Skip to content

P
pytensor
提交 d286cadb authored 作者: Gijs van Tulder's avatar Gijs van Tulder
浏览文件
操作

DnnConv grad ops in gpuarray backend check shape consistency.

上级 d01b5839
显示空白字符变更
内嵌 并排
正在显示 包含 103 行增加 和 24 行删除
theano/gpuarray/dnn_gi.c
浏览文件 @ d286cadb
...@@ -77,6 +77,48 @@ APPLY_SPECIFIC(conv_gi)(PyGpuArrayObject *kerns, PyGpuArrayObject *output, ...@@ -77,6 +77,48 @@ APPLY_SPECIFIC(conv_gi)(PyGpuArrayObject *kerns, PyGpuArrayObject *output,
cuda_enter(c->ctx); cuda_enter(c->ctx);
int expected_output_dims[5] = {0};
err = cudnnGetConvolutionNdForwardOutputDim(desc, APPLY_SPECIFIC(input), APPLY_SPECIFIC(kerns),
PyGpuArray_NDIM(im), expected_output_dims);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error computing convolution output dim: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
if (PyGpuArray_NDIM(im) == 4) {
if ((PyGpuArray_DIMS(output)[0] != expected_output_dims[0]) ||
(PyGpuArray_DIMS(output)[1] != expected_output_dims[1]) ||
(PyGpuArray_DIMS(output)[2] != expected_output_dims[2]) ||
(PyGpuArray_DIMS(output)[3] != expected_output_dims[3])) {
PyErr_Format(PyExc_ValueError, "impossible convolution output dim: expected %ldx%ldx%ldx%ld"
" but received gradient with shape %ldx%ldx%ldx%ld",
expected_output_dims[0], expected_output_dims[1],
expected_output_dims[2], expected_output_dims[3],
PyGpuArray_DIMS(output)[0], PyGpuArray_DIMS(output)[1],
PyGpuArray_DIMS(output)[2], PyGpuArray_DIMS(output)[3]);
cuda_exit(c->ctx);
return 1;
}
} else if (PyGpuArray_NDIM(im) == 5) {
if ((PyGpuArray_DIMS(output)[0] != expected_output_dims[0]) ||
(PyGpuArray_DIMS(output)[1] != expected_output_dims[1]) ||
(PyGpuArray_DIMS(output)[2] != expected_output_dims[2]) ||
(PyGpuArray_DIMS(output)[3] != expected_output_dims[3]) ||
(PyGpuArray_DIMS(output)[4] != expected_output_dims[4])) {
PyErr_Format(PyExc_ValueError, "impossible convolution output dim: expected %ldx%ldx%ldx%ldx%ld"
" but received gradient with shape %ldx%ldx%ldx%ldx%ld",
expected_output_dims[0], expected_output_dims[1],
expected_output_dims[2], expected_output_dims[3],
expected_output_dims[4],
PyGpuArray_DIMS(output)[0], PyGpuArray_DIMS(output)[1],
PyGpuArray_DIMS(output)[2], PyGpuArray_DIMS(output)[3],
PyGpuArray_DIMS(output)[4]);
cuda_exit(c->ctx);
return 1;
}
}
#ifdef CHOOSE_ALGO #ifdef CHOOSE_ALGO
#ifndef CHOOSE_ONCE #ifndef CHOOSE_ONCE
reuse_algo = 1; reuse_algo = 1;
......
theano/gpuarray/dnn_gw.c
浏览文件 @ d286cadb
...@@ -77,6 +77,48 @@ APPLY_SPECIFIC(conv_gw)(PyGpuArrayObject *input, PyGpuArrayObject *output, ...@@ -77,6 +77,48 @@ APPLY_SPECIFIC(conv_gw)(PyGpuArrayObject *input, PyGpuArrayObject *output,
cuda_enter(c->ctx); cuda_enter(c->ctx);
int expected_output_dims[5] = {0};
err = cudnnGetConvolutionNdForwardOutputDim(desc, APPLY_SPECIFIC(input), APPLY_SPECIFIC(kerns),
PyGpuArray_NDIM(input), expected_output_dims);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error computing convolution output dim: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
if (PyGpuArray_NDIM(input) == 4) {
if ((PyGpuArray_DIMS(output)[0] != expected_output_dims[0]) ||
(PyGpuArray_DIMS(output)[1] != expected_output_dims[1]) ||
(PyGpuArray_DIMS(output)[2] != expected_output_dims[2]) ||
(PyGpuArray_DIMS(output)[3] != expected_output_dims[3])) {
PyErr_Format(PyExc_ValueError, "impossible convolution output dim: expected %ldx%ldx%dx%ld"
" but received gradient with shape %ldx%ldx%dx%ld",
expected_output_dims[0], expected_output_dims[1],
expected_output_dims[2], expected_output_dims[3],
PyGpuArray_DIMS(output)[0], PyGpuArray_DIMS(output)[1],
PyGpuArray_DIMS(output)[2], PyGpuArray_DIMS(output)[3]);
cuda_exit(c->ctx);
return 1;
}
} else if (PyGpuArray_NDIM(input) == 5) {
if ((PyGpuArray_DIMS(output)[0] != expected_output_dims[0]) ||
(PyGpuArray_DIMS(output)[1] != expected_output_dims[1]) ||
(PyGpuArray_DIMS(output)[2] != expected_output_dims[2]) ||
(PyGpuArray_DIMS(output)[3] != expected_output_dims[3]) ||
(PyGpuArray_DIMS(output)[4] != expected_output_dims[4])) {
