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86 lines
3.6 KiB
86 lines
3.6 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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"""Evaluate MobilenetV2 on ImageNet"""
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import os
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import argparse
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from mindspore import context
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from mindspore import nn
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.compression.quant import QuantizationAwareTraining
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from src.mobilenetV2 import mobilenetV2
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from src.dataset import create_dataset
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from src.config import config_ascend_quant
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from src.config import config_gpu_quant
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--device_target', type=str, default=None, help='Run device target')
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args_opt = parser.parse_args()
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if __name__ == '__main__':
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config_device_target = None
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device_id = int(os.getenv('DEVICE_ID'))
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if args_opt.device_target == "Ascend":
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config_device_target = config_ascend_quant
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
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device_id=device_id, save_graphs=False)
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symmetric_list = [True, False]
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elif args_opt.device_target == "GPU":
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config_device_target = config_gpu_quant
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU",
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device_id=device_id, save_graphs=False)
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symmetric_list = [False, False]
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else:
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raise ValueError("Unsupported device target: {}.".format(args_opt.device_target))
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# define fusion network
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network = mobilenetV2(num_classes=config_device_target.num_classes)
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# convert fusion network to quantization aware network
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quantizer = QuantizationAwareTraining(bn_fold=True,
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per_channel=[True, False],
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symmetric=symmetric_list)
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network = quantizer.quantize(network)
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# define network loss
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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# define dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=False,
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config=config_device_target,
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device_target=args_opt.device_target,
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batch_size=config_device_target.batch_size)
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step_size = dataset.get_dataset_size()
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# load checkpoint
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if args_opt.checkpoint_path:
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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not_load_param = load_param_into_net(network, param_dict)
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if not_load_param:
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raise ValueError("Load param into net fail!")
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network.set_train(False)
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# define model
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model = Model(network, loss_fn=loss, metrics={'acc'})
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print("============== Starting Validation ==============")
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res = model.eval(dataset)
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print("result:", res, "ckpt=", args_opt.checkpoint_path)
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print("============== End Validation ==============")
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