def cmr_3d_sax_transform(self):
train_transform = ts.Compose([
ts.Pad(size=self.scale_size),
ts.ToTensor(),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'float']),
ts.RandomFlip(h=True, v=True, p=self.random_flip_prob),
ts.RandomAffine(rotation_range=self.rotate_val,
translation_range=self.shift_val,
zoom_range=self.scale_val,
interp=('bilinear', 'nearest')),
#ts.NormalizeMedicPercentile(norm_flag=(True, False)),
ts.NormalizeMedic(norm_flag=(True, False)),
ts.ChannelsLast(),
ts.AddChannel(axis=0),
ts.RandomCrop(size=self.patch_size),
ts.TypeCast(['float', 'long'])
])
valid_transform = ts.Compose([
ts.Pad(size=self.scale_size),
ts.ToTensor(),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'float']),
#ts.NormalizeMedicPercentile(norm_flag=(True, False)),
ts.NormalizeMedic(norm_flag=(True, False)),
ts.ChannelsLast(),
ts.AddChannel(axis=0),
ts.SpecialCrop(size=self.patch_size, crop_type=0),
ts.TypeCast(['float', 'long'])
])
return {'train': train_transform, 'valid': valid_transform}
def ct_82_transform(self):
train_transform = ts.Compose([
ts.ToTensor(),
ts.RangeNormalize(0, 1),
ts.Pad(size=self.scale_size),
ts.TypeCast(['float', 'float']),
RandomFlip3D(z=True, x=True, y=True, p=self.random_flip_prob),
RandomAffine3D(degrees=self.rotate_val, translate=self.shift_val,
scale=self.scale_val, resample=Image.NEAREST),
CustomCrop3D(size=self.patch_size, crop_type="random"),
ts.AddChannel(axis=0),
# ts.TypeCast(['float', 'float'])
])
valid_transform = ts.Compose([
ts.ToTensor(),
ts.RangeNormalize(0, 1),
ts.Pad(size=self.scale_size),
ts.TypeCast(['float', 'float']),
CustomCrop3D(size=self.patch_size, crop_type="center"),
ts.AddChannel(axis=0),
# ts.TypeCast(['float', 'float'])
])
return {'train': train_transform, 'valid': valid_transform}
def test_3d_sax_transform(self):
test_transform = ts.Compose([
ts.PadFactorNumpy(factor=self.division_factor),
ts.ToTensor(),
ts.ChannelsFirst(),
ts.TypeCast(['float']),
#ts.NormalizeMedicPercentile(norm_flag=True),
ts.NormalizeMedic(norm_flag=True),
ts.ChannelsLast(),
ts.AddChannel(axis=0),
])
return {'test': test_transform}
def ultrasound_transform(self):
train_transform = ts.Compose([ts.ToTensor(),
ts.TypeCast(['float']),
ts.AddChannel(axis=0),
ts.SpecialCrop(self.patch_size,0),
ts.RandomFlip(h=True, v=False, p=self.random_flip_prob),
ts.RandomAffine(rotation_range=self.rotate_val,
translation_range=self.shift_val,
zoom_range=self.scale_val,
interp=('bilinear')),
ts.StdNormalize(),
])
valid_transform = ts.Compose([ts.ToTensor(),
ts.TypeCast(['float']),
ts.AddChannel(axis=0),
ts.SpecialCrop(self.patch_size,0),
ts.StdNormalize(),
])
return {'train': train_transform, 'valid': valid_transform}
random.seed(c)
print("random seed: {}".format(c))
comment = "20/200 with parameter per slice and all slices"
print(comment)
patient_id_G_test = random.sample(patient_id_G, 20)
patient_id_H_test = random.sample(patient_id_H, 200)
patient_id_G_train = list(patient_id_G.difference(patient_id_G_test))
patient_id_H_train = list(patient_id_H.difference(patient_id_H_test))
transform_pipeline_train = tr.Compose([
AddGaussian(),
AddGaussian(ismulti=False),
tr.ToTensor(),
tr.AddChannel(axis=0),
tr.TypeCast('float'),
# Attenuation((-.001, .1)),
# tr.RangeNormalize(0,1),
tr.RandomBrightness(-.2, .2),
tr.RandomGamma(.9, 1.1),
tr.RandomFlip(),
tr.RandomAffine(rotation_range=5,
translation_range=0.2,
zoom_range=(0.9, 1.1))
])
transform_pipeline_test = tr.Compose([
tr.ToTensor(),
tr.AddChannel(axis=0),
tr.TypeCast('float')
for epoch in range(1, nb_epoch + 1):
seg_train(epoch, unet_model, seg_train_loader, criterion, optimizer)
# print("Test AUC:", auc_cal(model, testloader))
# test(model, testloader)
torch.save(unet_model, os.path.join(model_repo_dir, 'unet.pt'))
#Training UNET END
#Training Diag network START
transform_pipeline_train = tr.Compose(
[
# AddGaussian(),
# AddGaussian(ismulti=False),
tr.ToTensor(), tr.AddChannel(axis=0), tr.TypeCast('float'),
# Attenuation((-.001, .1)),
# tr.RangeNormalize(0,1),
tr.RandomBrightness(-.2, .2),
tr.RandomGamma(.9, 1.1),
tr.RandomFlip(),
tr.RandomAffine(rotation_range=5, translation_range=0.2
# zoom_range=(0.9, 1.1)
)])
transform_pipeline_test = tr.Compose([tr.ToTensor(), tr.AddChannel(axis=0), tr.TypeCast('float')
# tr.RangeNormalize(0, 1)
])
transformed_images = Beijing_diag_dataset(root_dir, patient_id_G_train, patient_id_H_train, resize_dim= (96,288),
transform=transform_pipeline_train)
