mirror of https://github.com/Jittor/Jittor
368 lines
15 KiB
Python
368 lines
15 KiB
Python
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# ***************************************************************
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# Copyright (c) 2022 Jittor. All Rights Reserved.
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# Maintainers:
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# Wenyang Zhou <576825820@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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#
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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import unittest
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import jittor as jt
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import numpy as np
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import jittor.nn as jnn
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skip_this_test = False
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try:
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jt.dirty_fix_pytorch_runtime_error()
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import torch
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import torch.nn as tnn
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import torchvision
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except:
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torch = None
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tnn = None
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torchvision = None
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skip_this_test = True
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def check_equal(res1, res2, eps=1e-5):
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assert np.allclose(res1.detach().numpy(), res2.numpy(), eps)
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@unittest.skipIf(skip_this_test, "No Torch found")
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class TestPad(unittest.TestCase):
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def test_index_add_(self):
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x = np.ones((5,3))
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a1 = torch.Tensor(x)
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a1.index_add_(0, torch.tensor([0,4,2]), torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float))
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a2 = jt.array(x)
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a2.index_add_(0, jt.array([0,4,2]), jt.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
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check_equal(a1, a2)
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x = np.ones((3,5))
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a1 = torch.Tensor(x)
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a1.index_add_(1, torch.tensor([0,4,2]), torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float))
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a2 = jt.array(x)
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a2.index_add_(1, jt.array([0,4,2]), jt.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
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check_equal(a1, a2)
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print('pass index_add_ test ...')
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def test_repeat(self):
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arr = np.random.randn(16,3,224,224)
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check_equal(torch.Tensor(arr).repeat(1,2,3,4), jt.array(arr).repeat(1,2,3,4))
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check_equal(torch.Tensor(arr).repeat(4,2,3,4), jt.array(arr).repeat(4,2,3,4))
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print('pass repeat test ...')
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def test_chunk(self):
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arr = np.random.randn(16,3,224,224)
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check_equal(torch.Tensor(arr).chunk(2,0)[0], jt.array(arr).chunk(2,0)[0])
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check_equal(torch.Tensor(arr).chunk(2,0)[1], jt.array(arr).chunk(2,0)[1])
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print('pass chunk test ...')
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def test_stack(self):
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arr1 = np.random.randn(16,3,224,224)
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arr2 = np.random.randn(16,3,224,224)
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check_equal(torch.stack([torch.Tensor(arr1), torch.Tensor(arr2)], 0), jt.stack([jt.array(arr1), jt.array(arr2)], 0))
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print('pass stack test ...')
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def test_flip(self):
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arr = np.random.randn(16,3,224,224)
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check_equal(torch.Tensor(arr).flip(0), jt.array(arr).flip(0))
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check_equal(torch.Tensor(arr).flip(1), jt.array(arr).flip(1))
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check_equal(torch.Tensor(arr).flip(2), jt.array(arr).flip(2))
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check_equal(torch.Tensor(arr).flip(3), jt.array(arr).flip(3))
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check_equal(torch.Tensor(arr).flip([2,3]), jt.array(arr).flip([2,3]))
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print('pass flip test ...')
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def test_cross(self):
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def check_equal(a, b, tol):
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np.testing.assert_allclose(a.detach().numpy(), b.numpy(), atol=1e-5)
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arr1 = np.random.randn(16,3,224,224,3)
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arr2 = np.random.randn(16,3,224,224,3)
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check_equal(torch.Tensor(arr1).cross(torch.Tensor(arr2), dim=1), jt.array(arr1).cross(jt.array(arr2), dim=1), 1e-1)
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check_equal(torch.Tensor(arr1).cross(torch.Tensor(arr2), dim=-4), jt.array(arr1).cross(jt.array(arr2), dim=-4), 1e-1)
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check_equal(torch.Tensor(arr1).cross(torch.Tensor(arr2), dim=-1), jt.array(arr1).cross(jt.array(arr2), dim=-1), 1e-1)
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check_equal(torch.Tensor(arr1).cross(torch.Tensor(arr2), dim=4), jt.array(arr1).cross(jt.array(arr2), dim=4), 1e-1)
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print('pass cross test ...')
