Browser finetuning: manifest finetune block (head; full/tfjs for 2D students), initializer offsets, weight maps, torch_version/exporter/opset, source_repo for 2D students

README.md

@@ -242,6 +242,37 @@ differs from PyTorch. A consistency check, not a re-run of F1 validation.
 | `setup_67/setup_67_er_8nm_in116_int8w.onnx` | 10.9 | 116³ → 28³ | 0.0902 | 0.0194 | 0.2870 |
 | `setup_67/setup_67_er_8nm_in116_int8w_fp16.onnx` | 10.9 | 116³ → 28³ | 0.0838 | 0.0206 | 0.2824 |
 
+## Finetuning in the browser and PyTorch originals
+
+Each folder's `manifest.json` has a `finetune` block listing the modes the model offers; a model without the block, or
+without a mode, is not offered that mode. `source_repo` points to the PyTorch twin (`model_state_dict.pt` + module code
+`model_def.py`, plus `model.ts` / `metadata.json` for the cellmap-flow server catalog); 2D students also name their
+3D teacher in `distilled_from_repo`.
+
+- **head**: train the final 1×1 conv on frozen features (`features` tensor, `weight` / `bias` initializers).
+- **full**: train the whole network with TF.js on WebGPU: `finetune/train_graph.json` (NHWC op list; parameters named as
+  the ONNX initializers, so `weight_map` is identity) + the reference runtime `finetune/cellmap_tfjs_train.js`.
+  2D students only for now.
+
+`finetune/initializers.json` gives every initializer's byte offset in each `.onnx` (raw_data inline, fixed shapes), so trained
+weights are written back in place (as fp16 in the fp16 files) and served as before; int8w files are not writable.
+In the 3D folders the Conv3d weights are split into `convs.<k>` per depth tap; `finetune/weight_map.json` maps each ONNX
+initializer to its PyTorch parameter.
+
+| folder | source_repo | modes | full: step at 556² / 300² (peak) |
+|---|---|---|---|
+| `setup_54` | [cellmap/mito-aff-resunet-setup-54](https://huggingface.co/cellmap/mito-aff-resunet-setup-54) | head | — |
+| `setup_53` | [cellmap/mito-aff-unetiso-setup-53](https://huggingface.co/cellmap/mito-aff-unetiso-setup-53) | head | — |
+| `setup_56` | [cellmap/nuc-resunet-setup-56](https://huggingface.co/cellmap/nuc-resunet-setup-56) | head | — |
+| `setup_55` | [cellmap/nuc-unetiso-setup-55](https://huggingface.co/cellmap/nuc-unetiso-setup-55) | head | — |
+| `setup_61` | [cellmap/nuc-resunet-setup-61](https://huggingface.co/cellmap/nuc-resunet-setup-61) | head | — |
+| `setup_67` | [cellmap/er-resunet-setup-67](https://huggingface.co/cellmap/er-resunet-setup-67) | head | — |
+| `setup_53_2d` | [cellmap/mito-aff-unetiso-setup-53-2d](https://huggingface.co/cellmap/mito-aff-unetiso-setup-53-2d) | head, full | 1178 ms (1.3 GB) / 294 ms (0.3 GB) |
+| `setup_54_2d` | [cellmap/mito-aff-resunet-setup-54-2d](https://huggingface.co/cellmap/mito-aff-resunet-setup-54-2d) | head, full | 1919 ms (3.7 GB) / 462 ms (0.9 GB) |
+
+Full-mode step times: TF.js 4.22 WebGPU, Chrome, Apple M-series GPU, batch 1, fp32, masked BCE + Adam (`manifest.json` → `finetune.full.spike`).
+
+
 ## Use in cellmap-flow serverless
 
 ONNX model → remote URL `https://huggingface.co/cellmap/onnx-web-models/resolve/main/<folder>/<file>.onnx`

