File size: 3,585 Bytes
792ff16 | 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 | """Inference for 2-frame simultaneous predictor with TTA."""
import json
import numpy as np
import torch
import sys
sys.path.insert(0, "/home/coder/code")
from flow_warp_attn_model import FlowWarpAttnUNet
def load_model(model_dir: str):
with open(f"{model_dir}/config.json") as f:
config = json.load(f)
model = FlowWarpAttnUNet(
in_channels=config["in_channels"],
channels=config["channels"],
out_channels=config.get("out_channels", 6)
)
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"],
"cached_pred2": None,
}
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_tta(model, device, ctx, last):
"""Run model with TTA (horizontal flip) and return both predictions."""
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
ctx_f = torch.from_numpy(ctx[:, :, :, ::-1].copy()).to(device)
last_f = torch.from_numpy(last[:, :, :, ::-1].copy()).to(device)
preds2, _ = model(ctx_f, last_f)
# Average normal and flipped-back predictions
pred1 = (preds1[0] + preds2[0].flip(-1)) / 2.0
pred2 = (preds1[1] + preds2[1].flip(-1)) / 2.0
return pred1, pred2
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"]
# Determine if this is an even or odd call
# Even-length context (8, 10, 12, 14): run model, return pred1, cache pred2
# Odd-length context (9, 11, 13, 15): return cached pred2
n = len(context_frames)
if n % 2 == 0:
# Run model
ctx, last = _prepare_input(context_frames, context_len)
pred1, pred2 = _run_model_tta(model, device, ctx, last)
# Cache pred2
pred2_np = pred2[0].cpu().numpy()
pred2_np = np.transpose(pred2_np, (1, 2, 0))
model_dict["cached_pred2"] = (pred2_np * 255.0).clip(0, 255).astype(np.uint8)
# Return pred1
pred1_np = pred1[0].cpu().numpy()
pred1_np = np.transpose(pred1_np, (1, 2, 0))
return (pred1_np * 255.0).clip(0, 255).astype(np.uint8)
else:
# Return cached pred2
if model_dict["cached_pred2"] is not None:
return model_dict["cached_pred2"]
else:
# Fallback: run model on context minus last frame
ctx, last = _prepare_input(context_frames[:-1], context_len)
pred1, pred2 = _run_model_tta(model, device, ctx, last)
pred2_np = pred2[0].cpu().numpy()
pred2_np = np.transpose(pred2_np, (1, 2, 0))
return (pred2_np * 255.0).clip(0, 255).astype(np.uint8)
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