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configs_and_mappings.py
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configs_and_mappings.py
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import torch
import yaml
import numpy as np
name_shift = {"net.stem_conv0.kernel":"net.stem.0.kernel",
"net.stem_bn0.weight":"net.stem.1.weight",
"net.stem_bn0.bias":"net.stem.1.bias",
"net.stem_bn0.running_mean":"net.stem.1.running_mean",
"net.stem_bn0.running_var":"net.stem.1.running_var",
"net.stem_conv1.kernel":"net.stem.3.kernel",
"net.stem_bn1.weight":"net.stem.4.weight",
"net.stem_bn1.bias":"net.stem.4.bias",
"net.stem_bn1.running_mean":"net.stem.4.running_mean",
"net.stem_bn1.running_var":"net.stem.4.running_var",
"net.stage1_BC_0.net_conv3d_0.kernel":"net.stage1.0.net.0.kernel",
"net.stage1_BC_0.net_bn_1.weight":"net.stage1.0.net.1.weight",
"net.stage1_BC_0.net_bn_1.bias":"net.stage1.0.net.1.bias",
"net.stage1_BC_0.net_bn_1.running_mean":"net.stage1.0.net.1.running_mean",
"net.stage1_BC_0.net_bn_1.running_var":"net.stage1.0.net.1.running_var",
"net.stage1_RB_1.net_conv3d_0.kernel": "net.stage1.1.net.0.kernel",
"net.stage1_RB_1.net_bn_1.weight": "net.stage1.1.net.1.weight",
"net.stage1_RB_1.net_bn_1.bias": "net.stage1.1.net.1.bias",
"net.stage1_RB_1.net_bn_1.running_mean":"net.stage1.1.net.1.running_mean",
"net.stage1_RB_1.net_bn_1.running_var":"net.stage1.1.net.1.running_var",
"net.stage1_RB_1.net_conv3d_2.kernel":"net.stage1.1.net.3.kernel",
"net.stage1_RB_1.net_bn_3.weight":"net.stage1.1.net.4.weight",
"net.stage1_RB_1.net_bn_3.bias":"net.stage1.1.net.4.bias",
"net.stage1_RB_1.net_bn_3.running_mean":"net.stage1.1.net.4.running_mean",
"net.stage1_RB_1.net_bn_3.running_var":"net.stage1.1.net.4.running_var",
"net.stage1_RB_2.net_conv3d_0.kernel":"net.stage1.2.net.0.kernel",
"net.stage1_RB_2.net_bn_1.weight":"net.stage1.2.net.1.weight",
"net.stage1_RB_2.net_bn_1.bias":"net.stage1.2.net.1.bias",
"net.stage1_RB_2.net_bn_1.running_mean":"net.stage1.2.net.1.running_mean",
"net.stage1_RB_2.net_bn_1.running_var":"net.stage1.2.net.1.running_var",
"net.stage1_RB_2.net_conv3d_2.kernel":"net.stage1.2.net.3.kernel",
"net.stage1_RB_2.net_bn_3.weight":"net.stage1.2.net.4.weight",
"net.stage1_RB_2.net_bn_3.bias":"net.stage1.2.net.4.bias",
"net.stage1_RB_2.net_bn_3.running_mean":"net.stage1.2.net.4.running_mean",
"net.stage1_RB_2.net_bn_3.running_var":"net.stage1.2.net.4.running_var",
"net.stage2_BC_0.net_conv3d_0.kernel": "net.stage2.0.net.0.kernel",
"net.stage2_BC_0.net_bn_1.weight":"net.stage2.0.net.1.weight",
"net.stage2_BC_0.net_bn_1.bias":"net.stage2.0.net.1.bias",
"net.stage2_BC_0.net_bn_1.running_mean":"net.stage2.0.net.1.running_mean",
"net.stage2_BC_0.net_bn_1.running_var":"net.stage2.0.net.1.running_var",
