pytorch中的模型总结
Model summary in pytorch
如何像 Keras 中的 model.summary() 方法一样在 PyTorch 中打印模型的摘要：
Model Summary:
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 1, 15, 27) 0
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, 8, 15, 27) 872 input_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 8, 7, 27) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1512) 0 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 1) 1513 flatten_1[0][0]
====================================================================================================
Total params: 2,385
Trainable params: 2,385
Non-trainable params: 0
虽然您不会像 Keras 中那样获得有关模型的详细信息 model.summary，但只需打印模型即可让您了解所涉及的不同层及其规范。
例如：
from torchvision import models
model = models.vgg16()
print(model)
这种情况下的输出如下：
VGG (
(features): Sequential (
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU (inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU (inplace)
(4): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU (inplace)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU (inplace)
(9): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU (inplace)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU (inplace)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU (inplace)
(16): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU (inplace)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU (inplace)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU (inplace)
(23): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU (inplace)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU (inplace)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU (inplace)
(30): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
)
(classifier): Sequential (
(0): Dropout (p = 0.5)
(1): Linear (25088 -> 4096)
(2): ReLU (inplace)
(3): Dropout (p = 0.5)
(4): Linear (4096 -> 4096)
(5): ReLU (inplace)
(6): Linear (4096 -> 1000)
)
)
现在您可以像 Kashyap 提到的那样，使用 state_dict 方法来获取不同层的权重。但是使用这个层列表可能会提供更多的方向是创建一个辅助函数来获得像模型摘要这样的 Keras！希望这对您有所帮助！
这将显示模型的权重和参数（但不显示输出形状）。
from torch.nn.modules.module import _addindent
import torch
import numpy as np
def torch_summarize(model, show_weights=True, show_parameters=True):
"""Summarizes torch model by showing trainable parameters and weights."""
tmpstr = model.__class__.__name__ + ' (\n'
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params and weights
if type(module) in [
torch.nn.modules.container.Container,
torch.nn.modules.container.Sequential
]:
modstr = torch_summarize(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
tmpstr = tmpstr + ')'
return tmpstr
# Test
import torchvision.models as models
model = models.alexnet()
print(torch_summarize(model))
# # Output
# AlexNet (
# (features): Sequential (
# (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2)), weights=((64, 3, 11, 11), (64,)), parameters=23296
# (1): ReLU (inplace), weights=(), parameters=0
# (2): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)), weights=((192, 64, 5, 5), (192,)), parameters=307392
# (4): ReLU (inplace), weights=(), parameters=0
# (5): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((384, 192, 3, 3), (384,)), parameters=663936
# (7): ReLU (inplace), weights=(), parameters=0
# (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((256, 384, 3, 3), (256,)), parameters=884992
# (9): ReLU (inplace), weights=(), parameters=0
# (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((256, 256, 3, 3), (256,)), parameters=590080
# (11): ReLU (inplace), weights=(), parameters=0
# (12): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# ), weights=((64, 3, 11, 11), (64,), (192, 64, 5, 5), (192,), (384, 192, 3, 3), (384,), (256, 384, 3, 3), (256,), (256, 256, 3, 3), (256,)), parameters=2469696
# (classifier): Sequential (
# (0): Dropout (p = 0.5), weights=(), parameters=0
# (1): Linear (9216 -> 4096), weights=((4096, 9216), (4096,)), parameters=37752832
# (2): ReLU (inplace), weights=(), parameters=0
# (3): Dropout (p = 0.5), weights=(), parameters=0
# (4): Linear (4096 -> 4096), weights=((4096, 4096), (4096,)), parameters=16781312
# (5): ReLU (inplace), weights=(), parameters=0
# (6): Linear (4096 -> 1000), weights=((1000, 4096), (1000,)), parameters=4097000
# ), weights=((4096, 9216), (4096,), (4096, 4096), (4096,), (1000, 4096), (1000,)), parameters=58631144
# )
编辑：isaykatsman 有一个 pytorch PR 添加一个 model.summary() 与 keras https://github.com/pytorch/pytorch/pull/3043/files
完全一样
最容易记住（不如 Keras 漂亮）：
print(model)
这也有效：
repr(model)
如果你只是想要参数个数：
sum([param.nelement() for param in model.parameters()])
发件人：Is there similar pytorch function as model.summary() as keras? (forum.PyTorch.org)
是的，您可以获得精确的 Keras 表示，使用 pytorch-summary 包。
VGG16 示例：
from torchvision import models
from torchsummary import summary
vgg = models.vgg16()
summary(vgg, (3, 224, 224))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 224, 224] 1,792
ReLU-2 [-1, 64, 224, 224] 0
Conv2d-3 [-1, 64, 224, 224] 36,928
ReLU-4 [-1, 64, 224, 224] 0
MaxPool2d-5 [-1, 64, 112, 112] 0
Conv2d-6 [-1, 128, 112, 112] 73,856
