def __init__(self,
num_hops=1,
batch_size=50,
validation_size=300,
sentence_embed_dim=200,
num_variants=5,
valid_num_variants=10):
self.valid_num_variants = valid_num_variants
self.sentence_output_embed_dim = self.sentence_input_embed_dim = self.question_embed_dim = self.memory_dim = sentence_embed_dim
self.batch_generator = batch_generator.BatchGenerator(
batch_size, num_variants=num_variants)
self.num_variants = self.batch_generator.num_variants
self.num_hops = num_hops
self.vocabluary_size = len(self.batch_generator.encode_dict)
self.batch_size = batch_size
self.validation_size = validation_size
self.session = tf.Session()
def __init__(self,
num_hops=1,
batch_size=100,
validation_size=400,
sentence_input_embed_dim=200,
sentence_output_embed_dim=200):
self.batch_generator = batch_generator.BatchGenerator(batch_size)
self.num_hops = num_hops
self.question_embed_dim = sentence_input_embed_dim
self.sentence_input_embed_dim = sentence_input_embed_dim
self.sentence_ouput_embed_dim = sentence_output_embed_dim
self.vocabluary_size = len(self.batch_generator.encode_dict)
self.memory_dim = sentence_output_embed_dim
self.batch_size = batch_size
self.validation_size = validation_size
self.session = tf.Session()
self.bb = tf.Variable(
tf.truncated_normal([1, self.question_embed_dim], stddev=0.1))
self.ba = tf.Variable(
tf.truncated_normal([1, self.sentence_input_embed_dim],
stddev=0.1))
self.bc = tf.Variable(
tf.truncated_normal([1, self.sentence_ouput_embed_dim],
stddev=0.1))
self.A = []
self.B = tf.Variable(
tf.truncated_normal(
[self.vocabluary_size, self.question_embed_dim], stddev=0.1))
self.C = []
self.W = tf.Variable(
tf.truncated_normal(
[self.sentence_ouput_embed_dim, self.vocabluary_size],
stddev=0.1))
for i in range(self.num_hops):
self.A.append(
tf.Variable(
tf.truncated_normal(
[self.vocabluary_size, self.sentence_input_embed_dim],
stddev=0.1)))
self.C.append(
tf.Variable(
tf.truncated_normal(
[self.vocabluary_size, self.sentence_ouput_embed_dim],
stddev=0.1)))
self.init = tf.initialize_all_variables()
FLAGS = tf.flags.FLAGS
is_result_sharpen = False
model = draw_model.DrawModel(with_attention, with_attention)
sess = tf.InteractiveSession()
saver = tf.train.Saver() # saves variables learned during training
tf.global_variables_initializer().run()
# to restore from model, uncomment the next line
ckpt_file = os.path.join(FLAGS.data_dir, "drawmodel.ckpt")
print(ckpt_file)
saver.restore(sess, ckpt_file)
# enter images
img_generator = batch_gen.BatchGenerator(const.batch_size, FLAGS.data_dir)
xtrain, ytrain = img_generator.next(direction=direction, debug=True)
print('xtrain.shape ', xtrain.shape)
#sys.exit(1)
feed_dict = {model.x: xtrain, model.y: ytrain}
canvases = sess.run(model.cs, feed_dict) # generate some examples
canvases = np.array(canvases) # T x batch x img_size
# visualize results
T, batch_size, img_size = canvases.shape
y_recons = 1.0 / (1.0 + np.exp(-canvases)) # x_recons = sigmoid(canvas)
print(y_recons.shape)
B = A = int(np.sqrt(img_size))
prefix = save_path + '/myattn_deploy3_withoutattenlc'
str(texture_id) + '_reconstructed.png',
output_img[:, :, :, idx] * 255)
generateTile.gif_step += 1
if __name__ == '__main__':
# load module
generateTile.gif_step = 0
dir = const.Direction.UP.value
size = const.A
with_attention = False
is_result_sharpen = False
B = A = const.A
FLAGS = tf.flags.FLAGS
img_generator = batch_gen.BatchGenerator(const.batch_size)
xtrain, ytrain = img_generator.next(direction=dir, debug=True)
radius = 3
# Tile around center
texture_list = [3]
input_img = np.reshape(np.transpose(xtrain[texture_list, :]),
(A, B, const.num_channels, len(texture_list)))
output_img = np.zeros(
(B * (2 * radius + 1), B * (2 * radius + 1), const.num_channels,
len(texture_list)))
output_img[B * radius:B * radius + B,
B * radius:B * radius + B, :, :] = input_img
if __name__ == '__main__':
# load module
dir = const.Direction.UP.value
size = const.A
with_attention = False
is_result_sharpen = False
B = A = const.A
prefix = './output/myattn_deploy3'
radius = 3
save_path = os.path.join(
"./train/",
'simple_d' + str(dir) + '_s' + str(size) + '_a' + str(with_attention))
FLAGS = tf.flags.FLAGS
img_generator = batch_gen.BatchGenerator(const.batch_size, save_path)
xtrain, ytrain = img_generator.next(direction=dir, debug=True)
texture_list = [0, 4, 10]
input_img = np.reshape(np.transpose(xtrain[texture_list, :]),
(A, B, len(texture_list)))
output_img = np.zeros(
(B * (2 * radius + 1), B * (2 * radius + 1), len(texture_list)))
output_img[B * radius:B * radius + B,
B * radius:B * radius + B, :] = input_img * 255
#gt_img =
## Do Left to right