Spade (#3343)

* add SPADE

PaddleCV/PaddleGAN/data_reader.py

@@ -23,6 +23,7 @@ import struct
 import os
 import paddle
 import random
+import sys
 
 
 def RandomCrop(img, crop_w, crop_h):
@@ -58,6 +59,19 @@ def get_preprocess_param(load_size, crop_size):
     }
 
 
+def get_preprocess_param(load_width, load_height, crop_width, crop_height):
+    if crop_width == load_width:
+        x = 0
+        y = 0
+    else:
+        x = np.random.randint(0, np.maximum(0, load_width - crop_width))
+        y = np.random.randint(0, np.maximum(0, load_height - crop_height))
+    flip = np.random.rand() > 0.5
+    return {
+        "crop_pos": (x, y),
+        "flip": flip}
+
+        
 class reader_creator(object):
     ''' read and preprocess dataset'''
 
@@ -209,6 +223,125 @@ class pair_reader_creator(reader_creator):
         return reader
 
 
+class triplex_reader_creator(reader_creator):
+    ''' read and preprocess dataset'''
+
+    def __init__(self,
+                 image_dir,
+                 list_filename,
+                 shuffle=False,
+                 batch_size=1,
+                 mode="TRAIN"):
+        super(triplex_reader_creator, self).__init__(
+            image_dir,
+            list_filename,
+            shuffle=shuffle,
+            batch_size=batch_size,
+            mode=mode)
+
+    def make_reader(self, args, return_name=False):
+        print(self.image_dir, self.list_filename)
+        print("files length:", len(self.lines))
+
+        def reader():
+            batch_out_1 = []
+            batch_out_2 = []
+            batch_out_3 = []
+            batch_out_name = []
+            if self.shuffle:
+                np.random.shuffle(self.lines)
+            for line in self.lines:
+                files = line.strip('\n\r\t ').split('\t')
+                if len(files) != 3:
+                    print("files is not equal to 3!")
+                    sys.exit(-1)
+                #label image instance
+                img1 = Image.open(os.path.join(self.image_dir, files[
+                    0]))
+                img2 = Image.open(os.path.join(self.image_dir, files[
+                    1])).convert('RGB')
+                if not args.no_instance:
+                    img3 = Image.open(os.path.join(self.image_dir, files[
+                        2]))
+
+                if self.mode == "TRAIN":
+                    param = get_preprocess_param(args.load_width, args.load_height,
+                                                 args.crop_width, args.crop_height)
+                    img1 = img1.resize((args.load_width, args.load_height),
+                                       Image.NEAREST)
+                    img2 = img2.resize((args.load_width, args.load_height),
+                                       Image.BICUBIC)
+                    if not args.no_instance:
+                        img3 = img3.resize((args.load_width, args.load_height),
+                                           Image.NEAREST)
+                    if args.crop_type == 'Centor':
+                        img1 = CentorCrop(img1, args.crop_width, args.crop_height)
+                        img2 = CentorCrop(img2, args.crop_width, args.crop_height)
+                        if not args.no_instance:
+                            img3 = CentorCrop(img3, args.crop_width, args.crop_height)
+                    elif args.crop_type == 'Random':
+                        x = param['crop_pos'][0]
+                        y = param['crop_pos'][1]
+                        img1 = img1.crop(
+                            (x, y, x + args.crop_width, y + args.crop_height))
+                        img2 = img2.crop(
+                            (x, y, x + args.crop_width, y + args.crop_height))
+                        if not args.no_instance:
+                            img3 = img3.crop(
+                                (x, y, x + args.crop_width, y + args.crop_height))
+                else:
+                    img1 = img1.resize((args.crop_width, args.crop_height),
+                                       Image.NEAREST)
+                    img2 = img2.resize((args.crop_width, args.crop_height),
+                                       Image.BICUBIC)
+                    if not args.no_instance:
+                        img3 = img3.resize((args.crop_width, args.crop_height),
+                                           Image.NEAREST)
+
+                img1 = np.array(img1)
+                index = img1[np.newaxis, :,:]
+                input_label = np.zeros((args.label_nc, index.shape[1], index.shape[2]))
+                np.put_along_axis(input_label,index,1.0,0)
+                img1 = input_label
+                img2 = (np.array(img2).astype('float32') / 255.0 - 0.5) / 0.5
+                img2 = img2.transpose([2, 0, 1])
+                if not args.no_instance:
+                    img3 = np.array(img3)[:, :, np.newaxis]
+                    img3 = img3.transpose([2, 0, 1])
+                    ###extracte edge from instance
+                    edge = np.zeros(img3.shape)
+                    edge = edge.astype('int8')
+                    edge[:, :, 1:] = edge[:, :, 1:] | (img3[:, :, 1:] != img3[:, :, :-1])
+                    edge[:, :, :-1] = edge[:, :, :-1] | (img3[:, :, 1:] != img3[:, :, :-1])
+                    edge[:, 1:, :] = edge[:, 1:, :] | (img3[:, 1:, :] != img3[:, :-1, :])
+                    edge[:, :-1, :] = edge[:, :-1, :] | (img3[:, 1:, :] != img3[:, :-1, :])
+                    img3 = edge.astype('float32')
+                    ###end extracte
+                batch_out_1.append(img1)
+                batch_out_2.append(img2)
+                if not args.no_instance:
+                    batch_out_3.append(img3)
+                if return_name:
+                    batch_out_name.append(os.path.basename(files[0]))
+                if len(batch_out_1) == self.batch_size:
+                    if return_name:
+                        if not args.no_instance:
+                            yield batch_out_1, batch_out_2, batch_out_3, batch_out_name
+                        else:
+                            yield batch_out_1, batch_out_2, batch_out_name
+                        batch_out_name = []
+                    else:
+                        if not args.no_instance:
+                            yield batch_out_1, batch_out_2, batch_out_3
+                        else:
+                            yield batch_out_1, batch_out_2
+                    batch_out_1 = []
+                    batch_out_2 = []
+                    batch_out_3 = []
+
+        return reader
+
+
 class celeba_reader_creator(reader_creator):
     ''' read and preprocess dataset'''
 
