Cherry pick gan0722 (#2891)

* fix_gan_gpu (#2864)

* fix dir and gpu

* infer_cgan_dcgan (#2806)

add run shell and infer shell for cgan and dcgan
update infer.py for gan and dcgan

PaddleCV/PaddleGAN/download.py

@@ -159,9 +159,9 @@ if __name__ == '__main__':
 
     if args.dataset == 'mnist':
         print('Download dataset: {}'.format(args.dataset))
-        download_mnist('./data/')
+        download_mnist('data')
     elif args.dataset in cycle_pix_dataset:
         print('Download dataset: {}'.format(args.dataset))
-        download_cycle_pix('./data/', args.dataset)
+        download_cycle_pix(os.path.join('data', args.dataset))
     else:
         print('Please download by yourself, thanks')

PaddleCV/PaddleGAN/infer.py

@@ -28,12 +28,17 @@ import glob
 from util.config import add_arguments, print_arguments
 from data_reader import celeba_reader_creator
 from util.utility import check_attribute_conflict, check_gpu, save_batch_image
+from util import utility
 import copy
 
+import matplotlib
+matplotlib.use('agg')
+import matplotlib.pyplot as plt
+
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
-add_arg('model_net',         str,   'cgan',            "The model used")
+add_arg('model_net',         str,   'CGAN',            "The model used")
 add_arg('net_G',             str,   "resnet_9block",   "Choose the CycleGAN and Pix2pix generator's network, choose in [resnet_9block|resnet_6block|unet_128|unet_256]")
 add_arg('init_model',        str,   None,              "The init model file of directory.")
 add_arg('output',            str,   "./infer_result",  "The directory the infer result to be saved to.")
@@ -54,6 +59,7 @@ add_arg('test_list',       str,   "./data/celeba/test_list_attr_celeba.txt",
 add_arg('dataset_dir',       str,   "./data/celeba/",                "the dataset directory to be infered")
 add_arg('n_layers',          int,   5,                 "default layers in generotor")
 add_arg('gru_n_layers',      int,   4,                 "default layers of GRU in generotor")
+add_arg('noise_size',        int,   100,               "the noise dimension")
 # yapf: enable
 
 
@@ -103,6 +109,22 @@ def infer(args):
             cfg=args,
             name='generator',
             is_test=True)
+    elif args.model_net == 'CGAN':
+        noise = fluid.layers.data(
+            name='noise', shape=[args.noise_size], dtype='float32')
+        conditions = fluid.layers.data(
+            name='conditions', shape=[1], dtype='float32')
+
+        from network.CGAN_network import CGAN_model
+        model = CGAN_model()
+        fake = model.network_G(noise, conditions, name="G")
+    elif args.model_net == 'DCGAN':
+        noise = fluid.layers.data(
+            name='noise', shape=[args.noise_size], dtype='float32')
+
+        from network.DCGAN_network import DCGAN_model
+        model = DCGAN_model()
+        fake = model.network_G(noise, name="G")
     else:
         raise NotImplementedError("model_net {} is not support".format(
             args.model_net))
@@ -116,7 +138,7 @@ def infer(args):
     for var in fluid.default_main_program().global_block().all_parameters():
         print(var.name)
     print(args.init_model + '/' + model_name)
-    fluid.io.load_persistables(exe, args.init_model + "/" + model_name)
+    fluid.io.load_persistables(exe, os.path.join(args.init_model, model_name))
     print('load params done')
     if not os.path.exists(args.output):
         os.makedirs(args.output)
@@ -230,6 +252,42 @@ def infer(args):
 
             imageio.imwrite(args.output + "/fake_" + image_name, (
                 (fake_temp + 1) * 127.5).astype(np.uint8))
+
+    elif args.model_net == 'CGAN':
+        noise_data = np.random.uniform(
+            low=-1.0, high=1.0,
+            size=[args.batch_size, args.noise_size]).astype('float32')
+        label = np.random.randint(
+            0, 9, size=[args.batch_size, 1]).astype('float32')
+        noise_tensor = fluid.LoDTensor()
+        conditions_tensor = fluid.LoDTensor()
+        noise_tensor.set(noise_data, place)
+        conditions_tensor.set(label, place)
+        fake_temp = exe.run(
+            fetch_list=[fake.name],
+            feed={"noise": noise_tensor,
+                  "conditions": conditions_tensor})[0]
+        fake_image = np.reshape(fake_temp, (args.batch_size, -1))
+
+        fig = utility.plot(fake_image)
+        plt.savefig(
+            os.path.join(args.output, 'fake_cgan.png'), bbox_inches='tight')
+        plt.close(fig)
+
+    elif args.model_net == 'DCGAN':
+        noise_data = np.random.uniform(
+            low=-1.0, high=1.0,
+            size=[args.batch_size, args.noise_size]).astype('float32')
+        noise_tensor = fluid.LoDTensor()
+        noise_tensor.set(noise_data, place)
+        fake_temp = exe.run(fetch_list=[fake.name],
+                            feed={"noise": noise_tensor})[0]
+        fake_image = np.reshape(fake_temp, (args.batch_size, -1))
+
+        fig = utility.plot(fake_image)
+        plt.savefig(
+            os.path.join(args.output, 'fake_dcgan.png'), bbox_inches='tight')
+        plt.close(fig)
     else:
         raise NotImplementedError("model_net {} is not support".format(
             args.model_net))

PaddleCV/PaddleGAN/scripts/infer_cgan.sh

+python infer.py --model_net CGAN --init_model ./output/checkpoints/9/ --batch_size 32 --noise_size 100