setup_53/finetune/weight_map.json

@@ -0,0 +1,482 @@
+{
+ "encoder0.conv1.convs.0.weight": {
+  "torch": "encoder0.conv1.weight",
+  "transform": "[:, :, 0]"
+ },
+ "encoder0.conv1.convs.0.bias": {
+  "torch": "encoder0.conv1.bias",
+  "transform": "identity"
+ },
+ "encoder0.conv1.convs.1.weight": {
+  "torch": "encoder0.conv1.weight",
+  "transform": "[:, :, 1]"
+ },
+ "encoder0.conv1.convs.2.weight": {
+  "torch": "encoder0.conv1.weight",
+  "transform": "[:, :, 2]"
+ },
+ "encoder0.bn1.weight": {
+  "torch": "encoder0.bn1.weight",
+  "transform": "identity"
+ },
+ "encoder0.bn1.bias": {
+  "torch": "encoder0.bn1.bias",
+  "transform": "identity"
+ },
+ "encoder0.bn1.running_mean": {
+  "torch": "encoder0.bn1.running_mean",
+  "transform": "identity"
+ },
+ "encoder0.bn1.running_var": {
+  "torch": "encoder0.bn1.running_var",
+  "transform": "identity"
+ },
+ "encoder0.conv2.convs.0.weight": {
+  "torch": "encoder0.conv2.weight",
+  "transform": "[:, :, 0]"
+ },
+ "encoder0.conv2.convs.0.bias": {
+  "torch": "encoder0.conv2.bias",
+  "transform": "identity"
+ },
+ "encoder0.conv2.convs.1.weight": {
+  "torch": "encoder0.conv2.weight",
+  "transform": "[:, :, 1]"
+ },
+ "encoder0.conv2.convs.2.weight": {
+  "torch": "encoder0.conv2.weight",
+  "transform": "[:, :, 2]"
+ },
+ "encoder0.bn2.weight": {
+  "torch": "encoder0.bn2.weight",
+  "transform": "identity"
+ },
+ "encoder0.bn2.bias": {
+  "torch": "encoder0.bn2.bias",
+  "transform": "identity"
+ },
+ "encoder0.bn2.running_mean": {
+  "torch": "encoder0.bn2.running_mean",
+  "transform": "identity"
+ },
+ "encoder0.bn2.running_var": {
+  "torch": "encoder0.bn2.running_var",
+  "transform": "identity"
+ },
+ "encoder1.conv1.convs.0.weight": {
+  "torch": "encoder1.conv1.weight",
+  "transform": "[:, :, 0]"
+ },
+ "encoder1.conv1.convs.0.bias": {
+  "torch": "encoder1.conv1.bias",
+  "transform": "identity"
+ },
+ "encoder1.conv1.convs.1.weight": {
+  "torch": "encoder1.conv1.weight",
+  "transform": "[:, :, 1]"
+ },
+ "encoder1.conv1.convs.2.weight": {
+  "torch": "encoder1.conv1.weight",
+  "transform": "[:, :, 2]"
+ },
+ "encoder1.bn1.weight": {
+  "torch": "encoder1.bn1.weight",
+  "transform": "identity"
+ },
+ "encoder1.bn1.bias": {
+  "torch": "encoder1.bn1.bias",
+  "transform": "identity"
+ },
+ "encoder1.bn1.running_mean": {
+  "torch": "encoder1.bn1.running_mean",
+  "transform": "identity"
+ },
+ "encoder1.bn1.running_var": {
+  "torch": "encoder1.bn1.running_var",
+  "transform": "identity"
+ },
+ "encoder1.conv2.convs.0.weight": {
+  "torch": "encoder1.conv2.weight",
+  "transform": "[:, :, 0]"
+ },
+ "encoder1.conv2.convs.0.bias": {
+  "torch": "encoder1.conv2.bias",
+  "transform": "identity"
+ },
+ "encoder1.conv2.convs.1.weight": {
+  "torch": "encoder1.conv2.weight",
+  "transform": "[:, :, 1]"
+ },
+ "encoder1.conv2.convs.2.weight": {
+  "torch": "encoder1.conv2.weight",
+  "transform": "[:, :, 2]"
+ },
+ "encoder1.bn2.weight": {
+  "torch": "encoder1.bn2.weight",
+  "transform": "identity"
+ },
+ "encoder1.bn2.bias": {
+  "torch": "encoder1.bn2.bias",
+  "transform": "identity"
+ },
+ "encoder1.bn2.running_mean": {
+  "torch": "encoder1.bn2.running_mean",
+  "transform": "identity"
+ },
+ "encoder1.bn2.running_var": {
+  "torch": "encoder1.bn2.running_var",
+  "transform": "identity"
+ },
+ "encoder2.conv1.convs.0.weight": {
+  "torch": "encoder2.conv1.weight",
+  "transform": "[:, :, 0]"
+ },
+ "encoder2.conv1.convs.0.bias": {
+  "torch": "encoder2.conv1.bias",
+  "transform": "identity"
+ },
+ "encoder2.conv1.convs.1.weight": {
+  "torch": "encoder2.conv1.weight",
+  "transform": "[:, :, 1]"
+ },
+ "encoder2.conv1.convs.2.weight": {
+  "torch": "encoder2.conv1.weight",
+  "transform": "[:, :, 2]"
+ },
+ "encoder2.bn1.weight": {
+  "torch": "encoder2.bn1.weight",
+  "transform": "identity"
+ },
+ "encoder2.bn1.bias": {
+  "torch": "encoder2.bn1.bias",
+  "transform": "identity"
+ },
+ "encoder2.bn1.running_mean": {
+  "torch": "encoder2.bn1.running_mean",
+  "transform": "identity"
+ },
+ "encoder2.bn1.running_var": {
+  "torch": "encoder2.bn1.running_var",
+  "transform": "identity"
+ },
+ "encoder2.conv2.convs.0.weight": {
+  "torch": "encoder2.conv2.weight",
+  "transform": "[:, :, 0]"
+ },
+ "encoder2.conv2.convs.0.bias": {
+  "torch": "encoder2.conv2.bias",
+  "transform": "identity"
+ },
+ "encoder2.conv2.convs.1.weight": {
+  "torch": "encoder2.conv2.weight",
+  "transform": "[:, :, 1]"
+ },
+ "encoder2.conv2.convs.2.weight": {
+  "torch": "encoder2.conv2.weight",
+  "transform": "[:, :, 2]"
+ },
+ "encoder2.bn2.weight": {
+  "torch": "encoder2.bn2.weight",
+  "transform": "identity"
+ },
+ "encoder2.bn2.bias": {
+  "torch": "encoder2.bn2.bias",
+  "transform": "identity"
+ },
+ "encoder2.bn2.running_mean": {
+  "torch": "encoder2.bn2.running_mean",
+  "transform": "identity"
+ },
+ "encoder2.bn2.running_var": {
+  "torch": "encoder2.bn2.running_var",
+  "transform": "identity"
+ },
+ "bottleneck.conv1.convs.0.weight": {
+  "torch": "bottleneck.conv1.weight",
+  "transform": "[:, :, 0]"
+ },
+ "bottleneck.conv1.convs.0.bias": {
+  "torch": "bottleneck.conv1.bias",
+  "transform": "identity"
+ },
+ "bottleneck.conv1.convs.1.weight": {
+  "torch": "bottleneck.conv1.weight",
+  "transform": "[:, :, 1]"
+ },
+ "bottleneck.conv1.convs.2.weight": {
+  "torch": "bottleneck.conv1.weight",
+  "transform": "[:, :, 2]"
+ },
+ "bottleneck.bn1.weight": {
+  "torch": "bottleneck.bn1.weight",
+  "transform": "identity"
+ },
+ "bottleneck.bn1.bias": {
+  "torch": "bottleneck.bn1.bias",
+  "transform": "identity"
+ },
+ "bottleneck.bn1.running_mean": {
+  "torch": "bottleneck.bn1.running_mean",
+  "transform": "identity"
+ },
+ "bottleneck.bn1.running_var": {
+  "torch": "bottleneck.bn1.running_var",
+  "transform": "identity"
+ },
+ "bottleneck.conv2.convs.0.weight": {
+  "torch": "bottleneck.conv2.weight",
+  "transform": "[:, :, 0]"
+ },
+ "bottleneck.conv2.convs.0.bias": {
+  "torch": "bottleneck.conv2.bias",
+  "transform": "identity"
+ },
+ "bottleneck.conv2.convs.1.weight": {
+  "torch": "bottleneck.conv2.weight",
+  "transform": "[:, :, 1]"
+ },
+ "bottleneck.conv2.convs.2.weight": {
+  "torch": "bottleneck.conv2.weight",
+  "transform": "[:, :, 2]"
