From 4861f034a72df0ceddb313fe137e9459f4cdd76b Mon Sep 17 00:00:00 2001
From: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>
Date: Tue, 21 Feb 2023 21:16:18 +0800
Subject: [PATCH] [Docs] Count FLOPs and parameters (#939)
* [Docs] Count FLOPs and parameters
* add the doc to index.rst
* fix table in HTML
* fix
* fix
* fix indent
* refine
---
docs/en/advanced_tutorials/model_analysis.md | 2 +-
.../advanced_tutorials/model_analysis.md | 3 +
docs/zh_cn/common_usage/model_analysis.md | 740 ++++++++++++++++++
docs/zh_cn/index.rst | 2 +
4 files changed, 746 insertions(+), 1 deletion(-)
create mode 100644 docs/zh_cn/advanced_tutorials/model_analysis.md
create mode 100644 docs/zh_cn/common_usage/model_analysis.md
diff --git a/docs/en/advanced_tutorials/model_analysis.md b/docs/en/advanced_tutorials/model_analysis.md
index 124ad3f6..77d43676 100644
--- a/docs/en/advanced_tutorials/model_analysis.md
+++ b/docs/en/advanced_tutorials/model_analysis.md
@@ -90,7 +90,7 @@ The return outputs is dict, which contains the following keys:
+---------------------+----------------------+--------+--------------+
```
-- `out_arch`: print related information by network layers
+- `out_arch`: print related information by network layers
```bash
TestNet(
diff --git a/docs/zh_cn/advanced_tutorials/model_analysis.md b/docs/zh_cn/advanced_tutorials/model_analysis.md
new file mode 100644
index 00000000..34a7a57d
--- /dev/null
+++ b/docs/zh_cn/advanced_tutorials/model_analysis.md
@@ -0,0 +1,3 @@
+# 模型复杂度分析
+
+翻译中，请暂时阅读英文文档 [Model Complexity Analysis](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/model_analysis.html)。
diff --git a/docs/zh_cn/common_usage/model_analysis.md b/docs/zh_cn/common_usage/model_analysis.md
new file mode 100644
index 00000000..5349cab9
--- /dev/null
+++ b/docs/zh_cn/common_usage/model_analysis.md
@@ -0,0 +1,740 @@
+# 统计模型计算量和参数量
+
+1. 定义模型
+
+ ```python
+ import torch.nn.functional as F
+ import torchvision
+ from mmengine.model import BaseModel
+
+ class MMResNet50(BaseModel):
+ def __init__(self):
+ super().__init__()
+ self.resnet = torchvision.models.resnet50()
+
+ def forward(self, imgs, labels=None, mode='tensor'):
+ x = self.resnet(imgs)
+ if mode == 'loss':
+ return {'loss': F.cross_entropy(x, labels)}
+ elif mode == 'predict':
+ return x, labels
+ elif mode == 'tensor':
+ return x
+ ```
+
+2. 统计计算量和参数量
+
+ ```python
+ from mmengine.analysis import get_model_complexity_info
+
+ input_shape = (3, 224, 224)
+ model = MMResNet50()
+ analysis_results = get_model_complexity_info(model, input_shape)
+ ```
+
+- 以表格的形式显示
+
+ ```python
+ print(analysis_results['out_table'])
+ ```
+
+
+ 点击展开
+
+ ```html
+ +------------------------+----------------------+------------+--------------+
+ | module | #parameters or shape | #flops | #activations |
+ +------------------------+----------------------+------------+--------------+
+ | resnet | 25.557M | 4.145G | 11.115M |
+ | conv1 | 9.408K | 0.118G | 0.803M |
+ | conv1.weight | (64, 3, 7, 7) | | |
+ | bn1 | 0.128K | 4.014M | 0 |
+ | bn1.weight | (64,) | | |
+ | bn1.bias | (64,) | | |
+ | layer1 | 0.216M | 0.69G | 4.415M |
+ | layer1.0 | 75.008K | 0.241G | 2.007M |
+ | layer1.0.conv1 | 4.096K | 12.845M | 0.201M |
+ | layer1.0.bn1 | 0.128K | 1.004M | 0 |
+ | layer1.0.conv2 | 36.864K | 0.116G | 0.201M |
+ | layer1.0.bn2 | 0.128K | 1.004M | 0 |
+ | layer1.0.conv3 | 16.384K | 51.38M | 0.803M |
+ | layer1.0.bn3 | 0.512K | 4.014M | 0 |
+ | layer1.0.downsample | 16.896K | 55.394M | 0.803M |
+ | layer1.1 | 70.4K | 0.224G | 1.204M |
+ | layer1.1.conv1 | 16.384K | 51.38M | 0.201M |
+ | layer1.1.bn1 | 0.128K | 1.004M | 0 |
