forked from huawei/mindspore2022
233 lines
9.2 KiB
Python
233 lines
9.2 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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# 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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"""train_imagenet."""
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import os
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import time
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import argparse
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import random
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import numpy as np
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from mindspore import context
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from mindspore import Tensor
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from mindspore import nn
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.nn.loss.loss import _Loss
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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from mindspore.train.model import Model, ParallelMode
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
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from mindspore.train.serialization import load_checkpoint
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from mindspore.communication.management import init
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import mindspore.dataset.engine as de
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from src.dataset import create_dataset
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from src.lr_generator import get_lr
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from src.config import config_ascend
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from src.mobilenetV2_quant import mobilenet_v2_quant
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random.seed(1)
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np.random.seed(1)
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de.config.set_seed(1)
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
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parser.add_argument('--platform', type=str, default=None, help='run platform')
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args_opt = parser.parse_args()
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if args_opt.platform == "Ascend":
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device_id = int(os.getenv('DEVICE_ID'))
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rank_id = int(os.getenv('RANK_ID'))
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rank_size = int(os.getenv('RANK_SIZE'))
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run_distribute = rank_size > 1
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Ascend",
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device_id=device_id, save_graphs=False)
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else:
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raise ValueError("Unsupport platform.")
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class CrossEntropyWithLabelSmooth(_Loss):
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"""
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CrossEntropyWith LabelSmooth.
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Args:
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smooth_factor (float): smooth factor, default=0.
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num_classes (int): num classes
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Returns:
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None.
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Examples:
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>>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
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"""
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def __init__(self, smooth_factor=0., num_classes=1000):
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super(CrossEntropyWithLabelSmooth, self).__init__()
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self.onehot = P.OneHot()
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor /
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(num_classes - 1), mstype.float32)
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self.ce = nn.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean(False)
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self.cast = P.Cast()
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def construct(self, logit, label):
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one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1],
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self.on_value, self.off_value)
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out_loss = self.ce(logit, one_hot_label)
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out_loss = self.mean(out_loss, 0)
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return out_loss
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class Monitor(Callback):
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"""
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Monitor loss and time.
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Args:
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lr_init (numpy array): train lr
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Returns:
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None
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Examples:
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>>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
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"""
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def __init__(self, lr_init=None):
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super(Monitor, self).__init__()
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self.lr_init = lr_init
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self.lr_init_len = len(lr_init)
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def epoch_begin(self, run_context):
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self.losses = []
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self.epoch_time = time.time()
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def epoch_end(self, run_context):
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cb_params = run_context.original_args()
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epoch_mseconds = (time.time() - self.epoch_time) * 1000
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per_step_mseconds = epoch_mseconds / cb_params.batch_num
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print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
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per_step_mseconds,
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np.mean(self.losses)))
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def step_begin(self, run_context):
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self.step_time = time.time()
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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step_mseconds = (time.time() - self.step_time) * 1000
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step_loss = cb_params.net_outputs
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if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
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step_loss = step_loss[0]
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if isinstance(step_loss, Tensor):
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step_loss = np.mean(step_loss.asnumpy())
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self.losses.append(step_loss)
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cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
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print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.5f}]".format(
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cb_params.cur_epoch_num -
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1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
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np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
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def _load_param_into_net(ori_model, ckpt_param_dict):
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"""
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load fp32 model parameters to quantization model.
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Args:
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ori_model: quantization model
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ckpt_param_dict: f32 param
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Returns:
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None
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"""
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iterable_dict = {
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'weight': iter([item for item in ckpt_param_dict.items() if item[0].endswith('weight')]),
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'bias': iter([item for item in ckpt_param_dict.items() if item[0].endswith('bias')]),
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'gamma': iter([item for item in ckpt_param_dict.items() if item[0].endswith('gamma')]),
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'beta': iter([item for item in ckpt_param_dict.items() if item[0].endswith('beta')]),
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'moving_mean': iter([item for item in ckpt_param_dict.items() if item[0].endswith('moving_mean')]),
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'moving_variance': iter(
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[item for item in ckpt_param_dict.items() if item[0].endswith('moving_variance')]),
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'minq': iter([item for item in ckpt_param_dict.items() if item[0].endswith('minq')]),
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'maxq': iter([item for item in ckpt_param_dict.items() if item[0].endswith('maxq')])
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}
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for name, param in ori_model.parameters_and_names():
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key_name = name.split(".")[-1]
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if key_name not in iterable_dict.keys():
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continue
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value_param = next(iterable_dict[key_name], None)
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if value_param is not None:
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param.set_parameter_data(value_param[1].data)
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print(f'init model param {name} with checkpoint param {value_param[0]}')
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if __name__ == '__main__':
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# train on ascend
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print("train args: ", args_opt, "\ncfg: ", config_ascend,
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"\nparallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))
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if run_distribute:
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context.set_auto_parallel_context(device_num=rank_size, parallel_mode=ParallelMode.DATA_PARALLEL,
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parameter_broadcast=True, mirror_mean=True)
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init()
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epoch_size = config_ascend.epoch_size
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net = mobilenet_v2_quant(num_classes=config_ascend.num_classes)
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if config_ascend.label_smooth > 0:
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loss = CrossEntropyWithLabelSmooth(
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smooth_factor=config_ascend.label_smooth, num_classes=config_ascend.num_classes)
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else:
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loss = SoftmaxCrossEntropyWithLogits(
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is_grad=False, sparse=True, reduction='mean')
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=True,
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config=config_ascend,
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platform=args_opt.platform,
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repeat_num=epoch_size,
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batch_size=config_ascend.batch_size)
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step_size = dataset.get_dataset_size()
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if args_opt.pre_trained:
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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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lr = Tensor(get_lr(global_step=config_ascend.start_epoch * step_size,
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lr_init=0,
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lr_end=0,
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lr_max=config_ascend.lr,
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warmup_epochs=config_ascend.warmup_epochs,
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total_epochs=epoch_size + config_ascend.start_epoch,
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steps_per_epoch=step_size))
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_ascend.momentum,
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config_ascend.weight_decay)
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model = Model(net, loss_fn=loss, optimizer=opt)
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cb = None
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if rank_id == 0:
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cb = [Monitor(lr_init=lr.asnumpy())]
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if config_ascend.save_checkpoint:
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config_ck = CheckpointConfig(save_checkpoint_steps=config_ascend.save_checkpoint_epochs * step_size,
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keep_checkpoint_max=config_ascend.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(
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prefix="mobilenet", directory=config_ascend.save_checkpoint_path, config=config_ck)
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cb += [ckpt_cb]
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model.train(epoch_size, dataset, callbacks=cb)
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