import torch
import torch.nn
import torch.utils.model_zoo
import thelper.nn
import thelper.nn.coordconv
[docs]class Module(torch.nn.Module):
expansion = 1
[docs] def __init__(self, inplanes, planes, stride=1, downsample=None, coordconv=False, radius_channel=True):
super().__init__()
self.inplanes = inplanes
self.planes = planes
self.stride = stride
self.downsample = downsample
self.coordconv = coordconv
self.radius_channel = radius_channel
def _make_conv2d(self, *args, **kwargs):
if self.coordconv:
return thelper.nn.coordconv.CoordConv2d(*args, radius_channel=self.radius_channel, **kwargs)
else:
return torch.nn.Conv2d(*args, **kwargs)
[docs]class BasicBlock(Module):
[docs] def __init__(self, inplanes, planes, stride=1, downsample=None, coordconv=False, radius_channel=True):
super().__init__(inplanes, planes, stride, downsample, coordconv, radius_channel)
self.conv1 = self._make_conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = torch.nn.BatchNorm2d(planes)
self.relu = torch.nn.ReLU(inplace=True)
self.conv2 = self._make_conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = torch.nn.BatchNorm2d(planes)
[docs] 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)
return out
[docs]class Bottleneck(Module):
expansion = 4
[docs] def __init__(self, inplanes, planes, stride=1, downsample=None, coordconv=False, radius_channel=True):
super().__init__(inplanes, planes, stride, downsample, coordconv, radius_channel)
self.conv1 = self._make_conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = torch.nn.BatchNorm2d(planes)
self.conv2 = self._make_conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = torch.nn.BatchNorm2d(planes)
self.conv3 = self._make_conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = torch.nn.BatchNorm2d(planes * self.expansion)
self.relu = torch.nn.ReLU(inplace=True)
[docs] 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)
out = self.relu(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)
return out
[docs]class SqueezeExcitationLayer(torch.nn.Module):
[docs] def __init__(self, channel, reduction=16):
super().__init__()
self.pool = torch.nn.AdaptiveAvgPool2d(1)
self.fc = torch.nn.Sequential(
torch.nn.Linear(channel, channel // reduction),
torch.nn.ReLU(inplace=True),
torch.nn.Linear(channel // reduction, channel),
torch.nn.Sigmoid()
)
[docs] def forward(self, x):
b, c, _, _ = x.size()
y = self.pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
[docs]class SqueezeExcitationBlock(Module):
[docs] def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16, coordconv=False, radius_channel=True):
super().__init__(inplanes, planes, stride, downsample, coordconv, radius_channel)
self.conv1 = self._make_conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = torch.nn.BatchNorm2d(planes)
self.relu = torch.nn.ReLU(inplace=True)
self.conv2 = self._make_conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = torch.nn.BatchNorm2d(planes)
self.se = SqueezeExcitationLayer(planes, reduction)
[docs] 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)
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
[docs]class ResNet(thelper.nn.Module):
[docs] def __init__(self, task, block=BasicBlock, layers=[3, 4, 6, 3], strides=[1, 2, 2, 2], input_channels=3,
flexible_input_res=False, pool_size=7, coordconv=False, radius_channel=True, pretrained=False):
# note: must always forward args to base class to keep backup
super().__init__(task, **{k: v for k, v in vars().items() if k not in ["self", "task", "__class__"]})
if isinstance(block, str):
block = thelper.utils.import_class(block)
if not issubclass(block, Module):
raise AssertionError("block type must be subclass of thelper.nn.resnet.Module")
if not isinstance(layers, list) or not isinstance(strides, list):
raise AssertionError("expected layers/strides to be provided as list of ints")
if len(layers) != len(strides):
raise AssertionError("layer/strides length mismatch")
self.inplanes = 64
self.coordconv = coordconv
self.radius_channel = radius_channel
self.conv1 = self._make_conv2d(in_channels=input_channels, out_channels=self.inplanes,
kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = torch.nn.BatchNorm2d(self.inplanes)
self.relu = torch.nn.ReLU(inplace=True)
self.maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1])
self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2])
self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3])
self.out_features = 512
self.layer5 = None
if len(layers) > 4:
self.layer5 = self._make_layer(block, 1024, layers[4], stride=strides[4])
self.out_features = 1024
if flexible_input_res:
self.avgpool = torch.nn.AdaptiveAvgPool2d(1)
else:
if pool_size < 1:
raise AssertionError("invalid avg pool size for non-flex resolution")
self.avgpool = torch.nn.AvgPool2d(pool_size, stride=1)
self.out_features *= block.expansion
self.fc = torch.nn.Linear(self.out_features, 1000) # output type/count will be specialized by task after init
for m in self.modules():
if isinstance(m, torch.nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, thelper.nn.coordconv.CoordConv2d):
torch.nn.init.kaiming_normal_(m.conv.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, torch.nn.BatchNorm2d):
torch.nn.init.constant_(m.weight, 1)
