1. Input source code:
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'''ResNet in PyTorch.

For Pre-activation ResNet, see 'preact_resnet.py'.

Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
	Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F


class BasicBlock(nn.Module):
	expansion = 1

	def __init__(self, in_planes, planes, stride=1):
		super(BasicBlock, self).__init__()
		self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
		self.bn1 = nn.BatchNorm2d(planes)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
		self.bn2 = nn.BatchNorm2d(planes)

		self.shortcut = nn.Sequential()
		if stride != 1 or in_planes != self.expansion*planes:
			self.shortcut = nn.Sequential(
				nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
				nn.BatchNorm2d(self.expansion*planes)
			)

	def forward(self, x):
		out = F.relu(self.bn1(self.conv1(x)))
		out = self.bn2(self.conv2(out))
		out += self.shortcut(x)
		out = F.relu(out)
		return out


class Bottleneck(nn.Module):
	expansion = 4

	def __init__(self, in_planes, planes, stride=1):
		super(Bottleneck, self).__init__()
		self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
		self.bn1 = nn.BatchNorm2d(planes)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
		self.bn2 = nn.BatchNorm2d(planes)
		self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
		self.bn3 = nn.BatchNorm2d(self.expansion*planes)

		self.shortcut = nn.Sequential()
		if stride != 1 or in_planes != self.expansion*planes:
			self.shortcut = nn.Sequential(
				nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
				nn.BatchNorm2d(self.expansion*planes)
			)

	def forward(self, x):
		out = F.relu(self.bn1(self.conv1(x)))
		out = F.relu(self.bn2(self.conv2(out)))
		out = self.bn3(self.conv3(out))
		out += self.shortcut(x)
		out = F.relu(out)
		return out


class ResNet(nn.Module):
	def __init__(self, block, num_blocks, num_classes=10):
		super(ResNet, self).__init__()
		self.in_planes = 64

		self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
		self.bn1 = nn.BatchNorm2d(64)
		self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
		self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
		self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
		self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
		self.linear = nn.Linear(512*block.expansion, num_classes)

	def _make_layer(self, block, planes, num_blocks, stride):
		strides = [stride] + [1]*(num_blocks-1)
		layers = []
		for stride in strides:
			layers.append(block(self.in_planes, planes, stride))
			self.in_planes = planes * block.expansion
		return nn.Sequential(*layers)

	def forward(self, x):
		out = F.relu(self.bn1(self.conv1(x)))
		out = self.layer1(out)
		out = self.layer2(out)
		out = self.layer3(out)
		out = self.layer4(out)
		out = F.avg_pool2d(out, 4)
		out = out.view(out.size(0), -1)
		out = self.linear(out)
		return out


def ResNet18():
	return ResNet(BasicBlock, [2,2,2,2])

def ResNet34():
	return ResNet(BasicBlock, [3,4,6,3])

def ResNet50():
	return ResNet(Bottleneck, [3,4,6,3])

def ResNet101():
	return ResNet(Bottleneck, [3,4,23,3])

def ResNet152():
	return ResNet(Bottleneck, [3,8,36,3])


def test():
	net = ResNet18()
	y = net(torch.randn(1,3,32,32))
	print(y.size())

# test()

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2\) Use this JSON I uploaded
3\) Use the data.csv I uploaded