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提交 ebab0a06 authored 作者: affanv14's avatar affanv14 提交者： Mohammed Affan
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change variable names in tests

上级 f8955c9e
隐藏空白字符变更
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正在显示 包含 40 行增加 和 40 行删除
theano/tensor/nnet/tests/test_abstract_conv.py
浏览文件 @ ebab0a06
......@@ -1727,23 +1727,23 @@ class Grouped_conv_noOptim(unittest.TestCase):
img_sym = theano.tensor.tensor4('img')
kern_sym = theano.tensor.tensor4('kern')
grouped_abstractconv_func = self.conv2d(border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation,
num_groups=groups)
grouped_abstractconv_op = self.conv2d(border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation,
num_groups=groups)
if self.flip_filter:
grouped_conv_output = grouped_abstractconv_func(img_sym, kern_sym[:, :, ::-1, ::-1])
grouped_conv_output = grouped_abstractconv_op(img_sym, kern_sym[:, :, ::-1, ::-1])
else:
grouped_conv_output = grouped_abstractconv_func(img_sym, kern_sym)
grouped_conv_output = grouped_abstractconv_op(img_sym, kern_sym)
grouped_func = theano.function([img_sym, kern_sym], grouped_conv_output, mode=self.mode)
grouped_output = grouped_func(img, kern)
normal_conv_output = conv2d_corr(img_sym,
kern_sym,
border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation)
normal_func = theano.function([img_sym, kern_sym], normal_conv_output,
normal_conv_op = conv2d_corr(img_sym,
kern_sym,
border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation)
normal_func = theano.function([img_sym, kern_sym], normal_conv_op,
mode=self.ref_mode)
normal_concat_output = [normal_func(img_arr, kern_arr)
for img_arr, kern_arr in zip(split_imgs, split_kern)]
......@@ -1751,7 +1751,7 @@ class Grouped_conv_noOptim(unittest.TestCase):
utt.assert_allclose(grouped_output, normal_concat_output)
utt.verify_grad(grouped_abstractconv_func,
utt.verify_grad(grouped_abstractconv_op,
[img, kern],
mode=self.mode)
......@@ -1764,23 +1764,23 @@ class Grouped_conv_noOptim(unittest.TestCase):
img_sym = theano.tensor.tensor4('img')
top_sym = theano.tensor.tensor4('top')
grouped_abstractconvgrad_func = self.conv2d_gradw(border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation,
num_groups=groups)
grouped_conv_output = grouped_abstractconvgrad_func(img_sym, top_sym, kshp[-2:])
grouped_abstractconvgrad_op = self.conv2d_gradw(border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation,
num_groups=groups)
grouped_conv_output = grouped_abstractconvgrad_op(img_sym, top_sym, kshp[-2:])
if self.flip_filter:
grouped_conv_output = grouped_conv_output[:, :, ::-1, ::-1]
grouped_func = theano.function([img_sym, top_sym], grouped_conv_output, mode=self.mode)
grouped_output = grouped_func(img, top)
normal_conv_output = conv2d_corr_gw(img_sym,
top_sym,
kshp,
border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation)
normal_func = theano.function([img_sym, top_sym], normal_conv_output,
normal_conv_op = conv2d_corr_gw(img_sym,
top_sym,
kshp,
border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation)
normal_func = theano.function([img_sym, top_sym], normal_conv_op,
mode=self.ref_mode)
normal_concat_output = [normal_func(img_arr, top_arr)
for img_arr, top_arr in zip(split_imgs, split_top)]
......@@ -1789,7 +1789,7 @@ class Grouped_conv_noOptim(unittest.TestCase):
utt.assert_allclose(grouped_output, normal_concat_output)
def abstract_conv_gradweight(inputs_val, output_val):
return grouped_abstractconvgrad_func(inputs_val, output_val, kshp[-2:])
return grouped_abstractconvgrad_op(inputs_val, output_val, kshp[-2:])
utt.verify_grad(abstract_conv_gradweight,
[img, top],
......@@ -1805,24 +1805,24 @@ class Grouped_conv_noOptim(unittest.TestCase):
kern_sym = theano.tensor.tensor4('kern')
top_sym = theano.tensor.tensor4('top')
grouped_abstractconvgrad_func = self.conv2d_gradi(border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation,
num_groups=groups)
grouped_abstractconvgrad_op = self.conv2d_gradi(border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation,
num_groups=groups)
if self.flip_filter:
grouped_conv_output = grouped_abstractconvgrad_func(kern_sym[:, :, ::-1, ::-1], top_sym, imshp[-2:])
grouped_conv_output = grouped_abstractconvgrad_op(kern_sym[:, :, ::-1, ::-1], top_sym, imshp[-2:])
else:
grouped_conv_output = grouped_abstractconvgrad_func(kern_sym, top_sym, imshp[-2:])
grouped_conv_output = grouped_abstractconvgrad_op(kern_sym, top_sym, imshp[-2:])
grouped_func = theano.function([kern_sym, top_sym], grouped_conv_output, mode=self.mode)
grouped_output = grouped_func(kern, top)
normal_conv_output = conv2d_corr_gi(kern_sym,
top_sym,
imshp,
border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation)
normal_func = theano.function([kern_sym, top_sym], normal_conv_output,
normal_conv_op = conv2d_corr_gi(kern_sym,
top_sym,
imshp,
border_mode=self.border_mode,
subsample=self.subsample,
filter_dilation=self.filter_dilation)
normal_func = theano.function([kern_sym, top_sym], normal_conv_op,
mode=self.ref_mode)
normal_concat_output = [normal_func(kern_arr, top_arr)
for kern_arr, top_arr in zip(split_kerns, split_top)]
......@@ -1831,7 +1831,7 @@ class Grouped_conv_noOptim(unittest.TestCase):
utt.assert_allclose(grouped_output, normal_concat_output)
def abstract_conv_gradinputs(filters_val, output_val):
return grouped_abstractconvgrad_func(filters_val, output_val, imshp[2:])
return grouped_abstractconvgrad_op(filters_val, output_val, imshp[2:])
utt.verify_grad(abstract_conv_gradinputs,
[kern, top],
......
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