ReLU-7 [-1, 128, 112, 112] 0
Conv2d-8 [-1, 128, 112, 112] 147,584
ReLU-9 [-1, 128, 112, 112] 0
MaxPool2d-10 [-1, 128, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 295,168
ReLU-12 [-1, 256, 56, 56] 0
Conv2d-13 [-1, 256, 56, 56] 590,080
ReLU-14 [-1, 256, 56, 56] 0
Conv2d-15 [-1, 256, 56, 56] 590,080
ReLU-16 [-1, 256, 56, 56] 0
MaxPool2d-17 [-1, 256, 28, 28] 0
Conv2d-18 [-1, 512, 28, 28] 1,180,160
ReLU-19 [-1, 512, 28, 28] 0
Conv2d-20 [-1, 512, 28, 28] 2,359,808
ReLU-21 [-1, 512, 28, 28] 0
Conv2d-22 [-1, 512, 28, 28] 2,359,808
ReLU-23 [-1, 512, 28, 28] 0
MaxPool2d-24 [-1, 512, 14, 14] 0
Conv2d-25 [-1, 512, 14, 14] 2,359,808
ReLU-26 [-1, 512, 14, 14] 0
Conv2d-27 [-1, 512, 14, 14] 2,359,808
ReLU-28 [-1, 512, 14, 14] 0
Conv2d-29 [-1, 512, 14, 14] 2,359,808
ReLU-30 [-1, 512, 14, 14] 0
MaxPool2d-31 [-1, 512, 7, 7] 0
Linear-32 [-1, 4096] 102,764,544
ReLU-33 [-1, 4096] 0
Dropout-34 [-1, 4096] 0
Linear-35 [-1, 4096] 16,781,312
ReLU-36 [-1, 4096] 0
Dropout-37 [-1, 4096] 0
Linear-38 [-1, 1000] 4,097,000
================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 218.59
Params size (MB): 527.79
Estimated Total Size (MB): 746.96
----------------------------------------------------------------
为模型定义对象后简单打印模型class
class RNN(nn.Module):
def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim):
super().__init__()
self.embedding = nn.Embedding(input_dim, embedding_dim)
self.rnn = nn.RNN(embedding_dim, hidden_dim)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward():
...
model = RNN(input_dim, embedding_dim, hidden_dim, output_dim)
print(model)
为了使用 torchsummary 类型：
from torchsummary import summary
如果没有请先安装。
pip install torchsummary
然后你可以试试，但请注意，由于某种原因它不起作用，除非我将模型设置为 cuda alexnet.cuda:
from torchsummary import summary
help(summary)
import torchvision.models as models
alexnet = models.alexnet(pretrained=False)
alexnet.cuda()
summary(alexnet, (3, 224, 224))
print(alexnet)
summary 必须采用输入大小，批大小设置为 -1，表示我们提供的任何批大小。
如果我们设置 summary(alexnet, (3, 224, 224), 32) 这意味着使用 bs=32.
summary(model, input_size, batch_size=-1, device='cuda')
输出：
Help on function summary in module torchsummary.torchsummary:
summary(model, input_size, batch_size=-1, device='cuda')
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [32, 64, 55, 55] 23,296
ReLU-2 [32, 64, 55, 55] 0
MaxPool2d-3 [32, 64, 27, 27] 0
Conv2d-4 [32, 192, 27, 27] 307,392
ReLU-5 [32, 192, 27, 27] 0
MaxPool2d-6 [32, 192, 13, 13] 0
Conv2d-7 [32, 384, 13, 13] 663,936
ReLU-8 [32, 384, 13, 13] 0
Conv2d-9 [32, 256, 13, 13] 884,992
ReLU-10 [32, 256, 13, 13] 0
Conv2d-11 [32, 256, 13, 13] 590,080
ReLU-12 [32, 256, 13, 13] 0
MaxPool2d-13 [32, 256, 6, 6] 0
AdaptiveAvgPool2d-14 [32, 256, 6, 6] 0
Dropout-15 [32, 9216] 0
Linear-16 [32, 4096] 37,752,832
ReLU-17 [32, 4096] 0
Dropout-18 [32, 4096] 0
Linear-19 [32, 4096] 16,781,312
ReLU-20 [32, 4096] 0
Linear-21 [32, 1000] 4,097,000
================================================================
Total params: 61,100,840
Trainable params: 61,100,840
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 18.38
Forward/backward pass size (MB): 268.12
Params size (MB): 233.08
Estimated Total Size (MB): 519.58
----------------------------------------------------------------
AlexNet(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): ReLU(inplace)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): ReLU(inplace)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace)
(8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace)
(12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
(classifier): Sequential(
(0): Dropout(p=0.5)
(1): Linear(in_features=9216, out_features=4096, bias=True)
(2): ReLU(inplace)
(3): Dropout(p=0.5)
(4): Linear(in_features=4096, out_features=4096, bias=True)
(5): ReLU(inplace)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
)
您可以使用
from torchsummary import summary
您可以指定设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
您可以创建一个网络，如果您使用的是 MNIST 数据集，那么以下命令将起作用并显示摘要
model = Network().to(device)
summary(model,(1,28,28))
Keras 喜欢使用 torchsummary 的模型摘要：
from torchsummary import summary
summary(model, input_size=(3, 224, 224))
torchinfo（以前的 torchsummary）包产生与 Keras1（对于给定的输入形状）类似的输出：2
from torchinfo import summary
model = ConvNet()
batch_size = 16
summary(model, input_size=(batch_size, 1, 28, 28))
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
├─Conv2d (conv1): 1-1 [5, 10, 24, 24] 260
├─Conv2d (conv2): 1-2 [5, 20, 8, 8] 5,020
├─Dropout2d (conv2_drop): 1-3 [5, 20, 8, 8] --
├─Linear (fc1): 1-4 [5, 50] 16,050
├─Linear (fc2): 1-5 [5, 10] 510
==========================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
Total mult-adds (M): 7.69
==========================================================================================
Input size (MB): 0.05
Forward/backward pass size (MB): 0.91
Params size (MB): 0.09
Estimated Total Size (MB): 1.05
==========================================================================================
备注：
-
Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensorflow's model.summary()...