+ },
+ "bottleneck.bn2.weight": {
+  "torch": "bottleneck.bn2.weight",
+  "transform": "identity"
+ },
+ "bottleneck.bn2.bias": {
+  "torch": "bottleneck.bn2.bias",
+  "transform": "identity"
+ },
+ "bottleneck.bn2.running_mean": {
+  "torch": "bottleneck.bn2.running_mean",
+  "transform": "identity"
+ },
+ "bottleneck.bn2.running_var": {
+  "torch": "bottleneck.bn2.running_var",
+  "transform": "identity"
+ },
+ "upconv2.conv.convs.0.weight": {
+  "torch": "upconv2.weight",
+  "transform": "permute(1,2,3,4,0).reshape(Cout*8, Cin, 1, 1)"
+ },
+ "upconv2.conv.convs.0.bias": {
+  "torch": "upconv2.bias",
+  "transform": "repeat_interleave(8)"
+ },
+ "decoder2.conv1.convs.0.weight": {
+  "torch": "decoder2.conv1.weight",
+  "transform": "[:, :, 0]"
+ },
+ "decoder2.conv1.convs.0.bias": {
+  "torch": "decoder2.conv1.bias",
+  "transform": "identity"
+ },
+ "decoder2.conv1.convs.1.weight": {
+  "torch": "decoder2.conv1.weight",
+  "transform": "[:, :, 1]"
+ },
+ "decoder2.conv1.convs.2.weight": {
+  "torch": "decoder2.conv1.weight",
+  "transform": "[:, :, 2]"
+ },
+ "decoder2.bn1.weight": {
+  "torch": "decoder2.bn1.weight",
+  "transform": "identity"
+ },
+ "decoder2.bn1.bias": {
+  "torch": "decoder2.bn1.bias",
+  "transform": "identity"
+ },
+ "decoder2.bn1.running_mean": {
+  "torch": "decoder2.bn1.running_mean",
+  "transform": "identity"
+ },
+ "decoder2.bn1.running_var": {
+  "torch": "decoder2.bn1.running_var",
+  "transform": "identity"
+ },
+ "decoder2.conv2.convs.0.weight": {
+  "torch": "decoder2.conv2.weight",
+  "transform": "[:, :, 0]"
+ },
+ "decoder2.conv2.convs.0.bias": {
+  "torch": "decoder2.conv2.bias",
+  "transform": "identity"
+ },
+ "decoder2.conv2.convs.1.weight": {
+  "torch": "decoder2.conv2.weight",
+  "transform": "[:, :, 1]"
+ },
+ "decoder2.conv2.convs.2.weight": {
+  "torch": "decoder2.conv2.weight",
+  "transform": "[:, :, 2]"
+ },
+ "decoder2.bn2.weight": {
+  "torch": "decoder2.bn2.weight",
+  "transform": "identity"
+ },
+ "decoder2.bn2.bias": {
+  "torch": "decoder2.bn2.bias",
+  "transform": "identity"
+ },
+ "decoder2.bn2.running_mean": {
+  "torch": "decoder2.bn2.running_mean",
+  "transform": "identity"
+ },
+ "decoder2.bn2.running_var": {
+  "torch": "decoder2.bn2.running_var",
+  "transform": "identity"
+ },
+ "upconv1.conv.convs.0.weight": {
+  "torch": "upconv1.weight",
+  "transform": "permute(1,2,3,4,0).reshape(Cout*8, Cin, 1, 1)"
+ },
+ "upconv1.conv.convs.0.bias": {
+  "torch": "upconv1.bias",
+  "transform": "repeat_interleave(8)"
+ },
+ "decoder1.conv1.convs.0.weight": {
+  "torch": "decoder1.conv1.weight",
+  "transform": "[:, :, 0]"
+ },
+ "decoder1.conv1.convs.0.bias": {
+  "torch": "decoder1.conv1.bias",
+  "transform": "identity"
+ },
+ "decoder1.conv1.convs.1.weight": {
+  "torch": "decoder1.conv1.weight",
+  "transform": "[:, :, 1]"
+ },
+ "decoder1.conv1.convs.2.weight": {
+  "torch": "decoder1.conv1.weight",
+  "transform": "[:, :, 2]"
+ },
+ "decoder1.bn1.weight": {
+  "torch": "decoder1.bn1.weight",
+  "transform": "identity"
+ },
+ "decoder1.bn1.bias": {
+  "torch": "decoder1.bn1.bias",
+  "transform": "identity"
+ },
+ "decoder1.bn1.running_mean": {
+  "torch": "decoder1.bn1.running_mean",
+  "transform": "identity"
+ },
+ "decoder1.bn1.running_var": {
+  "torch": "decoder1.bn1.running_var",
+  "transform": "identity"
+ },
+ "decoder1.conv2.convs.0.weight": {
+  "torch": "decoder1.conv2.weight",
+  "transform": "[:, :, 0]"
+ },
+ "decoder1.conv2.convs.0.bias": {
+  "torch": "decoder1.conv2.bias",
+  "transform": "identity"
+ },
+ "decoder1.conv2.convs.1.weight": {
+  "torch": "decoder1.conv2.weight",
+  "transform": "[:, :, 1]"
+ },
+ "decoder1.conv2.convs.2.weight": {
+  "torch": "decoder1.conv2.weight",
+  "transform": "[:, :, 2]"
+ },
+ "decoder1.bn2.weight": {
+  "torch": "decoder1.bn2.weight",
+  "transform": "identity"
+ },
+ "decoder1.bn2.bias": {
+  "torch": "decoder1.bn2.bias",
+  "transform": "identity"
+ },
+ "decoder1.bn2.running_mean": {
+  "torch": "decoder1.bn2.running_mean",
+  "transform": "identity"
+ },
+ "decoder1.bn2.running_var": {
+  "torch": "decoder1.bn2.running_var",
+  "transform": "identity"
+ },
+ "upconv0.conv.convs.0.weight": {
+  "torch": "upconv0.weight",
+  "transform": "permute(1,2,3,4,0).reshape(Cout*8, Cin, 1, 1)"
+ },
+ "upconv0.conv.convs.0.bias": {
+  "torch": "upconv0.bias",
+  "transform": "repeat_interleave(8)"
+ },
+ "decoder0.conv1.convs.0.weight": {
+  "torch": "decoder0.conv1.weight",
+  "transform": "[:, :, 0]"
+ },
+ "decoder0.conv1.convs.0.bias": {
+  "torch": "decoder0.conv1.bias",
+  "transform": "identity"
+ },
+ "decoder0.conv1.convs.1.weight": {
+  "torch": "decoder0.conv1.weight",
+  "transform": "[:, :, 1]"
+ },
+ "decoder0.conv1.convs.2.weight": {
+  "torch": "decoder0.conv1.weight",
+  "transform": "[:, :, 2]"
+ },
+ "decoder0.bn1.weight": {
+  "torch": "decoder0.bn1.weight",
+  "transform": "identity"
+ },
+ "decoder0.bn1.bias": {
+  "torch": "decoder0.bn1.bias",
+  "transform": "identity"
+ },
+ "decoder0.bn1.running_mean": {
+  "torch": "decoder0.bn1.running_mean",
+  "transform": "identity"
+ },
+ "decoder0.bn1.running_var": {
+  "torch": "decoder0.bn1.running_var",
+  "transform": "identity"
+ },
+ "decoder0.conv2.convs.0.weight": {
+  "torch": "decoder0.conv2.weight",
+  "transform": "[:, :, 0]"
+ },
+ "decoder0.conv2.convs.0.bias": {
+  "torch": "decoder0.conv2.bias",
+  "transform": "identity"
+ },
+ "decoder0.conv2.convs.1.weight": {
+  "torch": "decoder0.conv2.weight",
+  "transform": "[:, :, 1]"
+ },
+ "decoder0.conv2.convs.2.weight": {
+  "torch": "decoder0.conv2.weight",
+  "transform": "[:, :, 2]"
+ },
+ "decoder0.bn2.weight": {
+  "torch": "decoder0.bn2.weight",
+  "transform": "identity"
+ },
+ "decoder0.bn2.bias": {
+  "torch": "decoder0.bn2.bias",
+  "transform": "identity"
+ },
+ "decoder0.bn2.running_mean": {
+  "torch": "decoder0.bn2.running_mean",
+  "transform": "identity"
+ },
+ "decoder0.bn2.running_var": {
+  "torch": "decoder0.bn2.running_var",
+  "transform": "identity"
+ },
+ "final_conv.convs.0.weight": {
+  "torch": "final_conv.weight",
+  "transform": "[:, :, 0]"
+ },
+ "final_conv.convs.0.bias": {
+  "torch": "final_conv.bias",
+  "transform": "identity"
+ }
+}

