# 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 from __future__ import division import unittest import numpy as np import paddle.fluid.core as core from op_test import OpTest import paddle import paddle.fluid as fluid from paddle.fluid import Program, program_guard def adaptive_start_index(index, input_size, output_size): return int(np.floor(index * input_size / output_size)) def adaptive_end_index(index, input_size, output_size): return int(np.ceil((index + 1) * input_size / output_size)) def adaptive_pool2d_forward(x, output_size, data_format='NCHW', pool_type="max"): N = x.shape[0] C, H, W = [x.shape[1], x.shape[2], x.shape[3]] if data_format == 'NCHW' \ else [x.shape[3], x.shape[1], x.shape[2]] if (isinstance(output_size, int) or output_size == None): H_out = output_size W_out = output_size output_size = [H_out, W_out] else: H_out, W_out = output_size if output_size[0] == None: output_size[0] = H H_out = H if output_size[1] == None: output_size[1] = W W_out = W out = np.zeros((N, C, H_out, W_out)) if data_format=='NCHW' \ else np.zeros((N, H_out, W_out, C)) for i in range(H_out): in_h_start = adaptive_start_index(i, H, output_size[0]) in_h_end = adaptive_end_index(i, H, output_size[0]) for j in range(W_out): in_w_start = adaptive_start_index(j, W, output_size[1]) in_w_end = adaptive_end_index(j, W, output_size[1]) if data_format == 'NCHW': x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end] if pool_type == 'avg': field_size = ( (in_h_end - in_h_start) * (in_w_end - in_w_start)) out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size elif pool_type == 'max': out[:, :, i, j] = np.max(x_masked, axis=(2, 3)) elif data_format == 'NHWC': x_masked = x[:, in_h_start:in_h_end, in_w_start:in_w_end, :] if pool_type == 'avg': field_size = ( (in_h_end - in_h_start) * (in_w_end - in_w_start)) out[:, i, j, :] = np.sum(x_masked, axis=(1, 2)) / field_size elif pool_type == 'max': out[:, i, j, :] = np.max(x_masked, axis=(1, 2)) return out class TestAdaptiveMaxPool2DAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 7, 7]).astype("float32") self.res_1_np = adaptive_pool2d_forward( x=self.x_np, output_size=[3, 3], pool_type="max") self.res_2_np = adaptive_pool2d_forward( x=self.x_np, output_size=5, pool_type="max") self.res_3_np = adaptive_pool2d_forward( x=self.x_np, output_size=[2, 5], pool_type="max") """ self.res_4_np = adaptive_pool2d_forward( x=self.x_np, output_size=[3, 3], pool_type="max", data_format="NHWC") """ self.res_5_np = adaptive_pool2d_forward( x=self.x_np, output_size=[None, 3], pool_type="max") def test_static_graph(self): for use_cuda in ([False, True] if core.is_compiled_with_cuda() else [False]): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.enable_static() x = paddle.fluid.data(name="x", shape=[2, 3, 7, 7], dtype="float32") out_1 = paddle.nn.functional.adaptive_max_pool2d( x=x, output_size=[3, 3]) out_2 = paddle.nn.functional.adaptive_max_pool2d(x=x, output_size=5) out_3 = paddle.nn.functional.adaptive_max_pool2d( x=x, output_size=[2, 5]) #out_4 = paddle.nn.functional.adaptive_max_pool2d( # x=x, output_size=[3, 3], data_format="NHWC") out_5 = paddle.nn.functional.adaptive_max_pool2d( x=x, output_size=[None, 3]) exe = paddle.static.Executor(place=place) [res_1, res_2, res_3, res_5] = exe.run( fluid.default_main_program(), feed={"x": self.x_np}, fetch_list=[out_1, out_2, out_3, out_5]) assert np.allclose(res_1, self.res_1_np) assert np.allclose(res_2, self.res_2_np) assert np.allclose(res_3, self.res_3_np) #assert np.allclose(res_4, self.res_4_np) assert np.allclose(res_5, self.res_5_np) def test_dynamic_graph(self): for