Xinghao Chen
4 years ago
parent
ac5371b38f
commit
cd31275061
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# Copyright 2021 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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"""Inference Interface"""
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import sys
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import argparse
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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.nn import Loss, Top1CategoricalAccuracy, Top5CategoricalAccuracy
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from mindspore import context
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from mindspore import nn
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from src.dataset import create_dataset_cifar10
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from src.utils import count_params
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from src.hournasnet import hournasnet
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from easydict import EasyDict as edict
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parser = argparse.ArgumentParser(description='Evaluation')
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parser.add_argument('--data_path', type=str, default='/home/workspace/mindspore_dataset/',
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metavar='DIR', help='path to dataset')
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parser.add_argument('--model', default='hournas_f_c10', type=str, metavar='MODEL',
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help='Name of model to train (default: "tinynet_c"')
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parser.add_argument('--num-classes', type=int, default=10, metavar='N',
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help='number of label classes (default: 10)')
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parser.add_argument('-b', '--batch-size', type=int, default=256, metavar='N',
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help='input batch size for training (default: 256)')
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parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
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help='how many training processes to use (default: 4)')
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parser.add_argument('--ckpt', type=str, default='./ms_hournas_f_c10.ckpt',
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help='model checkpoint to load')
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parser.add_argument('--GPU', action='store_true', default=True,
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help='Use GPU for training (default: True)')
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parser.add_argument('--dataset_sink', action='store_true', default=True)
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parser.add_argument('--image-size', type=int, default=32, metavar='N',
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help='input image size (default: 32)')
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def main():
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"""Main entrance for training"""
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args = parser.parse_args()
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print(sys.argv)
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#context.set_context(mode=context.GRAPH_MODE)
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context.set_context(mode=context.PYNATIVE_MODE)
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if args.GPU:
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context.set_context(device_target='GPU')
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# parse model argument
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assert args.model.startswith(
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"hournas"), "Only Tinynet models are supported."
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#_, sub_name = args.model.split("_")
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net = hournasnet(args.model,
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num_classes=args.num_classes,
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drop_rate=0.0,
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drop_connect_rate=0.0,
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global_pool="avg",
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bn_tf=False,
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bn_momentum=None,
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bn_eps=None)
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print(net)
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print("Total number of parameters:", count_params(net))
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cfg = edict({'image_height': args.image_size, 'image_width': args.image_size,})
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cfg.batch_size = args.batch_size
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print(cfg)
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#input_size = net.default_cfg['input_size'][1]
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val_data_url = args.data_path #os.path.join(args.data_path, 'val')
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val_dataset = create_dataset_cifar10(val_data_url, repeat_num=1, training=False, cifar_cfg=cfg)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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eval_metrics = {'Validation-Loss': Loss(),
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'Top1-Acc': Top1CategoricalAccuracy(),
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'Top5-Acc': Top5CategoricalAccuracy()}
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ckpt = load_checkpoint(args.ckpt)
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load_param_into_net(net, ckpt)
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net.set_train(False)
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model = Model(net, loss, metrics=eval_metrics)
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metrics = model.eval(val_dataset, dataset_sink_mode=False)
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print(metrics)
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if __name__ == '__main__':
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main()
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# Copyright 2021 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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"""hub config."""
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from src.hournasnet import hournasnet
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def create_network(name, *args, **kwargs):
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if name == 'HourNAS':
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return hournasnet(*args, **kwargs)
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raise NotImplementedError(f"{name} is not implemented in the repo")
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# Copyright 2021 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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"""Architecture of HourNAS"""
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predefine_archs = {
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'hournas_f_c10': {
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'genotypes': [
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#'conv3bnrelu',
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'ir_k3_e1_se',
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'ir_k5_e6_se', 'ir_k5_e1_se', 'ir_k5_e1_se', 'ir_k3_e1_se',
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'ir_k5_e6_se', 'ir_k5_e1_se', 'ir_k3_e1_se', 'ir_k5_e1_se',
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'ir_k5_e6_se', 'ir_k3_e6_se', 'ir_k3_e6_se', 'ir_k3_e6_se',
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'ir_k5_e6_se', 'ir_k5_e3_se', 'ir_k5_e3_se', 'ir_k5_e3_se',
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'ir_k5_e6_se', 'ir_k5_e6_se', 'ir_k3_e6_se', 'ir_k5_e6_se',
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'ir_k5_e6_se',
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#'conv1', 'adaavgpool'
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],
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'strides': [
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#1,
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1,
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1, 1, 1, 1,
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1, 1, 1, 1,
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2, 1, 1, 1,
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1, 1, 1, 1,
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2, 1, 1, 1,
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1,
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#1, 1
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],
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'out_channels': [
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#32,
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16,
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24, 24, 24, 24,
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40, 40, 40, 40,
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80, 80, 80, 80,
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112, 112, 112, 112,
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192, 192, 192, 192,
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320,
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#1280, 1280,
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],
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'dropout_ratio': 0.2,
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'default_init': 'True',
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'se_ratio': '0.05'
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},
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}
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# Copyright 2021 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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"""Data operations, will be used in train.py and eval.py"""
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import math
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import os
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import numpy as np
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import mindspore.dataset.vision.py_transforms as py_vision
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import mindspore.dataset.transforms.py_transforms as py_transforms
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import mindspore.dataset.transforms.c_transforms as c_transforms
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import mindspore.common.dtype as mstype
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import mindspore.dataset as ds
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from mindspore.communication.management import get_rank, get_group_size
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from mindspore.dataset.vision import Inter
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import mindspore.dataset.vision.c_transforms as vision
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# values that should remain constant
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DEFAULT_CROP_PCT = 0.875
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IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
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# data preprocess configs
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SCALE = (0.08, 1.0)
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RATIO = (3./4., 4./3.)