setup_53/manifest.json

@@ -178,5 +178,40 @@
       "ort_cpu_s": 0.76
     }
   ],
-  "source_repo": "cellmap/mito-aff-unetiso-setup-53"
+  "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]"
+      },
+      "torch_bias": {
+        "torch": "final_conv.bias",
+        "transform": "identity"
+      },
+      "weight_map": "finetune/weight_map.json"
+    }
+  }
 }

setup_53_2d/finetune/cellmap_tfjs_train.js

@@ -0,0 +1,134 @@
+// Reference runtime for cellmap-flow browser finetuning ("full" mode, library tfjs).
+// Builds a trainable TF.js network from finetune/train_graph.json, takes its weights from the served .onnx
+// (finetune/initializers.json gives the byte offset of every initializer), and writes trained weights back into
+// the .onnx bytes in place, so the result is served by onnxruntime-web exactly as before.
+//
+//   const g = await (await fetch(base + "finetune/train_graph.json")).json();
+//   const offs = (await (await fetch(base + "finetune/initializers.json")).json())[onnxFile];
+//   const onnxBytes = new Uint8Array(await (await fetch(base + onnxFile)).arrayBuffer());
+//   const net = buildNet(tf, g, readOnnxWeights(onnxBytes, offs, Object.keys(g.params)));
+//   const opt = tf.train.adam(1e-4);
+//   const loss = trainStep(tf, net, opt, x, target, mask);   // x [1,H,W,1]; target/mask [1,H-ctx,W-ctx,C]
+//   const newBytes = writeOnnxWeights(onnxBytes, offs, net.exportWeights());
+//
+// Tensors are NHWC. ONNX conv weights [O,I,kh,kw] -> tf [kh,kw,I,O]; ConvTranspose [I,O,kh,kw] -> tf [kh,kw,O,I].
+
+function f16ToF32(h) {
+  const s = (h & 0x8000) ? -1 : 1, e = (h >> 10) & 0x1f, f = h & 0x3ff;
+  if (e === 0) return s * Math.pow(2, -14) * (f / 1024);
+  if (e === 31) return f ? NaN : s * Infinity;
+  return s * Math.pow(2, e - 15) * (1 + f / 1024);
+}
+const _f = new Float32Array(1), _u = new Uint32Array(_f.buffer);
+function f32ToF16(v) {
+  _f[0] = v; const x = _u[0];
+  const s = (x >>> 16) & 0x8000; let e = ((x >>> 23) & 0xff) - 112; let m = x & 0x7fffff;
+  if (e <= 0) { if (e < -10) return s; m |= 0x800000; const t = 14 - e; const r = (m >> t) + ((m >> (t - 1)) & 1); return s | r; }
+  if (e >= 31) return s | 0x7c00 | (((x >>> 23) & 0xff) === 0xff && m ? 0x200 : 0);
+  let r = s | (e << 10) | (m >> 13);
+  if (m & 0x1000) r += 1;  // round half up on the dropped bits (good enough for weights)
+  return r;
+}
+
+export function readOnnxWeights(bytes, offs, names) {
+  const out = {};
+  for (const n of names) {
+    const o = offs[n];
+    if (!o) throw new Error(`initializer ${n} not in file`);
+    const dv = new DataView(bytes.buffer, bytes.byteOffset + o.offset, o.nbytes);
+    if (o.dtype === "float32") {
+      const a = new Float32Array(o.nbytes / 4);
+      for (let i = 0; i < a.length; i++) a[i] = dv.getFloat32(4 * i, true);
+      out[n] = a;
+    } else if (o.dtype === "float16") {
+      const a = new Float32Array(o.nbytes / 2);
+      for (let i = 0; i < a.length; i++) a[i] = f16ToF32(dv.getUint16(2 * i, true));
+      out[n] = a;
+    } else throw new Error(`${n}: dtype ${o.dtype} is not writable`);
+  }
+  return out;
+}
+
+export function writeOnnxWeights(bytes, offs, weights) {
+  const b = new Uint8Array(bytes);  // copy
+  for (const [n, a] of Object.entries(weights)) {
+    const o = offs[n];
+    const dv = new DataView(b.buffer, b.byteOffset + o.offset, o.nbytes);
+    if (o.dtype === "float32") { if (a.length * 4 !== o.nbytes) throw new Error(n); a.forEach((v, i) => dv.setFloat32(4 * i, v, true)); }
+    else if (o.dtype === "float16") { if (a.length * 2 !== o.nbytes) throw new Error(n); a.forEach((v, i) => dv.setUint16(2 * i, f32ToF16(v), true)); }
+    else throw new Error(`${n}: dtype ${o.dtype} is not writable`);
+  }
+  return b;
+}
+
+export function buildNet(tf, graph, weights, { trainable = null } = {}) {
+  // trainable: optional Set of param names to train (default: graph's trainable flags); e.g. head only.
+  const P = {};
+  const kind = {};
+  for (const op of graph.ops) {
+    if (op.op === "conv") kind[op.w] = "conv";
+    if (op.op === "conv_t") kind[op.w] = "conv_t";
+  }
+  for (const [n, meta] of Object.entries(graph.params)) {
+    let t = tf.tensor(weights[n], meta.shape, "float32");
+    if (kind[n] === "conv") t = tf.transpose(t, [2, 3, 1, 0]);
+    else if (kind[n] === "conv_t") t = tf.transpose(t, [2, 3, 1, 0]);
+    const train = trainable ? trainable.has(n) : meta.trainable;
+    P[n] = train ? tf.variable(t, true) : t;  // unnamed: tf variable names are global, several nets may coexist
+    if (train) t.dispose();
+  }
+  function forward(x) {
+    const T = { [graph.input]: x };
+    for (const op of graph.ops) {
+      const a = T[op.x];
+      let y;
+      switch (op.op) {
+        case "conv": y = tf.conv2d(a, P[op.w], op.stride, "valid"); if (op.b) y = tf.add(y, P[op.b]); break;
+        case "conv_t": {
+          const [kh, kw, o] = P[op.w].shape, [n, h, w] = a.shape;
+          y = tf.conv2dTranspose(a, P[op.w], [n, (h - 1) * op.stride[0] + kh, (w - 1) * op.stride[1] + kw, o], op.stride, "valid");
+          if (op.b) y = tf.add(y, P[op.b]); break;
+        }
+        case "maxpool": y = tf.maxPool(a, op.k, op.stride, "valid"); break;
+        case "bn": y = tf.batchNorm(a, P[op.mean], P[op.var], P[op.bias], P[op.scale], op.eps); break;  // frozen stats
+        case "inorm": { const { mean, variance } = tf.moments(a, [1, 2], true);
+          y = tf.add(tf.mul(tf.div(tf.sub(a, mean), tf.sqrt(tf.add(variance, op.eps))), P[op.scale]), P[op.bias]); break; }
+        case "relu": y = tf.relu(a); break;
+        case "add": y = tf.add(a, T[op.x2]); break;
+        case "concat": y = tf.concat(op.xs.map(k => T[k]), 3); break;
+        case "crop": { const s = a.shape, ax = op.axis === "h" ? 1 : 2, begin = [0, 0, 0, 0], size = [-1, -1, -1, -1];
+          begin[ax] = op.start; size[ax] = s[ax] - op.start - op.end_margin; y = tf.slice(a, begin, size); break; }
+        case "upsample": { const [n, h, w, c] = a.shape, f = op.factor;  // nearest = repeat; rank-3 tiles (tf has no
+          const yw = tf.reshape(tf.tile(tf.reshape(a, [n * h * w, 1, c]), [1, f, 1]), [n * h, 1, w * f * c]);  // rank-6 tile grad)
+          y = tf.reshape(tf.tile(yw, [1, f, 1]), [n, h * f, w * f, c]); break; }
+        default: throw new Error(`op ${op.op}`);
+      }
+      T[op.y] = y;
+    }
+    return T[graph.output];
+  }
+  function exportWeights() {  // back to ONNX layout, trainable params only
+    const out = {};
+    for (const [n, t] of Object.entries(P)) {
+      if (!(t instanceof tf.Variable)) continue;
+      const v = kind[n] ? tf.transpose(t, [3, 2, 0, 1]) : t;
+      out[n] = v.dataSync().slice();
+      if (v !== t) v.dispose();
+    }
+    return out;
+  }
+  const vars = Object.values(P).filter(t => t instanceof tf.Variable);
+  return { forward, exportWeights, vars, params: P, context: graph.context };
+}
+
+// masked BCE on logits: mean over mask>0 of max(z,0) - z*t + log(1+exp(-|z|))
+export function maskedBce(tf, z, t, m) {
+  return tf.tidy(() => {
+    const l = tf.add(tf.sub(tf.relu(z), tf.mul(z, t)), tf.log1p(tf.exp(tf.neg(tf.abs(z)))));
+    return tf.div(tf.sum(tf.mul(l, m)), tf.maximum(tf.sum(m), 1));
+  });
+}
+
+export function trainStep(tf, net, opt, x, target, mask) {
+  return opt.minimize(() => maskedBce(tf, net.forward(x), target, mask), true, net.vars);
+}

