add dygraph fid score computation

ppgan/metric/compute_fid.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 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
+from paddle.fluid.dygraph.base import to_variable
+
+
+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 _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
+
+        images = to_variable(images)
+        param_dict, _ = fluid.load_dygraph(premodel_path)
+        model.set_dict(param_dict)
+        model.eval()
+        pred = model(images)[0][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
+
+        images = to_variable(images)
+        param_dict, _ = fluid.load_dygraph(premodel_path)
+        model.set_dict(param_dict)
+        model.eval()
+
+        pred = model(images)[0][0].numpy()
+
+        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], class_dim=1008)
+
+    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
+
+
+if __name__ == '__main__':
+    with fluid.dygraph.guard():
+        fid_value = calculate_fid_given_paths(
+            ('/workspace/color/fid_test/real',
+             '/workspace/color/fid_test/fake'), 1, True, 2048,
+            'pretrained/params_inceptionV3/compare.pdparams')
+        print('FID: ', fid_value)

ppgan/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
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.base import to_variable
+
+__all__ = ['InceptionV3']
+
+
+class InceptionV3(fluid.dygraph.Layer):
+    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],
+                 class_dim=1000,
+                 aux_logits=False,
+                 resize_input=True,
+                 normalize_input=True):
+        super(InceptionV3, self).__init__()
+        self.resize_input = resize_input
+        self.normalize_input = normalize_input
+        self.output_blocks = sorted(output_blocks)
+        self.last_needed_block = max(output_blocks)
+        self.class_dim = class_dim
+        self.aux_logits = aux_logits
+
+        assert self.last_needed_block <= 3, 'Last possible output block index is 3'
+        self.blocks = []
+
+        self.Conv2d_1a_3x3 = ConvBNLayer(3,
+                                         32,
+                                         3,
+                                         stride=2,
+                                         name='Conv2d_1a_3x3')
+        self.Conv2d_2a_3x3 = ConvBNLayer(32, 32, 3, name='Conv2d_2a_3x3')
+        self.Conv2d_2b_3x3 = ConvBNLayer(32,
+                                         64,
+                                         3,
+                                         padding=1,
+                                         name='Conv2d_2b_3x3')
+        self.maxpool1 = Pool2D(pool_size=3, pool_stride=2, pool_type='max')
+
+        block0 = [
+            self.Conv2d_1a_3x3, self.Conv2d_2a_3x3, self.Conv2d_2b_3x3,
+            self.maxpool1
+        ]
+        self.blocks.append(fluid.dygraph.Sequential(*block0))
+        ### block1
+
+        if self.last_needed_block >= 1:
+            self.Conv2d_3b_1x1 = ConvBNLayer(64, 80, 1, name='Conv2d_3b_1x1')
+            self.Conv2d_4a_3x3 = ConvBNLayer(80, 192, 3, name='Conv2d_4a_3x3')
+            self.maxpool2 = Pool2D(pool_size=3, pool_stride=2, pool_type='max')
+            block1 = [self.Conv2d_3b_1x1, self.Conv2d_4a_3x3, self.maxpool2]
+            self.blocks.append(fluid.dygraph.Sequential(*block1))
+
+        ### block2
+        ### Mixed_5b 5c 5d
+        if self.last_needed_block >= 2:
+            self.Mixed_5b = Fid_inceptionA(192,
+                                           pool_features=32,
+                                           name='Mixed_5b')
+            self.Mixed_5c = Fid_inceptionA(256,
+                                           pool_features=64,
+                                           name='Mixed_5c')
+            self.Mixed_5d = Fid_inceptionA(288,
+                                           pool_features=64,
+                                           name='Mixed_5d')
+
+            ### Mixed_6
+            self.Mixed_6a = InceptionB(288, name='Mixed_6a')
+            self.Mixed_6b = Fid_inceptionC(768, c7=128, name='Mixed_6b')
+            self.Mixed_6c = Fid_inceptionC(768, c7=160, name='Mixed_6c')
+            self.Mixed_6d = Fid_inceptionC(768, c7=160, name='Mixed_6d')
+            self.Mixed_6e = Fid_inceptionC(768, c7=192, name='Mixed_6e')
+
+            block2 = [
+                self.Mixed_5b, self.Mixed_5c, self.Mixed_5d, self.Mixed_6a,
+                self.Mixed_6b, self.Mixed_6c, self.Mixed_6d, self.Mixed_6e
+            ]
+            self.blocks.append(fluid.dygraph.Sequential(*block2))
+
+        if self.aux_logits:
+            self.AuxLogits = InceptionAux(768, self.class_dim, name='AuxLogits')
+        ### block3
+        ### Mixed_7
+        if self.last_needed_block >= 3:
+            self.Mixed_7a = InceptionD(768, name='Mixed_7a')
+            self.Mixed_7b = Fid_inceptionE_1(1280, name='Mixed_7b')
+            self.Mixed_7c = Fid_inceptionE_2(2048, name='Mixed_7c')
+            self.avgpool = Pool2D(global_pooling=True, pool_type='avg')
+
+            block3 = [self.Mixed_7a, self.Mixed_7b, self.Mixed_7c, self.avgpool]
+            self.blocks.append(fluid.dygraph.Sequential(*block3))
+
+    def forward(self, x):
