downstream/semseg/models/res16unet.py [48:381]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - stride=1, dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn0 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.conv1p1s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn1 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block1 = self._make_layer( self.BLOCK, self.PLANES[0], self.LAYERS[0], dilation=dilations[0], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv2p2s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn2 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block2 = self._make_layer( self.BLOCK, self.PLANES[1], self.LAYERS[1], dilation=dilations[1], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv3p4s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn3 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block3 = self._make_layer( self.BLOCK, self.PLANES[2], self.LAYERS[2], dilation=dilations[2], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv4p8s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn4 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block4 = self._make_layer( self.BLOCK, self.PLANES[3], self.LAYERS[3], dilation=dilations[3], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr4p16s2 = conv_tr( self.inplanes, self.PLANES[4], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr4 = get_norm(self.NORM_TYPE, self.PLANES[4], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion self.block5 = self._make_layer( self.BLOCK, self.PLANES[4], self.LAYERS[4], dilation=dilations[4], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr5p8s2 = conv_tr( self.inplanes, self.PLANES[5], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr5 = get_norm(self.NORM_TYPE, self.PLANES[5], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion self.block6 = self._make_layer( self.BLOCK, self.PLANES[5], self.LAYERS[5], dilation=dilations[5], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr6p4s2 = conv_tr( self.inplanes, self.PLANES[6], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr6 = get_norm(self.NORM_TYPE, self.PLANES[6], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion self.block7 = self._make_layer( self.BLOCK, self.PLANES[6], self.LAYERS[6], dilation=dilations[6], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr7p2s2 = conv_tr( self.inplanes, self.PLANES[7], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr7 = get_norm(self.NORM_TYPE, self.PLANES[7], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[7] + self.INIT_DIM self.block8 = self._make_layer( self.BLOCK, self.PLANES[7], self.LAYERS[7], dilation=dilations[7], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.final = conv(self.PLANES[7], out_channels, kernel_size=1, stride=1, bias=True, D=D) self.relu = MinkowskiReLU(inplace=True) def forward(self, x): out = self.conv0p1s1(x) out = self.bn0(out) out_p1 = self.relu(out) out = self.conv1p1s2(out_p1) out = self.bn1(out) out = self.relu(out) out_b1p2 = self.block1(out) out = self.conv2p2s2(out_b1p2) out = self.bn2(out) out = self.relu(out) out_b2p4 = self.block2(out) out = self.conv3p4s2(out_b2p4) out = self.bn3(out) out = self.relu(out) out_b3p8 = self.block3(out) # pixel_dist=16 out = self.conv4p8s2(out_b3p8) out = self.bn4(out) out = self.relu(out) out = self.block4(out) # pixel_dist=8 out = self.convtr4p16s2(out) out = self.bntr4(out) out = self.relu(out) out = me.cat(out, out_b3p8) out = self.block5(out) # pixel_dist=4 out = self.convtr5p8s2(out) out = self.bntr5(out) out = self.relu(out) out = me.cat(out, out_b2p4) out = self.block6(out) # pixel_dist=2 out = self.convtr6p4s2(out) out = self.bntr6(out) out = self.relu(out) out = me.cat(out, out_b1p2) out = self.block7(out) # pixel_dist=1 out = self.convtr7p2s2(out) out = self.bntr7(out) out = self.relu(out) out = me.cat(out, out_p1) out = self.block8(out) return self.final(out) class Res16UNet14(Res16UNetBase): BLOCK = BasicBlock LAYERS = (1, 1, 1, 1, 1, 1, 1, 1) class Res16UNet18(Res16UNetBase): BLOCK = BasicBlock LAYERS = (2, 2, 2, 2, 2, 2, 2, 2) class Res16UNet34(Res16UNetBase): BLOCK = BasicBlock LAYERS = (2, 3, 4, 6, 2, 2, 2, 2) class