()
plt.savefig("eff_receptive_field_dice_ce.png")