+        out = []
+        aux = None
+        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
+
+        for idx, block in enumerate(self.blocks):
+            x = block(x)
+            if self.aux_logits and (idx == 2):
+                aux = self.AuxLogits(x)
+            if idx in self.output_blocks:
+                out.append(x)
+            if idx == self.last_needed_block:
+                break
+
+        return out, aux
+
+
+class InceptionA(fluid.dygraph.Layer):
+    def __init__(self, in_channels, pool_features, name=None):
+        super(InceptionA, self).__init__()
+        self.branch1x1 = ConvBNLayer(in_channels,
+                                     64,
+                                     1,
+                                     name=name + '.branch1x1')
+
+        self.branch5x5_1 = ConvBNLayer(in_channels,
+                                       48,
+                                       1,
+                                       name=name + '.branch5x5_1')
+        self.branch5x5_2 = ConvBNLayer(48,
+                                       64,
+                                       5,
+                                       padding=2,
+                                       name=name + '.branch5x5_2')
+
+        self.branch3x3dbl_1 = ConvBNLayer(in_channels,
+                                          64,
+                                          1,
+                                          name=name + '.branch3x3dbl_1')
+        self.branch3x3dbl_2 = ConvBNLayer(64,
+                                          96,
+                                          3,
+                                          padding=1,
+                                          name=name + '.branch3x3dbl_2')
+        self.branch3x3dbl_3 = ConvBNLayer(96,
+                                          96,
+                                          3,
+                                          padding=1,
+                                          name=name + '.branch3x3dbl_3')
+
+        self.branch_pool0 = Pool2D(pool_size=3,
+                                   pool_stride=1,
+                                   pool_padding=1,
+                                   exclusive=True,
+                                   pool_type='avg')
+        self.branch_pool = ConvBNLayer(in_channels,
+                                       pool_features,
+                                       1,
+                                       name=name + '.branch_pool')
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch5x5 = self.branch5x5_1(x)
+        branch5x5 = self.branch5x5_2(branch5x5)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        branch_pool = self.branch_pool0(x)
+        branch_pool = self.branch_pool(branch_pool)
+        return fluid.layers.concat(
+            [branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
+
+
+class InceptionB(fluid.dygraph.Layer):
+    def __init__(self, in_channels, name=None):
+        super(InceptionB, self).__init__()
+        self.branch3x3 = ConvBNLayer(in_channels,
+                                     384,
+                                     3,
+                                     stride=2,
+                                     name=name + '.branch3x3')
+
+        self.branch3x3dbl_1 = ConvBNLayer(in_channels,
+                                          64,
+                                          1,
+                                          name=name + '.branch3x3dbl_1')
+        self.branch3x3dbl_2 = ConvBNLayer(64,
+                                          96,
+                                          3,
+                                          padding=1,
+                                          name=name + '.branch3x3dbl_2')
+        self.branch3x3dbl_3 = ConvBNLayer(96,
+                                          96,
+                                          3,
+                                          stride=2,
+                                          name=name + '.branch3x3dbl_3')
+
+        self.branch_pool = Pool2D(pool_size=3, pool_stride=2, pool_type='max')
+
+    def forward(self, x):
+        branch3x3 = self.branch3x3(x)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        branch_pool = self.branch_pool(x)
+        return fluid.layers.concat([branch3x3, branch3x3dbl, branch_pool],
+                                   axis=1)
+
+
+class InceptionC(fluid.dygraph.Layer):
+    def __init__(self, in_channels, c7, name=None):
+        super(InceptionC, self).__init__()
+        self.branch1x1 = ConvBNLayer(in_channels,
+                                     192,
+                                     1,
+                                     name=name + '.branch1x1')
+
+        self.branch7x7_1 = ConvBNLayer(in_channels,
+                                       c7,
+                                       1,
+                                       name=name + '.branch7x7_1')
+        self.branch7x7_2 = ConvBNLayer(c7,
+                                       c7, (1, 7),
+                                       padding=(0, 3),
+                                       name=name + '.branch7x7_2')
+        self.branch7x7_3 = ConvBNLayer(c7,
+                                       192, (7, 1),
+                                       padding=(3, 0),
