https://github.com/ayasyrev/model_constructor
Constructor for pytorch models.
https://github.com/ayasyrev/model_constructor
pytorch-model
Last synced: 2 months ago
JSON representation
Constructor for pytorch models.
- Host: GitHub
- URL: https://github.com/ayasyrev/model_constructor
- Owner: ayasyrev
- License: apache-2.0
- Created: 2020-01-09T07:54:29.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-07-27T08:33:10.000Z (11 months ago)
- Last Synced: 2025-03-25T20:51:17.502Z (3 months ago)
- Topics: pytorch-model
- Language: Jupyter Notebook
- Homepage: https://ayasyrev.github.io/model_constructor/
- Size: 2.14 MB
- Stars: 3
- Watchers: 1
- Forks: 2
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# model_constructor
> Constructor to create pytorch model.
## Install
`pip install model-constructor`
Or install from repo:
`pip install git+https://github.com/ayasyrev/model_constructor.git`
## How to use
First import constructor class, then create model constructor object.
Now you can change every part of model.
```python
from model_constructor import ModelConstructor
```
```python
mc = ModelConstructor()
```
Check base parameters:
```python
mc
```
output
ModelConstructor
in_chans: 3, num_classes: 1000
expansion: 1, groups: 1, dw: False, div_groups: None
act_fn: ReLU, sa: False, se: False
stem sizes: [64], stride on 0
body sizes [64, 128, 256, 512]
layers: [2, 2, 2, 2]
Check all parameters with `print_cfg` method:
```python
mc.print_cfg()
```
output
ModelConstructor(
in_chans=3
num_classes=1000
block='BasicBlock'
conv_layer='ConvBnAct'
block_sizes=[64, 128, 256, 512]
layers=[2, 2, 2, 2]
norm='BatchNorm2d'
act_fn='ReLU'
expansion=1
groups=1
bn_1st=True
zero_bn=True
stem_sizes=[64]
stem_pool="MaxPool2d {'kernel_size': 3, 'stride': 2, 'padding': 1}"
init_cnn='init_cnn'
make_stem='make_stem'
make_layer='make_layer'
make_body='make_body'
make_head='make_head')
Now we have model constructor, default setting as resnet18. And we can get model after call it.
```python
model = mc()
model
```
output
ModelConstructor(
(stem): Sequential(
(conv_1): ConvBnAct(
(conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(stem_pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
)
(body): Sequential(
(l_0): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
(l_1): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(id_conv): ConvBnAct(
(conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
(l_2): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(id_conv): ConvBnAct(
(conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
(l_3): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(id_conv): ConvBnAct(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
)
(head): Sequential(
(pool): AdaptiveAvgPool2d(output_size=1)
(flat): Flatten(start_dim=1, end_dim=-1)
(fc): Linear(in_features=512, out_features=1000, bias=True)
)
)
If you want to change model, just change constructor parameters.
Lets create resnet50.
```python
mc.expansion = 4
mc.layers = [3,4,6,3]
```
We can check, what we changed (compare to default constructor).
```python
mc.changed_fields
```
output
{'layers': [3, 4, 6, 3], 'expansion': 4}
```python
mc.print_changed_fields()
```
output
Changed fields:
layers: [3, 4, 6, 3]
expansion: 4
We can compare changed with defaults.
```python
mc.print_changed_fields(show_default=True)
```
output
Changed fields:
layers: [3, 4, 6, 3] | [2, 2, 2, 2]
expansion: 4 | 1
Now we can look at model parts - stem, body, head.
```python
mc.body
```
output
Sequential(
(l_0): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_2): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
(l_1): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(id_conv): ConvBnAct(
(conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_2): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_3): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
(l_2): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(id_conv): ConvBnAct(
(conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_2): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_3): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_4): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_5): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
(l_3): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(id_conv): ConvBnAct(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_2): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
)
## Create constructor from config.
Alternative we can create config first and than create constructor from it.
```python
from model_constructor import ModelCfg
```
```python
cfg = ModelCfg(
num_classes=10,
act_fn=nn.Mish,
)
print(cfg)
```
output
ModelCfg(
in_chans=3
num_classes=10
block='BasicBlock'
conv_layer='ConvBnAct'
block_sizes=[64, 128, 256, 512]
layers=[2, 2, 2, 2]
norm='BatchNorm2d'
act_fn='Mish'
expansion=1
groups=1
bn_1st=True
zero_bn=True
stem_sizes=[64]
stem_pool="MaxPool2d {'kernel_size': 3, 'stride': 2, 'padding': 1}")
When creating config or constructor we can use string annotation for nn.Modules - it useful when creating model from config files.
```python
cfg = ModelCfg(
num_classes=10,
act_fn="nn.SELU",
)
print(cfg.act_fn)
```
output
class 'torch.nn.modules.activation.SELU'
Now we can create constructor from config:
```python
mc = ModelConstructor.from_cfg(cfg)
mc
```
output
ModelConstructor
in_chans: 3, num_classes: 10
expansion: 1, groups: 1, dw: False, div_groups: None
act_fn: SELU, sa: , se: SEModule
stem sizes: [64], stride on 0
body sizes [64, 128, 256, 512]
layers: [2, 2, 2, 2]
## More modification.
Main purpose of this module - fast and easy modify model.
And here is the link to more modification to beat Imagenette leaderboard with add MaxBlurPool and modification to ResBlock [notebook](https://github.com/ayasyrev/imagenette_experiments/blob/master/ResnetTrick_create_model_fit.ipynb)
But now lets create model as mxresnet50 from [fastai forums tread](https://forums.fast.ai/t/how-we-beat-the-5-epoch-imagewoof-leaderboard-score-some-new-techniques-to-consider)
Lets create mxresnet constructor.
```python
mc = ModelConstructor(name='MxResNet')
```
Then lets modify stem.
```python
from model_constructor.xresnet import xresnet_stem
```
```python
mc.make_stem = xresnet_stem
mc.stem_sizes = [3,32,64,64]
```
Now lets change activation function to Mish.
Here is link to [forum discussion](https://forums.fast.ai/t/meet-mish-new-activation-function-possible-successor-to-relu)
We'v got Mish is in model_constructor.activations, but from pytorch 1.9 take it from torch:
```python
from torch.nn import Mish
```
```python
mc.act_fn = Mish
```
```python
mc
```
output
MxResNet
in_chans: 3, num_classes: 1000
expansion: 1, groups: 1, dw: False, div_groups: None
act_fn: Mish, sa: False, se: False
stem sizes: [3, 32, 64, 64], stride on 0
body sizes [64, 128, 256, 512]
layers: [2, 2, 2, 2]
```python
mc.print_changed_fields()
```
output
Changed fields:
name: MxResNet
act_fn: Mish
stem_sizes: [3, 32, 64, 64]
make_stem: xresnet_stem
Here is model:
```python
mc()
```
output
MxResNet(
act_fn: Mish, stem_sizes: [3, 32, 64, 64], make_stem: xresnet_stem
(stem): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_2): ConvBnAct(
(conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_3): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(stem_pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
)
(body): Sequential(
(l_0): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
)
(l_1): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(id_conv): ConvBnAct(
(conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
)
(l_2): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(id_conv): ConvBnAct(
(conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
)
(l_3): Sequential(
(bl_0): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(id_conv): ConvBnAct(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
(bl_1): BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
)
)
(head): Sequential(
(pool): AdaptiveAvgPool2d(output_size=1)
(flat): Flatten(start_dim=1, end_dim=-1)
(fc): Linear(in_features=512, out_features=1000, bias=True)
)
)
## MXResNet50
Now lets make MxResNet50
```python
mc.expansion = 4
mc.layers = [3,4,6,3]
mc.name = "mxresnet50"
```
```python
mc.print_changed_fields()
```
output
Changed fields:
name: mxresnet50
layers: [3, 4, 6, 3]
act_fn: Mish
expansion: 4
stem_sizes: [3, 32, 64, 64]
make_stem: xresnet_stem
Now we have mxresnet50 constructor.