setup_53_2d/finetune/finetune_spike.html

@@ -0,0 +1,57 @@
+<!doctype html>
+<meta charset="utf-8">
+<title>cellmap-flow finetune spike</title>
+<!-- Times one full-network training step (masked BCE on logits, Adam, batch 1, fp32) of a 2D student in TF.js WebGPU,
+     built from the model folder's finetune/ files, and checks the TF.js forward against onnxruntime-web.
+     Open over https (WebGPU needs a secure context), keep the tab in the foreground.
+     ?model=setup_53_2d&sizes=300,556  (repo cellmap/onnx-web-models) -->
+<pre id="log">loading…</pre>
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.22.0/dist/tf.min.js"></script>
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgpu@4.22.0/dist/tf-backend-webgpu.min.js"></script>
+<script type="module">
+const q = new URLSearchParams(location.search);
+const model = q.get("model") || "setup_53_2d", sizes = (q.get("sizes") || "300,556").split(",").map(Number);
+const repo = q.get("repo") || "https://huggingface.co/cellmap/onnx-web-models/resolve/main/";
+const base = repo + model + "/";
+const log = s => { document.getElementById("log").textContent += "\n" + s; console.log(s); };
+const rt = await import(base + "finetune/cellmap_tfjs_train.js");
+await tf.setBackend("webgpu"); await tf.ready();
+const man = await (await fetch(base + "manifest.json")).json();
+const graph = await (await fetch(base + "finetune/train_graph.json")).json();
+const offsAll = await (await fetch(base + "finetune/initializers.json")).json();
+const ort = await import("https://cdn.jsdelivr.net/npm/onnxruntime-web@1.27.0-dev.20260506-673c3320fc/dist/ort.webgpu.bundle.min.mjs");
+const results = [];
+for (const N of sizes) {
+  const file = man.variants.find(v => v.variant === "fp32" && v.input_size[1] === N)?.file;
+  if (!file) { log(`no fp32 file for ${N}`); continue; }
+  const bytes = new Uint8Array(await (await fetch(base + file)).arrayBuffer());
+  const net = rt.buildNet(tf, graph, rt.readOnnxWeights(bytes, offsAll[file], Object.keys(graph.params)));
+  const M = N - graph.context;
+  // forward check vs onnxruntime-web on a smooth synthetic slice
+  const xa = new Float32Array(N * N).map((_, i) => Math.sin(Math.floor(i / N) / 9) * Math.cos((i % N) / 13) * 0.8);
+  const s = await ort.InferenceSession.create(bytes, { executionProviders: ["webgpu"] });
+  const ro = (await s.run({ input: new ort.Tensor("float32", xa, [1, 1, 1, N, N]) })).output.data;
+  const zd = await tf.tidy(() => tf.transpose(net.forward(tf.tensor(xa, [1, N, N, 1])), [0, 3, 1, 2])).data();
+  let maxAbs = 0, flips = 0;
+  zd.forEach((v, i) => { maxAbs = Math.max(maxAbs, Math.abs(v - ro[i])); flips += (v > 0) !== (ro[i] > 0); });
+  // training steps
+  const C = man.channels.length;
+  const x = tf.randomUniform([1, N, N, 1], -1, 1);
+  const t = tf.cast(tf.greater(tf.randomUniform([1, M, M, C]), 0.5), "float32");
+  const m = tf.cast(tf.greater(tf.randomUniform([1, M, M, C]), 0.7), "float32");
+  const opt = tf.train.adam(1e-4);
+  const step = () => rt.trainStep(tf, net, opt, x, t, m);
+  for (let i = 0; i < 2; i++) { const l = step(); await l.data(); l.dispose(); }
+  const base0 = tf.memory().numBytes;
+  const prof = await tf.profile(async () => { const l = step(); await l.data(); l.dispose(); });
+  const times = [];
+  for (let i = 0; i < 8; i++) { const t0 = performance.now(); const l = step(); await l.data(); times.push(performance.now() - t0); l.dispose(); }
+  times.sort((a, b) => a - b);
+  const r = { model, N, step_ms_median: Math.round(times[4]), step_ms_min: Math.round(times[0]),
+              train_peak_mb: Math.round((prof.peakBytes - base0) / 1e6), vs_ort_max_abs: maxAbs, vs_ort_flips: flips };
+  results.push(r); log(JSON.stringify(r));
+  [x, t, m].forEach(a => a.dispose()); opt.dispose(); net.vars.forEach(v => v.dispose());
+}
+window.SPIKE = results;
+log("done");
+</script>

setup_53_2d/finetune/initializers.json

@@ -0,0 +1 @@
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setup_53_2d/manifest.json

@@ -166,5 +166,111 @@
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+        "setup_53_mito_affinities_16nm_2d_in300_fp32.onnx",
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+  "source_repo": "cellmap/mito-aff-unetiso-setup-53-2d"
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setup_54/finetune/weight_map.json

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+  "transform": "identity"
+ },
+ "dec_blocks.1.0.conv1.convs.1.weight": {
+  "torch": "dec_blocks.1.0.conv1.weight",
+  "transform": "[:, :, 1]"
+ },
+ "dec_blocks.1.0.conv1.convs.2.weight": {
+  "torch": "dec_blocks.1.0.conv1.weight",
+  "transform": "[:, :, 2]"
+ },
+ "dec_blocks.1.0.norm1.weight": {
+  "torch": "dec_blocks.1.0.norm1.weight",
+  "transform": "identity"
+ },
+ "dec_blocks.1.0.norm1.bias": {
+  "torch": "dec_blocks.1.0.norm1.bias",
+  "transform": "identity"
+ },
+ "dec_blocks.1.0.conv2.convs.0.weight": {
+  "torch": "dec_blocks.1.0.conv2.weight",
+  "transform": "[:, :, 0]"
+ },
+ "dec_blocks.1.0.conv2.convs.0.bias": {
+  "torch": "dec_blocks.1.0.conv2.bias",
+  "transform": "identity"
+ },
+ "dec_blocks.1.0.conv2.convs.1.weight": {
+  "torch": "dec_blocks.1.0.conv2.weight",
+  "transform": "[:, :, 1]"
+ },
+ "dec_blocks.1.0.conv2.convs.2.weight": {
+  "torch": "dec_blocks.1.0.conv2.weight",
+  "transform": "[:, :, 2]"
+ },
+ "dec_blocks.1.0.norm2.weight": {
+  "torch": "dec_blocks.1.0.norm2.weight",
+  "transform": "identity"
+ },
+ "dec_blocks.1.0.norm2.bias": {
+  "torch": "dec_blocks.1.0.norm2.bias",
+  "transform": "identity"
+ },
+ "dec_blocks.1.0.skip.convs.0.weight": {
+  "torch": "dec_blocks.1.0.skip.weight",
+  "transform": "[:, :, 0]"
+ },
+ "dec_blocks.1.0.skip.convs.0.bias": {
+  "torch": "dec_blocks.1.0.skip.bias",
+  "transform": "identity"
+ },
+ "dec_blocks.2.0.conv1.convs.0.weight": {
+  "torch": "dec_blocks.2.0.conv1.weight",
+  "transform": "[:, :, 0]"
+ },
+ "dec_blocks.2.0.conv1.convs.0.bias": {
+  "torch": "dec_blocks.2.0.conv1.bias",
+  "transform": "identity"
+ },
+ "dec_blocks.2.0.conv1.convs.1.weight": {
+  "torch": "dec_blocks.2.0.conv1.weight",
+  "transform": "[:, :, 1]"
+ },
+ "dec_blocks.2.0.conv1.convs.2.weight": {
+  "torch": "dec_blocks.2.0.conv1.weight",
+  "transform": "[:, :, 2]"
+ },
+ "dec_blocks.2.0.norm1.weight": {
+  "torch": "dec_blocks.2.0.norm1.weight",
+  "transform": "identity"
+ },
+ "dec_blocks.2.0.norm1.bias": {
+  "torch": "dec_blocks.2.0.norm1.bias",
+  "transform": "identity"
+ },
+ "dec_blocks.2.0.conv2.convs.0.weight": {
+  "torch": "dec_blocks.2.0.conv2.weight",
+  "transform": "[:, :, 0]"
+ },
+ "dec_blocks.2.0.conv2.convs.0.bias": {
+  "torch": "dec_blocks.2.0.conv2.bias",
+  "transform": "identity"
+ },
+ "dec_blocks.2.0.conv2.convs.1.weight": {
+  "torch": "dec_blocks.2.0.conv2.weight",
+  "transform": "[:, :, 1]"
+ },
+ "dec_blocks.2.0.conv2.convs.2.weight": {
+  "torch": "dec_blocks.2.0.conv2.weight",
+  "transform": "[:, :, 2]"
+ },
+ "dec_blocks.2.0.norm2.weight": {
+  "torch": "dec_blocks.2.0.norm2.weight",
+  "transform": "identity"
+ },
+ "dec_blocks.2.0.norm2.bias": {
+  "torch": "dec_blocks.2.0.norm2.bias",
+  "transform": "identity"
+ },
+ "dec_blocks.2.0.skip.convs.0.weight": {
+  "torch": "dec_blocks.2.0.skip.weight",
+  "transform": "[:, :, 0]"
+ },
+ "dec_blocks.2.0.skip.convs.0.bias": {
+  "torch": "dec_blocks.2.0.skip.bias",
+  "transform": "identity"
+ },
+ "final_conv.convs.0.weight": {
+  "torch": "final_conv.weight",
+  "transform": "[:, :, 0]"
+ },
+ "final_conv.convs.0.bias": {
+  "torch": "final_conv.bias",
+  "transform": "identity"
+ }
+}

setup_54/manifest.json

@@ -178,5 +178,40 @@
       "ort_cpu_s": 0.96
     }
   ],
-  "source_repo": "cellmap/mito-aff-resunet-setup-54"
+  "source_repo": "cellmap/mito-aff-resunet-setup-54",
+  "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": 3,
+      "activation": "none (logits)",
+      "files": [
+        "setup_54_mito_affinities_16nm_in116_fp16.onnx",
+        "setup_54_mito_affinities_16nm_in116_fp32.onnx",
+        "setup_54_mito_affinities_16nm_in132_fp16.onnx",
+        "setup_54_mito_affinities_16nm_in132_fp32.onnx",
+        "setup_54_mito_affinities_16nm_in148_fp16.onnx",
+        "setup_54_mito_affinities_16nm_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]"
+      },
+      "torch_bias": {
+        "torch": "final_conv.bias",
+        "transform": "identity"
+      },
+      "weight_map": "finetune/weight_map.json"
+    }
+  }
 }

