mindspore/model_zoo/official/cv/resnet50_quant/eval.py

# Copyright 2020 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.
# ============================================================================
"""Evaluate Resnet50 on ImageNet"""

import os
import argparse

from src.config import config_quant
from src.dataset import create_dataset
from src.crossentropy import CrossEntropy
#from models.resnet_quant import resnet50_quant #auto construct quantative network of resnet50
from models.resnet_quant_manual import resnet50_quant #manually construct quantative network of resnet50

from mindspore import context
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.compression.quant import QuantizationAwareTraining

parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
args_opt = parser.parse_args()

context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
config = config_quant

if args_opt.device_target == "Ascend":
    device_id = int(os.getenv('DEVICE_ID'))
    context.set_context(device_id=device_id)

if __name__ == '__main__':
    # define fusion network
    network = resnet50_quant(class_num=config.class_num)
    # convert fusion network to quantization aware network
    quantizer = QuantizationAwareTraining(bn_fold=True,
                                          per_channel=[True, False],
                                          symmetric=[True, False])
    network = quantizer.quantize(network)

    # define network loss
    if not config.use_label_smooth:
        config.label_smooth_factor = 0.0
    loss = CrossEntropy(smooth_factor=config.label_smooth_factor,
                        num_classes=config.class_num)

    # define dataset
    dataset = create_dataset(dataset_path=args_opt.dataset_path,
                             do_train=False,
                             batch_size=config.batch_size,
                             target=args_opt.device_target)
    step_size = dataset.get_dataset_size()

    # load checkpoint
    if args_opt.checkpoint_path:
        param_dict = load_checkpoint(args_opt.checkpoint_path)
        not_load_param = load_param_into_net(network, param_dict)
        if not_load_param:
            raise ValueError("Load param into network fail!")
    network.set_train(False)

    # define model
    model = Model(network, loss_fn=loss, metrics={'acc'})

    print("============== Starting Validation ==============")
    res = model.eval(dataset)
    print("result:", res, "ckpt=", args_opt.checkpoint_path)
    print("============== End Validation ==============")