Sync 1.6 to develop (#3797)

* sync 1.6

* sync 1.6

* update readme

PaddleCV/PaddleGAN/README.md

@@ -62,14 +62,14 @@
 
 ```
 
-同时推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/122272)
+同时推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/122272)
 
 ## 快速开始
 
 ### 安装说明
 **安装[PaddlePaddle](https://github.com/PaddlePaddle/Paddle)：**
 
-在当前目录下运行样例代码需要PadddlePaddle Fluid的v.1.5或以上的版本。如果你的运行环境中的PaddlePaddle低于此版本，请根据[安装文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.5/beginners_guide/install/index_cn.html)中的说明来更新PaddlePaddle。
+在当前目录下运行样例代码需要PadddlePaddle Fluid的v.1.6或以上的版本。如果你的运行环境中的PaddlePaddle低于此版本，请根据[安装文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.5/beginners_guide/install/index_cn.html)中的说明来更新PaddlePaddle。
 
 其他依赖包：
 1. `pip install imageio` 或者 `pip install -r requirements.txt` 安装imageio包（保存图片代码中所依赖的包）
@@ -114,7 +114,7 @@ SPADE使用的[cityscapes](https://www.cityscapes-dataset.com)数据集可以自
 
 - 每个GAN都给出了一份运行示例，放在scripts文件夹内，用户可以直接运行训练脚本快速开始训练。
 - 用户可以通过设置`--model_net`参数来选择想要训练的模型，通过设置`--dataset`参数来选择训练所需要的数据集。
-- SPADE模型的训练需要在主目录下新建一个VGG19_pretrained目录，从[该链接](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.tar)下载在ImageNet上预训练好的VGG19模型，解压之后把VGG19模型的参数名改成`vgg19_`开头的参数名。
+- SPADE模型的训练需要在主目录下新建一个VGG19_pretrained目录，从[该链接](https://paddle-gan-models.bj.bcebos.com/vgg19_spade.tar.gz)下载并解压在ImageNet上预训练好的VGG19模型，该模型是从分类模型页面下载的VGG19的预训练模型并为参数添加`vgg19_`的前缀用来区分和生成网络中的参数。
 
 ### 模型测试
 模型测试是利用训练完成的生成模型进行图像生成。infer.py是主要的执行程序，调用示例如下：
@@ -185,7 +185,7 @@ STGAN的效果图(图片属性分别为：original image, Bald, Bangs, Black Hai
 | StarGAN  | [StarGAN的预训练模型](https://paddle-gan-models.bj.bcebos.com/stargan_G.tar.gz)  |
 | AttGAN   | [AttGAN的预训练模型](https://paddle-gan-models.bj.bcebos.com/attgan_G.tar.gz)   |
 | STGAN    | [STGAN的预训练模型](https://paddle-gan-models.bj.bcebos.com/stgan_G.tar.gz)    |
-| SPADE    | [SPADE的预训练模型]()  ([SPADE需要的vgg预训练模型]())
+| SPADE    | [SPADE的预训练模型](https://paddle-gan-models.bj.bcebos.com/spade_G.tar.gz)  ([SPADE需要的vgg19预训练模型](https://paddle-gan-models.bj.bcebos.com/vgg19_spade.tar.gz))
 
 
 ## 进阶使用

PaddleCV/PaddleGAN/infer.py

@@ -27,7 +27,7 @@ import imageio
 import glob
 from util.config import add_arguments, print_arguments
 from data_reader import celeba_reader_creator, reader_creator, triplex_reader_creator
-from util.utility import check_attribute_conflict, check_gpu, save_batch_image
+from util.utility import check_attribute_conflict, check_gpu, save_batch_image, check_version
 from util import utility
 import copy
 
@@ -84,11 +84,12 @@ def infer(args):
     model_name = 'net_G'
 
     if args.model_net == 'CycleGAN':
-        py_reader = fluid.io.PyReader(
+        loader = fluid.io.DataLoader.from_generator(
             feed_list=[input, image_name],
             capacity=4,  ## batch_size * 4
             iterable=True,
             use_double_buffer=True)
+
         from network.CycleGAN_network import CycleGAN_model
         model = CycleGAN_model()
         if args.input_style == "A":
@@ -98,7 +99,7 @@ def infer(args):
         else:
             raise "Input with style [%s] is not supported." % args.input_style
     elif args.model_net == 'Pix2pix':
-        py_reader = fluid.io.PyReader(
+        loader = fluid.io.DataLoader.from_generator(
             feed_list=[input, image_name],
             capacity=4,  ## batch_size * 4
             iterable=True,
@@ -169,13 +170,13 @@ def infer(args):
         model = DCGAN_model(args.n_samples)
         fake = model.network_G(noise, name="G")
     elif args.model_net == 'SPADE':
-        label_shape = [-1, args.label_nc, args.crop_height, args.crop_width]
-        spade_data_shape = [-1, 1, args.crop_height, args.crop_width]
+        label_shape = [None, args.label_nc, args.crop_height, args.crop_width]
+        spade_data_shape = [None, 1, args.crop_height, args.crop_width]
         from network.SPADE_network import SPADE_model
         model = SPADE_model()
-        input_label = fluid.layers.data(
+        input_label = fluid.data(
