Skip to content

P
pytensor
提交 6877f6fb authored 作者: Laurent Dinh's avatar Laurent Dinh
浏览文件
操作

Added optimization test

上级 25e6df0f
隐藏空白字符变更
内嵌 并排
正在显示 包含 70 行增加 和 23 行删除
theano/sandbox/cuda/tests/test_abstractconv.py
浏览文件 @ 6877f6fb
......@@ -7,7 +7,11 @@ from theano.tests import unittest_tools as utt
import theano.tensor.nnet.abstract_conv as conv
from theano.sandbox.cuda import float32_shared_constructor as gpu_shared
from theano.compile import shared as cpu_shared
from theano.sandbox.cuda.dnn import dnn_available, dnn_conv, dnn_gradweight, dnn_gradinput
from theano.sandbox.cuda.dnn import (
dnn_available, dnn_conv, dnn_gradweight, dnn_gradinput,
GpuDnnConv, GpuDnnConvGradW, GpuDnnConvGradI)
from theano.sandbox.cuda.blas import (
GpuCorrMM, GpuCorrMM_gradWeights, GpuCorrMM_gradInputs)
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda
......@@ -51,7 +55,8 @@ class TestConv2d(unittest.TestCase):
def run_fwd(self, inputs_shape, filters_shape, ref=dnn_conv,
subsample=(1, 1), verify_grad=True, mode=mode_without_gpu,
border_mode='valid', filter_flip=True, device='cpu', provide_shape=False):
border_mode='valid', filter_flip=True, device='cpu', provide_shape=False,
target_op=None):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
......@@ -84,6 +89,11 @@ class TestConv2d(unittest.TestCase):
filter_shape=kshp)
f_ref = theano.function([], c_ref, mode=mode)
f = theano.function([], c, mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
......@@ -96,7 +106,7 @@ class TestConv2d(unittest.TestCase):
def run_gradweight(self, inputs_shape, filters_shape, output_shape,
ref=dnn_gradweight, subsample=(1, 1), filter_flip=True,
verify_grad=True, mode=mode_without_gpu, border_mode='valid',
device='cpu', provide_shape=False):
device='cpu', provide_shape=False, target_op=None):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
output_val = numpy.random.random(output_shape).astype('float32')
......@@ -128,6 +138,11 @@ class TestConv2d(unittest.TestCase):
conv_mode=conv_mode)
f = theano.function([], c, mode)
f_ref = theano.function([], c_ref, mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
......@@ -144,7 +159,8 @@ class TestConv2d(unittest.TestCase):
output_shape, ref=dnn_gradinput,
subsample=(1, 1), filter_flip=True,
verify_grad=True, mode=mode_without_gpu,
border_mode='valid', device='cpu', provide_shape=False):
border_mode='valid', device='cpu', provide_shape=False,
target_op=None):
output_val = numpy.random.random(output_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
......@@ -174,6 +190,11 @@ class TestConv2d(unittest.TestCase):
conv_mode=conv_mode)
f = theano.function([], c, mode)
f_ref = theano.function([], c_ref, mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
......@@ -200,17 +221,17 @@ class TestConv2d(unittest.TestCase):
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=GpuDnnConv)
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=GpuDnnConvGradW)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=GpuDnnConvGradI)
def test_gpucorrmm_conv(self):
if not dnn_available():
......@@ -228,14 +249,16 @@ class TestConv2d(unittest.TestCase):
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=GpuCorrMM)
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip,
target_op=GpuCorrMM_gradWeights)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip,
target_op=GpuCorrMM_gradInputs)
theano/sandbox/gpuarray/tests/test_abstractconv.py
浏览文件 @ 6877f6fb
......@@ -9,7 +9,13 @@ from theano.tests import unittest_tools as utt
import theano.tensor.nnet.abstract_conv as conv
from theano.compile import shared as cpu_shared
from ..type import gpuarray_shared_constructor as gpu_shared
from ..dnn import dnn_available, dnn_conv, dnn_gradweight, dnn_gradinput