use_cuda in ([False, True] if core.is_compiled_with_cuda() else [False]): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.disable_static(place=place) x = paddle.to_tensor(self.x_np) out_1 = paddle.nn.functional.adaptive_max_pool2d( x=x, return_mask=False, output_size=[3, 3]) out_2 = paddle.nn.functional.adaptive_max_pool2d(x=x, output_size=5) out_3 = paddle.nn.functional.adaptive_max_pool2d( x=x, output_size=[2, 5]) #out_4 = paddle.nn.functional.adaptive_max_pool2d( # x=x, output_size=[3, 3], data_format="NHWC") out_5 = paddle.nn.functional.adaptive_max_pool2d( x=x, output_size=[None, 3]) assert np.allclose(out_1.numpy(), self.res_1_np) assert np.allclose(out_2.numpy(), self.res_2_np) assert np.allclose(out_3.numpy(), self.res_3_np) #assert np.allclose(out_4.numpy(), self.res_4_np) assert np.allclose(out_5.numpy(), self.res_5_np) class TestAdaptiveMaxPool2DClassAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 7, 7]).astype("float32") self.res_1_np = adaptive_pool2d_forward( x=self.x_np, output_size=[3, 3], pool_type="max") self.res_2_np = adaptive_pool2d_forward( x=self.x_np, output_size=5, pool_type="max") self.res_3_np = adaptive_pool2d_forward( x=self.x_np, output_size=[2, 5], pool_type="max") #self.res_4_np = adaptive_pool2d_forward( # x=self.x_np, # output_size=[3, 3], # pool_type="max", # data_format="NHWC") self.res_5_np = adaptive_pool2d_forward( x=self.x_np, output_size=[None, 3], pool_type="max") def test_static_graph(self): for use_cuda in ([False, True] if core.is_compiled_with_cuda() else [False]): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.enable_static() x = paddle.fluid.data(name="x", shape=[2, 3, 7, 7], dtype="float32") adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[3, 3]) out_1 = adaptive_max_pool(x=x) adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=5) out_2 = adaptive_max_pool(x=x) adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[2, 5]) out_3 = adaptive_max_pool(x=x) # adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D( # output_size=[3, 3], data_format="NHWC") # out_4 = adaptive_max_pool(x=x) adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D( output_size=[None, 3]) out_5 = adaptive_max_pool(x=x) exe = paddle.static.Executor(place=place) [res_1, res_2, res_3, res_5] = exe.run( fluid.default_main_program(), feed={"x": self.x_np}, fetch_list=[out_1, out_2, out_3, out_5]) assert np.allclose(res_1, self.res_1_np) assert np.allclose(res_2, self.res_2_np) assert np.allclose(res_3, self.res_3_np) #assert np.allclose(res_4, self.res_4_np) assert np.allclose(res_5, self.res_5_np) def test_dynamic_graph(self): for use_cuda in ([False, True] if core.is_compiled_with_cuda() else [False]): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.disable_static(place=place) x = paddle.to_tensor(self.x_np) adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[3, 3]) out_1 = adaptive_max_pool(x=x) adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=5) out_2 = adaptive_max_pool(x=x) adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[2, 5]) out_3 = adaptive_max_pool(x=x) #adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D( # output_size=[3, 3], data_format="NHWC") #out_4 = adaptive_max_pool(x=x) adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D( output_size=[None, 3]) out_5 = adaptive_max_pool(x=x) assert np.allclose(out_1.numpy(), self.res_1_np) assert np.allclose(out_2.numpy(), self.res_2_np) assert np.allclose(out_3.numpy(), self.res_3_np) #assert np.allclose(out_4.numpy(), self.res_4_np) assert np.allclose(out_5.numpy(), self.res_5_np) if __name__ == '__main__': unittest.main()