def evaluate(self,
filename_testset,
stack_key="stack_val",
disp_key="disp_val",
image_size=(383, 552)):
#Calculate pad size for images
test_pad_size = (np.ceil(
(image_size[0] / 32)) * 32, np.ceil(
(image_size[1] / 32)) * 32) #32=2**numPoolings(=5)
#Create test set transforms
transform_test = [
FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.ToTensor(),
FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.ClipGroundTruth(
0.0202, 0.2825),
FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.PadSamples(
test_pad_size),
FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.Normalize(
mean_input=[0.485, 0.456, 0.406],
std_input=[0.229, 0.224, 0.225])
]
transform_test = torchvision.transforms.Compose(transform_test)
#Create dataloader
datareader = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(
filename_testset,
transform=transform_test,
stack_key=stack_key,
disp_key=disp_key)
dataloader = DataLoader(datareader,
batch_size=1,
shuffle=False,
num_workers=0)
return super(DDFFEval, self).evaluate(dataloader)
def evaluate(self,
filename_testset,
stack_key="stack_val",
disp_key="disp_val",
image_size=(383, 552)):
#Calculate pad size for images
test_pad_size = (np.ceil(
(image_size[0] / 32)) * 32, np.ceil(
(image_size[1] / 32)) * 32) #32=2**numPoolings(=5)
#Create test set transforms
transform_test = [
FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.ToTensor(),
FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.PadSamples(
test_pad_size)
]
if self.norm_mean is not None and self.norm_std is not None:
transform_test += [
FocalStackDDFFH5Reader.FocalStackDDFFH5Reader.Normalize(
mean_input=self.norm_mean, std_input=self.norm_std)
]
transform_test = torchvision.transforms.Compose(transform_test)
#Create dataloader
datareader = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(
filename_testset,
transform=transform_test,
stack_key=stack_key,
disp_key=disp_key)
dataloader = DataLoader(datareader,
batch_size=1,
shuffle=False,
num_workers=0)
return super(DDFFTFLearnEval, self).evaluate(dataloader)
def main():
#Set parameters
image_size = (383,552)
filename_testset = "../../ddff-dataset-trainval.h5"
checkpoint_file = "checkpoints/ddff_cc3_checkpoint_1.pt"
#Create validation reader
tmp_datareader = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(filename_testset, transform=None, stack_key="stack_val", disp_key="disp_val")
#Create PSPDDFF evaluator
evaluator = DDFFEval.DDFFEval(checkpoint_file, focal_stack_size=tmp_datareader.get_stack_size())
#Evaluate
metrics = evaluator.evaluate(filename_testset, image_size=image_size)
print(metrics)
import ddff.dataproviders.datareaders.FocalStackDDFFH5Reader as FocalStackDDFFH5Reader
import ddff.metricseval.DDFFTFLearnEval as DDFFTFLearnEval
if __name__ == "__main__":
#Set parameters
image_size = (383,552)
filename_testset = "ddff-dataset-trainval.h5"
checkpoint_file = "ddffnet-cc3-snapshot-121256.npz"
stack_key = "stack_val"
disp_key="disp_val"
#Create validation reader
tmp_datareader = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(filename_testset, transform=None, stack_key=stack_key, disp_key=disp_key)
#Create PSPDDFF evaluator
evaluator = DDFFTFLearnEval.DDFFTFLearnEval(checkpoint_file, focal_stack_size=tmp_datareader.get_stack_size(), norm_mean=None, norm_std=None)
#Evaluate
metrics = evaluator.evaluate(filename_testset, stack_key=stack_key, disp_key=disp_key, image_size=image_size)
print(metrics)
def from_h5_data(cls,
root_dir,
learning_rate=0.001,
cc1_enabled=False,
cc2_enabled=False,
cc3_enabled=True,
cc4_enabled=False,
cc5_enabled=False,
training_crop_size=None,
validation_crop_size=None,
pretrained='no_bn',
normalize_mean=[0.485, 0.456, 0.406],
normalize_std=[0.229, 0.224, 0.225],
scheduler_step_size=4,
scheduler_gama=0.9,
max_gradient=5.0,
deterministic=False,
optimizer='sgd',
normalize_loss=False,
epochs=20,
batch_size=2,
num_workers=4,
checkpoint_file=None,
checkpoint_frequency=50):
#Create data loaders
transform_train = cls.__create_preprocessing(
cls,
crop_size=training_crop_size,
mean=normalize_mean,
std=normalize_std)
transform_validation = cls.__create_preprocessing(
cls,
crop_size=validation_crop_size,
mean=normalize_mean,
std=normalize_std)
#Create h5 reader
dataset_train = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(
root_dir,
transform=transform_train,
stack_key="stack_train",
disp_key="disp_train")
dataset_validation = FocalStackDDFFH5Reader.FocalStackDDFFH5Reader(
root_dir,
transform=transform_validation,
stack_key="stack_val",
disp_key="disp_val")
#Create data loader
dataloader_train = DataLoader(dataset_train,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
dataloader_validation = DataLoader(dataset_validation,
batch_size=1,
shuffle=True,
num_workers=0)
#Call constructor
instance = cls(dataset_train.get_stack_size(),
learning_rate=learning_rate,
cc1_enabled=cc1_enabled,
cc2_enabled=cc2_enabled,
cc3_enabled=cc3_enabled,
cc4_enabled=cc4_enabled,
cc5_enabled=cc5_enabled,
pretrained=pretrained,
scheduler_step_size=scheduler_step_size,
scheduler_gama=scheduler_gama,
deterministic=deterministic,
optimizer=optimizer,
normalize_loss=normalize_loss)
#Save instance variables
instance.dataloader_validation = dataloader_validation
#Fit instance
epoch_losses = instance.train(
dataloader_train,
epochs,
checkpoint_file=checkpoint_file,
checkpoint_frequency=checkpoint_frequency,
max_gradient=max_gradient)
print("Losses per epoch: " + str(epoch_losses))
return instance