             name='input_label', shape=label_shape, dtype='float32')
-        input_ins = fluid.layers.data(
+        input_ins = fluid.data(
             name='input_ins', shape=spade_data_shape, dtype='float32')
         input_ = fluid.layers.concat([input_label, input_ins], 1)
         fake = model.network_G(input_, "generator", cfg=args, is_test=True)
@@ -298,11 +299,11 @@ def infer(args):
             batch_size=args.n_samples,
             mode="VAL")
         reader_test = test_reader.make_reader(args, return_name=True)
-        py_reader.decorate_batch_generator(
+        loader.set_batch_generator(
             reader_test,
             places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
         id2name = test_reader.id2name
-        for data in py_reader():
+        for data in loader():
             real_img, image_name = data[0]['input'], data[0]['image_name']
             image_name = id2name[np.array(image_name).astype('int32')[0]]
             print("read: ", image_name)
@@ -387,4 +388,5 @@ if __name__ == "__main__":
     args = parser.parse_args()
     print_arguments(args)
     check_gpu(args.use_gpu)
+    check_version()
     infer(args)

PaddleCV/PaddleGAN/requirements.txt

-imageio
+numpy >= 1.15.0

PaddleCV/PaddleGAN/train.py

@@ -66,6 +66,7 @@ if __name__ == "__main__":
     cfg = config.parse_args()
     config.print_arguments(cfg)
     utility.check_gpu(cfg.use_gpu)
+    utility.check_version()
     if cfg.profile:
         if cfg.use_gpu:
             with fluid.profiler.profiler('All', 'total',

PaddleCV/PaddleGAN/trainer/AttGAN.py

@@ -270,18 +270,18 @@ class AttGAN(object):
         self.id2name = id2name
 
     def build_model(self):
-        data_shape = [-1, 3, self.cfg.image_size, self.cfg.image_size]
+        data_shape = [None, 3, self.cfg.image_size, self.cfg.image_size]
 
-        image_real = fluid.layers.data(
+        image_real = fluid.data(
             name='image_real', shape=data_shape, dtype='float32')
-        label_org = fluid.layers.data(
-            name='label_org', shape=[self.cfg.c_dim], dtype='float32')
-        label_trg = fluid.layers.data(
-            name='label_trg', shape=[self.cfg.c_dim], dtype='float32')
-        label_org_ = fluid.layers.data(
-            name='label_org_', shape=[self.cfg.c_dim], dtype='float32')
-        label_trg_ = fluid.layers.data(
-            name='label_trg_', shape=[self.cfg.c_dim], dtype='float32')
+        label_org = fluid.data(
+            name='label_org', shape=[None, self.cfg.c_dim], dtype='float32')
+        label_trg = fluid.data(
+            name='label_trg', shape=[None, self.cfg.c_dim], dtype='float32')
+        label_org_ = fluid.data(
+            name='label_org_', shape=[None, self.cfg.c_dim], dtype='float32')
+        label_trg_ = fluid.data(
+            name='label_trg_', shape=[None, self.cfg.c_dim], dtype='float32')
 
         py_reader = fluid.io.PyReader(
             feed_list=[image_real, label_org, label_trg],
@@ -369,9 +369,9 @@ class AttGAN(object):
                 batch_id += 1
 
             if self.cfg.run_test:
-                image_name = fluid.layers.data(
+                image_name = fluid.data(
                     name='image_name',
-                    shape=[self.cfg.n_samples],
+                    shape=[None, self.cfg.n_samples],
                     dtype='int32')
                 test_py_reader = fluid.io.PyReader(
                     feed_list=[image_real, label_org, label_trg, image_name],

PaddleCV/PaddleGAN/trainer/CGAN.py

@@ -88,12 +88,12 @@ class CGAN(object):
 
     def build_model(self):
 
-        img = fluid.layers.data(name='img', shape=[784], dtype='float32')
-        condition = fluid.layers.data(
-            name='condition', shape=[1], dtype='float32')
-        noise = fluid.layers.data(
-            name='noise', shape=[self.cfg.noise_size], dtype='float32')
-        label = fluid.layers.data(name='label', shape=[1], dtype='float32')