+                                       name=name + '.branch7x7_3')
+
+        self.branch7x7dbl_1 = ConvBNLayer(in_channels,
+                                          c7,
+                                          1,
+                                          name=name + '.branch7x7dbl_1')
+        self.branch7x7dbl_2 = ConvBNLayer(c7,
+                                          c7, (7, 1),
+                                          padding=(3, 0),
+                                          name=name + '.branch7x7dbl_2')
+        self.branch7x7dbl_3 = ConvBNLayer(c7,
+                                          c7, (1, 7),
+                                          padding=(0, 3),
+                                          name=name + '.branch7x7dbl_3')
+        self.branch7x7dbl_4 = ConvBNLayer(c7,
+                                          c7, (7, 1),
+                                          padding=(3, 0),
+                                          name=name + '.branch7x7dbl_4')
+        self.branch7x7dbl_5 = ConvBNLayer(c7,
+                                          192, (1, 7),
+                                          padding=(0, 3),
+                                          name=name + '.branch7x7dbl_5')
+
+        self.branch_pool0 = Pool2D(pool_size=3,
+                                   pool_stride=1,
+                                   pool_padding=1,
+                                   exclusive=True,
+                                   pool_type='avg')
+        self.branch_pool = ConvBNLayer(in_channels,
+                                       192,
+                                       1,
+                                       name=name + '.branch_pool')
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch7x7 = self.branch7x7_1(x)
+        branch7x7 = self.branch7x7_2(branch7x7)
+        branch7x7 = self.branch7x7_3(branch7x7)
+
+        branch7x7dbl = self.branch7x7dbl_1(x)
+        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
+
+        branch_pool = self.branch_pool0(x)
+        branch_pool = self.branch_pool(branch_pool)
+
+        return fluid.layers.concat(
+            [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
+
+
+class InceptionD(fluid.dygraph.Layer):
+    def __init__(self, in_channels, name=None):
+        super(InceptionD, self).__init__()
+        self.branch3x3_1 = ConvBNLayer(in_channels,
+                                       192,
+                                       1,
+                                       name=name + '.branch3x3_1')
+        self.branch3x3_2 = ConvBNLayer(192,
+                                       320,
+                                       3,
+                                       stride=2,
+                                       name=name + '.branch3x3_2')
+
+        self.branch7x7x3_1 = ConvBNLayer(in_channels,
+                                         192,
+                                         1,
+                                         name=name + '.branch7x7x3_1')
+        self.branch7x7x3_2 = ConvBNLayer(192,
+                                         192, (1, 7),
+                                         padding=(0, 3),
+                                         name=name + '.branch7x7x3_2')
+        self.branch7x7x3_3 = ConvBNLayer(192,
+                                         192, (7, 1),
+                                         padding=(3, 0),
+                                         name=name + '.branch7x7x3_3')
+        self.branch7x7x3_4 = ConvBNLayer(192,
+                                         192,
+                                         3,
+                                         stride=2,
+                                         name=name + '.branch7x7x3_4')
+
+        self.branch_pool = Pool2D(pool_size=3, pool_stride=2, pool_type='max')
+
+    def forward(self, x):
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = self.branch3x3_2(branch3x3)
+
+        branch7x7x3 = self.branch7x7x3_1(x)
+        branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
+        branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
+        branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
+
+        branch_pool = self.branch_pool(x)
+
+        return fluid.layers.concat([branch3x3, branch7x7x3, branch_pool],
+                                   axis=1)
+
+
+class InceptionE(fluid.dygraph.Layer):
+    def __init__(self, in_channels, name=None):
+        super(InceptionE, self).__init__()
+        self.branch1x1 = ConvBNLayer(in_channels,
+                                     320,
+                                     1,
+                                     name=name + '.branch1x1')
+
+        self.branch3x3_1 = ConvBNLayer(in_channels,
+                                       384,
+                                       1,
+                                       name=name + '.branch3x3_1')
+        self.branch3x3_2a = ConvBNLayer(384,
+                                        384, (1, 3),
+                                        padding=(0, 1),
+                                        name=name + '.branch3x3_2a')
+        self.branch3x3_2b = ConvBNLayer(384,
+                                        384, (3, 1),
+                                        padding=(1, 0),
