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971 lines
42 KiB
971 lines
42 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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import paddle
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import paddle.fluid as fluid
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import numpy as np
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import unittest
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from op_test import OpTest
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def nll_loss_1d(logs, targets, weight=None, reduction='mean',
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ignore_index=-100):
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input_shape = logs.shape
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N = input_shape[0]
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C = input_shape[1]
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out = np.zeros_like(targets).astype(np.float64)
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total_weight = 0
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for i in range(N):
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cur_target = targets[i]
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if cur_target == ignore_index:
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out[i] = 0
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continue
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cur_weight = weight[cur_target] if weight is not None else 1
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total_weight += cur_weight
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out[i] = -logs[i][cur_target] * cur_weight
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if reduction == 'sum':
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return np.sum(out), np.array([total_weight]).astype('float64')
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elif reduction == 'mean':
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return out.sum() / total_weight, np.array(
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[total_weight]).astype('float64')
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elif reduction == 'none':
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return out
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def nll_loss_2d(logs, targets, weight=None, reduction='mean',
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ignore_index=-100):
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input_shape = logs.shape
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N = input_shape[0]
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H = input_shape[2]
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W = input_shape[3]
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out = np.zeros_like(targets).astype(np.float64)
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total_weight = 0
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for i in range(N):
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for h in range(H):
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for w in range(W):
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cur_target = targets[i][h][w]
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if cur_target == ignore_index:
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out[i][h][w] = 0
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continue
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cur_weight = weight[cur_target] if weight is not None else 1
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total_weight += cur_weight
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out[i][h][w] = -logs[i][cur_target][h][w] * cur_weight
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if reduction == 'sum':
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return np.sum(out), np.array([total_weight]).astype('float64')
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elif reduction == 'mean':
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return out.sum() / total_weight, np.array(
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[total_weight]).astype('float64')
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elif reduction == 'none':
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return out
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class TestNLLLoss(unittest.TestCase):
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def test_NLLLoss_1D_mean(self):
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input_np = np.random.random(size=(10, 10)).astype(np.float64)
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label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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#place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(name='input', shape=[10, 10], dtype='float64')
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label = fluid.data(name='label', shape=[10], dtype='int64')
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nll_loss = paddle.nn.loss.NLLLoss()
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(
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prog,
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feed={"input": input_np,
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"label": label_np},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss()
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dy_res = nll_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_res.numpy()
