def test_L1(inceptionv1):
objective = objectives.L1() # on input by default
assert_gradient_ascent(objective, inceptionv1)
def run(self,
layer,
class_,
channel=None,
style_template=None,
transforms=False,
opt_steps=500,
gram_coeff=1e-14):
"""
layer : layer_name to visualize
class_ : class to consider
style_template: template for comparision of generated activation maximization map
transforms : transforms required
opt_steps : number of optimization steps
"""
self.layer = layer
self.channel = channel if channel is not None else 0
with tf.Graph().as_default() as graph, tf.Session() as sess:
if style_template is not None:
try:
gram_template = tf.constant(
np.load(style_template), #[1:-1,:,:],
dtype=tf.float32)
except:
image = cv2.imread(style_template)
print(image.shape)
gram_template = tf.constant(
np.pad(cv2.imread(style_template),
((1, 1), (0, 0))), #[1:-1,:,:],
dtype=tf.float32)
else:
gram_template = None
obj = self._channel(self.layer + "/convolution",
self.channel,
gram=gram_template,
gram_coeff=gram_coeff)
obj += -self.L1 * objectives.L1(constant=.5)
obj += -self.TV * objectives.total_variation()
#obj += self.blur * objectives.blur_input_each_step()
if transforms == True:
transforms = [
transform.pad(self.jitter),
transform.jitter(self.jitter),
#transform.random_scale([self.scale ** (n/10.) for n in range(-10, 11)]),
#transform.random_rotate(range(-self.rotate, self.rotate + 1))
]
else:
transforms = []
T = render.make_vis_T(
self.model,
obj,
param_f=lambda: self.image(240,
channels=self.n_channels,
fft=self.decorrelate,
decorrelate=self.decorrelate),
optimizer=None,
transforms=transforms,
relu_gradient_override=False)
tf.initialize_all_variables().run()
images_array = []
for i in range(opt_steps):
T("vis_op").run()
images_array.append(
T("input").eval()[:, :, :, -1].reshape((240, 240)))
plt.figure(figsize=(10, 10))
# for i in range(1, self.n_channels+1):
# plt.imshow(np.load(style_template)[:, :, i-1], cmap='gray',
# interpolation='bilinear', vmin=0., vmax=1.)
# plt.savefig('gram_template_{}.png'.format(i), bbox_inches='tight')
texture_images = []
for i in range(1, self.n_channels + 1):
# plt.subplot(1, self.n_channels, i)
image = T("input").eval()[:, :, :, i - 1].reshape((240, 240))
print("channel: ", i, image.min(), image.max())
# plt.imshow(image, cmap='gray',
# interpolation='bilinear', vmin=0., vmax=1.)
# plt.xticks([])
# plt.yticks([])
texture_images.append(image)
# show(np.hstack(T("input").eval()))
os.makedirs(os.path.join(self.savepath, class_), exist_ok=True)
# print(self.savepath, class_, self.layer+'_' + str(self.channel) +'.png')
# plt.savefig(os.path.join(self.savepath, class_, self.layer+'_' + str(self.channel) + '_' + str(i) +'_noreg.png'), bbox_inches='tight')
# plt.show()
# print(np.array(texture_images).shape)
return np.array(texture_images), images_array