downstream/semseg/models/resunet.py [17:542]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class MinkUNetBase(ResNetBase): BLOCK = None PLANES = (64, 128, 256, 512, 256, 128, 128) DILATIONS = (1, 1, 1, 1, 1, 1) LAYERS = (2, 2, 2, 2, 2, 2) INIT_DIM = 64 OUT_PIXEL_DIST = 1 NORM_TYPE = NormType.BATCH_NORM NON_BLOCK_CONV_TYPE = ConvType.SPATIAL_HYPERCUBE CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS # To use the model, must call initialize_coords before forward pass. # Once data is processed, call clear to reset the model before calling initialize_coords def __init__(self, in_channels, out_channels, config, D=3, **kwargs): super(MinkUNetBase, self).__init__(in_channels, out_channels, config, D) def network_initialization(self, in_channels, out_channels, config, D): # Setup net_metadata dilations = self.DILATIONS bn_momentum = config.bn_momentum 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) # Output of the first conv concated to conv6 self.inplanes = self.INIT_DIM self.conv1p1s1 = conv( in_channels, self.inplanes, kernel_size=space_n_time_m(config.conv1_kernel_size, 1), stride=1, dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn1 = get_norm(self.NORM_TYPE, self.PLANES[0], 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.conv2p1s2 = 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.conv3p2s2 = 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.conv4p4s2 = 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.convtr4p8s2 = 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.convtr5p4s2 = 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.convtr6p2s2 = 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.relu = MinkowskiReLU(inplace=True) self.final = nn.Sequential( conv( self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion, 512, kernel_size=1, stride=1, dilation=1, bias=False, D=D), ME.MinkowskiBatchNorm(512), ME.MinkowskiReLU(), conv(512, out_channels, kernel_size=1, stride=1, dilation=1, bias=True, D=D)) def forward(self, x): out = self.conv1p1s1(x) out = self.bn1(out) out = self.relu(out) out_b1p1 = self.block1(out) out = self.conv2p1s2(out_b1p1) out = self.bn2(out) out = self.relu(out) out_b2p2 = self.block2(out) out = self.conv3p2s2(out_b2p2) out = self.bn3(out) out = self.relu(out) out_b3p4 = self.block3(out) out = self.conv4p4s2(out_b3p4) out = self.bn4(out) out = self.relu(out) # pixel_dist=8 out = self.block4(out) out = self.convtr4p8s2(out) out = self.bntr4(out) out = self.relu(out) out = me.cat(out, out_b3p4) out = self.block5(out) out = self.convtr5p4s2(out) out = self.bntr5(out) out = self.relu(out) out = me.cat(out, out_b2p2) out = self.block6(out) out = self.convtr6p2s2(out) out = self.bntr6(out) out = self.relu(out) out = me.cat(out, out_b1p1) return self.final(out) class ResUNet14(MinkUNetBase): BLOCK = BasicBlock LAYERS = (1, 1, 1, 1, 1, 1) class ResUNet18(MinkUNetBase): BLOCK = BasicBlock LAYERS = (2, 2, 2, 2, 2, 2) class ResUNet18INBN(ResUNet18): NORM_TYPE = NormType.INSTANCE_BATCH_NORM BLOCK = BasicBlockINBN class ResUNet34(MinkUNetBase): BLOCK = BasicBlock LAYERS = (3, 4, 6, 3, 2, 2) class ResUNet50(MinkUNetBase): BLOCK = Bottleneck LAYERS = (3, 4, 6, 3, 2, 2) class ResUNet101(MinkUNetBase): BLOCK = Bottleneck LAYERS = (3, 4, 23, 3, 2, 2) class ResUNet14D(ResUNet14): PLANES = (64, 128, 256, 512, 512, 512, 512) class ResUNet18D(ResUNet18): PLANES = (64, 128, 256, 512, 512, 512, 512) class ResUNet34D(ResUNet34): PLANES = (64, 128, 256, 512, 512, 512, 512) class ResUNet34E(ResUNet34): INIT_DIM = 32 PLANES = (32, 64, 128, 256, 128, 64, 64) class ResUNet34F(ResUNet34): INIT_DIM = 32 PLANES = (32, 64, 128, 256, 128, 64, 32) class MinkUNetHyper(MinkUNetBase): BLOCK = None PLANES = (64, 128, 256, 512, 256, 128, 128) DILATIONS = (1, 1, 1, 1, 1, 1) LAYERS = (2, 2, 2, 2, 2, 2) INIT_DIM = 64 OUT_PIXEL_DIST = 1 NORM_TYPE = NormType.BATCH_NORM NON_BLOCK_CONV_TYPE = ConvType.SPATIAL_HYPERCUBE CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS # To use the model, must call initialize_coords before forward pass. # Once data is processed, call clear to reset the model before calling initialize_coords def __init__(self, in_channels, out_channels, config, D=3, **kwargs): super(MinkUNetBase, self).