def check_avg_divisor(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) padding = 0 result = avg_pool3d(input, kernel_size=2, stride=2, padding=padding, divisor_override=8) result_np = pool3D_forward_naive(input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='avg') self.assertTrue(np.allclose(result.numpy(), result_np)) avg_pool3d_dg = paddle.nn.layer.AvgPool3d(kernel_size=2, stride=2, padding=0) result = avg_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) padding = [0, 0, 0, 0, 0, 0] result = avg_pool3d(input, kernel_size=2, stride=2, padding=padding, divisor_override=8) self.assertTrue(np.allclose(result.numpy(), result_np))
def check_max_dygraph_padding(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) padding = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]] result = max_pool3d(input, kernel_size=2, stride=2, padding=padding) result_np = pool3D_forward_naive(input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='max') self.assertTrue(np.allclose(result.numpy(), result_np)) max_pool3d_dg = paddle.nn.layer.MaxPool3d(kernel_size=2, stride=2, padding=0) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) padding = [0, 0, 0, 0, 0, 0] result = max_pool3d(input, kernel_size=2, stride=2, padding=padding) self.assertTrue(np.allclose(result.numpy(), result_np))
def check_avg_static_results(self, place): with fluid.program_guard(fluid.Program(), fluid.Program()): input = fluid.data(name="input", shape=[2, 3, 32, 32, 32], dtype="float32") result = avg_pool3d(input, kernel_size=2, stride=2, padding=0) input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") result_np = pool3D_forward_naive(input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='avg') exe = fluid.Executor(place) fetches = exe.run(fluid.default_main_program(), feed={"input": input_np}, fetch_list=[result]) self.assertTrue(np.allclose(fetches[0], result_np))
def check_avg_dygraph_results(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) result = avg_pool3d(input, kernel_size=2, stride=2, padding="SAME") result_np = pool3D_forward_naive(input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='avg', padding_algorithm="SAME") self.assertTrue(np.allclose(result.numpy(), result_np)) avg_pool3d_dg = paddle.nn.layer.AvgPool3d(kernel_size=2, stride=None, padding="SAME") result = avg_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np))
def check_max_dygraph_ndhwc_results(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") input = fluid.dygraph.to_variable( np.transpose(input_np, [0, 2, 3, 4, 1])) result = max_pool3d(input, kernel_size=2, stride=2, padding=0, data_format="NDHWC", return_indices=False) result_np = pool3D_forward_naive(input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='max') self.assertTrue( np.allclose(np.transpose(result.numpy(), [0, 4, 1, 2, 3]), result_np))
def check_max_dygraph_stride_is_none(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) result, indices = max_pool3d(input, kernel_size=2, stride=None, padding="SAME", return_indices=True) result_np = pool3D_forward_naive(input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='max', padding_algorithm="SAME") self.assertTrue(np.allclose(result.numpy(), result_np)) max_pool3d_dg = paddle.nn.layer.MaxPool3d(kernel_size=2, stride=2, padding=0) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np))