def extract_patches(img, N, scale=1.0, patch_size=positive_patches[0].shape):
extracted_patch_size = tuple((scale * np.array(patch_size)).astype(int))
extractor = PatchExtractor(patch_size=extracted_patch_size, max_patches=N, random_state=0)
patches = extractor.transform(img[np.newaxis])
if scale != 1:
patches = np.array([transform.resize(patch, patch_size) for patch in patches])
return patches
def learn_dictionary(X, n_filters, filter_size, n_sample=1000,
n_sample_patches=0, **kwargs):
"""
learn a dictionary of n_filters atoms from n_sample images from X
"""
n_channels = X.shape[1]
# subsample n_sample images randomly
rand_idx = np.random.choice(len(X), n_sample, replace=False)
# extract patches
patch_size = (filter_size, filter_size)
patches = PatchExtractor(patch_size).transform(
X[rand_idx, ...].reshape(n_sample, X.shape[2], X.shape[3], X.shape[1]))
patches = patches.reshape(patches.shape[0], -1)
patches -= np.mean(patches, axis=0)
patches /= np.std(patches, axis=0)
if n_sample_patches > 0 and (n_sample_patches < len(patches)):
np.random.shuffle(patches)
patches = patches[:n_sample_patches, ...]
# learn dictionary
print('Learning dictionary for weight initialization...')
dico = MiniBatchDictionaryLearning(n_components=n_filters, alpha=1, n_iter=1000, batch_size=10, shuffle=True,
verbose=True, **kwargs)
W = dico.fit(patches).components_
W = W.reshape(n_filters, n_channels, filter_size, filter_size)
print('Dictionary learned.')
return W.astype(np.float32)
def test_patch_extractor_color():
faces = _make_images(orange_face)
i_h, i_w = faces.shape[1:3]
p_h, p_w = 8, 8
expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1)
extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0)
patches = extr.transform(faces)
assert patches.shape == (expected_n_patches, p_h, p_w, 3)
def test_patch_extractor_color():
faces = _make_images(orange_face)
i_h, i_w = faces.shape[1:3]
p_h, p_w = 8, 8
expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1)
extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0)
patches = extr.transform(faces)
assert_true(patches.shape == (expected_n_patches, p_h, p_w, 3))
def test_patch_extractor_all_patches():
faces = face_collection
i_h, i_w = faces.shape[1:3]
p_h, p_w = 8, 8
expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1)
extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0)
patches = extr.transform(faces)
assert_true(patches.shape == (expected_n_patches, p_h, p_w))
def test_patch_extractor_all_patches():
faces = face_collection
i_h, i_w = faces.shape[1:3]
p_h, p_w = 8, 8
expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1)
extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0)
patches = extr.transform(faces)
assert patches.shape == (expected_n_patches, p_h, p_w)
def __init__(self, train, patch_size=(9, 9)):
self.patch_size = patch_size
X = []
toTensor = transforms.ToTensor()
for _input, _ in train:
X.append(toTensor(_input).permute(1, 2, 0).numpy())
X = np.array(X)
self.mean = (X.mean(axis=(0, 1, 2)))
X = np.add(X, -self.mean)
self.mean = torch.from_numpy(
self.mean.reshape(1, self.mean.shape[0], 1, 1)
)
_, _, _, n_channels = X.shape
# 1. Sample 10M random image patches (each with 3 colors)
patches = PatchExtractor(patch_size=self.patch_size,
max_patches=int(2.5e2)).transform(X)