from ..dnn import (
dnn_available, dnn_conv, dnn_gradweight, dnn_gradinput,
GpuDnnConv, GpuDnnConvGradW, GpuDnnConvGradI)
from theano.tensor.nnet.corr import (
CorrMM, CorrMM_gradWeights, CorrMM_gradInputs)
from theano.tensor.nnet.conv import ConvOp
from theano.tensor.nnet import ConvGrad3D, ConvTransp3D
from .config import mode_with_gpu, mode_without_gpu, test_ctx_name
......@@ -44,7 +50,7 @@ class TestConv2d(unittest.TestCase):
def run_fwd(self, inputs_shape, filters_shape, ref=dnn_conv,
subsample=(1, 1), verify_grad=True, mode=mode_without_gpu,
border_mode='valid', filter_flip=True, device='cpu',
provide_shape=False):
provide_shape=False, target_op=None):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
......@@ -77,6 +83,11 @@ class TestConv2d(unittest.TestCase):
filter_shape=kshp)
f_ref = theano.function([], c_ref, mode=mode)
f = theano.function([], c, mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
......@@ -89,7 +100,7 @@ class TestConv2d(unittest.TestCase):
def run_gradweight(self, inputs_shape, filters_shape, output_shape,
ref=dnn_gradweight, subsample=(1, 1), filter_flip=True,
verify_grad=True, mode=mode_without_gpu, border_mode='valid',
device='cpu', provide_shape=False):
device='cpu', provide_shape=False, target_op=None):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
output_val = numpy.random.random(output_shape).astype('float32')
......@@ -121,6 +132,11 @@ class TestConv2d(unittest.TestCase):
conv_mode=conv_mode)
f = theano.function([], c, mode)
f_ref = theano.function([], c_ref, mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
......@@ -135,7 +151,8 @@ class TestConv2d(unittest.TestCase):
def run_gradinput(self, inputs_shape, filters_shape, output_shape, ref=dnn_gradinput,
subsample=(1, 1), filter_flip=True, verify_grad=True, mode=mode_without_gpu,
border_mode='valid', device='cpu', provide_shape=False):
border_mode='valid', device='cpu', provide_shape=False,
target_op=None):
output_val = numpy.random.random(output_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
......@@ -165,6 +182,11 @@ class TestConv2d(unittest.TestCase):
conv_mode=conv_mode)
f = theano.function([], c, mode)
f_ref = theano.function([], c_ref, mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
......@@ -192,17 +214,17 @@ class TestConv2d(unittest.TestCase):
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=GpuDnnConv)
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=GpuDnnConvGradW)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=GpuDnnConvGradI)
def test_cormm_conv(self):
if not dnn_available(test_ctx_name):
......@@ -220,17 +242,17 @@ class TestConv2d(unittest.TestCase):
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=CorrMM)
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=CorrMM_gradWeights)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=CorrMM_gradInputs)
def test_cpu_conv(self):
if not dnn_available(test_ctx_name):
......@@ -271,7 +293,7 @@ class TestConv2d(unittest.TestCase):
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip, target_op=ConvOp)
else:
self.assertRaises(NotImplementedError,
self.run_fwd,
......@@ -290,7 +312,8 @@ class TestConv2d(unittest.TestCase):
output_shape=o, subsample=s,
verify_grad=False, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip,
target_op=(ConvOp, ConvGrad3D))
else:
self.assertRaises(NotImplementedError,
self.run_gradweight,
......@@ -310,7 +333,8 @@ class TestConv2d(unittest.TestCase):
output_shape=o, subsample=s,
verify_grad=False, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
filter_flip=flip,
target_op=(ConvOp, ConvTransp3D))
else:
self.assertRaises(NotImplementedError,
self.run_gradinput,
......
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