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116 lines
5.5 KiB
116 lines
5.5 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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# less 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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"""
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######################## train YOLOv3 example ########################
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train YOLOv3 and get network model files(.ckpt) :
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python train.py --image_dir dataset/coco/coco/train2017 --anno_path dataset/coco/train_coco.txt
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"""
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import argparse
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import numpy as np
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import mindspore.nn as nn
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from mindspore import context, Tensor
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from mindspore.common.initializer import initializer
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from mindspore.communication.management import init
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from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor
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from mindspore.train import Model, ParallelMode
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.model_zoo.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
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from dataset import create_yolo_dataset
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from config import ConfigYOLOV3ResNet18
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def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):
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"""Set learning rate"""
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lr_each_step = []
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lr = learning_rate
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for i in range(global_step):
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if steps:
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lr_each_step.append(lr * (decay_rate ** (i // decay_step)))
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else:
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lr_each_step.append(lr * (decay_rate ** (i / decay_step)))
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lr_each_step = np.array(lr_each_step).astype(np.float32)
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lr_each_step = lr_each_step[start_step:]
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return lr_each_step
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def init_net_param(net, init='ones'):
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"""Init the parameters in net."""
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params = net.trainable_params()
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for p in params:
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if isinstance(p.data, Tensor) and 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
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p.set_parameter_data(initializer(init, p.data.shape(), p.data.dtype()))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="YOLOv3")
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parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
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parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
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parser.add_argument("--mode", type=str, default="graph", help="Run graph mode or feed mode, default is graph")
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parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
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parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
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parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
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parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.")
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parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.")
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parser.add_argument("--image_dir", type=str, required=True, help="Dataset image dir.")
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parser.add_argument("--anno_path", type=str, required=True, help="Dataset anno path.")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
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context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True)
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if args_opt.distribute:
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device_num = args_opt.device_num
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context.reset_auto_parallel_context()
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context.set_context(enable_hccl=True)
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
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device_num=device_num)
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init()
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rank = args_opt.device_id
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else:
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context.set_context(enable_hccl=False)
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rank = 0
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device_num = 1
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loss_scale = float(args_opt.loss_scale)
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dataset = create_yolo_dataset(args_opt.image_dir, args_opt.anno_path, repeat_num=args_opt.epoch_size,
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batch_size=args_opt.batch_size, device_num=device_num, rank=rank)
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dataset_size = dataset.get_dataset_size()
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net = yolov3_resnet18(ConfigYOLOV3ResNet18())
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net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
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init_net_param(net, "XavierUniform")
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# checkpoint
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ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
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ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config)
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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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load_param_into_net(net, param_dict)
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lr = Tensor(get_lr(learning_rate=0.001, start_step=0, global_step=args_opt.epoch_size * dataset_size,
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decay_step=1000, decay_rate=0.95))
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opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
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net = TrainingWrapper(net, opt, loss_scale)
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callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
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model = Model(net)
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dataset_sink_mode = False
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if args_opt.mode == "graph":
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dataset_sink_mode = True
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print("Start train YOLOv3.")
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model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
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