PyErr_Format(PyExc_ValueError, "impossible convolution output dim: expected %ldx%ldx%ldx%ldx%ld"
" but received gradient with shape %ldx%ldx%ldx%ldx%ld",
expected_output_dims[0], expected_output_dims[1],
expected_output_dims[2], expected_output_dims[3],
expected_output_dims[4],
PyGpuArray_DIMS(output)[0], PyGpuArray_DIMS(output)[1],
PyGpuArray_DIMS(output)[2], PyGpuArray_DIMS(output)[3],
PyGpuArray_DIMS(output)[4]);
cuda_exit(c->ctx);
return 1;
}
}
#ifdef CHOOSE_ALGO #ifdef CHOOSE_ALGO
#ifndef CHOOSE_ONCE #ifndef CHOOSE_ONCE
reuse_algo = 1; reuse_algo = 1;
......
theano/gpuarray/tests/test_dnn.py
浏览文件 @ d286cadb
...@@ -12,6 +12,7 @@ import theano.tensor as T ...@@ -12,6 +12,7 @@ import theano.tensor as T
import theano.tests.unittest_tools as utt import theano.tests.unittest_tools as utt
from theano.tensor.signal.pool import pool_2d, pool_3d from theano.tensor.signal.pool import pool_2d, pool_3d
from theano.tensor.signal.pool import Pool, MaxPoolGrad, AveragePoolGrad from theano.tensor.signal.pool import Pool, MaxPoolGrad, AveragePoolGrad
from theano.tensor.nnet.abstract_conv import get_conv_output_shape
from .. import dnn from .. import dnn
from ..basic_ops import GpuAllocEmpty from ..basic_ops import GpuAllocEmpty
...@@ -628,56 +629,50 @@ class TestDnnInferShapes(utt.InferShapeTester): ...@@ -628,56 +629,50 @@ class TestDnnInferShapes(utt.InferShapeTester):
[(1, 1, 1), (2, 2, 2)], [(1, 1, 1), (2, 2, 2)],
'none') 'none')
def _test_conv_gradw(self, img, kerns, out, img_val, kern_vals, border_mode, conv_mode, subsample): def _test_conv_gradw(self, img, topgrad, kerns, img_shape, kerns_shape, border_mode, conv_mode, subsample):
if not dnn.dnn_available(test_ctx_name): if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg) raise SkipTest(dnn.dnn_available.msg)
topgrad_shape = get_conv_output_shape(img_shape, kerns_shape,
border_mode, subsample)
img_val = numpy.asarray( img_val = numpy.asarray(
img_val, numpy.random.rand(*img_shape),
dtype=theano.config.floatX dtype=theano.config.floatX
) )
kern_vals = numpy.asarray( topgrad_vals = numpy.asarray(
kern_vals, numpy.random.rand(*topgrad_shape),
dtype=theano.config.floatX dtype=theano.config.floatX
) )
temp_img = img.dimshuffle(1, 0, 2, 3) kerns_vals = numpy.zeros(kerns_shape, dtype=theano.config.floatX)
temp_kerns = kerns kerns_shape = theano.shared(numpy.asarray(kerns_shape))
if conv_mode == 'conv':
temp_kerns = temp_kerns[:, :, ::-1, ::-1]
temp_kerns = temp_kerns.dimshuffle(1, 0, 2, 3)
shape = (
kern_vals.shape[1], img_val.shape[1],
img_val.shape[2] - kern_vals.shape[2] + 1,
img_val.shape[3] - kern_vals.shape[3] + 1
)
out_vals = numpy.zeros(shape, dtype=theano.config.floatX)
desc = dnn.GpuDnnConvDesc( desc = dnn.GpuDnnConvDesc(
border_mode=border_mode, border_mode=border_mode,
subsample=subsample, subsample=subsample,
conv_mode=conv_mode, conv_mode=conv_mode,
precision=set_precision(theano.config.floatX) precision=set_precision(theano.config.floatX)
)(out.shape) )(kerns_shape)
conv_grad_w = dnn.GpuDnnConvGradW()( conv_grad_w = dnn.GpuDnnConvGradW()(
temp_img, img,
temp_kerns, topgrad,
out, kerns,
desc, desc,
) )
self._compile_and_check( self._compile_and_check(
[temp_img, temp_kerns, out], [img, topgrad, kerns],
[conv_grad_w], [conv_grad_w],
[img_val, kern_vals, out_vals], [img_val, topgrad_vals, kerns_vals],
dnn.GpuDnnConvGradW dnn.GpuDnnConvGradW
) )
@parameterized.expand(product(border_modes, conv_modes), utt.custom_name_func) @parameterized.expand(product(border_modes, conv_modes), utt.custom_name_func)
def test_conv_gradw(self, border_mode, conv_mode): def test_conv_gradw(self, border_mode, conv_mode):
self._test_conv_gradw(T.tensor4('img'), self._test_conv_gradw(T.tensor4('img'),
T.tensor4('topgrad'),
T.tensor4('kerns'), T.tensor4('kerns'),
T.tensor4('out'), (5, 2, 6, 13),
numpy.random.rand(2, 5, 6, 8), (1, 2, 3, 7),
numpy.random.rand(2, 1, 5, 6),
border_mode, border_mode,
conv_mode, conv_mode,
(1, 1)) (1, 1))
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论