diff --git a/paddle/phi/kernels/impl/fold_grad_kernel_impl.h b/paddle/phi/kernels/impl/fold_grad_kernel_impl.h index b9320eab85046..5b5cf5280285d 100644 --- a/paddle/phi/kernels/impl/fold_grad_kernel_impl.h +++ b/paddle/phi/kernels/impl/fold_grad_kernel_impl.h @@ -54,11 +54,8 @@ void FoldGradKernel(const Context& ctx, DDim out_shape = make_ddim({n_output_plane, output_sizes[0], output_sizes[1]}); - DDim input_matrix_shape = make_ddim({x_dims[0], - kernel_sizes[0], - kernel_sizes[1], - output_height, - output_width}); + DDim input_matrix_shape = make_ddim( + {1, kernel_sizes[0], kernel_sizes[1], output_height, output_width}); paddle::operators::math:: Im2ColFunctor diff --git a/paddle/phi/kernels/impl/fold_kernel_impl.h b/paddle/phi/kernels/impl/fold_kernel_impl.h index 415beca7bd928..72a86fb860414 100644 --- a/paddle/phi/kernels/impl/fold_kernel_impl.h +++ b/paddle/phi/kernels/impl/fold_kernel_impl.h @@ -56,11 +56,8 @@ void FoldKernel(const Context& ctx, DDim output_shape = make_ddim({n_output_plane, output_sizes[0], output_sizes[1]}); - DDim input_matrix_shape = make_ddim({x_dims[0], - kernel_sizes[0], - kernel_sizes[1], - output_height, - output_width}); + DDim input_matrix_shape = make_ddim( + {1, kernel_sizes[0], kernel_sizes[1], output_height, output_width}); phi::funcs::SetConstant set_zero; set_zero(ctx, out, static_cast(0)); @@ -68,6 +65,7 @@ void FoldKernel(const Context& ctx, for (int i = 0; i < batch_size; i++) { DenseTensor out_batch = out->Slice(i, i + 1).Resize(output_shape); // im size=3 + DenseTensor in_batch = x.Slice(i, i + 1).Resize(input_matrix_shape); // col size=5 col2im(ctx, in_batch, dilations, strides, paddings, &out_batch); diff --git a/python/paddle/fluid/tests/unittests/test_fold_op.py b/python/paddle/fluid/tests/unittests/test_fold_op.py index 0dec09ddf625d..960ef5f4f9984 100644 --- a/python/paddle/fluid/tests/unittests/test_fold_op.py +++ b/python/paddle/fluid/tests/unittests/test_fold_op.py @@ -14,6 +14,7 @@ from __future__ import print_function + import math import numpy as np import unittest @@ -45,34 +46,64 @@ def init_data(self): def calc_fold(self): output_shape = [0] * 4 output_shape[0] = self.batch_size - output_shape[1] = int(self.input_channels / - (self.kernel_sizes[0] * self.kernel_sizes[1])) + output_shape[1] = int( + self.input_channels / (self.kernel_sizes[0] * self.kernel_sizes[1]) + ) output_shape[2] = self.output_sizes[0] output_shape[3] = self.output_sizes[1] dkernel_h = self.dilations[0] * (self.kernel_sizes[0] - 1) + 1 dkernel_w = self.dilations[1] * (self.kernel_sizes[1] - 1) + 1 - col_height = int((self.output_sizes[0] + self.paddings[0] + - self.paddings[2] - dkernel_h) / self.strides[0]) + 1 - col_width = int((self.output_sizes[1] + self.paddings[1] + - self.paddings[3] - dkernel_w) / self.strides[1]) + 1 + col_height = ( + int( + ( + self.output_sizes[0] + + self.paddings[0] + + self.paddings[2] + - dkernel_h + ) + / self.strides[0] + ) + + 1 + ) + col_width = ( + int( + ( + self.output_sizes[1] + + self.paddings[1] + + self.paddings[3] + - dkernel_w + ) + / self.strides[1] + ) + + 1 + ) output = np.zeros(output_shape).astype(np.float64) ############ calculate output ############## for b in range(output_shape[0]): for c in range(self.input_channels): w_offset = int(c % self.kernel_sizes[1]) h_offset = int( - (c / self.kernel_sizes[1]) % self.kernel_sizes[0]) + (c / self.kernel_sizes[1]) % self.kernel_sizes[0] + ) c_out = int(c / self.kernel_sizes[0] / self.kernel_sizes[1]) for h in range(col_height): - h_out = int(h * self.strides[0] - self.paddings[0] + - h_offset * self.dilations[0]) + h_out = int( + h * self.strides[0] + - self.paddings[0] + + h_offset * self.dilations[0] + ) for w in range(col_width): - w_out = int(w * self.strides[1] - self.paddings[1] + - w_offset * self.dilations[1]) + w_out = int( + w * self.strides[1] + - self.paddings[1] + + w_offset * self.dilations[1] + ) if (h_out >= 0 and h_out < self.output_sizes[0]) and ( - w_out >= 0 and w_out < self.output_sizes[1]): - output[b, c_out, h_out, - w_out] += self.x[b, c, w + col_width * h] + w_out >= 0 and w_out < self.output_sizes[1] + ): + output[b, c_out, h_out, w_out] += self.x[ + b, c, w + col_width * h + ] self.outputs = output @@ -85,7 +116,7 @@ def set_data(self): 'paddings': self.paddings, 'dilations': self.dilations, 'strides': self.strides, - 'output_sizes': self.output_sizes + 'output_sizes': self.output_sizes, } self.outputs = {'Y': self.outputs} @@ -101,9 +132,23 @@ def test_check_grad(self): self.check_grad(['X'], 'Y', check_eager=True) +class TestFoldshape(TestFoldOp): + def init_data(self): + self.batch_size = 8 + self.input_channels = 3 * 3 * 3 + self.length = 6 + self.kernel_sizes = [3, 3] + self.strides = [1, 1] + self.paddings = [0, 0, 0, 0] + self.dilations = [1, 1] + self.output_sizes = [4, 5] + input_shape = [self.batch_size, self.input_channels, self.length] + self.x = np.random.rand(*input_shape).astype(np.float64) + + class TestFoldAPI(TestFoldOp): - #This is for test on paddle.nn.Fold + # This is for test on paddle.nn.Fold def setUp(self): self.op_type = 'fold' @@ -120,19 +165,19 @@ def test_api(self): m = paddle.nn.Fold(**self.attrs) m.eval() result = m(input) - np.testing.assert_allclose(result.numpy(), - self.outputs['Y'], - rtol=1e-05) + np.testing.assert_allclose( + result.numpy(), self.outputs['Y'], rtol=1e-05 + ) def test_info(self): str(paddle.nn.Fold(**self.attrs)) class TestFoldOpError(unittest.TestCase): - def test_errors(self): from paddle.nn.functional import fold from paddle.fluid.framework import Program, program_guard + with program_guard(Program(), Program()): def test_input_shape(): @@ -148,59 +193,67 @@ def test_kernel_shape(): def test_padding_shape(): # padding_size must be 2 or 4 x = paddle.randn(shape=[2, 6, 6], dtype="float32") - out = fold(x, - output_sizes=[2, 3], - kernel_sizes=[2, 2], - paddings=[2, 2, 3]) + out = fold( + x, + output_sizes=[2, 3], + kernel_sizes=[2, 2], + paddings=[2, 2, 3], + ) def test_dilations_shape(): # dialtions_size must be 2 x = paddle.randn(shape=[2, 6, 6], dtype="float32") - out = fold(x, - output_sizes=[2, 3], - kernel_sizes=[2, 2], - dilations=[2, 2, 3]) + out = fold( + x, + output_sizes=[2, 3], + kernel_sizes=[2, 2], + dilations=[2, 2, 3], + ) def test_strides_shape(): # strids_size must be 2 x = paddle.randn(shape=[2, 6, 6], dtype="float32") - out = fold(x, - output_sizes=[2, 3], - kernel_sizes=[2, 2], - strides=[2, 2, 3]) + out = fold( + x, + output_sizes=[2, 3], + kernel_sizes=[2, 2], + strides=[2, 2, 3], + ) def test_output_size(): # im_h * im_w must be L x = paddle.randn(shape=[2, 6, 6], dtype="float32") - out = fold(x, - output_sizes=[6, 6], - kernel_sizes=[2, 2], - strides=[1, 1]) + out = fold( + x, output_sizes=[6, 6], kernel_sizes=[2, 2], strides=[1, 1] + ) def test_output_size_2(): # out_size must GT 1 x = paddle.randn(shape=[2, 6, 6], dtype="float32") - out = fold(x, - output_sizes=[0.1, 0.2], - kernel_sizes=[2, 2], - strides=[1, 1]) + out = fold( + x, + output_sizes=[0.1, 0.2], + kernel_sizes=[2, 2], + strides=[1, 1], + ) def test_block_h_w(): # test_block_h_w GT 0 x = paddle.randn(shape=[2, 1, 1], dtype="float32") - out = fold(x, - output_sizes=[1, 1], - kernel_sizes=[2, 2], - strides=1) + out = fold( + x, output_sizes=[1, 1], kernel_sizes=[2, 2], strides=1 + ) def test_GT_0(): x = paddle.randn(shape=[2, 1, 1], dtype="float32") - out = fold(x, - output_sizes=[0, 0], - kernel_sizes=[0, 0], - dilations=0, - paddings=[0, 0], - strides=0) + out = fold( + x, + output_sizes=[0, 0], + kernel_sizes=[0, 0], + dilations=0, + paddings=[0, 0], + strides=0, + ) self.assertRaises(AssertionError, test_input_shape) self.assertRaises(AssertionError, test_kernel_shape)