__init__(in_channels, out_channels, config, D) def network_initialization(self, in_channels, out_channels, config, D): # Setup net_metadata dilations = self.DILATIONS bn_momentum = config.bn_momentum 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) # Output of the first conv concated to conv6 self.inplanes = self.INIT_DIM self.conv1p1s1 = conv( in_channels, self.inplanes, kernel_size=space_n_time_m(config.conv1_kernel_size, 1), stride=1, dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn1 = get_norm(self.NORM_TYPE, self.PLANES[0], 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.conv2p1s2 = 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.conv3p2s2 = 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.conv4p4s2 = 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.pool_tr4 = ME.MinkowskiPoolingTranspose(kernel_size=8, stride=8, dimension=D) out_pool4 = self.inplanes self.convtr4p8s2 = 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.pool_tr5 = ME.MinkowskiPoolingTranspose(kernel_size=4, stride=4, dimension=D) out_pool5 = self.inplanes self.convtr5p4s2 = 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.pool_tr6 = ME.MinkowskiPoolingTranspose(kernel_size=2, stride=2, dimension=D) out_pool6 = self.inplanes self.convtr6p2s2 = 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.relu = MinkowskiReLU(inplace=True) self.final = nn.Sequential( conv( out_pool5 + out_pool6 + self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion, 512, kernel_size=1, bias=False, D=D), ME.MinkowskiBatchNorm(512), ME.MinkowskiReLU(), conv(512, out_channels, kernel_size=1, bias=True, D=D)) def forward(self, x): out = self.conv1p1s1(x) out = self.bn1(out) out = self.relu(out) out_b1p1 = self.block1(out) out = self.conv2p1s2(out_b1p1) out = self.bn2(out) out = self.relu(out) out_b2p2 = self.block2(out) out = self.conv3p2s2(out_b2p2) out = self.bn3(out) out = self.relu(out) out_b3p4 = self.block3(out) out = self.conv4p4s2(out_b3p4) out = self.bn4(out) out = self.relu(out) # pixel_dist=8 out = self.block4(out) out = self.convtr4p8s2(out) out = self.bntr4(out) out = self.relu(out) out = me.cat(out, out_b3p4) out = self.block5(out) out_5 = self.pool_tr5(out) out = self.convtr5p4s2(out) out = self.bntr5(out) out = self.relu(out) out = me.cat(out, out_b2p2) out = self.block6(out) out_6 = self.pool_tr6(out) out = self.convtr6p2s2(out) out = self.bntr6(out) out = self.relu(out) out = me.cat(out, out_b1p1, out_6, out_5) return self.final(out) class MinkUNetHyper14INBN(MinkUNetHyper): NORM_TYPE = NormType.INSTANCE_BATCH_NORM BLOCK = BasicBlockINBN class STMinkUNetBase(MinkUNetBase): CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(STMinkUNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class STResUNet14(STMinkUNetBase, ResUNet14): pass class STResUNet18(STMinkUNetBase, ResUNet18): pass class STResUNet34(STMinkUNetBase, ResUNet34): pass class STResUNet50(STMinkUNetBase, ResUNet50): pass class STResUNet101(STMinkUNetBase, ResUNet101): pass class STResTesseractUNetBase(STMinkUNetBase): CONV_TYPE = ConvType.HYPERCUBE class STResTesseractUNet14(STResTesseractUNetBase, ResUNet14): pass class STResTesseractUNet18(STResTesseractUNetBase, ResUNet18): pass class STResTesseractUNet34(STResTesseractUNetBase, ResUNet34): pass class STResTesseractUNet50(STResTesseractUNetBase, ResUNet50): pass class STResTesseractUNet101(STResTesseractUNetBase, ResUNet101): pass - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - downstream/votenet_det_new/models/backbone/sparseconv/models/resunet.py [17:542]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class MinkUNetBase(ResNetBase): BLOCK = None PLANES = (64, 128, 256, 512, 256, 128, 128) DILATIONS = (1, 1, 1, 1, 1, 1) LAYERS = (2, 2, 2, 2, 2, 2) INIT_DIM = 64 OUT_PIXEL_DIST = 1 NORM_TYPE = NormType.BATCH_NORM NON_BLOCK_CONV_TYPE = ConvType.SPATIAL_HYPERCUBE CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS # To use the model, must call initialize_coords before forward pass. # Once data is processed, call clear to reset the model before calling initialize_coords def __init__(self, in_channels, out_channels, config, D=3, **kwargs): super(MinkUNetBase, self).__init__(in_channels, out_channels, config, D) def network_initialization(self, in_channels, out_channels, config, D): # Setup net_metadata dilations = self.DILATIONS bn_momentum = config.bn_momentum 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) # Output of the first conv concated to conv6 self.inplanes = self.INIT_DIM self.conv1p1s1 = conv( in_channels, self.inplanes, kernel_size=space_n_time_m(config.conv1_kernel_size, 1), stride=1, dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn1 = get_norm(self.NORM_TYPE, self.PLANES[0], 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.conv2p1s2 = 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.conv3p2s2 = 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.conv4p4s2 = 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.convtr4p8s2 = 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.convtr5p4s2 = 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.convtr6p2s2 = 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.relu = MinkowskiReLU(inplace=True) self.final = nn.Sequential( conv( self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion, 512, kernel_size=1, stride=1, dilation=1, bias=False, D=D), ME.MinkowskiBatchNorm(512), ME.MinkowskiReLU(), conv(512, out_channels, kernel_size=1, stride=1, dilation=1, bias=True, D=D)) def forward(self, x): out = self.conv1p1s1(x) out = self.bn1(out) out = self.relu(out) out_b1p1 = self.block1(out) out = self.conv2p1s2(out_b1p1) out = self.bn2(out) out = self.relu(out) out_b2p2 = self.block2(out) out = self.conv3p2s2(out_b2p2) out = self.bn3(out) out = self.relu(out) out_b3p4 = self.block3(out) out = self.conv4p4s2(out_b3p4) out = self.bn4(out) out = self.relu(out) # pixel_dist=8 out = self.block4(out) out = self.convtr4p8s2(out) out = self.bntr4(out) out = self.relu(out) out = me.cat(out, out_b3p4) out = self.block5(out) out = self.convtr5p4s2(out) out = self.bntr5(out) out = self.relu(out) out = me.cat(out, out_b2p2) out = self.block6(out) out = self.convtr6p2s2(out) out = self.bntr6(out) out = self.relu(out) out = me.cat(out, out_b1p1) return self.final(out) class ResUNet14(MinkUNetBase): BLOCK = BasicBlock LAYERS = (1, 1, 1, 1, 1, 1) class ResUNet18(MinkUNetBase): BLOCK = BasicBlock LAYERS = (2, 2, 2, 2, 2, 2) class ResUNet18INBN(ResUNet18): NORM_TYPE = NormType.INSTANCE_BATCH_NORM BLOCK = BasicBlockINBN class ResUNet34(MinkUNetBase): BLOCK = BasicBlock LAYERS = (3, 4, 6, 3, 2, 2) class ResUNet50(MinkUNetBase): BLOCK = Bottleneck LAYERS = (3, 4, 6, 3, 2, 2) class ResUNet101(MinkUNetBase): BLOCK = Bottleneck LAYERS = (3, 4, 23, 3, 2, 2) class ResUNet14D(ResUNet14): PLANES = (64, 128, 256, 512, 512, 512, 512) class ResUNet18D(ResUNet18): PLANES = (64, 128, 256, 512, 512, 512, 512) class ResUNet34D(ResUNet34): PLANES = (64, 128, 256, 512, 512, 512, 512) class ResUNet34E(ResUNet34): INIT_DIM = 32 PLANES = (32, 64, 128, 256, 128, 64, 64) class ResUNet34F(ResUNet34): INIT_DIM = 32 PLANES = (32, 64, 128, 256, 128, 64, 32) class MinkUNetHyper(MinkUNetBase): BLOCK = None PLANES = (64, 128, 256, 512, 256, 128, 128) DILATIONS = (1, 1, 1, 1, 1, 1) LAYERS = (2, 2, 2, 2, 2, 2) INIT_DIM = 64 OUT_PIXEL_DIST = 1 NORM_TYPE = NormType.BATCH_NORM NON_BLOCK_CONV_TYPE = ConvType.SPATIAL_HYPERCUBE CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS # To use the model, must call initialize_coords before forward pass. # Once data is processed, call clear to reset the model before calling initialize_coords def __init__(self, in_channels, out_channels, config, D=3, **kwargs): super(MinkUNetBase, self).