+ | layer1.1.conv2 | 36.864K | 0.116G | 0.201M |
+ | layer1.1.bn2 | 0.128K | 1.004M | 0 |
+ | layer1.1.conv3 | 16.384K | 51.38M | 0.803M |
+ | layer1.1.bn3 | 0.512K | 4.014M | 0 |
+ | layer1.2 | 70.4K | 0.224G | 1.204M |
+ | layer1.2.conv1 | 16.384K | 51.38M | 0.201M |
+ | layer1.2.bn1 | 0.128K | 1.004M | 0 |
+ | layer1.2.conv2 | 36.864K | 0.116G | 0.201M |
+ | layer1.2.bn2 | 0.128K | 1.004M | 0 |
+ | layer1.2.conv3 | 16.384K | 51.38M | 0.803M |
+ | layer1.2.bn3 | 0.512K | 4.014M | 0 |
+ | layer2 | 1.22M | 1.043G | 3.111M |
+ | layer2.0 | 0.379M | 0.379G | 1.305M |
+ | layer2.0.conv1 | 32.768K | 0.103G | 0.401M |
+ | layer2.0.bn1 | 0.256K | 2.007M | 0 |
+ | layer2.0.conv2 | 0.147M | 0.116G | 0.1M |
+ | layer2.0.bn2 | 0.256K | 0.502M | 0 |
+ | layer2.0.conv3 | 65.536K | 51.38M | 0.401M |
+ | layer2.0.bn3 | 1.024K | 2.007M | 0 |
+ | layer2.0.downsample | 0.132M | 0.105G | 0.401M |
+ | layer2.1 | 0.28M | 0.221G | 0.602M |
+ | layer2.1.conv1 | 65.536K | 51.38M | 0.1M |
+ | layer2.1.bn1 | 0.256K | 0.502M | 0 |
+ | layer2.1.conv2 | 0.147M | 0.116G | 0.1M |
+ | layer2.1.bn2 | 0.256K | 0.502M | 0 |
+ | layer2.1.conv3 | 65.536K | 51.38M | 0.401M |
+ | layer2.1.bn3 | 1.024K | 2.007M | 0 |
+ | layer2.2 | 0.28M | 0.221G | 0.602M |
+ | layer2.2.conv1 | 65.536K | 51.38M | 0.1M |
+ | layer2.2.bn1 | 0.256K | 0.502M | 0 |
+ | layer2.2.conv2 | 0.147M | 0.116G | 0.1M |
+ | layer2.2.bn2 | 0.256K | 0.502M | 0 |
+ | layer2.2.conv3 | 65.536K | 51.38M | 0.401M |
+ | layer2.2.bn3 | 1.024K | 2.007M | 0 |
+ | layer2.3 | 0.28M | 0.221G | 0.602M |
+ | layer2.3.conv1 | 65.536K | 51.38M | 0.1M |
+ | layer2.3.bn1 | 0.256K | 0.502M | 0 |
+ | layer2.3.conv2 | 0.147M | 0.116G | 0.1M |
+ | layer2.3.bn2 | 0.256K | 0.502M | 0 |
+ | layer2.3.conv3 | 65.536K | 51.38M | 0.401M |
+ | layer2.3.bn3 | 1.024K | 2.007M | 0 |
+ | layer3 | 7.098M | 1.475G | 2.158M |
+ | layer3.0 | 1.512M | 0.376G | 0.652M |
+ | layer3.0.conv1 | 0.131M | 0.103G | 0.201M |
+ | layer3.0.bn1 | 0.512K | 1.004M | 0 |
+ | layer3.0.conv2 | 0.59M | 0.116G | 50.176K |
+ | layer3.0.bn2 | 0.512K | 0.251M | 0 |
+ | layer3.0.conv3 | 0.262M | 51.38M | 0.201M |
+ | layer3.0.bn3 | 2.048K | 1.004M | 0 |
+ | layer3.0.downsample | 0.526M | 0.104G | 0.201M |
+ | layer3.1 | 1.117M | 0.22G | 0.301M |
+ | layer3.1.conv1 | 0.262M | 51.38M | 50.176K |
+ | layer3.1.bn1 | 0.512K | 0.251M | 0 |
+ | layer3.1.conv2 | 0.59M | 0.116G | 50.176K |
+ | layer3.1.bn2 | 0.512K | 0.251M | 0 |
+ | layer3.1.conv3 | 0.262M | 51.38M | 0.201M |
+ | layer3.1.bn3 | 2.048K | 1.004M | 0 |
+ | layer3.2 | 1.117M | 0.22G | 0.301M |
+ | layer3.2.conv1 | 0.262M | 51.38M | 50.176K |
+ | layer3.2.bn1 | 0.512K | 0.251M | 0 |
+ | layer3.2.conv2 | 0.59M | 0.116G | 50.176K |
+ | layer3.2.bn2 | 0.512K | 0.251M | 0 |
+ | layer3.2.conv3 | 0.262M | 51.38M | 0.201M |
+ | layer3.2.bn3 | 2.048K | 1.004M | 0 |
+ | layer3.3 | 1.117M | 0.22G | 0.301M |
+ | layer3.3.conv1 | 0.262M | 51.38M | 50.176K |
+ | layer3.3.bn1 | 0.512K | 0.251M | 0 |
+ | layer3.3.conv2 | 0.59M | 0.116G | 50.176K |
+ | layer3.3.bn2 | 0.512K | 0.251M | 0 |
+ | layer3.3.conv3 | 0.262M | 51.38M | 0.201M |
+ | layer3.3.bn3 | 2.048K | 1.004M | 0 |
+ | layer3.4 | 1.117M | 0.22G | 0.301M |
+ | layer3.4.conv1 | 0.262M | 51.38M | 50.176K |
+ | layer3.4.bn1 | 0.512K | 0.251M | 0 |
+ | layer3.4.conv2 | 0.59M | 0.116G | 50.176K |
+ | layer3.4.bn2 | 0.512K | 0.251M | 0 |
+ | layer3.4.conv3 | 0.262M | 51.38M | 0.201M |
+ | layer3.4.bn3 | 2.048K | 1.004M | 0 |
+ | layer3.5 | 1.117M | 0.22G | 0.301M |
+ | layer3.5.conv1 | 0.262M | 51.38M | 50.176K |
+ | layer3.5.bn1 | 0.512K | 0.251M | 0 |