PaddleCV/PaddleGAN/scripts/infer_dcgan.sh

+python infer.py --model_net DCGAN --init_model ./output/checkpoints/9/ --batch_size 32 --noise_size 100

PaddleCV/PaddleGAN/scripts/run_cgan.sh

+python train.py --model_net CGAN --dataset mnist --noise_size 100 --batch_size 32 --epoch 10 >log_out 2>log_err

PaddleCV/PaddleGAN/scripts/run_dcgan.sh

+python train.py --model_net DCGAN --dataset mnist --noise_size 100 --batch_size 32 --epoch 10 >log_out 2>log_err

PaddleCV/PaddleGAN/trainer/CGAN.py

@@ -111,7 +111,7 @@ class CGAN(object):
             utility.init_checkpoints(self.cfg, exe, g_trainer, "net_G")
             utility.init_checkpoints(self.cfg, exe, d_trainer, "net_D")
 
-### memory optim
+        ### memory optim
         build_strategy = fluid.BuildStrategy()
         build_strategy.enable_inplace = True
         build_strategy.memory_optimize = False
@@ -181,7 +181,7 @@ class CGAN(object):
                 t_time += batch_time
 
                 if batch_id % self.cfg.print_freq == 0:
-                    image_path = self.cfg.output + '/images'
+                    image_path = os.path.join(self.cfg.output, 'images')
                     if not os.path.exists(image_path):
                         os.makedirs(image_path)
                     generate_const_image = exe.run(
@@ -201,10 +201,9 @@ class CGAN(object):
                                batch_time))
                     plt.title('Epoch ID={}, Batch ID={}'.format(epoch_id,
                                                                 batch_id))
+                    img_name = '{:04d}_{:04d}.png'.format(epoch_id, batch_id)
                     plt.savefig(
-                        '{}/{:04d}_{:04d}.png'.format(image_path, epoch_id,
-                                                      batch_id),
-                        bbox_inches='tight')
+                        os.path.join(image_path, img_name), bbox_inches='tight')
                     plt.close(fig)
 
             if self.cfg.save_checkpoints:

PaddleCV/PaddleGAN/trainer/DCGAN.py

@@ -107,7 +107,7 @@ class DCGAN(object):
             utility.init_checkpoints(self.cfg, exe, g_trainer, "net_G")
             utility.init_checkpoints(self.cfg, exe, d_trainer, "net_D")
 
-### memory optim
+        ### memory optim
         build_strategy = fluid.BuildStrategy()
         build_strategy.enable_inplace = True
         build_strategy.memory_optimize = False
@@ -166,7 +166,7 @@ class DCGAN(object):
                 t_time += batch_time
 
                 if batch_id % self.cfg.print_freq == 0:
-                    image_path = self.cfg.output + '/images'
+                    image_path = os.path.join(self.cfg.output, 'images')
                     if not os.path.exists(image_path):
                         os.makedirs(image_path)
                     generate_const_image = exe.run(
@@ -185,10 +185,9 @@ class DCGAN(object):
                                batch_time))
                     plt.title('Epoch ID={}, Batch ID={}'.format(epoch_id,
                                                                 batch_id))
+                    img_name = '{:04d}_{:04d}.png'.format(epoch_id, batch_id)
                     plt.savefig(
-                        '{}/{:04d}_{:04d}.png'.format(image_path, epoch_id,
-                                                      batch_id),
-                        bbox_inches='tight')
+                        os.path.join(image_path, img_name), bbox_inches='tight')
                     plt.close(fig)
 
             if self.cfg.save_checkpoints:

PaddleCV/PaddleGAN/util/utility.py

@@ -49,7 +49,7 @@ def plot(gen_data):
 
 
 def checkpoints(epoch, cfg, exe, trainer, name):
-    output_path = cfg.output + '/checkpoints/' + str(epoch)
+    output_path = os.path.join(cfg.output, 'checkpoints', str(epoch))
     if not os.path.exists(output_path):
         os.makedirs(output_path)
     fluid.io.save_persistables(
@@ -75,7 +75,7 @@ def save_test_image(epoch,
                     g_trainer,
                     A_test_reader,
                     B_test_reader=None):
-    out_path = cfg.output + '/test'
+    out_path = os.path.join(cfg.output, 'test')
     if not os.path.exists(out_path):
         os.makedirs(out_path)
     if cfg.model_net == "Pix2pix":
@@ -242,6 +242,8 @@ class ImagePool(object):
 
 
 def check_attribute_conflict(label_batch, attr, attrs):
+    ''' Based on https://github.com/LynnHo/AttGAN-Tensorflow'''
+
     def _set(label, value, attr):
         if attr in attrs:
             label[attrs.index(attr)] = value
@@ -262,10 +264,6 @@ def check_attribute_conflict(label_batch, attr, attrs):
             for a in ['Straight_Hair', 'Wavy_Hair']:
                 if a != attr:
                     _set(label, 0, a)
-        elif attr in ['Mustache', 'No_Beard'] and attrs[attr_id] != 0:
-            for a in ['Mustache', 'No_Beard']:
-                if a != attr:
-                    _set(label, 0, a)
     return label_batch
 
 
@@ -290,7 +288,7 @@ def check_gpu(use_gpu):
 
     try:
         if use_gpu and not fluid.is_compiled_with_cuda():
-            logger.error(err)
+            print(err)
             sys.exit(1)
     except Exception as e:
         pass