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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """ test WGAN """
- import os
- import json
- import mindspore.common.dtype as mstype
- import mindspore.ops as ops
- from mindspore import Tensor
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore import context
- import numpy as np
- from PIL import Image
-
- from src.dcgan_model import DcganG
- from src.dcgannobn_model import DcgannobnG
- from src.args import get_args
-
-
- if __name__ == "__main__":
-
- args_opt = get_args('eval')
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
- context.set_context(device_id=args_opt.device_id)
-
- with open(args_opt.config, 'r') as gencfg:
- generator_config = json.loads(gencfg.read())
-
- imageSize = generator_config["imageSize"]
- nz = generator_config["nz"]
- nc = generator_config["nc"]
- ngf = generator_config["ngf"]
- noBN = generator_config["noBN"]
- n_extra_layers = generator_config["n_extra_layers"]
-
- # generator
- if noBN:
- netG = DcgannobnG(imageSize, nz, nc, ngf, n_extra_layers)
- else:
- netG = DcganG(imageSize, nz, nc, ngf, n_extra_layers)
-
- # load weights
- load_param_into_net(netG, load_checkpoint(args_opt.ckpt_file))
-
- # initialize noise
- fixed_noise = Tensor(np.random.normal(size=[args_opt.nimages, nz, 1, 1]), dtype=mstype.float32)
-
- fake = netG(fixed_noise)
- mul = ops.Mul()
- add = ops.Add()
- reshape = ops.Reshape()
- fake = mul(fake, 0.5*255)
- fake = add(fake, 0.5*255)
-
- for i in range(args_opt.nimages):
- img_pil = reshape(fake[i, ...], (1, nc, imageSize, imageSize))
- img_pil = img_pil.asnumpy()[0].astype(np.uint8).transpose((1, 2, 0))
- img_pil = Image.fromarray(img_pil)
- img_pil.save(os.path.join(args_opt.output_dir, "generated_%02d.png" % i))
-
- print("Generate images success!")
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