+                                        name=name + '.branch3x3_2b')
+
+        self.branch3x3dbl_1 = ConvBNLayer(in_channels,
+                                          448,
+                                          1,
+                                          name=name + '.branch3x3dbl_1')
+        self.branch3x3dbl_2 = ConvBNLayer(448,
+                                          384,
+                                          3,
+                                          padding=1,
+                                          name=name + '.branch3x3dbl_2')
+        self.branch3x3dbl_3a = ConvBNLayer(384,
+                                           384, (1, 3),
+                                           padding=(0, 1),
+                                           name=name + '.branch3x3dbl_3a')
+        self.branch3x3dbl_3b = ConvBNLayer(384,
+                                           384, (3, 1),
+                                           padding=(1, 0),
+                                           name=name + '.branch3x3dbl_3b')
+
+        self.branch_pool0 = Pool2D(pool_size=3,
+                                   pool_stride=1,
+                                   pool_padding=1,
+                                   exclusive=True,
+                                   pool_type='avg')
+        self.branch_pool = ConvBNLayer(in_channels,
+                                       192,
+                                       1,
+                                       name=name + '.branch_pool')
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+        branch3x3_1 = self.branch3x3_1(x)
+        branch3x3_2a = self.branch3x3_2a(branch3x3_1)
+        branch3x3_2b = self.branch3x3_2b(branch3x3_1)
+        branch3x3 = fluid.layers.concat([branch3x3_2a, branch3x3_2b], axis=1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl_3a = self.branch3x3dbl_3a(branch3x3dbl)
+        branch3x3dbl_3b = self.branch3x3dbl_3b(branch3x3dbl)
+        branch3x3dbl = fluid.layers.concat([branch3x3dbl_3a, branch3x3dbl_3b],
+                                           axis=1)
+
+        branch_pool = self.branch_pool0(x)
+        branch_pool = self.branch_pool(branch_pool)
+
+        return fluid.layers.concat(
+            [branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
+
+
+class InceptionAux(fluid.dygraph.Layer):
+    def __init__(self, in_channels, num_classes, name=None):
+        super(InceptionAux, self).__init__()
+        self.num_classes = num_classes
+        self.pool0 = Pool2D(pool_size=5, pool_stride=3, pool_type='avg')
+        self.conv0 = ConvBNLayer(in_channels, 128, 1, name=name + '.conv0')
+        self.conv1 = ConvBNLayer(128, 768, 5, name=name + '.conv1')
+        self.pool1 = Pool2D(global_pooling=True, pool_type='avg')
+
+    def forward(self, x):
+        x = self.pool0(x)
+        x = self.conv0(x)
+        x = self.conv1(x)
+        x = self.pool1(x)
+        x = fluid.layers.flatten(x, axis=1)
+        x = fluid.layers.fc(x, size=self.num_classes)
+        return x
+
+
+class Fid_inceptionA(fluid.dygraph.Layer):
+    """ FID block in inception v3
+    """
+    def __init__(self, in_channels, pool_features, name=None):
+        super(Fid_inceptionA, self).__init__()
+        self.branch1x1 = ConvBNLayer(in_channels,
+                                     64,
+                                     1,
+                                     name=name + '.branch1x1')
+
+        self.branch5x5_1 = ConvBNLayer(in_channels,
+                                       48,
+                                       1,
+                                       name=name + '.branch5x5_1')
+        self.branch5x5_2 = ConvBNLayer(48,
+                                       64,
+                                       5,
+                                       padding=2,
+                                       name=name + '.branch5x5_2')
+
+        self.branch3x3dbl_1 = ConvBNLayer(in_channels,
+                                          64,
+                                          1,
+                                          name=name + '.branch3x3dbl_1')
+        self.branch3x3dbl_2 = ConvBNLayer(64,
+                                          96,
+                                          3,
+                                          padding=1,
+                                          name=name + '.branch3x3dbl_2')
+        self.branch3x3dbl_3 = ConvBNLayer(96,
+                                          96,
+                                          3,
+                                          padding=1,
+                                          name=name + '.branch3x3dbl_3')
+
+        self.branch_pool0 = Pool2D(pool_size=3,
+                                   pool_stride=1,
+                                   pool_padding=1,
+                                   exclusive=True,
+                                   pool_type='avg')
+        self.branch_pool = ConvBNLayer(in_channels,
+                                       pool_features,
+                                       1,
+                                       name=name + '.branch_pool')
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch5x5 = self.branch5x5_1(x)
