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- from mindspore import context
- import os
- import random
- import argparse
- import ast
- import numpy as np
- from mindspore import Tensor
- from mindspore import dataset as de
- import mindspore.ops as ops
- from mindspore import dtype as mstype
- from mindspore.parallel._auto_parallel_context import auto_parallel_context
- from mindspore.nn.optim.momentum import Momentum
- from mindspore.train.model import Model, ParallelMode
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
- from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
- from mindspore.train.loss_scale_manager import FixedLossScaleManager
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.communication.management import init, get_rank, get_group_size
- import mindspore.nn as nn
- import mindspore.common.initializer as weight_init
- from easydict import EasyDict
- import moxing as mox
-
- from src.lr_generator import get_lr
- from src.glore_resnet50 import glore_resnet50
- from src.dataset import create_dataset_ImageNet as ImageNet
- from src.dataset import create_dataset_Cifar10 as Cifar10
-
-
- parser = argparse.ArgumentParser(description='Image classification with glore_resnet50')
- parser.add_argument('--use_glore', type=bool, default=True, help='Enable GloreUnit')
- parser.add_argument('--run_distribute', type=ast.literal_eval, default=True, help='Run distribute')
- parser.add_argument('--device_num', type=int, default=8, help='Device num.')
- parser.add_argument('--data_url', type=str, default='/opt_data/xidian_wks/imagenet_original/train/',
- help='Dataset path')
- parser.add_argument('--train_url', type=str)
- parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
- parser.add_argument('--pre_trained', type=bool, default=False)
- parser.add_argument('--pre_ckpt_path', type=str,
- default='')
- parser.add_argument('--parameter_server', type=ast.literal_eval, default=False, help='Run parameter server train')
- args_opt = parser.parse_args()
-
- config = EasyDict({
- "class_num": 1000,
- "batch_size": 256,
- "loss_scale": 1024,
- "momentum": 0.92,
- "weight_decay": 0.0001,
- "epoch_size": 180,
- "pretrain_epoch_size": 0,
- "save_checkpoint": True,
- "save_checkpoint_epochs": 5,
- "keep_checkpoint_max": 10,
- "save_checkpoint_path": "./",
- "warmup_epochs": 0,
- "lr_decay_mode": "steps",
- "lr_init": 0.1,
- "lr_end": 0,
- "lr_max": 0.4
- })
-
-
- class SoftmaxCrossEntropyExpand(nn.Cell): # pylint: disable=missing-docstring
- def __init__(self, sparse=False):
- super(SoftmaxCrossEntropyExpand, self).__init__()
- self.exp = ops.Exp()
- self.sum = ops.ReduceSum(keep_dims=True)
- self.onehot = ops.OneHot()
- self.on_value = Tensor(1.0, mstype.float32)
- self.off_value = Tensor(0.0, mstype.float32)
- self.div = ops.RealDiv()
- self.log = ops.Log()
- self.sum_cross_entropy = ops.ReduceSum(keep_dims=False)
- self.mul = ops.Mul()
- self.mul2 = ops.Mul()
- self.mean = ops.ReduceMean(keep_dims=False)
- self.sparse = sparse
- self.max = ops.ReduceMax(keep_dims=True)
- self.sub = ops.Sub()
- self.eps = Tensor(1e-24, mstype.float32)
-
- def construct(self, logit, label): # pylint: disable=missing-docstring
- logit_max = self.max(logit, -1)
- exp = self.exp(self.sub(logit, logit_max))
- exp_sum = self.sum(exp, -1)
- softmax_result = self.div(exp, exp_sum)
- if self.sparse:
- label = self.onehot(label, ops.shape(logit)[1], self.on_value, self.off_value)
-
- softmax_result_log = self.log(softmax_result + self.eps)
- loss = self.sum_cross_entropy((self.mul(softmax_result_log, label)), -1)
- loss = self.mul2(ops.scalar_to_array(-1.0), loss)
- loss = self.mean(loss, -1)
-
- return loss
-
-
- if __name__ == '__main__':
- target = args_opt.device_target