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def test_normalize(self):
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arr = np.random.randn(16,3,224,224,3)
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check_equal(tnn.functional.normalize(torch.Tensor(arr)), jt.normalize(jt.array(arr)))
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=0), jt.normalize(jt.array(arr), dim=0), 1e-1)
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=1), jt.normalize(jt.array(arr), dim=1), 1e-1)
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=-1), jt.normalize(jt.array(arr), dim=-1), 1e-1)
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=2), jt.normalize(jt.array(arr), dim=2), 1e-1)
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=3), jt.normalize(jt.array(arr), dim=3), 1e-1)
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print('pass normalize test ...')
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def test_make_grid(self):
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arr = np.random.randn(16,3,10,10)
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr)), jt.make_grid(jt.array(arr)))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=2), jt.make_grid(jt.array(arr), nrow=2))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=3), jt.make_grid(jt.array(arr), nrow=3))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=3, padding=4), jt.make_grid(jt.array(arr), nrow=3, padding=4))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=3, padding=4, pad_value=-1), jt.make_grid(jt.array(arr), nrow=3, padding=4, pad_value=-1))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=3, normalize=True, padding=4, pad_value=-1), jt.make_grid(jt.array(arr), nrow=3, normalize=True, padding=4, pad_value=-1))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=3, normalize=True, padding=4, pad_value=-1, range=(-100,100)), jt.make_grid(jt.array(arr), nrow=3, normalize=True, padding=4, pad_value=-1, range=(-100,100)))
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print('pass make_grid test ...')
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def test_make_grid2(self):
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def check(shape):
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arr = np.random.randn(*shape)
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr)), jt.make_grid(jt.array(arr)))
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check((3,100,200))
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check((1,100,200))
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check((100,200))
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check((1,3,100,200))
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check((4,3,100,200))
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check((10,3,100,200))
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def test_make_grid3(self):
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arr=np.random.randn(3,10,10)
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr)), jt.make_grid(jt.array(arr)))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), normalize=True), jt.make_grid(jt.array(arr), normalize=True))
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def test_save_image(self):
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arr = jt.array(np.random.randn(16,3,10,10))
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jt.save_image(arr, jt.flags.cache_path+"/tmp/a.jpg")
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def test_unbind(self):
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arr = np.random.randn(2,3,4)
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for dim in range(len(arr.shape)):
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t_res = torch.unbind(torch.Tensor(arr), dim=dim)
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j_res = jt.unbind(jt.array(arr), dim=dim)
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for idx in range(len(t_res)):
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assert np.allclose(t_res[idx].numpy(), j_res[idx].numpy())
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print('pass unbind test ...')
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def test_expand(self):
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a = jt.zeros((3,1))
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b = a.expand(3, 4)
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assert b.shape == (3,4)
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b = a.expand(-1, 4)
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assert b.shape == (3,4)
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b = a.expand((3, 4))
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assert b.shape == (3,4)
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b = a.expand((-1, 4))
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assert b.shape == (3,4)
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def test_bilinear(self):
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from jittor import nn
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m = nn.Bilinear(20, 30, 40)
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input1 = jt.randn(128, 20)
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input2 = jt.randn(128, 30)
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output = m(input1, input2)
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assert output.shape == [128,40]
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m2 = torch.nn.Bilinear(20, 30, 40)
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m2.weight = torch.nn.Parameter(torch.Tensor(m.weight.data))
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m2.bias = torch.nn.Parameter(torch.Tensor(m.bias.data))
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in1 = torch.Tensor(input1.data)
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in2 = torch.Tensor(input2.data)
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out = m2(in1, in2)
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np.testing.assert_allclose(
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out.detach().numpy(), output.data,
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atol=1e-4)
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def test_ctc_loss(self):
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def check(T,C,N,S,S_min):
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jt.set_global_seed(0)
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# Initialize random batch of input vectors, for *size = (T,N,C)
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input = jt.randn(T, N, C).log_softmax(2)
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# input = -jt.ones((T, N, C))
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# input[0,0,1] += 0.01