与 Keras 不同，PyTorch 有一个 可以适应任何兼容的跨多个调用的输入形状，例如任何足够大的图像尺寸（对于完全卷积网络）。
因此，它不能为每一层呈现 固有 组 input/output 形状，因为这些是输入相关的，为什么在上面的包中你必须指定输入维度。
summary(my_model, (3, 224, 224), device = 'cpu') 将解决问题。
我更喜欢这个简单的片段 -
net = model
modules = [module for module in net.modules()]
params = [param.shape for param in net.parameters()]
# Print Model Summary
print(modules[0])
total_params=0
for i in range(1,len(modules)):
j = 2*i
param = (params[j-2][1]*params[j-2][0])+params[j-1][0]
total_params += param
print("Layer",i,"->\t",end="")
print("Weights:", params[j-2][0],"x",params[j-2][1],
"\tBias: ",params[j-1][0], "\tParameters: ", param)
print("\nTotal Params: ", total_params)
这打印了我需要的一切 -
Net(
(hLayer1): Linear(in_features=1024, out_features=256, bias=True)
(hLayer2): Linear(in_features=256, out_features=128, bias=True)
(hLayer3): Linear(in_features=128, out_features=64, bias=True)
(outLayer): Linear(in_features=64, out_features=10, bias=True)
)
Layer 1 -> Weights: 256 x 1024 Bias: 256 Parameters: 262400
Layer 2 -> Weights: 128 x 256 Bias: 128 Parameters: 32896
Layer 3 -> Weights: 64 x 128 Bias: 64 Parameters: 8256
Layer 4 -> Weights: 10 x 64 Bias: 10 Parameters: 650
Total Parameters: 304202
对于复杂的模型或更深入的模型统计
安装 torchstat
pip install torchstat
获取统计数据
from torchstat import stat
import torchvision.models as models
model = models.vgg19()
stat(model, (3, 224, 224))
输出-
module name input shape output shape params memory(MB) MAdd Flops MemRead(B) MemWrite(B) duration[%] MemR+W(B)
0 features.0 3 224 224 64 224 224 1792.0 12.25 173,408,256.0 89,915,392.0 609280.0 12845056.0 21.56% 13454336.0
1 features.1 64 224 224 64 224 224 0.0 12.25 3,211,264.0 3,211,264.0 12845056.0 12845056.0 0.92% 25690112.0
2 features.2 64 224 224 64 224 224 36928.0 12.25 3,699,376,128.0 1,852,899,328.0 12992768.0 12845056.0 4.74% 25837824.0
3 features.3 64 224 224 64 224 224 0.0 12.25 3,211,264.0 3,211,264.0 12845056.0 12845056.0 0.92% 25690112.0
4 features.4 64 224 224 64 112 112 0.0 3.06 2,408,448.0 3,211,264.0 12845056.0 3211264.0 1.22% 16056320.0
5 features.5 64 112 112 128 112 112 73856.0 6.12 1,849,688,064.0 926,449,664.0 3506688.0 6422528.0 4.71% 9929216.0
6 features.6 128 112 112 128 112 112 0.0 6.12 1,605,632.0 1,605,632.0 6422528.0 6422528.0 0.94% 12845056.0
7 features.7 128 112 112 128 112 112 147584.0 6.12 3,699,376,128.0 1,851,293,696.0 7012864.0 6422528.0 4.36% 13435392.0
8 features.8 128 112 112 128 112 112 0.0 6.12 1,605,632.0 1,605,632.0 6422528.0 6422528.0 0.91% 12845056.0
9 features.9 128 112 112 128 56 56 0.0 1.53 1,204,224.0 1,605,632.0 6422528.0 1605632.0 1.51% 8028160.0
10 features.10 128 56 56 256 56 56 295168.0 3.06 1,849,688,064.0 925,646,848.0 2786304.0 3211264.0 3.57% 5997568.0
11 features.11 256 56 56 256 56 56 0.0 3.06 802,816.0 802,816.0 3211264.0 3211264.0 0.90% 6422528.0
12 features.12 256 56 56 256 56 56 590080.0 3.06 3,699,376,128.0 1,850,490,880.0 5571584.0 3211264.0 4.30% 8782848.0
13 features.13 256 56 56 256 56 56 0.0 3.06 802,816.0 802,816.0 3211264.0 3211264.0 0.90% 6422528.0
14 features.14 256 56 56 256 56 56 590080.0 3.06 3,699,376,128.0 1,850,490,880.0 5571584.0 3211264.0 4.38% 8782848.0
15 features.15 256 56 56 256 56 56 0.0 3.06 802,816.0 802,816.0 3211264.0 3211264.0 0.94% 6422528.0
16 features.16 256 56 56 256 56 56 590080.0 3.06 3,699,376,128.0 1,850,490,880.0 5571584.0 3211264.0 4.33% 8782848.0
17 features.17 256 56 56 256 56 56 0.0 3.06 802,816.0 802,816.0 3211264.0 3211264.0 0.90% 6422528.0
18 features.18 256 56 56 256 28 28 0.0 0.77 602,112.0 802,816.0 3211264.0 802816.0 1.44% 4014080.0
19 features.19 256 28 28 512 28 28 1180160.0 1.53 1,849,688,064.0 925,245,440.0 5523456.0 1605632.0 3.60% 7129088.0
20 features.20 512 28 28 512 28 28 0.0 1.53 401,408.0 401,408.0 1605632.0 1605632.0 0.92% 3211264.0
21 features.21 512 28 28 512 28 28 2359808.0 1.53 3,699,376,128.0 1,850,089,472.0 11044864.0 1605632.0 4.45% 12650496.0