@@ -461,6 +594,33 @@ class data_reader(object):
                         mode="TEST")
                     reader_test = test_reader.make_reader(
                         self.cfg, return_name=True)
+                batch_num = train_reader.len()
+                reader = train_reader.make_reader(self.cfg)
+                return reader, reader_test, batch_num
+            elif self.cfg.model_net in ['SPADE']:
+                dataset_dir = os.path.join(self.cfg.data_dir, self.cfg.dataset)
+                train_list = os.path.join(dataset_dir, 'train.txt')
+                if self.cfg.train_list is not None:
+                    train_list = self.cfg.train_list
+                train_reader = triplex_reader_creator(
+                    image_dir=dataset_dir,
+                    list_filename=train_list,
+                    shuffle=self.cfg.shuffle,
+                    batch_size=self.cfg.batch_size,
+                    mode="TRAIN")
+                reader_test = None
+                if self.cfg.run_test:
+                    test_list = os.path.join(dataset_dir, "test.txt")
+                    if self.cfg.test_list is not None:
+                        test_list = self.cfg.test_list
+                    test_reader = triplex_reader_creator(
+                        image_dir=dataset_dir,
+                        list_filename=test_list,
+                        shuffle=False,
+                        batch_size=1,
+                        mode="TEST")
+                    reader_test = test_reader.make_reader(
+                        self.cfg, return_name=True)
                     id2name = test_reader.id2name
                 batch_num = train_reader.len()
                 reader = train_reader.make_reader(self.cfg)

PaddleCV/PaddleGAN/infer.py

@@ -26,7 +26,7 @@ import numpy as np
 import imageio
 import glob
 from util.config import add_arguments, print_arguments
-from data_reader import celeba_reader_creator, reader_creator
+from data_reader import celeba_reader_creator, reader_creator, triplex_reader_creato
 from util.utility import check_attribute_conflict, check_gpu, save_batch_image
 from util import utility
 import copy
@@ -44,13 +44,19 @@ add_arg('init_model',        str,   None,              "The init model file of d
 add_arg('output',            str,   "./infer_result",  "The directory the infer result to be saved to.")
 add_arg('input_style',       str,   "A",               "The style of the input, A or B")
 add_arg('norm_type',         str,   "batch_norm",      "Which normalization to used")
+add_arg('crop_type',         str,   None,      "Which crop type to use")
 add_arg('use_gpu',           bool,  True,              "Whether to use GPU to train.")
 add_arg('dropout',           bool,  False,             "Whether to use dropout")
 add_arg('g_base_dims',       int,   64,                "Base channels in CycleGAN generator")
+add_arg('ngf',       int,   64,                "Base channels in SPADE generator")
 add_arg('c_dim',             int,   13,                "the size of attrs")
 add_arg('use_gru',           bool,  False,             "Whether to use GRU")
 add_arg('crop_size',         int,   178,               "crop size")
 add_arg('image_size',        int,   128,               "image size")
+add_arg('load_height',        int,   128,               "image size")
+add_arg('load_width',        int,   128,               "image size")
+add_arg('crop_height',        int,   128,               "height of crop size")
+add_arg('crop_width',        int,   128,               "width of crop size")
 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")
@@ -60,6 +66,8 @@ add_arg('dataset_dir',       str,   "./data/celeba/",                "the datase
 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")
+add_arg('label_nc',        int,   36,               "label numbers of SPADE")
+add_arg('no_instance', type=bool, default=False, help="Whether to use instance label.")
 # yapf: enable
 
 
@@ -159,6 +167,13 @@ def infer(args):
         from network.DCGAN_network import DCGAN_model
         model = DCGAN_model(args.n_samples)
         fake = model.network_G(noise, name="G")
+    elif args.model_net == 'SPADE':
+        from network.SPADE_network import SPADE_model
+        model = SPADE_model()
+        input_label = fluid.layers.data(name='input_label', shape=data_shape, dtype='float32')
+        input_ins = fluid.layers.data(name='input_ins', shape=data_shape, dtype='float32')
+        input_ = fluid.layers.concat([input_label, input_ins], 1)
+        fake = model.network_G(input_, "generator", cfg=args, is_test=True)
     else:
         raise NotImplementedError("model_net {} is not support".format(
             args.model_net))
@@ -294,6 +309,33 @@ def infer(args):
             imageio.imwrite(
                 os.path.join(args.output, "fake_" + image_name), (
                     (fake_temp + 1) * 127.5).astype(np.uint8))
+    elif args.model_net == 'SPADE':
+        test_reader = triplex_reader_creator(
+            image_dir=args.dataset_dir,
+            list_filename=args.test_list,
+            shuffle=False,
+            batch_size=1,
+            mode="TEST")
+        reader_test = test_reader.make_reader(
+            args, return_name=True)
+        for data in zip(reader_test()):
+            data_A, data_B, data_C, name = data[0]
+            name = name[0]
+            tensor_A = fluid.LoDTensor()
+            tensor_C = fluid.LoDTensor()
+            tensor_A.set(data_A, place)
+            tensor_C.set(data_C, place)
+            fake_B_temp = exe.run(
+                fetch_list=[fake.name],
+                feed={"input_label": tensor_A,
+                      "input_ins": tensor_C})
+            fake_B_temp = np.squeeze(fake_B_temp[0]).transpose([1, 2, 0])
+            input_B_temp = np.squeeze(data_B[0]).transpose([1, 2, 0])
+
+            imageio.imwrite(args.output + "/fakeB_" + "_" + name, (
+                (fake_B_temp + 1) * 127.5).astype(np.uint8))
+            imageio.imwrite(args.output + "/real_" + "_" + name, (
+                (input_B_temp + 1) * 127.5).astype(np.uint8))
 