# 2. Perform PCA on these to get eigenvectors V and eigenvalues D.
pca = PCA()
pca.fit(patches.reshape(patches.shape[0], -1))
dim = (-1,) + self.patch_size + (n_channels,)
eigenvectors = torch.from_numpy(
pca.components_.reshape(dim).transpose(0, 3, 1, 2).astype(
X.dtype)
)
eigenvalues = torch.from_numpy(
np.diag(1. / np.sqrt(pca.explained_variance_))
)
# 4. Construct the whitening kernel k:
# for each pair of colors (ci,cj),
# set k[j,i, :, :] = V[:, j, x0, y0]^T * D^{-1/2} * V[:, i, :, :]
# where (x0, y0) is the center pixel location
# (e.g. (5,5) for a 9x9 kernel)
x_0 = int(np.floor(self.patch_size[0] / 2))
y_0 = int(np.floor(self.patch_size[1] / 2))
filter_shape = (n_channels,
n_channels,
self.patch_size[0],
self.patch_size[1])
self.kernel = torch.zeros(filter_shape)
eigenvectorsT = eigenvectors.permute(2, 3, 1, 0)
# build the kernel
for i in range(n_channels):
for j in range(n_channels):
a = torch.mm(
eigenvectorsT[x_0, y_0, j, :].contiguous().view(1, -1),
eigenvalues.float()
)
b = eigenvectors[:, i, :, :].contiguous().view(
-1, self.patch_size[0] * self.patch_size[1]
)
c = torch.mm(a, b).contiguous().view(self.patch_size[0],
self.patch_size[1])
self.kernel[j, i, :, :] = c
self.padding = (self.patch_size[0] - 1), (self.patch_size[1] - 1)
def extract_patches(img, N, patch_size, scale=1.0):
# takes the size we need from the negative pics
extracted_patch_size = tuple((scale * np.array(patch_size)).astype(int))
extractor = PatchExtractor(patch_size=extracted_patch_size,
max_patches=N,
random_state=0)
patches = extractor.transform(img[np.newaxis]) # TO DO:
if scale != 1:
patches = np.array(
[transform.resize(patch, patch_size) for patch in patches])
return patches
def extract_patches(path, max_patches, patch_size):
"""
Extract a patch of images of the same `patch_size` from the original image.
Output is a 4D-Numpy array with shape (max_patches, *patch_size, num_channels=3).
"""
img = cv2.imread(path)
img = np.expand_dims(img, axis=0)
patch_extractor = PatchExtractor(max_patches=max_patches,
patch_size=patch_size)
patch_extractor.fit(img)
return patch_extractor.transform(img)
def get_feature_patches(FV, patch_size, patch_shift, input_shape):
FV = StandardScaler(copy=False).fit_transform(FV)
# FV should be of the shape (nFeatures, nFrames)
if any(np.array([9,10,21,22,39])==np.shape(FV)[1]):
FV = FV.T
patches = np.empty([])
if np.shape(FV)[1]<patch_size:
# print('Size append: ', np.shape(FV), patch_size)
FV1 = FV.copy()
while np.shape(FV)[1]<=patch_size:
FV = np.append(FV, FV1, axis=1)
numPatches = int(np.ceil(np.shape(FV)[1]/patch_shift))
patches = PatchExtractor(patch_size=(np.shape(FV)[0], patch_size), max_patches=numPatches).transform(np.expand_dims(FV, axis=0))
patches_mean = np.mean(patches, axis=2)
patches_var = np.var(patches, axis=2)
patches_mean_var = np.append(patches_mean, patches_var, axis=1)
# print('sklearn splitting: ', time.clock()-startTime, np.shape(patches))
# print('Patches: ', np.shape(patches))
if np.shape(patches_mean_var)[1]!=2*input_shape[0]: # This condition checks for 39CC
if np.shape(patches_mean_var)[1]==44:
patches_mean_var = patches_mean_var[:,list(range(0,21))+list(range(22,43))]
elif np.shape(patches_mean_var)[1]==78:
first_7_cep_dim = np.array(list(range(0,7))+list(range(13,20))+list(range(26,33))+list(range(39,46))+list(range(52,59))+list(range(65,72)))
patches_mean_var = patches_mean_var[:, first_7_cep_dim]
# print('patches_mean_var: ', np.shape(patches_mean_var))
return patches_mean_var
def _extract_random_patches(X: np.ndarray, image_size: Tuple,
patch_size: Tuple,
n_patches: Union[int, np.integer],
random_state: Union[
None, int, np.random.RandomState] = None)\
-> np.ndarray:
"""
Extract random patches from image array.
