mirror of https://github.com/Jittor/Jittor
175 lines
8.6 KiB
Python
175 lines
8.6 KiB
Python
# ***************************************************************
|
|
# Copyright (c) 2022 Jittor. All Rights Reserved.
|
|
# Maintainers:
|
|
# Dun Liang <randonlang@gmail.com>.
|
|
#
|
|
# This file is subject to the terms and conditions defined in
|
|
# file 'LICENSE.txt', which is part of this source code package.
|
|
# ***************************************************************
|
|
import unittest
|
|
import jittor as jt
|
|
import os
|
|
import numpy as np
|
|
import jittor.nn as jnn
|
|
|
|
skip_this_test = False
|
|
|
|
try:
|
|
jt.dirty_fix_pytorch_runtime_error()
|
|
import torch
|
|
import torch.nn as tnn
|
|
except:
|
|
skip_this_test = True
|
|
|
|
@unittest.skipIf(skip_this_test, "No Torch found")
|
|
class TestLoss(unittest.TestCase):
|
|
def test_l1_loss(self):
|
|
jt_loss=jnn.L1Loss()
|
|
tc_loss=tnn.L1Loss()
|
|
output=np.random.randn(10,100).astype(np.float32)
|
|
target=np.random.randn(10,100).astype(np.float32)
|
|
jt_y=jt_loss(jt.array(output), jt.array(target))
|
|
tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
def test_mse_loss(self):
|
|
jt_loss=jnn.MSELoss()
|
|
tc_loss=tnn.MSELoss()
|
|
output=np.random.randn(10,100).astype(np.float32)
|
|
target=np.random.randn(10,100).astype(np.float32)
|
|
jt_y=jt_loss(jt.array(output), jt.array(target))
|
|
tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
def test_nll_loss(self):
|
|
tc_loss = tnn.functional.nll_loss
|
|
jt_loss = jnn.nll_loss
|
|
output=np.random.randn(10,10).astype(np.float32)
|
|
target=np.random.randint(10, size=(10))
|
|
jt_y=jt_loss(jt.array(output), jt.array(target),reduction='mean')
|
|
tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target),reduction='mean')
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
output=np.random.randn(10,10).astype(np.float32)
|
|
target=np.random.randint(10, size=(10))
|
|
weight=np.random.randn(10,).astype(np.float32)
|
|
jt_y=jt_loss(jt.array(output), jt.array(target),jt.array(weight),reduction='mean')
|
|
tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target),torch.from_numpy(weight),reduction='mean')
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
def test_cross_entropy_loss(self):
|
|
jt_loss=jnn.CrossEntropyLoss()
|
|
tc_loss=tnn.CrossEntropyLoss()
|
|
output=np.random.randn(10,10).astype(np.float32)
|
|
target=np.random.randint(10, size=(10))
|
|
jt_y=jt_loss(jt.array(output), jt.array(target))
|
|
tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
def test_cross_entropy_loss_v2(self):
|
|
B = 100
|
|
C = 5
|
|
for shape in [[100,1],[],[100,20]]:
|
|
s1 = [B,C]+shape
|
|
s2 = [B]+shape
|
|
a = np.random.randn(*s1).astype(np.float32)
|
|
b = np.random.randint(0,C,size=s2).astype(np.int32)
|
|
weight = np.random.randn(C).astype(np.float32)
|
|
|
|
for r in ['mean','sum','none']:
|
|
r1 = torch.nn.functional.cross_entropy(torch.tensor(a),torch.tensor(b.astype(np.int64)),weight=torch.tensor(weight),reduction=r)
|
|
r2 = jnn.cross_entropy_loss(jt.array(a),jt.array(b),weight=jt.array(weight),reduction=r)
|
|
np.testing.assert_allclose(r1.numpy(),r2.numpy(),rtol=1e-3, atol=1e-3)
|
|
|
|
for r in ['mean','sum','none']:
|
|
r1 = torch.nn.functional.cross_entropy(torch.tensor(a),torch.tensor(b.astype(np.int64)),reduction=r)
|
|
r2 = jnn.cross_entropy_loss(jt.array(a),jt.array(b),reduction=r)
|
|
np.testing.assert_allclose(r1.numpy(),r2.numpy(),rtol=1e-3, atol=1e-3)
|
|
|
|
r1 = torch.nn.functional.cross_entropy(torch.tensor(a),torch.tensor(b.astype(np.int64)))
|
|
r2 = jnn.cross_entropy_loss(jt.array(a),jt.array(b))
|
|
np.testing.assert_allclose(r1.numpy(),r2.numpy(),rtol=1e-3, atol=1e-3)
|
|
|
|
r1 = torch.nn.functional.cross_entropy(torch.tensor(a),torch.tensor(b.astype(np.int64)),weight=torch.tensor(weight))
|
|
r2 = jnn.cross_entropy_loss(jt.array(a),jt.array(b),weight=jt.array(weight))
|
|
np.testing.assert_allclose(r1.numpy(),r2.numpy(),rtol=1e-3, atol=1e-3)