     elif args.model_net == 'CGAN':
         noise_data = np.random.uniform(

PaddleCV/PaddleGAN/network/SPADE_network.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from .base_network import conv2d, deconv2d, norm_layer, conv2d_spectral_norm
+import paddle.fluid as fluid
+import numpy as np
+
+class SPADE_model(object):
+    def __init__(self):
+        pass
+
+    def network_G(self, input, name, cfg, is_test=False):
+        nf = cfg.ngf
+        num_up_layers = 5
+        sw = cfg.crop_width // (2**num_up_layers)
+        sh = cfg.crop_height // (2**num_up_layers)
+        seg = input
+        x = fluid.layers.resize_nearest(seg, out_shape=(sh, sw), align_corners=False)
+        x = conv2d(x, 16*nf,3,padding=1,name=name + "_fc",use_bias=True, is_test=is_test)
+        x = self.SPADEResnetBlock(x, seg, 16 * nf, 16 * nf, cfg, name=name+"_head_0", is_test=is_test)
+        x = fluid.layers.resize_nearest(x, scale=2, align_corners=False)
+        x = self.SPADEResnetBlock(x, seg, 16 * nf, 16 * nf, cfg, name=name+"_G_middle_0", is_test=is_test)
+        x = self.SPADEResnetBlock(x, seg, 16 * nf, 16 * nf, cfg, name=name+"_G_middle_1", is_test=is_test)
+        x = fluid.layers.resize_nearest(x, scale=2, align_corners=False)
+
+        x = self.SPADEResnetBlock(x, seg, 16 * nf, 8 * nf, cfg, name=name+"_up_0", is_test=is_test)
+        x = fluid.layers.resize_nearest(x, scale=2, align_corners=False)
+        x = self.SPADEResnetBlock(x, seg, 8 * nf, 4 * nf, cfg, name=name+"_up_1", is_test=is_test)
+        x = fluid.layers.resize_nearest(x, scale=2, align_corners=False)
+        x = self.SPADEResnetBlock(x, seg, 4 * nf, 2 * nf, cfg, name=name+"_up_2", is_test=is_test)
+        x = fluid.layers.resize_nearest(x, scale=2, align_corners=False)
+        x = self.SPADEResnetBlock(x, seg, 2 * nf, 1 * nf, cfg, name=name+"_up_3", is_test=is_test)
+        x = fluid.layers.leaky_relu(
+            x, alpha=0.2, name=name + '_conv_img_leaky_relu')
+        x = conv2d(x, 3,3,padding=1,name=name + "_conv_img",use_bias=True, is_test=is_test)
+        x = fluid.layers.tanh(x)
+
+        return x
+    def SPADEResnetBlock(self, x, seg, fin, fout, opt, name, is_test=False):
+        learn_shortcut = (fin != fout)
+        fmiddle = min(fin, fout)
+        semantic_nc = opt.label_nc + (0 if opt.no_instance else 1)
+        if learn_shortcut:
+            x_s = self.SPADE(x, seg, fin, name=name+".norm_s", is_test=is_test)
+            x_s = conv2d_spectral_norm(x_s, fout,1,use_bias=False, name=name + ".conv_s", is_test=is_test)
+        else:
+            x_s = x
+        dx = self.SPADE(x, seg, fin, name=name+".norm_0", is_test=is_test)
+        dx = fluid.layers.leaky_relu(dx, alpha=0.2, name=name+'_leaky_relu0')
+        dx = conv2d_spectral_norm(dx, fmiddle,3,padding=1,name=name + ".conv_0", use_bias=True, is_test=is_test)
+        
+        dx = self.SPADE(dx, seg, fmiddle, name=name+".norm_1", is_test=is_test)
+        dx = fluid.layers.leaky_relu(dx, alpha=0.2, name=name+'_leaky_relu1')
+        dx = conv2d_spectral_norm(dx, fout,3,padding=1,name=name + ".conv_1", use_bias=True, is_test=is_test)
+
+        output = dx + x_s
+        return output
+
+
+    def SPADE(self, input, seg_map, norm_nc, name, is_test=False):
+        nhidden = 128
+        ks = 3
+        pw = ks // 2
+        seg_map = fluid.layers.resize_nearest(seg_map, out_shape=input.shape[2:], align_corners=False)
+        actv = conv2d(seg_map, nhidden, ks, padding=pw, activation_fn='relu', name=name+".mlp_shared.0", use_bias=True)
+        gamma = conv2d(actv, norm_nc, ks, padding=pw, name=name+".mlp_gamma", use_bias=True)
+        beta = conv2d(actv, norm_nc, ks, padding=pw, name=name+".mlp_beta", use_bias=True)
+        param_attr = fluid.ParamAttr(
+            name=name + ".param_free_norm.weight",
+            initializer=fluid.initializer.Constant(value=1.0), trainable=False)
+        bias_attr = fluid.ParamAttr(
+            name=name+".param_free_norm.bias", initializer=fluid.initializer.Constant(0.0), trainable=False)
+
+        norm = fluid.layers.batch_norm(input=input, name=name, param_attr=param_attr,
+        bias_attr=bias_attr, moving_mean_name=name+".param_free_norm.running_mean", moving_variance_name=name+".param_free_norm.running_var", is_test=is_test)
+        out = norm * (1 + gamma) + beta
+        return out    
+
+
+    def network_D(self, input, name, cfg):
+        num_D = 2
+        result = []
+        for i in range(num_D):
+            out = build_discriminator_Nlayers(input, name=name+"_%d"%i)
+            result.append(out)
+            input = fluid.layers.pool2d(input, pool_size=3, pool_type="avg", pool_stride=2, pool_padding=1, name=name+"_pool%d"%i)
+
+        return result
+
+
+
+def build_discriminator_Nlayers(input,
+                                name="discriminator",
+                                d_nlayers=4,
+                                d_base_dims=64,
+                                norm_type='instance_norm'):
+    kw = 4
+    padw = int(np.ceil((kw - 1.0) / 2))
+    nf = d_base_dims
+    res_list = []
+    res1 = conv2d(
+        input,
+        nf,
+        kw,
+        2,
+        0.02,
+        1,
+        name=name + ".model0.0",
+        activation_fn='leaky_relu',
+        relufactor=0.2,
+        use_bias=True)
+    d_dims = d_base_dims
+    res_list.append(res1)
+    for i in range(1, d_nlayers):
+        conv_name = name + ".model{}.0.0".format(i)
+        nf = min(nf*2, 512)
+        stride = 1 if i == d_nlayers - 1 else 2
+        dis_output = conv2d_spectral_norm(
+            res_list[-1],
+            nf,
+            kw,
+            stride,
+            0.02,
+            1,
+            name=conv_name,
+            norm=norm_type,
+            activation_fn='leaky_relu',
+            relufactor=0.2,
+            use_bias=False, norm_affine=False)
+        res_list.append(dis_output)
+    o_c4 = conv2d(
+        res_list[-1],
+        1,
+        4,
+        1,
+        0.02,
+        1,
+        name + ".model{}.0".format(d_nlayers),
+        use_bias=True)
+    res_list.append(o_c4)
+    return res_list
+

