pytorch 调用环境 npu用不了 pytorch没有调用gpu

 检查使用的GPU的索引

• torch.cuda.is_available()   cuda是否可用；
• torch.cuda.device_count()   返回gpu数量；
• torch.cuda.get_device_name(0)  返回gpu名字，设备索引默认从0开始；
• torch.cuda.current_device()  返回当前设备索引；

如果使用GPU时，出现找不到cudnn可用，可能是因为GPU卡太老，pytorch不支持。

 

卷积Conv2d

示例：nn.Conv2d(1, 64, 2, 1, 1)，分别对应conv2d( in_channels, out_channels, kernel_size, stide, padding)

torch.nn.Conv2d( in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)

• dilation ( int )——控制卷积核元素之间的距离，默认1
• groups ( int )——控制输入通道和输出通道的阻塞连接数，默认1

• group=1，输出是所有输入的卷积；
• group=2，相当于有两个并排的卷积层，每个卷积层计算输入通道的一半，输出也是输出通道的一半，随后将两个输出连接起来得到最后结果；
• group=in_channels，每一个输入通道分别和它对应的卷积核进行卷积

• kernel_size, stride, padding, dilation: 

• 是int数时，表示height和width值相同
• 是tuple数组时，则分别表示height和weight

• bias ( bool ) —— 是否添加可学习的偏置到输出中

 

二维卷积层，输入到输出尺寸的计算：

W_out = (W_in + 2*padding - k) / stride + 1

其中 W_in 是输入图像尺寸，padding补0数目，k 是卷积核尺寸，stride是步长

如果dilation 大于 1， 代表是空洞卷积，则需计算 空洞卷积 的等效卷积核 尺寸K，带入上式 k 中

K = k_d + ( k_d − 1 ) ∗ ( d− 1)

其中 K 代表等效卷积核尺寸，k_d 代表实际卷积核尺寸， d 代表 dilation--空洞卷积的参数

参数变量：

• weight（tensor）——卷积的权重，（out_channels, in_channels, kernel_size）
• bias（tensor）——卷积的偏置，（out_channels）

 

BatchNorm2d

示例，nn.BatchNorm2d(64),

括号内写传入的channel数

 

MaxPool2d

示例，nn.MaxPool2d(2, 2, 0)，分别对应MaxPool2d(kernel_size, stride, padding)

torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)

 

Transforms

transforms.ToTensor(),  # range [0, 255] -> [0.0,1.0]，自动每个数除以255。相当于归一化到 [0, 1]之间。

transforms.Normalize()

channel=（channel-mean）/std (因为transforms.ToTensor()已经把数据处理成[0,1],那么(x-0.5)/0.5就是[-1.0, 1.0])

transforms.Normalize(mean = (0.5, 0.5, 0.5), std = (0.5, 0.5, 0.5))

transforms完整使用方法：

train_transform = transforms.Compose([
    transforms.ToPILImage(),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(15),
    transforms.ToTensor(), # 将图片转成tensor，并把数值normalize到[0,1]
    transforms.Normalize(mean = (0.5, 0.5, 0.5), std = (0.5, 0.5, 0.5)),
])

View Code

然后在DataSet函数

__init__时，self.transform=transform

 __getitem__(index)时， x = self.transform(X[index])

 

那么问题来了，一般是normalize到哪个区间中呢？？

 