-
- # init context
- context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
- if args_opt.run_distribute:
- if target == "Ascend":
- device_id = int(os.getenv('DEVICE_ID'))
- context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
- context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True,
- auto_parallel_search_mode="recursive_programming")
- init()
- # create dataset
-
- # download dataset from obs to cache
- mox.file.copy_parallel(src_url=args_opt.data_url, dst_url='/cache/data_mmq')
- dataset_path = '/cache/data_mmq'
-
- dataset = ImageNet(dataset_path=dataset_path,
- do_train=True,
- use_randaugment=True,
- repeat_num=1,
- batch_size=config.batch_size,
- target=target)
-
- # dataset = Cifar10(dataset_path=dataset_path, do_train=True, repeat_num=1,
- # batch_size=config.batch_size, target=target)
-
- step_size = dataset.get_dataset_size()
-
- # define net
-
- net = glore_resnet50(num_classes=config.class_num, use_glore=args_opt.use_glore)
-
- # init weight
- if args_opt.pre_trained:
- param_dict = load_checkpoint(args_opt.pre_ckpt_path)
- load_param_into_net(net, param_dict)
- else:
- for _, cell in net.cells_and_names():
- if isinstance(cell, nn.Conv2d):
- cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
- cell.weight.shape,
- cell.weight.dtype)
-
- # cell.weight.default_input = weight_init.initializer(weight_init.HeNormal(mode='fan_out', ),
- # cell.weight.shape,
- # cell.weight.dtype)
-
- if isinstance(cell, nn.Dense):
- cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
- cell.weight.shape,
- cell.weight.dtype)
-
- # init lr
- # lr = power_lr(0.4, config.epoch_size, step_size)
- lr = get_lr(lr_init=config.lr_init,
- lr_end=config.lr_end,
- lr_max=config.lr_max,
- warmup_epochs=config.warmup_epochs,
- total_epochs=config.epoch_size,
- steps_per_epoch=step_size,
- lr_decay_mode=config.lr_decay_mode)
- lr = Tensor(lr)
-
- #
- # define opt
- decayed_params = []
- no_decayed_params = []
- for param in net.trainable_params():
- if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
- decayed_params.append(param)
- else:
- no_decayed_params.append(param)
-
- group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
- {'params': no_decayed_params},
- {'order_params': net.trainable_params()}]
- net_opt = nn.SGD(group_params, learning_rate=lr, momentum=config.momentum,
- weight_decay=config.weight_decay, loss_scale=config.loss_scale,
- nesterov=True)
- # net_opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
- # define loss, model
- loss = SoftmaxCrossEntropyExpand(sparse=True)
- loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
- model = Model(net, loss_fn=loss, optimizer=net_opt, loss_scale_manager=loss_scale)
-
- # define callbacks
- time_cb = TimeMonitor(data_size=step_size)
- loss_cb = LossMonitor()
- cb = [time_cb, loss_cb]
- if config.save_checkpoint:
- config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
- keep_checkpoint_max=config.keep_checkpoint_max)
- ckpt_cb = ModelCheckpoint(prefix="glore_resnet50", directory='/cache/train_output/device_' +
- os.getenv('DEVICE_ID') + '/', config=config_ck)
- cb += [ckpt_cb]
-
- # train model
- print("===========================================")
- print("Total epoch: {}".format(config.epoch_size))
- print("Class num: {}".format(config.class_num))
- print("Backbone resnet50")
- print("Enable glore: {}".format(args_opt.use_glore))
- print("=======Multiple Training Begin========")
- model.train(config.epoch_size - config.pretrain_epoch_size, dataset,
- callbacks=cb, dataset_sink_mode=True)
-
- # copy train result from cache to obs
- mox.file.copy_parallel(src_url='/cache/train_output', dst_url=args_opt.train_url)
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