I have a quantized model which is basically a resnet18. the quantization seems to go just fine until, when I try to load the quantized model from disk using sth like this :
def load_quantized(quantized_checkpoint_file_path):
model = fvmodels.resnet18(pretrained=False, use_se=True)
model.eval()
model.fuse_model()
# print(f'model: {model}')
# Specify quantization configuration
model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
# print(model.qconfig)
torch.quantization.prepare(model, inplace=True)
# Convert to quantized model
torch.quantization.convert(model, inplace=True)
checkpoint = torch.load(quantized_checkpoint_file_path, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint, strict=False)
# model = torch.jit.load(quantized_checkpoint_file_path, map_location=torch.device('cpu'))
fvmodels.print_size_of_model(model)
return model
and while trying to use that :
model = load_quantized('path to model')
model.eval()
with torch.no_grad():
for img, lbl in dtloader:
features = model(img.unsqueeze(0))
I face the following error :
RuntimeError: Could not run 'aten::native_batch_norm' with arguments from the 'QuantizedCPUTensorId' backend. 'aten::native_batch_norm' is only available for these backends: [CPUTensorId, MkldnnCPUTensorId, VariableTensorId].
This seems to be casued by the fact that the batchnorm layer is not fused! and the issue is I dont know how to fuse it. to be more specific here is the resnet model I have at hand :
class ResNet(nn.Module):
def __init__(self, block, layers, use_se=True):
self.inplanes = 64
self.use_se = use_se
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
# self.prelu = nn.PReLU()
self.prelu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.bn2 = nn.BatchNorm2d(512)
self.dropout = nn.Dropout()
self.fc = nn.Linear(512 * 7 * 7, 512)
self.bn3 = nn.BatchNorm1d(512)
self.quant = torch.quantization.QuantStub()
self.dequant = torch.quantization.DeQuantStub()
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
self.inplanes = planes
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, use_se=self.use_se))
return nn.Sequential(*layers)
def forward(self, x):
x = self.quant(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn2(x)
x = self.dropout(x)
# x = x.view(x.size(0), -1)
x = x.reshape(x.size(0), -1)
x = self.fc(x)
x = self.bn3(x)
x = self.dequant(x)
return x
def fuse_model(self):
r"""Fuse conv/bn/relu modules in resnet models
Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
Model is modified in place. Note that this operation does not change numerics
and the model after modification is in floating point
"""
fuse_modules(self, [['conv1', 'bn1', 'prelu'],
['bn2'],
['bn3']], inplace=True)
for m in self.modules():
# print(m)
if type(m) == Bottleneck or type(m) == BasicBlock or type(m) == IRBlock:
m.fuse_model()
as you can see in the forward pass we have :
...
x = self.bn2(x)
x = self.dropout(x)
which is followed by a dropout and unlike previous ones, doesnt come with neither conv or relu!
the same thing goes to bn3 a couple of lines later:
...
x = self.fc(x)
x = self.bn3(x)
x = self.dequant(x)
....
So I’m not sure how I’m supposed to get around this. obviously the way I’m fusing is wrong:
def fuse_model(self):
fuse_modules(self, [['conv1', 'bn1', 'prelu'],
['bn2'],
['bn3']], inplace=True)
for m in self.modules():
# print(m)
if type(m) == Bottleneck or type(m) == BasicBlock or type(m) == IRBlock:
m.fuse_model()
For the sake of completeness here are the whole models :
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.add_relu = torch.nn.quantized.FloatFunctional()
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
# out += residual
# out = self.relu(out)
out = self.add_relu.add_relu(out, residual)
return out
def fuse_model(self):
torch.quantization.fuse_modules(self, [['conv1', 'bn1', 'relu'],
['conv2', 'bn2']], inplace=True)
if self.downsample:
torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, 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, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu1 = nn.ReLU(inplace=False)
self.relu2 = nn.ReLU(inplace=False)
self.downsample = downsample
self.stride = stride
self.skip_add_relu = nn.quantized.FloatFunctional()
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu2(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
# out += residual
# out = self.relu(out)
out = self.skip_add_relu.add_relu(out, residual)
return out
def fuse_model(self):
fuse_modules(self, [['conv1', 'bn1', 'relu1'],
['conv2', 'bn2', 'relu2'],
['conv3', 'bn3']], inplace=True)
if self.downsample:
torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)
class SEBlock(nn.Module):
def __init__(self, channel, reduction=16):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.mult_xy = nn.quantized.FloatFunctional()
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction),
# nn.PReLU(),
nn.ReLU(),
nn.Linear(channel // reduction, channel),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
# out = x*y
out = self.mult_xy.mul(x, y)
return out
class IRBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
super().__init__()
self.bn0 = nn.BatchNorm2d(inplanes)
self.conv1 = conv3x3(inplanes, inplanes)
self.bn1 = nn.BatchNorm2d(inplanes)
# self.prelu = nn.PReLU()
self.prelu = nn.ReLU()
self.conv2 = conv3x3(inplanes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.use_se = use_se
if self.use_se:
self.se = SEBlock(planes)
self.add_residual_relu = nn.quantized.FloatFunctional()
def forward(self, x):
residual = x
out = self.bn0(x)
out = self.conv1(out)
out = self.bn1(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.use_se:
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
# out += residual
# out = self.prelu(out)
# we may need to change prelu into relu and this, instead of add, use add_relu here
out = self.add_residual_relu.add_relu(out, residual)
# out = self.prelu(out)
return out
def fuse_model(self):
fuse_modules(self, [['conv1', 'bn1', 'prelu'],
['conv2', 'bn2']], inplace=True)
if self.downsample:
torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)
class ResNet(nn.Module):
def __init__(self, block, layers, use_se=True):
self.inplanes = 64
self.use_se = use_se
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
# self.prelu = nn.PReLU()
self.prelu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.bn2 = nn.BatchNorm2d(512)
self.dropout = nn.Dropout()
self.fc = nn.Linear(512 * 7 * 7, 512)
self.bn3 = nn.BatchNorm1d(512)
self.quant = torch.quantization.QuantStub()
self.dequant = torch.quantization.DeQuantStub()
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
self.inplanes = planes
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, use_se=self.use_se))
return nn.Sequential(*layers)
def forward(self, x):
x = self.quant(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn2(x)
x = self.dropout(x)
# x = x.view(x.size(0), -1)
x = x.reshape(x.size(0), -1)
x = self.fc(x)
x = self.bn3(x)
x = self.dequant(x)
return x
def fuse_model(self):
r"""Fuse conv/bn/relu modules in resnet models
Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
Model is modified in place. Note that this operation does not change numerics
and the model after modification is in floating point
"""
fuse_modules(self, [['conv1', 'bn1', 'prelu'],
['bn2'],
['bn3']], inplace=True)
for m in self.modules():
# print(m)
if type(m) == Bottleneck or type(m) == BasicBlock or type(m) == IRBlock:
m.fuse_model()
def resnet18(pretrained, use_se, **kwargs):
model = ResNet(IRBlock, [2, 2, 2, 2], use_se=use_se, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
side note:
Also the actual model (resnet18 can be found from this link in case someone might need it)
Config:
Im using Pytorch 1.5.0+cpu on windows 10 x64 v1803
Any help is greatly appreciated