PaddleCV/PaddleGAN/network/base_network.py

@@ -34,12 +34,18 @@ def cal_padding(img_size, stride, filter_size, dilation=1):
     return out_size // 2, out_size - out_size // 2
 
 
-def norm_layer(input, norm_type='batch_norm', name=None, is_test=False):
+def norm_layer(input, norm_type='batch_norm', name=None, is_test=False, affine=True):
     if norm_type == 'batch_norm':
+        if affine == True:
             param_attr = fluid.ParamAttr(
                 name=name + '_w', initializer=fluid.initializer.Constant(1.0))
             bias_attr = fluid.ParamAttr(
                 name=name + '_b', initializer=fluid.initializer.Constant(value=0.0))
+        else:
+            param_attr = fluid.ParamAttr(
+                name=name + '_w', initializer=fluid.initializer.Constant(1.0), trainable=False)
+            bias_attr = fluid.ParamAttr(
+                name=name + '_b', initializer=fluid.initializer.Constant(value=0.0), trainable=False)
         return fluid.layers.batch_norm(
             input,
             param_attr=param_attr,
@@ -58,6 +64,7 @@ def norm_layer(input, norm_type='batch_norm', name=None, is_test=False):
         if name is not None:
             scale_name = name + "_scale"
             offset_name = name + "_offset"
+        if affine:
             scale_param = fluid.ParamAttr(
                 name=scale_name,
                 initializer=fluid.initializer.Constant(1.0),
@@ -66,6 +73,15 @@ def norm_layer(input, norm_type='batch_norm', name=None, is_test=False):
                 name=offset_name,
                 initializer=fluid.initializer.Constant(0.0),
                 trainable=True)
+        else:
+            scale_param = fluid.ParamAttr(
+                name=scale_name,
+                initializer=fluid.initializer.Constant(1.0),
+                trainable=False)
+            offset_param = fluid.ParamAttr(
+                name=offset_name,
+                initializer=fluid.initializer.Constant(0.0),
+                trainable=False)
         scale = helper.create_parameter(
             attr=scale_param, shape=input.shape[1:2], dtype=dtype)
         offset = helper.create_parameter(
@@ -375,3 +391,149 @@ def conv_and_pool(x, num_filters, name, stddev=0.02, act=None):
         bias_attr=bias_attr,
         act=act)
     return out
+def conv2d_spectral_norm(input,
+                         num_filters=64,
+                         filter_size=7,
+                         stride=1,
+                         stddev=0.02,
+                         padding=0,
+                         name="conv2d_spectral_norm",
+                         norm=None,
+                         activation_fn=None,
+                         relufactor=0.0,
+                         use_bias=False,
+                         padding_type=None,
+                         initial="normal",
+                         is_test=False, norm_affine=True):
+    b, c, h, w = input.shape
+    height = num_filters
+    width = c * filter_size * filter_size
+    helper = fluid.layer_helper.LayerHelper("conv2d_spectral_norm", **locals())
+    dtype = helper.input_dtype()
+    weight_param = fluid.ParamAttr(
+        name=name+".weight_orig",
+        initializer=fluid.initializer.Constant(1.0),
+        trainable=True)
+    weight = helper.create_parameter(
+        attr=weight_param, shape=(num_filters, c, filter_size, filter_size), dtype=dtype)
+    weight_spectral_norm = fluid.layers.spectral_norm(weight, dim=0, name=name+".spectral_norm")
+    weight = weight_spectral_norm
+    if use_bias:
+        bias_attr = fluid.ParamAttr(
+            name=name + "_b", initializer=fluid.initializer.Constant(0.0))
+    else:
+        bias_attr = False
+    conv = conv2d_with_filter(input, weight, stride, padding, bias_attr=bias_attr, name=name)
+    if norm is not None:
+        conv = norm_layer(
+            input=conv, norm_type=norm, name=name + "_norm", is_test=is_test, affine=norm_affine)
+    if activation_fn == 'relu':
+        conv = fluid.layers.relu(conv, name=name + '_relu')
+    elif activation_fn == 'leaky_relu':
+        conv = fluid.layers.leaky_relu(
+            conv, alpha=relufactor, name=name + '_leaky_relu')
+    elif activation_fn == 'tanh':
+        conv = fluid.layers.tanh(conv, name=name + '_tanh')
+    elif activation_fn == 'sigmoid':
+        conv = fluid.layers.sigmoid(conv, name=name + '_sigmoid')
+    elif activation_fn == None:
+        conv = conv
+    else:
+        raise NotImplementedError("activation: [%s] is not support" %
+                                  activation_fn)
+    return conv
+
+
+def conv2d_with_filter(input,
+                       filter,
+                       stride=1,
+                       padding=0,
+                       dilation=1,
+                       groups=None,
+                       bias_attr=None,
+                       use_cudnn=True,
+                       act=None,
+                       name=None):
+    """ 
+    Similar with conv2d, this is a convolution2D layers. Difference
+    is filter can be token as input directly instead of setting filter size
+    and number of fliters. Filter is a  4-D tensor with shape 
+    [num_filter, num_channel, filter_size_h, filter_size_w].
+     Args:
+        input (Variable): The input image with [N, C, H, W] format.
+        filter(Variable): The input filter with [N, C, H, W] format.
+        stride (int|tuple): The stride size. If stride is a tuple, it must
+            contain two integers, (stride_H, stride_W). Otherwise, the
+            stride_H = stride_W = stride. Default: stride = 1.
+        padding (int|tuple): The padding size. If padding is a tuple, it must
+            contain two integers, (padding_H, padding_W). Otherwise, the
+            padding_H = padding_W = padding. Default: padding = 0.
+        dilation (int|tuple): The dilation size. If dilation is a tuple, it must
+            contain two integers, (dilation_H, dilation_W). Otherwise, the
+            dilation_H = dilation_W = dilation. Default: dilation = 1.
+        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d.
+            If it is set to False, no bias will be added to the output units.
+            If it is set to None or one attribute of ParamAttr, conv2d
+            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
+            is not set, the bias is initialized zero. Default: None.
+        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
+            library is installed. Default: True
+        act (str): Activation type, if it is set to None, activation is not appended.
+            Default: None
+        name (str|None): A name for this layer(optional). If set None, the layer
+            will be named automatically. Default: None
+    Returns:
+        Variable: The tensor variable storing the convolution and \
+                  non-linearity activation result.
+    Raises:
+        ValueError: If the shapes of input, filter_size, stride, padding and
+                    groups mismatch.
+    Examples:
+        .. code-block:: python
+          data = fluid.layers.data(name='data', shape=[3, 32, 32], \
+                                  dtype='float32')
+          filter = fluid.layers.data(name='filter',shape=[10,3,3,3], \
+                                    dtype='float32',append_batch_size=False)
+          conv2d = fluid.layers.conv2d(input=data, 
+                                       filter=filter,
+                                       act="relu") 
+    """
+    helper = fluid.layer_helper.LayerHelper("conv2d_with_filter", **locals())
+    num_channels = input.shape[1]
+    num_filters = filter.shape[0]
+    num_filter_channels = filter.shape[1]
+    l_type = 'conv2d'
+    if (num_channels == groups and num_filters % num_channels == 0 and
+            not use_cudnn):
+        l_type = 'depthwise_conv2d'
+    if groups is None:
+        assert num_filter_channels == num_channels
+    else:
+        if num_channels % groups != 0:
+            raise ValueError("num_channels must be divisible by groups.")
+        if num_channels // groups != num_filter_channels:
+            raise ValueError("num_filter_channels must equal to num_channels\
+                              divided by groups.")
+    stride = fluid.layers.utils.convert_to_list(stride, 2, 'stride')
+    padding = fluid.layers.utils.convert_to_list(padding, 2, 'padding')
+    dilation = fluid.layers.utils.convert_to_list(dilation, 2, 'dilation')
+    if not isinstance(use_cudnn, bool):
+        raise ValueError("use_cudnn should be True or False")
+    pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
+    helper.append_op(
+        type=l_type,
+        inputs={
+            'Input': input,
+            'Filter': filter,
+        },
+        outputs={"Output": pre_bias},
+        attrs={
+            'strides': stride,
+            'paddings': padding,
+            'dilations': dilation,
+            'groups': groups,
+            'use_cudnn': use_cudnn,
+            'use_mkldnn': False
+        })
+    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
+    return helper.append_activation(pre_act)

