Note
Go to the end to download the full example code.
Diving inside Neural Networks#
This tutorial provides a short practical overview of the
Recorder class, which is designed to ease
interactions with the internal states of PyTorch Neural Network
objects (torch.nn.Module) during or after inference.
from scio.recorder import Recorder
Wrapping and visualizing your Neural Network#
Let us first load an arbitrary Neural Network. We use a lightweight Tiniest architecture trained on CIFAR10 and hosted on our hub. We also prepare future input data for this tutorial.
import torch
inputs = torch.rand(5, 3, 32, 32) # Random inputs with 5 samples
net = torch.hub.load("ThalesGroup/scio:hub", "tiniest", trust_repo=True, verbose=False)
net = net.to(inputs)
To wrap it into a Recorder Net, rnet,
one simply needs to specify an input_size (including batch
dimension) or provide input_data. This will directly analyze and
store the control flow of the model, using the torchinfo library.
rnet = Recorder(net, input_data=inputs[[0]])
rnet # Visualize layers
Recorder instance for the following network
============================================================================================================================================
Layer (type (var_name):depth-idx) Input Shape Output Shape Param # Param %
============================================================================================================================================
Tiniest (Tiniest) [1, 3, 32, 32] [1, 10] -- --
├─Conv2d (conv1): 1-1 [1, 3, 32, 32] [1, 48, 32, 32] 1,344 1.38%
├─LayerNorm2d (ln1): 1-2 [1, 48, 32, 32] [1, 48, 32, 32] -- --
│ └─LayerNorm (ln): 2-1 [1, 32, 32, 48] [1, 32, 32, 48] 96 0.10%
├─Block (l1): 1-3 [1, 48, 32, 32] [1, 48, 32, 32] 48 0.05%
│ └─Conv2d (dwconv1): 2-2 [1, 12, 32, 32] [1, 12, 32, 32] 120 0.12%
│ └─Conv2d (dwconv2): 2-3 [1, 12, 32, 32] [1, 12, 32, 32] 600 0.62%
│ └─Conv2d (dwconv3): 2-4 [1, 12, 32, 32] [1, 12, 32, 32] 600 0.62%
│ └─LayerNorm2d (ln): 2-5 [1, 48, 32, 32] [1, 48, 32, 32] -- --
│ │ └─LayerNorm (ln): 3-1 [1, 32, 32, 48] [1, 32, 32, 48] 96 0.10%
│ └─Conv2d (fc1): 2-6 [1, 48, 32, 32] [1, 96, 32, 32] 4,704 4.82%
│ └─Conv2d (fc2): 2-7 [1, 48, 32, 32] [1, 48, 32, 32] 2,352 2.41%
├─Block (l2): 1-4 [1, 48, 32, 32] [1, 48, 32, 32] 48 0.05%
│ └─Conv2d (dwconv1): 2-8 [1, 12, 32, 32] [1, 12, 32, 32] 120 0.12%
│ └─Conv2d (dwconv2): 2-9 [1, 12, 32, 32] [1, 12, 32, 32] 600 0.62%
│ └─Conv2d (dwconv3): 2-10 [1, 12, 32, 32] [1, 12, 32, 32] 600 0.62%
│ └─LayerNorm2d (ln): 2-11 [1, 48, 32, 32] [1, 48, 32, 32] -- --
│ │ └─LayerNorm (ln): 3-2 [1, 32, 32, 48] [1, 32, 32, 48] 96 0.10%
│ └─Conv2d (fc1): 2-12 [1, 48, 32, 32] [1, 96, 32, 32] 4,704 4.82%
│ └─Conv2d (fc2): 2-13 [1, 48, 32, 32] [1, 48, 32, 32] 2,352 2.41%
├─Block (l3): 1-5 [1, 48, 32, 32] [1, 48, 32, 32] 48 0.05%
│ └─Conv2d (dwconv1): 2-14 [1, 12, 32, 32] [1, 12, 32, 32] 120 0.12%
│ └─Conv2d (dwconv2): 2-15 [1, 12, 32, 32] [1, 12, 32, 32] 600 0.62%
│ └─Conv2d (dwconv3): 2-16 [1, 12, 32, 32] [1, 12, 32, 32] 600 0.62%
│ └─LayerNorm2d (ln): 2-17 [1, 48, 32, 32] [1, 48, 32, 32] -- --