Parameters
----------
X : np.ndarray of shape (n_samples, n_image_array)
image_size : Tuple (n_height, n_width)
or (n_height, n_width, n_channels)
Image size
patch_size : Tuple (n_height, n_width)
or (n_height, n_width, n_channels)
Patch size, features to extract
n_patches : Union[int, np.integer]
Number of patches to extract
random_state : Union[None, int, np.random.RandomState], default=None
Returns
-------
ndarray of shape (n_samples, n_patch_features)
"""
rs = check_random_state(random_state)
random_images = Coates._reshape_arrays_to_images(
X[rs.randint(0, high=X.shape[0], size=n_patches)], image_size)
random_patches = PatchExtractor(
patch_size=(patch_size[0], patch_size[1]),
max_patches=1,
random_state=rs).transform(random_images)
return Coates._reshape_images_to_arrays(random_patches, patch_size)
def get_feature_patches(FV, patch_size, patch_shift, input_shape):
FV = StandardScaler(copy=False).fit_transform(FV)
# FV should be of the shape (nFeatures, nFrames)
if any(np.array([9, 10, 21, 22, 39]) == np.shape(FV)[1]):
FV = FV.T
patches = np.empty([])
if np.shape(FV)[1] < patch_size:
FV1 = FV.copy()
while np.shape(FV)[1] <= patch_size:
FV = np.append(FV, FV1, axis=1)
numPatches = int(np.ceil(np.shape(FV)[1] / patch_shift))
patches = PatchExtractor(patch_size=(np.shape(FV)[0], patch_size),
max_patches=numPatches).transform(
np.expand_dims(FV, axis=0))
# print('sklearn splitting: ', time.clock()-startTime, np.shape(patches))
# print('Patches: ', np.shape(patches))
if (np.shape(patches)[1] == 9) or (np.shape(patches)[1] == 10):
diff_dim = input_shape[0] - np.shape(patches)[1]
zero_padding = np.zeros(
(np.shape(patches)[0], diff_dim, np.shape(patches)[2]))
patches = np.append(patches, zero_padding, axis=1)
elif np.shape(patches)[1] == 22:
patches = patches[:, :21, :]
elif np.shape(patches)[1] == 39:
first_7_cep_dim = np.array(
list(range(0, 7)) + list(range(13, 20)) + list(range(26, 33)))
patches = patches[:, first_7_cep_dim, :]
# print('Patches: ', np.shape(patches))
return patches
def _extract_random_patches(X,
image_size,
patch_size,
n_patches,
random_state=None):
"""
Extracts random patches from image array
:param X: (n_samples, n_image_array)
:param image_size: (n_height, n_width) or (n_height, n_width, n_channels) Image size
:param patch_size: (n_height, n_width) or (n_height, n_width, n_channels) Patch size, features to extract
:param n_patches: Number of patches to extract
:param random_state: None or int or np.RandomState
:return: (n_samples, n_patch_features)
"""
rs = check_random_state(random_state)
random_images = Coates._reshape_arrays_to_images(
X[rs.randint(0, high=X.shape[0], size=n_patches)], image_size)
random_patches = PatchExtractor(
patch_size=(patch_size[0], patch_size[1]),
max_patches=1,
random_state=rs).transform(random_images)
return Coates._reshape_images_to_arrays(random_patches, patch_size)
def test_patch_extractor_max_patches():
faces = face_collection
i_h, i_w = faces.shape[1:3]
p_h, p_w = 8, 8
max_patches = 100
expected_n_patches = len(faces) * max_patches
extr = PatchExtractor(patch_size=(p_h, p_w), max_patches=max_patches,
random_state=0)
patches = extr.transform(faces)
assert patches.shape == (expected_n_patches, p_h, p_w)
max_patches = 0.5
expected_n_patches = len(faces) * int((i_h - p_h + 1) * (i_w - p_w + 1)
* max_patches)
extr = PatchExtractor(patch_size=(p_h, p_w), max_patches=max_patches,
random_state=0)
patches = extr.transform(faces)
assert patches.shape == (expected_n_patches, p_h, p_w)
def extract_patch(images, patch_side=33, max_patches=20):
"""
Extracts patches from a collection of images.
'patch': refer to patch generated by single image
'images': refer to the set of all images
'patches': refer to the patches generated by the set of all images
Input: images = (image_lr, image_hr)
Return: patches_lr, patches_hr
"""
image_lr, image_hr = images
N = len(image_lr)
patches_lr = np.empty([0, patch_side, patch_side, 3])
patches_hr = np.empty([0, patch_side, patch_side, 3])
patchextractor = PatchExtractor()
