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Copy pathvisualize_filters.py
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123 lines (98 loc) · 3.8 KB
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import tensorflow as tf
import sys
import cPickle as pickle
import numpy as np
from math import sqrt
import scipy.misc as misc
sys.path.insert(0, 'ops/')
sys.path.insert(0, 'nets/')
from tf_ops import *
def put_kernels_on_grid (kernel, pad = 1):
'''Visualize conv. filters as an image (mostly for the 1st layer).
Arranges filters into a grid, with some paddings between adjacent filters.
Args:
kernel: tensor of shape [Y, X, NumChannels, NumKernels]
pad: number of black pixels around each filter (between them)
Return:
Tensor of shape [1, (Y+2*pad)*grid_Y, (X+2*pad)*grid_X, NumChannels].
'''
# get shape of the grid. NumKernels == grid_Y * grid_X
def factorization(n):
for i in range(int(sqrt(float(n))), 0, -1):
if n % i == 0:
if i == 1: print('Who would enter a prime number of filters')
return (i, int(n / i))
(grid_Y, grid_X) = factorization (kernel.get_shape()[3].value)
print ('grid: %d = (%d, %d)' % (kernel.get_shape()[3].value, grid_Y, grid_X))
x_min = tf.reduce_min(kernel)
x_max = tf.reduce_max(kernel)
kernel = (kernel - x_min) / (x_max - x_min)
# pad X and Y
x = tf.pad(kernel, tf.constant( [[pad,pad],[pad, pad],[0,0],[0,0]] ), mode = 'CONSTANT')
# X and Y dimensions, w.r.t. padding
Y = kernel.get_shape()[0] + 2 * pad
X = kernel.get_shape()[1] + 2 * pad
channels = kernel.get_shape()[2]
print 'Y:',Y
print 'X:',X
print 'channels:',channels
# put NumKernels to the 1st dimension
x = tf.transpose(x, (3, 0, 1, 2))
# organize grid on Y axis
x = tf.reshape(x, tf.stack([grid_X, Y * grid_Y, X, channels]))
# switch X and Y axes
x = tf.transpose(x, (0, 2, 1, 3))
# organize grid on X axis
x = tf.reshape(x, tf.stack([1, X * grid_X, Y * grid_Y, channels]))
# back to normal order (not combining with the next step for clarity)
x = tf.transpose(x, (2, 1, 3, 0))
# to tf.image_summary order [batch_size, height, width, channels],
# where in this case batch_size == 1
x = tf.transpose(x, (3, 0, 1, 2))
# scaling to [0, 255] is not necessary for tensorboard
return x
if __name__ == '__main__':
if len(sys.argv) < 2:
print 'You must provide an info.pkl file'
exit()
pkl_file = open(sys.argv[1], 'rb')
a = pickle.load(pkl_file)
LEARNING_RATE = a['LEARNING_RATE']
LOSS_METHOD = a['LOSS_METHOD']
BATCH_SIZE = a['BATCH_SIZE']
L1_WEIGHT = a['L1_WEIGHT']
IG_WEIGHT = a['IG_WEIGHT']
NETWORK = a['NETWORK']
AUGMENT = a['AUGMENT']
EPOCHS = a['EPOCHS']
DATA = a['DATA']
EXPERIMENT_DIR = 'checkpoints/LOSS_METHOD_'+LOSS_METHOD\
+'/NETWORK_'+NETWORK\
+'/L1_WEIGHT_'+str(L1_WEIGHT)\
+'/IG_WEIGHT_'+str(IG_WEIGHT)\
+'/AUGMENT_'+str(AUGMENT)\
+'/DATA_'+DATA+'/'\
if NETWORK == 'pix2pix': from pix2pix import *
if NETWORK == 'resnet': from resnet import *
image_u = tf.placeholder(tf.float32, shape=(1, 256, 256, 3), name='image_u')
layers = netG_encoder(image_u)
gen_image = netG_decoder(layers)
saver = tf.train.Saver()
init = tf.group(tf.local_variables_initializer(), tf.global_variables_initializer())
sess = tf.Session()
sess.run(init)
ckpt = tf.train.get_checkpoint_state(EXPERIMENT_DIR)
if ckpt and ckpt.model_checkpoint_path:
print "Restoring previous model..."
try:
saver.restore(sess, ckpt.model_checkpoint_path)
print "Model restored"
except:
print "Could not restore model"
pass
with tf.variable_scope('g_enc_conv1'):
tf.get_variable_scope().reuse_variables()
weights = tf.get_variable('weights')
grid = put_kernels_on_grid (weights)
g = np.squeeze(sess.run(grid))
misc.imsave('filters_enc_conv1.png', g)