+        img = fluid.data(name='img', shape=[None, 784], dtype='float32')
+        condition = fluid.data(
+            name='condition', shape=[None, 1], dtype='float32')
+        noise = fluid.data(
+            name='noise', shape=[None, self.cfg.noise_size], dtype='float32')
+        label = fluid.data(name='label', shape=[None, 1], dtype='float32')
 
         g_trainer = GTrainer(noise, condition, self.cfg)
         d_trainer = DTrainer(img, condition, label, self.cfg)

PaddleCV/PaddleGAN/trainer/CycleGAN.py

@@ -229,15 +229,13 @@ class CycleGAN(object):
         self.B_id2name = B_id2name
 
     def build_model(self):
-        data_shape = [-1, 3, self.cfg.crop_size, self.cfg.crop_size]
+        data_shape = [None, 3, self.cfg.crop_size, self.cfg.crop_size]
 
-        input_A = fluid.layers.data(
-            name='input_A', shape=data_shape, dtype='float32')
-        input_B = fluid.layers.data(
-            name='input_B', shape=data_shape, dtype='float32')
-        fake_pool_A = fluid.layers.data(
+        input_A = fluid.data(name='input_A', shape=data_shape, dtype='float32')
+        input_B = fluid.data(name='input_B', shape=data_shape, dtype='float32')
+        fake_pool_A = fluid.data(
             name='fake_pool_A', shape=data_shape, dtype='float32')
-        fake_pool_B = fluid.layers.data(
+        fake_pool_B = fluid.data(
             name='fake_pool_B', shape=data_shape, dtype='float32')
 
         A_py_reader = fluid.io.PyReader(
@@ -348,10 +346,10 @@ class CycleGAN(object):
                 batch_id += 1
 
             if self.cfg.run_test:
-                A_image_name = fluid.layers.data(
-                    name='A_image_name', shape=[1], dtype='int32')
-                B_image_name = fluid.layers.data(
-                    name='B_image_name', shape=[1], dtype='int32')
+                A_image_name = fluid.data(
+                    name='A_image_name', shape=[None, 1], dtype='int32')
+                B_image_name = fluid.data(
+                    name='B_image_name', shape=[None, 1], dtype='int32')
                 A_test_py_reader = fluid.io.PyReader(
                     feed_list=[input_A, A_image_name],
                     capacity=4,

PaddleCV/PaddleGAN/trainer/DCGAN.py

@@ -86,10 +86,10 @@ class DCGAN(object):
         self.train_reader = train_reader
 
     def build_model(self):
-        img = fluid.layers.data(name='img', shape=[784], dtype='float32')
-        noise = fluid.layers.data(
-            name='noise', shape=[self.cfg.noise_size], dtype='float32')
-        label = fluid.layers.data(name='label', shape=[1], dtype='float32')
+        img = fluid.data(name='img', shape=[None, 784], dtype='float32')
+        noise = fluid.data(
+            name='noise', shape=[None, self.cfg.noise_size], dtype='float32')
+        label = fluid.data(name='label', shape=[None, 1], dtype='float32')
 
         g_trainer = GTrainer(noise, label, self.cfg)
         d_trainer = DTrainer(img, label, self.cfg)

PaddleCV/PaddleGAN/trainer/Pix2pix.py

@@ -211,18 +211,16 @@ class Pix2pix(object):
         self.id2name = id2name
 
     def build_model(self):
-        data_shape = [-1, 3, self.cfg.crop_size, self.cfg.crop_size]
+        data_shape = [None, 3, self.cfg.crop_size, self.cfg.crop_size]
 
-        input_A = fluid.layers.data(
-            name='input_A', shape=data_shape, dtype='float32')
-        input_B = fluid.layers.data(
-            name='input_B', shape=data_shape, dtype='float32')
-        input_fake = fluid.layers.data(
+        input_A = fluid.data(name='input_A', shape=data_shape, dtype='float32')
+        input_B = fluid.data(name='input_B', shape=data_shape, dtype='float32')
+        input_fake = fluid.data(
             name='input_fake', shape=data_shape, dtype='float32')
 
-        py_reader = fluid.io.PyReader(
+        loader = fluid.io.DataLoader.from_generator(
             feed_list=[input_A, input_B],
-            capacity=4,  ## batch_size * 4
