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提交 58c3d08e authored 作者: Vikram's avatar Vikram
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Unshared convolution python code

Errors fixed. Suggestions implemented. Minor changes Minor changes Gradient calculation added. Test for forward added. Errors fixed Grad wrt weights done. Grad wrt inputs incomplete Grad inp Fix typo Tests and bug fix for Grad Inp Modified flops to raise error Mostly convdim agnostic. Cleaner code for gradInput Some corr changes MiChecks for convdim=2 added. Some more misc changes Unshared code moved into one func Re-added unshared flag to get_conv_output_shape Simpler grad inputs. Unshared removeded from get_conv_output_shape. C code changes in corr.py wdim bug fix opt and abstract_conv changes CPU code for fwd and gradWeights. Added tests. Some errors gemv increment fixed. Values for fwd still don't match Forward perfect. Gradweights inverts regions; to be corrected. Added grad inputs and tests but allclose error Python gradInputs simplified Grad input fixed gradweights flipping problem solved Weight dimension order changed. C cache version updated. Docstring changes if unshared is True -> if unshared. Specific error messages for unshared in C code. Unshared tests integrated with AbstractConv. Subsampling errors fixed. Allclose errors with optimiser enabled Kern flip in optimiser fixed. Still some errors Errors fixed GPU corr_gemm code (untested) Unnecessary changes rolled back More GPU code but gemm error 11 Fixed mistakes caused while copying from CPU Errors fixed Fixed error with .data for gpuarray GPU tests Suggestions implemented for error messages Jenkins errors fixed Commits squashed Small errors fixed. Tests need to be rewritten Tests moved to separate class. Mistakes fixed Tests sped up Suggestions implemented. Tests modified
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正在显示 包含 311 行增加 和 53 行删除
theano/gpuarray/blas.py
浏览文件 @ 58c3d08e
差异被折叠。
theano/gpuarray/dnn.py
浏览文件 @ 58c3d08e
......@@ -3035,6 +3035,9 @@ def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
if version(raises=False) < 6000 and op.filter_dilation != (1, 1):
return None
if op.unshared:
return None
inp1 = inputs[0]
inp2 = inputs[1]
......@@ -3129,6 +3132,8 @@ def local_abstractconv_cudnn(node):
ctx = infer_context_name(*node.inputs)
if not isinstance(node.inputs[0].type, GpuArrayType):
return
if node.op.unshared:
return None
if isinstance(node.op, AbstractConv2d):
return local_abstractconv_cudnn_graph(node.op, ctx, node.inputs, node.outputs)
elif isinstance(node.op, AbstractConv3d):
......@@ -3143,6 +3148,8 @@ def local_abstractconv_cudnn_alt(node):
if version(raises=False) < 6000 and node.op.filter_dilation != (1, 1):
return None
if node.op.unshared:
return None
inp1 = node.inputs[0]
inp2 = node.inputs[1]
......@@ -3349,6 +3356,8 @@ def local_abstractconv_gw_cudnn(node):
ctx = infer_context_name(*node.inputs)
if not isinstance(node.inputs[0].type, GpuArrayType):
return
if node.op.unshared:
return None
if isinstance(node.op, AbstractConv2d_gradWeights):
return local_abstractconv_cudnn_graph(node.op, ctx, node.inputs, node.outputs)
elif isinstance(node.op, AbstractConv3d_gradWeights):
......@@ -3360,6 +3369,8 @@ def local_abstractconv_gi_cudnn(node):
ctx = infer_context_name(*node.inputs)
if not isinstance(node.inputs[0].type, GpuArrayType):
return
if node.op.unshared:
return None
if isinstance(node.op, AbstractConv2d_gradInputs):
return local_abstractconv_cudnn_graph(node.op, ctx, node.inputs, node.outputs)
elif isinstance(node.op, AbstractConv3d_gradInputs):
......