+ | layer3.5.conv2 | 0.59M | 0.116G | 50.176K |
+ | layer3.5.bn2 | 0.512K | 0.251M | 0 |
+ | layer3.5.conv3 | 0.262M | 51.38M | 0.201M |
+ | layer3.5.bn3 | 2.048K | 1.004M | 0 |
+ | layer4 | 14.965M | 0.812G | 0.627M |
+ | layer4.0 | 6.04M | 0.374G | 0.326M |
+ | layer4.0.conv1 | 0.524M | 0.103G | 0.1M |
+ | layer4.0.bn1 | 1.024K | 0.502M | 0 |
+ | layer4.0.conv2 | 2.359M | 0.116G | 25.088K |
+ | layer4.0.bn2 | 1.024K | 0.125M | 0 |
+ | layer4.0.conv3 | 1.049M | 51.38M | 0.1M |
+ | layer4.0.bn3 | 4.096K | 0.502M | 0 |
+ | layer4.0.downsample | 2.101M | 0.103G | 0.1M |
+ | layer4.1 | 4.463M | 0.219G | 0.151M |
+ | layer4.1.conv1 | 1.049M | 51.38M | 25.088K |
+ | layer4.1.bn1 | 1.024K | 0.125M | 0 |
+ | layer4.1.conv2 | 2.359M | 0.116G | 25.088K |
+ | layer4.1.bn2 | 1.024K | 0.125M | 0 |
+ | layer4.1.conv3 | 1.049M | 51.38M | 0.1M |
+ | layer4.1.bn3 | 4.096K | 0.502M | 0 |
+ | layer4.2 | 4.463M | 0.219G | 0.151M |
+ | layer4.2.conv1 | 1.049M | 51.38M | 25.088K |
+ | layer4.2.bn1 | 1.024K | 0.125M | 0 |
+ | layer4.2.conv2 | 2.359M | 0.116G | 25.088K |
+ | layer4.2.bn2 | 1.024K | 0.125M | 0 |
+ | layer4.2.conv3 | 1.049M | 51.38M | 0.1M |
+ | layer4.2.bn3 | 4.096K | 0.502M | 0 |
+ | fc | 2.049M | 2.048M | 1K |
+ | fc.weight | (1000, 2048) | | |
+ | fc.bias | (1000,) | | |
+ | avgpool | | 0.1M | 0 |
+ +------------------------+----------------------+------------+--------------+
+ ```
+
+
+
+- 以模型结构的形式显示
+
+ ```python
+ print(analysis_results['out_arch'])
+ ```
+
+
+ 点击展开
+
+ ```python
+ MMResNet50(
+ #params: 25.56M, #flops: 4.14G, #acts: 11.11M
+ (data_preprocessor): BaseDataPreprocessor(#params: 0, #flops: N/A, #acts: N/A)
+ (resnet): ResNet(
+ #params: 25.56M, #flops: 4.14G, #acts: 11.11M
+ (conv1): Conv2d(
+ 3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
+ #params: 9.41K, #flops: 0.12G, #acts: 0.8M
+ )
+ (bn1): BatchNorm2d(
+ 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.13K, #flops: 4.01M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
+ (layer1): Sequential(
+ #params: 0.22M, #flops: 0.69G, #acts: 4.42M
+ (0): Bottleneck(
+ #params: 75.01K, #flops: 0.24G, #acts: 2.01M
+ (conv1): Conv2d(
+ 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 4.1K, #flops: 12.85M, #acts: 0.2M
+ )
+ (bn1): BatchNorm2d(
+ 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.13K, #flops: 1M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 36.86K, #flops: 0.12G, #acts: 0.2M
+ )
+ (bn2): BatchNorm2d(
+ 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.13K, #flops: 1M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 16.38K, #flops: 51.38M, #acts: 0.8M
+ )
+ (bn3): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 4.01M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ (downsample): Sequential(
+ #params: 16.9K, #flops: 55.39M, #acts: 0.8M
+ (0): Conv2d(
+ 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 16.38K, #flops: 51.38M, #acts: 0.8M
+ )
+ (1): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 4.01M, #acts: 0
+ )
+ )
+ )
+ (1): Bottleneck(
+ #params: 70.4K, #flops: 0.22G, #acts: 1.2M
+ (conv1): Conv2d(
+ 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 16.38K, #flops: 51.38M, #acts: 0.2M
+ )
+ (bn1): BatchNorm2d(
+ 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.13K, #flops: 1M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 36.86K, #flops: 0.12G, #acts: 0.2M
+ )
+ (bn2): BatchNorm2d(
+ 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.13K, #flops: 1M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 16.38K, #flops: 51.38M, #acts: 0.8M
+ )