setup_54_2d/finetune/cellmap_tfjs_train.js

@@ -0,0 +1,134 @@
+// Reference runtime for cellmap-flow browser finetuning ("full" mode, library tfjs).
+// Builds a trainable TF.js network from finetune/train_graph.json, takes its weights from the served .onnx
+// (finetune/initializers.json gives the byte offset of every initializer), and writes trained weights back into
+// the .onnx bytes in place, so the result is served by onnxruntime-web exactly as before.
+//
+//   const g = await (await fetch(base + "finetune/train_graph.json")).json();
+//   const offs = (await (await fetch(base + "finetune/initializers.json")).json())[onnxFile];
+//   const onnxBytes = new Uint8Array(await (await fetch(base + onnxFile)).arrayBuffer());
+//   const net = buildNet(tf, g, readOnnxWeights(onnxBytes, offs, Object.keys(g.params)));
+//   const opt = tf.train.adam(1e-4);
+//   const loss = trainStep(tf, net, opt, x, target, mask);   // x [1,H,W,1]; target/mask [1,H-ctx,W-ctx,C]
+//   const newBytes = writeOnnxWeights(onnxBytes, offs, net.exportWeights());
+//
+// Tensors are NHWC. ONNX conv weights [O,I,kh,kw] -> tf [kh,kw,I,O]; ConvTranspose [I,O,kh,kw] -> tf [kh,kw,O,I].
+
+function f16ToF32(h) {
+  const s = (h & 0x8000) ? -1 : 1, e = (h >> 10) & 0x1f, f = h & 0x3ff;
+  if (e === 0) return s * Math.pow(2, -14) * (f / 1024);
+  if (e === 31) return f ? NaN : s * Infinity;
+  return s * Math.pow(2, e - 15) * (1 + f / 1024);
+}
+const _f = new Float32Array(1), _u = new Uint32Array(_f.buffer);
+function f32ToF16(v) {
+  _f[0] = v; const x = _u[0];
+  const s = (x >>> 16) & 0x8000; let e = ((x >>> 23) & 0xff) - 112; let m = x & 0x7fffff;
+  if (e <= 0) { if (e < -10) return s; m |= 0x800000; const t = 14 - e; const r = (m >> t) + ((m >> (t - 1)) & 1); return s | r; }
+  if (e >= 31) return s | 0x7c00 | (((x >>> 23) & 0xff) === 0xff && m ? 0x200 : 0);
+  let r = s | (e << 10) | (m >> 13);
+  if (m & 0x1000) r += 1;  // round half up on the dropped bits (good enough for weights)
+  return r;
+}
+
+export function readOnnxWeights(bytes, offs, names) {
+  const out = {};
+  for (const n of names) {
+    const o = offs[n];
+    if (!o) throw new Error(`initializer ${n} not in file`);
+    const dv = new DataView(bytes.buffer, bytes.byteOffset + o.offset, o.nbytes);
+    if (o.dtype === "float32") {
+      const a = new Float32Array(o.nbytes / 4);
+      for (let i = 0; i < a.length; i++) a[i] = dv.getFloat32(4 * i, true);
+      out[n] = a;
+    } else if (o.dtype === "float16") {
+      const a = new Float32Array(o.nbytes / 2);
+      for (let i = 0; i < a.length; i++) a[i] = f16ToF32(dv.getUint16(2 * i, true));
+      out[n] = a;
+    } else throw new Error(`${n}: dtype ${o.dtype} is not writable`);
+  }
+  return out;
+}
+
+export function writeOnnxWeights(bytes, offs, weights) {
+  const b = new Uint8Array(bytes);  // copy
+  for (const [n, a] of Object.entries(weights)) {
+    const o = offs[n];
+    const dv = new DataView(b.buffer, b.byteOffset + o.offset, o.nbytes);
+    if (o.dtype === "float32") { if (a.length * 4 !== o.nbytes) throw new Error(n); a.forEach((v, i) => dv.setFloat32(4 * i, v, true)); }
+    else if (o.dtype === "float16") { if (a.length * 2 !== o.nbytes) throw new Error(n); a.forEach((v, i) => dv.setUint16(2 * i, f32ToF16(v), true)); }
+    else throw new Error(`${n}: dtype ${o.dtype} is not writable`);
+  }
+  return b;
+}
+
+export function buildNet(tf, graph, weights, { trainable = null } = {}) {
+  // trainable: optional Set of param names to train (default: graph's trainable flags); e.g. head only.
+  const P = {};
+  const kind = {};
+  for (const op of graph.ops) {
+    if (op.op === "conv") kind[op.w] = "conv";
+    if (op.op === "conv_t") kind[op.w] = "conv_t";
+  }
+  for (const [n, meta] of Object.entries(graph.params)) {
+    let t = tf.tensor(weights[n], meta.shape, "float32");
+    if (kind[n] === "conv") t = tf.transpose(t, [2, 3, 1, 0]);
+    else if (kind[n] === "conv_t") t = tf.transpose(t, [2, 3, 1, 0]);
+    const train = trainable ? trainable.has(n) : meta.trainable;
+    P[n] = train ? tf.variable(t, true) : t;  // unnamed: tf variable names are global, several nets may coexist
+    if (train) t.dispose();
+  }
+  function forward(x) {
+    const T = { [graph.input]: x };
+    for (const op of graph.ops) {
+      const a = T[op.x];
+      let y;
+      switch (op.op) {
+        case "conv": y = tf.conv2d(a, P[op.w], op.stride, "valid"); if (op.b) y = tf.add(y, P[op.b]); break;
+        case "conv_t": {
+          const [kh, kw, o] = P[op.w].shape, [n, h, w] = a.shape;
+          y = tf.conv2dTranspose(a, P[op.w], [n, (h - 1) * op.stride[0] + kh, (w - 1) * op.stride[1] + kw, o], op.stride, "valid");
+          if (op.b) y = tf.add(y, P[op.b]); break;
+        }
+        case "maxpool": y = tf.maxPool(a, op.k, op.stride, "valid"); break;
+        case "bn": y = tf.batchNorm(a, P[op.mean], P[op.var], P[op.bias], P[op.scale], op.eps); break;  // frozen stats
+        case "inorm": { const { mean, variance } = tf.moments(a, [1, 2], true);
+          y = tf.add(tf.mul(tf.div(tf.sub(a, mean), tf.sqrt(tf.add(variance, op.eps))), P[op.scale]), P[op.bias]); break; }
+        case "relu": y = tf.relu(a); break;
+        case "add": y = tf.add(a, T[op.x2]); break;
+        case "concat": y = tf.concat(op.xs.map(k => T[k]), 3); break;
+        case "crop": { const s = a.shape, ax = op.axis === "h" ? 1 : 2, begin = [0, 0, 0, 0], size = [-1, -1, -1, -1];
+          begin[ax] = op.start; size[ax] = s[ax] - op.start - op.end_margin; y = tf.slice(a, begin, size); break; }
+        case "upsample": { const [n, h, w, c] = a.shape, f = op.factor;  // nearest = repeat; rank-3 tiles (tf has no
+          const yw = tf.reshape(tf.tile(tf.reshape(a, [n * h * w, 1, c]), [1, f, 1]), [n * h, 1, w * f * c]);  // rank-6 tile grad)
+          y = tf.reshape(tf.tile(yw, [1, f, 1]), [n, h * f, w * f, c]); break; }
+        default: throw new Error(`op ${op.op}`);
+      }
+      T[op.y] = y;
+    }
+    return T[graph.output];
+  }
+  function exportWeights() {  // back to ONNX layout, trainable params only
+    const out = {};
+    for (const [n, t] of Object.entries(P)) {
+      if (!(t instanceof tf.Variable)) continue;
+      const v = kind[n] ? tf.transpose(t, [3, 2, 0, 1]) : t;
+      out[n] = v.dataSync().slice();
+      if (v !== t) v.dispose();
+    }
+    return out;
+  }
+  const vars = Object.values(P).filter(t => t instanceof tf.Variable);
+  return { forward, exportWeights, vars, params: P, context: graph.context };
+}
+
+// masked BCE on logits: mean over mask>0 of max(z,0) - z*t + log(1+exp(-|z|))
+export function maskedBce(tf, z, t, m) {
+  return tf.tidy(() => {
+    const l = tf.add(tf.sub(tf.relu(z), tf.mul(z, t)), tf.log1p(tf.exp(tf.neg(tf.abs(z)))));
+    return tf.div(tf.sum(tf.mul(l, m)), tf.maximum(tf.sum(m), 1));
+  });
+}
+
+export function trainStep(tf, net, opt, x, target, mask) {
+  return opt.minimize(() => maskedBce(tf, net.forward(x), target, mask), true, net.vars);
+}

