A library to inspect itermediate layers of PyTorch models.

Last update: Dec 28, 2022

Overview

A library to inspect itermediate layers of PyTorch models.

Why?

It's often the case that we want to inspect intermediate layers of a model without modifying the code e.g. visualize attention matrices of language models, get values from an intermediate layer to feed to another layer, or applying a loss function to intermediate layers.

Install

$ pip install surgeon-pytorch

Usage

Inspect

Given a PyTorch model we can display all layers using get_layers:

import torch
import torch.nn as nn

from surgeon_pytorch import Inspect, get_layers

class SomeModel(nn.Module):

    def __init__(self):
        super().__init__()
        self.layer1 = nn.Linear(5, 3)
        self.layer2 = nn.Linear(3, 2)
        self.layer3 = nn.Linear(2, 1)

    def forward(self, x):
        x1 = self.layer1(x)
        x2 = self.layer2(x1)
        y = self.layer3(x2)
        return y


model = SomeModel()
print(get_layers(model)) # ['layer1', 'layer2', 'layer3']

Then we can wrap our model to be inspected using Inspect and in every forward call the new model we will also output the provided layer outputs (in second return value):

model_wrapped = Inspect(model, layer='layer2')
x = torch.rand(1, 5)
y, x2 = model_wrapped(x)
print(x2) # tensor([[-0.2726,  0.0910]], grad_fn=<AddmmBackward0>)

We can also provide a list of layers:

model_wrapped = Inspect(model, layer=['layer1', 'layer2'])
x = torch.rand(1, 5)
y, [x1, x2] = model_wrapped(x)
print(x1) # tensor([[ 0.1739,  0.3844, -0.4724]], grad_fn=<AddmmBackward0>)
print(x2) # tensor([[-0.2238,  0.0107]], grad_fn=<AddmmBackward0>)

Or a dictionary to get named outputs:

model_wrapped = Inspect(model, layer={'x1': 'layer1', 'x2': 'layer2'})
x = torch.rand(1, 5)
y, layers = model_wrapped(x)
print(layers)
"""
{
    'x1': tensor([[ 0.3707,  0.6584, -0.2970]], grad_fn=<AddmmBackward0>),
    'x2': tensor([[-0.1953, -0.3408]], grad_fn=<AddmmBackward0>)
}
"""

TODO

add extract function to get intermediate block

Comments

Use one backbone with different heads

Is it possible to save the results from the backbone and apply them on the heads of the all the other models. My goal was to try to save time by avoiding repeating the backbone part. Instead of running the 3 complete models (left), only run the backbone 1 time and switch only the heads for the 3 models (right), therefore not repeating executing the backbone every time in yolov5 model.

Thank you for the help!
question

opened by brunopatricio2012 4
Support for DataParallel?

Hi, I noticed that the current version does not support parallel models (at least those created using torch.nn.DataParallel) since the forward hook does not differentiate between the different copies of the model and a model wrapped with Inspect will just return the intermediate features of the last copy of the parallelized model to run.

Are you planning on fixing this issue/supporting this use case?

opened by zimmerrol 1

A library to inspect itermediate layers of PyTorch models.

Related tags

Overview

Why?

Install

Usage

Inspect

TODO

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Comments

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Support for DataParallel?

Releases(0.0.4)

0.0.4(May 3, 2022)

0.0.3(May 3, 2022)

0.0.2(Apr 21, 2022)

0.0.1(Apr 21, 2022)

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