_archive/inspect_graph_deep.py

"""Deep-dive into model_11 and model_22 graph structure to understand OneOCRFeatureExtract."""
import onnx
import numpy as np
from pathlib import Path

models_dir = Path("oneocr_extracted/onnx_models")

for idx in [11, 22]:
    matches = list(models_dir.glob(f"model_{idx:02d}_*"))
    model = onnx.load(str(matches[0]))
    
    print(f"\n{'='*70}")
    print(f"FULL GRAPH: model_{idx:02d}")
    print(f"{'='*70}")
    
    # All initializers (weights)
    print(f"\n  Initializers ({len(model.graph.initializer)}):")
    for init in model.graph.initializer:
        data = onnx.numpy_helper.to_array(init)
        print(f"    {init.name}: shape={data.shape}, dtype={data.dtype}, "
              f"range=[{data.min():.4f}, {data.max():.4f}]")
    
    # All nodes
    print(f"\n  Nodes ({len(model.graph.node)}):")
    for i, node in enumerate(model.graph.node):
        domain_str = f" (domain={node.domain!r})" if node.domain else ""
        print(f"    [{i}] {node.op_type}{domain_str}")
        print(f"        in:  {list(node.input)}")
        print(f"        out: {list(node.output)}")
        for attr in node.attribute:
            if attr.type == 2:  # INT
                print(f"        {attr.name} = {attr.i}")
            elif attr.type == 1:  # FLOAT
                print(f"        {attr.name} = {attr.f}")
            elif attr.type == 3:  # STRING 
                val = attr.s
                if len(val) > 100:
                    print(f"        {attr.name} = bytes({len(val)})")
                else:
                    print(f"        {attr.name} = {val!r}")
            elif attr.type == 4:  # TENSOR
                t = attr.t
                print(f"        {attr.name} = tensor(dtype={t.data_type}, dims={list(t.dims)}, "
                      f"raw_bytes={len(t.raw_data)})")
            elif attr.type == 7:  # INTS
                print(f"        {attr.name} = {list(attr.ints)}")
            elif attr.type == 6:  # FLOATS
                print(f"        {attr.name} = {list(attr.floats)[:10]}...")

    # Show feature/config initializer details
    for init in model.graph.initializer:
        if "config" in init.name.lower() or "feature" in init.name.lower():
            raw = init.raw_data
            print(f"\n  feature/config blob: {len(raw)} bytes")
            print(f"  First 64 bytes (hex): {raw[:64].hex()}")
            print(f"  Last 32 bytes (hex): {raw[-32:].hex()}")
            # Try to interpret structure
            # Check if starts with dimension info
            print(f"  As uint32 first 8 values: {[int.from_bytes(raw[i:i+4], 'little') for i in range(0, 32, 4)]}")
            print(f"  As float32 first 8 values: {list(np.frombuffer(raw[:32], dtype=np.float32))}")