"net.stage2_RB_1.net_conv3d_0.kernel":"net.stage2.1.net.0.kernel",
"net.stage2_RB_1.net_bn_1.weight":"net.stage2.1.net.1.weight",
"net.stage2_RB_1.net_bn_1.bias":"net.stage2.1.net.1.bias",
"net.stage2_RB_1.net_bn_1.running_mean":"net.stage2.1.net.1.running_mean",
"net.stage2_RB_1.net_bn_1.running_var":"net.stage2.1.net.1.running_var",
"net.stage2_RB_1.net_conv3d_2.kernel":"net.stage2.1.net.3.kernel",
"net.stage2_RB_1.net_bn_3.weight":"net.stage2.1.net.4.weight",
"net.stage2_RB_1.net_bn_3.bias":"net.stage2.1.net.4.bias",
"net.stage2_RB_1.net_bn_3.running_mean":"net.stage2.1.net.4.running_mean",
"net.stage2_RB_1.net_bn_3.running_var":"net.stage2.1.net.4.running_var",
"net.stage2_RB_1.downsample_conv3d_0.kernel":"net.stage2.1.downsample.0.kernel",
"net.stage2_RB_1.downsample_bn_1.weight":"net.stage2.1.downsample.1.weight",
"net.stage2_RB_1.downsample_bn_1.bias":"net.stage2.1.downsample.1.bias",
"net.stage2_RB_1.downsample_bn_1.running_mean":"net.stage2.1.downsample.1.running_mean",
"net.stage2_RB_1.downsample_bn_1.running_var":"net.stage2.1.downsample.1.running_var",
"net.stage2_RB_2.net_conv3d_0.kernel":"net.stage2.2.net.0.kernel",
"net.stage2_RB_2.net_bn_1.weight":"net.stage2.2.net.1.weight",
"net.stage2_RB_2.net_bn_1.bias": "net.stage2.2.net.1.bias",
"net.stage2_RB_2.net_bn_1.running_mean":"net.stage2.2.net.1.running_mean",
"net.stage2_RB_2.net_bn_1.running_var":"net.stage2.2.net.1.running_var",
"net.stage2_RB_2.net_conv3d_2.kernel":"net.stage2.2.net.3.kernel",
"net.stage2_RB_2.net_bn_3.weight":"net.stage2.2.net.4.weight",
"net.stage2_RB_2.net_bn_3.bias":"net.stage2.2.net.4.bias",
"net.stage2_RB_2.net_bn_3.running_mean":"net.stage2.2.net.4.running_mean",
"net.stage2_RB_2.net_bn_3.running_var":"net.stage2.2.net.4.running_var",
"net.stage3_BC_0.net_conv3d_0.kernel":"net.stage3.0.net.0.kernel",
"net.stage3_BC_0.net_bn_1.weight":"net.stage3.0.net.1.weight",
"net.stage3_BC_0.net_bn_1.bias":"net.stage3.0.net.1.bias",
"net.stage3_BC_0.net_bn_1.running_mean":"net.stage3.0.net.1.running_mean",
"net.stage3_BC_0.net_bn_1.running_var":"net.stage3.0.net.1.running_var",
"net.stage3_RB_1.net_conv3d_0.kernel":"net.stage3.1.net.0.kernel",
"net.stage3_RB_1.net_bn_1.weight":"net.stage3.1.net.1.weight",
"net.stage3_RB_1.net_bn_1.bias":"net.stage3.1.net.1.bias",
"net.stage3_RB_1.net_bn_1.running_mean":"net.stage3.1.net.1.running_mean",
"net.stage3_RB_1.net_bn_1.running_var":"net.stage3.1.net.1.running_var",
"net.stage3_RB_1.net_conv3d_2.kernel":"net.stage3.1.net.3.kernel",
"net.stage3_RB_1.net_bn_3.weight":"net.stage3.1.net.4.weight",
"net.stage3_RB_1.net_bn_3.bias":"net.stage3.1.net.4.bias",
"net.stage3_RB_1.net_bn_3.running_mean":"net.stage3.1.net.4.running_mean",
"net.stage3_RB_1.net_bn_3.running_var":"net.stage3.1.net.4.running_var",