│ │ └─LayerNorm (ln): 3-3 [1, 32, 32, 48] [1, 32, 32, 48] 96 0.10%
│ └─Conv2d (fc1): 2-18 [1, 48, 32, 32] [1, 96, 32, 32] 4,704 4.82%
│ └─Conv2d (fc2): 2-19 [1, 48, 32, 32] [1, 48, 32, 32] 2,352 2.41%
├─Conv2d (dsconv): 1-6 [1, 48, 32, 32] [1, 80, 16, 16] 3,920 4.02%
├─LayerNorm2d (ln2): 1-7 [1, 80, 16, 16] [1, 80, 16, 16] -- --
│ └─LayerNorm (ln): 2-20 [1, 16, 16, 80] [1, 16, 16, 80] 160 0.16%
├─Block (l4): 1-8 [1, 80, 16, 16] [1, 80, 16, 16] 80 0.08%
│ └─Conv2d (dwconv1): 2-21 [1, 20, 16, 16] [1, 20, 16, 16] 200 0.21%
│ └─Conv2d (dwconv2): 2-22 [1, 20, 16, 16] [1, 20, 16, 16] 1,000 1.03%
│ └─Conv2d (dwconv3): 2-23 [1, 20, 16, 16] [1, 20, 16, 16] 1,000 1.03%
│ └─LayerNorm2d (ln): 2-24 [1, 80, 16, 16] [1, 80, 16, 16] -- --
│ │ └─LayerNorm (ln): 3-4 [1, 16, 16, 80] [1, 16, 16, 80] 160 0.16%
│ └─Conv2d (fc1): 2-25 [1, 80, 16, 16] [1, 160, 16, 16] 12,960 13.29%
│ └─Conv2d (fc2): 2-26 [1, 80, 16, 16] [1, 80, 16, 16] 6,480 6.64%
├─Block (l5): 1-9 [1, 80, 16, 16] [1, 80, 16, 16] 80 0.08%
│ └─Conv2d (dwconv1): 2-27 [1, 20, 16, 16] [1, 20, 16, 16] 200 0.21%
│ └─Conv2d (dwconv2): 2-28 [1, 20, 16, 16] [1, 20, 16, 16] 1,000 1.03%
│ └─Conv2d (dwconv3): 2-29 [1, 20, 16, 16] [1, 20, 16, 16] 1,000 1.03%
│ └─LayerNorm2d (ln): 2-30 [1, 80, 16, 16] [1, 80, 16, 16] -- --
│ │ └─LayerNorm (ln): 3-5 [1, 16, 16, 80] [1, 16, 16, 80] 160 0.16%
│ └─Conv2d (fc1): 2-31 [1, 80, 16, 16] [1, 160, 16, 16] 12,960 13.29%
│ └─Conv2d (fc2): 2-32 [1, 80, 16, 16] [1, 80, 16, 16] 6,480 6.64%
├─Block (l6): 1-10 [1, 80, 16, 16] [1, 80, 16, 16] 80 0.08%
│ └─Conv2d (dwconv1): 2-33 [1, 20, 16, 16] [1, 20, 16, 16] 200 0.21%
│ └─Conv2d (dwconv2): 2-34 [1, 20, 16, 16] [1, 20, 16, 16] 1,000 1.03%
│ └─Conv2d (dwconv3): 2-35 [1, 20, 16, 16] [1, 20, 16, 16] 1,000 1.03%
│ └─LayerNorm2d (ln): 2-36 [1, 80, 16, 16] [1, 80, 16, 16] -- --
│ │ └─LayerNorm (ln): 3-6 [1, 16, 16, 80] [1, 16, 16, 80] 160 0.16%
│ └─Conv2d (fc1): 2-37 [1, 80, 16, 16] [1, 160, 16, 16] 12,960 13.29%
│ └─Conv2d (fc2): 2-38 [1, 80, 16, 16] [1, 80, 16, 16] 6,480 6.64%
├─AdaptiveAvgPool2d (avgpool): 1-11 [1, 80, 16, 16] [1, 80, 1, 1] -- --
├─Linear (fc): 1-12 [1, 80] [1, 10] 810 0.83%
============================================================================================================================================
Total params: 97,530
Trainable params: 97,530
Non-trainable params: 0
Total mult-adds (Units.MEGABYTES): 44.73
============================================================================================================================================
Input size (MB): 0.01
Forward/backward pass size (MB): 9.05
Params size (MB): 0.39
Estimated Total Size (MB): 9.45
============================================================================================================================================
Currently recording: None
============================================================================================================================================
Tip
For summary customization, refer to torchinfo.summary options.