theano/gpuarray/opt.py
浏览文件 @ 58c3d08e
......@@ -1595,12 +1595,17 @@ def local_abstractconv_gemm(node):
border_mode = node.op.border_mode
subsample = node.op.subsample
filter_dilation = node.op.filter_dilation
num_groups = node.op.num_groups
unshared = node.op.unshared
if ((border_mode == 'full') and (subsample == (1, 1)) and node.op.num_groups == 1):
flip = (slice(None),) * (kern.ndim - 2) + \
(slice(None, None, -1),) * 2
kern_axes = (1, 0) + tuple(i for i in range(2, kern.ndim))
if ((border_mode == 'full') and (subsample == (1, 1)) and num_groups == 1 and not unshared):
if not node.op.filter_flip:
kern = kern[:, :, ::-1, ::-1]
kern = kern[flip]
# need to dimshuffle the kernel for full convolution
kern = kern.dimshuffle(1, 0, 2, 3)
kern = kern.dimshuffle(kern_axes)
# call GpuCorrMM_gradInputs
rval = GpuCorrMM_gradInputs('valid',
subsample,
......@@ -1609,13 +1614,14 @@ def local_abstractconv_gemm(node):
else:
# need to flip the kernel if necessary
if node.op.filter_flip:
kern = kern[:, :, ::-1, ::-1]
kern = kern[flip]
# By default use GpuCorrMM
rval = GpuCorrMM(border_mode,
subsample,
filter_dilation,
node.op.num_groups)(gpu_contiguous(img),
gpu_contiguous(kern))
num_groups,
unshared)(gpu_contiguous(img),
gpu_contiguous(kern))
# call GpuCorrMM_gradWeights if good
# (the latter is faster if batchsize * kernelHeight * kernelWidth
......@@ -1628,11 +1634,12 @@ def local_abstractconv_gemm(node):
(node.op.kshp is not None) and
(None not in node.op.kshp) and
border_mode != "half" and
node.op.num_groups == 1):
num_groups == 1 and
not unshared):
# we know the kernel and output size
prod1 = node.op.kshp[0] * node.op.kshp[1]
prod1 = node.op.kshp[0] * node.op.kshp[-3]
prod2 = ((node.op.imshp[-2] - node.op.kshp[0] + 1) *
(node.op.imshp[-1] - node.op.kshp[1] + 1))
(node.op.imshp[-1] - node.op.kshp[-3] + 1))
if (None not in node.op.imshp[:1]):
# we also know batchsize and input channels
prod1 *= node.op.imshp[0]
......@@ -1641,7 +1648,8 @@ def local_abstractconv_gemm(node):
if prod1 > prod2:
rval = GpuCorrMM_gradWeights(border_mode,
subsample,
filter_dilation)(
filter_dilation,
unshared)(
gpu_contiguous(img.dimshuffle(1, 0, 2, 3)),
gpu_contiguous(kern.dimshuffle(1, 0, 2, 3)))
# (we need to wrap the result in as_gpuarray_variable,
......@@ -1690,8 +1698,9 @@ def local_abstractconv_gemm_alt(node):
subsample = node.op.subsample
filter_dilation = node.op.filter_dilation
num_groups = node.op.num_groups
unshared = node.op.unshared
if border_mode == 'full' and subsample == (1, 1) and num_groups == 1:
if border_mode == 'full' and subsample == (1, 1) and num_groups == 1 and not unshared:
if not node.op.filter_flip:
kern = kern[:, :, ::-1, ::-1]
......@@ -1702,7 +1711,7 @@ def local_abstractconv_gemm_alt(node):
gpu_contiguous(kern), gpu_contiguous(img))
elif (border_mode == 'valid' and subsample == (1, 1) and filter_dilation == (1, 1) and
num_groups == 1):
num_groups == 1 and not unshared):
if node.op.filter_flip:
kern = kern[:, :, ::-1, ::-1]
......@@ -1896,10 +1905,13 @@ def local_abstractconv_gradweights_gemm(node):
rval = GpuCorrMM_gradWeights(border_mode=node.op.border_mode,
subsample=node.op.subsample,
filter_dilation=node.op.filter_dilation,
num_groups=node.op.num_groups)(
num_groups=node.op.num_groups,
unshared=node.op.unshared)(
gpu_contiguous(img), gpu_contiguous(topgrad), shape)
flip = (slice(None),) * (rval.ndim - 2) + \
(slice(None, None, -1),) * 2
if node.op.filter_flip:
rval = rval[:, :, ::-1, ::-1]
rval = rval[flip]
rval = tensor.patternbroadcast(rval, node.outputs[0].broadcastable)
rval = as_gpuarray_variable(rval, context_name=ctx)
return [rval]
......@@ -1918,9 +1930,10 @@ def local_abstractconv_gemm_gradweights_alt(node):
subsample = node.op.subsample
filter_dilation = node.op.filter_dilation
num_groups = node.op.num_groups
unshared = node.op.unshared
if(border_mode == 'valid' and subsample == (1, 1) and filter_dilation == (1, 1) and
num_groups == 1):
num_groups == 1 and not unshared):
rval = GpuCorrMM(border_mode,
subsample,
filter_dilation)(
......@@ -2001,12 +2014,15 @@ def local_abstractconv_gradinputs_gemm(node):
return None
if node.op.filter_flip:
kern = kern[:, :, ::-1, ::-1]
flip = (slice(None),) * (kern.ndim - 2) + \
(slice(None, None, -1),) * 2
kern = kern[flip]
rval = GpuCorrMM_gradInputs(border_mode=node.op.border_mode,
subsample=node.op.subsample,
filter_dilation=node.op.filter_dilation,
num_groups=node.op.num_groups)(
num_groups=node.op.num_groups,
unshared=node.op.unshared)(
gpu_contiguous(kern), gpu_contiguous(topgrad), shape)
return [rval]
......@@ -2023,8 +2039,9 @@ def local_abstractconv_gradinputs_gemm_alt(node):
subsample = node.op.subsample
filter_dilation = node.op.filter_dilation
num_groups = node.op.num_groups
unshared = node.op.unshared
if border_mode == 'valid' and subsample == (1, 1) and num_groups == 1:
if border_mode == 'valid' and subsample == (1, 1) and num_groups == 1 and not unshared:
if not node.op.filter_flip:
kern = kern[:, :, ::-1, ::-1]
......
theano/gpuarray/tests/test_gemmcorr.py
浏览文件 @ 58c3d08e
......@@ -8,10 +8,10 @@ from theano.tests import unittest_tools as utt
from theano.tensor.nnet.corr import CorrMM, CorrMM_gradWeights, CorrMM_gradInputs
from ..type import gpuarray_shared_constructor
from ..blas import GpuCorrMM, GpuCorrMM_gradWeights, GpuCorrMM_gradInputs
from .config import mode_with_gpu, mode_without_gpu, ref_cast
from theano.tensor.nnet.tests.test_abstract_conv import Grouped_conv_noOptim
from theano.gpuarray.type import gpuarray_shared_constructor
from theano.gpuarray.blas import GpuCorrMM, GpuCorrMM_gradWeights, GpuCorrMM_gradInputs
from config import mode_with_gpu, mode_without_gpu, ref_cast
from theano.tensor.nnet.tests.test_abstract_conv import Grouped_conv_noOptim, TestUnsharedConv
class TestCorrMM(unittest.TestCase):
......@@ -20,9 +20,13 @@ class TestCorrMM(unittest.TestCase):
border_mode='valid',
filter_dilation=(1, 1),
subsample=(1, 1),
unshared=False,
verify_grad=False):
inputs_shape = [inputs_shape[i] for i in (0, 3, 1, 2)]
filters_shape = [filters_shape[i] for i in (0, 3, 1, 2)]
if unshared:
filters_shape = [filters_shape[i] for i in (0, 1, 2, 5, 3, 4)]
else:
filters_shape = [filters_shape[i] for i in (0, 3, 1, 2)]
inputs_val = np.random.random(inputs_shape).astype(config.floatX)
filters_val = np.random.random(filters_shape).astype(config.floatX)
......@@ -32,13 +36,15 @@ class TestCorrMM(unittest.TestCase):
conv_ref = CorrMM(border_mode=border_mode,
filter_dilation=filter_dilation,
subsample=subsample)(ref_cast(inputs),
ref_cast(filters))
subsample=subsample,
unshared=unshared)(ref_cast(inputs),
ref_cast(filters))
f_ref = theano.function([], conv_ref, mode=mode_without_gpu)
conv = GpuCorrMM(border_mode=border_mode,
filter_dilation=filter_dilation,
subsample=subsample)(inputs, filters)
subsample=subsample,
unshared=unshared)(inputs, filters)
f = theano.function([], conv, mode=mode_with_gpu)
res_ref = f_ref()
......@@ -48,7 +54,8 @@ class TestCorrMM(unittest.TestCase):
if verify_grad:
utt.verify_grad(GpuCorrMM(border_mode=border_mode,
filter_dilation=filter_dilation,
subsample=subsample),
subsample=subsample,
unshared=unshared),
[inputs_val, filters_val], mode=mode_with_gpu)
def test_valid(self):
......@@ -57,12 +64,6 @@ class TestCorrMM(unittest.TestCase):
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 6, 12, 1),
subsample=(2, 2))
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 6, 12, 1),
subsample=(2, 2))
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 6, 12, 1),
subsample=(3, 3))
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 6, 12, 1),
subsample=(3, 3))
......@@ -117,6 +118,41 @@ class TestCorrMM(unittest.TestCase):
border_mode=border_mode,
verify_grad=True)
def test_unshared(self):
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 15, 1, 6, 12, 1),
unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 8, 1, 6, 12, 1),
subsample=(2, 2), unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 5, 1, 6, 12, 1),
subsample=(3, 3), unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 5, 1, 6, 12, 1),
subsample=(3, 2), unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 15, 1, 6, 12, 1),
subsample=(1, 2), unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 15, 1, 6, 12, 1),
border_mode='valid', unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 21, 13, 6, 12, 1),
border_mode='half', unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 25, 23, 6, 12, 1),
border_mode='full', unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 15, 1, 6, 12, 1),
border_mode=(0, 0), unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 17, 5, 6, 12, 1),
border_mode=(1, 2), unshared=True)
self.run_conv_valid(inputs_shape=(16, 20, 12, 1),
filters_shape=(10, 21, 5, 6, 12, 1),
border_mode=(3, 2), unshared=True)
def run_gradweight(self, inputs_shape, filters_shape, dCdH_shape,
subsample=(1, 1)):
inputs_shape = [inputs_shape[i] for i in (0, 3, 1, 2)]
......@@ -227,3 +263,17 @@ class TestGroupGpuCorr2d(Grouped_conv_noOptim):
conv_op = GpuCorrMM
conv_gradw_op = GpuCorrMM_gradWeights
conv_gradi_op = GpuCorrMM_gradInputs
flip_filter = True
is_dnn = False
class TestUnsharedGpuCorr2d(TestUnsharedConv):
mode = theano.compile.get_mode("FAST_RUN")
conv2d = GpuCorrMM
conv2d_gradw = GpuCorrMM_gradWeights
conv2d_gradi = GpuCorrMM_gradInputs
conv2d_op = GpuCorrMM
conv2d_gradw_op = GpuCorrMM_gradWeights
conv2d_gradi_op = GpuCorrMM_gradInputs
flip_filter = True
is_dnn = False
theano/tensor/nnet/__init__.py
浏览文件 @ 58c3d08e
......@@ -37,7 +37,7 @@ from .abstract_conv import separable_conv2d
def conv2d(input, filters, input_shape=None, filter_shape=None,
border_mode='valid', subsample=(1, 1), filter_flip=True,
image_shape=None, filter_dilation=(1, 1), num_groups=1, **kwargs):
image_shape=None, filter_dilation=(1, 1), num_groups=1, unshared=False, **kwargs):
"""
This function will build the symbolic graph for convolving a mini-batch of a
stack of 2D inputs with a set of 2D filters. The implementation is modelled
......@@ -51,18 +51,22 @@ def conv2d(input, filters, input_shape=None, filter_shape=None,
(batch size, input channels, input rows, input columns).
See the optional parameter ``input_shape``.
filters: symbolic 4D tensor
filters: symbolic 4D or 6D tensor
Set of filters used in CNN layer of shape
(output channels, input channels, filter rows, filter columns).
(output channels, input channels, filter rows, filter columns)
for normal convolution and
(output channels, output rows, output columns, input channels,
filter rows, filter columns)
for unshared convolution.
See the optional parameter ``filter_shape``.
input_shape: None, tuple/list of len 4 of int or Constant variable
input_shape: None, tuple/list of len 4 or 6 of int or Constant variable
The shape of the input parameter.
Optional, possibly used to choose an optimal implementation.
You can give ``None`` for any element of the list to specify that this
element is not known at compile time.
filter_shape: None, tuple/list of len 4 of int or Constant variable
filter_shape: None, tuple/list of len 4 or 6 of int or Constant variable
The shape of the filters parameter.
Optional, possibly used to choose an optimal implementation.
You can give ``None`` for any element of the list to specify that this
......@@ -105,6 +109,11 @@ def conv2d(input, filters, input_shape=None, filter_shape=None,
Divides the image, kernel and output tensors into num_groups
separate groups. Each which carry out convolutions separately
unshared: bool
If true, then unshared or 'locally connected' convolution will be
performed. A different kernel will be used for each region of the
input.
kwargs: Any other keyword arguments are accepted for backwards
compatibility, but will be ignored.
......@@ -154,12 +163,12 @@ def conv2d(input, filters, input_shape=None, filter_shape=None,
return abstract_conv2d(input, filters, input_shape, filter_shape,
border_mode, subsample, filter_flip,
filter_dilation, num_groups)
filter_dilation, num_groups, unshared)
def conv2d_transpose(input, filters, output_shape, filter_shape=None,
border_mode='valid', input_dilation=(1, 1),
filter_flip=True, filter_dilation=(1, 1), num_groups=1):
filter_flip=True, filter_dilation=(1, 1), num_groups=1, unshared=False):
"""
This function will build the symbolic graph for applying a transposed
convolution over a mini-batch of a stack of 2D inputs with a set of 2D
......@@ -215,6 +224,11 @@ def conv2d_transpose(input, filters, output_shape, filter_shape=None,
Divides the image, kernel and output tensors into num_groups
separate groups. Each which carry out convolutions separately
unshared: bool
If true, then unshared or 'locally connected' convolution will be
performed. A different kernel will be used for each region of the
input.