PaddleCV/PaddleGAN/network/vgg.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle
+import paddle.fluid as fluid
+
+__all__ = ["VGGNet", "VGG11", "VGG13", "VGG16", "VGG19"]
+
+train_parameters = {
+    "input_size": [3, 224, 224],
+    "input_mean": [0.485, 0.456, 0.406],
+    "input_std": [0.229, 0.224, 0.225],
+    "learning_strategy": {
+        "name": "piecewise_decay",
+        "batch_size": 256,
+        "epochs": [30, 60, 90],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    }
+}
+
+
+class VGGNet():
+    def __init__(self, layers=16, name=""):
+        self.params = train_parameters
+        self.layers = layers
+        self.name=name
+
+    def net(self, input, class_dim=1000):
+        layers = self.layers
+        vgg_spec = {
+            11: ([1, 1, 2, 2, 2]),
+            13: ([2, 2, 2, 2, 2]),
+            16: ([2, 2, 3, 3, 3]),
+            19: ([2, 2, 4, 4, 4])
+        }
+        assert layers in vgg_spec.keys(), \
+            "supported layers are {} but input layer is {}".format(vgg_spec.keys(), layers)
+
+        nums = vgg_spec[layers]
+        conv1, res = self.conv_block(input, 64, nums[0], name=self.name+"_conv1_")
+        conv2, res = self.conv_block(res, 128, nums[1], name=self.name+"_conv2_")
+        conv3, res = self.conv_block(res, 256, nums[2], name=self.name+"_conv3_")
+        conv4, res = self.conv_block(res, 512, nums[3], name=self.name+"_conv4_")
+        conv5, res = self.conv_block(res, 512, nums[4], name=self.name+"_conv5_")
+
+        if self.layers == 16:
+            return [conv1, conv2, conv3]
+        elif self.layers == 19:
+            return [conv1, conv2, conv3, conv4, conv5]
+
+    def conv_block(self, input, num_filter, groups, name=""):
+        conv = input
+        for i in range(groups):
+            conv = fluid.layers.conv2d(
+                input=conv,
+                num_filters=num_filter,
+                filter_size=3,
+                stride=1,
+                padding=1,
+                act='relu',
+                param_attr=fluid.param_attr.ParamAttr(
+                    name=name + str(i + 1) + "_weights", trainable=False),
+                bias_attr=False
+                )
+            if i == 0:
+                relu_res = conv
+        return relu_res, fluid.layers.pool2d(
+            input=conv, pool_size=2, pool_type='max', pool_stride=2)
+
+
+    def load_vars(self, exe, program, pretrained_model):
+        vars = []
+        for var in program.list_vars():
+            if fluid.io.is_parameter(var) and var.name.startswith("vgg"):
+                vars.append(var)
+                print(var.name)
+        fluid.io.load_vars(exe, pretrained_model, program, vars)
+
+
+def VGG11():
+    model = VGGNet(layers=11)
+    return model
+
+
+def VGG13():
+    model = VGGNet(layers=13)
+    return model
+
+
+def VGG16():
+    model = VGGNet(layers=16, name="vgg16")
+    return model
+
+
+def VGG19(name="vgg19"):
+    model = VGGNet(layers=19, name=name)
+    return model