+        branch5x5 = self.branch5x5_2(branch5x5)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        branch_pool = self.branch_pool0(x)
+        branch_pool = self.branch_pool(branch_pool)
+        return fluid.layers.concat(
+            [branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
+
+
+class Fid_inceptionC(fluid.dygraph.Layer):
+    """ FID block in inception v3
+    """
+    def __init__(self, in_channels, c7, name=None):
+        super(Fid_inceptionC, self).__init__()
+        self.branch1x1 = ConvBNLayer(in_channels,
+                                     192,
+                                     1,
+                                     name=name + '.branch1x1')
+
+        self.branch7x7_1 = ConvBNLayer(in_channels,
+                                       c7,
+                                       1,
+                                       name=name + '.branch7x7_1')
+        self.branch7x7_2 = ConvBNLayer(c7,
+                                       c7, (1, 7),
+                                       padding=(0, 3),
+                                       name=name + '.branch7x7_2')
+        self.branch7x7_3 = ConvBNLayer(c7,
+                                       192, (7, 1),
+                                       padding=(3, 0),
+                                       name=name + '.branch7x7_3')
+
+        self.branch7x7dbl_1 = ConvBNLayer(in_channels,
+                                          c7,
+                                          1,
+                                          name=name + '.branch7x7dbl_1')
+        self.branch7x7dbl_2 = ConvBNLayer(c7,
+                                          c7, (7, 1),
+                                          padding=(3, 0),
+                                          name=name + '.branch7x7dbl_2')
+        self.branch7x7dbl_3 = ConvBNLayer(c7,
+                                          c7, (1, 7),
+                                          padding=(0, 3),
+                                          name=name + '.branch7x7dbl_3')
+        self.branch7x7dbl_4 = ConvBNLayer(c7,
+                                          c7, (7, 1),
+                                          padding=(3, 0),
+                                          name=name + '.branch7x7dbl_4')
+        self.branch7x7dbl_5 = ConvBNLayer(c7,
+                                          192, (1, 7),
+                                          padding=(0, 3),
+                                          name=name + '.branch7x7dbl_5')
+
+        self.branch_pool0 = Pool2D(pool_size=3,
+                                   pool_stride=1,
+                                   pool_padding=1,
+                                   exclusive=True,
+                                   pool_type='avg')
+        self.branch_pool = ConvBNLayer(in_channels,
+                                       192,
+                                       1,
+                                       name=name + '.branch_pool')
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch7x7 = self.branch7x7_1(x)
+        branch7x7 = self.branch7x7_2(branch7x7)
+        branch7x7 = self.branch7x7_3(branch7x7)
+
+        branch7x7dbl = self.branch7x7dbl_1(x)
+        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
+
+        branch_pool = self.branch_pool0(x)
+        branch_pool = self.branch_pool(branch_pool)
+
+        return fluid.layers.concat(
+            [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
+
+
+class Fid_inceptionE_1(fluid.dygraph.Layer):
+    """ FID block in inception v3
+        """
+    def __init__(self, in_channels, name=None):
+        super(Fid_inceptionE_1, self).__init__()
+        self.branch1x1 = ConvBNLayer(in_channels,
+                                     320,
+                                     1,
+                                     name=name + '.branch1x1')
+
+        self.branch3x3_1 = ConvBNLayer(in_channels,
+                                       384,
+                                       1,
+                                       name=name + '.branch3x3_1')
+        self.branch3x3_2a = ConvBNLayer(384,
+                                        384, (1, 3),
+                                        padding=(0, 1),
+                                        name=name + '.branch3x3_2a')
+        self.branch3x3_2b = ConvBNLayer(384,
+                                        384, (3, 1),
+                                        padding=(1, 0),
+                                        name=name + '.branch3x3_2b')
+
+        self.branch3x3dbl_1 = ConvBNLayer(in_channels,
+                                          448,
+                                          1,
+                                          name=name + '.branch3x3dbl_1')
+        self.branch3x3dbl_2 = ConvBNLayer(448,
+                                          384,
+                                          3,
+                                          padding=1,
+                                          name=name + '.branch3x3dbl_2')
+        self.branch3x3dbl_3a = ConvBNLayer(384,
+                                           384, (1, 3),
+                                           padding=(0, 1),
+                                           name=name + '.branch3x3dbl_3a')
+        self.branch3x3dbl_3b = ConvBNLayer(384,
