File "train_txt2im.py", line 458, in main_train() File "train_txt2im.py", line 133, in main_train load_and_assign_npz(sess=sess, name=net_rnn_name, model=net_rnn) File "train_txt2im.py", line 458, in main_train() File "train_txt2im.py", line 133, in main_train load_and_assign_npz(sess=sess, name=net_rnn_name, model=net_rnn) File "/home/siddanath/importantforprojects/text-to-image/utils.py", line 20, in load_and_assign_npz params = tl.files.load_npz(name=name) File "/home/siddanath/importantforprojects/text-to-image/tensorlayer/files.py", line 600, in load_npz return d['params'] File "/home/siddanath/anaconda3/lib/python3.7/site-packages/numpy/lib/npyio.py", line 262, in getitem pickle_kwargs=self.pickle_kwargs) File "/home/siddanath/anaconda3/lib/python3.7/site-packages/numpy/lib/format.py", line 722, in read_array raise ValueError("Object arrays cannot be loaded when " ValueError: Object arrays cannot be loaded when allow_pickle=False

I got a problem, how can I solve it?

Attempt to have a second RNNCell use the weights of a variable scope that already has weights: 'rnnftxt/rnn/dynamic/rnn/basic_lstm_cell'; and the cell was not constructed as BasicLSTMCell(..., reuse=True). To share the weights of an RNNCell, simply reuse it in your second calculation, or create a new one with the argument reuse=True.

What is the next step to do after download.py? I tried python data_loader.py, but it has FileNotFoundError: FileNotFoundError: [Errno 2] No such file or directory: '/home/ly/src/lib/text-to-image/102flowers/text_c10'

code -

sample_sentence = ["a"] * int(sample_size/ni) + ["e"] * int(sample_size/ni) + ["i"] * int(sample_size/ni) + ["o"] * int(sample_size/ni) + ["u"] * int(sample_size/ni)

for i, sentence in enumerate(sample_sentence): print("seed: %s" % sentence) sentence = preprocess_caption(sentence) sample_sentence[i] = [vocab.word_to_id(word) for word in nltk.tokenize.word_tokenize( sentence)] + [vocab.end_id] # add END_ID

sample_sentence = tl.prepro.pad_sequences(sample_sentence, padding='post')

img_gen, rnn_out = sess.run([net_g_res.outputs, net_rnn_res.outputs], feed_dict={
    t_real_caption: sample_sentence,
    t_z: sample_seed})

save_images(img_gen, [ni, ni], 'samples/gen_samples/gen.png')

import tensorflow as tf import tensorlayer as tl from tensorlayer.layers import * from tensorlayer.prepro import * from tensorlayer.cost import * import numpy as np import scipy from scipy.io import loadmat import time, os, re, nltk

from utils import * from model import * import model import pickle

###======================== PREPARE DATA ====================================### print("Loading data from pickle ...") import pickle with open("_vocab.pickle", 'rb') as f: vocab = pickle.load(f) with open("_image_train.pickle", 'rb') as f: _, images_train = pickle.load(f) with open("_image_test.pickle", 'rb') as f: _, images_test = pickle.load(f) with open("_n.pickle", 'rb') as f: n_captions_train, n_captions_test, n_captions_per_image, n_images_train, n_images_test = pickle.load(f) with open("_caption.pickle", 'rb') as f: captions_ids_train, captions_ids_test = pickle.load(f)

images_train_256 = np.array(images_train_256)

images_test_256 = np.array(images_test_256)

images_train = np.array(images_train) images_test = np.array(images_test)

ni = int(np.ceil(np.sqrt(batch_size))) save_dir = "checkpoint"

t_real_image = tf.placeholder('float32', [batch_size, image_size, image_size, 3], name = 'real_image')

t_real_caption = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name='real_caption_input')

t_z = tf.placeholder(tf.float32, [batch_size, z_dim], name='z_noise') generator_txt2img = model.generator_txt2img_resnet

net_rnn = rnn_embed(t_real_caption, is_train=False, reuse=False) net_g, _ = generator_txt2img(t_z, net_rnn.outputs, is_train=False, reuse=False, batch_size=batch_size)

sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) tl.layers.initialize_global_variables(sess)

net_rnn_name = os.path.join(save_dir, 'net_rnn.npz400.npz') net_cnn_name = os.path.join(save_dir, 'net_cnn.npz400.npz') net_g_name = os.path.join(save_dir, 'net_g.npz400.npz') net_d_name = os.path.join(save_dir, 'net_d.npz400.npz')

net_rnn_res = tl.files.load_and_assign_npz(sess=sess, name=net_rnn_name, network=net_rnn)

net_g_res = tl.files.load_and_assign_npz(sess=sess, name=net_g_name, network=net_g)

sample_size = batch_size sample_seed = np.random.normal(loc=0.0, scale=1.0, size=(sample_size, z_dim)).astype(np.float32)

n = int(sample_size / ni) sample_sentence = ["the flower shown has yellow anther red pistil and bright red petals."] * n +
["this flower has petals that are yellow, white and purple and has dark lines"] * n +
["the petals on this flower are white with a yellow center"] * n +
["this flower has a lot of small round pink petals."] * n +
["this flower is orange in color, and has petals that are ruffled and rounded."] * n +
["the flower has yellow petals and the center of it is brown."] * n +
["this flower has petals that are blue and white."] * n +
["these white flowers have petals that start off white in color and end in a white towards the tips."] * n

for i, sentence in enumerate(sample_sentence): print("seed: %s" % sentence) sentence = preprocess_caption(sentence) sample_sentence[i] = [vocab.word_to_id(word) for word in nltk.tokenize.word_tokenize(sentence)] + [vocab.end_id] # add END_ID

sample_sentence = tl.prepro.pad_sequences(sample_sentence, padding='post')

img_gen, rnn_out = sess.run([net_g_res.outputs, net_rnn_res.outputs], feed_dict={ t_real_caption : sample_sentence, t_z : sample_seed})

save_images(img_gen, [ni, ni], 'samples/gen_samples/gen.png')

Hello,

are there any plans in the near future to update this git to the latest Tensorflow and Tensorlayer versions? I've been trying making the code run with backwards compat (compat.tf1. ...) but I've keep bumping on errors which are a bit too big of mouth full for me.

Fyi: I've succesfully run the DCGAN Tensorlayer implementation with Tensorlayer 2.2 and a self build Tensorflow 2.1 (with 3.0 compute compatibility) from source in Python 3.7.

So, an update would be greatly appreciated!