__init__(in_channels, out_channels, config, D) def network_initialization(self, in_channels, out_channels, config, D): # Setup net_metadata dilations = self.DILATIONS bn_momentum = config.bn_momentum 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) # Output of the first conv concated to conv6 self.inplanes = self.INIT_DIM self.conv1p1s1 = conv( in_channels, self.inplanes, kernel_size=space_n_time_m(config.conv1_kernel_size, 1), stride=1, dilation=1, conv_type=self.NON_BLOCK_CONV_TYPE, D=D) self.bn1 = get_norm(self.NORM_TYPE, self.PLANES[0], 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.conv2p1s2 = 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.conv3p2s2 = 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.conv4p4s2 = 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.pool_tr4 = ME.MinkowskiPoolingTranspose(kernel_size=8, stride=8, dimension=D) out_pool4 = self.inplanes self.convtr4p8s2 = 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.pool_tr5 = ME.MinkowskiPoolingTranspose(kernel_size=4, stride=4, dimension=D) out_pool5 = self.inplanes self.convtr5p4s2 = 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.pool_tr6 = ME.MinkowskiPoolingTranspose(kernel_size=2, stride=2, dimension=D) out_pool6 = self.inplanes self.convtr6p2s2 = 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.relu = MinkowskiReLU(inplace=True) self.final = nn.Sequential( conv( out_pool5 + out_pool6 + self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion, 512, kernel_size=1, bias=False, D=D), ME.MinkowskiBatchNorm(512), ME.MinkowskiReLU(), conv(512, out_channels, kernel_size=1, bias=True, D=D)) def forward(self, x): out = self.conv1p1s1(x) out = self.bn1(out) out = self.relu(out) out_b1p1 = self.block1(out) out = self.conv2p1s2(out_b1p1) out = self.bn2(out) out = self.relu(out) out_b2p2 = self.block2(out) out = self.conv3p2s2(out_b2p2) out = self.bn3(out) out = self.relu(out) out_b3p4 = self.block3(out) out = self.conv4p4s2(out_b3p4) out = self.bn4(out) out = self.relu(out) # pixel_dist=8 out = self.block4(out) out = self.convtr4p8s2(out) out = self.bntr4(out) out = self.relu(out) out = me.cat(out, out_b3p4) out = self.block5(out) out_5 = self.pool_tr5(out) out = self.convtr5p4s2(out) out = self.bntr5(out) out = self.relu(out) out = me.cat(out, out_b2p2) out = self.block6(out) out_6 = self.pool_tr6(out) out = self.convtr6p2s2(out) out = self.bntr6(out) out = self.relu(out) out = me.cat(out, out_b1p1, out_6, out_5) return self.final(out) class MinkUNetHyper14INBN(MinkUNetHyper): NORM_TYPE = NormType.INSTANCE_BATCH_NORM BLOCK = BasicBlockINBN class STMinkUNetBase(MinkUNetBase): CONV_TYPE = ConvType.SPATIAL_HYPERCUBE_TEMPORAL_HYPERCROSS def __init__(self, in_channels, out_channels, config, D=4, **kwargs): super(STMinkUNetBase, self).__init__(in_channels, out_channels, config, D, **kwargs) class STResUNet14(STMinkUNetBase, ResUNet14): pass class STResUNet18(STMinkUNetBase, ResUNet18): pass class STResUNet34(STMinkUNetBase, ResUNet34): pass class STResUNet50(STMinkUNetBase, ResUNet50): pass class STResUNet101(STMinkUNetBase, ResUNet101): pass class STResTesseractUNetBase(STMinkUNetBase): CONV_TYPE = ConvType.HYPERCUBE class STResTesseractUNet14(STResTesseractUNetBase, ResUNet14): pass class STResTesseractUNet18(STResTesseractUNetBase, ResUNet18): pass class STResTesseractUNet34(STResTesseractUNetBase, ResUNet34): pass class STResTesseractUNet50(STResTesseractUNetBase, ResUNet50): pass class STResTesseractUNet101(STResTesseractUNetBase, ResUNet101): pass - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -