carrot/tinygrad_repo/test/test_tensor_variable.py
carrot efee1712aa
KerryGoldModel, AGNOS12.3, ButtonMode3, autoDetectLFA2, (#181)
* fix.. speed_limit error...

* draw tpms settings.

* fix.. traffic light stopping only..

* fix.. waze cam

* fix.. waze...

* add setting (Enable comma connect )

* auto detect LFA2

* fix.. cruisespeed1

* vff2 driving model.

* fix..

* agnos 12.3

* fix..

* ff

* ff

* test

* ff

* fix.. drawTurnInfo..

* Update drive_helpers.py

* fix..

support eng  voice

eng sounds

fix settings... english

fix.. mph..

fix.. roadlimit speed bug..

* new vff model.. 250608

* fix soundd..

* fix safe exit speed..

* fix.. sounds.

* fix.. radar timeStep..

* KerryGold model

* Update drive_helpers.py

* fix.. model.

* fix..

* fix..

* Revert "fix.."

This reverts commit b09ec459afb855c533d47fd7e8a1a6b1a09466e7.

* Revert "fix.."

This reverts commit 290bec6b83a4554ca232d531a911edccf94a2156.

* fix esim

* add more acc table. 10kph

* kg update..

* fix cruisebutton mode3

* test atc..cond.

* fix.. canfd

* fix.. angle control limit
2025-06-13 15:59:36 +09:00

97 lines
3.0 KiB
Python

import unittest
import numpy as np
from tinygrad import Tensor, Variable
from tinygrad.helpers import Context
class TestTensorVariable(unittest.TestCase):
def test_add_tvar(self):
vv = Variable("a", 0, 10).bind(1)
ret = (Tensor(vv) + 3).item()
assert ret == 4
def test_inner_tvar_node(self):
vv = Variable("w", 0, 10).bind(2)
ret = Tensor.from_uop(vv * 4).item()
assert ret == 8
def test_inner_tvar_mul(self):
vv = Variable("w", 0, 10).bind(2)
assert (Tensor(3) * vv).item() == 6
def test_inner_tvar_mul_node(self):
vv = Variable("w", 0, 10).bind(2)
assert (Tensor(3) * (vv * 4)).item() == 24
def test_symbolic_mean(self):
with Context(IGNORE_OOB=1):
vv = Variable("a", 1, 10).bind(2)
t = Tensor.ones(2, 2).contiguous().reshape(2, vv)
ret = t.mean().item()
assert ret == 1
def test_symbolic_mean_2d(self):
with Context(IGNORE_OOB=1):
vv = Variable("a", 1, 10).bind(2)
vv2 = Variable("b", 1, 10).bind(2)
t = Tensor.ones(2, 2).contiguous().reshape(vv2, vv)
ret = t.mean().item()
assert ret == 1
def test_symbolic_mean_2d_axis_1(self):
with Context(IGNORE_OOB=1):
vv = Variable("a", 1, 10).bind(2)
vv2 = Variable("b", 1, 10).bind(2)
t = Tensor.ones(2, 2).contiguous().reshape(vv2, vv)
ret = t.mean(axis=1).reshape(2, 1).numpy()
assert np.all(ret == 1)
def test_symbolic_mean_2d_add(self):
with Context(IGNORE_OOB=1):
add_term = Variable("c", 0, 10).bind(1)
vv = Variable("a", 1, 10).bind(1)
vv2 = Variable("b", 1, 10).bind(1)
t = Tensor.ones(2, 2).contiguous().reshape(vv2+add_term, vv+add_term)
ret = t.mean().item()
assert ret == 1
def test_symbolic_var(self):
with Context(IGNORE_OOB=1):
vv = Variable("a", 1, 10).bind(2)
t = Tensor.ones(2, 2).contiguous().reshape(2, vv)
ret = t.var().item()
assert ret == 0
def test_symbolic_pad(self):
vv = Variable("a", 1, 10).bind(2)
t = Tensor.ones(2, 2).contiguous()
t = t.pad([vv, vv, vv, vv]).mean()
ones = 4
zeros = 6+6+4+4+6+6
self.assertAlmostEqual(t.item(), ones/(ones+zeros))
def test_symbolic_arange(self):
vv = Variable("a", 1, 10)
ret = Tensor.arange(0, vv.bind(4))
self.assertListEqual(ret.reshape(4).tolist(), [0,1,2,3])
def test_symbolic_arange_sym_start(self):
vv = Variable("a", 1, 6)
ret = Tensor.arange(vv.bind(4), 7)
self.assertListEqual(ret.reshape(3).tolist(), [4,5,6])
# TODO: add vmin/vmax pattern for symbolic denominator
@unittest.expectedFailure
def test_symbolic_arange_sym_step(self):
vv = Variable("step", 1, 3)
ret = Tensor.arange(0, 10, vv.bind(2))
self.assertListEqual(ret.reshape(5).tolist(), [0,2,4,6,8])
def test_symbolic_arange_two_vars(self):
begin = Variable("b", 1, 5)
end = Variable("e", 6, 10)
ret = Tensor.arange(begin.bind(4), end.bind(7))
self.assertListEqual(ret.reshape(3).tolist(), [4,5,6])
if __name__ == '__main__':
unittest.main()