openpilot warp (#13283)

* openpilot image warp test

* 0.4 ms on metal, 1 ms on CPU

* new inputs each time

* reshape

test/external/external_openpilot_image_warp.py

@@ -0,0 +1,55 @@
+import time
+from tinygrad.tensor import Tensor, Device
+
+MODEL_WIDTH = 512
+MODEL_HEIGHT = 256
+MODEL_FRAME_SIZE = MODEL_WIDTH * MODEL_HEIGHT * 3 // 2
+IMG_INPUT_SHAPE = (1, 12, 128, 256)
+
+def tensor_arange(end): return Tensor([float(i) for i in range(end)])
+def tensor_round(tensor:Tensor): return (tensor + 0.5).floor()
+
+h_src, w_src = 1208, 1928
+h_dst, w_dst = MODEL_HEIGHT, MODEL_WIDTH
+x = tensor_arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst)
+y = tensor_arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst)
+ones = Tensor.ones_like(x)
+dst_coords = x.reshape((1,-1)).cat(y.reshape((1,-1))).cat(ones.reshape((1,-1)))
+
+def warp_perspective_tinygrad(src:Tensor, M_inv:Tensor) -> Tensor:
+  src_coords = M_inv @ dst_coords
+  src_coords = src_coords / src_coords[2:3, :]
+
+  x_src = src_coords[0].reshape(h_dst, w_dst)
+  y_src = src_coords[1].reshape(h_dst, w_dst)
+
+  x_nearest = tensor_round(x_src).clip(0, w_src - 1).cast('int')
+  y_nearest = tensor_round(y_src).clip(0, h_src - 1).cast('int')
+
+  # TODO: make 2d indexing fast
+  idx = y_nearest*src.shape[1] + x_nearest
+  dst = src.flatten()[idx]
+  return dst.reshape(h_dst, w_dst)
+
+if __name__ == "__main__":
+  from tinygrad.engine.jit import TinyJit
+  update_img_jit = TinyJit(warp_perspective_tinygrad, prune=True)
+
+  step_times = []
+  for _ in range(10):
+    # regenerate inputs
+    inputs = [Tensor.randn(1928,1208), Tensor.randn(3,3)]
+    Tensor.realize(*inputs)
+    Device.default.synchronize()
+
+    # do the warp
+    st = time.perf_counter()
+    out = update_img_jit(*inputs)
+    mt = time.perf_counter()
+    val = out.contiguous().realize()
+    Device.default.synchronize()
+    et = time.perf_counter()
+
+    # measure the time
+    step_times.append((et-st)*1e3)
+    print(f"enqueue {(mt-st)*1e3:6.2f} ms -- total run {step_times[-1]:6.2f} ms")