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66 lines
2.6 KiB
66 lines
2.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Cycle GAN predict."""
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import os
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from mindspore import Tensor
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from src.models import get_generator
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from src.utils import get_args, load_ckpt, save_image, Reporter
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from src.dataset import create_dataset
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def predict():
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"""Predict function."""
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args = get_args("predict")
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G_A = get_generator(args)
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G_B = get_generator(args)
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# Use BatchNorm2d with batchsize=1, affine=False, training=True instead of InstanceNorm2d
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# Use real mean and varance rather than moving_men and moving_varance in BatchNorm2d
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G_A.set_train(True)
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G_B.set_train(True)
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load_ckpt(args, G_A, G_B)
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imgs_out = os.path.join(args.outputs_dir, "predict")
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if not os.path.exists(imgs_out):
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os.makedirs(imgs_out)
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if not os.path.exists(os.path.join(imgs_out, "fake_A")):
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os.makedirs(os.path.join(imgs_out, "fake_A"))
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if not os.path.exists(os.path.join(imgs_out, "fake_B")):
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os.makedirs(os.path.join(imgs_out, "fake_B"))
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args.data_dir = 'testA'
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ds = create_dataset(args)
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reporter = Reporter(args)
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reporter.start_predict("A to B")
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for data in ds.create_dict_iterator(output_numpy=True):
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img_A = Tensor(data["image"])
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path_A = str(data["image_name"][0], encoding="utf-8")
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fake_B = G_A(img_A)
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save_image(fake_B, os.path.join(imgs_out, "fake_B", path_A))
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reporter.info('save fake_B at %s', os.path.join(imgs_out, "fake_B", path_A))
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reporter.end_predict()
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args.data_dir = 'testB'
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ds = create_dataset(args)
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reporter.dataset_size = args.dataset_size
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reporter.start_predict("B to A")
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for data in ds.create_dict_iterator(output_numpy=True):
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img_B = Tensor(data["image"])
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path_B = str(data["image_name"][0], encoding="utf-8")
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fake_A = G_B(img_B)
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save_image(fake_A, os.path.join(imgs_out, "fake_A", path_B))
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reporter.info('save fake_A at %s', os.path.join(imgs_out, "fake_A", path_B))
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reporter.end_predict()
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if __name__ == "__main__":
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predict()
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