File size: 3,693 Bytes
f6eebf5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | """Inference for multi-frame predictor with caching + TTA."""
import json
import numpy as np
import torch
import sys
sys.path.insert(0, "/home/coder/code")
from multi_frame_model import MultiFrameFlowWarpUNet
def load_model(model_dir: str):
with open(f"{model_dir}/config.json") as f:
config = json.load(f)
model = MultiFrameFlowWarpUNet(
in_channels=config["in_channels"],
channels=config["channels"],
num_future=config.get("num_future", 8),
)
sd = torch.load(f"{model_dir}/model.pt", map_location="cpu", weights_only=True)
sd = {k: v.float() for k, v in sd.items()}
model.load_state_dict(sd)
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
return {
"model": model,
"device": device,
"context_len": config["context_len"],
"num_future": config.get("num_future", 8),
"cache": None, # Will store (context_hash, predictions_list)
"call_count": 0,
}
def _context_hash(context_frames):
"""Hash first frame to identify a rollout."""
return context_frames[0].tobytes()[:1024]
def _prepare_input(context_frames, context_len):
N = len(context_frames)
if N >= context_len:
frames = context_frames[-context_len:]
else:
pad = np.repeat(context_frames[:1], context_len - N, axis=0)
frames = np.concatenate([pad, context_frames], axis=0)
frames_f = frames.astype(np.float32) / 255.0
frames_f = np.transpose(frames_f, (0, 3, 1, 2))
context = frames_f.reshape(1, -1, 64, 64)
last_frame = frames_f[-1:]
return context, last_frame
def _run_model_with_tta(model, device, context_frames, context_len):
"""Run model with TTA (horizontal flip) and return all 8 predictions."""
ctx, last = _prepare_input(context_frames, context_len)
with torch.no_grad():
ctx_t = torch.from_numpy(ctx).to(device)
last_t = torch.from_numpy(last).to(device)
preds1, _ = model(ctx_t, last_t)
flipped_frames = context_frames[:, :, ::-1, :].copy()
ctx_f, last_f = _prepare_input(flipped_frames, context_len)
with torch.no_grad():
ctx_ft = torch.from_numpy(ctx_f).to(device)
last_ft = torch.from_numpy(last_f).to(device)
preds2, _ = model(ctx_ft, last_ft)
result = []
for p1, p2 in zip(preds1, preds2):
p2_flipped = p2.flip(-1)
avg = (p1 + p2_flipped) / 2.0
pred_np = avg[0].cpu().numpy()
pred_np = np.transpose(pred_np, (1, 2, 0))
result.append((pred_np * 255.0).clip(0, 255).astype(np.uint8))
return result
def predict_next_frame(model_dict, context_frames: np.ndarray) -> np.ndarray:
model = model_dict["model"]
device = model_dict["device"]
context_len = model_dict["context_len"]
# Check if we have a cached prediction for this rollout
ctx_hash = _context_hash(context_frames)
if model_dict["cache"] is not None:
cached_hash, cached_preds, cached_step = model_dict["cache"]
if cached_hash == ctx_hash and cached_step < len(cached_preds):
pred = cached_preds[cached_step]
model_dict["cache"] = (cached_hash, cached_preds, cached_step + 1)
return pred
# No cache hit - run full model prediction
# Use only the original context (first context_len frames)
base_context = context_frames[:context_len] if len(context_frames) >= context_len else context_frames
all_preds = _run_model_with_tta(model, device, base_context, context_len)
# Return first prediction and cache the rest
model_dict["cache"] = (ctx_hash, all_preds, 1)
return all_preds[0]
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