| { |
| "model": "setup_53", |
| "task": "mito_affinities", |
| "architecture": "Unet3dIso", |
| "checkpoint": "model_checkpoint_340000", |
| "params": 5602595, |
| "channels": [ |
| "mito_aff_z", |
| "mito_aff_y", |
| "mito_aff_x" |
| ], |
| "input_voxel_size_nm": [ |
| 16, |
| 16, |
| 16 |
| ], |
| "output_voxel_size_nm": [ |
| 16, |
| 16, |
| 16 |
| ], |
| "output": "raw logits (apply sigmoid / threshold at 0)", |
| "input_norm": [ |
| { |
| "name": "MinMaxNormalizer", |
| "min_value": 0, |
| "max_value": 255 |
| }, |
| { |
| "name": "LambdaNormalizer", |
| "expression": "2*x-1" |
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| "io": "input 'input' float32 [1,1,z,y,x]; output 'output' float32 [1,C,z',y',x']", |
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| { |
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| "file": "setup_53_mito_affinities_16nm_in124_fp16.onnx", |
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| { |
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| "output_size": 36, |
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| "file": "setup_53_mito_affinities_16nm_in124_int8w.onnx", |
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| { |
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| "variant": "int8w_fp16", |
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| "output_size": 36, |
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| "file": "setup_53_mito_affinities_16nm_in124_int8w_fp16.onnx", |
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| ], |
| "source_repo": "cellmap/mito-aff-unetiso-setup-53", |
| "torch_version": "2.9.1", |
| "exporter": "torch.onnx.export (TorchScript, dynamo=False)", |
| "opset": 18, |
| "finetune": { |
| "head": { |
| "final_layer": "final_conv.convs.0", |
| "onnx_node": "/final_conv/convs.0/Conv", |
| "features": "/final_conv/Slice_output_0", |
| "weight": "final_conv.convs.0.weight", |
| "bias": "final_conv.convs.0.bias", |
| "in_channels": 32, |
| "out_channels": 3, |
| "activation": "none (logits)", |
| "files": [ |
| "setup_53_mito_affinities_16nm_in124_fp16.onnx", |
| "setup_53_mito_affinities_16nm_in124_fp32.onnx", |
| "setup_53_mito_affinities_16nm_in140_fp16.onnx", |
| "setup_53_mito_affinities_16nm_in140_fp32.onnx", |
| "setup_53_mito_affinities_16nm_in172_fp16.onnx", |
| "setup_53_mito_affinities_16nm_in172_fp32.onnx" |
| ], |
| "initializers": "finetune/initializers.json", |
| "loss": "masked BCE on logits", |
| "note": "train the final 1x1 conv on frozen features", |
| "torch_weight": { |
| "torch": "final_conv.weight", |
| "transform": "[:, :, 0]", |
| "to_onnx": [ |
| { |
| "index": [ |
| 2, |
| 0 |
| ] |
| } |
| ] |
| }, |
| "torch_bias": { |
| "torch": "final_conv.bias", |
| "transform": "identity", |
| "to_onnx": [] |
| }, |
| "weight_map": "finetune/weight_map.json" |
| }, |
| "full": { |
| "library": "tfjs", |
| "files": [ |
| "finetune/train_graph.json", |
| "finetune/cellmap_tfjs_train.js" |
| ], |
| "weight_map": "finetune/weight_map.json", |
| "torch_params": "finetune/torch_params.json", |
| "initializers": "finetune/initializers.json", |
| "write_back": [ |
| "setup_53_mito_affinities_16nm_in124_fp16.onnx", |
| "setup_53_mito_affinities_16nm_in124_fp32.onnx", |
| "setup_53_mito_affinities_16nm_in140_fp16.onnx", |
| "setup_53_mito_affinities_16nm_in140_fp32.onnx", |
| "setup_53_mito_affinities_16nm_in172_fp16.onnx", |
| "setup_53_mito_affinities_16nm_in172_fp32.onnx" |
| ], |
| "frozen": [ |
| "encoder0.bn1.running_mean", |
| "encoder0.bn1.running_var", |
| "encoder0.bn2.running_mean", |
| "encoder0.bn2.running_var", |
| "encoder1.bn1.running_mean", |
| "encoder1.bn1.running_var", |
| "encoder1.bn2.running_mean", |
| "encoder1.bn2.running_var", |
| "encoder2.bn1.running_mean", |
| "encoder2.bn1.running_var", |
| "encoder2.bn2.running_mean", |
| "encoder2.bn2.running_var", |
| "bottleneck.bn1.running_mean", |
| "bottleneck.bn1.running_var", |
| "bottleneck.bn2.running_mean", |
| "bottleneck.bn2.running_var", |
| "decoder2.bn1.running_mean", |
| "decoder2.bn1.running_var", |
| "decoder2.bn2.running_mean", |
| "decoder2.bn2.running_var", |
| "decoder1.bn1.running_mean", |
| "decoder1.bn1.running_var", |
| "decoder1.bn2.running_mean", |
| "decoder1.bn2.running_var", |
| "decoder0.bn1.running_mean", |
| "decoder0.bn1.running_var", |
| "decoder0.bn2.running_mean", |
| "decoder0.bn2.running_var" |
| ], |
| "note": "NDHWC graph, params named as the PyTorch state_dict (Conv3d [O,I,kd,kh,kw], ConvTranspose3d [I,O,2,2,2]); read them from the .onnx with torch_params.json from_onnx, write back with weight_map.json to_onnx; BatchNorm running stats frozen. Plain TF.js autodiff keeps every intermediate until the backward pass: use the smallest block (3D memory grows with the block volume)" |
| } |
| } |
| } |