22 features.22 512 28 28 512 28 28 0.0 1.53 401,408.0 401,408.0 1605632.0 1605632.0 0.94% 3211264.0
23 features.23 512 28 28 512 28 28 2359808.0 1.53 3,699,376,128.0 1,850,089,472.0 11044864.0 1605632.0 4.39% 12650496.0
24 features.24 512 28 28 512 28 28 0.0 1.53 401,408.0 401,408.0 1605632.0 1605632.0 0.90% 3211264.0
25 features.25 512 28 28 512 28 28 2359808.0 1.53 3,699,376,128.0 1,850,089,472.0 11044864.0 1605632.0 4.34% 12650496.0
26 features.26 512 28 28 512 28 28 0.0 1.53 401,408.0 401,408.0 1605632.0 1605632.0 0.90% 3211264.0
27 features.27 512 28 28 512 14 14 0.0 0.38 301,056.0 401,408.0 1605632.0 401408.0 0.96% 2007040.0
28 features.28 512 14 14 512 14 14 2359808.0 0.38 924,844,032.0 462,522,368.0 9840640.0 401408.0 0.99% 10242048.0
29 features.29 512 14 14 512 14 14 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
30 features.30 512 14 14 512 14 14 2359808.0 0.38 924,844,032.0 462,522,368.0 9840640.0 401408.0 0.11% 10242048.0
31 features.31 512 14 14 512 14 14 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
32 features.32 512 14 14 512 14 14 2359808.0 0.38 924,844,032.0 462,522,368.0 9840640.0 401408.0 0.11% 10242048.0
33 features.33 512 14 14 512 14 14 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
34 features.34 512 14 14 512 14 14 2359808.0 0.38 924,844,032.0 462,522,368.0 9840640.0 401408.0 0.11% 10242048.0
35 features.35 512 14 14 512 14 14 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
36 features.36 512 14 14 512 7 7 0.0 0.10 75,264.0 100,352.0 401408.0 100352.0 0.01% 501760.0
37 avgpool 512 7 7 512 7 7 0.0 0.10 0.0 0.0 0.0 0.0 0.49% 0.0
38 classifier.0 25088 4096 102764544.0 0.02 205,516,800.0 102,760,448.0 411158528.0 16384.0 11.27% 411174912.0
39 classifier.1 4096 4096 0.0 0.02 4,096.0 4,096.0 16384.0 16384.0 0.00% 32768.0
40 classifier.2 4096 4096 0.0 0.02 0.0 0.0 0.0 0.0 0.01% 0.0
41 classifier.3 4096 4096 16781312.0 0.02 33,550,336.0 16,777,216.0 67141632.0 16384.0 1.08% 67158016.0
42 classifier.4 4096 4096 0.0 0.02 4,096.0 4,096.0 16384.0 16384.0 0.00% 32768.0
43 classifier.5 4096 4096 0.0 0.02 0.0 0.0 0.0 0.0 0.00% 0.0
44 classifier.6 4096 1000 4097000.0 0.00 8,191,000.0 4,096,000.0 16404384.0 4000.0 0.93% 16408384.0
total 143667240.0 119.34 39,283,567,128.0 19,667,896,320.0 16404384.0 4000.0 100.00% 825282624.0
============================================================================================================================================================
Total params: 143,667,240
------------------------------------------------------------------------------------------------------------------------------------------------------------
Total memory: 119.34 MB
Total MAdd: 39.28 GMAdd
Total Flops: 19.67 GFlops
Total MemR+W: 787.05 MB
如何像 Keras 中的 model.summary() 方法一样在 PyTorch 中打印模型的摘要：
Model Summary:
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 1, 15, 27) 0
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, 8, 15, 27) 872 input_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 8, 7, 27) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1512) 0 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 1) 1513 flatten_1[0][0]
====================================================================================================
Total params: 2,385
Trainable params: 2,385
Non-trainable params: 0
虽然您不会像 Keras 中那样获得有关模型的详细信息 model.summary，但只需打印模型即可让您了解所涉及的不同层及其规范。
例如：
from torchvision import models
model = models.vgg16()
print(model)
这种情况下的输出如下：
VGG (
(features): Sequential (
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU (inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU (inplace)
(4): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU (inplace)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU (inplace)
(9): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU (inplace)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU (inplace)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU (inplace)
