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提交 4eda607b authored 作者: Tegan Maharaj's avatar Tegan Maharaj
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fixed pep and flake errors

上级 e183ab3b
隐藏空白字符变更
内嵌 并排
正在显示 包含 57 行增加 和 78 行删除
theano/tensor/nnet/tests/test_abstract_conv.py
浏览文件 @ 4eda607b
......@@ -33,9 +33,6 @@ from theano.tensor.nnet.ConvGrad3D import ConvGrad3D
from theano.tensor.nnet.ConvTransp3D import ConvTransp3D
def conv2d_corr(inputs, filters, border_mode="valid",
subsample=(1, 1), conv_mode='conv',
filter_dilation=(1, 1)):
......@@ -1592,107 +1589,90 @@ class TestConv2dTranspose(unittest.TestCase):
#TM copying the TestConvTypes setup, not sure exactly what unittest.TestCase is
# TM copying the TestConvTypes setup, not sure exactly what unittest.TestCase is
class TestConv2dGrads(unittest.TestCase):
def __init__(self):
self.random_stream = numpy.random.RandomState(unittest_tools.fetch_seed())
self.inputs_shapes = [(8,1,12,12), (1,1,5,5), (1,1,5,6), (1,1,6,6)]
self.filters_shapes = [(5,1,2,2), (1,1,3,3)]
def setUp(self):
self.random_stream = numpy.random.RandomState(utt.fetch_seed())
self.inputs_shapes = [(8, 1, 12, 12), (1, 1, 5, 5), (1, 1, 5, 6), (1, 1, 6, 6)]
self.filters_shapes = [(5, 1, 2, 2), (1, 1, 3, 3)]
self.subsamples = [(1,1), (2,2)]
self.subsamples = [(1, 1), (2, 2)]
self.border_modes = ["valid", "full"]
self.filter_flip = [True, False]
self.output_grad = T.tensor4()
self.output_grad_wrt = T.tensor4()
self.filters = T.tensor4()
self.output_grad = theano.tensor.tensor4()
self.output_grad_wrt = theano.tensor.tensor4()
self.filters = theano.tensor.tensor4()
self.x = T.tensor4('x', theano.config.floatX) #inputs
self.w = T.tensor4('w', theano.config.floatX) #weights
#first two must be wrong; I'm not sure how to use TestCase
if unittest.TestCase == 'inputs':
test_conv2d_grad_wrt_inputs()
elif unittest.TestCase =='weights':
test_conv2d_grad_wrt_weights():
else:
self.test_conv2_grads_wrt_input_and_weights()
self.x = theano.tensor.tensor4('x', theano.config.floatX) # inputs
self.w = theano.tensor.tensor4('w', theano.config.floatX) # weights
def test_conv2d_grad_wrt_inputs():
def test_conv2d_grad_wrt_inputs(self):
"""Compares calculated abstract grads wrt inputs with the fwd grads
This method checks the outputs of conv2_grad_wrt_inputs against
the outputs of T.nnet.conv forward grads to make sure the
results are the same.
"""
for (in_shape, fltr_shape) in zip(self.inputs_shapes, self.filters_shapes):
for bm in self.border_modes:
for ss in self.subsamples:
for ff in self.filter_flip:
if filter_flip = True:
fltr_shape = transpose(fltr_shape) #conv2d doesn't seem to have filter_flip
conv_out = T.nnet.conv.conv2d(self.x,
filters = self.filters,
border_mode = bm,
subsample = ss,
image_shape = in_shape
filter_shape = fltr_shape
)
conv_grad = theano.grad(wrt=[x], known_grads={conv_out: self.output_grad})
f_prime = theano.function([x, output_grad, filters], conv_grad)
conv_wrt_i_out = T.nnet.conv.abstract_conv.conv2d_grad_wrt_inputs(self.output_grad_wrt,
filters = self.filters,
border_mode = bm,
subsample = ss,
input_shape = in_shape,
filter_shape = fltr_shape,
filter_flip = ff
)
f = theano.function([x, output_grad_wrt, filters], conv_wrt_i_out)
if self.filter_flip:
fltr_shape = fltr_shape[::1] # conv2d doesn't seem to have filter_flip
conv_out = theano.tensor.nnet.conv.conv2d(self.x,
filters=self.filters,
border_mode=bm,
subsample=ss,
image_shape=in_shape,
filter_shape=fltr_shape
)
conv_grad = theano.grad(conv_out.sum(), wrt=[self.x], known_grads={conv_out: self.output_grad})
f_prime = theano.function([self.x, self.output_grad, self.filters], conv_grad)
conv_wrt_i_out = theano.tensor.nnet.conv.abstract_conv.conv2d_grad_wrt_inputs(self.output_grad_wrt,
filters=self.filters,
border_mode=bm,
subsample=ss,
input_shape=in_shape,
filter_shape=fltr_shape,
filter_flip=ff
)
f = theano.function([self.x, self.output_grad_wrt, self.filters], conv_wrt_i_out)
utt.assert_allclose(f, f_prime)
def test_conv2d_grad_wrt_weights():
def test_conv2d_grad_wrt_weights(self):
"""Compares calculated abstract grads wrt weights with the fwd grads
This method checks the outputs of conv2_grad_wrt_weights against
the outputs of T.nnet.conv forward grads to make sure the
results are the same.
"""
for (in_shape, fltr_shape) in zip(self.inputs_shapes, self.filters_shapes):
for bm in self.border_modes:
for ss in self.subsamples:
for ff in self.filter_flip:
if filter_flip = True:
fltr_shape = transpose(fltr_shape) #conv2d doesn't seem to have filter_flip
conv_out = T.nnet.conv.conv2d(self.w,
filters = self.filters,
border_mode = bm,
subsample = ss,
image_shape = in_shape,
filter_shape = fltr_shape
)
conv_grad = theano.grad(wrt=[w], known_grads={conv_out: output_grad})
f_prime = theano.function([w, output_grad, filters], conv_grad)
conv_wrt_w_out = T.nnet.conv.abstract_conv.conv2d_grad_wrt_weights(self.output_grad_wrt,
filters = self.filters,
border_mode = bm,
subsample = ss,
input_shape = in_shape,
filter_shape = fltr_shape,
filter_flip = ff
)
f = theano.function([w, output_grad_wrt, filters], conv_wrt_w_out)
if self.filter_flip:
fltr_shape = fltr_shape[::1] # conv2d doesn't seem to have filter_flip
conv_out = theano.tensor.nnet.conv.conv2d(self.w,
filters=self.filters,
border_mode=bm,
subsample=ss,
image_shape=in_shape,
filter_shape=fltr_shape
)
conv_grad = theano.grad(conv_out.sum(), wrt=[self.w], known_grads={conv_out: self.output_grad})
f_prime = theano.function([self.w, self.output_grad, self.filters], conv_grad)
conv_wrt_w_out = theano.tensor.nnet.conv.abstract_conv.conv2d_grad_wrt_weights(self.output_grad_wrt,
filters=self.filters,
border_mode=bm,
subsample=ss,
input_shape=in_shape,
filter_shape=fltr_shape,
filter_flip=ff
)
f = theano.function([self.w, self.output_grad_wrt, self.filters], conv_wrt_w_out)
utt.assert_allclose(f, f_prime)
def test_conv2_grads_wrt_input_and_weights():
test_conv2d_grad_wrt_inputs()
test_conv2d_grad_wrt_weights()
\ No newline at end of file
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