infer_cgan_dcgan (#2806)

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

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.")
@@ -50,10 +55,11 @@ add_arg('selected_attrs',    str,
     "Bald,Bangs,Black_Hair,Blond_Hair,Brown_Hair,Bushy_Eyebrows,Eyeglasses,Male,Mouth_Slightly_Open,Mustache,No_Beard,Pale_Skin,Young",
 "the attributes we selected to change")
 add_arg('batch_size',        int,   16,                "batch size when test")
-add_arg('test_list',       str,   "./data/celeba/test_list_attr_celeba.txt",                "the test list file")
+add_arg('test_list',         str,   "./data/celeba/test_list_attr_celeba.txt",                "the test list file")
 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('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))
@@ -230,6 +252,40 @@ 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(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(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

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