def get_minibatch(self, train=True, Aug=config.Augment):
if train:
multiple_batches = self.train_dataset
else:
multiple_batches = self.val_dataset
if Aug:
for image_batch, seg_batch in multiple_batches:
yield jitter_image(train_batch=image_batch.numpy(), train_seg_batch=seg_batch.numpy())
else:
for image_batch, seg_batch in multiple_batches:
randomVariable = random()
if randomVariable > 0.5 or not train:
yield tf.cast(image_batch, tf.float32).numpy(), tf.cast(seg_batch, tf.float32).numpy()
else:
yield jitter_image(train_batch=image_batch.numpy(), train_seg_batch=seg_batch.numpy())
def get_minibatch(self, train=True):
if train:
multiple_batches = self.train_dataset
else:
multiple_batches = self.val_dataset
for image_batch, seg_batch in multiple_batches:
yield jitter_image(train_batch=image_batch.numpy(),
train_seg_batch=seg_batch.numpy())
def get_minibatch(self, train=True, Aug=True):
if train:
multiple_batches = self.train_dataset
else:
multiple_batches = self.val_dataset
if Aug:
for image_batch, seg_batch in multiple_batches:
yield jitter_image(train_batch=image_batch.numpy(), train_seg_batch=seg_batch.numpy())
else:
for image_batch, seg_batch in multiple_batches:
yield tf.cast(image_batch, tf.float32).numpy(), tf.cast(seg_batch, tf.float32).numpy()
def get_one_batch(self):
for image_batch, seg_batch in self.train_dataset:
return jitter_image(train_batch=image_batch.numpy(), train_seg_batch=seg_batch.numpy())