+            capacity=4,
             iterable=True,
             use_double_buffer=True)
 
@@ -232,7 +230,7 @@ class Pix2pix(object):
 
         # prepare environment
         place = fluid.CUDAPlace(0) if self.cfg.use_gpu else fluid.CPUPlace()
-        py_reader.decorate_batch_generator(
+        loader.set_batch_generator(
             self.train_reader,
             places=fluid.cuda_places()
             if self.cfg.use_gpu else fluid.cpu_places())
@@ -259,7 +257,7 @@ class Pix2pix(object):
 
         for epoch_id in range(self.cfg.epoch):
             batch_id = 0
-            for tensor in py_reader():
+            for tensor in loader():
                 s_time = time.time()
 
                 tensor_A, tensor_B = tensor[0]['input_A'], tensor[0]['input_B']
@@ -298,16 +296,16 @@ class Pix2pix(object):
                 batch_id += 1
 
             if self.cfg.run_test:
-                image_name = fluid.layers.data(
+                image_name = fluid.data(
                     name='image_name',
-                    shape=[self.cfg.batch_size],
+                    shape=[None, self.cfg.batch_size],
                     dtype="int32")
-                test_py_reader = fluid.io.PyReader(
+                test_loader = fluid.io.DataLoader.from_generator(
                     feed_list=[input_A, input_B, image_name],
-                    capacity=4,  ## batch_size * 4
+                    capacity=4,
                     iterable=True,
                     use_double_buffer=True)
-                test_py_reader.decorate_batch_generator(
+                test_loader.set_batch_generator(
                     self.test_reader,
                     places=fluid.cuda_places()
                     if self.cfg.use_gpu else fluid.cpu_places())
@@ -319,7 +317,7 @@ class Pix2pix(object):
                     place,
                     test_program,
                     gen_trainer,
-                    test_py_reader,
+                    test_loader,
                     A_id2name=self.id2name)
 
             if self.cfg.save_checkpoints:

PaddleCV/PaddleGAN/trainer/SPADE.py

@@ -284,19 +284,19 @@ class SPADE(object):
         self.id2name = id2name
 
     def build_model(self):
-        data_shape = [-1, 3, self.cfg.crop_height, self.cfg.crop_width]
+        data_shape = [None, 3, self.cfg.crop_height, self.cfg.crop_width]
         label_shape = [
-            -1, self.cfg.label_nc, self.cfg.crop_height, self.cfg.crop_width
+            None, self.cfg.label_nc, self.cfg.crop_height, self.cfg.crop_width
         ]
-        edge_shape = [-1, 1, self.cfg.crop_height, self.cfg.crop_width]
+        edge_shape = [None, 1, self.cfg.crop_height, self.cfg.crop_width]
 
-        input_A = fluid.layers.data(
+        input_A = fluid.data(
             name='input_label', shape=label_shape, dtype='float32')
-        input_B = fluid.layers.data(
+        input_B = fluid.data(
             name='input_img', shape=data_shape, dtype='float32')
-        input_C = fluid.layers.data(
+        input_C = fluid.data(
             name='input_ins', shape=edge_shape, dtype='float32')
-        input_fake = fluid.layers.data(
+        input_fake = fluid.data(
             name='input_fake', shape=data_shape, dtype='float32')
 
         gen_trainer = GTrainer(input_A, input_B, input_C, self.cfg,
@@ -394,9 +394,9 @@ class SPADE(object):
 
             if self.cfg.run_test:
                 test_program = gen_trainer.infer_program
-                image_name = fluid.layers.data(
+                image_name = fluid.data(
                     name='image_name',
-                    shape=[self.cfg.batch_size],
+                    shape=[None, self.cfg.batch_size],
                     dtype="int32")
                 test_py_reader = fluid.io.PyReader(
                     feed_list=[input_A, input_B, input_C, image_name],

PaddleCV/PaddleGAN/trainer/STGAN.py

@@ -282,18 +282,18 @@ class STGAN(object):
         self.batch_num = batch_num
 