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expected = nll_loss_1d(input_np, label_np)[0]
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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def test_NLLLoss_1D_sum(self):
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input_np = np.random.random(size=(10, 10)).astype(np.float64)
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label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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#place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(name='input', shape=[10, 10], dtype='float64')
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label = fluid.data(name='label', shape=[10], dtype='int64')
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nll_loss = paddle.nn.loss.NLLLoss(reduction='sum')
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(
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prog,
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feed={"input": input_np,
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"label": label_np},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss(reduction='sum')
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dy_res = nll_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_res.numpy()
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expected = nll_loss_1d(input_np, label_np, reduction='sum')[0]
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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def test_NLLLoss_1D_with_weight_mean(self):
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input_np = np.random.random(size=(10, 10)).astype(np.float64)
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label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
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weight_np = np.random.random(size=(10, )).astype(np.float64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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# place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(name='input', shape=[10, 10], dtype='float64')
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label = fluid.data(name='label', shape=[10], dtype='int64')
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weight = fluid.data(name='weight', shape=[10], dtype='float64')
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nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(prog,
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feed={
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"input": input_np,
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"label": label_np,
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"weight": weight_np
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},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss(
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weight=fluid.dygraph.to_variable(weight_np))
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dy_res = nll_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_res.numpy()
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expected = nll_loss_1d(input_np, label_np, weight=weight_np)[0]
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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def test_NLLLoss_1D_with_weight_sum(self):
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input_np = np.random.random(size=(10, 10)).astype(np.float64)
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label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
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weight_np = np.random.random(size=(10, )).astype(np.float64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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# place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(name='input', shape=[10, 10], dtype='float64')
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label = fluid.data(name='label', shape=[10], dtype='int64')
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weight = fluid.data(name='weight', shape=[10], dtype='float64')
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nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='sum')
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(prog,
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feed={
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"input": input_np,
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"label": label_np,
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"weight": weight_np
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},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss(
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weight=fluid.dygraph.to_variable(weight_np), reduction='sum')
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dy_res = nll_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_res.numpy()
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expected = nll_loss_1d(
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input_np, label_np, weight=weight_np, reduction='sum')[0]
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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def test_NLLLoss_1D_with_weight_mean_cpu(self):
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input_np = np.random.random(size=(10, 10)).astype(np.float64)
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label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