Returns
-------
Symbolic 4D tensor
......@@ -242,4 +256,5 @@ def conv2d_transpose(input, filters, output_shape, filter_shape=None,
subsample=input_dilation,
filter_flip=filter_flip,
filter_dilation=filter_dilation,
num_groups=num_groups)
num_groups=num_groups,
unshared=unshared)
theano/tensor/nnet/corr.py
浏览文件 @ 58c3d08e
差异被折叠。
theano/tensor/nnet/opt.py
浏览文件 @ 58c3d08e
......@@ -82,12 +82,14 @@ def local_abstractconv_gemm(node):
# need to flip the kernel if necessary
if node.op.filter_flip:
kern = kern[:, :, ::-1, ::-1]
flip = (slice(None),) * (kern.ndim - 2) + \
(slice(None, None, -1),) * 2
kern = kern[flip]
rval = CorrMM(border_mode=node.op.border_mode,
subsample=node.op.subsample,
filter_dilation=node.op.filter_dilation,
num_groups=node.op.num_groups)(img, kern)
num_groups=node.op.num_groups,
unshared=node.op.unshared)(img, kern)
copy_stack_trace(node.outputs[0], rval)
return [rval]
......@@ -134,12 +136,15 @@ def local_abstractconv_gradweight_gemm(node):
rval = CorrMM_gradWeights(border_mode=node.op.border_mode,
subsample=node.op.subsample,
filter_dilation=node.op.filter_dilation,
num_groups=node.op.num_groups)(img, topgrad, shape)
num_groups=node.op.num_groups,
unshared=node.op.unshared)(img, topgrad, shape)
copy_stack_trace(node.outputs[0], rval)
# need to flip the kernel if necessary
if node.op.filter_flip:
rval = rval[:, :, ::-1, ::-1]
flip = (slice(None),) * (rval.ndim - 2) + \
(slice(None, None, -1),) * 2
rval = rval[flip]
rval = theano.tensor.patternbroadcast(rval, node.outputs[0].broadcastable)
copy_stack_trace(node.outputs[0], rval)
......@@ -189,12 +194,14 @@ def local_abstractconv_gradinputs_gemm(node):
# need to flip the kernel if necessary
if node.op.filter_flip:
kern = kern[:, :, ::-1, ::-1]
flip = (slice(None),) * (kern.ndim - 2) + \
(slice(None, None, -1),) * 2
kern = kern[flip]
rval = CorrMM_gradInputs(border_mode=node.op.border_mode,
subsample=node.op.subsample,
filter_dilation=node.op.filter_dilation,
num_groups=node.op.num_groups)(kern, topgrad,
shape)
num_groups=node.op.num_groups,
unshared=node.op.unshared)(kern, topgrad, shape)
copy_stack_trace(node.outputs[0], rval)
return [rval]
......@@ -242,7 +249,7 @@ def local_conv2d_cpu(node):
if not node.op.filter_flip:
# Not tested yet
return None
if node.op.num_groups > 1:
if node.op.num_groups > 1 or node.op.unshared:
return None
rval = conv2d(img, kern,
......@@ -270,7 +277,7 @@ def local_conv2d_gradweight_cpu(node):
if not node.op.filter_flip:
# Not tested yet
return
if node.op.num_groups > 1:
if node.op.num_groups > 1 or node.op.unshared:
return None
if node.op.border_mode == 'valid' and \
......@@ -370,7 +377,7 @@ def local_conv2d_gradinputs_cpu(node):
if not node.op.filter_flip:
# Not tested yet
return None
if node.op.num_groups > 1:
if node.op.num_groups > 1 or node.op.unshared:
return None
# Conv 3d implementation, needed when subsample > 2
......
theano/tensor/nnet/tests/test_abstract_conv.py
浏览文件 @ 58c3d08e
......@@ -1744,3 +1744,146 @@ class Separable_conv(unittest.TestCase):
fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN')
top = fun(x[:, :, :3, :3, :3], depthwise_filter, pointwise_filter)
utt.assert_allclose(top, precomp_output)
class TestUnsharedConv(unittest.TestCase):
conv2d = theano.tensor.nnet.abstract_conv.AbstractConv2d
conv2d_gradw = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
conv2d_gradi = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
