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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.
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
import numpy as np
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from tinygrad.tensor import Tensor, _to_np_dtype
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def mask_like(like, mask_inx, mask_value = 1.0):
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mask = np.zeros(like.shape, dtype=_to_np_dtype(like.dtype)).reshape(-1)
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mask[mask_inx] = mask_value
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return mask.reshape(like.shape)
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def jacobian(func, input):
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output = func(input)
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ji = input.numpy().reshape(-1).shape[-1]
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jo = output.numpy().reshape(-1).shape[-1]
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J = np.zeros((jo,ji), dtype=np.float32)
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for o in range(jo):
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input.grad = None
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output = func(input)
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# tinygrad doesn't support slicing, tiny-hack to select
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# the needed scalar an backpropagate only through it
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o_scalar = Tensor(mask_like(output, o, 1.)).mul(output).sum()
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o_scalar = Tensor(mask_like(output, o, 1.)).mul(output).sum()
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o_scalar.backward()
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for i, grad in enumerate(input.grad.numpy().reshape(-1)):
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J[o,i] = grad
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return J
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def numerical_jacobian(func, input, eps = 1e-3):
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output = func(input)
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ji = input.numpy().reshape(-1).shape[-1]
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jo = output.numpy().reshape(-1).shape[-1]
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NJ = np.zeros((jo, ji), dtype=np.float32)
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for i in range(ji):
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eps_perturb = mask_like(input, i, mask_value = eps)
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output_perturb_add = func(Tensor(input.numpy() + eps_perturb)).numpy().reshape(-1)
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output_perturb_sub = func(Tensor(input.numpy() - eps_perturb)).numpy().reshape(-1)
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grad_approx = ((output_perturb_add) - (output_perturb_sub)) / (2*eps)
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NJ[:,i] = grad_approx
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return NJ
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def gradcheck(func, input, eps = 1e-3, atol = 1e-3, rtol = 1e-3):
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NJ = numerical_jacobian(func, input, eps)
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J = jacobian(func, input)
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return np.allclose(J, NJ, atol = atol, rtol = rtol)
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