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：
Wout = (Win + 2*padding - k) / stride + 1
其中 Win 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() 使用如下公式进行归一化：
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)), ])
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()
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)
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'))
保存、读取完整模型
# 保存 torch.save(model, 'model.pkl') # 加载 model = torch.load('model.pkl')
个人学习记录，如有描述欠妥之处，欢迎大家指出交流~(*^__^*)
参考：
https://www.jianshu.com/p/6ba95579082c