def print_samples(images,
forward,
model_name,
epoch,
suffix='',
columns=1,
directory='plots'):
"""
images is an 4D array of images: (index, channel, height, width)
forward is a theano.function which maps images to outputs
note: images can be tensor4 or np.ndarray
"""
where = directory + "/samples_" + model_name
if (not os.path.exists(where)):
os.makedirs(where)
if (not isinstance(images, np.ndarray)):
images = np.array(images.eval())
# now images is np.ndarray
n_images = images.shape[0]
print "==> Printing %d images to %s" % (n_images, where)
# we set random_greyscale flag false when showing greyscale images
grey_images = get_greyscale(images, rgb=True)
out_images = forward(images)
all_images = np.array([]).reshape((0, ) + images.shape[1:])
for index in xrange(n_images):
all_images = np.concatenate([all_images, images[index:(index + 1)]],
axis=0)
all_images = np.concatenate(
[all_images, grey_images[index:(index + 1)]], axis=0)
all_images = np.concatenate(
[all_images, out_images[index:(index + 1)]], axis=0)
R = all_images[..., 0, ..., ...]
G = all_images[..., 1, ..., ...]
B = all_images[..., 2, ..., ...]
all_images = tile_raster_images((R, G, B, None), (32, 32),
(-(-len(images) // columns), 3 * columns),
tile_spacing=(1, 1))
image = Image.fromarray(all_images)
image_name = "epoch %d" % epoch
image.save(where + '/' + image_name + suffix + ".png")
def print_samples(images, forward, model_name, epoch, suffix = '', columns = 1, directory = 'plots'): """ images is an 4D array of images: (index, channel, height, width) forward is a theano.function which maps images to outputs note: images can be tensor4 or np.ndarray """ where = directory + "/samples_" + model_name if (not os.path.exists(where)): os.makedirs(where) if (not isinstance(images, np.ndarray)): images = np.array(images.eval()) # now images is np.ndarray n_images = images.shape[0] print "==> Printing %d images to %s" % (n_images, where) # we set random_greyscale flag false when showing greyscale images grey_images = get_greyscale(images, rgb = True) out_images = forward(images) all_images = np.array([]).reshape((0,) + images.shape[1:]) for index in xrange(n_images): all_images = np.concatenate([all_images, images[index:(index + 1)]], axis = 0) all_images = np.concatenate([all_images, grey_images[index:(index + 1)]], axis = 0) all_images = np.concatenate([all_images, out_images[index:(index + 1)]], axis = 0) R = all_images[..., 0, ..., ...] G = all_images[..., 1, ..., ...] B = all_images[..., 2, ..., ...] all_images = tile_raster_images( (R, G, B, None), (32, 32), (-(-len(images) // columns), 3 * columns), tile_spacing = (1, 1) ) image = Image.fromarray(all_images) image_name = "epoch %d" % epoch image.save(where + '/' + image_name + suffix + ".png")
def plot_filters(filters,
model_name,
epoch,
suffix='',
max_num_filters=100,
columns=1,
repeat=5,
directory='plots'):
""" Plots filters of convolution layers
filters is 4D nd.array (filter_index, channels, width, height), channels = 1 or 3
one filters size = repeat * kernerl size
"""
where = directory + "/filters_" + model_name
if (not os.path.exists(where)):
os.makedirs(where)
num_filters = min(filters.shape[0], max_num_filters)
print "==> Printing %d images to %s" % (num_filters, where)
filters = filters[0:num_filters]
filters = filters.repeat(repeat, axis=2).repeat(repeat, axis=3)
if (filters.shape[1] == 3):
R = filters[..., 0, ..., ...]
G = filters[..., 1, ..., ...]
B = filters[..., 2, ..., ...]
else:
R = filters[..., 0, ..., ...]
G = R
B = R
filters = tile_raster_images((R, G, B, None),
filters.shape[2:],
(-(-filters.shape[0] // columns), columns),
tile_spacing=(2, 2))
image = Image.fromarray(filters)
image_name = "epoch %d" % epoch
image.save(where + '/' + image_name + suffix + ".png")
def plot_filters(filters, model_name, epoch, suffix = '', max_num_filters = 100, columns = 1, repeat = 5, directory = 'plots'): """ Plots filters of convolution layers filters is 4D nd.array (filter_index, channels, width, height), channels = 1 or 3 one filters size = repeat * kernerl size """ where = directory + "/filters_" + model_name if (not os.path.exists(where)): os.makedirs(where) num_filters = min(filters.shape[0], max_num_filters) print "==> Printing %d images to %s" % (num_filters, where) filters = filters[0:num_filters] filters = filters.repeat(repeat, axis = 2).repeat(repeat, axis = 3) if (filters.shape[1] == 3): R = filters[..., 0, ..., ...] G = filters[..., 1, ..., ...] B = filters[..., 2, ..., ...] else: R = filters[..., 0, ..., ...] G = R B = R filters = tile_raster_images( (R, G, B, None), filters.shape[2:], (-(-filters.shape[0] // columns), columns), tile_spacing = (2, 2) ) image = Image.fromarray(filters) image_name = "epoch %d" % epoch image.save(where + '/' + image_name + suffix + ".png")