patchextractor.set_params(patch_size=(patch_side, patch_side),
max_patches=max_patches)
for i in range(N):
# set the ramdom sate
randint = np.random.randint(0, 2**16 - 1)
# patchify the low resolution images
patchextractor.set_params(random_state=randint)
# the low resolution need to be bicubiced
patch_lr = patchextractor.transform(np.expand_dims(image_lr[i],
axis=0))
patches_lr = np.append(patches_lr, patch_lr, axis=0)
# patchify the low resolution images
patchextractor.set_params(random_state=randint)
patch_hr = patchextractor.transform(np.expand_dims(image_hr[i],
axis=0))
patches_hr = np.append(patches_hr, patch_hr, axis=0)
return patches_lr, patches_hr
def generate_data(img_folder, max_patches=0.001):
for fpath in get_img_filepaths(img_folder):
print ('Reading image', fpath)
patch_extractor = PatchExtractor(patch_size=(32,32),
max_patches=max_patches)
img_tensor = imread(fpath, mode='RGB')
# shape : (row, col, channels)
input_matrix = np.array([img_tensor])
# shape : (1, row, col, channels)
input_matrix = input_matrix/255.0 # Casting into 0 to 1 space which DNN models learn faster
patches = patch_extractor.transform(input_matrix)
# shape : (n_samples, row, col, channels)
patches = np.rollaxis(patches, axis=3, start=1)
# shape : (n_samples, channels, row, col)
small_patches = np.array([resize(patch) for patch in patches])
# shape : (n_samples, channels, max_x, max_y)
patches = np.array([p.reshape(p.shape[0] * p.shape[1] * p.shape[2])
for p in patches])
# shape : (n_samples, output_vector_size)
if False:
# Print out values to debug
print ("Shapes of tensors", small_patches.shape, patches.shape)
for i, (small, big) in enumerate(zip(small_patches, patches)):
small_img = np.rollaxis(small, axis=0, start=3)
if not os.path.exists('debug'):
os.makedirs('debug')
imsave('debug/small_patch_{}.jpg'.format(i), small_img)
imsave('debug/big_patch_{}.jpg'.format(i), vec2img(big))
yield small_patches, patches
def generate_data(img_folder, max_patches=0.001):
for fpath in get_img_filepaths(img_folder):
print ('Reading image', fpath)
patch_extractor = PatchExtractor(patch_size=(32,32),
max_patches=max_patches)
img_tensor = imread(fpath, mode='RGB')
# shape : (row, col, channels)
input_matrix = np.array([img_tensor])
# shape : (1, row, col, channels)
input_matrix = input_matrix/255.0 # Casting into 0 to 1 space which DNN models learn faster
patches = patch_extractor.transform(input_matrix)
# shape : (n_samples, row, col, channels)
patches = np.rollaxis(patches, axis=3, start=1)
# shape : (n_samples, channels, row, col)
small_patches = np.array([resize(patch) for patch in patches])
# shape : (n_samples, channels, max_x, max_y)
patches = np.array([p.reshape(p.shape[0] * p.shape[1] * p.shape[2])
for p in patches])
# shape : (n_samples, output_vector_size)
if False:
# Print out values to debug
print ("Shapes of tensors", small_patches.shape, patches.shape)
for i, (small, big) in enumerate(zip(small_patches, patches)):
small_img = np.rollaxis(small, axis=0, start=3)
if not os.path.exists('debug'):
os.makedirs('debug')
imsave('debug/small_patch_{}.jpg'.format(i), small_img)
imsave('debug/big_patch_{}.jpg'.format(i), vec2img(big))
yield small_patches, patches
def get_feature_patches(PARAMS, Xin, segment_duration, segment_shift):
Xin -= np.mean(Xin)
Xin /= np.max(Xin) - np.min(Xin)
# print('Xin: ', np.shape(Xin))
seg_patches = np.empty([])
if len(Xin) < segment_duration:
Xin1 = Xin.copy()
while len(Xin) <= segment_duration:
Xin = np.append(Xin, Xin1)
# startTime = time.process_time()
numPatches = int(np.ceil(len(Xin) / segment_shift))
Xin = np.expand_dims(np.expand_dims(Xin, axis=0), axis=2)
# print('Xin: ', np.shape(Xin))
seg_patches = PatchExtractor(patch_size=(segment_duration, 1),
max_patches=numPatches).transform(Xin)
# print('sklearn splitting: ', time.process_time()-startTime, np.shape(seg_patches))
return seg_patches
def test_patch_extractor_max_patches():
faces = face_collection
i_h, i_w = faces.shape[1:3]
p_h, p_w = 8, 8
max_patches = 100