Res16UNet50(Res16UNetBase): BLOCK = Bottleneck LAYERS = (2, 3, 4, 6, 2, 2, 2, 2) class Res16UNet101(Res16UNetBase): BLOCK = Bottleneck LAYERS = (2, 3, 4, 23, 2, 2, 2, 2) class Res16UNet14A(Res16UNet14): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class Res16UNet14A2(Res16UNet14A): LAYERS = (1, 1, 1, 1, 2, 2, 2, 2) class Res16UNet14B(Res16UNet14): PLANES = (32, 64, 128, 256, 128, 128, 128, 128) class Res16UNet14B2(Res16UNet14B): LAYERS = (1, 1, 1, 1, 2, 2, 2, 2) class Res16UNet14B3(Res16UNet14B): LAYERS = (2, 2, 2, 2, 1, 1, 1, 1) class Res16UNet14C(Res16UNet14): PLANES = (32, 64, 128, 256, 192, 192, 128, 128) class Res16UNet14D(Res16UNet14): PLANES = (32, 64, 128, 256, 384, 384, 384, 384) class Res16UNet18A(Res16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class Res16UNet18B(Res16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 128, 128) class Res16UNet18D(Res16UNet18): PLANES = (32, 64, 128, 256, 384, 384, 384, 384) class Res16UNet34A(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 64, 64) class Res16UNet34B(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 64, 32) class Res16UNet34C(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 96, 96) class STRes16UNetBase(Res16UNetBase): CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(STRes16UNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class STRes16UNet14(STRes16UNetBase, Res16UNet14): pass class STRes16UNet14A(STRes16UNetBase, Res16UNet14A): pass class STRes16UNet18(STRes16UNetBase, Res16UNet18): pass class STRes16UNet34(STRes16UNetBase, Res16UNet34): pass class STRes16UNet50(STRes16UNetBase, Res16UNet50): pass class STRes16UNet101(STRes16UNetBase, Res16UNet101): pass class STRes16UNet18A(STRes16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class STResTesseract16UNetBase(STRes16UNetBase): CONV_TYPE = ConvType.HYPERCUBE class STResTesseract16UNet18A(STRes16UNet18A, STResTesseract16UNetBase): pass - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - downstream/votenet_det_new/models/backbone/sparseconv/models/res16unet.py [47:380]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - stride=1, dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn0 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.conv1p1s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn1 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block1 = self._make_layer( self.BLOCK, self.PLANES[0], self.LAYERS[0], dilation=dilations[0], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv2p2s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn2 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block2 = self._make_layer( self.BLOCK, self.PLANES[1], self.LAYERS[1], dilation=dilations[1], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv3p4s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn3 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block3 = self._make_layer( self.BLOCK, self.PLANES[2], self.LAYERS[2], dilation=dilations[2], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.conv4p8s2 = conv( self.inplanes, self.inplanes, kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn4 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum) self.block4 = self._make_layer( self.BLOCK, self.PLANES[3], self.LAYERS[3], dilation=dilations[3], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr4p16s2 = conv_tr( self.inplanes, self.PLANES[4], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr4 = get_norm(self.NORM_TYPE, self.PLANES[4], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion self.block5 = self._make_layer( self.BLOCK, self.PLANES[4], self.LAYERS[4], dilation=dilations[4], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr5p8s2 = conv_tr( self.inplanes, self.PLANES[5], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr5 = get_norm(self.NORM_TYPE, self.PLANES[5], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion self.block6 = self._make_layer( self.BLOCK, self.PLANES[5], self.LAYERS[5], dilation=dilations[5], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr6p4s2 = conv_tr( self.inplanes, self.PLANES[6], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr6 = get_norm(self.NORM_TYPE, self.PLANES[6], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion self.block7 = self._make_layer( self.BLOCK, self.PLANES[6], self.LAYERS[6], dilation=dilations[6], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.convtr7p2s2 = conv_tr( self.inplanes, self.PLANES[7], kernel_size=space_n_time_m(2, 1), upsample_stride=space_n_time_m(2, 1), dilation=1, bias=False, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bntr7 = get_norm(self.NORM_TYPE, self.PLANES[7], D, bn_momentum=bn_momentum) self.inplanes = self.PLANES[7] + self.INIT_DIM self.block8 = self._make_layer( self.BLOCK, self.PLANES[7], self.LAYERS[7], dilation=dilations[7], norm_type=self.NORM_TYPE, bn_momentum=bn_momentum) self.final = conv(self.PLANES[7], out_channels, kernel_size=1, stride=1, bias=True, D=D) self.relu = MinkowskiReLU(inplace=True) def forward(self, x): out = self.conv0p1s1(x) out = self.bn0(out) out_p1 = self.relu(out) out = self.conv1p1s2(out_p1) out = self.bn1(out) out = self.relu(out) out_b1p2 = self.block1(out) out = self.conv2p2s2(out_b1p2) out = self.bn2(out) out = self.relu(out) out_b2p4 = self.block2(out) out = self.conv3p4s2(out_b2p4) out = self.bn3(out) out = self.relu(out) out_b3p8 = self.block3(out) # pixel_dist=16 out = self.conv4p8s2(out_b3p8) out = self.bn4(out) out = self.relu(out) out = self.block4(out) # pixel_dist=8 out = self.convtr4p16s2(out) out = self.bntr4(out) out = self.relu(out) out = me.cat(out, out_b3p8) out = self.block5(out) # pixel_dist=4 out = self.convtr5p8s2(out) out = self.bntr5(out) out = self.relu(out) out = me.cat(out, out_b2p4) out = self.block6(out) # pixel_dist=2 out = self.convtr6p4s2(out) out = self.bntr6(out) out = self.relu(out) out = me.cat(out, out_b1p2) out = self.block7(out) # pixel_dist=1 out = self.convtr7p2s2(out) out = self.bntr7(out) out = self.relu(out) out = me.cat(out, out_p1) out = self.block8(out) return self.final(out) class Res16UNet14(Res16UNetBase): BLOCK = BasicBlock LAYERS = (1, 1, 1, 1, 1, 1, 1, 1) class Res16UNet18(Res16UNetBase): BLOCK = BasicBlock LAYERS = (2, 2, 2, 2, 2, 2, 2, 2) class Res16UNet34(Res16UNetBase): BLOCK = BasicBlock LAYERS = (2, 3, 4, 6, 2, 2, 2, 2) class Res16UNet50(Res16UNetBase): BLOCK = Bottleneck LAYERS = (2, 3, 4, 6, 2, 2, 2, 2) class Res16UNet101(Res16UNetBase): BLOCK = Bottleneck LAYERS = (2, 3, 4, 23, 2, 2, 2, 2) class Res16UNet14A(Res16UNet14): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class Res16UNet14A2(Res16UNet14A): LAYERS = (1, 1, 1, 1, 2, 2, 2, 2) class Res16UNet14B(Res16UNet14): PLANES = (32, 64, 128, 256, 128, 128, 128, 128) class Res16UNet14B2(Res16UNet14B): LAYERS = (1, 1, 1, 1, 2, 2, 2, 2) class Res16UNet14B3(Res16UNet14B): LAYERS = (2, 2, 2, 2, 1, 1, 1, 1) class Res16UNet14C(Res16UNet14): PLANES = (32, 64, 128, 256, 192, 192, 128, 128) class Res16UNet14D(Res16UNet14): PLANES = (32, 64, 128, 256, 384, 384, 384, 384) class Res16UNet18A(Res16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class Res16UNet18B(Res16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 128, 128) class Res16UNet18D(Res16UNet18): PLANES = (32, 64, 128, 256, 384, 384, 384, 384) class Res16UNet34A(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 64, 64) class Res16UNet34B(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 64, 32) class Res16UNet34C(Res16UNet34): PLANES = (32, 64, 128, 256, 256, 128, 96, 96) class STRes16UNetBase(Res16UNetBase): CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(STRes16UNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class STRes16UNet14(STRes16UNetBase, Res16UNet14): pass class STRes16UNet14A(STRes16UNetBase, Res16UNet14A): pass class STRes16UNet18(STRes16UNetBase, Res16UNet18): pass class STRes16UNet34(STRes16UNetBase, Res16UNet34): pass class STRes16UNet50(STRes16UNetBase, Res16UNet50): pass class STRes16UNet101(STRes16UNetBase, Res16UNet101): pass class STRes16UNet18A(STRes16UNet18): PLANES = (32, 64, 128, 256, 128, 128, 96, 96) class STResTesseract16UNetBase(STRes16UNetBase): CONV_TYPE = ConvType.HYPERCUBE class STResTesseract16UNet18A(STRes16UNet18A, STResTesseract16UNetBase): pass - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -