downstream/semseg/models/resnet.py [15:221]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class ResNetBase(Model): BLOCK = None LAYERS = () INIT_DIM = 64 PLANES = (64, 128, 256, 512) OUT_PIXEL_DIST = 32 HAS_LAST_BLOCK = False CONV_TYPE = ConvType.HYPERCUBE def __init__(self, in_channels, out_channels, config, D=3, **kwargs): assert self.BLOCK is not None assert self.OUT_PIXEL_DIST > 0 super(ResNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) self.network_initialization(in_channels, out_channels, config, D) self.weight_initialization() def network_initialization(self, in_channels, out_channels, config, D): def space_n_time_m(n, m): return n if D == 3 else [n, n, n, m] if D == 4: self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1) dilations = config.dilations bn_momentum = config.bn_momentum self.inplanes = self.INIT_DIM self.conv1 = conv( in_channels, self.inplanes, kernel_size=space_n_time_m(config.conv1_kernel_size, 1), stride=1, D=D) self.bn1 = get_norm(NormType.BATCH_NORM, self.inplanes, D=self.D, bn_momentum=bn_momentum) self.relu = ME.MinkowskiReLU(inplace=True) self.pool = sum_pool(kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), D=D) self.layer1 = self._make_layer( self.BLOCK, self.PLANES[0], self.LAYERS[0], stride=space_n_time_m(2, 1), dilation=space_n_time_m(dilations[0], 1)) self.layer2 = self._make_layer( self.BLOCK, self.PLANES[1], self.LAYERS[1], stride=space_n_time_m(2, 1), dilation=space_n_time_m(dilations[1], 1)) self.layer3 = self._make_layer( self.BLOCK, self.PLANES[2], self.LAYERS[2], stride=space_n_time_m(2, 1), dilation=space_n_time_m(dilations[2], 1)) self.layer4 = self._make_layer( self.BLOCK, self.PLANES[3], self.LAYERS[3], stride=space_n_time_m(2, 1), dilation=space_n_time_m(dilations[3], 1)) self.final = conv( self.PLANES[3] * self.BLOCK.expansion, out_channels, kernel_size=1, bias=True, D=D) def weight_initialization(self): for m in self.modules(): if isinstance(m, ME.MinkowskiBatchNorm): nn.init.constant_(m.bn.weight, 1) nn.init.constant_(m.bn.bias, 0) def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_type=NormType.BATCH_NORM, bn_momentum=0.1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv( self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False, D=self.D), get_norm(norm_type, planes * block.expansion, D=self.D, bn_momentum=bn_momentum), ) layers = [] layers.append( block( self.inplanes, planes, stride=stride, dilation=dilation, downsample=downsample, conv_type=self.CONV_TYPE, D=self.D)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append( block( self.inplanes, planes, stride=1, dilation=dilation, conv_type=self.CONV_TYPE, D=self.D)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.pool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.final(x) return x class ResNet14(ResNetBase): BLOCK = BasicBlock LAYERS = (1, 1, 1, 1) class ResNet18(ResNetBase): BLOCK = BasicBlock LAYERS = (2, 2, 2, 2) class ResNet34(ResNetBase): BLOCK = BasicBlock LAYERS = (3, 4, 6, 3) class ResNet50(ResNetBase): BLOCK = Bottleneck LAYERS = (3, 4, 6, 3) class ResNet101(ResNetBase): BLOCK = Bottleneck LAYERS = (3, 4, 23, 3) class STResNetBase(ResNetBase): CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(STResNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class STResNet14(STResNetBase, ResNet14): pass class STResNet18(STResNetBase, ResNet18): pass class STResNet34(STResNetBase, ResNet34): pass class STResNet50(STResNetBase, ResNet50): pass class STResNet101(STResNetBase, ResNet101): pass class STResTesseractNetBase(STResNetBase): CONV_TYPE = ConvType.HYPERCUBE class STResTesseractNet14(STResTesseractNetBase, STResNet14): pass class STResTesseractNet18(STResTesseractNetBase, STResNet18): pass class STResTesseractNet34(STResTesseractNetBase, STResNet34): pass class STResTesseractNet50(STResTesseractNetBase, STResNet50): pass class STResTesseractNet101(STResTesseractNetBase, STResNet101): pass - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - downstream/votenet_det_new/models/backbone/sparseconv/models/resnet.py [15:221]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class ResNetBase(Model): BLOCK = None LAYERS = () INIT_DIM = 64 PLANES = (64, 128, 256, 512) OUT_PIXEL_DIST = 32 HAS_LAST_BLOCK = False CONV_TYPE = ConvType.HYPERCUBE def __init__(self, in_channels, out_channels, config, D=3, **kwargs): assert self.BLOCK is not None assert self.OUT_PIXEL_DIST > 0 super(ResNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) self.network_initialization(in_channels, out_channels, config, D) self.weight_initialization() def network_initialization(self, in_channels, out_channels, config, D): def space_n_time_m(n, m): return n if D == 3 else [n, n, n, m] if D == 4: self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1) dilations = config.dilations bn_momentum = config.bn_momentum self.inplanes = self.INIT_DIM self.conv1 = conv( in_channels, self.inplanes, kernel_size=space_n_time_m(config.conv1_kernel_size, 1), stride=1, D=D) self.bn1 = get_norm(NormType.BATCH_NORM, self.inplanes, D=self.D, bn_momentum=bn_momentum) self.relu = ME.MinkowskiReLU(inplace=True) self.pool = sum_pool(kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), D=D) self.layer1 = self._make_layer( self.BLOCK, self.PLANES[0], self.LAYERS[0], stride=space_n_time_m(2, 1), dilation=space_n_time_m(dilations[0], 1)) self.layer2 = self._make_layer( self.BLOCK, self.PLANES[1], self.LAYERS[1], stride=space_n_time_m(2, 1), dilation=space_n_time_m(dilations[1], 1)) self.layer3 = self._make_layer( self.BLOCK, self.PLANES[2], self.LAYERS[2], stride=space_n_time_m(2, 1), dilation=space_n_time_m(dilations[2], 1)) self.layer4 = self._make_layer( self.BLOCK, self.PLANES[3], self.LAYERS[3], stride=space_n_time_m(2, 1), dilation=space_n_time_m(dilations[3], 1)) self.final = conv( self.PLANES[3] * self.BLOCK.expansion, out_channels, kernel_size=1, bias=True, D=D) def weight_initialization(self): for m in self.modules(): if isinstance(m, ME.MinkowskiBatchNorm): nn.init.constant_(m.bn.weight, 1) nn.init.constant_(m.bn.bias, 0) def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_type=NormType.BATCH_NORM, bn_momentum=0.1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv( self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False, D=self.D), get_norm(norm_type, planes * block.expansion, D=self.D, bn_momentum=bn_momentum), ) layers = [] layers.append( block( self.inplanes, planes, stride=stride, dilation=dilation, downsample=downsample, conv_type=self.CONV_TYPE, D=self.D)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append( block( self.inplanes, planes, stride=1, dilation=dilation, conv_type=self.CONV_TYPE, D=self.D)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.pool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.final(x) return x class ResNet14(ResNetBase): BLOCK = BasicBlock LAYERS = (1, 1, 1, 1) class ResNet18(ResNetBase): BLOCK = BasicBlock LAYERS = (2, 2, 2, 2) class ResNet34(ResNetBase): BLOCK = BasicBlock LAYERS = (3, 4, 6, 3) class ResNet50(ResNetBase): BLOCK = Bottleneck LAYERS = (3, 4, 6, 3) class ResNet101(ResNetBase): BLOCK = Bottleneck LAYERS = (3, 4, 23, 3) class STResNetBase(ResNetBase): CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(STResNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class STResNet14(STResNetBase, ResNet14): pass class STResNet18(STResNetBase, ResNet18): pass class STResNet34(STResNetBase, ResNet34): pass class STResNet50(STResNetBase, ResNet50): pass class STResNet101(STResNetBase, ResNet101): pass class STResTesseractNetBase(STResNetBase): CONV_TYPE = ConvType.HYPERCUBE class STResTesseractNet14(STResTesseractNetBase, STResNet14): pass class STResTesseractNet18(STResTesseractNetBase, STResNet18): pass class STResTesseractNet34(STResTesseractNetBase, STResNet34): pass class STResTesseractNet50(STResTesseractNetBase, STResNet50): pass class STResTesseractNet101(STResTesseractNetBase, STResNet101): pass - 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