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ds.config.set_seed(1)
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def split_imgs_and_labels(imgs, labels, batchInfo):
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"""split data into labels and images"""
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ret_imgs = []
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ret_labels = []
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for i, image in enumerate(imgs):
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ret_imgs.append(image)
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ret_labels.append(labels[i])
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return np.array(ret_imgs), np.array(ret_labels)
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def create_dataset(batch_size, train_data_url='', workers=8, distributed=False,
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input_size=224, color_jitter=0.4):
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"""Create ImageNet training dataset"""
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if not os.path.exists(train_data_url):
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raise ValueError('Path not exists')
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decode_op = py_vision.Decode()
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type_cast_op = c_transforms.TypeCast(mstype.int32)
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random_resize_crop_bicubic = py_vision.RandomResizedCrop(size=(input_size, input_size),
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scale=SCALE, ratio=RATIO,
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interpolation=Inter.BICUBIC)
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random_horizontal_flip_op = py_vision.RandomHorizontalFlip(0.5)
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adjust_range = (max(0, 1 - color_jitter), 1 + color_jitter)
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random_color_jitter_op = py_vision.RandomColorAdjust(brightness=adjust_range,
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contrast=adjust_range,
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saturation=adjust_range)
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to_tensor = py_vision.ToTensor()
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normalize_op = py_vision.Normalize(
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IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
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# assemble all the transforms
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image_ops = py_transforms.Compose([decode_op, random_resize_crop_bicubic,
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random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
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rank_id = get_rank() if distributed else 0
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rank_size = get_group_size() if distributed else 1
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dataset_train = ds.ImageFolderDataset(train_data_url,
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num_parallel_workers=workers,
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shuffle=True,
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num_shards=rank_size,
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shard_id=rank_id)
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dataset_train = dataset_train.map(input_columns=["image"],
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operations=image_ops,
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num_parallel_workers=workers)
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dataset_train = dataset_train.map(input_columns=["label"],
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operations=type_cast_op,
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num_parallel_workers=workers)
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# batch dealing
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ds_train = dataset_train.batch(batch_size,
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per_batch_map=split_imgs_and_labels,
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input_columns=["image", "label"],
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num_parallel_workers=2,
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drop_remainder=True)
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ds_train = ds_train.repeat(1)
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return ds_train
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def create_dataset_val(batch_size=128, val_data_url='', workers=8, distributed=False,
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input_size=224):
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"""Create ImageNet validation dataset"""
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if not os.path.exists(val_data_url):
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raise ValueError('Path not exists')
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rank_id = get_rank() if distributed else 0
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rank_size = get_group_size() if distributed else 1
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dataset = ds.ImageFolderDataset(val_data_url, num_parallel_workers=workers,
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num_shards=rank_size, shard_id=rank_id)
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scale_size = None
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if isinstance(input_size, tuple):
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assert len(input_size) == 2
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if input_size[-1] == input_size[-2]:
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scale_size = int(math.floor(input_size[0] / DEFAULT_CROP_PCT))
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else:
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scale_size = tuple([int(x / DEFAULT_CROP_PCT) for x in input_size])
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else:
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scale_size = int(math.floor(input_size / DEFAULT_CROP_PCT))
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type_cast_op = c_transforms.TypeCast(mstype.int32)
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decode_op = py_vision.Decode()
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resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
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center_crop = py_vision.CenterCrop(size=input_size)
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to_tensor = py_vision.ToTensor()
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normalize_op = py_vision.Normalize(
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IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
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image_ops = py_transforms.Compose([decode_op, resize_op, center_crop,
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to_tensor, normalize_op])
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dataset = dataset.map(input_columns=["label"], operations=type_cast_op,
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num_parallel_workers=workers)
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dataset = dataset.map(input_columns=["image"], operations=image_ops,
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num_parallel_workers=workers)
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dataset = dataset.batch(batch_size, per_batch_map=split_imgs_and_labels,
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input_columns=["image", "label"],
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num_parallel_workers=2,
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drop_remainder=True)
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dataset = dataset.repeat(1)
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return dataset
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def _get_rank_info():
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"""
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get rank size and rank id
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"""
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rank_size = int(os.environ.get("RANK_SIZE", 1))
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if rank_size > 1:
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rank_size = get_group_size()
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rank_id = get_rank()
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else:
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rank_size = rank_id = None
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return rank_size, rank_id
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def create_dataset_cifar10(data_home, repeat_num=1, training=True, cifar_cfg=None):
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"""Data operations."""
