144 lines
4.2 KiB
Python
144 lines
4.2 KiB
Python
import unittest
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import numpy as np
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from tinygrad.tensor import Tensor, Device
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class TestConv(unittest.TestCase):
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def test_simple(self):
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x = Tensor.ones(1,12,128,256).contiguous().realize()
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w = Tensor.ones(32,12,3,3).contiguous().realize()
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ret = x.conv2d(w, stride=(2,2), padding=(1,1)).numpy()
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# it's not 108 around the padding
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assert (ret[:, :, 1:-1, 1:-1] == 108).all()
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assert ret[0,0,0,0] == 48
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assert ret[0,0,0,1] == 72
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def test_simple_rand(self):
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x = Tensor.rand(1,12,128,256)
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w = Tensor.rand(32,12,3,3)
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x.conv2d(w, stride=(2,2), padding=(1,1)).numpy()
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def test_many_simple(self):
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x = Tensor(np.arange(8*2*8).reshape(1,8,2,8).astype(np.float32))
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#w = Tensor(np.arange(8*8*1*1).reshape(8,8,1,1).astype(np.float32))
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w = Tensor.eye(8).reshape((8,8,1,1))
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ret = x.conv2d(w, stride=(1,2), padding=(0,0)).numpy()
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print(ret)
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def test_lazycache(self):
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Tensor.no_grad = True
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x = Tensor.rand(1, 32)
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y = Tensor.rand(32)
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out = x + y.reshape((1,32,1)).reshape((1,32)) + y.reshape((1,32,1)).reshape((1,32))
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out.numpy()
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Tensor.no_grad = False
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def test_simple_biased(self):
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C = 8
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x = Tensor.rand(1,C,5,5)
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w = Tensor.eye(C).reshape((C,C,1,1))
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b = Tensor(np.arange(C).astype(np.float32))
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ret = Tensor.conv2d(x,w,b).relu().conv2d(w,b)
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print(ret.numpy())
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def test_two_binops_no_rerun(self):
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Tensor.no_grad = True
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x = Tensor.randn(1,12,128,256)
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w = Tensor.randn(32,12,3,3)
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out = x.conv2d(w, stride=(2,2), padding=(1,1))
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r1, r2 = out.relu(), (out-1)
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np.testing.assert_allclose(r1.numpy(), np.maximum(out.numpy(), 0))
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np.testing.assert_allclose(r2.numpy(), out.numpy() - 1)
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Tensor.no_grad = False
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def test_two_overlapping_binops_no_rerun(self):
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Tensor.no_grad = True
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x = Tensor.randn(1,12,128,256)
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w = Tensor.randn(32,12,3,3)
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out = x.conv2d(w, stride=(2,2), padding=(1,1))
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r1, r2 = out.relu(), out.elu()
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np.testing.assert_allclose(r1.numpy(), np.maximum(out.numpy(), 0))
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np.testing.assert_allclose(r2.numpy(), np.where(out.numpy() > 0, out.numpy(), (np.exp(out.numpy()) - 1)), atol=1e-5)
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Tensor.no_grad = False
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@unittest.skipIf(Device.DEFAULT != "TORCH", "Takes too long to compile for Compiled backends")
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def test_two_overlapping_binops_no_rerun_wino(self):
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Tensor.no_grad = True
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old_wino = Tensor.wino
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Tensor.wino = True
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x = Tensor.randn(1,4,16,16)
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w = Tensor.randn(6,4,3,3)
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out = x.conv2d(w, padding=(1,1))
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r1, r2 = out.relu(), out.elu()
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np.testing.assert_allclose(r1.numpy(), np.maximum(out.numpy(), 0))
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np.testing.assert_allclose(r2.numpy(), np.where(out.numpy() > 0, out.numpy(), (np.exp(out.numpy()) - 1)), atol=1e-5)
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Tensor.wino = old_wino
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Tensor.no_grad = False
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def test_first_three(self):
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Tensor.no_grad = True
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x = Tensor.rand(1,12,128,256)
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w = Tensor.rand(32,12,3,3)
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x = x.conv2d(w, stride=(2,2), padding=(1,1)).elu()
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w = Tensor.rand(32,1,3,3)
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x = x.conv2d(w, padding=(1,1), groups=32).elu()
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w = Tensor.rand(16,32,1,1)
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x = x.conv2d(w).elu()
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x = x.numpy()
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print(x.shape)
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Tensor.no_grad = False
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def test_elu(self):
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Tensor.no_grad = True
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x = Tensor.rand(1,12,128,256)
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w = Tensor.rand(32,12,3,3)
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x = x.conv2d(w, stride=(2,2), padding=(1,1))
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x = x.elu()
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w = Tensor.rand(32,1,3,3)
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x = x.conv2d(w, padding=(1,1), groups=32)
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x.numpy()
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Tensor.no_grad = False
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def test_reduce_relu(self):
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Tensor.no_grad = True
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x = Tensor.rand(1,12,128,256)
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x = x.sum(keepdim=True).relu()
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x.numpy()
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Tensor.no_grad = False
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def test_bias(self):
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Tensor.no_grad = True
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from tinygrad.nn import Conv2d
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x = Tensor.rand(1,12,128,256)
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c = Conv2d(12, 32, 3)
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x = c(x).relu()
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w = Tensor.uniform(32, 1, 3, 3)
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x = x.conv2d(w, groups=32)
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x.numpy()
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Tensor.no_grad = False
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def test_multiadd(self):
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w = Tensor.rand(32)
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x = Tensor.rand(32).relu()
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(w+x).numpy()
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def test_reorder(self):
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x = Tensor.rand(1,12,128,256)
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w = Tensor.rand(12,12,3,3)
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x = x.conv2d(w, padding=(1,1))
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print(x.shape)
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x = x.reshape((1, 12, 256, 128))
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x += 1
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x += 1
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x = x.reshape((1, 12, 128, 256))
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x.numpy()
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if __name__ == '__main__':
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unittest.main() |