"net.stage3_RB_1.downsample_conv3d_0.kernel":"net.stage3.1.downsample.0.kernel",
"net.stage3_RB_1.downsample_bn_1.weight":"net.stage3.1.downsample.1.weight",
"net.stage3_RB_1.downsample_bn_1.bias":"net.stage3.1.downsample.1.bias",
"net.stage3_RB_1.downsample_bn_1.running_mean":"net.stage3.1.downsample.1.running_mean",
"net.stage3_RB_1.downsample_bn_1.running_var":"net.stage3.1.downsample.1.running_var",
"net.stage3_RB_2.net_conv3d_0.kernel":"net.stage3.2.net.0.kernel",
"net.stage3_RB_2.net_bn_1.weight":"net.stage3.2.net.1.weight",
"net.stage3_RB_2.net_bn_1.bias":"net.stage3.2.net.1.bias",
"net.stage3_RB_2.net_bn_1.running_mean":"net.stage3.2.net.1.running_mean",
"net.stage3_RB_2.net_bn_1.running_var":"net.stage3.2.net.1.running_var",
"net.stage3_RB_2.net_conv3d_2.kernel":"net.stage3.2.net.3.kernel",
"net.stage3_RB_2.net_bn_3.weight":"net.stage3.2.net.4.weight",
"net.stage3_RB_2.net_bn_3.bias":"net.stage3.2.net.4.bias",
"net.stage3_RB_2.net_bn_3.running_mean":"net.stage3.2.net.4.running_mean",
"net.stage3_RB_2.net_bn_3.running_var":"net.stage3.2.net.4.running_var",
"net.stage4_BC_0.net_conv3d_0.kernel":"net.stage4.0.net.0.kernel",
"net.stage4_BC_0.net_bn_1.weight": "net.stage4.0.net.1.weight",
"net.stage4_BC_0.net_bn_1.bias":"net.stage4.0.net.1.bias",
"net.stage4_BC_0.net_bn_1.running_mean":"net.stage4.0.net.1.running_mean",
"net.stage4_BC_0.net_bn_1.running_var":"net.stage4.0.net.1.running_var",
"net.stage4_RB_1.net_conv3d_0.kernel":"net.stage4.1.net.0.kernel",
"net.stage4_RB_1.net_bn_1.weight":"net.stage4.1.net.1.weight",
"net.stage4_RB_1.net_bn_1.bias":"net.stage4.1.net.1.bias",
"net.stage4_RB_1.net_bn_1.running_mean":"net.stage4.1.net.1.running_mean",
"net.stage4_RB_1.net_bn_1.running_var":"net.stage4.1.net.1.running_var",
"net.stage4_RB_1.net_conv3d_2.kernel":"net.stage4.1.net.3.kernel",
"net.stage4_RB_1.net_bn_3.weight":"net.stage4.1.net.4.weight",
"net.stage4_RB_1.net_bn_3.bias":"net.stage4.1.net.4.bias",
"net.stage4_RB_1.net_bn_3.running_mean":"net.stage4.1.net.4.running_mean",
"net.stage4_RB_1.net_bn_3.running_var":"net.stage4.1.net.4.running_var",
"net.stage4_RB_1.downsample_conv3d_0.kernel": "net.stage4.1.downsample.0.kernel",
"net.stage4_RB_1.downsample_bn_1.weight":"net.stage4.1.downsample.1.weight",
"net.stage4_RB_1.downsample_bn_1.bias":"net.stage4.1.downsample.1.bias",
"net.stage4_RB_1.downsample_bn_1.running_mean":"net.stage4.1.downsample.1.running_mean",
"net.stage4_RB_1.downsample_bn_1.running_var":"net.stage4.1.downsample.1.running_var",
"net.stage4_RB_2.net_conv3d_0.kernel":"net.stage4.2.net.0.kernel",
"net.stage4_RB_2.net_bn_1.weight":"net.stage4.2.net.1.weight",
"net.stage4_RB_2.net_bn_1.bias":"net.stage4.2.net.1.bias",
"net.stage4_RB_2.net_bn_1.running_mean":"net.stage4.2.net.1.running_mean",
