downstream/semseg/models/resunet.py [18:113]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - downstream/votenet_det_new/models/backbone/sparseconv/models/resunet.py [276:371]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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) - 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