expected_n_patches = len(faces) * max_patches
extr = PatchExtractor(patch_size=(p_h, p_w), max_patches=max_patches,
random_state=0)
patches = extr.transform(faces)
assert_true(patches.shape == (expected_n_patches, p_h, p_w))
max_patches = 0.5
expected_n_patches = len(faces) * int((i_h - p_h + 1) * (i_w - p_w + 1)
* max_patches)
extr = PatchExtractor(patch_size=(p_h, p_w), max_patches=max_patches,
random_state=0)
patches = extr.transform(faces)
assert_true(patches.shape == (expected_n_patches, p_h, p_w))
def get_patches(img, tb_size, sb_size, mask_tb=None, mask_sb=None):
tb_extractor = PatchExtractor(tuple(tb_size))
sb_extractor = PatchExtractor(tuple(sb_size))
tb_images = tb_extractor.transform(img)
tb_images = tb_images.reshape(
(img.shape[0], -1, tb_images.shape[-2], tb_images.shape[-1]))
if mask_tb is not None:
tb_images = tb_images[:, mask_tb.ravel(), :, :]
tb_images = np.rollaxis(tb_images, 1, 4)
sb_images = sb_extractor.transform(tb_images)
sb_images = sb_images.reshape((img.shape[0], -1, sb_images.shape[-3],
sb_images.shape[-2], sb_images.shape[-1]))
sb_images = np.rollaxis(sb_images, 4, 1)
if mask_sb is not None:
sb_images = sb_images[:, :, mask_sb.ravel(), :, :]
sb_images = sb_images.reshape(
list(sb_images.shape[:-2]) + [np.prod(sb_images.shape[-2:])])
return sb_images
def test_patch_extractor_max_patches_default():
faces = face_collection
extr = PatchExtractor(max_patches=100, random_state=0)
patches = extr.transform(faces)
assert patches.shape == (len(faces) * 100, 19, 25)
def test_patch_extractor_fit():
faces = face_collection
extr = PatchExtractor(patch_size=(8, 8), max_patches=100, random_state=0)
assert extr == extr.fit(faces)
def train(self):
patch_extractor = PatchExtractor((self.params['receptive_field'], self.params['receptive_field']), max_patches=self.params['training_patches_num'])
self.trainingPatches = patch_extractor.transform(self.dataset.train.images).reshape(-1, self.params['receptive_field'] **2)
self.trainingPatches = self._img_preprocessing(self.trainingPatches)
super().train()
def convolutional_zca(input, patch_size=(9, 9), max_patches=int(1e5)):
"""
This is an implementation of the convolutional ZCA whitening presented by
David Eigen in his phd thesis
http://www.cs.nyu.edu/~deigen/deigen-thesis.pdf
"Predicting Images using Convolutional Networks:
Visual Scene Understanding with Pixel Maps"
From paragraph 8.4:
A simple adaptation of ZCA to convolutional application is to find the
ZCA whitening transformation for a sample of local image patches across
the dataset, and then apply this transform to every patch in a larger image.
We then use the center pixel of each ZCA patch to create the conv-ZCA
output image. The operations of applying local ZCA and selecting the center
pixel can be combined into a single convolution kernel,
resulting in the following algorithm
(explained using RGB inputs and 9x9 kernel):
1. Sample 10M random 9x9 image patches (each with 3 colors)
2. Perform PCA on these to get eigenvectors V and eigenvalues D.
3. Optionally remove small eigenvalues, so V has shape [npca x 3 x 9 x 9].
4. Construct the whitening kernel k:
for each pair of colors (ci,cj),
set k[j,i, :, :] = V[:, j, x0, y0]^T * D^{-1/2} * V[:, i, :, :]
where (x0, y0) is the center pixel location (e.g. (5,5) for a 9x9 kernel)
:param input: 4D tensor of shape [batch_size, rows, col, channels]
:param patch_size: size of the patches extracted from the dataset
:param max_patches: max number of patches extracted from the dataset
:return: conv-zca whitened dataset
"""
# I don't know if it's correct or not.. but it seems to work
mean = np.mean(input, axis=(0, 1, 2))
input -= mean # center the data
n_imgs, h, w, n_channels = input.shape
patch_size = (patch_size, patch_size)
patches = PatchExtractor(patch_size=patch_size,
max_patches=max_patches).transform(input)
pca = PCA()
pca.fit(patches.reshape(patches.shape[0], -1))
# Transpose the components into theano convolution filter type
dim = (-1,) + patch_size + (n_channels,)
V = shared(pca.components_.reshape(dim).