(16): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU (inplace)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU (inplace)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU (inplace)
(23): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU (inplace)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU (inplace)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU (inplace)
(30): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
)
(classifier): Sequential (
(0): Dropout (p = 0.5)
(1): Linear (25088 -> 4096)
(2): ReLU (inplace)
(3): Dropout (p = 0.5)
(4): Linear (4096 -> 4096)
(5): ReLU (inplace)
(6): Linear (4096 -> 1000)
)
)
现在您可以像 Kashyap 提到的那样，使用 state_dict 方法来获取不同层的权重。但是使用这个层列表可能会提供更多的方向是创建一个辅助函数来获得像模型摘要这样的 Keras！希望这对您有所帮助！
这将显示模型的权重和参数（但不显示输出形状）。
from torch.nn.modules.module import _addindent
import torch
import numpy as np
def torch_summarize(model, show_weights=True, show_parameters=True):
"""Summarizes torch model by showing trainable parameters and weights."""
tmpstr = model.__class__.__name__ + ' (\n'
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params and weights
if type(module) in [
torch.nn.modules.container.Container,
torch.nn.modules.container.Sequential
]:
modstr = torch_summarize(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
tmpstr = tmpstr + ')'
return tmpstr
# Test
import torchvision.models as models
model = models.alexnet()
print(torch_summarize(model))
# # Output
# AlexNet (
# (features): Sequential (
# (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2)), weights=((64, 3, 11, 11), (64,)), parameters=23296
# (1): ReLU (inplace), weights=(), parameters=0
# (2): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)), weights=((192, 64, 5, 5), (192,)), parameters=307392
# (4): ReLU (inplace), weights=(), parameters=0
# (5): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((384, 192, 3, 3), (384,)), parameters=663936
# (7): ReLU (inplace), weights=(), parameters=0
# (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((256, 384, 3, 3), (256,)), parameters=884992
# (9): ReLU (inplace), weights=(), parameters=0
# (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((256, 256, 3, 3), (256,)), parameters=590080
# (11): ReLU (inplace), weights=(), parameters=0
# (12): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# ), weights=((64, 3, 11, 11), (64,), (192, 64, 5, 5), (192,), (384, 192, 3, 3), (384,), (256, 384, 3, 3), (256,), (256, 256, 3, 3), (256,)), parameters=2469696
# (classifier): Sequential (
# (0): Dropout (p = 0.5), weights=(), parameters=0
# (1): Linear (9216 -> 4096), weights=((4096, 9216), (4096,)), parameters=37752832
# (2): ReLU (inplace), weights=(), parameters=0
# (3): Dropout (p = 0.5), weights=(), parameters=0
# (4): Linear (4096 -> 4096), weights=((4096, 4096), (4096,)), parameters=16781312
# (5): ReLU (inplace), weights=(), parameters=0
# (6): Linear (4096 -> 1000), weights=((1000, 4096), (1000,)), parameters=4097000
# ), weights=((4096, 9216), (4096,), (4096, 4096), (4096,), (1000, 4096), (1000,)), parameters=58631144
# )
编辑：isaykatsman 有一个 pytorch PR 添加一个 model.summary() 与 keras https://github.com/pytorch/pytorch/pull/3043/files
最容易记住（不如 Keras 漂亮）：
print(model)
这也有效：
repr(model)
如果你只是想要参数个数：
sum([param.nelement() for param in model.parameters()])
发件人：Is there similar pytorch function as model.summary() as keras? (forum.PyTorch.org)
是的，您可以获得精确的 Keras 表示，使用 pytorch-summary 包。
VGG16 示例：
from torchvision import models
from torchsummary import summary
vgg = models.vgg16()
summary(vgg, (3, 224, 224))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 224, 224] 1,792
ReLU-2 [-1, 64, 224, 224] 0
Conv2d-3 [-1, 64, 224, 224] 36,928
ReLU-4 [-1, 64, 224, 224] 0
MaxPool2d-5 [-1, 64, 112, 112] 0
Conv2d-6 [-1, 128, 112, 112] 73,856
ReLU-7 [-1, 128, 112, 112] 0