For example, it is possible to bound the depth of the
representation tree with depth=2.
Note
In the case of dynamic control flow, refer to the
force_static_flow argument of
Recorder.
In many ways, this wrapper is transparent to the user. For example,
one can naturally process data with rnet(inputs).
Selecting layers of interest#
The penultimate line of the above summary reports the layers that are
currently set to be recorded (stored in rnet.recording). By default after instantiation,
there are none.
print(repr(rnet).split("\n")[-2]) # Show penultimate summary line
Currently recording: None
One can arbitrarily set this using rnet.record() with the depth-idx identifiers
from the summary (e.g. 1-9). For example, the following
specifies that the output of the first Block and the penultimate
layer should be recorded.
rnet.record((1, 3), (1, 11))
print(repr(rnet).split("\n")[-2]) # Show penultimate summary line
Currently recording: 1-3, 1-11
Note
Though not shown in the summary, it is possible to select 0-1
to refer to the entire model.
Warning
torchinfo.summary can only detect torch.nn.Module calls.
As such, if a Neural Network uses activation functions, it should
call their Module implementation (instead of their
functional counterpart) and declare them as an attribute,
for them to be visible as a layer in the summary. It is not
necessary to declare a different attribute for every activation
call (e.g. one self.relu = nn.ReLU() can be used multiple
times in forward()).
Capturing internal states#
Once the recording layers are set, every forward pass will
automatically store the corresponding internal states in the
rnet.activations mapping.
Its keys are the (depth, idx) 2-tuples.
out = rnet(inputs) # Forward pass, records the activations
rnet.activations
mappingproxy({(1, 3): tensor([[[[-1.7022e+00, -8.1401e+00, 1.3161e+00, ..., 4.2003e+00,
3.1560e+00, -5.7395e+00],
[ 8.2100e+00, 3.4759e+00, 3.0745e+00, ..., 3.8526e-01,
4.9458e+00, 7.8325e-01],
[ 7.4736e+00, 7.1036e+00, 5.7739e+00, ..., -3.3250e-01,
5.7480e-01, -2.9331e+00],
...,
[ 7.0612e+00, 2.4737e+00, -9.3712e+00, ..., 7.5873e-01,
-3.0715e+00, 1.3497e+00],
[-6.2940e+00, 4.0016e-02, 4.7528e+00, ..., 7.5221e-01,
5.2391e+00, 2.6424e+00],
[ 2.3914e+00, -2.5762e+00, 5.1090e+00, ..., 6.8672e+00,
2.0650e+00, -5.4759e+00]],
[[ 1.6582e+00, 8.6526e+00, -1.0749e+00, ..., 2.8766e+00,
-5.5018e-01, 5.1222e+00],
[-1.4082e+00, -1.4155e+00, 3.1035e+00, ..., 3.2815e+00,
-1.9658e-01, -8.0176e-01],
[-4.5562e+00, 1.3145e+00, 6.4768e+00, ..., 4.0439e+00,
-3.5790e-02, 6.9798e+00],
...,
[-3.3441e+00, 1.0621e+00, 1.3430e+01, ..., 4.6467e+00,
-8.9313e-02, 2.4199e+00],
[ 1.1811e+01, 1.9927e+00, -3.3585e+00, ..., 1.3413e-02,
-2.6428e+00, 2.1664e+00],
[ 7.7002e-01, 7.3239e+00, 1.8310e+00, ..., 8.6775e-01,
-4.2041e-01, 1.0785e+01]],
[[-2.5241e-01, 1.5138e+00, 5.0879e+00, ..., 6.6563e+00,
4.2072e+00, 3.8277e+00],