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weight_np = np.random.random(size=(10, )).astype(np.float64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(name='input', shape=[10, 10], dtype='float64')
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label = fluid.data(name='label', shape=[10], dtype='int64')
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weight = fluid.data(name='weight', shape=[10], dtype='float64')
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nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(prog,
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feed={
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"input": input_np,
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"label": label_np,
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"weight": weight_np
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},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss(
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weight=fluid.dygraph.to_variable(weight_np))
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dy_res = nll_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_res.numpy()
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expected = nll_loss_1d(input_np, label_np, weight=weight_np)[0]
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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def test_NLLLoss_1D_with_weight_no_reduce_cpu(self):
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input_np = np.random.random(size=(10, 10)).astype(np.float64)
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label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
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weight_np = np.random.random(size=(10, )).astype(np.float64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(name='input', shape=[10, 10], dtype='float64')
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label = fluid.data(name='label', shape=[10], dtype='int64')
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weight = fluid.data(name='weight', shape=[10], dtype='float64')
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nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='none')
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(prog,
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feed={
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"input": input_np,
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"label": label_np,
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"weight": weight_np
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},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss(
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weight=fluid.dygraph.to_variable(weight_np), reduction='none')
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dy_res = nll_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_res.numpy()
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expected = nll_loss_1d(
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input_np, label_np, weight=weight_np, reduction='none')
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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def test_NLLLoss_2D_mean(self):
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input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
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label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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#place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(
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name='input', shape=[5, 3, 5, 5], dtype='float64')
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label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
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nll_loss = paddle.nn.loss.NLLLoss()
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(
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prog,
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feed={"input": input_np,
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"label": label_np},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss()
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dy_res = nll_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_res.numpy()
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expected = nll_loss_2d(input_np, label_np)[0]
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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def test_NLLLoss_2D_sum(self):
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input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
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label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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#place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(
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name='input', shape=[5, 3, 5, 5], dtype='float64')
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label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
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nll_loss = paddle.nn.loss.NLLLoss(reduction='sum')
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(
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prog,
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feed={"input": input_np,
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"label": label_np},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss(reduction='sum')
