Merge pull request #13 from gbstack/master

Add StarGAN-v2 style FID calculation

ppgan/metric/README.md

@@ -8,3 +8,12 @@ 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
 ```
+
+### Inception-V3 weights converted from torchvision
+
+Download: https://aistudio.baidu.com/aistudio/datasetdetail/51890
+
+This model weights file is converted from official torchvision inception-v3 model. And both BigGAN and StarGAN-v2 is using it to calculate FID score.
+
+Note that this model weights is different from above one (which is converted from tensorflow unofficial version)
+

ppgan/metric/compute_fid.py

@@ -16,15 +16,18 @@
 import fnmatch
 import numpy as np
 import cv2
+from PIL import Image
 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
+try:
+    from tqdm import tqdm
+except:
+    def tqdm(x):
+        return x
 
 
 """ based on https://github.com/mit-han-lab/gan-compression/blob/master/metric/fid_score.py
@@ -128,7 +131,7 @@ def calculate_fid_given_img(img_fake,
     return fid_value
 
 
-def _get_activations(files, model, batch_size, dims, use_gpu, premodel_path):
+def _get_activations(files, model, batch_size, dims, use_gpu, premodel_path, style=None):
     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.'))
@@ -144,8 +147,23 @@ def _get_activations(files, model, batch_size, dims, use_gpu, premodel_path):
     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]])
+
+        # same as stargan-v2 official implementation: resize to 256 first, then resize to 299
+        if style == 'stargan':
+            img_list = []
+            for f in files[start:end]:
+                im = Image.open(str(f)).convert('RGB')
+                if im.size[0] != 299:
+                    im = im.resize((256, 256), 2)
+                    im = im.resize((299, 299), 2)
+
+                img_list.append(np.array(im).astype('float32'))
+
+            images = np.array(
+                img_list)
+        else:
+            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]))
@@ -155,33 +173,53 @@ def _get_activations(files, model, batch_size, dims, use_gpu, premodel_path):
         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()
+        # imagenet normalization
+        if style == 'stargan':
+            mean = np.array([0.485, 0.456, 0.406]).astype('float32')
+            std = np.array([0.229, 0.224, 0.225]).astype('float32')
+            images[:] = (images[:] - mean[:, None, None]) / std[:, None, None]
 
-        pred = model(images)[0][0].numpy()
+        if style=='stargan':
+            pred_arr[start:end] = inception_infer(images, premodel_path)
+        else:
+            with fluid.dygraph.guard():
+                images = to_variable(images)
+                param_dict, _ = fluid.load_dygraph(premodel_path)
+                model.set_dict(param_dict)
+                model.eval()
 
-        pred_arr[start:end] = pred.reshape(end - start, -1)
+                pred = model(images)[0][0].numpy()
+
+                pred_arr[start:end] = pred.reshape(end - start, -1)
 
     return pred_arr
 
 
+def inception_infer(x, model_path):
+    exe = fluid.Executor()
+    [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(model_path, exe)
+    results = exe.run(inference_program,
+                  feed={feed_target_names[0]: x},
+                  fetch_list=fetch_targets)
+    return results[0]
+
+
 def _calculate_activation_statistics(files,
                                      model,
                                      premodel_path,
                                      batch_size=50,
                                      dims=2048,
-                                     use_gpu=False):
+                                     use_gpu=False,
+                                     style = None):
     act = _get_activations(files, model, batch_size, dims, use_gpu,
-                           premodel_path)
+                           premodel_path, style)
     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):
+                                premodel_path, style=None):
     if path.endswith('.npz'):
         f = np.load(path)
         m, s = f['mu'][:], f['sigma'][:]
@@ -193,7 +231,7 @@ def _compute_statistics_of_path(path, model, batch_size, dims, use_gpu,
                     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)
+                                                batch_size, dims, use_gpu, style)
     return m, s
 
 
@@ -202,7 +240,8 @@ def calculate_fid_given_paths(paths,
                               batch_size,
                               use_gpu,
                               dims,
-                              model=None):
+                              model=None,
+                              style = None):
     assert os.path.exists(
         premodel_path
     ), 'pretrain_model path {} is not exists! Please download it first'.format(
@@ -211,14 +250,15 @@ def calculate_fid_given_paths(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)
+    if model is None and style != 'stargan':
+        with fluid.dygraph.guard():
+            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)
+                                         use_gpu, premodel_path, style)
     m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size, dims,
-                                         use_gpu, premodel_path)
+                                         use_gpu, premodel_path, style)
 
     fid_value = _calculate_frechet_distance(m1, s1, m2, s2)
     return fid_value

ppgan/metric/test_fid_score.py

@@ -38,6 +38,9 @@ def parse_args():
                         type=int,
                         default=1,
                         help='sample number in a batch for inference.')
+    parser.add_argument('--style',
+                        type=str,
+                        help='calculation style: stargan or default (gan-compression style)')
     args = parser.parse_args()
     return args
 
@@ -50,10 +53,9 @@ def main():
     inference_model_path = args.inference_model
     batch_size = args.batch_size
 
-    with fluid.dygraph.guard():
-        fid_value = calculate_fid_given_paths(paths, inference_model_path,
-                                              batch_size, args.use_gpu, 2048)
-        print('FID: ', fid_value)
+    fid_value = calculate_fid_given_paths(paths, inference_model_path,
+                                          batch_size, args.use_gpu, 2048, style=args.style)
+    print('FID: ', fid_value)
 
 
 if __name__ == "__main__":