[ 1.7377e+00, 3.1053e+00, 1.7281e+00, ..., -9.0835e-01,
-5.1616e+00, -3.9332e+00],
[ 4.0045e+00, -1.3305e+00, -4.0025e+00, ..., 5.4383e-01,
4.8545e+00, 2.0072e+00],
...,
[ 3.1167e+00, -9.8798e-02, -7.7456e+00, ..., -3.2560e+00,
-4.4654e+00, -7.2924e-01],
[-4.1589e+00, -1.8304e+00, -2.9414e-02, ..., -2.2777e+00,
4.6770e-01, -1.9580e+00],
[-6.0022e+00, -8.6861e+00, -9.0680e+00, ..., -4.7578e+00,
-6.5775e+00, -6.8521e+00]],
...,
[[-2.3116e+00, -5.4705e+00, -2.1156e+00, ..., -3.0074e+00,
-6.0479e+00, -3.9592e+00],
[-8.3428e+00, -2.0116e+00, -4.2231e+00, ..., -4.6542e+00,
-6.2577e-01, -2.7182e+00],
[-2.2370e+00, -7.2099e+00, -4.8415e+00, ..., -9.0038e-01,
4.2641e+00, -6.0504e+00],
...,
[-7.6772e+00, 5.7089e-01, -4.6675e+00, ..., -6.8624e+00,
-2.1480e+00, -3.0441e+00],
[-1.1076e+01, -7.1043e-01, -1.0415e+00, ..., -1.0344e-01,
8.1842e-01, -1.0041e+00],
[-1.1128e+00, -4.1235e+00, -5.3697e+00, ..., -2.0745e+00,
-1.9154e+00, -5.4500e+00]],
[[-3.6294e+00, -1.2266e+00, -1.4711e+00, ..., -4.7060e+00,
-1.9022e-01, -1.3691e+00],
[-1.2468e+00, -3.0143e-01, -9.9140e-01, ..., 4.5021e-02,
6.1783e-01, -1.7144e+00],
[-1.9860e+00, -4.8642e-01, -4.3711e+00, ..., -1.7553e+00,
-1.6518e+00, -3.4629e+00],
...,
[-7.6630e+00, -9.3625e-01, -3.5440e+00, ..., -2.4302e+00,
-4.6675e+00, -2.9561e+00],
[-2.3291e+00, 5.9916e-02, -5.8214e-01, ..., -3.6602e+00,
-1.1828e+00, 6.9605e-01],
[-3.1220e+00, -6.2251e-01, -3.7096e+00, ..., -2.9578e+00,
-2.4020e+00, -1.4033e+00]],
[[-4.3595e-01, -8.9710e+00, -2.4228e-01, ..., -4.7376e+00,
1.0235e+00, 5.8505e-01],
[ 3.3992e+00, 3.0944e+00, -5.5038e-01, ..., -4.6174e+00,
-3.4045e-01, -7.0661e-01],
[ 2.3746e+00, 9.6190e-01, -1.1166e+01, ..., -1.8727e+00,
-2.0663e+00, -4.8873e+00],
...,
[-1.8387e+00, -6.9693e-01, -9.4641e+00, ..., -6.7464e+00,
-1.2005e+00, -3.1699e+00],
[-8.2991e+00, -1.2437e+00, 6.0767e+00, ..., -6.4197e-01,
6.6454e-01, -1.3952e-01],
[-1.7444e+00, -4.1268e+00, -2.1140e+00, ..., -3.7640e+00,
-1.1238e+00, -7.8603e+00]]],
[[[ 5.0805e+00, 8.7671e-02, 3.4758e+00, ..., 3.1559e+00,
3.5970e+00, -7.2715e-01],
[-3.0254e+00, 2.2791e+00, -6.0707e+00, ..., -4.3978e-02,
-3.6069e+00, -2.4760e+00],
[ 5.1218e+00, 1.0057e+00, 1.5490e+00, ..., -3.0058e+00,
-5.9112e+00, -1.2956e+00],
...,
[ 2.1297e-01, 5.1988e+00, 3.5929e-01, ..., 3.2178e+00,
-9.8183e+00, -1.1763e+00],
[ 5.3548e+00, -1.0457e+01, 4.2351e-01, ..., 4.9426e+00,
-5.5472e+00, -5.5033e+00],
[ 3.0098e+00, -6.6520e+00, 2.4066e+00, ..., -6.9228e+00,
5.4740e+00, 2.2548e+00]],
[[ 1.9567e+00, 3.2346e+00, -2.1096e+00, ..., 4.4348e+00,
8.5156e-01, -2.2350e+00],
[ 1.0953e+01, -2.1557e+00, 1.0032e+01, ..., 1.4487e+00,
9.7532e+00, 3.1700e+00],
[-2.7019e+00, 3.3467e+00, -1.4208e+00, ..., 2.7078e+00,
1.3399e+01, 6.6587e-01],
...,
[ 2.9796e+00, -1.6890e+00, -3.2654e+00, ..., -2.0565e+00,
1.4040e+01, 2.8258e+00],
[-5.8443e-02, 1.2664e+01, 1.4248e-02, ..., 6.1573e-01,
1.6862e+01, 2.0706e+00],
