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.

from model_constructor import ModelConstructor

mc = ModelConstructor()

Check base parameters:

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:

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.

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.

mc.expansion = 4
mc.layers = [3,4,6,3]

We can check, what we changed (compare to default constructor).

mc.changed_fields

output

{'layers': [3, 4, 6, 3], 'expansion': 4}

mc.print_changed_fields()

output

Changed fields:
    layers: [3, 4, 6, 3]
    expansion: 4

We can compare changed with defaults.

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.

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.

from model_constructor import ModelCfg

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.

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:

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

But now lets create model as mxresnet50 from fastai forums tread

Lets create mxresnet constructor.

mc = ModelConstructor(name='MxResNet')

Then lets modify stem.

from model_constructor.xresnet import xresnet_stem

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
We'v got Mish is in model_constructor.activations, but from pytorch 1.9 take it from torch:

from torch.nn import Mish

mc.act_fn = Mish

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]

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:

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

mc.expansion = 4
mc.layers = [3,4,6,3]
mc.name = "mxresnet50"

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.

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]

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)
    )

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:

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:

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

from model_constructor.yaresnet import YaBasicBlock

mc = ModelConstructor(name="YaResNet")
mc.block = YaBasicBlock

Or in one line:

mc = ModelConstructor(name="YaResNet", block=YaBasicBlock)

That all. Now we have YaResNet constructor

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.

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:

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

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:

class YaResnet50(YaResnet34):
    expansion: int = 4

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]

Name		Name	Last commit message	Last commit date
Latest commit History 320 Commits
.github/workflows		.github/workflows
Nbs		Nbs
docs		docs
src/model_constructor		src/model_constructor
tests		tests
.flake8		.flake8
.gitignore		.gitignore
.pylintrc		.pylintrc
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Makefile		Makefile
README.md		README.md
mkdocs.yaml		mkdocs.yaml
nbdocs.ini		nbdocs.ini
noxfile.py		noxfile.py
noxfile_conda.py		noxfile_conda.py
noxfile_conda_lint.py		noxfile_conda_lint.py
noxfile_cov.py		noxfile_cov.py
noxfile_lint.py		noxfile_lint.py
requirements.txt		requirements.txt
requirements_test.txt		requirements_test.txt
requirements_test_extra.txt		requirements_test_extra.txt
settings.ini		settings.ini
setup.cfg		setup.cfg
setup.py		setup.py

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model_constructor

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model_constructor

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Create constructor from config.

More modification.

MXResNet50

YaResNet

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