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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.
- # ============================================================================
- """
- python eval.py
- """
- import argparse
- import os
- import mindspore.nn as nn
- from mindspore import context
- from mindspore.train.model import Model
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.common import set_seed
- from mindspore import Tensor
- from mindspore.common import dtype as mstype
- from mindspore.nn.loss.loss import _Loss
- from mindspore.ops import functional as F
- from mindspore.ops import operations as P
-
- from src.config import imagenet_cfg
- from src.dataset import create_dataset_imagenet
-
- import src.fishnet_ms as net_ms
-
- set_seed(1)
-
- parser = argparse.ArgumentParser(description='fishnet99')
- parser.add_argument('--dataset_name', type=str, default='imagenet', choices=['imagenet', 'cifar10'],
- help='dataset name.')
- parser.add_argument('--checkpoint_path', type=str, default='./ckpt_0', help='Checkpoint file path')
- args_opt = parser.parse_args()
-
-
- class CrossEntropySmooth(_Loss):
- """CrossEntropy"""
- def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
- super(CrossEntropySmooth, self).__init__()
- self.onehot = P.OneHot()
- self.sparse = sparse
- self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
- self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
- self.ce = nn.SoftmaxCrossEntropyWithLogits(reduction=reduction)
-
- def construct(self, logit, label):
- if self.sparse:
- label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
- loss_ = self.ce(logit, label)
- return loss_
-
-
- if __name__ == '__main__':
-
- if args_opt.dataset_name == "imagenet":
- cfg = imagenet_cfg
- dataset = create_dataset_imagenet(cfg.val_data_path, 1, False)
- if not cfg.use_label_smooth:
- cfg.label_smooth_factor = 0.0
- loss = CrossEntropySmooth(sparse=True, reduction="mean",
- smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
- net = net_ms.fish99()
- model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
-
- else:
- raise ValueError("dataset is not support.")
-
- device_target = cfg.device_target
- context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
- context.set_context(device_id=cfg.device_id)
-
- file_list = os.listdir(args_opt.checkpoint_path)
- for filename in file_list:
- de_path = os.path.join(args_opt.checkpoint_path, filename)
- if de_path.endswith('.ckpt'):
- param_dict = load_checkpoint(de_path)
- load_param_into_net(net, param_dict)
- net.set_train(False)
-
- acc = model.eval(dataset)
- print(f"model {de_path}'s accuracy is {acc}")
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