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# Initialize random batch of targets (0 = blank, 1:C = classes)
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target = jt.randint(low=1, high=C, shape=(N, S), dtype=jt.int)
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_input_jt = input
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input_lengths = jt.full((N,), T, dtype=jt.int)
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target_lengths = jt.randint(low=S_min, high=S+1, shape=(N,), dtype=jt.int)
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# ctc_loss = nn.CTCLoss()
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loss = jt.ctc_loss(input, target, input_lengths, target_lengths, reduction='none')
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_loss_jt = loss
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loss_jt = loss.numpy()
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input = torch.Tensor(input.numpy()).detach().requires_grad_()
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input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long)
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target_lengths = torch.LongTensor(target_lengths.numpy())
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input_lengths = torch.LongTensor(input_lengths.numpy())
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target = torch.LongTensor(target.numpy())
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loss = tnn.CTCLoss(reduction='none')(input, target, input_lengths, target_lengths)
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np.testing.assert_allclose(loss.detach().numpy(), loss_jt, rtol=1e-5, atol=1e-5)
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dinput_jt = jt.grad(_loss_jt, _input_jt)
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dinput_jt.sync()
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loss.sum().backward()
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# print(input.grad)
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# print(dinput_jt)
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# print(loss)
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def check_gpu_with_cpu(T,C,N,S,S_min):
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jt.set_global_seed(1)
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# Initialize random batch of input vectors, for *size = (T,N,C)
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input = jt.randn(T, N, C).log_softmax(2)
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# input = -jt.ones((T, N, C))
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# input[0,0,1] += 0.01
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# Initialize random batch of targets (0 = blank, 1:C = classes)
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target = jt.randint(low=1, high=C, shape=(N, S), dtype=jt.int)
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_input_jt = input
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input_lengths = jt.full((N,), T, dtype=jt.int)
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target_lengths = jt.randint(low=S_min, high=S+1, shape=(N,), dtype=jt.int)
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# ctc_loss = nn.CTCLoss()
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loss = jt.ctc_loss(input, target, input_lengths, target_lengths, reduction='none')
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_loss_jt = loss
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loss_jt = loss.numpy()
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dinput_jt = jt.grad(_loss_jt, _input_jt)
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dinput_jt.sync()
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with jt.flag_scope(use_cuda=1):
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input = input.copy()
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target = target.copy()
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input_lengths = input_lengths.copy()
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target_lengths = target_lengths.copy()
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loss = jt.ctc_loss(input, target, input_lengths, target_lengths, reduction='none')
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grad = jt.grad(loss, input)
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np.testing.assert_allclose(_loss_jt.numpy(), loss.numpy(), atol=1e-5, rtol=1e-5)
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np.testing.assert_allclose(dinput_jt.numpy(), grad.numpy(), atol=1e-5, rtol=1e-5)
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check(2,2,1,1,1)
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check(50,20,16,30,10)
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if jt.has_cuda:
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with jt.flag_scope(use_cuda=1):
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check(2,2,1,1,1)
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check(50,20,16,30,10)
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check_gpu_with_cpu(50,20,16,30,10)
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class TestOther(unittest.TestCase):
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def test_save(self):
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pp = [1,2,jt.array([1,2,3]), {"a":[1,2,3], "b":jt.array([1,2,3])}]
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name = jt.flags.cache_path+"/xx.pkl"
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jt.save(pp, name)
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x = jt.load(name)
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assert x[:2] == [1,2]
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assert (x[2] == np.array([1,2,3])).all()
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assert x[3]['a'] == [1,2,3]
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assert (x[3]['b'] == np.array([1,2,3])).all()
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def test_arctan2(self):
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x = jt.float32([1,1,-1,-1, 1,-1,0,0,0])
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y = jt.float32([-1,1,-1,1, 0,0,1,-1,0])
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z = jt.arctan2(y, x)
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z2 = np.arctan2(y.data, x.data)
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np.testing.assert_allclose(z.data, z2, atol=1e-6)
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y = jt.random((100,)) * 2 - 1
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x = jt.random((100,)) * 2 - 1
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z = jt.arctan2(y, x)
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z2 = np.arctan2(y.data, x.data)