"net.stage4_RB_2.net_bn_1.running_var":"net.stage4.2.net.1.running_var",
"net.stage4_RB_2.net_conv3d_2.kernel":"net.stage4.2.net.3.kernel",
"net.stage4_RB_2.net_bn_3.weight":"net.stage4.2.net.4.weight",
"net.stage4_RB_2.net_bn_3.bias":"net.stage4.2.net.4.bias",
"net.stage4_RB_2.net_bn_3.running_mean":"net.stage4.2.net.4.running_mean",
"net.stage4_RB_2.net_bn_3.running_var":"net.stage4.2.net.4.running_var",
"net.up1_BC_0.net_conv3d_0.kernel":"net.up1.0.net.0.kernel",
"net.up1_BC_0.net_bn_1.weight":"net.up1.0.net.1.weight",
"net.up1_BC_0.net_bn_1.bias":"net.up1.0.net.1.bias",
"net.up1_BC_0.net_bn_1.running_mean":"net.up1.0.net.1.running_mean",
"net.up1_BC_0.net_bn_1.running_var":"net.up1.0.net.1.running_var",
"net.up1_2_RB_1.net_conv3d_0.kernel":"net.up1.1.0.net.0.kernel",
"net.up1_2_RB_1.net_bn_1.weight":"net.up1.1.0.net.1.weight",
"net.up1_2_RB_1.net_bn_1.bias":"net.up1.1.0.net.1.bias",
"net.up1_2_RB_1.net_bn_1.running_mean":"net.up1.1.0.net.1.running_mean",
"net.up1_2_RB_1.net_bn_1.running_var":"net.up1.1.0.net.1.running_var",
"net.up1_2_RB_1.net_conv3d_2.kernel":"net.up1.1.0.net.3.kernel",
"net.up1_2_RB_1.net_bn_3.weight":"net.up1.1.0.net.4.weight",
"net.up1_2_RB_1.net_bn_3.bias": "net.up1.1.0.net.4.bias",
"net.up1_2_RB_1.net_bn_3.running_mean": "net.up1.1.0.net.4.running_mean",
"net.up1_2_RB_1.net_bn_3.running_var": "net.up1.1.0.net.4.running_var",
"net.up1_2_RB_1.downsample_conv3d_0.kernel": "net.up1.1.0.downsample.0.kernel",
"net.up1_2_RB_1.downsample_bn_1.weight": "net.up1.1.0.downsample.1.weight",
"net.up1_2_RB_1.downsample_bn_1.bias": "net.up1.1.0.downsample.1.bias",
"net.up1_2_RB_1.downsample_bn_1.running_mean": "net.up1.1.0.downsample.1.running_mean",
"net.up1_2_RB_1.downsample_bn_1.running_var": "net.up1.1.0.downsample.1.running_var",
"net.up1_2_RB_2.net_conv3d_0.kernel": "net.up1.1.1.net.0.kernel",
"net.up1_2_RB_2.net_bn_1.weight": "net.up1.1.1.net.1.weight",
"net.up1_2_RB_2.net_bn_1.bias": "net.up1.1.1.net.1.bias",
"net.up1_2_RB_2.net_bn_1.running_mean": "net.up1.1.1.net.1.running_mean",
"net.up1_2_RB_2.net_bn_1.running_var": "net.up1.1.1.net.1.running_var",
"net.up1_2_RB_2.net_conv3d_2.kernel": "net.up1.1.1.net.3.kernel",
"net.up1_2_RB_2.net_bn_3.weight": "net.up1.1.1.net.4.weight",
"net.up1_2_RB_2.net_bn_3.bias": "net.up1.1.1.net.4.bias",
"net.up1_2_RB_2.net_bn_3.running_mean": "net.up1.1.1.net.4.running_mean",
"net.up1_2_RB_2.net_bn_3.running_var": "net.up1.1.1.net.4.running_var",
"net.up2_BC_0.net_conv3d_0.kernel": "net.up2.0.net.0.kernel",
"net.up2_BC_0.net_bn_1.weight": "net.up2.0.net.1.weight",
"net.up2_BC_0.net_bn_1.bias": "net.up2.0.net.1.bias",
"net.up2_BC_0.net_bn_1.running_mean": "net.up2.0.net.1.running_mean",
"net.up2_BC_0.net_bn_1.running_var": "net.up2.0.net.1.running_var",
"net.up2_2_RB_1.net_conv3d_0.kernel": "net.up2.1.0.net.0.kernel",
