# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import paddle import paddle.fluid as fluid import numpy as np import unittest def stable_softmax(x): shiftx = (x - np.max(x)).clip(-64.) exps = np.exp(shiftx) return exps / np.sum(exps) def log_softmax(x, axis=-1): softmax_out = np.apply_along_axis(stable_softmax, axis, x) return np.log(softmax_out) def cross_entropy_loss_1d(input, label, weight=None, reduction='mean', ignore_index=-100): log_softmax_out = log_softmax(input) input_shape = log_softmax_out.shape N = input_shape[0] C = input_shape[1] out = np.zeros_like(label).astype(np.float64) total_weight = 0 for i in range(N): cur_target = label[i] if cur_target == ignore_index: out[i] = 0 continue cur_weight = weight[cur_target] if weight is not None else 1 total_weight += cur_weight out[i] = -log_softmax_out[i][cur_target] * cur_weight if reduction == 'sum': return np.sum(out), np.array([total_weight]).astype('float64') elif reduction == 'mean': return out.sum() / total_weight, np.array( [total_weight]).astype('float64') elif reduction == 'none': return out def cross_entropy_loss_2d(input, label, weight=None, reduction='mean', ignore_index=-100): log_softmax_out = log_softmax(input) input_shape = log_softmax_out.shape N = input_shape[0] H = input_shape[1] W = input_shape[2] out = np.zeros_like(label).astype(np.float64) total_weight = 0 for i in range(N): for h in range(H): for w in range(W): cur_target = label[i][h][w] if cur_target == ignore_index: out[i][h][w] = 0 continue cur_weight = weight[cur_target] if weight is not None else 1 total_weight += cur_weight out[i][h][w] = -log_softmax_out[i][h][w][ cur_target] * cur_weight if reduction == 'sum': return np.sum(out), np.array([total_weight]).astype('float64') elif reduction == 'mean': return out.sum() / total_weight, np.array( [total_weight]).astype('float64') elif reduction == 'none': return out class CrossEntropyLoss(unittest.TestCase): def test_cross_entropy_loss_1d_with_mean_ignore(self): input_np = np.random.random([2, 4]).astype(np.float64) label_np = np.random.randint(0, 4, size=(2)).astype(np.int64) paddle.enable_static() 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=[2, 4], dtype='float64') label = fluid.data(name='label', shape=[2], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(ignore_index=0) ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, }, fetch_list=[ret]) self.assertIsNotNone(static_ret) expected = cross_entropy_loss_1d(input_np, label_np)[0] with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( axis=1, ignore_index=0) dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_1d(input_np, label_np, ignore_index=0)[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_with_weight_mean_ignore(self): input_np = np.random.random([2, 4]).astype(np.float64) label_np = np.random.randint(0, 4, size=(2)).astype(np.int64) weight_np = np.random.random([4]).astype(np.float64) #shape:C paddle.enable_static() 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=[2, 4], dtype='float64') label = fluid.data(name='label', shape=[2], dtype='int64') weight = fluid.data( name='weight', shape=[4], dtype='float64') #weight for each class cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, ignore_index=0) ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, "weight": weight_np }, fetch_list=[ret]) self.assertIsNotNone(static_ret) expected = cross_entropy_loss_1d( input_np, label_np, weight=weight_np)[0] with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=fluid.dygraph.to_variable(weight_np), axis=1, ignore_index=0) dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_1d( input_np, label_np, weight=weight_np, ignore_index=0)[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_with_weight_mean(self): input_np = np.random.random([2, 4]).astype(np.float64) label_np = np.random.randint(0, 4, size=(2)).astype(np.int64) weight_np = np.random.random([4]).astype(np.float64) #shape:C paddle.enable_static() 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=[2, 4], dtype='float64') label = fluid.data(name='label', shape=[2], dtype='int64') weight = fluid.data( name='weight', shape=[4], dtype='float64') #weight for each class cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight) ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, "weight": weight_np }, fetch_list=[ret]) self.assertIsNotNone(static_ret) expected = cross_entropy_loss_1d( input_np, label_np, weight=weight_np)[0] with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=fluid.dygraph.to_variable(weight_np), axis=1) dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_1d( input_np, label_np, weight=weight_np)[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_with_weight_sum(self): input_np = np.random.random([100, 200]).astype(np.float64) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 weight_np = np.random.random([200]).astype(np.float64) #C paddle.enable_static() 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=[100, 200], dtype='float64') label = fluid.data(name='label', shape=[100], dtype='int64') weight = fluid.data(name='weight', shape=[200], dtype='float64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='sum') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, "weight": weight_np }, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=fluid.dygraph.to_variable(weight_np), reduction='sum') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_1d( input_np, label_np, weight=weight_np, reduction='sum')[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_with_weight_none(self): input_np = np.random.random([100, 200]).astype(np.float64) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 weight_np = np.random.random([200]).astype(np.float64) #C paddle.enable_static() 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=[100, 200], dtype='float64') label = fluid.data(name='label', shape=[100], dtype='int64') weight = fluid.data(name='weight', shape=[200], dtype='float64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='none') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, "weight": weight_np }, fetch_list=[ret]) static_ret = np.squeeze(static_ret) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=fluid.dygraph.to_variable(weight_np), reduction='none') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() dy_ret_value = np.squeeze(dy_ret_value) self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_1d( input_np, label_np, weight=weight_np, reduction='none') self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_with_weight_none_func(self): input_np = np.random.random([100, 200]).astype(np.float64) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N weight_np = np.random.random([200]).astype(np.float64) #C paddle.enable_static() 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=[100, 200], dtype='float64') label = fluid.data(name='label', shape=[100], dtype='int64') weight = fluid.data(name='weight', shape=[200], dtype='float64') ret = paddle.nn.functional.cross_entropy( input, label, weight=weight, reduction='none') exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, "weight": weight_np }, fetch_list=[ret]) static_ret = np.squeeze(static_ret) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): dy_ret = paddle.nn.functional.cross_entropy( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np), weight=fluid.dygraph.to_variable(weight_np), reduction='none') dy_ret_value = dy_ret.numpy() dy_ret_value = np.squeeze(dy_ret_value) self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_1d( input_np, label_np, weight=weight_np, reduction='none') self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_mean(self): input_np = np.random.random([100, 200]).astype(np.float64) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 weight_np = np.random.random([200]).astype(np.float64) #C paddle.enable_static() 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=[100, 200], dtype='float64') label = fluid.data(name='label', shape=[100], dtype='int64') weight = fluid.data(name='weight', shape=[100], dtype='float64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss() ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={'input': input_np, 'label': label_np}, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss() dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_1d(input_np, label_np)[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_sum(self): input_np = np.random.random([100, 200]).astype(np.float64) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 paddle.enable_static() 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=[100, 200], dtype='float64') label = fluid.data(name='label', shape=[100], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='sum') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={'input': input_np, 'label': label_np}, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='sum') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_1d(input_np, label_np, reduction='sum')[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_none(self): input_np = np.random.random([100, 200]).astype(np.float64) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 paddle.enable_static() 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=[100, 200], dtype='float64') label = fluid.data(name='label', shape=[100], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='none') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={'input': input_np, 'label': label_np}, fetch_list=[ret]) static_ret = np.squeeze(static_ret) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='none') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() dy_ret_value = np.squeeze(dy_ret_value) self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_1d(input_np, label_np, reduction='none') self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_with_weight_none(self): input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW1 weight_np = np.random.random(size=(3, )).astype(np.float64) #C paddle.enable_static() 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=[2, 2, 2, 3], dtype='float64') label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') weight = fluid.data(name='weight', shape=[3], dtype='float64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='none') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, "weight": weight_np }, fetch_list=[ret]) static_ret = np.squeeze(static_ret) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=fluid.dygraph.to_variable(weight_np), reduction='none') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() dy_ret_value = np.squeeze(dy_ret_value) self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_2d( input_np, label_np, weight=weight_np, reduction='none') self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_with_weight_mean(self): input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW weight_np = np.random.random(size=(3, )).astype(np.float64) #C paddle.enable_static() 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=[2, 2, 2, 3], dtype='float64') label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') weight = fluid.data(name='weight', shape=[3], dtype='float64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='mean') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, "weight": weight_np }, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=fluid.dygraph.to_variable(weight_np), reduction='mean') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_2d( input_np, label_np, weight=weight_np, reduction='mean')[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_with_weight_sum(self): input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW weight_np = np.random.random(size=(3, )).astype(np.float64) #C paddle.enable_static() 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=[2, 2, 2, 3], dtype='float64') label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') weight = fluid.data(name='weight', shape=[3], dtype='float64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='sum') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, "weight": weight_np }, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=fluid.dygraph.to_variable(weight_np), reduction='sum') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_2d( input_np, label_np, weight=weight_np, reduction='sum')[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_none(self): input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW paddle.enable_static() 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=[2, 2, 2, 3], dtype='float64') label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='none') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, }, fetch_list=[ret]) static_ret = np.squeeze(static_ret) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='none') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() dy_ret_value = np.squeeze(dy_ret_value) self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_2d(input_np, label_np, reduction='none') self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_mean(self): input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW paddle.enable_static() 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=[2, 2, 2, 3], dtype='float64') label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='mean') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, }, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='mean') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_2d( input_np, label_np, reduction='mean')[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_sum(self): input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW paddle.enable_static() 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=[2, 2, 2, 3], dtype='float64') label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='sum') ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, }, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='sum') dy_ret = cross_entropy_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = cross_entropy_loss_2d(input_np, label_np, reduction='sum')[0] self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) if __name__ == "__main__": unittest.main()