forked from mindspore-Ecosystem/mindspore
163 lines
8.3 KiB
Python
163 lines
8.3 KiB
Python
# 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 /data --anno_path /data/coco/train_coco.txt --mindrecord_dir=/data/Mindrecord_train
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If the mindrecord_dir is empty, it wil generate mindrecord file by image_dir and anno_path.
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Note if mindrecord_dir isn't empty, it will use mindrecord_dir rather than image_dir and anno_path.
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"""
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import os
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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.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.common.initializer import initializer
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from src.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
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from src.dataset import create_yolo_dataset, data_to_mindrecord_byte_image
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from src.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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for i in range(global_step):
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if steps:
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lr_each_step.append(learning_rate * (decay_rate ** (i // decay_step)))
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else:
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lr_each_step.append(learning_rate * (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(network, init_value='ones'):
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"""Init:wq the parameters in network."""
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params = network.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_value, p.data.shape, p.data.dtype))
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def main():
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parser = argparse.ArgumentParser(description="YOLOv3 train")
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parser.add_argument("--only_create_dataset", type=bool, default=False, help="If set it true, only create "
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"Mindrecord, default is false.")
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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("--lr", type=float, default=0.001, help="Learning rate, default is 0.001.")
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parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink")
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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("--pre_trained", type=str, default=None, help="Pretrained checkpoint file path")
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parser.add_argument("--pre_trained_epoch_size", type=int, default=0, help="Pretrained epoch size")
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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("--mindrecord_dir", type=str, default="./Mindrecord_train",
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help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by"
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"image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir "
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"rather than image_dir and anno_path. Default is ./Mindrecord_train")
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parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, "
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"the absolute image path is joined by the image_dir "
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"and the relative path in anno_path")
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parser.add_argument("--anno_path", type=str, default="", help="Annotation 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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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_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 % device_num
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else:
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rank = 0
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device_num = 1
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print("Start create dataset!")
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# It will generate mindrecord file in args_opt.mindrecord_dir,
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# and the file name is yolo.mindrecord0, 1, ... file_num.
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if not os.path.isdir(args_opt.mindrecord_dir):
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os.makedirs(args_opt.mindrecord_dir)
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prefix = "yolo.mindrecord"
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mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0")
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if not os.path.exists(mindrecord_file):
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if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path):
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print("Create Mindrecord.")
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data_to_mindrecord_byte_image(args_opt.image_dir,
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args_opt.anno_path,
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args_opt.mindrecord_dir,
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prefix=prefix,
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file_num=8)
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print("Create Mindrecord Done, at {}".format(args_opt.mindrecord_dir))
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else:
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print("image_dir or anno_path not exits.")
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if not args_opt.only_create_dataset:
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loss_scale = float(args_opt.loss_scale)
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# When create MindDataset, using the fitst mindrecord file, such as yolo.mindrecord0.
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dataset = create_yolo_dataset(mindrecord_file, 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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print("Create dataset done!")
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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.pre_trained:
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if args_opt.pre_trained_epoch_size <= 0:
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raise KeyError("pre_trained_epoch_size must be greater than 0.")
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param_dict = load_checkpoint(args_opt.pre_trained)
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load_param_into_net(net, param_dict)
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total_epoch_size = 60
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if args_opt.distribute:
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total_epoch_size = 160
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lr = Tensor(get_lr(learning_rate=args_opt.lr, start_step=args_opt.pre_trained_epoch_size * dataset_size,
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global_step=total_epoch_size * dataset_size,
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decay_step=1000, decay_rate=0.95, steps=True))
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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 == "sink":
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print("In sink mode, one epoch return a loss.")
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dataset_sink_mode = True
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print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.")
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model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
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if __name__ == '__main__':
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main()
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