torch.nn.init.constant_(m.bias, 0)
if pretrained:
# note: if using a non-default setup in the constructor, loading the pre-trained weights will most
# likely fail as the weights are downloaded from the pytorch model zoo for the regular resnet impls
import torchvision
default_weights_mapping = {
str([2, 2, 2, 2]) + str("BasicBlock"): "resnet18",
str([3, 4, 6, 3]) + str("BasicBlock"): "resnet34",
str([3, 4, 6, 3]) + str("Bottleneck"): "resnet50",
str([3, 4, 23, 3]) + str("Bottleneck"): "resnet101",
str([3, 8, 36, 3]) + str("Bottleneck"): "resnet152"
}
tag = str(layers) + block.__name__
assert tag in default_weights_mapping, "could not find corresponding weight url"
weights_url = torchvision.models.resnet.model_urls[default_weights_mapping[tag]]
state_dict = torchvision.models.utils.load_state_dict_from_url(weights_url)
self.load_state_dict(state_dict)
self.set_task(task)
def _make_conv2d(self, *args, **kwargs):
if self.coordconv:
return thelper.nn.coordconv.CoordConv2d(*args, radius_channel=self.radius_channel, **kwargs)
else:
return torch.nn.Conv2d(*args, **kwargs)
def _make_layer(self, block, planes, blocks, stride):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = torch.nn.Sequential(
self._make_conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
torch.nn.BatchNorm2d(planes * block.expansion),
)
layers = [block(self.inplanes, planes, stride, downsample)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return torch.nn.Sequential(*layers)
[docs] def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
if self.layer5 is not None:
x = self.layer5(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
[docs] def set_task(self, task):
assert isinstance(task, thelper.tasks.Classification), "missing impl for non-classif task type"
num_classes = len(task.class_names)
if self.fc.out_features != num_classes:
self.fc = torch.nn.Linear(self.out_features, num_classes)
self.task = task
[docs]class ConvTailNet(torch.nn.Module):
[docs] def __init__(self, n_inputs, num_classes):
super(ConvTailNet, self).__init__()
self.conv1 = torch.nn.Conv2d(n_inputs, n_inputs, kernel_size=1, bias=False)
self.relu = torch.nn.ReLU(True)
self.conv2 = torch.nn.Conv2d(n_inputs, n_inputs, kernel_size=1, bias=False)
self.conv3 = torch.nn.Conv2d(n_inputs, num_classes, kernel_size=1, bias=False)
[docs] def forward(self, x):
x0 = x
x = self.conv1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.relu(x)
x = torch.add(x0, x)
x = self.conv3(x)
return x
[docs]class ResNetFullyConv(ResNet):
[docs] def __init__(self, task, block=BasicBlock, layers=[3, 4, 6, 3], strides=[1, 2, 2, 2], input_channels=3,
flexible_input_res=False, pool_size=7, coordconv=False, radius_channel=True, pretrained=False):
super().__init__(task=task, block=block, layers=layers, strides=strides, input_channels=input_channels,
flexible_input_res=flexible_input_res, pool_size=pool_size, coordconv=coordconv,
radius_channel=radius_channel, pretrained=pretrained)
self.set_task(task)
[docs] def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
if self.layer5 is not None:
x = self.layer5(x)
x = self.avgpool(x)
x = self.fc(x)
x = torch.squeeze(x)
return x
[docs] def set_task(self, task):
assert isinstance(task, thelper.tasks.Classification), "missing impl for non-classif task type"
num_classes = len(task.class_names)
self.fc = ConvTailNet(self.out_features, num_classes)
self.task = task
[docs]class FCResNet(ResNet):
[docs] def __init__(self, task, ckptdata, map_location="cpu", avgpool_size=0):
if isinstance(ckptdata, str):
ckptdata = thelper.utils.load_checkpoint(ckptdata, map_location=map_location)
model_type = ckptdata["model_type"]
if model_type != "thelper.nn.resnet.ResNet":
raise AssertionError("cannot convert non-resnet model to fully conv with this impl")
model_params = ckptdata["model_params"]
if isinstance(ckptdata["task"], str):
old_model_task = thelper.tasks.create_task(ckptdata["task"])
else:
old_model_task = ckptdata["task"]
self.task = None
self.avgpool_size = avgpool_size
super().__init__(old_model_task, **model_params)
self.load_state_dict(ckptdata["model"], strict=False) # assumes model always stored as weight dict
self.finallayer = torch.nn.Conv2d(self.out_features, self.fc.out_features, kernel_size=1)
self.finallayer.weight = torch.nn.Parameter(self.fc.weight.view(self.fc.out_features, self.out_features, 1, 1))
self.finallayer.bias = torch.nn.Parameter(self.fc.bias)
self.set_task(task)
[docs] def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
if self.layer5 is not None:
x = self.layer5(x)
if self.avgpool_size > 0:
x = torch.nn.functional.avg_pool2d(x, kernel_size=self.avgpool_size, stride=1)
x = self.finallayer(x)
return x
[docs] def set_task(self, task):
assert isinstance(task, (thelper.tasks.Segmentation, thelper.tasks.Classification)), \
"missing impl for non-segm/classif task type"
num_classes = len(task.class_names)
if self.fc.out_features != num_classes:
self.fc = torch.nn.Linear(self.out_features, num_classes)
self.finallayer = torch.nn.Conv2d(self.out_features, num_classes, kernel_size=1)
self.finallayer.weight = torch.nn.Parameter(self.fc.weight.view(self.fc.out_features, self.out_features, 1, 1))
self.finallayer.bias = torch.nn.Parameter(self.fc.bias)
self.task = task