Conv2d-8 [-1, 128, 112, 112] 147,584
ReLU-9 [-1, 128, 112, 112] 0
MaxPool2d-10 [-1, 128, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 295,168
ReLU-12 [-1, 256, 56, 56] 0
Conv2d-13 [-1, 256, 56, 56] 590,080
ReLU-14 [-1, 256, 56, 56] 0
Conv2d-15 [-1, 256, 56, 56] 590,080
ReLU-16 [-1, 256, 56, 56] 0
MaxPool2d-17 [-1, 256, 28, 28] 0
Conv2d-18 [-1, 512, 28, 28] 1,180,160
ReLU-19 [-1, 512, 28, 28] 0
Conv2d-20 [-1, 512, 28, 28] 2,359,808
ReLU-21 [-1, 512, 28, 28] 0
Conv2d-22 [-1, 512, 28, 28] 2,359,808
ReLU-23 [-1, 512, 28, 28] 0
MaxPool2d-24 [-1, 512, 14, 14] 0
Conv2d-25 [-1, 512, 14, 14] 2,359,808
ReLU-26 [-1, 512, 14, 14] 0
Conv2d-27 [-1, 512, 14, 14] 2,359,808
ReLU-28 [-1, 512, 14, 14] 0
Conv2d-29 [-1, 512, 14, 14] 2,359,808
ReLU-30 [-1, 512, 14, 14] 0
MaxPool2d-31 [-1, 512, 7, 7] 0
Linear-32 [-1, 4096] 102,764,544
ReLU-33 [-1, 4096] 0
Dropout-34 [-1, 4096] 0
Linear-35 [-1, 4096] 16,781,312
ReLU-36 [-1, 4096] 0
Dropout-37 [-1, 4096] 0
Linear-38 [-1, 1000] 4,097,000
================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 218.59
Params size (MB): 527.79
Estimated Total Size (MB): 746.96
----------------------------------------------------------------
为模型定义对象后简单打印模型class
class RNN(nn.Module):
def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim):
super().__init__()
self.embedding = nn.Embedding(input_dim, embedding_dim)
self.rnn = nn.RNN(embedding_dim, hidden_dim)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward():
...
model = RNN(input_dim, embedding_dim, hidden_dim, output_dim)
print(model)
为了使用 torchsummary 类型：
from torchsummary import summary
如果没有请先安装。
pip install torchsummary
然后你可以试试，但请注意，由于某种原因它不起作用，除非我将模型设置为 cuda alexnet.cuda:
from torchsummary import summary
help(summary)
import torchvision.models as models
alexnet = models.alexnet(pretrained=False)
alexnet.cuda()
summary(alexnet, (3, 224, 224))
print(alexnet)
summary 必须采用输入大小，批大小设置为 -1，表示我们提供的任何批大小。
如果我们设置 summary(alexnet, (3, 224, 224), 32) 这意味着使用 bs=32.
summary(model, input_size, batch_size=-1, device='cuda')
输出：
Help on function summary in module torchsummary.torchsummary:
summary(model, input_size, batch_size=-1, device='cuda')
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [32, 64, 55, 55] 23,296
ReLU-2 [32, 64, 55, 55] 0
MaxPool2d-3 [32, 64, 27, 27] 0
Conv2d-4 [32, 192, 27, 27] 307,392
ReLU-5 [32, 192, 27, 27] 0
MaxPool2d-6 [32, 192, 13, 13] 0
Conv2d-7 [32, 384, 13, 13] 663,936
ReLU-8 [32, 384, 13, 13] 0
Conv2d-9 [32, 256, 13, 13] 884,992
ReLU-10 [32, 256, 13, 13] 0
Conv2d-11 [32, 256, 13, 13] 590,080
ReLU-12 [32, 256, 13, 13] 0
MaxPool2d-13 [32, 256, 6, 6] 0
AdaptiveAvgPool2d-14 [32, 256, 6, 6] 0
Dropout-15 [32, 9216] 0
Linear-16 [32, 4096] 37,752,832
ReLU-17 [32, 4096] 0
Dropout-18 [32, 4096] 0
Linear-19 [32, 4096] 16,781,312
ReLU-20 [32, 4096] 0
Linear-21 [32, 1000] 4,097,000
================================================================
Total params: 61,100,840
Trainable params: 61,100,840
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 18.38
Forward/backward pass size (MB): 268.12
Params size (MB): 233.08
Estimated Total Size (MB): 519.58
----------------------------------------------------------------
AlexNet(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): ReLU(inplace)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): ReLU(inplace)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace)
(8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace)
(12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
(classifier): Sequential(
(0): Dropout(p=0.5)
(1): Linear(in_features=9216, out_features=4096, bias=True)
(2): ReLU(inplace)
(3): Dropout(p=0.5)
(4): Linear(in_features=4096, out_features=4096, bias=True)
(5): ReLU(inplace)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
)
您可以使用
from torchsummary import summary
您可以指定设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