[ 3.0496e+00, 1.1344e+01, 1.5120e+00, ..., 1.4912e+01,
6.4290e-01, 4.0607e-01]],
[[ 1.3388e+00, 1.6310e+00, 6.3092e+00, ..., 5.9953e+00,
7.9956e+00, 7.2546e+00],
[-2.7757e-01, 1.3050e+00, -3.9240e+00, ..., -4.0540e-01,
-3.9568e+00, -3.2103e+00],
[ 2.7136e+00, 2.2255e+00, 2.7000e+00, ..., -1.7754e+00,
-6.4103e+00, -1.9679e+00],
...,
[-2.9834e+00, 1.7282e+00, 6.7993e-01, ..., 1.9443e+00,
7.1873e-02, 3.7831e+00],
[-1.4561e-01, -8.4292e+00, -7.0686e+00, ..., -8.1948e-01,
-4.6235e+00, -2.3658e+00],
[-5.3289e+00, -8.2413e+00, -7.1389e+00, ..., -1.0153e+01,
-6.2089e+00, -4.4526e+00]],
...,
[[-5.7656e+00, -2.2378e+00, -6.3310e+00, ..., -5.1985e+00,
-9.4067e+00, -7.1324e+00],
[-1.0993e+01, 4.1962e-01, -7.7093e+00, ..., -2.6705e+00,
-8.7259e+00, -8.8013e-01],
[-2.1185e+00, -4.3493e+00, 8.9117e-01, ..., 1.5759e+00,
-6.3580e+00, 9.0258e-01],
...,
[-9.2017e-01, -7.5772e+00, -4.9637e-01, ..., -5.8232e+00,
-8.7223e+00, -3.3486e+00],
[-2.9168e+00, -4.0060e+00, -3.4872e+00, ..., -5.7944e+00,
-8.8498e+00, 3.0686e-01],
[-3.4334e+00, -7.0884e+00, -2.7922e-01, ..., -1.0622e+01,
-6.7335e+00, -4.0388e+00]],
[[ 1.7408e-01, -1.4576e+00, -1.2563e+00, ..., -4.4072e+00,
-6.4268e+00, -7.1240e+00],
[-2.6519e+00, -3.4047e+00, -1.7169e+00, ..., -5.6319e+00,
8.7324e-01, -2.0666e+00],
[ 2.4200e-01, -1.7536e+00, -2.0500e+00, ..., -3.9415e+00,
-2.8769e+00, -3.6237e+00],
...,
[-2.0481e+00, -8.9259e-01, -8.5338e-01, ..., -9.2954e+00,
-1.9070e+00, -6.3516e+00],
[-1.4993e+00, -4.0809e+00, -5.8545e+00, ..., -6.6398e+00,
-1.1979e+00, -1.4924e+00],
[-4.5012e+00, -2.6363e+00, -2.7986e+00, ..., -9.6162e-01,
-7.9291e+00, -4.7604e+00]],
[[ 7.8305e-01, -2.0886e+00, 3.4647e+00, ..., -2.8440e+00,
-1.1117e+00, 8.7459e-01],
[-1.0883e+01, 1.8911e+00, -9.3761e+00, ..., -2.1934e+00,
-4.7214e+00, -2.8249e+00],
[ 2.2531e+00, -4.9591e-01, 2.7125e+00, ..., -3.9317e+00,
-9.3989e+00, -1.9396e+00],
...,
[ 8.0485e-01, 3.3610e+00, -2.2048e+00, ..., -2.9378e+00,
-5.2954e+00, -9.4283e-01],
[ 4.9259e+00, -5.7080e+00, -7.3379e+00, ..., -3.1136e+00,
-9.6568e+00, 1.5648e+00],
[-7.1825e-01, -8.1123e+00, -3.0114e+00, ..., -1.4047e+01,
-2.4005e+00, -3.6580e+00]]],
[[[ 4.9511e+00, -1.5027e+00, -1.9901e+00, ..., 5.0412e+00,
1.0706e+00, 1.0697e+00],
[ 6.2415e+00, 5.0564e+00, 7.1823e+00, ..., 2.6606e+00,
-4.2820e+00, -2.6922e+00],
[ 2.6145e+00, -7.9739e+00, -3.2277e+00, ..., -5.7815e-01,
2.0001e+00, -6.0351e-01],
...,
[ 2.4288e+00, -6.2262e+00, -1.1152e+00, ..., 2.1734e-01,
-2.6232e+00, -1.4114e+00],
[ 7.7376e+00, 1.9148e+00, 5.6097e+00, ..., -8.7782e+00,
-2.0215e+00, -5.4768e+00],
[-3.0471e+00, 3.3768e+00, -9.9078e+00, ..., 3.4497e+00,
5.2545e+00, -1.2969e+00]],
[[ 4.1481e+00, -9.4472e-01, 4.9418e+00, ..., 2.6200e+00,
-1.1429e+00, 1.1250e+00],
[ 4.8664e+00, -8.3640e-01, -2.8931e+00, ..., 3.2391e+00,
7.3649e+00, 7.5707e-01],
[ 2.4969e+00, 1.1368e+01, 3.4130e+00, ..., 2.6504e+00,