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dy_res = nll_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_res.numpy()
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expected = nll_loss_2d(input_np, label_np, reduction='sum')[0]
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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def test_NLLLoss_2D_with_weight_mean(self):
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input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
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label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
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weight_np = np.random.random(size=(3, )).astype(np.float64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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#place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(
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name='input', shape=[5, 3, 5, 5], dtype='float64')
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label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
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weight = fluid.data(name='weight', shape=[3], dtype='float64')
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nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(prog,
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feed={
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"input": input_np,
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"label": label_np,
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"weight": weight_np
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},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss(
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weight=fluid.dygraph.to_variable(weight_np))
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dy_res = nll_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_res.numpy()
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expected = nll_loss_2d(input_np, label_np, weight=weight_np)[0]
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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def test_NLLLoss_2D_with_weight_mean_cpu(self):
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input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
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label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
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weight_np = np.random.random(size=(3, )).astype(np.float64)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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place = fluid.CPUPlace()
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with fluid.program_guard(prog, startup_prog):
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input = fluid.data(
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name='input', shape=[5, 3, 5, 5], dtype='float64')
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label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
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weight = fluid.data(name='weight', shape=[3], dtype='float64')
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nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
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res = nll_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(prog,
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feed={
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"input": input_np,
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"label": label_np,
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"weight": weight_np
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},
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fetch_list=[res])
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with fluid.dygraph.guard():
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nll_loss = paddle.nn.loss.NLLLoss(
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weight=fluid.dygraph.to_variable(weight_np))
|
|
dy_res = nll_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_res.numpy()
|
|
|
|
expected = nll_loss_2d(input_np, label_np, weight=weight_np)[0]
|
|
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
|
|
def test_NLLLoss_2D_with_weight_sum(self):
|
|
input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
|
|
label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
|
|
weight_np = np.random.random(size=(3, )).astype(np.float64)
|
|
prog = fluid.Program()
|
|
startup_prog = fluid.Program()
|
|
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
|
|
) else fluid.CPUPlace()
|
|
with fluid.program_guard(prog, startup_prog):
|
|
input = fluid.data(
|
|
name='input', shape=[5, 3, 5, 5], dtype='float64')
|
|
label = fluid.data(name='label', shape=[5, 5, 5], dtype='int64')
|
|
weight = fluid.data(name='weight', shape=[3], dtype='float64')
|
|
|
|
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='sum')
|
|
res = nll_loss(input, label)
|
|
|
|
exe = fluid.Executor(place)
|
|
static_result = exe.run(prog,
|
|
feed={
|
|
"input": input_np,
|
|
"label": label_np,
|
|
"weight": weight_np
|
|
},
|
|
fetch_list=[res])
|
|
|
|
with fluid.dygraph.guard():
|
|
nll_loss = paddle.nn.loss.NLLLoss(
|
|
weight=fluid.dygraph.to_variable(weight_np), reduction='sum')
|
|
dy_res = nll_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_res.numpy()
|
|
|
|
expected = nll_loss_2d(
|
|
input_np, label_np, weight=weight_np, reduction='sum')[0]