您可以创建一个网络，如果您使用的是 MNIST 数据集，那么以下命令将起作用并显示摘要
model = Network().to(device)
summary(model,(1,28,28))
Keras 喜欢使用 torchsummary 的模型摘要：
from torchsummary import summary
summary(model, input_size=(3, 224, 224))
torchinfo（以前的 torchsummary）包产生与 Keras1（对于给定的输入形状）类似的输出：2
from torchinfo import summary
model = ConvNet()
batch_size = 16
summary(model, input_size=(batch_size, 1, 28, 28))
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
├─Conv2d (conv1): 1-1 [5, 10, 24, 24] 260
├─Conv2d (conv2): 1-2 [5, 20, 8, 8] 5,020
├─Dropout2d (conv2_drop): 1-3 [5, 20, 8, 8] --
├─Linear (fc1): 1-4 [5, 50] 16,050
├─Linear (fc2): 1-5 [5, 10] 510
==========================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
Total mult-adds (M): 7.69
==========================================================================================
Input size (MB): 0.05
Forward/backward pass size (MB): 0.91
Params size (MB): 0.09
Estimated Total Size (MB): 1.05
==========================================================================================
备注：
-
Torchinfo provides information complementary to what is provided by
print(your_model)in PyTorch, similar to Tensorflow'smodel.summary()... 与 Keras 不同，PyTorch 有一个
因此，它不能为每一层呈现 固有 组 input/output 形状，因为这些是输入相关的，为什么在上面的包中你必须指定输入维度。
summary(my_model, (3, 224, 224), device = 'cpu') 将解决问题。
我更喜欢这个简单的片段 -
net = model
modules = [module for module in net.modules()]
params = [param.shape for param in net.parameters()]
# Print Model Summary
print(modules[0])
total_params=0
for i in range(1,len(modules)):
j = 2*i
param = (params[j-2][1]*params[j-2][0])+params[j-1][0]
total_params += param
print("Layer",i,"->\t",end="")
print("Weights:", params[j-2][0],"x",params[j-2][1],
"\tBias: ",params[j-1][0], "\tParameters: ", param)
print("\nTotal Params: ", total_params)
这打印了我需要的一切 -
Net(
(hLayer1): Linear(in_features=1024, out_features=256, bias=True)
(hLayer2): Linear(in_features=256, out_features=128, bias=True)
(hLayer3): Linear(in_features=128, out_features=64, bias=True)
(outLayer): Linear(in_features=64, out_features=10, bias=True)
)
Layer 1 -> Weights: 256 x 1024 Bias: 256 Parameters: 262400
Layer 2 -> Weights: 128 x 256 Bias: 128 Parameters: 32896
Layer 3 -> Weights: 64 x 128 Bias: 64 Parameters: 8256
Layer 4 -> Weights: 10 x 64 Bias: 10 Parameters: 650
Total Parameters: 304202
对于复杂的模型或更深入的模型统计
安装 torchstat
pip install torchstat
获取统计数据
from torchstat import stat
import torchvision.models as models
model = models.vgg19()
stat(model, (3, 224, 224))
输出-
module name input shape output shape params memory(MB) MAdd Flops MemRead(B) MemWrite(B) duration[%] MemR+W(B)
0 features.0 3 224 224 64 224 224 1792.0 12.25 173,408,256.0 89,915,392.0 609280.0 12845056.0 21.56% 13454336.0
1 features.1 64 224 224 64 224 224 0.0 12.25 3,211,264.0 3,211,264.0 12845056.0 12845056.0 0.92% 25690112.0
2 features.2 64 224 224 64 224 224 36928.0 12.25 3,699,376,128.0 1,852,899,328.0 12992768.0 12845056.0 4.74% 25837824.0
3 features.3 64 224 224 64 224 224 0.0 12.25 3,211,264.0 3,211,264.0 12845056.0 12845056.0 0.92% 25690112.0
4 features.4 64 224 224 64 112 112 0.0 3.06 2,408,448.0 3,211,264.0 12845056.0 3211264.0 1.22% 16056320.0
5 features.5 64 112 112 128 112 112 73856.0 6.12 1,849,688,064.0 926,449,664.0 3506688.0 6422528.0 4.71% 9929216.0
6 features.6 128 112 112 128 112 112 0.0 6.12 1,605,632.0 1,605,632.0 6422528.0 6422528.0 0.94% 12845056.0
7 features.7 128 112 112 128 112 112 147584.0 6.12 3,699,376,128.0 1,851,293,696.0 7012864.0 6422528.0 4.36% 13435392.0
8 features.8 128 112 112 128 112 112 0.0 6.12 1,605,632.0 1,605,632.0 6422528.0 6422528.0 0.91% 12845056.0
9 features.9 128 112 112 128 56 56 0.0 1.53 1,204,224.0 1,605,632.0 6422528.0 1605632.0 1.51% 8028160.0
10 features.10 128 56 56 256 56 56 295168.0 3.06 1,849,688,064.0 925,646,848.0 2786304.0 3211264.0 3.57% 5997568.0
11 features.11 256 56 56 256 56 56 0.0 3.06 802,816.0 802,816.0 3211264.0 3211264.0 0.90% 6422528.0
