update to TL2

README.md

@@ -1,28 +1,30 @@
 # DCGAN in TensorLayer
 
-TensorLayer implementation of [Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434).
 
+This is the TensorLayer implementation of [Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434).
 Looking for Text to Image Synthesis ? [click here](https://github.com/zsdonghao/text-to-image)
 
 ![alt tag](img/DCGAN.png)
 
+
+- 🆕 🔥 2019 May: We just update this project to support TF2 and TL2. Enjoy!
+- 🆕 🔥 2019 May: This project is chosen as the default template of TL projects.
+
+
 ## Prerequisites
 
-- Python3
-- TensorFlow==1.13
-- TensorLayer (self-contained)
+- Python3.5 3.6
+- TensorFlow==2.0.0a0  `pip3 install tensorflow-gpu==2.0.0a0`
+- TensorLayer=2.0.0		`pip3 install tensorlayer==2.0.0`
 
 ## Usage
 
 First, download the aligned face images from [google](https://drive.google.com/open?id=0B7EVK8r0v71pWEZsZE9oNnFzTm8) or [baidu](https://pan.baidu.com/s/1eSNpdRG#list/path=%2F) to a `data` folder.
 
 Second, train the GAN:
 
-    $ python main_eager_mode.py   # single GPU for TF>=1.13
-    $ python main_graph_mode.py   # single GPU for TF<=1.13
-    $ python main_eager_mode_horovod.py   # multiple GPU (TODO)
-    $ python main_eager_mode_tlmagic.py   # multiple GPU (TODO)    
-
+    $ python train.py
+
 ## Result on celebA
 
 

data.py

@@ -0,0 +1,57 @@
+import os
+import numpy as np
+import tensorflow as tf
+import tensorlayer as tl
+## enable debug logging
+tl.logging.set_verbosity(tl.logging.DEBUG)
+
+class FLAGS(object):
+    def __init__(self):
+        self.n_epoch = 25 # "Epoch to train [25]"
+        self.z_dim = 100 # "Num of noise value]"
+        self.learning_rate = 0.0002 # "Learning rate of for adam [0.0002]")
+        self.beta1 = 0.5 # "Momentum term of adam [0.5]")
+        self.batch_size = 64 # "The number of batch images [64]")
+        self.output_size = 64 # "The size of the output images to produce [64]")
+        self.sample_size = 64 # "The number of sample images [64]")
+        self.c_dim = 3 # "Number of image channels. [3]")
+        self.save_step = 500 # "The interval of saveing checkpoints. [500]")
+        # self.dataset = "celebA" # "The name of dataset [celebA, mnist, lsun]")
+        self.checkpoint_dir = "checkpoint" # "Directory name to save the checkpoints [checkpoint]")
+        self.sample_dir = "samples" # "Directory name to save the image samples [samples]")
+        assert np.sqrt(self.sample_size) % 1 == 0., 'Flag `sample_size` needs to be a perfect square'
+flags = FLAGS()
+
+tl.files.exists_or_mkdir(flags.checkpoint_dir) # save model
+tl.files.exists_or_mkdir(flags.sample_dir) # save generated image
+
+def get_celebA(output_size, n_epoch, batch_size):
+    # dataset API and augmentation
+    images_path = tl.files.load_file_list(path='data', regx='.*.jpg', keep_prefix=True, printable=False)
+    def generator_train():
+        for image_path in images_path:
+            yield image_path.encode('utf-8')
+    def _map_fn(image_path):
+        image = tf.io.read_file(image_path)
+        image = tf.image.decode_jpeg(image, channels=3)  # get RGB with 0~1
+        image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+        # image = tf.image.crop_central(image, [FLAGS.output_size, FLAGS.output_size, FLAGS.c_dim])
+        # image = tf.image.resize_images(image, FLAGS.output_size])
+        image = image[45:173, 25:153, :] # central crop
+        image = tf.image.resize([image], (output_size, output_size))[0]
+        # image = tf.image.crop_and_resize(image, boxes=[[]], crop_size=[64, 64])
+        # image = tf.image.resize_image_with_crop_or_pad(image, FLAGS.output_size, FLAGS.output_size) # central crop
+        image = tf.image.random_flip_left_right(image)
+        image = image * 2 - 1
+        return image
+    train_ds = tf.data.Dataset.from_generator(generator_train, output_types=tf.string)
+    ds = train_ds.shuffle(buffer_size=4096)
+    # ds = ds.shard(num_shards=hvd.size(), index=hvd.rank())
+    ds = ds.repeat(n_epoch)
+    ds = ds.map(_map_fn, num_parallel_calls=4)
+    ds = ds.batch(batch_size)
+    ds = ds.prefetch(buffer_size=2)
+    return ds, images_path
+    # for batch_images in train_ds:
+    #     print(batch_images.shape)
+    # value = ds.make_one_shot_iterator().get_next()

model.py

@@ -2,13 +2,11 @@
 import tensorlayer as tl
 from tensorlayer.layers import Input, Dense, DeConv2d, Reshape, BatchNorm2d, Conv2d, Flatten, BatchNorm
 
-flags = tf.app.flags
-FLAGS = flags.FLAGS
-
 def get_generator(shape, gf_dim=64): # Dimension of gen filters in first conv layer. [64]
     image_size = 64
     s16 = image_size // 16
-    w_init = tf.glorot_normal_initializer()
+    # w_init = tf.glorot_normal_initializer()
+    w_init = tf.random_normal_initializer(stddev=0.02)
     gamma_init = tf.random_normal_initializer(1., 0.02)
 
     ni = Input(shape)
@@ -26,7 +24,8 @@ def get_generator(shape, gf_dim=64): # Dimension of gen filters in first conv la
     return tl.models.Model(inputs=ni, outputs=nn, name='generator')
 
 def get_discriminator(shape, df_dim=64): # Dimension of discrim filters in first conv layer. [64]
-    w_init = tf.glorot_normal_initializer()
+    # w_init = tf.glorot_normal_initializer()
+    w_init = tf.random_normal_initializer(stddev=0.02)
     gamma_init = tf.random_normal_initializer(1., 0.02)
     lrelu = lambda x : tf.nn.leaky_relu(x, 0.2)