setup_54_2d/finetune/finetune_spike.html

@@ -0,0 +1,57 @@
+<!doctype html>
+<meta charset="utf-8">
+<title>cellmap-flow finetune spike</title>
+<!-- Times one full-network training step (masked BCE on logits, Adam, batch 1, fp32) of a 2D student in TF.js WebGPU,
+     built from the model folder's finetune/ files, and checks the TF.js forward against onnxruntime-web.
+     Open over https (WebGPU needs a secure context), keep the tab in the foreground.
+     ?model=setup_53_2d&sizes=300,556  (repo cellmap/onnx-web-models) -->
+<pre id="log">loading…</pre>
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.22.0/dist/tf.min.js"></script>
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgpu@4.22.0/dist/tf-backend-webgpu.min.js"></script>
+<script type="module">
+const q = new URLSearchParams(location.search);
+const model = q.get("model") || "setup_53_2d", sizes = (q.get("sizes") || "300,556").split(",").map(Number);
+const repo = q.get("repo") || "https://huggingface.co/cellmap/onnx-web-models/resolve/main/";
+const base = repo + model + "/";
+const log = s => { document.getElementById("log").textContent += "\n" + s; console.log(s); };
+const rt = await import(base + "finetune/cellmap_tfjs_train.js");
+await tf.setBackend("webgpu"); await tf.ready();
+const man = await (await fetch(base + "manifest.json")).json();
+const graph = await (await fetch(base + "finetune/train_graph.json")).json();
+const offsAll = await (await fetch(base + "finetune/initializers.json")).json();
+const ort = await import("https://cdn.jsdelivr.net/npm/onnxruntime-web@1.27.0-dev.20260506-673c3320fc/dist/ort.webgpu.bundle.min.mjs");
+const results = [];
+for (const N of sizes) {
+  const file = man.variants.find(v => v.variant === "fp32" && v.input_size[1] === N)?.file;
+  if (!file) { log(`no fp32 file for ${N}`); continue; }
+  const bytes = new Uint8Array(await (await fetch(base + file)).arrayBuffer());
+  const net = rt.buildNet(tf, graph, rt.readOnnxWeights(bytes, offsAll[file], Object.keys(graph.params)));
+  const M = N - graph.context;
+  // forward check vs onnxruntime-web on a smooth synthetic slice
+  const xa = new Float32Array(N * N).map((_, i) => Math.sin(Math.floor(i / N) / 9) * Math.cos((i % N) / 13) * 0.8);
+  const s = await ort.InferenceSession.create(bytes, { executionProviders: ["webgpu"] });
+  const ro = (await s.run({ input: new ort.Tensor("float32", xa, [1, 1, 1, N, N]) })).output.data;
+  const zd = await tf.tidy(() => tf.transpose(net.forward(tf.tensor(xa, [1, N, N, 1])), [0, 3, 1, 2])).data();
+  let maxAbs = 0, flips = 0;
+  zd.forEach((v, i) => { maxAbs = Math.max(maxAbs, Math.abs(v - ro[i])); flips += (v > 0) !== (ro[i] > 0); });
+  // training steps
+  const C = man.channels.length;
+  const x = tf.randomUniform([1, N, N, 1], -1, 1);
+  const t = tf.cast(tf.greater(tf.randomUniform([1, M, M, C]), 0.5), "float32");
+  const m = tf.cast(tf.greater(tf.randomUniform([1, M, M, C]), 0.7), "float32");
+  const opt = tf.train.adam(1e-4);
+  const step = () => rt.trainStep(tf, net, opt, x, t, m);
+  for (let i = 0; i < 2; i++) { const l = step(); await l.data(); l.dispose(); }
+  const base0 = tf.memory().numBytes;
+  const prof = await tf.profile(async () => { const l = step(); await l.data(); l.dispose(); });
+  const times = [];
+  for (let i = 0; i < 8; i++) { const t0 = performance.now(); const l = step(); await l.data(); times.push(performance.now() - t0); l.dispose(); }
+  times.sort((a, b) => a - b);
+  const r = { model, N, step_ms_median: Math.round(times[4]), step_ms_min: Math.round(times[0]),
+              train_peak_mb: Math.round((prof.peakBytes - base0) / 1e6), vs_ort_max_abs: maxAbs, vs_ort_flips: flips };
+  results.push(r); log(JSON.stringify(r));
+  [x, t, m].forEach(a => a.dispose()); opt.dispose(); net.vars.forEach(v => v.dispose());
+}
+window.SPIKE = results;
+log("done");
+</script>

setup_54_2d/finetune/initializers.json

@@ -0,0 +1 @@
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setup_54_2d/manifest.json

@@ -166,5 +166,74 @@
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setup_55/manifest.json

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setup_56/manifest.json

@@ -176,5 +176,40 @@
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+        "setup_56_nuc_128nm_in132_fp16.onnx",
+        "setup_56_nuc_128nm_in132_fp32.onnx",
+        "setup_56_nuc_128nm_in148_fp16.onnx",
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setup_61/finetune/weight_map.json

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+}

setup_61/manifest.json

@@ -176,5 +176,40 @@
       "ort_cpu_s": 0.98
     }
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+        "setup_61_nuc_256nm_in132_fp16.onnx",
+        "setup_61_nuc_256nm_in132_fp32.onnx",
+        "setup_61_nuc_256nm_in148_fp16.onnx",
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 }

setup_67/finetune/weight_map.json

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setup_67/manifest.json

@@ -176,5 +176,40 @@
       "ort_cpu_s": 0.88
     }
   ],
-  "source_repo": "cellmap/er-resunet-setup-67"
+  "source_repo": "cellmap/er-resunet-setup-67",
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+  "exporter": "torch.onnx.export (TorchScript, dynamo=False)",
+  "opset": 18,
+  "finetune": {
+    "head": {
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+      "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": {
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+        "transform": "[:, :, 0]"
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+      "weight_map": "finetune/weight_map.json"
+    }
+  }
 }