conv2d_op = theano.tensor.nnet.abstract_conv.AbstractConv2d
conv2d_gradw_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
conv2d_gradi_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
def setUp(self):
self.mode = theano.compile.mode.Mode(optimizer='None')
self.img_shape = [(2, 1, 4, 4), (1, 2, 4, 2), (1, 3, 5, 3), (1, 4, 4, 4)]
self.kern_shape = [(2, 2, 2, 1, 3, 3), (2, 4, 2, 2, 4, 2), (3, 2, 1, 1, 3, 3), (4, 3, 3, 2, 4, 2)]
self.topgrad_shape = [(2, 2, 2, 2), (1, 2, 4, 2), (1, 3, 2, 1), (1, 4, 3, 3)]
self.border_mode = ['valid', 'full', 'valid', 'full']
self.subsample = [(1, 1), (2, 2), (2, 1), (3, 2)]
self.filter_dilation = (1, 1)
self.num_groups = [1, 1, 3, 2]
self.verify_flags = np.random.choice([True, False], 4, [1.0, 0.0])
self.ref_mode = 'FAST_RUN'
if theano.config.cxx == "":
raise SkipTest("CorrMM needs cxx")
def test_fwd(self):
tensor6 = theano.tensor.TensorType(theano.config.floatX, (False,) * 6)
img_sym = theano.tensor.tensor4('img')
kern_sym = tensor6('kern')
ref_kern_sym = theano.tensor.tensor4('ref_kern')
for imshp, kshp, mode, sub, groups, verify in zip(self.img_shape, self.kern_shape, self.border_mode,
self.subsample, self.num_groups, self.verify_flags):
img = np.random.random(imshp).astype(theano.config.floatX)
kern = np.random.random(kshp).astype(theano.config.floatX)
unshared_conv_op = self.conv2d(border_mode=mode, subsample=sub,
filter_dilation=self.filter_dilation,
num_groups=groups, unshared=True)
unshared_out_sym = unshared_conv_op(img_sym, kern_sym)
unshared_func = theano.function([img_sym, kern_sym], unshared_out_sym, mode=self.mode)
assert any([isinstance(node.op, self.conv2d_op)
for node in unshared_func.maker.fgraph.toposort()])
unshared_output = unshared_func(img, kern)
single_kshp = kshp[:1] + kshp[3:]
ref_conv_op = self.conv2d(border_mode=mode, subsample=sub,
filter_dilation=self.filter_dilation,
num_groups=groups, unshared=False)
ref_out_sym = ref_conv_op(img_sym, ref_kern_sym)
ref_func = theano.function([img_sym, ref_kern_sym], ref_out_sym, mode=self.mode)
for i in range(0, kshp[1]):
for j in range(0, kshp[2]):
single_kern = kern[:, i, j, ...].reshape(single_kshp)
ref_val = ref_func(img, single_kern)
utt.assert_allclose(ref_val[:, :, i, j], unshared_output[:, :, i, j])
if verify:
utt.verify_grad(unshared_conv_op, [img, kern], mode=self.mode, eps=1)
def test_gradweight(self):
img_sym = theano.tensor.tensor4('img')
top_sym = theano.tensor.tensor4('top')
for imshp, kshp, topshp, mode, sub, groups, verify in zip(self.img_shape, self.kern_shape, self.topgrad_shape,
self.border_mode, self.subsample, self.num_groups,
self.verify_flags):
img = np.random.random(imshp).astype(theano.config.floatX)
top = np.random.random(topshp).astype(theano.config.floatX)
unshared_conv_op = self.conv2d_gradw(border_mode=mode, subsample=sub,
filter_dilation=self.filter_dilation,
num_groups=groups, unshared=True)
unshared_out_sym = unshared_conv_op(img_sym, top_sym, tensor.as_tensor_variable(kshp[-2:]))
unshared_func = theano.function([img_sym, top_sym], unshared_out_sym, mode=self.mode)
assert any([isinstance(node.op, self.conv2d_gradw_op)
for node in unshared_func.maker.fgraph.toposort()])
unshared_output = unshared_func(img, top)
single_kshp = kshp[:1] + kshp[3:]
ref_conv_op = self.conv2d_gradw(border_mode=mode, subsample=sub,
filter_dilation=self.filter_dilation,
num_groups=groups, unshared=False)
