def test_softmax(t):
q = minitorch.softmax(t, 3)
x = q.sum(dim=3)
assert_close(x[0, 0, 0, 0], 1.0)
q = minitorch.softmax(t, 1)
x = q.sum(dim=1)
assert_close(x[0, 0, 0, 0], 1.0)
minitorch.grad_check(lambda a: minitorch.softmax(a, dim=2), t)
def test_conv2():
t = minitorch.tensor_fromlist([[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3],
[0, 1, 2, 3]]).view(1, 1, 4, 4)
t.requires_grad_(True)
t2 = minitorch.tensor_fromlist([[1, 1], [1, 1]]).view(1, 1, 2, 2)
t2.requires_grad_(True)
out = minitorch.Conv2dFun.apply(t, t2)
out.sum().backward()
minitorch.grad_check(minitorch.Conv2dFun.apply, t, t2)
def test_avg(t):
out = minitorch.avgpool2d(t, (2, 2))
assert (out[0, 0, 0,
0] == sum([t[0, 0, i, j] for i in range(2)
for j in range(2)]) / 4.0)
out = minitorch.avgpool2d(t, (2, 1))
assert (out[0, 0, 0,
0] == sum([t[0, 0, i, j] for i in range(2)
for j in range(1)]) / 2.0)
out = minitorch.avgpool2d(t, (1, 2))
assert (out[0, 0, 0,
0] == sum([t[0, 0, i, j] for i in range(1)
for j in range(2)]) / 2.0)
minitorch.grad_check(lambda t: minitorch.avgpool2d(t, (2, 2)), t)
def test_two_grad_broadcast(fn, ts):
t1, t2 = ts
minitorch.grad_check(fn[1], t1, t2)
# broadcast check
minitorch.grad_check(fn[1], t1.sum(0), t2)
minitorch.grad_check(fn[1], t1, t2.sum(0))
def test_two_grad(fn, ts):
t1, t2 = ts
minitorch.grad_check(fn[1], t1, t2)
def test_reduce(fn, t1):
minitorch.grad_check(fn[1], t1)
def test_one_derivative(fn, t1):
minitorch.grad_check(fn[1], t1)
def test_conv_channel(input, weight):
minitorch.grad_check(minitorch.Conv2dFun.apply, input, weight)
