Pytorch models constructor.
Project description
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: [32, 32, 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='ResBlock'
conv_layer='ConvBnAct'
block_sizes=[64, 128, 256, 512]
layers=[2, 2, 2, 2]
norm='BatchNorm2d'
act_fn='ReLU'
pool="AvgPool2d {'kernel_size': 2, 'ceil_mode': True}"
expansion=1
groups=1
bn_1st=True
zero_bn=True
stem_sizes=[32, 32, 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 xresnet18. And we can get model after call it.
model = mc()
model
output
ModelConstructor(
(stem): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(32, 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): ReLU(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): 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): ResBlock(
(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): ResBlock(
(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): ResBlock(
(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(
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
(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)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): ResBlock(
(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): ResBlock(
(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(
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
(id_conv): ConvBnAct(
(conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(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_1): ResBlock(
(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): ResBlock(
(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(
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
(id_conv): ConvBnAct(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(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_1): ResBlock(
(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 xresnet50.
mc.expansion = 4
mc.layers = [3,4,6,3]
Now we can look at model parts - stem, body, head.
mc.body
output
Sequential(
(l_0): Sequential(
(bl_0): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(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(64, 256, kernel_size=(1, 1), stride=(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_1): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(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): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(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_1): Sequential(
(bl_0): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(256, 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)
(act_fn): ReLU(inplace=True)
)
(conv_1): ConvBnAct(
(conv): Conv2d(128, 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_2): ConvBnAct(
(conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
(id_conv): ConvBnAct(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(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_1): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(512, 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)
(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(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): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(512, 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)
(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(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_3): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(512, 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)
(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
(l_2): Sequential(
(bl_0): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(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=(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_2): ConvBnAct(
(conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
(id_conv): ConvBnAct(
(conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_2): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_3): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_4): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_5): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
)
(l_3): Sequential(
(bl_0): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(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=(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_2): ConvBnAct(
(conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(id_conv): Sequential(
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
(id_conv): ConvBnAct(
(conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_1): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): ReLU(inplace=True)
)
(bl_2): ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(2048, 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()
print(cfg)
output
in_chans=3 num_classes=1000 block='ResBlock' conv_layer='ConvBnAct' block_sizes=[64, 128, 256, 512] layers=[2, 2, 2, 2] norm='BatchNorm2d' act_fn='ReLU' pool="AvgPool2d {'kernel_size': 2, 'ceil_mode': True}" expansion=1 groups=1 bn_1st=True zero_bn=True stem_sizes=[32, 32, 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 can create constructor from config:
mc = ModelConstructor.from_cfg(cfg)
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: [32, 32, 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.
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 model_constructor.activations import Mish
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]Here is model:
mc()
output
MxResNet(
stem_sizes: [3, 32, 64, 64], act_fn: Mish
(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): ResBlock(
(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): ResBlock(
(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): ResBlock(
(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(
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
(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)
)
)
(act_fn): Mish(inplace=True)
)
(bl_1): ResBlock(
(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): ResBlock(
(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(
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
(id_conv): ConvBnAct(
(conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
(bl_1): ResBlock(
(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): ResBlock(
(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(
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
(id_conv): ConvBnAct(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(act_fn): Mish(inplace=True)
)
(bl_1): ResBlock(
(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'
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
ResBlock(
(convs): Sequential(
(conv_0): ConvBnAct(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(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)
)
(conv_2): ConvBnAct(
(conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(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(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, 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, stem_sizes=[32,64,64])
model = mc()
Or create with config:
mc = ModelConstructor.from_cfg(
ModelCfg(name="MxResNet", act_fn=Mish, layers=[3,4,6,3], expansion=4, stem_sizes=[32,64,64])
)
model = mc()
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 YaResBlock
mc = ModelConstructor(name="YaResNet")
mc.block = YaResBlock
Or in one line:
mc = ModelConstructor(name="YaResNet", block=YaResBlock)
That all. Now we have YaResNet constructor
mc.print_cfg()
output
ModelConstructor(
name='YaResNet'
in_chans=3
num_classes=1000
block='YaResBlock'
conv_layer='ConvBnAct'
block_sizes=[64, 128, 256, 512]
layers=[2, 2, 2, 2]
norm='BatchNorm2d'
act_fn='ReLU'
pool="AvgPool2d {'kernel_size': 2, 'ceil_mode': True}"
expansion=1
groups=1
bn_1st=True
zero_bn=True
stem_sizes=[32, 32, 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
YaResBlock(
(reduce): AvgPool2d(kernel_size=2, stride=2, padding=0)
(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 Resnet34 like model constructor:
class YaResnet34(ModelConstructor):
block: type[nn.Module] = YaResBlock
layers: list[int] = [3, 4, 6, 3]
mc = YaResnet34()
mc.print_cfg()
output
YaResnet34(
in_chans=3
num_classes=1000
block='YaResBlock'
conv_layer='ConvBnAct'
block_sizes=[64, 128, 256, 512]
layers=[3, 4, 6, 3]
norm='BatchNorm2d'
act_fn='ReLU'
pool="AvgPool2d {'kernel_size': 2, 'ceil_mode': True}"
expansion=1
groups=1
bn_1st=True
zero_bn=True
stem_sizes=[32, 32, 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')
And Resnet50 like model can be inherited from YaResnet34:
class YaResnet50(YaResnet34):
expansion = 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: [32, 32, 64], stride on 0
body sizes [64, 128, 256, 512]
layers: [3, 4, 6, 3]
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