+ (bn3): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 4.01M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ (2): Bottleneck(
+ #params: 70.4K, #flops: 0.22G, #acts: 1.2M
+ (conv1): Conv2d(
+ 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 16.38K, #flops: 51.38M, #acts: 0.2M
+ )
+ (bn1): BatchNorm2d(
+ 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.13K, #flops: 1M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 36.86K, #flops: 0.12G, #acts: 0.2M
+ )
+ (bn2): BatchNorm2d(
+ 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.13K, #flops: 1M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 16.38K, #flops: 51.38M, #acts: 0.8M
+ )
+ (bn3): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 4.01M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ )
+ (layer2): Sequential(
+ #params: 1.22M, #flops: 1.04G, #acts: 3.11M
+ (0): Bottleneck(
+ #params: 0.38M, #flops: 0.38G, #acts: 1.3M
+ (conv1): Conv2d(
+ 256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 32.77K, #flops: 0.1G, #acts: 0.4M
+ )
+ (bn1): BatchNorm2d(
+ 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.26K, #flops: 2.01M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
+ #params: 0.15M, #flops: 0.12G, #acts: 0.1M
+ )
+ (bn2): BatchNorm2d(
+ 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.26K, #flops: 0.5M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 65.54K, #flops: 51.38M, #acts: 0.4M
+ )
+ (bn3): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 2.01M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ (downsample): Sequential(
+ #params: 0.13M, #flops: 0.1G, #acts: 0.4M
+ (0): Conv2d(
+ 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
+ #params: 0.13M, #flops: 0.1G, #acts: 0.4M
+ )
+ (1): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 2.01M, #acts: 0
+ )
+ )
+ )
+ (1): Bottleneck(
+ #params: 0.28M, #flops: 0.22G, #acts: 0.6M
+ (conv1): Conv2d(
+ 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 65.54K, #flops: 51.38M, #acts: 0.1M
+ )
+ (bn1): BatchNorm2d(
+ 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.26K, #flops: 0.5M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 0.15M, #flops: 0.12G, #acts: 0.1M
+ )
+ (bn2): BatchNorm2d(
+ 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.26K, #flops: 0.5M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 65.54K, #flops: 51.38M, #acts: 0.4M
+ )
+ (bn3): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 2.01M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ (2): Bottleneck(
+ #params: 0.28M, #flops: 0.22G, #acts: 0.6M
+ (conv1): Conv2d(
+ 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 65.54K, #flops: 51.38M, #acts: 0.1M
+ )
+ (bn1): BatchNorm2d(
+ 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.26K, #flops: 0.5M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 0.15M, #flops: 0.12G, #acts: 0.1M
+ )
+ (bn2): BatchNorm2d(
+ 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.26K, #flops: 0.5M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 65.54K, #flops: 51.38M, #acts: 0.4M
+ )
+ (bn3): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 2.01M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ (3): Bottleneck(
+ #params: 0.28M, #flops: 0.22G, #acts: 0.6M
+ (conv1): Conv2d(
+ 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 65.54K, #flops: 51.38M, #acts: 0.1M
+ )
+ (bn1): BatchNorm2d(
+ 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.26K, #flops: 0.5M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 0.15M, #flops: 0.12G, #acts: 0.1M
+ )
+ (bn2): BatchNorm2d(
