| """fp32 depth graphs vs upstream PyTorch depth (light model, exact fp32 weights) on 20 golden frames.""" |
| import numpy as np, onnxruntime as ort, torch |
| from tools.maya_export.common import build_light_model, work_dir |
| from tts.model.depth_v2 import GROUP_OF_LEVEL |
| B = work_dir() / 'depth_build' / 'fp32' |
| z = np.load(work_dir() / 'golden' / 'ref' / 'conv_01_s0.npz') |
| codes = z['codes'][:, :20].T.astype(np.int64); h = z['h_frames'][:20].astype(np.float32) |
| model = build_light_model(); d = model.depth.eval() |
| mem_t = d.null_mem.detach().float().expand(20, -1, -1).contiguous() |
| worst_rel = 0.0; worst_abs = 0.0 |
| with torch.inference_mode(): |
| for L in range(1, 16): |
| x = d._prefix(torch.from_numpy(h), torch.from_numpy(codes), L) |
| ref = d.heads[L - 1](d._run(x, GROUP_OF_LEVEL[L], mem_t, None)).float().numpy() |
| s = ort.InferenceSession(str(B / f'level_{L:02d}.onnx'), providers=['CPUExecutionProvider']) |
| got = s.run(None, {'h': h, 'codes': codes, 'mem': mem_t.numpy()})[0] |
| del s |
| err = np.abs(got - ref) |
| worst_abs = max(worst_abs, float(err.max())); worst_rel = max(worst_rel, float(err.max() / np.abs(ref).max())) |
| print(f'fp32 vs upstream PyTorch depth: WORST abs {worst_abs:.3e} rel {worst_rel:.3e}') |
|
|
