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https://github.com/firestar5683/StarPilot.git
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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
import time
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from tinygrad.tensor import Tensor, Device
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MODEL_WIDTH = 512
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MODEL_HEIGHT = 256
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MODEL_FRAME_SIZE = MODEL_WIDTH * MODEL_HEIGHT * 3 // 2
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IMG_INPUT_SHAPE = (1, 12, 128, 256)
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def tensor_arange(end): return Tensor([float(i) for i in range(end)])
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def tensor_round(tensor:Tensor): return (tensor + 0.5).floor()
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h_src, w_src = 1208, 1928
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h_dst, w_dst = MODEL_HEIGHT, MODEL_WIDTH
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x = tensor_arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst)
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y = tensor_arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst)
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ones = Tensor.ones_like(x)
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dst_coords = x.reshape((1,-1)).cat(y.reshape((1,-1))).cat(ones.reshape((1,-1)))
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def warp_perspective_tinygrad(src:Tensor, M_inv:Tensor) -> Tensor:
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src_coords = M_inv @ dst_coords
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src_coords = src_coords / src_coords[2:3, :]
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x_src = src_coords[0].reshape(h_dst, w_dst)
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y_src = src_coords[1].reshape(h_dst, w_dst)
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x_nearest = tensor_round(x_src).clip(0, w_src - 1).cast('int')
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y_nearest = tensor_round(y_src).clip(0, h_src - 1).cast('int')
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# TODO: make 2d indexing fast
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idx = y_nearest*src.shape[1] + x_nearest
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dst = src.flatten()[idx]
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return dst.reshape(h_dst, w_dst)
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if __name__ == "__main__":
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from tinygrad.engine.jit import TinyJit
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update_img_jit = TinyJit(warp_perspective_tinygrad, prune=True)
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step_times = []
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for _ in range(10):
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# regenerate inputs
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inputs = [Tensor.randn(1928,1208), Tensor.randn(3,3)]
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Tensor.realize(*inputs)
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Device.default.synchronize()
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# do the warp
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st = time.perf_counter()
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out = update_img_jit(*inputs)
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mt = time.perf_counter()
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val = out.contiguous().realize()
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Device.default.synchronize()
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et = time.perf_counter()
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# measure the time
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step_times.append((et-st)*1e3)
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print(f"enqueue {(mt-st)*1e3:6.2f} ms -- total run {step_times[-1]:6.2f} ms")
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