We can inspect every parts of it.
And after call it we got model.
```python
mc
```
output
mxresnet50
in_chans: 3, num_classes: 1000
expansion: 4, groups: 1, dw: False, div_groups: None
act_fn: Mish, sa: False, se: False
stem sizes: [3, 32, 64, 64], stride on 0
body sizes [64, 128, 256, 512]
layers: [3, 4, 6, 3]
```python
mc.stem.conv_1
```
output
ConvBnAct(
(conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
```python
mc.body.l_0.bl_0
```
output
BasicBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): Mish(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
We can get model direct way:
```python
mc = ModelConstructor(
name="MxResNet",
act_fn=Mish,
layers=[3,4,6,3],
expansion=4,
make_stem=xresnet_stem,
stem_sizes=[32,64,64]
)
model = mc()
```
Another way:
```python
model = ModelConstructor.create_model(
name="MxResNet",
act_fn=Mish,
layers=[3,4,6,3],
expansion=4,
make_stem=xresnet_stem,
stem_sizes=[32,64,64]
)
```
## YaResNet
Now lets change Resblock to YaResBlock (Yet another ResNet, former NewResBlock) is in lib from version 0.1.0
```python
from model_constructor.yaresnet import YaBasicBlock
```
```python
mc = ModelConstructor(name="YaResNet")
mc.block = YaBasicBlock
```
Or in one line:
```python
mc = ModelConstructor(name="YaResNet", block=YaBasicBlock)
```
That all. Now we have YaResNet constructor
```python
mc.print_cfg()
```
output
ModelConstructor(
name='YaResNet'
in_chans=3
num_classes=1000
block='YaBasicBlock'
conv_layer='ConvBnAct'
block_sizes=[64, 128, 256, 512]
layers=[2, 2, 2, 2]
norm='BatchNorm2d'
act_fn='ReLU'
expansion=1
groups=1
bn_1st=True
zero_bn=True
stem_sizes=[64]
stem_pool="MaxPool2d {'kernel_size': 3, 'stride': 2, 'padding': 1}"
init_cnn='init_cnn'
make_stem='make_stem'
make_layer='make_layer'
make_body='make_body'
make_head='make_head')
Let see what we have.
```python
mc.body.l_1.bl_0
```
output
YaBasicBlock(
(reduce): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): ConvBnAct(
(conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(merge): ReLU(inplace=True)
)
Lets create `xResnet34` like model constructor:
```python
from typing import Callable
from model_constructor.helpers import ModSeq
class YaResnet34(ModelConstructor):
block: type[nn.Module] = YaBasicBlock
layers: list[int] = [3, 4, 6, 3]
make_stem: Callable[[ModelCfg], ModSeq] = xresnet_stem
```
```python
mc = YaResnet34()
mc.print_cfg()
```
output
YaResnet34(
in_chans=3
num_classes=1000
block='YaBasicBlock'
conv_layer='ConvBnAct'
block_sizes=[64, 128, 256, 512]
layers=[3, 4, 6, 3]
norm='BatchNorm2d'
act_fn='ReLU'
expansion=1
groups=1
bn_1st=True
zero_bn=True
stem_sizes=[64]
stem_pool="MaxPool2d {'kernel_size': 3, 'stride': 2, 'padding': 1}"
init_cnn='init_cnn'
make_stem='xresnet_stem'
make_layer='make_layer'
make_body='make_body'
make_head='make_head')
And `xResnet50` like model can be inherited from `YaResnet34`:
```python
class YaResnet50(YaResnet34):
expansion: int = 4
```
```python
mc = YaResnet50()
mc
```
output
YaResnet50
in_chans: 3, num_classes: 1000
expansion: 4, groups: 1, dw: False, div_groups: None
act_fn: ReLU, sa: False, se: False
stem sizes: [64], stride on 0
body sizes [64, 128, 256, 512]
layers: [3, 4, 6, 3]