1.0609e+00, 5.4673e-01],
...,
[-1.8373e+00, 6.1022e+00, -6.7085e-01, ..., 2.4950e+00,
1.8695e+00, 3.7362e-01],
[ 5.4703e-01, 1.7698e+00, 8.2906e-01, ..., 1.3463e+01,
7.3126e+00, 4.2411e+00],
[ 5.5332e+00, 4.1572e-01, 1.9763e+01, ..., -5.6383e-01,
2.3872e-01, -1.6456e+00]],
[[ 1.6635e+00, 4.7899e+00, 4.6182e+00, ..., 3.7803e+00,
5.6378e+00, 3.1067e+00],
[ 1.4200e-01, 4.7036e+00, 2.7844e+00, ..., -3.7973e+00,
-5.9692e+00, -5.1718e+00],
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[[ 2.2686e-02]],
[[ 5.3278e-02]],
[[-1.1578e+00]],
[[ 1.7661e-01]],
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[[-8.5253e-02]],
[[-1.0406e-02]],
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[[[-9.3577e-01]],
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[[[-3.3189e-01]],
[[-1.9446e-02]],
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[[ 1.0625e-02]],
[[ 8.1591e-02]],
[[-2.1832e-02]],
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[[-3.3994e-02]],
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[[-1.4032e-01]],
[[-2.3860e-02]],
[[-9.0546e-02]],
[[-9.8386e-02]],
[[-4.7722e-02]],
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[[-3.6645e-03]],
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[[ 1.9925e-02]],
[[-1.2176e-01]],
[[ 4.7967e-03]],
[[-1.4316e-03]],
[[-4.7118e-04]],
[[-5.6228e-02]],
[[ 6.4555e-02]],
[[ 7.8806e-03]],
[[ 1.1947e-01]],
[[-6.1449e-02]],
[[ 2.2647e-03]],
[[-3.3782e-02]],
[[-7.3577e-02]],
[[ 6.5389e-02]],
[[-7.7591e-02]],
[[-1.1609e-02]],
[[-4.7356e-02]],
[[ 7.8632e-03]],
[[ 1.0340e-02]],
[[ 3.5183e-02]],
[[-1.4570e-01]],
[[ 1.0716e-01]],
[[ 2.1980e-02]],
[[ 1.2434e-02]],
[[-6.8202e-02]],
[[ 2.0452e-02]],
[[ 7.7962e-02]],
[[-4.5192e-02]],
[[ 7.6807e-03]],
[[-8.4734e-02]],
[[-2.8607e-02]],
[[-4.5014e-03]],
[[ 8.4037e-02]],
[[-9.3664e-01]],
[[-6.6527e-02]],
[[ 9.7697e-02]],
[[ 5.0429e-02]],
[[ 4.1691e-02]],
[[-8.6985e-02]],
[[-3.5593e-03]],
[[-1.3485e-01]]]], grad_fn=<AsStridedBackward1>)})
Other functionalities#
In addition to recording, the following functionalities are available
and documented in Recorder:
One-the-fly postprocessing of activations during inference (e.g. clipping).
Local disabling of the recording during forward pass.
Access the recorded layers’
Moduleobjects directly.Get the named parameters of the recorded layers.
Implementation details#
This utility works by affecting hooks to every layer of net with
Module.register_forward_hook. However, since layers are not aware
of the context in which they are called, these hooks carry references
to rnet and with it, the sufficient context to know when to
trigger. This means that two different
Recorder Nets can wrap the same net
without any conflict. As an implementation detail, note that these
references are made weak in order to be
properly cleaned up upon deletion of rnet.
Total running time of the script: (0 minutes 0.153 seconds)