|
|
|
|
for r in ['mean','sum','none']:
|
|
r1 = torch.nn.functional.cross_entropy(torch.tensor(a),torch.tensor(b.astype(np.int64)),weight=torch.tensor(weight),reduction=r,ignore_index=C//2)
|
|
r2 = jnn.cross_entropy_loss(jt.array(a),jt.array(b),weight=jt.array(weight),reduction=r,ignore_index=C//2)
|
|
np.testing.assert_allclose(r1.numpy(),r2.numpy(),rtol=1e-3, atol=1e-3)
|
|
|
|
for r in ['mean','sum','none']:
|
|
r1 = torch.nn.functional.cross_entropy(torch.tensor(a),torch.tensor(b.astype(np.int64)),reduction=r,ignore_index=C//2)
|
|
r2 = jnn.cross_entropy_loss(jt.array(a),jt.array(b),reduction=r,ignore_index=C//2)
|
|
np.testing.assert_allclose(r1.numpy(),r2.numpy(),rtol=1e-3, atol=1e-3)
|
|
|
|
r1 = torch.nn.functional.cross_entropy(torch.tensor(a),torch.tensor(b.astype(np.int64)),ignore_index=C//2)
|
|
r2 = jnn.cross_entropy_loss(jt.array(a),jt.array(b),ignore_index=C//2)
|
|
np.testing.assert_allclose(r1.numpy(),r2.numpy(),rtol=1e-3, atol=1e-3)
|
|
|
|
r1 = torch.nn.functional.cross_entropy(torch.tensor(a),torch.tensor(b.astype(np.int64)),weight=torch.tensor(weight),ignore_index=C//2)
|
|
r2 = jnn.cross_entropy_loss(jt.array(a),jt.array(b),weight=jt.array(weight),ignore_index=C//2)
|
|
np.testing.assert_allclose(r1.numpy(),r2.numpy(),rtol=1e-3, atol=1e-3)
|
|
|
|
|
|
def test_cross_entropy_ignore_index(self):
|
|
ignore_index = np.random.randint(0, 10)
|
|
jt_loss = jnn.CrossEntropyLoss(ignore_index=ignore_index)
|
|
tc_loss = tnn.CrossEntropyLoss(ignore_index=ignore_index)
|
|
output = np.random.rand(100, 10).astype(np.float32)
|
|
target = np.random.randint(10, size=(100))
|
|
jt_y=jt_loss(jt.array(output), jt.array(target))
|
|
tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
def test_cross_entropy_weight(self):
|
|
weight = np.random.rand(10).astype('float32')
|
|
jt_loss = jnn.CrossEntropyLoss(weight=jt.array(weight))
|
|
tc_loss = tnn.CrossEntropyLoss(weight=torch.from_numpy(weight))
|
|
output = np.random.rand(100, 10).astype(np.float32)
|
|
target = np.random.randint(10, size=(100))
|
|
jt_y=jt_loss(jt.array(output), jt.array(target))
|
|
tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
def test_cross_entropy_weight_ignore(self):
|
|
weight = np.random.rand(4).astype('float32')
|
|
jt_loss = jnn.CrossEntropyLoss(weight=jt.array(weight), ignore_index=1)
|
|
tc_loss = tnn.CrossEntropyLoss(weight=torch.from_numpy(weight), ignore_index=1)
|
|
output = np.random.rand(3, 4, 2,2).astype(np.float32)
|
|
target = np.random.randint(4, size=(3, 2,2))
|
|
jt_y=jt_loss(jt.array(output), jt.array(target))
|
|
tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
|
|
np.testing.assert_allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
|
|
def test_bce_loss(self):
|
|
jt_loss=jnn.BCELoss()
|
|
tc_loss=tnn.BCELoss()
|
|
jt_sig = jnn.Sigmoid()
|
|
tc_sig = tnn.Sigmoid()
|
|
output=np.random.randn(100).astype(np.float32)
|
|
target=np.random.randint(2, size=(100)).astype(np.float32)
|
|
jt_y=jt_loss(jt_sig(jt.array(output)), jt.array(target))
|
|
tc_y=tc_loss(tc_sig(torch.from_numpy(output)), torch.from_numpy(target))
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
weight=np.random.randn(100).astype(np.float32)
|
|
jt_loss=jnn.BCELoss(weight=jt.array(weight), size_average=False)
|
|
tc_loss=tnn.BCELoss(weight=torch.Tensor(weight), size_average=False)
|
|
jt_y=jt_loss(jt_sig(jt.array(output)), jt.array(target))
|
|
tc_y=tc_loss(tc_sig(torch.from_numpy(output)), torch.from_numpy(target))
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
def test_bce_with_logits_loss(self):
|
|
jt_loss=jnn.BCEWithLogitsLoss()
|
|
tc_loss=tnn.BCEWithLogitsLoss()
|
|
output=np.random.randn(100).astype(np.float32)
|
|
target=np.random.randint(2, size=(100)).astype(np.float32)
|
|
jt_y=jt_loss(jt.array(output), jt.array(target))
|
|
tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
|
|
assert np.allclose(jt_y.numpy(), tc_y.numpy())
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|