PaddleCV/PaddleGAN/scripts/run_SPADE.sh

+export FLAGS_eager_delete_tensor_gb=0.0
+export FLAGS_fast_eager_deletion_mode=1
+export FLAGS_fraction_of_gpu_memory_to_use=0.01
+CUDA_VISIBLE_DEVICES=0 python train.py --model_net SPADE --dataset cityscapes --train_list train_list --test_list val_list --crop_type Random --batch_size 1 --epoch 200 --load_height 612 --load_width 1124 --crop_height 512 --crop_width 1024 --label_nc 36

PaddleCV/PaddleGAN/train.py

@@ -30,7 +30,7 @@ import trainer
 def train(cfg):
 
     MODELS = [
-        "CGAN", "DCGAN", "Pix2pix", "CycleGAN", "StarGAN", "AttGAN", "STGAN"
+        "CGAN", "DCGAN", "Pix2pix", "CycleGAN", "StarGAN", "AttGAN", "STGAN", "SPADE"
     ]
     if cfg.model_net not in MODELS:
         raise NotImplementedError("{} is not support!".format(cfg.model_net))

PaddleCV/PaddleGAN/trainer/SPADE.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from network.SPADE_network import SPADE_model
+from util import utility
+import paddle.fluid as fluid
+import sys
+import time
+import network.vgg as vgg
+import pickle as pkl
+import numpy as np
+class GTrainer():
+    def __init__(self, input_label, input_img, input_ins, cfg, step_per_epoch):
+        self.cfg = cfg
+        self.program = fluid.default_main_program().clone()
+        with fluid.program_guard(self.program):
+            model = SPADE_model()
+            input = input_label
+            if not cfg.no_instance:
+                input = fluid.layers.concat([input_label, input_ins], 1)
+            self.fake_B = model.network_G(input, "generator", cfg=cfg)
+            self.fake_B.persistable = True
+            self.infer_program = self.program.clone()
+            fake_concat = fluid.layers.concat([input, self.fake_B], 1)
+            real_concat = fluid.layers.concat([input, input_img], 1)
+            fake_and_real = fluid.layers.concat([fake_concat, real_concat], 0)
+            pred = model.network_D(fake_and_real, "discriminator", cfg)
+            if type(pred) == list:
+                self.pred_fake = []
+                self.pred_real = []
+                for p in pred:
+                    self.pred_fake.append([tensor[:tensor.shape[0] // 2] for tensor in p])
+                    self.pred_real.append([tensor[tensor.shape[0] // 2:] for tensor in p])
+            else:
+                self.pred_fake = pred[:pred.shape[0] // 2]
+                self.pred_real = pred[pred.shape[0] // 2:]
+
+            ###GAN Loss hinge
+            if isinstance(self.pred_fake, list):
+                self.gan_loss = 0
+                for pred_i in self.pred_fake:
+                    if isinstance(pred_i, list):
+                        pred_i = pred_i[-1]
+                    loss_i = -1 * fluid.layers.reduce_mean(pred_i)
+                    self.gan_loss += loss_i
+                self.gan_loss /= len(self.pred_fake)
+            else:
+                self.gan_loss = -1 * fluid.layers.reduce_mean(self.pred_fake)
+            self.gan_loss.persistable = True
+            #####GAN Feat loss
+            num_D = len(self.pred_fake)
+            self.gan_feat_loss = 0.0
+            for i in range(num_D):
+                num_intermediate_outputs = len(self.pred_fake[i]) - 1
+                for j in range(num_intermediate_outputs):
+                    self.gan_feat_loss = fluid.layers.reduce_mean(fluid.layers.abs(fluid.layers.elementwise_sub(
+                        x=self.pred_fake[i][j], y=self.pred_real[i][j]))) * cfg.lambda_feat / num_D
+            self.gan_feat_loss.persistable = True
+            ########VGG Feat loss
+            weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]
+            self.vgg = vgg.VGG19()
+            fake_vgg = self.vgg.net(self.fake_B)
+            real_vgg = self.vgg.net(input_img)
+            self.vgg_loss = 0.0
+            for i in range(len(fake_vgg)):
+                self.vgg_loss += weights[i] * fluid.layers.reduce_mean(fluid.layers.abs(fluid.layers.elementwise_sub(
+                        x=fake_vgg[i], y=real_vgg[i])))
+            self.vgg_loss.persistable = True
+            self.g_loss = (self.gan_loss + self.gan_feat_loss + self.vgg_loss)/3 
+            lr = cfg.learning_rate
+            vars = []
+            for var in self.program.list_vars():
+                if fluid.io.is_parameter(var) and var.name.startswith(
+                        "generator"):
+                    vars.append(var.name)
+            self.param = vars
+            if cfg.epoch <= 100:
+                optimizer = fluid.optimizer.Adam(
+                    learning_rate=lr, beta1=0.5, beta2=0.999, name="net_G")
+            else:
+                optimizer = fluid.optimizer.Adam(
+                    learning_rate=fluid.layers.piecewise_decay(
+                        boundaries=[99 * step_per_epoch] + [
+                            x * step_per_epoch
+                            for x in range(100, cfg.epoch - 1)
+                        ],
+                        values=[lr] + [
+                            lr * (1.0 - (x - 99.0) / 101.0)
+                            for x in range(100, cfg.epoch)
+                        ]),
+                    beta1=0.5,
+                    beta2=0.999,
+                    name="net_G")
+            optimizer.minimize(self.g_loss, parameter_list=vars)
+
+
+class DTrainer():
+    def __init__(self, input_label, input_img, input_ins, fake_B, cfg, step_per_epoch):
+        self.program = fluid.default_main_program().clone()
+        lr = cfg.learning_rate
+        with fluid.program_guard(self.program):
+            model = SPADE_model()
+            input = input_label
+            if not cfg.no_instance:
+                input = fluid.layers.concat([input_label, input_ins], 1)
+            fake_concat = fluid.layers.concat([input, fake_B], 1)
+            real_concat = fluid.layers.concat([input, input_img], 1)
+            fake_and_real = fluid.layers.concat([fake_concat, real_concat], 0)
+            pred = model.network_D(fake_and_real, "discriminator", cfg)
+            if type(pred) == list:
+                self.pred_fake = []
+                self.pred_real = []
+                for p in pred:
+                    self.pred_fake.append([tensor[:tensor.shape[0] // 2] for tensor in p])
+                    self.pred_real.append([tensor[tensor.shape[0] // 2:] for tensor in p])
+            else:
+                self.pred_fake = pred[:pred.shape[0] // 2]
+                self.pred_real = pred[pred.shape[0] // 2:]
+
+            #####gan loss
+            self.gan_loss_fake = 0
+            for pred_i in self.pred_fake:
+                zeros = fluid.layers.fill_constant_batch_size_like(input=pred_i[-1],shape=pred_i[-1].shape,value=0,dtype='float32')
+                if isinstance(pred_i, list):
+                    pred_i = pred_i[-1]
+                minval = fluid.layers.elementwise_min(-1 * pred_i-1, zeros)
+                loss_i = -1 * fluid.layers.reduce_mean(minval)
+                self.gan_loss_fake += loss_i
+            self.gan_loss_fake /= len(self.pred_fake)
+            
+            self.gan_loss_real = 0
+            for pred_i in self.pred_real:
+                zeros = fluid.layers.fill_constant_batch_size_like(input=pred_i[-1],shape=pred_i[-1].shape,value=0,dtype='float32')
+                if isinstance(pred_i, list):
+                    pred_i = pred_i[-1]
+                minval = fluid.layers.elementwise_min(pred_i-1, zeros)
+                loss_i = -1 * fluid.layers.reduce_mean(minval)
+                self.gan_loss_real += loss_i
+            self.gan_loss_real /= len(self.pred_real)
+            self.gan_loss_real.persistable = True
+            self.gan_loss_fake.persistable = True
+
+            self.d_loss = 0.5 * (self.gan_loss_real + self.gan_loss_fake)
+            vars = []
+            for var in self.program.list_vars():
+                if fluid.io.is_parameter(var) and var.name.startswith(
+                        "discriminator"):
+                    vars.append(var.name)
+            self.param = vars
+            if cfg.epoch <= 100:
+                optimizer = fluid.optimizer.Adam(
+                    learning_rate=lr, beta1=0.5, beta2=0.999, name="net_D")
+            else:
+                optimizer = fluid.optimizer.Adam(
+                    learning_rate=fluid.layers.piecewise_decay(
+                        boundaries=[99 * step_per_epoch] + [
+                            x * step_per_epoch
+                            for x in range(100, cfg.epoch - 1)
+                        ],
+                        values=[lr] + [
+                            lr * (1.0 - (x - 99.0) / 101.0)
+                            for x in range(100, cfg.epoch)
+                        ]),
+                    beta1=0.5,
+                    beta2=0.999,
+                    name="net_D")
+
+            optimizer.minimize(self.d_loss, parameter_list=vars)
+
+
+class SPADE(object):
+    def add_special_args(self, parser):
+        parser.add_argument(
+            '--vgg19_pretrain',
+            type=str,
+            default="./VGG19_pretrained",
+            help="VGG19 pretrained model for vgg loss"
+        )
+        parser.add_argument(
+            '--crop_width',
+            type=int,
+            default=1024,
+            help="crop width for training SPADE")
+        parser.add_argument(
+            '--crop_height',
+            type=int,
+            default=512,
+            help="crop height for training SPADE")
+        parser.add_argument(
+            '--load_width',
+            type=int,
+            default=1124,
+            help="load width for training SPADE")
+        parser.add_argument(
+            '--load_height',
+            type=int,
+            default=612,
+            help="load height for training SPADE")
+        parser.add_argument(
+            '--d_nlayers',
+            type=int,
+            default=4,
+            help="num of discriminator layers for SPADE")
+        parser.add_argument(
+            '--label_nc',
+            type=int,
+            default=36,
+            help="label numbers of SPADE")
+        parser.add_argument(
+            '--ngf',
+            type=int,
+            default=64,
+            help="base channels of generator in SPADE")
+        parser.add_argument(
+            '--ndf',
+            type=int,
+            default=64,
+            help="base channels of discriminator in SPADE")
+        parser.add_argument(
+            '--num_D',
+            type=int,
+            default=2,
+            help="number of discriminators in SPADE")
+        parser.add_argument(
+            '--lambda_feat',
+            type=float,
+            default=10,
+            help="weight term of feature loss")
+        parser.add_argument(
+            '--lambda_vgg',
+            type=float,
+            default=10,
+            help="weight term of vgg loss")
+        parser.add_argument('--no_instance', type=bool, default=False, help="Whether to use instance label.")
+
+        return parser
+
+    def __init__(self,
+                 cfg=None,
+                 train_reader=None,
+                 test_reader=None,
+                 batch_num=1):
+        self.cfg = cfg
+        self.train_reader = train_reader
+        self.test_reader = test_reader
+        self.batch_num = batch_num
+
+    def build_model(self):
+        data_shape = [-1, 3, self.cfg.crop_height, self.cfg.crop_width]
+        label_shape = [-1, self.cfg.label_nc, self.cfg.crop_height, self.cfg.crop_width]
+        edge_shape = [-1, 1, self.cfg.crop_height, self.cfg.crop_width]
+
+        input_A = fluid.layers.data(
+            name='input_label', shape=label_shape, dtype='float32')
+        input_B = fluid.layers.data(
+            name='input_img', shape=data_shape, dtype='float32')
+        input_C = fluid.layers.data(
+            name='input_ins', shape=edge_shape, dtype='float32')
+        input_fake = fluid.layers.data(
+            name='input_fake', shape=data_shape, dtype='float32')
+
+        gen_trainer = GTrainer(input_A, input_B, input_C, self.cfg, self.batch_num)
+        dis_trainer = DTrainer(input_A, input_B, input_C, input_fake, self.cfg,
+                               self.batch_num)
+        py_reader = fluid.io.PyReader(
+            feed_list=[input_A, input_B, input_C],
+            capacity=4,  ## batch_size * 4
+            iterable=True,
+            use_double_buffer=True)
+       py_reader.decorate_batch_generator(
+            self.train_reader,
+            places=fluid.cuda_places()
+            if self.cfg.use_gpu else fluid.cpu_places())
+
+        # prepare environment
+        place = fluid.CUDAPlace(0) if self.cfg.use_gpu else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+        gen_trainer.vgg.load_vars(exe, gen_trainer.program, self.cfg.vgg19_pretrain)
+                 
+        if self.cfg.init_model:
+            utility.init_checkpoints(self.cfg, exe, gen_trainer, "net_G")
+            utility.init_checkpoints(self.cfg, exe, dis_trainer, "net_D")
+
+        ### memory optim
+        build_strategy = fluid.BuildStrategy()
+        build_strategy.enable_inplace = False
+        build_strategy.sync_batch_norm = True
+
+        gen_trainer_program = fluid.CompiledProgram(
+            gen_trainer.program).with_data_parallel(
+                loss_name=gen_trainer.g_loss.name,
+                build_strategy=build_strategy)
+        dis_trainer_program = fluid.CompiledProgram(
+            dis_trainer.program).with_data_parallel(
+                loss_name=dis_trainer.d_loss.name,
+                build_strategy=build_strategy)
+
+        t_time = 0
+
+        for epoch_id in range(self.cfg.epoch):
+            batch_id = 0
+            for tensor in py_reader():
+                data_A, data_B, data_C = tensor[0]['input_A'], tensor[0]['input_B'], tensor[0]['input_C']
+                tensor_A = fluid.LoDTensor()
+                tensor_B = fluid.LoDTensor()
+                tensor_C = fluid.LoDTensor()
+                tensor_A.set(data_A, place)
+                tensor_B.set(data_B, place)
+                tensor_C.set(data_C, place)
+                s_time = time.time()
+                # optimize the generator network
+                g_loss_gan, g_loss_vgg, g_loss_feat, fake_B_tmp = exe.run(
+                    gen_trainer_program,
+                    fetch_list=[
+                        gen_trainer.gan_loss, gen_trainer.vgg_loss, gen_trainer.gan_feat_loss,
+                        gen_trainer.fake_B
+                    ],
+                    feed={"input_label": tensor_A,
+                          "input_img": tensor_B,
+                          "input_ins": tensor_C})
+                
+                # optimize the discriminator network
+                d_loss_real, d_loss_fake = exe.run(dis_trainer_program,
+                                                   fetch_list=[
+                                                       dis_trainer.gan_loss_real,
+                                                       dis_trainer.gan_loss_fake
+                                                   ],
+                                                   feed={
+                                                       "input_label": tensor_A,
+                                                       "input_img": tensor_B,
+                                                       "input_ins": tensor_C,
+                                                       "input_fake": fake_B_tmp
+                                                   })
+
+                batch_time = time.time() - s_time
+                t_time += batch_time
+                if batch_id % self.cfg.print_freq == 0:
+                    print("epoch{}: batch{}: \n\
+                         g_loss_gan: {}; g_loss_vgg: {}; g_loss_feat: {} \n\
+                         d_loss_real: {}; d_loss_fake: {}; \n\
+                         Batch_time_cost: {:.2f}"
+                          .format(epoch_id, batch_id, g_loss_gan[0], g_loss_vgg[
+                              0], g_loss_feat[0], d_loss_real[0], d_loss_fake[0], batch_time))
+
+                sys.stdout.flush()
+                batch_id += 1
+
+            if self.cfg.run_test:
+                test_program = gen_trainer.infer_program
+                image_name = fluid.layers.data(
+                    name='image_name',
+                    shape=[self.cfg.batch_size],
+                    dtype="int32")
+                test_py_reader = fluid.io.PyReader(
+                    feed_list=[input_A, input_B, image_name],
+                    capacity=4,  ## batch_size * 4
+                    iterable=True,
+                    use_double_buffer=True)
+                test_py_reader.decorate_batch_generator(
+                    self.test_reader,
+                    places=fluid.cuda_places()
+                    if self.cfg.use_gpu else fluid.cpu_places())
+                utility.save_test_image(epoch_id, self.cfg, exe, place,
+                                        test_program, gen_trainer,
+                                        test_py_reader)
+
+            if self.cfg.save_checkpoints:
+                utility.checkpoints(epoch_id, self.cfg, exe, gen_trainer,
+                                    "net_G")
+                utility.checkpoints(epoch_id, self.cfg, exe, dis_trainer,
+                                    "net_D")

PaddleCV/PaddleGAN/util/utility.py

@@ -170,6 +170,30 @@ def save_test_image(epoch,
             res_inputB = Image.fromarray(((input_B_temp + 1) * 127.5).astype(
                 np.uint8))
             res_inputB.save(os.path.join(out_path, inputB_name))
+    elif cfg.model_net == "SPADE":
+        for data in A_test_reader():
+            data_A, data_B, data_C, name = data[0]['input_A'], data[0]['input_B'], data[0]['input_C'], data[0]['image_name']
+            tensor_A = fluid.LoDTensor()
+            tensor_B = fluid.LoDTensor()
+            tensor_C = fluid.LoDTensor()
+            tensor_A.set(data_A, place)
+            tensor_B.set(data_B, place)
+            tensor_C.set(data_C, place)
+            fake_B_temp = exe.run(
+                test_program,
+                fetch_list=[g_trainer.fake_B],
+                feed={"input_label": tensor_A,
+                      "input_img": tensor_B,
+                      "input_ins": tensor_C})
+            fake_B_temp = np.squeeze(fake_B_temp[0]).transpose([1, 2, 0])
+            input_B_temp = np.squeeze(data_B[0]).transpose([1, 2, 0])
+
+            res_fakeB = Image.fromarray(((fake_B_temp + 1) * 127.5).astype(
+                np.uint8))
+            res_fakeB.save(out_path+"/fakeB_"+str(epoch)+"_"+name)
+            res_real = Image.fromarray(((input_B_temp + 1) * 127.5).astype(
+                np.uint8))
+            res_real.save(out_path+"/real_"+str(epoch)+"_"+name)
     elif cfg.model_net == "StarGAN":
         for data in A_test_reader():
             real_img, label_org, label_trg, image_name = data[0][