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np.testing.assert_allclose(z.data, z2, atol=1e-6)
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np.testing.assert_allclose(jt.array([1]).arctan().item(), 0.7853982)
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def test_softmax_precision(self):
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# jt.flags.use_cuda = 1
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a = -jt.array([1.0,2.0,1e5])
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b = a.log_softmax(0)
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assert b.isfinite().all().item()
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print("test_softmax_precision cpu ok")
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if not jt.has_cuda: return
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jt.flags.use_cuda = 1
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a = -jt.array([1.0,2.0,1e5])
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b = a.log_softmax(0)
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assert b.isfinite().all().item()
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print("test_softmax_precision gpu ok")
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def test_code_softmax(self):
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if not jt.has_cuda: return
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def softmax(x, dim = None, log=False):
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if dim is None:
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x = (x - x.max()).exp()
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ret = x / x.sum()
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else:
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x = (x-x.max(dim, keepdims=True)).exp()
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ret = x / x.sum(dim, keepdims=True)
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if log: return ret.log()
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return ret
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from jittor.other.code_softmax import softmax_v1
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with jt.flag_scope(use_cuda = 1):
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shape = (120, 2000, 2000)
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shape = (3,3)
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for log in [0,1]:
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for shape in [(3,3),
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(12, 200, 2000),
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(12, 200, 2048),
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(12, 200, 2049)]:
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print(shape)
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a = jt.rand(shape)
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c = jt.rand(shape)
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b = softmax(a, -1, log=log)
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bb = softmax_v1(a, log=log)
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err = (bb - b).abs().max()
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assert err.item() < 1e-5, (err, bb, b)
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d1 = jt.grad(b*c, a)
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d2 = jt.grad(bb*c, a)
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err = (d1 - d2).abs().max()
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if log:
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assert err.item() < 1e-2, (err.item())
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else:
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assert err.item() < 1e-5, (err.item())
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def test_nan(self):
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a = np.array([1.0,0.0,1.0,-1.0], "float32") / np.array([1.0,0.0,0.0,0.0], "float32")
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np.testing.assert_allclose(jt.isnan(jt.array(a)).data, [0,1,0,0])
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np.testing.assert_allclose(jt.isfinite(jt.array(a)).data, [1,0,0,0])
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np.testing.assert_allclose(jt.isinf(jt.array(a)).data, [0,0,1,1])
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np.testing.assert_allclose(jt.isneginf(jt.array(a)).data, [0,0,0,1])
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np.testing.assert_allclose(jt.isposinf(jt.array(a)).data, [0,0,1,0])
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def test_nan_cuda(self):
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if not jt.has_cuda: return
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with jt.flag_scope(use_cuda=1):
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self.test_nan()
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def test_dropout2d(self):
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m = jt.nn.Dropout2d(p=0.2)
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m.train()
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input = jt.randn(1, 10, 4, 3)
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output = m(input)
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output.sync()
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def test_tri(self):
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a = jt.ones(3, 3)
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b = jt.triu(a)
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assert jt.all_equal(b, [[1,1,1],[0,1,1],[0,0,1]])
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b = jt.triu(a, diagonal=1)
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assert jt.all_equal(b, [[0,1,1],[0,0,1],[0,0,0]])
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b = jt.triu(a, diagonal=-1)
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assert jt.all_equal(b, [[1,1,1],[1,1,1],[0,1,1]])
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a = jt.ones(3, 3)
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b = jt.tril(a)
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assert jt.all_equal(b, [[1,0,0],[1,1,0],[1,1,1]])
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b = jt.tril(a, diagonal=1)
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assert jt.all_equal(b, [[1,1,0],[1,1,1],[1,1,1]])
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b = jt.tril(a, diagonal=-1)
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assert jt.all_equal(b, [[0,0,0],[1,0,0],[1,1,0]])
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if __name__ == "__main__":
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unittest.main() |