ref_out_sym = ref_conv_op(img_sym, top_sym, tensor.as_tensor_variable(single_kshp[-2:]))
ref_func = theano.function([img_sym, top_sym], ref_out_sym, mode=self.mode)
for i in range(0, topshp[2]):
for j in range(0, topshp[3]):
top_single = np.zeros_like(top)
top_single[:, :, i, j] = top[:, :, i, j]
ref_output = ref_func(img, top_single)
utt.assert_allclose(unshared_output[:, i, j, ...], ref_output)
def conv_gradweight(inputs_val, output_val):
return unshared_conv_op(inputs_val, output_val, tensor.as_tensor_variable(kshp[-2:]))
if verify:
utt.verify_grad(conv_gradweight, [img, top], mode=self.mode, eps=1)
def test_gradinput(self):
tensor6 = theano.tensor.TensorType(theano.config.floatX, (False,) * 6)
kern_sym = tensor6('kern')
top_sym = theano.tensor.tensor4('top')
ref_kern_sym = theano.tensor.tensor4('ref_kern')
for imshp, kshp, topshp, mode, sub, groups, verify in zip(self.img_shape, self.kern_shape, self.topgrad_shape,
self.border_mode, self.subsample, self.num_groups,
self.verify_flags):
single_kshp = kshp[:1] + kshp[3:]
single_kern = np.random.random(single_kshp).astype(theano.config.floatX)
kern = single_kern.reshape((kshp[:1] + (1, 1) + kshp[3:]))
kern = np.tile(kern, (1, kshp[1], kshp[2], 1, 1, 1))
top = np.random.random(topshp).astype(theano.config.floatX)
unshared_conv_op = self.conv2d_gradi(border_mode=mode, subsample=sub,
filter_dilation=self.filter_dilation,
num_groups=groups, unshared=True)
unshared_out_sym = unshared_conv_op(kern_sym, top_sym, tensor.as_tensor_variable(imshp[-2:]))
unshared_func = theano.function([kern_sym, top_sym], unshared_out_sym, mode=self.mode)
assert any([isinstance(node.op, self.conv2d_gradi_op)
for node in unshared_func.maker.fgraph.toposort()])
unshared_output = unshared_func(kern, top)
ref_conv_op = self.conv2d_gradi(border_mode=mode, subsample=sub,
filter_dilation=self.filter_dilation,
num_groups=groups, unshared=False)
ref_out_sym = ref_conv_op(ref_kern_sym, top_sym, tensor.as_tensor_variable(imshp[-2:]))
ref_func = theano.function([ref_kern_sym, top_sym], ref_out_sym, mode=self.mode)
ref_output = ref_func(single_kern, top)
utt.assert_allclose(ref_output, unshared_output)
def conv_gradinputs(filters_val, output_val):
return unshared_conv_op(filters_val, output_val, tensor.as_tensor_variable(imshp[-2:]))
if verify:
utt.verify_grad(conv_gradinputs, [kern, top], mode=self.mode, eps=1)
theano/tensor/nnet/tests/test_corr.py
浏览文件 @ 58c3d08e
......@@ -10,7 +10,7 @@ import theano
import theano.tensor as T
from theano.tests import unittest_tools as utt
from theano.tensor.nnet import corr, conv
from theano.tensor.nnet.tests.test_abstract_conv import Grouped_conv_noOptim
from theano.tensor.nnet.tests.test_abstract_conv import Grouped_conv_noOptim, TestUnsharedConv
class TestCorr2D(utt.InferShapeTester):
......@@ -452,6 +452,21 @@ class TestGroupCorr2d(Grouped_conv_noOptim):
utt.assert_allclose(gconv_output, conv_output)
class TestUnsharedCorr2D(TestUnsharedConv):
if theano.config.mode == "FAST_COMPILE":
mode = theano.compile.get_mode("FAST_RUN").excluding('gpuarray')
else:
mode = None
conv2d = corr.CorrMM
conv2d_gradw = corr.CorrMM_gradWeights
conv2d_gradi = corr.CorrMM_gradInputs
conv2d_op = corr.CorrMM
conv2d_gradw_op = corr.CorrMM_gradWeights
conv2d_gradi_op = corr.CorrMM_gradInputs
flip_filter = True
is_dnn = False
if __name__ == '__main__':
t = TestCorr2D('setUp')
......
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