Source code for thelper.nn.resnet

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