|
|
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
|
|
def test_NLLLoss_in_dims_not_2or4_mean(self):
|
|
input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
|
|
label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
|
|
prog = fluid.Program()
|
|
startup_prog = fluid.Program()
|
|
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
|
|
) else fluid.CPUPlace()
|
|
#place = fluid.CPUPlace()
|
|
with fluid.program_guard(prog, startup_prog):
|
|
input = fluid.data(
|
|
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
|
|
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
|
|
nll_loss = paddle.nn.loss.NLLLoss()
|
|
res = nll_loss(input, label)
|
|
|
|
exe = fluid.Executor(place)
|
|
static_result = exe.run(
|
|
prog,
|
|
feed={"input": input_np,
|
|
"label": label_np},
|
|
fetch_list=[res])
|
|
|
|
with fluid.dygraph.guard():
|
|
nll_loss = paddle.nn.loss.NLLLoss()
|
|
dy_res = nll_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_res.numpy()
|
|
|
|
input_shape = input_np.shape
|
|
label_shape = label_np.shape
|
|
input_np_reshape = np.reshape(input_np,
|
|
(input_shape[0], input_shape[1], 1, -1))
|
|
label_np_reshape = np.reshape(label_np, (label_shape[0], 1, -1))
|
|
expected = nll_loss_2d(input_np_reshape, label_np_reshape)[0]
|
|
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
|
|
def test_NLLLoss_in_dims_not_2or4_with_weight_mean(self):
|
|
input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
|
|
label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
|
|
weight_np = np.random.random(size=(3, )).astype(np.float64)
|
|
prog = fluid.Program()
|
|
startup_prog = fluid.Program()
|
|
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
|
|
) else fluid.CPUPlace()
|
|
#place = fluid.CPUPlace()
|
|
with fluid.program_guard(prog, startup_prog):
|
|
input = fluid.data(
|
|
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
|
|
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
|
|
weight = fluid.data(name='weight', shape=[3], dtype='float64')
|
|
nll_loss = paddle.nn.loss.NLLLoss(weight=weight)
|
|
res = nll_loss(input, label)
|
|
|
|
exe = fluid.Executor(place)
|
|
static_result = exe.run(prog,
|
|
feed={
|
|
"input": input_np,
|
|
"label": label_np,
|
|
"weight": weight_np
|
|
},
|
|
fetch_list=[res])
|
|
|
|
with fluid.dygraph.guard():
|
|
nll_loss = paddle.nn.loss.NLLLoss(
|
|
weight=fluid.dygraph.to_variable(weight_np))
|
|
dy_res = nll_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_res.numpy()
|
|
|
|
input_shape = input_np.shape
|
|
label_shape = label_np.shape
|
|
input_np_reshape = np.reshape(input_np,
|
|
(input_shape[0], input_shape[1], 1, -1))
|
|
label_np_reshape = np.reshape(label_np, (label_shape[0], 1, -1))
|
|
expected = nll_loss_2d(
|
|
input_np_reshape, label_np_reshape, weight=weight_np)[0]
|
|
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
|
|
def test_NLLLoss_in_dims_not_2or4_with_weight_sum(self):
|
|
input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
|
|
label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
|
|
weight_np = np.random.random(size=(3, )).astype(np.float64)
|
|
prog = fluid.Program()
|
|
startup_prog = fluid.Program()
|
|
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
|
|
) else fluid.CPUPlace()
|
|
place = fluid.CPUPlace()
|
|
with fluid.program_guard(prog, startup_prog):
|
|
input = fluid.data(
|
|
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
|
|
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
|
|
weight = fluid.data(name='weight', shape=[3], dtype='float64')
|
|
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='sum')
|
|
res = nll_loss(input, label)
|
|
|
|
exe = fluid.Executor(place)
|
|
static_result = exe.run(prog,
|
|
feed={
|
|
"input": input_np,
|
|
"label": label_np,
|
|
"weight": weight_np
|
|
},
|
|
fetch_list=[res])
|
|
|
|
with fluid.dygraph.guard():
|
|
nll_loss = paddle.nn.loss.NLLLoss(
|
|
weight=fluid.dygraph.to_variable(weight_np), reduction='sum')
|
|
dy_res = nll_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_res.numpy()
|
|
|
|
input_shape = input_np.shape
|
|
label_shape = label_np.shape
|
|
input_np_reshape = np.reshape(input_np,
|
|
(input_shape[0], input_shape[1], 1, -1))
|
|
label_np_reshape = np.reshape(label_np, (label_shape[0], 1, -1))
|
|
expected = nll_loss_2d(
|
|
input_np_reshape,
|
|
label_np_reshape,
|
|
weight=weight_np,
|
|
reduction='sum')[0]
|
|
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
|
|
def test_NLLLoss_in_dims_not_2or4_with_weight_no_reduce(self):
|
|
input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
|
|
label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
|
|
weight_np = np.random.random(size=(3, )).astype(np.float64)
|
|
prog = fluid.Program()
|
|
startup_prog = fluid.Program()
|
|
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
|
|
) else fluid.CPUPlace()
|
|
#place = fluid.CPUPlace()
|
|
with fluid.program_guard(prog, startup_prog):
|
|
input = fluid.data(
|
|
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
|
|
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
|
|
weight = fluid.data(name='weight', shape=[3], dtype='float64')
|
|
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='none')
|
|
res = nll_loss(input, label)
|
|
|
|
exe = fluid.Executor(place)
|
|
static_result = exe.run(prog,
|
|
feed={
|
|
"input": input_np,
|
|
"label": label_np,
|
|
"weight": weight_np
|
|
},
|
|
fetch_list=[res])
|
|
|
|
with fluid.dygraph.guard():
|
|
nll_loss = paddle.nn.loss.NLLLoss(
|
|
weight=fluid.dygraph.to_variable(weight_np), reduction='none')