transpose(0, 3, 1, 2).astype(input.dtype))
D = T.nlinalg.diag(1. / np.sqrt(pca.explained_variance_))
x_0 = int(np.floor(patch_size[0] / 2))
y_0 = int(np.floor(patch_size[1] / 2))
filter_shape = [n_channels, n_channels, patch_size[0], patch_size[1]]
image_shape = [n_imgs, n_channels, h, w]
kernel = T.zeros(filter_shape)
VT = V.dimshuffle(2, 3, 1, 0)
# V : 243 x 3 x 9 x 9
# VT : 9 x 9 x 3 x 243
# build the kernel
for i in range(n_channels):
for j in range(n_channels):
a = T.dot(VT[x_0, y_0, j, :], D).reshape([1, -1])
b = V[:, i, :, :].reshape([-1, patch_size[0] * patch_size[1]])
c = T.dot(a, b).reshape([patch_size[0], patch_size[1]])
kernel = T.set_subtensor(kernel[j, i, :, :], c)
kernel = kernel.astype(floatX)
input = input.astype(floatX)
input_images = T.tensor4(dtype=floatX)
conv_whitening = conv2d(input_images.dimshuffle((0, 3, 1, 2)),
kernel,
input_shape=image_shape,
filter_shape=filter_shape,
border_mode='full')
s_crop = [(patch_size[0] - 1) // 2,
(patch_size[1] - 1) // 2]
# e_crop = [s_crop[0] if (s_crop[0] % 2) != 0 else s_crop[0] + 1,
# s_crop[1] if (s_crop[1] % 2) != 0 else s_crop[1] + 1]
conv_whitening = conv_whitening[:, :, s_crop[0]:-s_crop[0], s_crop[
1]:-s_crop[1]]
conv_whitening = conv_whitening.dimshuffle(0, 2, 3, 1)
f_convZCA = function([input_images], conv_whitening)
return f_convZCA(input)
def convolutional_zca(input, patch_size=(9, 9), max_patches=int(1e5)):
"""
This is an implementation of the convolutional ZCA whitening presented by
David Eigen in his phd thesis
http://www.cs.nyu.edu/~deigen/deigen-thesis.pdf
"Predicting Images using Convolutional Networks:
Visual Scene Understanding with Pixel Maps"
From paragraph 8.4:
A simple adaptation of ZCA to convolutional application is to find the
ZCA whitening transformation for a sample of local image patches across
the dataset, and then apply this transform to every patch in a larger image.
We then use the center pixel of each ZCA patch to create the conv-ZCA
output image. The operations of applying local ZCA and selecting the center
pixel can be combined into a single convolution kernel,
resulting in the following algorithm
(explained using RGB inputs and 9x9 kernel):
1. Sample 10M random 9x9 image patches (each with 3 colors)
2. Perform PCA on these to get eigenvectors V and eigenvalues D.
3. Optionally remove small eigenvalues, so V has shape [npca x 3 x 9 x 9].
4. Construct the whitening kernel k:
for each pair of colors (ci,cj),
set k[j,i, :, :] = V[:, j, x0, y0]^T * D^{-1/2} * V[:, i, :, :]
where (x0, y0) is the center pixel location (e.g. (5,5) for a 9x9 kernel)
:param input: 4D tensor of shape [batch_size, rows, col, channels]
:param patch_size: size of the patches extracted from the dataset
:param max_patches: max number of patches extracted from the dataset
:return: conv-zca whitened dataset
"""
# I don't know if it's correct or not.. but it seems to work
mean = np.mean(input, axis=(0, 1, 2))
input -= mean # center the data
n_imgs, h, w, n_channels = input.shape
patch_size = (patch_size, patch_size)
patches = PatchExtractor(patch_size=patch_size,
max_patches=max_patches).transform(input)
pca = PCA()
pca.fit(patches.reshape(patches.shape[0], -1))
# Transpose the components into theano convolution filter type
dim = (-1, ) + patch_size + (n_channels, )
V = shared(
pca.components_.reshape(dim).transpose(0, 3, 1, 2).astype(input.dtype))
D = T.nlinalg.diag(1. / np.sqrt(pca.explained_variance_))
x_0 = int(np.floor(patch_size[0] / 2))
y_0 = int(np.floor(patch_size[1] / 2))
filter_shape = [n_channels, n_channels, patch_size[0], patch_size[1]]
image_shape = [n_imgs, n_channels, h, w]
kernel = T.zeros(filter_shape)
VT = V.dimshuffle(2, 3, 1, 0)
# V : 243 x 3 x 9 x 9
# VT : 9 x 9 x 3 x 243
# build the kernel
for i in range(n_channels):
for j in range(n_channels):
a = T.dot(VT[x_0, y_0, j, :], D).reshape([1, -1])
b = V[:, i, :, :].reshape([-1, patch_size[0] * patch_size[1]])
c = T.dot(a, b).reshape([patch_size[0], patch_size[1]])
kernel = T.set_subtensor(kernel[j, i, :, :], c)
kernel = kernel.astype(floatX)
input = input.astype(floatX)
input_images = T.tensor4(dtype=floatX)
conv_whitening = conv2d(input_images.dimshuffle((0, 3, 1, 2)),
kernel,
input_shape=image_shape,
filter_shape=filter_shape,
border_mode='full')
s_crop = [(patch_size[0] - 1) // 2, (patch_size[1] - 1) // 2]
# e_crop = [s_crop[0] if (s_crop[0] % 2) != 0 else s_crop[0] + 1,
# s_crop[1] if (s_crop[1] % 2) != 0 else s_crop[1] + 1]
conv_whitening = conv_whitening[:, :, s_crop[0]:-s_crop[0],
s_crop[1]:-s_crop[1]]
conv_whitening = conv_whitening.dimshuffle(0, 2, 3, 1)
f_convZCA = function([input_images], conv_whitening)
return f_convZCA(input)
def __call__(self, _input):
_input = _input.permute(1, 2, 0).numpy()
_input = _input.reshape(1,
_input.shape[0],
_input.shape[1],
_input.shape[2])
mean = (_input.mean(axis=(0, 1, 2)))
_input = np.add(_input, -mean)
_, _, _, n_channels = _input.shape
# 1. Sample 10M random image patches (each with 3 colors)
patches = PatchExtractor(patch_size=self.patch_size).transform(_input)
