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Browse files- __pycache__/predict.cpython-311.pyc +0 -0
- predict.py +2 -2
__pycache__/predict.cpython-311.pyc
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Binary files a/__pycache__/predict.cpython-311.pyc and b/__pycache__/predict.cpython-311.pyc differ
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predict.py
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@@ -213,8 +213,8 @@ def predict_next_frame(ens, context_frames: np.ndarray) -> np.ndarray:
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for step in range(PRED_FRAMES):
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ctx_in = ctx if noise_std == 0 else torch.clamp(ctx + torch.randn_like(ctx) * noise_std, 0, 1)
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ctx_flip_in = ctx_flip if noise_std == 0 else torch.clamp(ctx_flip + torch.randn_like(ctx_flip) * noise_std, 0, 1)
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ar_orig = _predict_ar_frame(ens.sonic_ar, ctx_in, last_t, residual_scale=
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ar_flip = _predict_ar_frame(ens.sonic_ar, ctx_flip_in, last_f, residual_scale=
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ar_flip_back = torch.flip(ar_flip, dims=[3])
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ar_frame = (ar_orig + ar_flip_back) / 2.0
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ar_preds_run.append(ar_frame)
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for step in range(PRED_FRAMES):
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ctx_in = ctx if noise_std == 0 else torch.clamp(ctx + torch.randn_like(ctx) * noise_std, 0, 1)
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ctx_flip_in = ctx_flip if noise_std == 0 else torch.clamp(ctx_flip + torch.randn_like(ctx_flip) * noise_std, 0, 1)
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ar_orig = _predict_ar_frame(ens.sonic_ar, ctx_in, last_t, residual_scale=1.03)
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ar_flip = _predict_ar_frame(ens.sonic_ar, ctx_flip_in, last_f, residual_scale=1.03)
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ar_flip_back = torch.flip(ar_flip, dims=[3])
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ar_frame = (ar_orig + ar_flip_back) / 2.0
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ar_preds_run.append(ar_frame)
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