打印网络各层参数及图形输出尺寸

1 def show_summary():
  2     from collections import OrderedDict
  3     import pandas as pd
  4     import numpy as np
  5 
  6     import torch
  7     from torch.autograd import Variable
  8     import torch.nn.functional as F
  9     from torch import nn
 10 
 11 
 12     def get_names_dict(model):
 13         """
 14         Recursive walk to get names including path
 15         """
 16         names = {}
 17         def _get_names(module, parent_name=''):
 18             for key, module in module.named_children():
 19                 name = parent_name + '.' + key if parent_name else key
 20                 names[name]=module
 21                 if isinstance(module, torch.nn.Module):
 22                     _get_names(module, parent_name=name)
 23         _get_names(model)
 24         return names
 25 
 26 
 27     def torch_summarize_df(input_size, model, weights=False, input_shape=True, nb_trainable=False):
 28         """
 29         Summarizes torch model by showing trainable parameters and weights.
 30         
 31         author: wassname
 32         url: https://gist.github.com/wassname/0fb8f95e4272e6bdd27bd7df386716b7
 33         license: MIT
 34         
 35         Modified from:
 36         - https://github.com/pytorch/pytorch/issues/2001#issuecomment-313735757
 37         - https://gist.github.com/wassname/0fb8f95e4272e6bdd27bd7df386716b7/
 38         
 39         Usage:
 40             import torchvision.models as models
 41             model = models.alexnet()
 42             df = torch_summarize_df(input_size=(3, 224,224), model=model)
 43             print(df)
 44             
 45             #              name class_name        input_shape       output_shape  nb_params
 46             # 1     features=>0     Conv2d  (-1, 3, 224, 224)   (-1, 64, 55, 55)      23296#(3*11*11+1)*64
 47             # 2     features=>1       ReLU   (-1, 64, 55, 55)   (-1, 64, 55, 55)          0
 48             # ...
 49         """
 50 
 51         def register_hook(module):
 52             def hook(module, input, output):
 53                 name = ''
 54                 for key, item in names.items():
 55                     if item == module:
 56                         name = key
 57                 #<class 'torch.nn.modules.conv.Conv2d'>
 58                 class_name = str(module.__class__).split('.')[-1].split("'")[0]
 59                 module_idx = len(summary)
 60 
 61                 m_key = module_idx + 1
 62 
 63                 summary[m_key] = OrderedDict()
 64                 summary[m_key]['name'] = name
 65                 summary[m_key]['class_name'] = class_name
 66                 if input_shape:
 67                     summary[m_key][
 68                         'input_shape'] = (-1, ) + tuple(input[0].size())[1:]
 69                 summary[m_key]['output_shape'] = (-1, ) + tuple(output.size())[1:]
 70                 if weights:
 71                     summary[m_key]['weights'] = list(
 72                         [tuple(p.size()) for p in module.parameters()])
 73 
 74     #             summary[m_key]['trainable'] = any([p.requires_grad for p in module.parameters()])
 75                 if nb_trainable:
 76                     params_trainable = sum([torch.LongTensor(list(p.size())).prod() for p in module.parameters() if p.requires_grad])
 77                     summary[m_key]['nb_trainable'] = params_trainable
 78                 params = sum([torch.LongTensor(list(p.size())).prod() for p in module.parameters()])
 79                 summary[m_key]['nb_params'] = params
 80                 
 81 
 82             if  not isinstance(module, nn.Sequential) and \
 83                 not isinstance(module, nn.ModuleList) and \
 84                 not (module == model):
 85                 hooks.append(module.register_forward_hook(hook))
 86                 
 87         # Names are stored in parent and path+name is unique not the name
 88         names = get_names_dict(model)
 89 
 90         # check if there are multiple inputs to the network
 91         if isinstance(input_size[0], (list, tuple)):
 92             x = [Variable(torch.rand(1, *in_size)) for in_size in input_size]
 93         else:
 94             x = Variable(torch.rand(1, *input_size))
 95 
 96         if next(model.parameters()).is_cuda:
 97             x = x.cuda()
 98 
 99         # create properties
100         summary = OrderedDict()
101         hooks = []
102 
103         # register hook
104         model.apply(register_hook)
105 
106         # make a forward pass
107         model(x)
108 
109         # remove these hooks
110         for h in hooks:
111             h.remove()
112 
113         # make dataframe
114         df_summary = pd.DataFrame.from_dict(summary, orient='index')
115 
116         return df_summary
117 
118 
119     # Test on alexnet
120     import torchvision.models as models
121     model = Classifier_1()
122     df = torch_summarize_df(input_size=(1, 48, 48), model=model)
123     print(df)
124 
125 show_summary()

View Code

效果如下：

name   class_name        input_shape       output_shape        nb_params
1    cnn.0       Conv2d    (-1, 1, 48, 48)   (-1, 64, 49, 49)      tensor(320)
2    cnn.1  BatchNorm2d   (-1, 64, 49, 49)   (-1, 64, 49, 49)      tensor(128)
3    cnn.2         ReLU   (-1, 64, 49, 49)   (-1, 64, 49, 49)                0
4    cnn.3    MaxPool2d   (-1, 64, 49, 49)   (-1, 64, 24, 24)                0
5    cnn.4       Conv2d   (-1, 64, 24, 24)  (-1, 128, 25, 25)    tensor(32896)
6    cnn.5  BatchNorm2d  (-1, 128, 25, 25)  (-1, 128, 25, 25)      tensor(256)
7    cnn.6         ReLU  (-1, 128, 25, 25)  (-1, 128, 25, 25)                0
8    cnn.7    MaxPool2d  (-1, 128, 25, 25)  (-1, 128, 12, 12)                0
9    cnn.8       Conv2d  (-1, 128, 12, 12)  (-1, 256, 12, 12)   tensor(295168)
10   cnn.9  BatchNorm2d  (-1, 256, 12, 12)  (-1, 256, 12, 12)      tensor(512)
11  cnn.10         ReLU  (-1, 256, 12, 12)  (-1, 256, 12, 12)                0
12  cnn.11    MaxPool2d  (-1, 256, 12, 12)    (-1, 256, 6, 6)                0
13  cnn.12       Conv2d    (-1, 256, 6, 6)    (-1, 256, 6, 6)   tensor(590080)
14  cnn.13  BatchNorm2d    (-1, 256, 6, 6)    (-1, 256, 6, 6)      tensor(512)
15  cnn.14         ReLU    (-1, 256, 6, 6)    (-1, 256, 6, 6)                0
16  cnn.15    MaxPool2d    (-1, 256, 6, 6)    (-1, 256, 3, 3)                0
17    fc.0       Linear         (-1, 2304)          (-1, 512)  tensor(1180160)
18    fc.1      Dropout          (-1, 512)          (-1, 512)                0
19    fc.2         ReLU          (-1, 512)          (-1, 512)                0
20    fc.3       Linear          (-1, 512)          (-1, 256)   tensor(131328)
21    fc.4      Dropout          (-1, 256)          (-1, 256)                0
22    fc.5       Linear          (-1, 256)            (-1, 7)     tensor(1799)

View Code

参考：https://zhuanlan.zhihu.com/p/33992733

 

保存模型，读取模型

只保存、读取模型参数

# 保存
torch.save(model.state_dict(), 'parameter.pkl')
# 加载
model = TheModelClass(...)
model.load_state_dict(torch.load('parameter.pkl'))

View Code

保存、读取完整模型

# 保存
torch.save(model, 'model.pkl')
# 加载
model = torch.load('model.pkl')

View Code

 

 

 

 

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