提交 ca9ff7e6 编写于 作者: L lijianshe02

add dygraph fid score computation

上级 5a890132
......@@ -198,10 +198,10 @@ def _compute_statistics_of_path(path, model, batch_size, dims, use_gpu,
def calculate_fid_given_paths(paths,
premodel_path,
batch_size,
use_gpu,
dims,
premodel_path,
model=None):
assert os.path.exists(
premodel_path
......@@ -222,12 +222,3 @@ def calculate_fid_given_paths(paths,
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)
#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 argparse
from compute_fid import *
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--image_data_path1',
type=str,
default='./real',
help='path of image data')
parser.add_argument('--image_data_path2',
type=str,
default='./fake',
help='path of image data')
parser.add_argument('--inference_model',
type=str,
default='./pretrained/params_inceptionV3',
help='path of inference_model.')
parser.add_argument('--use_gpu',
type=bool,
default=True,
help='default use gpu.')
parser.add_argument('--batch_size',
type=int,
default=1,
help='sample number in a batch for inference.')
args = parser.parse_args()
return args
def main():
args = parse_args()
path1 = args.image_data_path1
path2 = args.image_data_path2
paths = (path1, path2)
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, 1,
True, 2048)
print('FID: ', fid_value)
if __name__ == "__main__":
main()
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