12 features.12 256 56 56 256 56 56 590080.0 3.06 3,699,376,128.0 1,850,490,880.0 5571584.0 3211264.0 4.30% 8782848.0
13 features.13 256 56 56 256 56 56 0.0 3.06 802,816.0 802,816.0 3211264.0 3211264.0 0.90% 6422528.0
14 features.14 256 56 56 256 56 56 590080.0 3.06 3,699,376,128.0 1,850,490,880.0 5571584.0 3211264.0 4.38% 8782848.0
15 features.15 256 56 56 256 56 56 0.0 3.06 802,816.0 802,816.0 3211264.0 3211264.0 0.94% 6422528.0
16 features.16 256 56 56 256 56 56 590080.0 3.06 3,699,376,128.0 1,850,490,880.0 5571584.0 3211264.0 4.33% 8782848.0
17 features.17 256 56 56 256 56 56 0.0 3.06 802,816.0 802,816.0 3211264.0 3211264.0 0.90% 6422528.0
18 features.18 256 56 56 256 28 28 0.0 0.77 602,112.0 802,816.0 3211264.0 802816.0 1.44% 4014080.0
19 features.19 256 28 28 512 28 28 1180160.0 1.53 1,849,688,064.0 925,245,440.0 5523456.0 1605632.0 3.60% 7129088.0
20 features.20 512 28 28 512 28 28 0.0 1.53 401,408.0 401,408.0 1605632.0 1605632.0 0.92% 3211264.0
21 features.21 512 28 28 512 28 28 2359808.0 1.53 3,699,376,128.0 1,850,089,472.0 11044864.0 1605632.0 4.45% 12650496.0
22 features.22 512 28 28 512 28 28 0.0 1.53 401,408.0 401,408.0 1605632.0 1605632.0 0.94% 3211264.0
23 features.23 512 28 28 512 28 28 2359808.0 1.53 3,699,376,128.0 1,850,089,472.0 11044864.0 1605632.0 4.39% 12650496.0
24 features.24 512 28 28 512 28 28 0.0 1.53 401,408.0 401,408.0 1605632.0 1605632.0 0.90% 3211264.0
25 features.25 512 28 28 512 28 28 2359808.0 1.53 3,699,376,128.0 1,850,089,472.0 11044864.0 1605632.0 4.34% 12650496.0
26 features.26 512 28 28 512 28 28 0.0 1.53 401,408.0 401,408.0 1605632.0 1605632.0 0.90% 3211264.0
27 features.27 512 28 28 512 14 14 0.0 0.38 301,056.0 401,408.0 1605632.0 401408.0 0.96% 2007040.0
28 features.28 512 14 14 512 14 14 2359808.0 0.38 924,844,032.0 462,522,368.0 9840640.0 401408.0 0.99% 10242048.0
29 features.29 512 14 14 512 14 14 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
30 features.30 512 14 14 512 14 14 2359808.0 0.38 924,844,032.0 462,522,368.0 9840640.0 401408.0 0.11% 10242048.0
31 features.31 512 14 14 512 14 14 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
32 features.32 512 14 14 512 14 14 2359808.0 0.38 924,844,032.0 462,522,368.0 9840640.0 401408.0 0.11% 10242048.0
33 features.33 512 14 14 512 14 14 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
34 features.34 512 14 14 512 14 14 2359808.0 0.38 924,844,032.0 462,522,368.0 9840640.0 401408.0 0.11% 10242048.0
35 features.35 512 14 14 512 14 14 0.0 0.38 100,352.0 100,352.0 401408.0 401408.0 0.00% 802816.0
36 features.36 512 14 14 512 7 7 0.0 0.10 75,264.0 100,352.0 401408.0 100352.0 0.01% 501760.0
37 avgpool 512 7 7 512 7 7 0.0 0.10 0.0 0.0 0.0 0.0 0.49% 0.0
38 classifier.0 25088 4096 102764544.0 0.02 205,516,800.0 102,760,448.0 411158528.0 16384.0 11.27% 411174912.0
39 classifier.1 4096 4096 0.0 0.02 4,096.0 4,096.0 16384.0 16384.0 0.00% 32768.0
40 classifier.2 4096 4096 0.0 0.02 0.0 0.0 0.0 0.0 0.01% 0.0
41 classifier.3 4096 4096 16781312.0 0.02 33,550,336.0 16,777,216.0 67141632.0 16384.0 1.08% 67158016.0
42 classifier.4 4096 4096 0.0 0.02 4,096.0 4,096.0 16384.0 16384.0 0.00% 32768.0
43 classifier.5 4096 4096 0.0 0.02 0.0 0.0 0.0 0.0 0.00% 0.0
44 classifier.6 4096 1000 4097000.0 0.00 8,191,000.0 4,096,000.0 16404384.0 4000.0 0.93% 16408384.0
total 143667240.0 119.34 39,283,567,128.0 19,667,896,320.0 16404384.0 4000.0 100.00% 825282624.0
============================================================================================================================================================
Total params: 143,667,240
------------------------------------------------------------------------------------------------------------------------------------------------------------
Total memory: 119.34 MB
Total MAdd: 39.28 GMAdd
Total Flops: 19.67 GFlops
Total MemR+W: 787.05 MB