I have been trying to follow this code snippet and adapt it to a ResNet20 to reproduce results at different bitwidths without any luck, meaning that I always get validation accuracy equal to the bitwidth **B** I’m using (8% for 8 bits, 4% for 4 bits, and so on). It’s not clear at all to me where and how many times to put the **self.quant** and **self.dequant** code lines in the resnet definition, and also how to correctly **fuse** the model.

These are the code changes I did to the BasicBlock (bold):

class BasicBlock(nn.Module):

expansion = 1

```
def __init__(self, in_planes, planes, stride=1, option='A'):
super(BasicBlock, self).__init__()
**self.quant = torch.quantization.QuantStub()**
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 != planes:
if option == 'A':
self.shortcut = LambdaLayer(lambda x:
F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes // 4, planes // 4), "constant",
0))
elif option == 'B':
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
**self.dequant = torch.quantization.DeQuantStub()**
def forward(self, x):
**out = self.quant(x)**
out = F.relu(self.bn1(self.conv1(out)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
**out = self.dequant(out)**
return out
```

and to the ResNet module (bold):

class ResNet(nn.Module):

def **init**(self, block, num_blocks, num_classes=10):

super(ResNet, self).**init**()

**self.quant = torch.quantization.QuantStub()**

self.in_planes = 16

```
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
self.linear = nn.Linear(64, num_classes)
self.apply(_weights_init)
**self.dequant = torch.quantization.DeQuantStub()**
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 = self.quant(x)**
out = F.relu(self.bn1(self.conv1(out)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = F.avg_pool2d(out, out.size()[3])
out = out.view(out.size(0), -1)
out = self.linear(out)
**out = self.dequant(out)**
return out
```

And this is how I fuse the model:

model_fp32_fused = torch.quantization.fuse_modules(model_fp32, [[“conv1”, “bn1”]], inplace=True)

for module_name, module in model_fp32_fused.named_children():

if “layer” in module_name:

for basic_block_name, basic_block in module.named_children():

torch.quantization.fuse_modules(

basic_block, [[“conv1”, “bn1”], [“conv2”, “bn2”]],

inplace=True)

What am I doing wrong? I’m sorry if it’s trivial but it’s very difficult to understand how to implement this for more complex models from the documentation. Thank you