add metric(fid_score) for gan (#4553)

* fix init

* update

* add fid_score for gan

* update readme

PaddleCV/gan/README.md

@@ -18,12 +18,12 @@
 本图像生成模型库包含CGAN\[[3](#参考文献)\], DCGAN\[[4](#参考文献)\], Pix2Pix\[[5](#参考文献)\], CycleGAN\[[6](#参考文献)\], StarGAN\[[7](#参考文献)\], AttGAN\[[8](#参考文献)\], STGAN\[[9](#参考文献)\], SPADE\[[13](#参考文献)\]。
 
 注意：
-1. StarGAN，AttGAN和STGAN由于梯度惩罚所需的操作目前只支持GPU，需使用GPU训练。
-2. GAN模型目前仅仅验证了单机单卡训练和预测结果。
-3. CGAN和DCGAN两个模型训练使用的数据集为MNIST数据集；StarGAN，AttGAN和STGAN的数据集为CelebA数据集。Pix2Pix和CycleGAN支持的数据集可以参考download.py中的cycle_pix_dataset。cityscapes数据集需要从[官方](https://www.cityscapes-dataset.com)下载数据，下载完之后使用`scripts/prepare_cityscapes_dataset.py`处理，处理后的文件夹命名为cityscapes并放入data目录下即可。
-4. PaddlePaddle1.5.1及之前的版本不支持在AttGAN和STGAN模型里的判别器加上的instance norm。如果要在判别器中加上instance norm，请源码编译develop分支并安装。
-5. 中间效果图保存在${output_dir}/test文件夹中。对于Pix2Pix来说，inputA 和inputB 代表输入的两种风格的图片，fakeB表示生成图片；对于CycleGAN来说，inputA表示输入图片，fakeB表示inputA根据生成的图片，cycA表示fakeB经过生成器重构出来的对应于inputA的重构图片；对于StarGAN，AttGAN和STGAN来说，第一行表示原图，之后的每一行都代表一种属性变换。
-6. infer过程使用的test_list文件和训练过程中使用的train_list具有相同格式，第一行为样本数量，第二行为属性，之后的行中第一个表示图片名称，之后的-1和1表示该图片是否拥有该属性(1为有该属性，-1为没有该属性)。
+1. GAN模型目前仅仅验证了单机单卡训练和预测结果。
+2. CGAN和DCGAN两个模型训练使用的数据集为MNIST数据集；StarGAN，AttGAN和STGAN的数据集为CelebA数据集。Pix2Pix和CycleGAN支持的数据集可以参考download.py中的cycle_pix_dataset。cityscapes数据集需要从[官方](https://www.cityscapes-dataset.com)下载数据，下载完之后使用`scripts/prepare_cityscapes_dataset.py`处理，处理后的文件夹命名为cityscapes并放入data目录下即可。
+3. PaddlePaddle1.5.1及之前的版本不支持在AttGAN和STGAN模型里的判别器加上的instance norm。如果要在判别器中加上instance norm，请源码编译develop分支并安装。
+4. 中间效果图保存在${output_dir}/test文件夹中。对于Pix2Pix来说，inputA 和inputB 代表输入的两种风格的图片，fakeB表示生成图片；对于CycleGAN来说，inputA表示输入图片，fakeB表示inputA根据生成的图片，cycA表示fakeB经过生成器重构出来的对应于inputA的重构图片；对于StarGAN，AttGAN和STGAN来说，第一行表示原图，之后的每一行都代表一种属性变换。
+5. infer过程使用的test_list文件和训练过程中使用的train_list具有相同格式，第一行为样本数量，第二行为属性，之后的行中第一个表示图片名称，之后的-1和1表示该图片是否拥有该属性(1为有该属性，-1为没有该属性)。
+6. metric中的fid评价指标需要先下载inceptionV3模型参数，模型参数下载链接：[inceptionV3](https://paddle-gan-models.bj.bcebos.com/params_inceptionV3.tar.gz)
 
