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[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
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docs/en/advanced_tutorials/model_analysis.md
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@ -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(

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docs/zh_cn/advanced_tutorials/model_analysis.md 100644
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@ -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)。

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docs/zh_cn/common_usage/model_analysis.md 100644
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# 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'])
```
<details>
<summary>点击展开</summary>
```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 |
+------------------------+----------------------+------------+--------------+
```
</details>
- 以模型结构的形式显示
```python
print(analysis_results['out_arch'])
```
<details>
<summary>点击展开</summary>
```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
)
)
)
```
</details>
- 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)。

2
docs/zh_cn/index.rst
View File

@ -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