def get_batch(self, subset, batch_size, use_target_distribution=False):
# Select a subset
if subset == 'train':
X = self.X_train
y = self.y_train
elif subset == 'valid':
X = self.X_val
y = self.y_val
elif subset == 'test':
X = self.X_test
y = self.y_test
# Random choice of samples
idx = np.random.choice(X.shape[0], batch_size)
batch = X[idx, 0, :].reshape((batch_size, 28, 28))
# Resize from 28x28 to 64x64
batch_resized = []
factor = self.image_size / 28.0
for i in range(batch.shape[0]):
# resize to 64x64 pixels
batch_resized.append(scipy.ndimage.zoom(batch[i, :, :], factor, order=1))
batch = np.stack(batch_resized)
batch = batch.reshape((batch_size, 1, self.image_size, self.image_size))
# Convert to RGB
batch = np.concatenate([batch, batch, batch], axis=1)
# Modify images if target distribution requested
if use_target_distribution:
# Binarize images
batch[batch >= 0.5] = 1
batch[batch < 0.5] = 0
# For each image in the mini batch
for i in range(batch_size):
# Take a random crop of the Lena image (background)
x_c = np.random.randint(0, self.lena.size[0] - self.image_size)
y_c = np.random.randint(0, self.lena.size[1] - self.image_size)
image = self.lena.crop((x_c, y_c, x_c + self.image_size, y_c + self.image_size))
image = np.asarray(image).transpose((2, 0, 1)) / 255.0
# Randomly alter the color distribution of the crop
for j in range(3):
image[j, :, :] = (image[j, :, :] + np.random.uniform(0, 1)) / 2.0
# Invert the color of pixels where there is a number
image[batch[i, :, :, :] == 1] = 1 - image[batch[i, :, :, :] == 1]
batch[i, :, :, :] = image
# Rescale to range [-1, +1]
# batch = batch * 2 - 1
# Image label
labels = y[idx]
return batch.astype('float32'), labels.astype('int32')
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