 图像生成模型库库的目录结构如下：
 ```
@@ -58,6 +58,10 @@
 │   ├── celeba
 │       ├── ${image_dir} 存放实际图片
 │       ├── list 文件
+│
+├── metric 评价指标
+│   ├── compute_fid.py 计算fid_score的文件
+│   ├── inception.py 计算fid_score所需要的inceptionV3网络结构
 
 ```
 
@@ -71,7 +75,7 @@
 在当前目录下运行样例代码需要PadddlePaddle Fluid的v.1.7.1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）
+1. `pip install -r requirements.txt` 安装imageio包（保存图片代码中所依赖的包）
 
 ### 任务简介
 

PaddleCV/gan/metric/compute_fid.py

+import os
+import fnmatch
+import numpy as np
+import cv2
+from cv2 import imread
+from scipy import linalg
+import paddle.fluid as fluid
+from inception import InceptionV3
+
+def tqdm(x): return x
+
+""" based on https://github.com/mit-han-lab/gan-compression/blob/master/metric/fid_score.py
+"""
+
+"""
+inceptionV3 pretrain model is convert from pytorch, pretrain_model url is https://paddle-gan-models.bj.bcebos.com/params_inceptionV3.tar.gz
+"""
+
+def _calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    m1 = np.atleast_1d(mu1)
+    m2 = np.atleast_1d(mu2)
+ 
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, 'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, 'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    t = sigma1.dot(sigma2)
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean)
+
+
+def _build_program(model):
+    main_program = fluid.Program()
+    startup_program = fluid.Program()
+    with fluid.program_guard(main_program, startup_program):
+        images = fluid.data(name='images', shape = [None, 3, None, None])
+        output = model.network(images, class_dim=1008)
+        pred = fluid.layers.pool2d(output[0], global_pooling=True)
+
+    test_program = main_program.clone(for_test=True)
+    return pred, test_program, startup_program
+
+def _get_activations_from_ims(img, model, batch_size, dims, use_gpu, premodel_path):
+    n_batches = (len(img) + batch_size - 1) // batch_size
+    n_used_img = len(img)
+
+    pred_arr = np.empty((n_used_img, dims))
+
+    for i in tqdm(range(n_batches)):
+        start = i * batch_size
+        end = start + batch_size
+        if end > len(img):
+            end = len(img)
+        images = img[start: end]
+        if images.shape[1] != 3:
+            images = images.transpose((0, 3, 1, 2))
+        images /= 255
+
+        output, main_program, startup_program = _build_program(model)
+        place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_program)
+
+        fluid.load(main_program, os.path.join(premodel_path, 'paddle_inceptionv3'), exe)
+        pred = exe.run(main_program, feed = {'images': images}, fetch_list = [output])[0]
+
+        pred_arr[start: end] = pred.reshape(end - start, -1)
+
+    return pred_arr
+
+def _compute_statistic_of_img(img, model, batch_size, dims, use_gpu, premodel_path):
+    act = _get_activations_from_ims(img, model, batch_size, dims, use_gpu, premodel_path) 
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu,sigma
+
+def calculate_fid_given_img(img_fake, img_real, batch_size, use_gpu, dims, premodel_path, model=None):
+    assert os.path.exists(premodel_path), 'pretrain_model path {} is not exists! Please download it first'.format(premodel_path)
+    if model is None:
+        block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+        model = InceptionV3([block_idx])
+
+    m1, s1 = _compute_statistic_of_img(img_fake, model, batch_size, dims, use_gpu, premodel_path)
+    m2, s2 = _compute_statistic_of_img(img_real, model, batch_size, dims, use_gpu, premodel_path)
+
+    fid_value = _calculate_frechet_distance(m1, s1, m2, s2)
+    return fid_value
+
+
+def _get_activations(files, model, batch_size, dims, use_gpu, premodel_path):
+    if len(files) % batch_size != 0:
+        print(('Warning: number of images is not a multiple of the '
+            'batch size. Some samples are going to be ignored.'))
+    if batch_size > len(files):
+        print(('Warning: batch size is bigger than the datasets size. '
+               'Setting batch size to datasets size'))
+        batch_size = len(files)
+
+    n_batches = len(files) // batch_size
+    n_used_imgs = n_batches * batch_size
+
+    pred_arr = np.empty((n_used_imgs, dims))
+    for i in tqdm(range(n_batches)):
+        start = i * batch_size
+        end = start + batch_size
+        images = np.array([imread(str(f)).astype(np.float32) for f in files[start:end]])
+
+        if len(images.shape) != 4:
+            images = imread(str(files[start]))
+            images = cv2.cvtColor(images, cv2.COLOR_BGR2GRAY)
+            images = np.array([images.astype(np.float32)])
+
+        images = images.transpose((0, 3, 1, 2))
+        images /= 255
+
+        output, main_program, startup_program = _build_program(model)
+        place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_program)
+
+        fluid.load(main_program, os.path.join(premodel_path, 'paddle_inceptionv3'), exe)
+        pred = exe.run(main_program, feed = {'images': images}, fetch_list = [output])[0]
+
+        pred_arr[start: end] = pred.reshape(end - start, -1)
+
+    return pred_arr
+
+def _calculate_activation_statistics(files, model, premodel_path, batch_size=50, dims=2048, use_gpu=False):
+    act = _get_activations(files, model, batch_size, dims, use_gpu, premodel_path)
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+def _compute_statistics_of_path(path, model, batch_size, dims, use_gpu, premodel_path):
+    if path.endswith('.npz'):
+        f = np.load(path)
+        m, s = f['mu'][:], f['sigma'][:]
+        f.close()
+    else:
+        files = []
+        for root, dirnames, filenames in os.walk(path):
+            for filename in fnmatch.filter(filenames, '*.jpg') or fnmatch.filter(filenames, '*.png'):
+                files.append(os.path.join(root, filename))
+        m, s = _calculate_activation_statistics(files, model, premodel_path, batch_size, dims, use_gpu)
+    return m, s
+
+def calculate_fid_given_paths(paths, batch_size, use_gpu, dims, premodel_path, model=None):
+    assert os.path.exists(premodel_path), 'pretrain_model path {} is not exists! Please download it first'.format(premodel_path)
+    for p in paths:
+        if not os.path.exists(p):
+            raise RuntimeError('Invalid path: %s' % p)
+
+    if model is None:
+        block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+        model = InceptionV3([block_idx])
+
+    m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size, dims, use_gpu, premodel_path)
+    m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size, dims, use_gpu, premodel_path)
+
+    fid_value = _calculate_frechet_distance(m1, s1, m2, s2)
+    return fid_value
+    
+

PaddleCV/gan/metric/inception.py

+#copyright (c) 2020 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.
+
+import math
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+
+__all__ = ['InceptionV3']
+
+
+class InceptionV3:
+    DEFAULT_BLOCK_INDEX = 3
+    BLOCK_INDEX_BY_DIM = {
+        64: 0,   # First max pooling features
+        192: 1,  # Second max pooling featurs
+        768: 2,  # Pre-aux classifier features
+        2048: 3  # Final average pooling features
+    }
+
+    def __init__(self, output_blocks = [DEFAULT_BLOCK_INDEX], resize_input=True, normalize_input=True):
+        self.resize_input = resize_input
+        self.normalize_input = normalize_input
+        self.output_blocks = sorted(output_blocks)
+        self.last_needed_block = max(output_blocks)
+
+        assert self.last_needed_block <= 3, 'Last possible output block index is 3'
+
+    def network(self, x, class_dim=1000, aux_logits=False):
+        if self.resize_input:
+            x = fluid.layers.resize_bilinear(x, out_shape=[299, 299], align_corners=False, align_mode=0)
+
+        if self.normalize_input:
+            x = x * 2 - 1
+
+        out, _, = self.fid_inceptionv3(x, class_dim, aux_logits)
+        return out
+        
+
+    def fid_inceptionv3(self, x, num_classes=1000, aux_logits=False):
+        """ inception v3 model for FID computation
+        """
+        out = []
+        aux = None
+
+        ### block0
+        x = self.conv_bn_layer(x, 32, 3, stride=2, name='Conv2d_1a_3x3')
+        x = self.conv_bn_layer(x, 32, 3, name='Conv2d_2a_3x3')
+        x = self.conv_bn_layer(x, 64, 3, padding=1, name='Conv2d_2b_3x3')
+        x = fluid.layers.pool2d(x, pool_size=3, pool_stride=2, pool_type='max')
+        if 0 in self.output_blocks:
+            out.append(x)
+
+        if self.last_needed_block >= 1:
+            ### block1
+            x = self.conv_bn_layer(x, 80, 1, name='Conv2d_3b_1x1')
+            x = self.conv_bn_layer(x, 192, 3, name='Conv2d_4a_3x3')
+            x = fluid.layers.pool2d(x, pool_size=3, pool_stride=2, pool_type='max')
+            if 1 in self.output_blocks:
+                out.append(x)
+
+        if self.last_needed_block >= 2:
+            ### block2
+            ### Mixed_5b 5c 5d
+            x = self.fid_inceptionA(x, pool_features=32, name='Mixed_5b')
+            x = self.fid_inceptionA(x, pool_features=64, name='Mixed_5c')
+            x = self.fid_inceptionA(x, pool_features=64, name='Mixed_5d')
+
+            ### Mixed_6
+            x = self.inceptionB(x, name='Mixed_6a')
+            x = self.fid_inceptionC(x, c7=128, name='Mixed_6b')
+            x = self.fid_inceptionC(x, c7=160, name='Mixed_6c')
+            x = self.fid_inceptionC(x, c7=160, name='Mixed_6d')
+            x = self.fid_inceptionC(x, c7=192, name='Mixed_6e')
+            if 2 in self.output_blocks:
+                out.append(x)
+
+            if aux_logits:
+                aux = self.inceptionAux(x, num_classes, name='AuxLogits')
+
+        if self.last_needed_block >= 3:
+            ### block3
+            ### Mixed_7
+            x = self.inceptionD(x, name='Mixed_7a')
+            x = self.fid_inceptionE_1(x, name='Mixed_7b')
+            x = self.fid_inceptionE_2(x, name='Mixed_7c')
+
+            x = fluid.layers.pool2d(x, global_pooling=True, pool_type='avg')
+            out.append(x)
+
+            #x = fluid.layers.dropout(x, dropout_prob=0.5)
+            #x = fluid.layers.flatten(x, axis=1)
+            #x = fluid.layers.fc(x, size=num_classes, param_attr=ParamAttr(name='fc.weight'), bias_attr=ParamAttr(name='fc.bias'))
+
+        return out, aux
+
+    def inceptionA(self, x, pool_features, name=None):
+        branch1x1 = self.conv_bn_layer(x, 64, 1, name=name+'.branch1x1')
+
+        branch5x5 = self.conv_bn_layer(x, 48, 1, name=name+'.branch5x5_1')