"net.up2_2_RB_1.net_bn_1.weight": "net.up2.1.0.net.1.weight",
"net.up2_2_RB_1.net_bn_1.bias": "net.up2.1.0.net.1.bias",
"net.up2_2_RB_1.net_bn_1.running_mean": "net.up2.1.0.net.1.running_mean",
"net.up2_2_RB_1.net_bn_1.running_var": "net.up2.1.0.net.1.running_var",
"net.up2_2_RB_1.net_conv3d_2.kernel": "net.up2.1.0.net.3.kernel",
"net.up2_2_RB_1.net_bn_3.weight": "net.up2.1.0.net.4.weight",
"net.up2_2_RB_1.net_bn_3.bias": "net.up2.1.0.net.4.bias",
"net.up2_2_RB_1.net_bn_3.running_mean": "net.up2.1.0.net.4.running_mean",
"net.up2_2_RB_1.net_bn_3.running_var": "net.up2.1.0.net.4.running_var",
"net.up2_2_RB_1.downsample_conv3d_0.kernel":"net.up2.1.0.downsample.0.kernel",
"net.up2_2_RB_1.downsample_bn_1.weight": "net.up2.1.0.downsample.1.weight",
"net.up2_2_RB_1.downsample_bn_1.bias": "net.up2.1.0.downsample.1.bias",
"net.up2_2_RB_1.downsample_bn_1.running_mean": "net.up2.1.0.downsample.1.running_mean",
"net.up2_2_RB_1.downsample_bn_1.running_var": "net.up2.1.0.downsample.1.running_var",
"net.up2_2_RB_2.net_conv3d_0.kernel": "net.up2.1.1.net.0.kernel",
"net.up2_2_RB_2.net_bn_1.weight": "net.up2.1.1.net.1.weight",
"net.up2_2_RB_2.net_bn_1.bias": "net.up2.1.1.net.1.bias",
"net.up2_2_RB_2.net_bn_1.running_mean": "net.up2.1.1.net.1.running_mean",
"net.up2_2_RB_2.net_bn_1.running_var": "net.up2.1.1.net.1.running_var",
"net.up2_2_RB_2.net_conv3d_2.kernel": "net.up2.1.1.net.3.kernel",
"net.up2_2_RB_2.net_bn_3.weight": "net.up2.1.1.net.4.weight",
"net.up2_2_RB_2.net_bn_3.bias": "net.up2.1.1.net.4.bias",
"net.up2_2_RB_2.net_bn_3.running_mean": "net.up2.1.1.net.4.running_mean",
"net.up2_2_RB_2.net_bn_3.running_var": "net.up2.1.1.net.4.running_var",
"net.up3_BC_0.net_conv3d_0.kernel": "net.up3.0.net.0.kernel",
"net.up3_BC_0.net_bn_1.weight": "net.up3.0.net.1.weight",
"net.up3_BC_0.net_bn_1.bias": "net.up3.0.net.1.bias",
"net.up3_BC_0.net_bn_1.running_mean": "net.up3.0.net.1.running_mean",
"net.up3_BC_0.net_bn_1.running_var": "net.up3.0.net.1.running_var",
"net.up3_2_RB_1.net_conv3d_0.kernel": "net.up3.1.0.net.0.kernel",
"net.up3_2_RB_1.net_bn_1.weight": "net.up3.1.0.net.1.weight",
"net.up3_2_RB_1.net_bn_1.bias": "net.up3.1.0.net.1.bias",
"net.up3_2_RB_1.net_bn_1.running_mean": "net.up3.1.0.net.1.running_mean",
"net.up3_2_RB_1.net_bn_1.running_var": "net.up3.1.0.net.1.running_var",
"net.up3_2_RB_1.net_conv3d_2.kernel": "net.up3.1.0.net.3.kernel",
"net.up3_2_RB_1.net_bn_3.weight": "net.up3.1.0.net.4.weight",
"net.up3_2_RB_1.net_bn_3.bias": "net.up3.1.0.net.4.bias",
"net.up3_2_RB_1.net_bn_3.running_mean": "net.up3.1.0.net.4.running_mean",
"net.up3_2_RB_1.net_bn_3.running_var": "net.up3.1.0.net.4.running_var",
"net.up3_2_RB_1.downsample_conv3d_0.kernel": "net.up3.1.0.downsample.0.kernel",
