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"setup_67_er_8nm_in116_fp16.onnx", "max_abs": 0.009019851684570312, "mean_abs": 0.0021523048635572195, "flip_frac": 0.00036443148688046647, "ort_cpu_s": 0.97 }, { "model": "setup_67", "variant": "int8w", "input_size": 116, "output_size": 28, "file_mb": 10.88, "file": "setup_67_er_8nm_in116_int8w.onnx", "max_abs": 0.09022176265716553, "mean_abs": 0.019372668117284775, "flip_frac": 0.0028698979591836736, "ort_cpu_s": 0.57 }, { "model": "setup_67", "variant": "int8w_fp16", "input_size": 116, "output_size": 28, "file_mb": 10.91, "file": "setup_67_er_8nm_in116_int8w_fp16.onnx", "max_abs": 0.08383440971374512, "mean_abs": 0.020597415044903755, "flip_frac": 0.002824344023323615, "ort_cpu_s": 0.88 } ], "source_repo": "cellmap/er-resunet-setup-67", "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": 40, "out_channels": 1, "activation": "none (logits)", "files": [ "setup_67_er_8nm_in116_fp16.onnx", "setup_67_er_8nm_in116_fp32.onnx", "setup_67_er_8nm_in132_fp16.onnx", "setup_67_er_8nm_in132_fp32.onnx", "setup_67_er_8nm_in148_fp16.onnx", "setup_67_er_8nm_in148_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_67_er_8nm_in116_fp16.onnx", "setup_67_er_8nm_in116_fp32.onnx", "setup_67_er_8nm_in132_fp16.onnx", "setup_67_er_8nm_in132_fp32.onnx", "setup_67_er_8nm_in148_fp16.onnx", "setup_67_er_8nm_in148_fp32.onnx" ], "frozen": [], "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)" } } }