import os

import apex

import torch

import torch_npu

from apex import amp

from torch import nn



from ascend_utils.common.utils import count_parameters

from modelslim.pytorch import sparse



device = torch.device("npu:{}".format(os.getenv('DEVICE_ID', 0)))

torch.npu.set_device(device)

model = nn.Sequential(

      nn.Conv2d(3, 32, 1, 1, bias=False),

      nn.Sequential(nn.Conv2d(32, 64, 1, 1, bias=False), nn.BatchNorm2d(64), nn.Conv2d(64, 32, 1, 1, bias=False)),

      nn.Sequential(nn.Conv2d(32, 64, 1, 1, bias=False), nn.BatchNorm2d(64), nn.Conv2d(64, 32, 1, 1, bias=False)),

      nn.Sequential(nn.Conv2d(32, 64, 1, 1, bias=False), nn.BatchNorm2d(64), nn.Conv2d(64, 32, 1, 1, bias=False)),

      nn.Sequential(nn.Conv2d(32, 64, 1, 1, bias=False), nn.BatchNorm2d(64), nn.Conv2d(64, 32, 1, 1, bias=False)),

      nn.AdaptiveAvgPool2d(1),

      nn.Flatten(),

      nn.Linear(32, 10, bias=False),

).to(device)



optimizer = apex.optimizers.NpuFusedSGD(model.parameters(), lr=0.1)

steps_per_epoch, epochs_each_stage = 10, [2, 3, 1]

oring_model_params = count_parameters(model)  # 10826

model, optimizer = apex.amp.initialize(model, optimizer, opt_level="O2", combine_grad=False)



  # 添加宽度稀疏化训练方式

model = sparse.sparse_model_depth(

    model, optimizer, steps_per_epoch=steps_per_epoch, epochs_each_stage=epochs_each_stage

)



 # 模型训练

for _ in range(steps_per_epoch * sum(epochs_each_stage)):

    optimizer.zero_grad()

    output = model(torch.ones([1, 3, 32, 32]).npu())

    loss = torch.mean(output)

    with amp.scale_loss(loss, optimizer) as scaled_loss:

        scaled_loss.backward()

    optimizer.step()