forked from mindspore-Ecosystem/mindspore
190 lines
6.3 KiB
Python
190 lines
6.3 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops.operations import _grad_ops as G
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, reduction):
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super(Net, self).__init__()
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self.loss = P.NLLLoss(reduction=reduction)
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def construct(self, predict, target, weight):
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return self.loss(predict, target, weight)
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class NLLLossGradNet(nn.Cell):
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def __init__(self, reduction):
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super(NLLLossGradNet, self).__init__()
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self.grad = G.NLLLossGrad(reduction=reduction)
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def construct(self, x, dout_x, target, weight, total_weight):
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gout = self.grad(x, dout_x, target, weight, total_weight)
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return gout
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def nll_loss_template(nptype_input, nptype_weight, reduction):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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nll_loss_net = Net(reduction)
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predict = Tensor(
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np.array([[0.53, 0.74, -2.12], [1.29, -0.34, -1.13]]).astype(nptype_input))
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target = Tensor(np.array([0, 1]).astype(np.int32))
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weight = Tensor(np.array([0.45, -0.32, 1.21]).astype(nptype_weight))
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loss, total_weight = nll_loss_net(predict, target, weight)
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loss_np = loss.asnumpy()
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total_weight_np = total_weight.asnumpy()
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expected_tot_weight = np.array(0.129999995)
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if reduction == 'none':
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expected_loss = np.array([-0.238499984, -0.108800001])
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elif reduction == 'mean':
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expected_loss = np.array(-2.67153859)
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elif reduction == 'sum':
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expected_loss = np.array(-0.347299993)
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if nptype_input == np.float32 and nptype_weight == np.float32:
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ertol_loss = 1e-06
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elif nptype_input == np.float16 or nptype_weight == np.float16:
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ertol_loss = 1e-03
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if nptype_weight == np.float32:
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ertol_weight = 1e-06
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elif nptype_weight == np.float16:
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ertol_weight = 1e-03
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np.testing.assert_allclose(loss_np, expected_loss, ertol_loss)
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np.testing.assert_allclose(
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total_weight_np, expected_tot_weight, ertol_weight)
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def nll_loss_grad_template(nptype_input, nptype_weight, reduction):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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nll_loss_grad_net = NLLLossGradNet(reduction)
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x = Tensor(
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np.array([[0.53, 0.74, -2.12], [1.29, -0.34, -1.13]]).astype(nptype_input))
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if reduction == "none":
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dloss = Tensor(
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np.array([3.24, -2.13]).astype(nptype_input))
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else:
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dloss = Tensor(np.array(1.23).astype(nptype_input))
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target = Tensor(np.array([0, 1]).astype(np.int32))
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weight = Tensor(np.array([0.45, -0.32, 1.21]).astype(nptype_weight))
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total_weight = Tensor(np.array(0.13).astype(nptype_weight))
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dx = nll_loss_grad_net(x, dloss, target, weight, total_weight)
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dx_np = dx.asnumpy()
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print(dx)
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if reduction == "none":
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dx_expected = np.array([[-1.45799994, 0, 0], [0, -0.681600034, 0]])
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elif reduction == "mean":
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dx_expected = np.array([[-4.25769234, 0, 0], [0, 3.02769232, 0]])
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else:
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dx_expected = np.array([[-0.553499997, 0, 0], [0, 0.393599987, 0]])
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if nptype_input == np.float32 and nptype_weight == np.float32:
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ertol_loss = 1e-06
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else:
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ertol_loss = 1e-02
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np.testing.assert_allclose(dx_np, dx_expected, ertol_loss)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nll_loss_no_reduction():
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# Four combinations of fp32 and fp16 inputs and weights
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nll_loss_template(np.float32, np.float32, "none")
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nll_loss_template(np.float32, np.float16, "none")
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nll_loss_template(np.float16, np.float32, "none")
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nll_loss_template(np.float16, np.float16, "none")
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nll_loss_mean_reduction():
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# Four combinations of fp32 and fp16 inputs and weights
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nll_loss_template(np.float32, np.float32, "mean")
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nll_loss_template(np.float32, np.float16, "mean")
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nll_loss_template(np.float16, np.float32, "mean")
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nll_loss_template(np.float16, np.float16, "mean")
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nll_loss_sum_reduction():
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# Four combinations of fp32 and fp16 inputs and weights
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nll_loss_template(np.float32, np.float32, "sum")
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nll_loss_template(np.float32, np.float16, "sum")
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nll_loss_template(np.float16, np.float32, "sum")
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nll_loss_template(np.float16, np.float16, "sum")
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nll_loss_grad_mean_reduction():
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# Four combinations of fp32 and fp16 inputs and weights
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nll_loss_grad_template(np.float32, np.float32, "mean")
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nll_loss_grad_template(np.float32, np.float16, "mean")
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nll_loss_grad_template(np.float16, np.float32, "mean")
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nll_loss_grad_template(np.float16, np.float16, "mean")
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nll_loss_grad_sum_reduction():
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# Four combinations of fp32 and fp16 inputs and weights
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nll_loss_grad_template(np.float32, np.float32, "sum")
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nll_loss_grad_template(np.float32, np.float16, "sum")
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nll_loss_grad_template(np.float16, np.float32, "sum")
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nll_loss_grad_template(np.float16, np.float16, "sum")
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_nll_loss_grad_no_reduction():
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# Four combinations of fp32 and fp16 inputs and weights
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nll_loss_grad_template(np.float32, np.float32, "none")
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nll_loss_grad_template(np.float32, np.float16, "none")
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nll_loss_grad_template(np.float16, np.float32, "none")
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nll_loss_grad_template(np.float16, np.float16, "none")
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