|
|
dy_res = nll_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_res.numpy()
|
|
|
|
input_shape = input_np.shape
|
|
label_shape = label_np.shape
|
|
out_shape = (input_shape[0], ) + input_shape[2:]
|
|
input_np_reshape = np.reshape(input_np,
|
|
(input_shape[0], input_shape[1], 1, -1))
|
|
label_np_reshape = np.reshape(label_np, (label_shape[0], 1, -1))
|
|
expected = nll_loss_2d(
|
|
input_np_reshape,
|
|
label_np_reshape,
|
|
weight=weight_np,
|
|
reduction='none')
|
|
expected = np.reshape(expected, out_shape)
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
|
|
def test_NLLLoss_in_dims_not_2or4_with_weight_no_reduce_cpu(self):
|
|
input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
|
|
label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
|
|
weight_np = np.random.random(size=(3, )).astype(np.float64)
|
|
prog = fluid.Program()
|
|
startup_prog = fluid.Program()
|
|
place = fluid.CPUPlace()
|
|
with fluid.program_guard(prog, startup_prog):
|
|
input = fluid.data(
|
|
name='input', shape=[5, 3, 5, 5, 5], dtype='float64')
|
|
label = fluid.data(name='label', shape=[5, 5, 5, 5], dtype='int64')
|
|
weight = fluid.data(name='weight', shape=[3], dtype='float64')
|
|
nll_loss = paddle.nn.loss.NLLLoss(weight=weight, reduction='none')
|
|
res = nll_loss(input, label)
|
|
|
|
exe = fluid.Executor(place)
|
|
static_result = exe.run(prog,
|
|
feed={
|
|
"input": input_np,
|
|
"label": label_np,
|
|
"weight": weight_np
|
|
},
|
|
fetch_list=[res])
|
|
|
|
with fluid.dygraph.guard():
|
|
nll_loss = paddle.nn.loss.NLLLoss(
|
|
weight=fluid.dygraph.to_variable(weight_np), reduction='none')
|
|
dy_res = nll_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_res.numpy()
|
|
|
|
input_shape = input_np.shape
|
|
label_shape = label_np.shape
|
|
out_shape = (input_shape[0], ) + input_shape[2:]
|
|
input_np_reshape = np.reshape(input_np,
|
|
(input_shape[0], input_shape[1], 1, -1))
|
|
label_np_reshape = np.reshape(label_np, (label_shape[0], 1, -1))
|
|
expected = nll_loss_2d(
|
|
input_np_reshape,
|
|
label_np_reshape,
|
|
weight=weight_np,
|
|
reduction='none')
|
|
expected = np.reshape(expected, out_shape)
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
|
|
|
|
class TestNLLLossOp1DWithReduce(OpTest):
|
|
def setUp(self):
|
|
self.init_test_case()
|
|
self.op_type = "nll_loss"
|
|
self.with_weight = False
|
|
input_np = np.random.uniform(0.1, 0.8,
|
|
self.input_shape).astype("float64")
|
|
label_np = np.random.randint(0, self.input_shape[1],
|
|
self.label_shape).astype("int64")
|
|
output_np, total_weight_np = nll_loss_1d(input_np, label_np)
|
|
self.inputs = {'X': input_np, 'Label': label_np}
|
|
if self.with_weight:
|
|
weight_np = np.random.uniform(0.1, 0.8,
|
|
self.input_shape[1]).astype("float64")
|
|
output_np, total_weight_np = nll_loss_1d(
|
|
input_np, label_np, weight=weight_np)
|
|
self.inputs['Weight'] = weight_np
|
|
|
|
self.outputs = {'Out': output_np, 'Total_weight': total_weight_np}
|
|
self.attrs = {'reduction': 'mean', 'ignore_index': -100}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_output_with_weight(self):
|
|
self.with_weight = True
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.with_weight = True
|
|
place = fluid.CPUPlace()
|
|
self.check_grad_with_place(place, ['X'], 'Out')
|
|
if fluid.core.is_compiled_with_cuda():
|
|
place = fluid.CUDAPlace(0)
|
|
self.check_grad_with_place(place, ['X'], 'Out')
|
|
|
|
def init_test_case(self):
|
|
self.input_shape = [10, 10]
|
|
self.label_shape = [10]
|
|
|
|
|
|
class TestNLLLossOp1DNoReduce(OpTest):
|
|
def setUp(self):
|
|
self.init_test_case()
|
|
self.op_type = "nll_loss"
|
|
self.with_weight = False
|
|
input_np = np.random.uniform(0.1, 0.8,
|
|
self.input_shape).astype("float64")
|
|
label_np = np.random.randint(0, self.input_shape[1],
|
|
self.label_shape).astype("int64")
|
|
output_np = nll_loss_1d(input_np, label_np, reduction='none')
|
|
total_weight_np = np.array([0]).astype('float64')
|
|
self.inputs = {'X': input_np, 'Label': label_np}
|
|
if self.with_weight:
|
|
weight_np = np.random.uniform(0.1, 0.8,
|
|
self.input_shape[1]).astype("float64")
|
|
output_np, total_weight_np = nll_loss_1d(
|
|
input_np, label_np, weight=weight_np, reduction='none')
|
|
self.inputs['Weight'] = weight_np
|
|
|
|
self.outputs = {'Out': output_np, 'Total_weight': total_weight_np}
|
|
self.attrs = {'reduction': 'none', 'ignore_index': -100}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_output_with_weight(self):
|
|
self.with_weight = True
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.with_weight = True
|
|
place = fluid.CPUPlace()
|
|
self.check_grad_with_place(place, ['X'], 'Out')
|
|
if fluid.core.is_compiled_with_cuda():
|
|
place = fluid.CUDAPlace(0)
|
|
self.check_grad_with_place(place, ['X'], 'Out')
|
|
|
|
def init_test_case(self):
|
|
self.input_shape = [10, 10]
|
|
self.label_shape = [10]
|
|
|
|
|
|
class TestNLLLossOp2DWithReduce(OpTest):
|
|
def setUp(self):
|
|
self.init_test_case()
|
|
self.op_type = "nll_loss"
|
|
self.with_weight = False
|
|
input_np = np.random.uniform(0.1, 0.8,
|
|
self.input_shape).astype("float64")
|
|
label_np = np.random.randint(0, self.input_shape[1],
|
|
self.label_shape).astype("int64")
|
|
output_np, total_weight_np = nll_loss_2d(input_np, label_np)
|
|
self.inputs = {'X': input_np, 'Label': label_np}
|
|
if self.with_weight:
|
|
weight_np = np.random.uniform(0.1, 0.8,
|
|
self.input_shape[1]).astype("float64")
|
|
output_np, total_weight_np = nll_loss_2d(
|
|
input_np, label_np, weight=weight_np)
|
|
self.inputs['Weight'] = weight_np
|
|
|
|
self.outputs = {'Out': output_np, 'Total_weight': total_weight_np}
|
|
self.attrs = {'reduction': 'mean', 'ignore_index': -100}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_output_with_weight(self):
|
|
self.with_weight = True
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.with_weight = True
|
|
place = fluid.CPUPlace()
|
|
self.check_grad_with_place(place, ['X'], 'Out')
|
|
if fluid.core.is_compiled_with_cuda():
|
|
place = fluid.CUDAPlace(0)
|
|
self.check_grad_with_place(place, ['X'], 'Out')
|
|
|
|
def init_test_case(self):
|
|
self.input_shape = [5, 3, 5, 5]
|
|
self.label_shape = [5, 5, 5]
|
|
|
|
|
|
class TestNLLLossOp2DNoReduce(OpTest):
|
|
def setUp(self):
|
|
self.init_test_case()
|
|
self.op_type = "nll_loss"
|
|
self.with_weight = False
|
|
input_np = np.random.uniform(0.1, 0.8,
|
|
self.input_shape).astype("float64")
|
|
label_np = np.random.randint(0, self.input_shape[1],
|
|
self.label_shape).astype("int64")
|
|
output_np = nll_loss_2d(input_np, label_np, reduction='none')
|
|
total_weight_np = np.array([0]).astype('float64')
|
|
self.inputs = {'X': input_np, 'Label': label_np}
|
|
if self.with_weight:
|
|
weight_np = np.random.uniform(0.1, 0.8,
|
|
self.input_shape[1]).astype("float64")
|
|
output_np, total_weight_np = nll_loss_2d(
|
|
input_np, label_np, weight=weight_np, reduction='none')
|
|
self.inputs['Weight'] = weight_np
|
|
|
|
self.outputs = {'Out': output_np, 'Total_weight': total_weight_np}
|
|
self.attrs = {'reduction': 'none', 'ignore_index': -100}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_output_with_weight(self):
|
|
self.with_weight = True
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.with_weight = True
|
|
place = fluid.CPUPlace()
|
|
self.check_grad_with_place(place, ['X'], 'Out')
|
|
if fluid.core.is_compiled_with_cuda():
|
|
place = fluid.CUDAPlace(0)
|
|
self.check_grad_with_place(place, ['X'], 'Out')
|
|
|
|
def init_test_case(self):
|
|
self.input_shape = [5, 3, 5, 5]
|
|
self.label_shape = [5, 5, 5]
|
|
|
|
|
|
class TestNLLLossName(unittest.TestCase):
|
|
def test_name(self):
|
|
prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
place = paddle.CPUPlace()
|
|
with paddle.static.program_guard(prog, startup_prog):
|
|
x = paddle.fluid.data(name='x', shape=[10, 10], dtype='float64')
|
|
label = paddle.fluid.data(name='label', shape=[10], dtype='int64')
|
|
nll_loss = paddle.nn.loss.NLLLoss(name='nll_loss')
|
|
res = nll_loss(x, label)
|
|
self.assertTrue(res.name.startswith('nll_loss'))
|
|
|
|
|
|
class TestNLLLossInvalidArgs(unittest.TestCase):
|
|
def test_x_dim_value_error(self):
|
|
def test_x_dim_lt_2():
|
|
prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
place = paddle.CPUPlace()
|
|
with paddle.static.program_guard(prog, startup_prog):
|
|
x = paddle.fluid.data(name='x', shape=[10, ], dtype='float64')
|
|
label = paddle.fluid.data(name='label', shape=[10, ], dtype='float64')
|
|
nll_loss = paddle.nn.loss.NLLLoss()
|
|
res = nll_loss(x, label)
|
|
|
|
self.assertRaises(ValueError, test_x_dim_lt_2)
|
|
|
|
def test_x_dim_imperative_lt_2():
|
|
with fluid.dygraph.guard():
|
|
x_np = np.random.random(size=(5, )).astype(np.float64)
|
|
label_np = np.random.randint(0, 10, size=(5, )).astype(np.int64)
|
|
x = paddle.to_tensor(x_np)
|
|
label = paddle.to_tensor(label_np)
|
|
nll_loss = paddle.nn.loss.NLLLoss()
|
|
res = nll_loss(x, label)
|
|
|
|
self.assertRaises(ValueError, test_x_dim_imperative_lt_2)
|
|
|
|
def test_reduction_value_error(self):
|
|
def test_NLLLoss_reduction_not_sum_mean_none():
|
|
prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
place = paddle.CPUPlace()
|
|
with paddle.static.program_guard(prog, startup_prog):
|
|
x = paddle.fluid.data(name='x', shape=[10, 10], dtype='float64')
|
|
label = paddle.fluid.data(name='label', shape=[10], dtype='int64')
|
|
nll_loss = paddle.nn.loss.NLLLoss(reduction='')
|
|
res = nll_loss(x, label)
|
|
|
|
self.assertRaises(ValueError, test_NLLLoss_reduction_not_sum_mean_none)
|
|
|
|
def test_NLLLoss_reduction_imperative_not_sum_mean_none():
|
|
with fluid.dygraph.guard():
|
|
x_np = np.random.random(size=(5, 3)).astype(np.float64)
|
|
label_np = np.random.randint(0, 3, size=(5, )).astype(np.int64)
|
|
x = paddle.to_tensor(x_np)
|
|
label = paddle.to_tensor(label_np)
|
|
nll_loss = paddle.nn.loss.NLLLoss(reduction='')
|
|
res = nll_loss(x, label)
|
|
|
|
self.assertRaises(ValueError,
|
|
test_NLLLoss_reduction_imperative_not_sum_mean_none)
|
|
|
|
def test_nll_loss_function_reduction_not_sum_mean_none():
|
|
prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
place = paddle.CPUPlace()
|
|
with paddle.static.program_guard(prog, startup_prog):
|
|
x = paddle.fluid.data(name='x', shape=[10, 10], dtype='float64')
|
|
label = paddle.fluid.data(name='label', shape=[10], dtype='int64')
|
|
res = paddle.nn.functional.nll_loss(x, label, reduction='')
|
|
|
|
self.assertRaises(ValueError,
|
|
test_nll_loss_function_reduction_not_sum_mean_none)
|
|
|
|
def test_nll_loss_function_reduction_imperative_not_sum_mean_none():
|
|
with fluid.dygraph.guard():
|
|
x_np = np.random.random(size=(5, 3)).astype(np.float64)
|
|
label_np = np.random.randint(0, 3, size=(5, )).astype(np.int64)
|
|
x = paddle.to_tensor(x_np)
|
|
label = paddle.to_tensor(label_np)
|
|
res = paddle.nn.functional.nll_loss(x, label, reduction='')
|
|
|
|
self.assertRaises(
|
|
ValueError,
|
|
test_nll_loss_function_reduction_imperative_not_sum_mean_none)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|