"net.up3_2_RB_1.downsample_bn_1.weight": "net.up3.1.0.downsample.1.weight",
"net.up3_2_RB_1.downsample_bn_1.bias": "net.up3.1.0.downsample.1.bias",
"net.up3_2_RB_1.downsample_bn_1.running_mean": "net.up3.1.0.downsample.1.running_mean",
"net.up3_2_RB_1.downsample_bn_1.running_var": "net.up3.1.0.downsample.1.running_var",
"net.up3_2_RB_2.net_conv3d_0.kernel": "net.up3.1.1.net.0.kernel",
"net.up3_2_RB_2.net_bn_1.weight": "net.up3.1.1.net.1.weight",
"net.up3_2_RB_2.net_bn_1.bias": "net.up3.1.1.net.1.bias",
"net.up3_2_RB_2.net_bn_1.running_mean": "net.up3.1.1.net.1.running_mean",
"net.up3_2_RB_2.net_bn_1.running_var": "net.up3.1.1.net.1.running_var",
"net.up3_2_RB_2.net_conv3d_2.kernel": "net.up3.1.1.net.3.kernel",
"net.up3_2_RB_2.net_bn_3.weight": "net.up3.1.1.net.4.weight",
"net.up3_2_RB_2.net_bn_3.bias": "net.up3.1.1.net.4.bias",
"net.up3_2_RB_2.net_bn_3.running_mean": "net.up3.1.1.net.4.running_mean",
"net.up3_2_RB_2.net_bn_3.running_var": "net.up3.1.1.net.4.running_var",
"net.up4_BC_0.net_conv3d_0.kernel": "net.up4.0.net.0.kernel",
"net.up4_BC_0.net_bn_1.weight": "net.up4.0.net.1.weight",
"net.up4_BC_0.net_bn_1.bias": "net.up4.0.net.1.bias",
"net.up4_BC_0.net_bn_1.running_mean": "net.up4.0.net.1.running_mean",
"net.up4_BC_0.net_bn_1.running_var": "net.up4.0.net.1.running_var",
"net.up4_2_RB_1.net_conv3d_0.kernel": "net.up4.1.0.net.0.kernel",
"net.up4_2_RB_1.net_bn_1.weight": "net.up4.1.0.net.1.weight",
"net.up4_2_RB_1.net_bn_1.bias": "net.up4.1.0.net.1.bias",
"net.up4_2_RB_1.net_bn_1.running_mean": "net.up4.1.0.net.1.running_mean",
"net.up4_2_RB_1.net_bn_1.running_var": "net.up4.1.0.net.1.running_var",
"net.up4_2_RB_1.net_conv3d_2.kernel": "net.up4.1.0.net.3.kernel",
"net.up4_2_RB_1.net_bn_3.weight": "net.up4.1.0.net.4.weight",
"net.up4_2_RB_1.net_bn_3.bias": "net.up4.1.0.net.4.bias",
"net.up4_2_RB_1.net_bn_3.running_mean": "net.up4.1.0.net.4.running_mean",
"net.up4_2_RB_1.net_bn_3.running_var": "net.up4.1.0.net.4.running_var",
"net.up4_2_RB_1.downsample_conv3d_0.kernel":"net.up4.1.0.downsample.0.kernel",
"net.up4_2_RB_1.downsample_bn_1.weight": "net.up4.1.0.downsample.1.weight",
"net.up4_2_RB_1.downsample_bn_1.bias": "net.up4.1.0.downsample.1.bias",
"net.up4_2_RB_1.downsample_bn_1.running_mean": "net.up4.1.0.downsample.1.running_mean",
"net.up4_2_RB_1.downsample_bn_1.running_var": "net.up4.1.0.downsample.1.running_var",
"net.up4_2_RB_2.net_conv3d_0.kernel": "net.up4.1.1.net.0.kernel",
"net.up4_2_RB_2.net_bn_1.weight": "net.up4.1.1.net.1.weight",
"net.up4_2_RB_2.net_bn_1.bias": "net.up4.1.1.net.1.bias",
"net.up4_2_RB_2.net_bn_1.running_mean": "net.up4.1.1.net.1.running_mean",
"net.up4_2_RB_2.net_bn_1.running_var": "net.up4.1.1.net.1.running_var",
"net.up4_2_RB_2.net_conv3d_2.kernel": "net.up4.1.1.net.3.kernel",
"net.up4_2_RB_2.net_bn_3.weight": "net.up4.1.1.net.4.weight",