+        branch5x5 = self.conv_bn_layer(branch5x5, 64, 5, padding=2, name=name+'.branch5x5_2')
+
+        branch3x3dbl = self.conv_bn_layer(x, 64, 1, name=name+'.branch3x3dbl_1')
+        branch3x3dbl = self.conv_bn_layer(branch3x3dbl, 96, 3, padding=1, name=name+'.branch3x3dbl_2')
+        branch3x3dbl = self.conv_bn_layer(branch3x3dbl, 96, 3, padding=1, name=name+'.branch3x3dbl_3')
+
+        branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=1, pool_padding=1, pool_type='avg')
+        branch_pool = self.conv_bn_layer(branch_pool, pool_features, 1, name=name+'.branch_pool')
+
+        return fluid.layers.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
+       
+
+    def inceptionB(self, x, name=None):
+        branch3x3 = self.conv_bn_layer(x, 384, 3, stride=2, name=name+'.branch3x3')
+ 
+        branch3x3dbl = self.conv_bn_layer(x, 64, 1, name=name+'.branch3x3dbl_1')
+        branch3x3dbl = self.conv_bn_layer(branch3x3dbl, 96, 3, padding=1, name=name+'.branch3x3dbl_2')
+        branch3x3dbl = self.conv_bn_layer(branch3x3dbl, 96, 3, stride=2, name=name+'.branch3x3dbl_3')
+
+        branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=2, pool_type='max')
+
+        return fluid.layers.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
+
+    def inceptionC(self, x, c7, name=None):
+        branch1x1 = self.conv_bn_layer(x, 192, 1, name=name+'.branch1x1')
+
+        branch7x7 = self.conv_bn_layer(x, c7, 1, name=name+'.branch7x7_1')
+        branch7x7 = self.conv_bn_layer(branch7x7, c7, (1, 7), padding=(0, 3), name=name+'.branch7x7_2')
+        branch7x7 = self.conv_bn_layer(branch7x7, 192, (7, 1), padding=(3, 0), name=name+'.branch7x7_3')
+
+        branch7x7dbl = self.conv_bn_layer(x, c7, 1, name=name+'.branch7x7dbl_1')
+        branch7x7dbl = self.conv_bn_layer(branch7x7dbl, c7, (7, 1), padding=(3, 0), name=name+'.branch7x7dbl_2')
+        branch7x7dbl = self.conv_bn_layer(branch7x7dbl, c7, (1, 7), padding=(0, 3), name=name+'.branch7x7dbl_3')
+        branch7x7dbl = self.conv_bn_layer(branch7x7dbl, c7, (7, 1), padding=(3, 0), name=name+'.branch7x7dbl_4')
+        branch7x7dbl = self.conv_bn_layer(branch7x7dbl, 192, (1, 7), padding=(0, 3), name=name+'.branch7x7dbl_5')
+
+        branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=1, pool_padding=1, pool_type='avg')
+        branch_pool = self.conv_bn_layer(branch_pool, 192, 1, name=name+'.branch_pool')
+
+        return fluid.layers.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
+
+    def inceptionD(self, x, name=None):
+        branch3x3 = self.conv_bn_layer(x, 192, 1, name=name+'.branch3x3_1')
+        branch3x3 = self.conv_bn_layer(branch3x3, 320, 3, stride=2, name=name+'.branch3x3_2')
+
+        branch7x7x3 = self.conv_bn_layer(x, 192, 1, name=name+'.branch7x7x3_1')
+        branch7x7x3 = self.conv_bn_layer(branch7x7x3, 192, (1, 7), padding=(0, 3), name=name+'.branch7x7x3_2')
+        branch7x7x3 = self.conv_bn_layer(branch7x7x3, 192, (7, 1), padding=(3, 0), name=name+'.branch7x7x3_3')
+        branch7x7x3 = self.conv_bn_layer(branch7x7x3, 192, 3, stride=2, name=name+'.branch7x7x3_4')
+
+        branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=2, pool_type='max')
+        return fluid.layers.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
+
+    def inceptionE(self, x, name=None):
+        branch1x1 = self.conv_bn_layer(x, 320, 1, name=name+'.branch1x1')
+
+        branch3x3 = self.conv_bn_layer(x, 384, 1, name=name+'.branch3x3_1')
+        branch3x3_2a = self.conv_bn_layer(branch3x3, 384, (1, 3), padding=(0, 1), name=name+'.branch3x3_2a')
+        branch3x3_2b = self.conv_bn_layer(branch3x3, 384, (3, 1), padding=(1, 0), name=name+'.branch3x3_2b')
+        branch3x3 = fluid.layers.concat([branch3x3_2a, branch3x3_2b], axis=1)
+
+        branch3x3dbl = self.conv_bn_layer(x, 448, 1, name=name+'.branch3x3dbl_1')
+        branch3x3dbl = self.conv_bn_layer(branch3x3dbl, 384, 3, padding=1, name=name+'.branch3x3dbl_2')
+        branch3x3dbl_3a = self.conv_bn_layer(branch3x3dbl, 384, (1, 3), padding=(0, 1), name=name+'.branch3x3dbl_3a')
+        branch3x3dbl_3b = self.conv_bn_layer(branch3x3dbl, 384, (3, 1), padding=(1, 0), name=name+'.branch3x3dbl_3b')
+        branch3x3dbl = fluid.layers.concat([branch3x3dbl_3a, branch3x3dbl_3b], axis=1)
+
+        branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=1, pool_padding=1, pool_type='avg')
+        branch_pool = self.conv_bn_layer(branch_pool, 192, 1, name=name+'.branch_pool')
+
+        return fluid.layers.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
+
+    def inceptionAux(self, x, num_classes, name=None):
+        x = fluid.layers.pool2d(x, pool_size=5, pool_stride=3, pool_type='avg')
+        x = self.conv_bn_layer(x, 128, 1, name=name+'.conv0')
+        x = self.conv_bn_layer(x, 768, 5, name=name+'.conv1')
+        x = fluid.layers.pool2d(x, global_pooling=True, pool_type='avg')
+        x = fluid.layers.flatten(x, axis=1)
+        x = fluid.layers.fc(x, size=num_classes)
+        return x
+
+
+    def fid_inceptionA(self, x, pool_features, name=None):
+        """ FID block in inception v3
+        """
+        branch1x1 = self.conv_bn_layer(x, 64, 1, name=name+'.branch1x1')
+
+        branch5x5 = self.conv_bn_layer(x, 48, 1, name=name+'.branch5x5_1')
+        branch5x5 = self.conv_bn_layer(branch5x5, 64, 5, padding=2, name=name+'.branch5x5_2')
+
+        branch3x3dbl = self.conv_bn_layer(x, 64, 1, name=name+'.branch3x3dbl_1')
+        branch3x3dbl = self.conv_bn_layer(branch3x3dbl, 96, 3, padding=1, name=name+'.branch3x3dbl_2')
+        branch3x3dbl = self.conv_bn_layer(branch3x3dbl, 96, 3, padding=1, name=name+'.branch3x3dbl_3')
+
+        branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=1, pool_padding=1, exclusive=True, pool_type='avg')
+        branch_pool = self.conv_bn_layer(branch_pool, pool_features, 1, name=name+'.branch_pool')
+
+        return fluid.layers.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
+
+    def fid_inceptionC(self, x, c7, name=None):
+        """ FID block in inception v3
+        """
+        branch1x1 = self.conv_bn_layer(x, 192, 1, name=name+'.branch1x1')
+
+        branch7x7 = self.conv_bn_layer(x, c7, 1, name=name+'.branch7x7_1')
+        branch7x7 = self.conv_bn_layer(branch7x7, c7, (1, 7), padding=(0, 3), name=name+'.branch7x7_2')
+        branch7x7 = self.conv_bn_layer(branch7x7, 192, (7, 1), padding=(3, 0), name=name+'.branch7x7_3')
+
+        branch7x7dbl = self.conv_bn_layer(x, c7, 1, name=name+'.branch7x7dbl_1')
+        branch7x7dbl = self.conv_bn_layer(branch7x7dbl, c7, (7, 1), padding=(3, 0), name=name+'.branch7x7dbl_2')
+        branch7x7dbl = self.conv_bn_layer(branch7x7dbl, c7, (1, 7), padding=(0, 3), name=name+'.branch7x7dbl_3')
+        branch7x7dbl = self.conv_bn_layer(branch7x7dbl, c7, (7, 1), padding=(3, 0), name=name+'.branch7x7dbl_4')
+        branch7x7dbl = self.conv_bn_layer(branch7x7dbl, 192, (1, 7), padding=(0, 3), name=name+'.branch7x7dbl_5')
+
+        branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=1, pool_padding=1, exclusive=True, pool_type='avg')
+        branch_pool = self.conv_bn_layer(branch_pool, 192, 1, name=name+'.branch_pool')
+
+        return fluid.layers.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
+
+    def fid_inceptionE_1(self, x, name=None):
+        """ FID block in inception v3
+        """
+        branch1x1 = self.conv_bn_layer(x, 320, 1, name=name+'.branch1x1')
+
+        branch3x3 = self.conv_bn_layer(x, 384, 1, name=name+'.branch3x3_1')
+        branch3x3_2a = self.conv_bn_layer(branch3x3, 384, (1, 3), padding=(0, 1), name=name+'.branch3x3_2a')
