Merge pull request #8 from lijianshe02/add_fid

add dygraph fid score computation

ppgan/metric/README.md

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+English (./README.md)
+
+# Usage
+
+To compute the FID score between two datasets, where images of each dataset are contained in an individual folder:
+
+wget https://paddlegan.bj.bcebos.com/InceptionV3.pdparams
+```
+python test_fid_score.py --image_data_path1 /path/to/dataset1 --image_data_path2 /path/to/dataset2 --inference_model ./InceptionV3.pdparams
+```

ppgan/metric/compute_fid.py

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+#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,
+                              premodel_path,
+                              batch_size,
+                              use_gpu,
+                              dims,
+                              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