Torch Model Manager
Torch Model Manager is an open-source python project designed for Deep Learning developpers that aims to make the use of pytorch library easy. The version 
Examples of Use
- Initialization
from torchvision import
from torch_model_manager import TorchModelManager
# Assume you have a PyTorch model 'model'
model = models.vgg16(pretrained=True)
model_manager = TorchModelManager(model)
- Get Named Layers
named_layers = model_manager.get_named_layers()
# output
>>> {
'features': {
'0': 'Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'1': 'ReLU(inplace=True)',
'2': 'Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'3': 'ReLU(inplace=True)',
'4': 'MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)',
'5': 'Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'6': 'ReLU(inplace=True)',
'7': 'Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'8': 'ReLU(inplace=True)',
'9': 'MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)',
'10': 'Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'11': 'ReLU(inplace=True)',
'12': 'Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'13': 'ReLU(inplace=True)',
'14': 'Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'15': 'ReLU(inplace=True)',
'16': 'MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)',
'17': 'Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'18': 'ReLU(inplace=True)',
'19': 'Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'20': 'ReLU(inplace=True)',
'21': 'Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'22': 'ReLU(inplace=True)',
'23': 'MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)',
'24': 'Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'25': 'ReLU(inplace=True)',
'26': 'Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'27': 'ReLU(inplace=True)',
'28': 'Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))',
'29': 'ReLU(inplace=True)',
'30': 'MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)',
},
'avgpool': 'AdaptiveAvgPool2d(output_size=(7, 7))',
'classifier': {
'0': 'Linear(in_features=25088, out_features=4096, bias=True)',
'1': 'ReLU(inplace=True)',
'2': 'Dropout(p=0.5, inplace=False)',
'3': 'Linear(in_features=4096, out_features=4096, bias=True)',
'4': 'ReLU(inplace=True)',
'5': 'Dropout(p=0.5, inplace=False)',
'6': 'Linear(in_features=4096, out_features=1000, bias=True)'
}
}
This method allows the user to access the overall architecture of the model in dictionnary format.
- Get Layer by Index
layer_index = ['classifier', 6]
layer = model_manager.get_layer_by_index(layer_index)
>>> Linear(in_features=4096, out_features=1000, bias=True)
The index is represented by a list, where each position represents a level. For instance, in the previous example, 'classifier' is the index to access the first level of the model architecture, and 6 is the index of the layer at the second level.
- Get Layer by Attribute
layers = model_manager.get_layer_by_attribute('out_features', 1000, '==')
>>> {('classifier', 6): Linear(in_features=4096, out_features=1000, bias=True)}
Retrieves all the layers satifying the given condition out_features = 1000.
- Get Layers by Conditions
# Retrieve layers that satisfy the given conditions
conditions = {
'and': [{'==': ('kernel_size', (1, 1))}, {'==': ('stride', (1, 1))}],
'or': [{'==': ('kernel_size', (3, 3))}]
}
layers = model_manager.get_layer_by_attributes(conditions)
>>> {('features', 0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))}
It is almost the same as the previous one, but this time it extracts the layers that satisfy a set of conditions.
- Get Layer by Instance
# Search for layers in the model by their instance type
layers = model_manager.get_layer_by_instance(nn.Conv2d)
>>> {('features', 0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), ('features', 28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))}
The get_layer_by_instance method allows you to extract layers of a specific type from the model. In the previous example, the extracted layers are convolutional layers.
-
Delete Layer by Index The deletion process involves the following steps:
-
Search for the layers and retrieve their indexes.
-
Delete the layers at the corresponding indexes.
Here is an example of how to delete layers using different methods:
- Delete a layer by index:
# Delete a layer from the model using its index
model_manager.delete_layer_by_index(['features', 0])
- Delete Layer by Attribute
# Delete layers from the model based on a specific attribute
model_manager.delete_layer_by_attribute('activation', 'relu', '==')
- Delete Layers by Conditions
# Delete layers from the model based on multiple conditions
conditions = {
'and': [{'==': ('kernel_size', (1, 1))}, {'==': ('stride', (1, 1))}],
'or': [{'==': ('kernel_size', (3, 3))}]
}
model_manager.delete_layer_by_attributes(conditions)
- Delete Layer by Instance
# Delete layers from the model by their instance type
model_manager.delete_layer_by_instance(nn.Conv2d)
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