From 179298e3a291470ef30e850f23d98c2fb9055202 Mon Sep 17 00:00:00 2001
From: Christian Clauss <cclauss@me.com>
Date: Sat, 8 Apr 2023 02:52:26 +0200
Subject: [PATCH] Revert "Add LeNet Implementation in PyTorch (#7070)" (#8621)

This reverts commit b2b8585e63664a0c7aa18b95528e345c2738c4ae.
---
 computer_vision/lenet_pytorch.py | 82 --------------------------------
 requirements.txt                 |  1 -
 2 files changed, 83 deletions(-)
 delete mode 100644 computer_vision/lenet_pytorch.py

diff --git a/computer_vision/lenet_pytorch.py b/computer_vision/lenet_pytorch.py
deleted file mode 100644
index 177a5ebfc..000000000
--- a/computer_vision/lenet_pytorch.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""
-LeNet Network
-
-Paper: http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf
-"""
-
-import numpy
-import torch
-import torch.nn as nn
-
-
-class LeNet(nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-
-        self.tanh = nn.Tanh()
-        self.avgpool = nn.AvgPool2d(kernel_size=2, stride=2)
-
-        self.conv1 = nn.Conv2d(
-            in_channels=1,
-            out_channels=6,
-            kernel_size=(5, 5),
-            stride=(1, 1),
-            padding=(0, 0),
-        )
-        self.conv2 = nn.Conv2d(
-            in_channels=6,
-            out_channels=16,
-            kernel_size=(5, 5),
-            stride=(1, 1),
-            padding=(0, 0),
-        )
-        self.conv3 = nn.Conv2d(
-            in_channels=16,
-            out_channels=120,
-            kernel_size=(5, 5),
-            stride=(1, 1),
-            padding=(0, 0),
-        )
-
-        self.linear1 = nn.Linear(120, 84)
-        self.linear2 = nn.Linear(84, 10)
-
-    def forward(self, image_array: numpy.ndarray) -> numpy.ndarray:
-        image_array = self.tanh(self.conv1(image_array))
-        image_array = self.avgpool(image_array)
-        image_array = self.tanh(self.conv2(image_array))
-        image_array = self.avgpool(image_array)
-        image_array = self.tanh(self.conv3(image_array))
-
-        image_array = image_array.reshape(image_array.shape[0], -1)
-        image_array = self.tanh(self.linear1(image_array))
-        image_array = self.linear2(image_array)
-        return image_array
-
-
-def test_model(image_tensor: torch.tensor) -> bool:
-    """
-    Test the model on an input batch of 64 images
-
-    Args:
-        image_tensor (torch.tensor): Batch of Images for the model
-
-    >>> test_model(torch.randn(64, 1, 32, 32))
-    True
-
-    """
-    try:
-        model = LeNet()
-        output = model(image_tensor)
-    except RuntimeError:
-        return False
-
-    return output.shape == torch.zeros([64, 10]).shape
-
-
-if __name__ == "__main__":
-    random_image_1 = torch.randn(64, 1, 32, 32)
-    random_image_2 = torch.randn(1, 32, 32)
-
-    print(f"random_image_1 Model Passed: {test_model(random_image_1)}")
-    print(f"\nrandom_image_2 Model Passed: {test_model(random_image_2)}")
diff --git a/requirements.txt b/requirements.txt
index e159fe010..acfbc823e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -17,7 +17,6 @@ statsmodels
 sympy
 tensorflow
 texttable
-torch
 tweepy
 xgboost
 yulewalker