# 2. Perform PCA on these to get eigenvectors V and eigenvalues D.
pca = PCA()
pca.fit(patches.reshape(patches.shape[0], -1))
dim = (-1,) + self.patch_size + (n_channels,)
eigenvectors = torch.from_numpy(
pca.components_.reshape(dim).transpose(0, 3, 1, 2).astype(
_input.dtype)
)
eigenvalues = torch.from_numpy(
np.diag(1. / np.sqrt(pca.explained_variance_))
)
# 4. Construct the whitening kernel k:
# for each pair of colors (ci,cj),
# set k[j,i, :, :] = V[:, j, x0, y0]^T * D^{-1/2} * V[:, i, :, :]
# where (x0, y0) is the center pixel location
# (e.g. (5,5) for a 9x9 kernel)
x_0 = int(np.floor(self.patch_size[0] / 2))
y_0 = int(np.floor(self.patch_size[1] / 2))
filter_shape = (n_channels,
n_channels,
self.patch_size[0],
self.patch_size[1])
kernel = torch.zeros(filter_shape)
eigenvectorsT = eigenvectors.permute(2, 3, 1, 0)
# build the kernel
for i in range(n_channels):
for j in range(n_channels):
a = torch.mm(
eigenvectorsT[x_0, y_0, j, :].contiguous().view(1, -1),
eigenvalues.float()
)
b = eigenvectors[:, i, :, :].contiguous().view(
-1, self.patch_size[0] * self.patch_size[1]
)
c = torch.mm(a, b).contiguous().view(self.patch_size[0],
self.patch_size[1])
kernel[j, i, :, :] = c
padding = (self.patch_size[0] - 1), (self.patch_size[1] - 1)
input_tensor = torch.from_numpy(_input).permute(0, 3, 1, 2)
conv_whitening = torch.nn.functional.conv2d(
input=input_tensor,
weight=kernel,
padding=padding
)
s_crop = [(self.patch_size[0] - 1) // 2, (self.patch_size[1] - 1) // 2]
conv_whitening = conv_whitening[
:, :, s_crop[0]:-s_crop[0], s_crop[1]:-s_crop[1]
]
return conv_whitening.view(conv_whitening.shape[1],
conv_whitening.shape[2],
conv_whitening.shape[3])
def test_patch_extractor_fit():
faces = face_collection
extr = PatchExtractor(patch_size=(8, 8), max_patches=100, random_state=0)
assert_true(extr == extr.fit(faces))
X_train, X_test = X[train], X[test]
y_train, y_test = y[train], y[test]
###############################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
#TODO: Support for "non-square" numbers of atoms to display
#This number should be square i.e. 16, 25, 36, 49, 64 etc.
