timm_custom
Description
In the case of your data having only 1 channel while want to use timm models (with or without pretrained weights), run the following command to get the model with appropriate number of input channels.
python load_timm.py --model "densenet" --model_depth 121 --pretrained True --num_classes=4 --n_input_channels 1
Argument
The argument explanation is below:
usage: load_timm.py [-h] [--model MODEL] [--model_depth MODEL_DEPTH]
[--pretrained PRETRAINED] [--num_classes NUM_CLASSES]
[--n_input_channels N_INPUT_CHANNELS]
optional arguments:
-h, --help show this help message and exit
--model MODEL Model name
--model_depth MODEL_DEPTH
Depth of the model
--pretrained PRETRAINED
If true, will use ImageNet pretrained weight
--num_classes NUM_CLASSES
number of classes
--n_input_channels N_INPUT_CHANNELS
number of input channels
Notice that the script will check if the given model + model_depth is in timm models. Additionally, if pretrained=True, it will check if that particular model has a pretraiend weight or not. If not, it will just set pretrained=False.
Usage
In the case of a project where it handles CT images, it only has 1 channel since there is no RGB channel.
Example
When running python load_timm.py --model "densenet" --model_depth 121 --pretrained True --num_classes=4 --n_input_channels 3, the model architecture is shown below:
DenseNet(
(features): Sequential(
(conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(norm0): BatchNormAct2d(
64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(act): ReLU(inplace=True)
)
...
When running python load_timm.py --model "densenet" --model_depth 121 --pretrained True --num_classes=4 --n_input_channels 1, the model architecture has been updated as shown below:
DenseNet(
(features): Sequential(
(conv0): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(norm0): BatchNormAct2d(
64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(act): ReLU(inplace=True)
)
...
Warning
Since timm models pretrained weights are trained on the ImageNet dataset (which has 3 channels), it will lose the “initial benefit” of pretrained weights.