+ 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.26K, #flops: 0.5M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 65.54K, #flops: 51.38M, #acts: 0.4M
+ )
+ (bn3): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 2.01M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ )
+ (layer3): Sequential(
+ #params: 7.1M, #flops: 1.48G, #acts: 2.16M
+ (0): Bottleneck(
+ #params: 1.51M, #flops: 0.38G, #acts: 0.65M
+ (conv1): Conv2d(
+ 512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.13M, #flops: 0.1G, #acts: 0.2M
+ )
+ (bn1): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 1M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
+ #params: 0.59M, #flops: 0.12G, #acts: 50.18K
+ )
+ (bn2): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 0.2M
+ )
+ (bn3): BatchNorm2d(
+ 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 2.05K, #flops: 1M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ (downsample): Sequential(
+ #params: 0.53M, #flops: 0.1G, #acts: 0.2M
+ (0): Conv2d(
+ 512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
+ #params: 0.52M, #flops: 0.1G, #acts: 0.2M
+ )
+ (1): BatchNorm2d(
+ 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 2.05K, #flops: 1M, #acts: 0
+ )
+ )
+ )
+ (1): Bottleneck(
+ #params: 1.12M, #flops: 0.22G, #acts: 0.3M
+ (conv1): Conv2d(
+ 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 50.18K
+ )
+ (bn1): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 0.59M, #flops: 0.12G, #acts: 50.18K
+ )
+ (bn2): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 0.2M
+ )
+ (bn3): BatchNorm2d(
+ 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 2.05K, #flops: 1M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ (2): Bottleneck(
+ #params: 1.12M, #flops: 0.22G, #acts: 0.3M
+ (conv1): Conv2d(
+ 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 50.18K
+ )
+ (bn1): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 0.59M, #flops: 0.12G, #acts: 50.18K
+ )
+ (bn2): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 0.2M
+ )
+ (bn3): BatchNorm2d(
+ 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 2.05K, #flops: 1M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ (3): Bottleneck(
+ #params: 1.12M, #flops: 0.22G, #acts: 0.3M
+ (conv1): Conv2d(
+ 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 50.18K
+ )
+ (bn1): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 0.59M, #flops: 0.12G, #acts: 50.18K
+ )
+ (bn2): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 0.2M
+ )
+ (bn3): BatchNorm2d(
+ 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 2.05K, #flops: 1M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ (4): Bottleneck(
+ #params: 1.12M, #flops: 0.22G, #acts: 0.3M
+ (conv1): Conv2d(
+ 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 50.18K
+ )
+ (bn1): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 0.59M, #flops: 0.12G, #acts: 50.18K
+ )
+ (bn2): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 0.2M
+ )
+ (bn3): BatchNorm2d(
+ 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 2.05K, #flops: 1M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ (5): Bottleneck(
+ #params: 1.12M, #flops: 0.22G, #acts: 0.3M
+ (conv1): Conv2d(
+ 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 50.18K
+ )
+ (bn1): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 0.59M, #flops: 0.12G, #acts: 50.18K
+ )
+ (bn2): BatchNorm2d(
+ 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 0.51K, #flops: 0.25M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.26M, #flops: 51.38M, #acts: 0.2M
+ )
+ (bn3): BatchNorm2d(
+ 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 2.05K, #flops: 1M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ )
+ (layer4): Sequential(
+ #params: 14.96M, #flops: 0.81G, #acts: 0.63M
+ (0): Bottleneck(
+ #params: 6.04M, #flops: 0.37G, #acts: 0.33M
+ (conv1): Conv2d(
+ 1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 0.52M, #flops: 0.1G, #acts: 0.1M
+ )
+ (bn1): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 0.5M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
+ #params: 2.36M, #flops: 0.12G, #acts: 25.09K
+ )
+ (bn2): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 0.13M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 1.05M, #flops: 51.38M, #acts: 0.1M
+ )
+ (bn3): BatchNorm2d(
+ 2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 4.1K, #flops: 0.5M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ (downsample): Sequential(
+ #params: 2.1M, #flops: 0.1G, #acts: 0.1M
+ (0): Conv2d(
+ 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
+ #params: 2.1M, #flops: 0.1G, #acts: 0.1M
+ )
+ (1): BatchNorm2d(
+ 2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 4.1K, #flops: 0.5M, #acts: 0
+ )
+ )
+ )
+ (1): Bottleneck(
+ #params: 4.46M, #flops: 0.22G, #acts: 0.15M
+ (conv1): Conv2d(
+ 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 1.05M, #flops: 51.38M, #acts: 25.09K
+ )
+ (bn1): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 0.13M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 2.36M, #flops: 0.12G, #acts: 25.09K
+ )
+ (bn2): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 0.13M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 1.05M, #flops: 51.38M, #acts: 0.1M
+ )
+ (bn3): BatchNorm2d(
+ 2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 4.1K, #flops: 0.5M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ (2): Bottleneck(
+ #params: 4.46M, #flops: 0.22G, #acts: 0.15M
+ (conv1): Conv2d(
+ 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 1.05M, #flops: 51.38M, #acts: 25.09K
+ )
+ (bn1): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 0.13M, #acts: 0
+ )
+ (conv2): Conv2d(
+ 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
+ #params: 2.36M, #flops: 0.12G, #acts: 25.09K
+ )
+ (bn2): BatchNorm2d(
+ 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 1.02K, #flops: 0.13M, #acts: 0
+ )
+ (conv3): Conv2d(
+ 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
+ #params: 1.05M, #flops: 51.38M, #acts: 0.1M
+ )
+ (bn3): BatchNorm2d(
+ 2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
+ #params: 4.1K, #flops: 0.5M, #acts: 0
+ )
+ (relu): ReLU(inplace=True)
+ )
+ )
+ (avgpool): AdaptiveAvgPool2d(
+ output_size=(1, 1)
+ #params: 0, #flops: 0.1M, #acts: 0
+ )
+ (fc): Linear(
+ in_features=2048, out_features=1000, bias=True
+ #params: 2.05M, #flops: 2.05M, #acts: 1K
+ )
+ )
+ )
+ ```
+
+
+
+- 总的计算量
+
+ ```python
+ print("Model Flops:{}".format(analysis_results['flops_str']))
+ # Model Flops:4.145G
+ ```
+
+- 总的参数量
+
+ ```python
+ print("Model Parameters:{}".format(analysis_results['params_str']))
+ # Model Parameters:25.557M
+ ```
+
+关于模型计算量和参数量的定义以及更多用法请阅读[模型复杂度分析](../advanced_tutorials/model_analysis.md)。
diff --git a/docs/zh_cn/index.rst b/docs/zh_cn/index.rst
index 95af4ce4..aacfe808 100644
--- a/docs/zh_cn/index.rst
+++ b/docs/zh_cn/index.rst
@@ -24,6 +24,7 @@
common_usage/speed_up_training.md
common_usage/save_gpu_memory.md
common_usage/set_random_seed.md
+ common_usage/model_analysis.md
common_usage/set_interval.md
common_usage/epoch_to_iter.md
@@ -56,6 +57,7 @@
advanced_tutorials/manager_mixin.md
advanced_tutorials/cross_library.md
advanced_tutorials/test_time_augmentation.md
+ advanced_tutorials/model_analysis.md
.. toctree::
:maxdepth: 1