"net.up4_2_RB_2.net_bn_3.bias": "net.up4.1.1.net.4.bias",
"net.up4_2_RB_2.net_bn_3.running_mean": "net.up4.1.1.net.4.running_mean",
"net.up4_2_RB_2.net_bn_3.running_var": "net.up4.1.1.net.4.running_var"}
config={
"fast_rep_flag": False,
# Dataset configs
"dataset_name" : 'UnknwonDataset',
"dataset_root" : 'UnknownDatasetPath',
"source_dataset_name" : 'UnknwonDataset',
"source_dataset_root" : 'UnknownDatasetPath',
"target_dataset_name" : 'UnknwonDataset',
"target_dataset_root" : 'UnknownDatasetPath',
"save_dir" : 'results_ckpt',
"ns_dataset_version":'v1.0-trainval',
#"ns_dataset_version":'v1.0-mini',
# splits
"train_split" : 'train',
"val_split" : 'val',
"test_split" : 'val',
"nb_classes" : 1,
# Method parameters
"input_intensities" : False,
"input_dirs":False,
"input_normals":False,
"source_input_intensities":False,
"source_input_dirs": False,
"target_input_intensities":False,
"target_input_dirs" :False,
"manifold_points" : 10000,
"non_manifold_points" :2048,
# Training parameters
"da_flag": False,
"dual_seg_head" : True,
"training_iter_nbr" : 50000,
"training_batch_size" : 4,
"test_batch_size" : 1,
"training_lr_start" : 0.001,
"training_lr_start_head" : None,
"optimizer" : "AdamW",
"lr_scheduler" : None,
"step_lr_step_size" :200000, #Step size for steplr scheduler
"step_lr_gamma" : 0.7, #Gamma for steplr scheduler
"voxel_size" : 0.1,
"val_interval" :5,
"resume" :False,
# Network parameter
"network_backbone" : 'TorchSparseMinkUNet_inside',
"network_latent_size": 128,
"network_decoder" : 'InterpAllRadiusNet',
"network_decoder_k" : 2.0,
"network_n_labels" : 1,
"use_no_dirs_rec_head_flag" : False,
"rotation_head_flag" : False,
# Technical parameter
"device" : 'cuda',
"threads" :6,
"interactive_log" : True,
"logging" : 'INFO',
"use_amp" : False,
# Data augmentation
"randRotationZ" : True,
"randFlip" : True,
"no_augmentation" : False,
# Ckpt path
"ckpt_path_model" : "UnknownPath",
# Ignorance idx
"ignore_idx" : 0,
"get_latent" : False,
# Test flag
"test_flag_eval" : False,
"target_training" : True,
"source_training" : True,
# Which ckpt to load from in eval
"ckpt_number" : -1,
"da_flag" : True,
"dual_seg_head" : True,
"source_dataset_name" : 'NuScenes',
"source_dataset_root" : 'data/nuscenes',
"source_input_intensities" : False,
"source_input_dirs" : False,
"nb_classes" : 11,
"target_dataset_name" : 'SemanticKITTI',
"target_dataset_root" : 'data/SemanticKITTI',
"target_input_intensities" : False,
"target_input_dirs" : False,
"lr_scheduler":"step_lr",
"training_iter_nbr":200000,
"step_lr_gamma":0.7,
}
def read_yaml_file(file_path) -> dict:
with open(file_path, 'r') as file:
try:
data = yaml.safe_load(file)
return data
except yaml.YAMLError as e:
print(f"Error while reading the YAML file: {e}")
return {}