+        branch3x3_2b = self.conv_bn_layer(branch3x3, 384, (3, 1), padding=(1, 0), name=name+'.branch3x3_2b')
+        branch3x3 = fluid.layers.concat([branch3x3_2a, branch3x3_2b], axis=1)
+
+        branch3x3dbl = self.conv_bn_layer(x, 448, 1, name=name+'.branch3x3dbl_1')
+        branch3x3dbl = self.conv_bn_layer(branch3x3dbl, 384, 3, padding=1, name=name+'.branch3x3dbl_2')
+        branch3x3dbl_3a = self.conv_bn_layer(branch3x3dbl, 384, (1, 3), padding=(0, 1), name=name+'.branch3x3dbl_3a')
+        branch3x3dbl_3b = self.conv_bn_layer(branch3x3dbl, 384, (3, 1), padding=(1, 0), name=name+'.branch3x3dbl_3b')
+        branch3x3dbl = fluid.layers.concat([branch3x3dbl_3a, branch3x3dbl_3b], axis=1)
+
+        branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=1, pool_padding=1, exclusive=True, pool_type='avg')
+        branch_pool = self.conv_bn_layer(branch_pool, 192, 1, name=name+'.branch_pool')
+
+        return fluid.layers.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
+
+    def fid_inceptionE_2(self, x, name=None):
+        """ FID block in inception v3
+        """
+        branch1x1 = self.conv_bn_layer(x, 320, 1, name=name+'.branch1x1')
+
+        branch3x3 = self.conv_bn_layer(x, 384, 1, name=name+'.branch3x3_1')
+        branch3x3_2a = self.conv_bn_layer(branch3x3, 384, (1, 3), padding=(0, 1), name=name+'.branch3x3_2a')
+        branch3x3_2b = self.conv_bn_layer(branch3x3, 384, (3, 1), padding=(1, 0), name=name+'.branch3x3_2b')
+        branch3x3 = fluid.layers.concat([branch3x3_2a, branch3x3_2b], axis=1)
+
+        branch3x3dbl = self.conv_bn_layer(x, 448, 1, name=name+'.branch3x3dbl_1')
+        branch3x3dbl = self.conv_bn_layer(branch3x3dbl, 384, 3, padding=1, name=name+'.branch3x3dbl_2')
+        branch3x3dbl_3a = self.conv_bn_layer(branch3x3dbl, 384, (1, 3), padding=(0, 1), name=name+'.branch3x3dbl_3a')
+        branch3x3dbl_3b = self.conv_bn_layer(branch3x3dbl, 384, (3, 1), padding=(1, 0), name=name+'.branch3x3dbl_3b')
+        branch3x3dbl = fluid.layers.concat([branch3x3dbl_3a, branch3x3dbl_3b], axis=1)
+        
+        ### same with paper
+        branch_pool = fluid.layers.pool2d(x, pool_size=3, pool_stride=1, pool_padding=1, pool_type='max')
+        branch_pool = self.conv_bn_layer(branch_pool, 192, 1, name=name+'.branch_pool')
+
+        return fluid.layers.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
+
+    def conv_bn_layer(self,
+                      data,
+                      num_filters,
+                      filter_size,
+                      stride=1,
+                      padding=0,
+                      groups=1,
+                      act='relu',
+                      name=None):
+        conv = fluid.layers.conv2d(
+            input=data,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + ".conv.weight"),
+            bias_attr=False,
+            name=name)
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            epsilon=0.001,
+            name=name+'.bn',
+            param_attr=ParamAttr(name=name + ".bn.weight"),
+            bias_attr=ParamAttr(name=name + ".bn.bias"),
+            moving_mean_name=name + '.bn.running_mean',
+            moving_variance_name=name + '.bn.running_var')

PaddleCV/gan/network/base_network.py

@@ -42,7 +42,7 @@ def norm_layer(input,
     if norm_type == 'batch_norm':
         if affine == True:
             param_attr = fluid.ParamAttr(
-                name=name + '_w', initializer=fluid.initializer.Constant(1.0))
+                name=name + '_w', initializer=fluid.initializer.Normal(loc=1.0, scale=0.02))
             bias_attr = fluid.ParamAttr(
                 name=name + '_b',
                 initializer=fluid.initializer.Constant(value=0.0))

PaddleCV/gan/requirements.txt

 numpy >= 1.15.0
+opencv-python