+                                           384, (3, 1),
+                                           padding=(1, 0),
+                                           name=name + '.branch3x3dbl_3b')
+
+        self.branch_pool0 = Pool2D(pool_size=3,
+                                   pool_stride=1,
+                                   pool_padding=1,
+                                   exclusive=True,
+                                   pool_type='avg')
+        self.branch_pool = ConvBNLayer(in_channels,
+                                       192,
+                                       1,
+                                       name=name + '.branch_pool')
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+        branch3x3_1 = self.branch3x3_1(x)
+        branch3x3_2a = self.branch3x3_2a(branch3x3_1)
+        branch3x3_2b = self.branch3x3_2b(branch3x3_1)
+        branch3x3 = fluid.layers.concat([branch3x3_2a, branch3x3_2b], axis=1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl_3a = self.branch3x3dbl_3a(branch3x3dbl)
+        branch3x3dbl_3b = self.branch3x3dbl_3b(branch3x3dbl)
+        branch3x3dbl = fluid.layers.concat([branch3x3dbl_3a, branch3x3dbl_3b],
+                                           axis=1)
+
+        branch_pool = self.branch_pool0(x)
+        branch_pool = self.branch_pool(branch_pool)
+
+        return fluid.layers.concat(
+            [branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
+
+
+class Fid_inceptionE_2(fluid.dygraph.Layer):
+    """ FID block in inception v3
+    """
+    def __init__(self, in_channels, name=None):
+        super(Fid_inceptionE_2, self).__init__()
+        self.branch1x1 = ConvBNLayer(in_channels,
+                                     320,
+                                     1,
+                                     name=name + '.branch1x1')
+
+        self.branch3x3_1 = ConvBNLayer(in_channels,
+                                       384,
+                                       1,
+                                       name=name + '.branch3x3_1')
+        self.branch3x3_2a = ConvBNLayer(384,
+                                        384, (1, 3),
+                                        padding=(0, 1),
+                                        name=name + '.branch3x3_2a')
+        self.branch3x3_2b = ConvBNLayer(384,
+                                        384, (3, 1),
+                                        padding=(1, 0),
+                                        name=name + '.branch3x3_2b')
+
+        self.branch3x3dbl_1 = ConvBNLayer(in_channels,
+                                          448,
+                                          1,
+                                          name=name + '.branch3x3dbl_1')
+        self.branch3x3dbl_2 = ConvBNLayer(448,
+                                          384,
+                                          3,
+                                          padding=1,
+                                          name=name + '.branch3x3dbl_2')
+        self.branch3x3dbl_3a = ConvBNLayer(384,
+                                           384, (1, 3),
+                                           padding=(0, 1),
+                                           name=name + '.branch3x3dbl_3a')
+        self.branch3x3dbl_3b = ConvBNLayer(384,
+                                           384, (3, 1),
+                                           padding=(1, 0),
+                                           name=name + '.branch3x3dbl_3b')
+        ### same with paper
+        self.branch_pool0 = Pool2D(pool_size=3,
+                                   pool_stride=1,
+                                   pool_padding=1,
+                                   pool_type='max')
+        self.branch_pool = ConvBNLayer(in_channels,
+                                       192,
+                                       1,
+                                       name=name + '.branch_pool')
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+        branch3x3_1 = self.branch3x3_1(x)
+        branch3x3_2a = self.branch3x3_2a(branch3x3_1)
+        branch3x3_2b = self.branch3x3_2b(branch3x3_1)
+        branch3x3 = fluid.layers.concat([branch3x3_2a, branch3x3_2b], axis=1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl_3a = self.branch3x3dbl_3a(branch3x3dbl)
+        branch3x3dbl_3b = self.branch3x3dbl_3b(branch3x3dbl)
+        branch3x3dbl = fluid.layers.concat([branch3x3dbl_3a, branch3x3dbl_3b],
+                                           axis=1)
+
+        branch_pool = self.branch_pool0(x)
+        branch_pool = self.branch_pool(branch_pool)
+
+        return fluid.layers.concat(
+            [branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 in_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 padding=0,
+                 groups=1,
+                 act='relu',
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+        self.conv = Conv2D(num_channels=in_channels,
+                           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)
+        self.bn = BatchNorm(num_filters,
+                            act=act,
+                            epsilon=0.001,
+                            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')
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.bn(y)
+        return y