n_atoms = 144
patch_size = (8, 8)
print "Extracting image patches from %d faces" % len(X_train)
t0 = time()
extr = PatchExtractor(patch_size=patch_size,
max_patches=100,
random_state=0)
patches = extr.transform(X_train)
print "done in %0.3fs" % (time() - t0)
print "Extracting %d atoms from %d patches" % (n_atoms, len(patches))
t0 = time()
patches = patches.reshape(
(patches.shape[0], patches.shape[1] * patches.shape[2]))
sc1 = KSVDCoder(n_atoms, verbose=True, n_iter=5)
rc1 = RandomDataCoder(n_atoms)
kc1 = KMeansCoder(n_atoms, verbose=True)
steps = [('pre', ZCA()), ('dict', kc1)]
p_kmeans = Pipeline(steps)
p_kmeans.fit(patches)
def extract_patches(self, patch_size, max_patches=None, random_state=None):
patch_extractor = PatchExtractor(patch_size=patch_size, max_patches=np.int(
max_patches / self.num_images()), random_state=random_state)
return patch_extractor.transform(self._images).astype(np.uint8)
def test_patch_extractor_fit():
lenas = lena_collection
extr = PatchExtractor(patch_size=(8, 8), max_patches=100, random_state=0)
assert_true(extr == extr.fit(lenas))
def test_patch_extractor_max_patches_default():
lenas = lena_collection
extr = PatchExtractor(max_patches=100, random_state=0)
patches = extr.transform(lenas)
assert_equal(patches.shape, (len(lenas) * 100, 12, 12))
def test_patch_extractor_max_patches_default():
faces = face_collection
extr = PatchExtractor(max_patches=100, random_state=0)
patches = extr.transform(faces)
assert_equal(patches.shape, (len(faces) * 100, 19, 25))
def test_patch_extractor_max_patches_default():
lenas = lena_collection
extr = PatchExtractor(max_patches=100, random_state=0)
patches = extr.transform(lenas)
assert_equal(patches.shape, (len(lenas) * 100, 12, 12))
def get_feature_patches(PARAMS, FV, patch_size, patch_shift, input_shape):
# Removing NaN and Inf
if any(np.array([9, 10, 21, 22, 39]) == np.shape(FV)[1]):
FV = FV[~np.isnan(FV).any(axis=1), :]
FV = FV[~np.isinf(FV).any(axis=1), :]
else:
FV = FV[:, ~np.isnan(FV).any(axis=0)]
FV = FV[:, ~np.isinf(FV).any(axis=0)]
FV = StandardScaler(copy=False).fit_transform(FV)
# FV should be of the shape (nFeatures, nFrames)
if any(np.array([9, 10, 21, 22, 39]) == np.shape(FV)[1]):
FV = FV.T
frmStart = 0
frmEnd = 0
patchNum = 0
patches = np.empty([])
if np.shape(FV)[1] < patch_size:
FV1 = FV.copy()
while np.shape(FV)[1] <= patch_size:
FV = np.append(FV, FV1, axis=1)
# while frmEnd<np.shape(FV)[1]:
# # print('get_feature_patches: ', frmStart, frmEnd, np.shape(FV))
# frmStart = patchNum*patch_shift
# frmEnd = np.min([patchNum*patch_shift+patch_size, np.shape(FV)[1]])
# if frmEnd-frmStart<patch_size:
# frmStart = frmEnd - patch_size
# if np.size(patches)<=1:
# patches = np.expand_dims(FV[:, frmStart:frmEnd], axis=0)
# else:
# patches = np.append(patches, np.expand_dims(FV[:, frmStart:frmEnd], axis=0), axis=0)
# patchNum += 1
# startTime = time.clock()
# for frmStart in range(0, np.shape(FV)[1], patch_shift):
# # print('get_feature_patches: ', frmStart, frmEnd, np.shape(FV))
# frmEnd = np.min([frmStart+patch_size, np.shape(FV)[1]])
# if frmEnd-frmStart<patch_size:
# frmStart = frmEnd - patch_size
# if np.size(patches)<=1:
# patches = np.array(FV[:, frmStart:frmEnd], ndmin=3)
# else:
# patches = np.append(patches, np.array(FV[:, frmStart:frmEnd], ndmin=3), axis=0)
# print('My splitting: ', time.clock()-startTime, np.shape(patches))
startTime = time.clock()
numPatches = int(np.ceil(np.shape(FV)[1] / patch_shift))
patches = PatchExtractor(patch_size=(np.shape(FV)[0], patch_size),
max_patches=numPatches).transform(
np.expand_dims(FV, axis=0))
# print('sklearn splitting: ', time.clock()-startTime, np.shape(patches))
# print('Patches: ', np.shape(patches))
if (np.shape(patches)[1] == 9) or (np.shape(patches)[1] == 10):
diff_dim = input_shape[0] - np.shape(patches)[1]
zero_padding = np.zeros(
(np.shape(patches)[0], diff_dim, np.shape(patches)[2]))
patches = np.append(patches, zero_padding, axis=1)
elif np.shape(patches)[1] == 22:
patches = patches[:, :21, :]
elif np.shape(patches)[1] == 39:
if not PARAMS['39_dim_CC_feat']:
first_7_cep_dim = np.array(
list(range(0, 7)) + list(range(13, 20)) + list(range(26, 33)))
patches = patches[:, first_7_cep_dim, :]
# print('Patches: ', np.shape(patches))
return patches