     def build_model(self):
-        data_shape = [-1, 3, self.cfg.image_size, self.cfg.image_size]
+        data_shape = [None, 3, self.cfg.image_size, self.cfg.image_size]
 
-        image_real = fluid.layers.data(
+        image_real = fluid.data(
             name='image_real', shape=data_shape, dtype='float32')
-        label_org = fluid.layers.data(
-            name='label_org', shape=[self.cfg.c_dim], dtype='float32')
-        label_trg = fluid.layers.data(
-            name='label_trg', shape=[self.cfg.c_dim], dtype='float32')
-        label_org_ = fluid.layers.data(
-            name='label_org_', shape=[self.cfg.c_dim], dtype='float32')
-        label_trg_ = fluid.layers.data(
-            name='label_trg_', shape=[self.cfg.c_dim], dtype='float32')
+        label_org = fluid.data(
+            name='label_org', shape=[None, self.cfg.c_dim], dtype='float32')
+        label_trg = fluid.data(
+            name='label_trg', shape=[None, self.cfg.c_dim], dtype='float32')
+        label_org_ = fluid.data(
+            name='label_org_', shape=[None, self.cfg.c_dim], dtype='float32')
+        label_trg_ = fluid.data(
+            name='label_trg_', shape=[None, self.cfg.c_dim], dtype='float32')
 
         test_gen_trainer = GTrainer(image_real, label_org, label_org_,
                                     label_trg, label_trg_, self.cfg,
@@ -378,9 +378,9 @@ class STGAN(object):
                 batch_id += 1
 
             if self.cfg.run_test:
-                image_name = fluid.layers.data(
+                image_name = fluid.data(
                     name='image_name',
-                    shape=[self.cfg.n_samples],
+                    shape=[None, self.cfg.n_samples],
                     dtype='int32')
                 test_py_reader = fluid.io.PyReader(
                     feed_list=[image_real, label_org, label_trg, image_name],

PaddleCV/PaddleGAN/trainer/StarGAN.py

@@ -259,14 +259,14 @@ class StarGAN(object):
         self.batch_num = batch_num
 
     def build_model(self):
-        data_shape = [-1, 3, self.cfg.image_size, self.cfg.image_size]
+        data_shape = [None, 3, self.cfg.image_size, self.cfg.image_size]
 
-        image_real = fluid.layers.data(
+        image_real = fluid.data(
             name='image_real', shape=data_shape, dtype='float32')
-        label_org = fluid.layers.data(
-            name='label_org', shape=[self.cfg.c_dim], dtype='float32')
-        label_trg = fluid.layers.data(
-            name='label_trg', shape=[self.cfg.c_dim], dtype='float32')
+        label_org = fluid.data(
+            name='label_org', shape=[None, self.cfg.c_dim], dtype='float32')
+        label_trg = fluid.data(
+            name='label_trg', shape=[None, self.cfg.c_dim], dtype='float32')
 
         py_reader = fluid.io.PyReader(
             feed_list=[image_real, label_org, label_trg],
@@ -346,9 +346,9 @@ class StarGAN(object):
                 batch_id += 1
 
             if self.cfg.run_test:
-                image_name = fluid.layers.data(
+                image_name = fluid.data(
                     name='image_name',
-                    shape=[self.cfg.n_samples],
+                    shape=[None, self.cfg.n_samples],
                     dtype='int32')
                 test_py_reader = fluid.io.PyReader(
                     feed_list=[image_real, label_org, label_trg, image_name],

PaddleCV/PaddleGAN/util/utility.py

@@ -409,3 +409,19 @@ def check_gpu(use_gpu):
             sys.exit(1)
     except Exception as e:
         pass
+
+
+def check_version():
+    """
+    Log error and exit when the installed version of paddlepaddle is
+    not satisfied.
+    """
+    err = "PaddlePaddle version 1.6 or higher is required, " \
+          "or a suitable develop version is satisfied as well. \n" \
+          "Please make sure the version is good with your code." \
+
+    try:
+        fluid.require_version('1.6.0')
+    except Exception as e:
+        print(err)
+        sys.exit(1)