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data_dir = os.path.join(data_home, "cifar-10-batches-bin")
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if not training:
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data_dir = os.path.join(data_home, "cifar-10-verify-bin")
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rank_size, rank_id = _get_rank_info()
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if training:
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data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, shuffle=True)
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else:
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data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, shuffle=False)
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resize_height = cifar_cfg.image_height
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resize_width = cifar_cfg.image_width
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# define map operations
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random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
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random_horizontal_op = vision.RandomHorizontalFlip()
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resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
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rescale_op = vision.Rescale(1.0 / 255.0, 0.0)
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#normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
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normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.24703233, 0.24348505, 0.26158768))
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changeswap_op = vision.HWC2CHW()
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type_cast_op = c_transforms.TypeCast(mstype.int32)
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c_trans = []
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if training:
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c_trans = [random_crop_op, random_horizontal_op]
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c_trans += [resize_op, rescale_op, normalize_op, changeswap_op]
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# apply map operations on images
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data_set = data_set.map(operations=type_cast_op, input_columns="label")
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data_set = data_set.map(operations=c_trans, input_columns="image")
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# apply batch operations
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data_set = data_set.batch(batch_size=cifar_cfg.batch_size, drop_remainder=True)
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# apply repeat operations
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data_set = data_set.repeat(repeat_num)
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return data_set
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,89 @@
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# Copyright 2021 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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"""model utils"""
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import math
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import argparse
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import numpy as np
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def str2bool(value):
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"""Convert string arguments to bool type"""
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if value.lower() in ('yes', 'true', 't', 'y', '1'):
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return True
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if value.lower() in ('no', 'false', 'f', 'n', '0'):
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return False
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raise argparse.ArgumentTypeError('Boolean value expected.')
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def get_lr(base_lr, total_epochs, steps_per_epoch, decay_epochs=1, decay_rate=0.9,
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warmup_epochs=0., warmup_lr_init=0., global_epoch=0):
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"""Get scheduled learning rate"""
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lr_each_step = []
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total_steps = steps_per_epoch * total_epochs
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global_steps = steps_per_epoch * global_epoch
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self_warmup_delta = ((base_lr - warmup_lr_init) / \
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warmup_epochs) if warmup_epochs > 0 else 0
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self_decay_rate = decay_rate if decay_rate < 1 else 1/decay_rate
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for i in range(total_steps):
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epochs = math.floor(i/steps_per_epoch)
|
||||
cond = 1 if (epochs < warmup_epochs) else 0
|
||||
warmup_lr = warmup_lr_init + epochs * self_warmup_delta
|
||||
decay_nums = math.floor(epochs / decay_epochs)
|
||||
decay_rate = math.pow(self_decay_rate, decay_nums)
|
||||
decay_lr = base_lr * decay_rate
|
||||
lr = cond * warmup_lr + (1 - cond) * decay_lr
|
||||
lr_each_step.append(lr)
|
||||
lr_each_step = lr_each_step[global_steps:]
|
||||
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
||||
return lr_each_step
|
||||
|
||||
|
||||
def add_weight_decay(net, weight_decay=1e-5, skip_list=None):
|
||||
"""Apply weight decay to only conv and dense layers (len(shape) > =2)
|
||||
Args:
|
||||
net (mindspore.nn.Cell): Mindspore network instance
|
||||
weight_decay (float): weight decay tobe used.
|
||||
skip_list (tuple): list of parameter names without weight decay
|
||||
Returns:
|
||||
A list of group of parameters, separated by different weight decay.
|
||||
"""
|
||||
decay = []
|
||||
no_decay = []
|
||||
if not skip_list:
|
||||
skip_list = ()
|
||||
for param in net.trainable_params():
|
||||
if len(param.shape) == 1 or \
|
||||
param.name.endswith(".bias") or \
|
||||
param.name in skip_list:
|
||||
no_decay.append(param)
|
||||
else:
|
||||
decay.append(param)
|
||||
return [
|
||||
{'params': no_decay, 'weight_decay': 0.},
|
||||
{'params': decay, 'weight_decay': weight_decay}]
|
||||
|
||||
|
||||
def count_params(net):
|
||||
"""Count number of parameters in the network
|
||||
Args:
|
||||
net (mindspore.nn.Cell): Mindspore network instance
|
||||
Returns:
|
||||
total_params (int): Total number of trainable params
|
||||
"""
|
||||
total_params = 0
|
||||
for param in net.trainable_params():
|
||||
total_params += np.prod(param.shape)
|
||||
return total_params
|
||||
Loading…
Reference in new issue