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提交 238f0c87 authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Refactor the cuda tests.

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正在显示 包含 23 行增加 和 249 行删除
theano/sandbox/cuda/tests/test_abstractconv.py
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import unittest
import numpy
import itertools
import theano
from theano import tensor
from theano.tests import unittest_tools as utt
import theano.tensor.nnet.abstract_conv as conv
from theano.tensor.nnet.tests import test_abstract_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,
dnn_available,
GpuDnnConv, GpuDnnConvGradW, GpuDnnConvGradI)
from theano.sandbox.cuda.blas import (
GpuCorrMM, GpuCorrMM_gradWeights, GpuCorrMM_gradInputs)
......@@ -21,194 +17,14 @@ if not cuda.cuda_available:
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
mode_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
else:
mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
mode_without_gpu = theano.compile.get_default_mode().excluding('gpu')
class TestConv2d(unittest.TestCase):
class TestDnnConv2d(test_abstract_conv.TestConv2d):
def setUp(self):
super(TestConv2d, self).setUp()
self.inputs_shapes = [(8, 1, 12, 12), (8, 1, 18, 18), (2, 1, 4, 4),
(6, 1, 10, 11), (2, 1, 6, 5), (1, 5, 9, 9)]
self.filters_shapes = [(5, 1, 2, 2), (4, 1, 3, 3), (2, 1, 3, 3),
(1, 1, 2, 5), (4, 1, 2, 2), (4, 5, 2, 2)]
self.subsamples = [(1, 1), (2, 2), (2, 4)]
self.border_modes = ["valid", "full", "half",
(0, 0), (1, 1), (5, 5), (5, 2)]
self.filter_flip = [True, False]
def get_output_shape(self, inputs_shape, filters_shape,
subsample, border_mode):
if border_mode == "valid":
border_mode = (0, 0)
elif border_mode == "full":
border_mode = (filters_shape[2] - 1, filters_shape[3] - 1)
elif border_mode == "half":
border_mode = (filters_shape[2] // 2, filters_shape[3] // 2)
batch_size = inputs_shape[0]
num_filters = filters_shape[0]
return (batch_size, num_filters,) \
+ tuple(None if i is None or k is None
else ((i + 2 * pad - k) // d + 1)
for i, k, d, pad in zip(inputs_shape[2:], filters_shape[2:],
subsample, border_mode))
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,
target_op=None):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
if device == 'gpu':
inputs = gpu_shared(inputs_val)
filters = gpu_shared(filters_val)
else:
inputs = theano.tensor.as_tensor_variable(cpu_shared(inputs_val))
filters = theano.tensor.as_tensor_variable(cpu_shared(filters_val))
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c_ref = ref(inputs, filters,
border_mode=border_mode,
subsample=subsample,
conv_mode=conv_mode)
c = conv.conv2d(inputs, filters,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
input_shape=imshp,
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)
if verify_grad:
utt.verify_grad(conv.AbstractConv2d(border_mode="valid", imshp=imshp, kshp=kshp,
subsample=subsample),
[inputs_val, filters_val],
mode=mode)
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, target_op=None):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
output_val = numpy.random.random(output_shape).astype('float32')
if device == 'gpu':
inputs = gpu_shared(inputs_val)
output = gpu_shared(output_val)
else:
inputs = theano.tensor.as_tensor_variable(cpu_shared(inputs_val))
output = theano.tensor.as_tensor_variable(cpu_shared(output_val))
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c = conv.AbstractConv2d_gradWeights(border_mode=border_mode,
filter_flip=filter_flip,
subsample=subsample,
imshp=imshp, kshp=kshp)
c = c(inputs, output, filters_shape[-2:])
c_ref = ref(inputs, output,
filters_shape,
border_mode=border_mode,
subsample=subsample,
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)
def abstract_conv2d_gradweight(inputs_val, output_val):
conv_op = conv.AbstractConv2d_gradWeights(border_mode=border_mode, subsample=subsample)
return conv_op(inputs_val, output_val, filters_shape[-2:])
if verify_grad:
utt.verify_grad(abstract_conv2d_gradweight, [inputs_val, output_val],
mode=mode, eps=1)
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,
target_op=None):
output_val = numpy.random.random(output_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
if device == 'gpu':
output = gpu_shared(output_val)
filters = gpu_shared(filters_val)
else:
output = theano.tensor.as_tensor_variable(cpu_shared(output_val))
filters = theano.tensor.as_tensor_variable(cpu_shared(filters_val))
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c = conv.AbstractConv2d_gradInputs(border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
imshp=imshp, kshp=kshp)
c = c(filters, output, inputs_shape[-2:])
c_ref = ref(filters, output, inputs_shape,
border_mode=border_mode, subsample=subsample,
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)
def abstract_conv2d_gradinputs(filters_val, output_val):
conv_op = conv.AbstractConv2d_gradInputs(border_mode=border_mode, subsample=subsample)
return conv_op(filters_val, output_val, inputs_shape[-2:])
if verify_grad:
utt.verify_grad(abstract_conv2d_gradinputs, [filters_val, output_val],
mode=mode, eps=1)
super(TestDnnConv2d, self).setUp()
self.shared = gpu_shared
def test_dnn_conv(self):
if not dnn_available():
......@@ -223,24 +39,27 @@ class TestConv2d(unittest.TestCase):
self.filter_flip):
o = self.get_output_shape(i, f, s, b)
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='gpu',
verify_grad=True, mode=mode,
provide_shape=provide_shape, border_mode=b,
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',
verify_grad=True, mode=mode,
provide_shape=provide_shape, border_mode=b,
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',
verify_grad=True, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=GpuDnnConvGradI)
def test_gpucorrmm_conv(self):
if not dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
class TestCorrMMConv2d(test_abstract_conv.TestConv2d):
def setUp(self):
super(TestDnnConv2d, self).setUp()
self.shared = gpu_shared
def test_gpucorrmm_conv(self):
mode = mode_with_gpu.excluding('cudnn')
for (i, f), s, b, flip, provide_shape in itertools.product(
zip(self.inputs_shapes, self.filters_shapes),
......@@ -251,7 +70,7 @@ class TestConv2d(unittest.TestCase):
o = self.get_output_shape(i, f, s, b)
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='gpu',
verify_grad=True, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip,
target_op=(GpuCorrMM,
......@@ -259,65 +78,20 @@ class TestConv2d(unittest.TestCase):
GpuCorrMM_gradInputs))
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='gpu',
verify_grad=True, mode=mode,
provide_shape=provide_shape, border_mode=b,
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',
verify_grad=True, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip,
target_op=GpuCorrMM_gradInputs)
def test_grad_types(self):
# This function simply tests the behaviour of the AbstractConv
# Ops, not their optimizations
cpu_input = tensor.ftensor4()
cpu_filters = tensor.ftensor4()
cpu_topgrad = tensor.ftensor4()
gpu_input = cuda.ftensor4()
gpu_filters = cuda.ftensor4()
gpu_topgrad = cuda.ftensor4()
out_shape = tensor.lvector()
# Check the gradient of the forward conv2d
for input, filters in itertools.product(
(cpu_input, gpu_input),
(cpu_filters, gpu_filters)):
output = conv.conv2d(input, filters)
grad_input, grad_filters = theano.grad(output.sum(),
wrt=(input, filters))
assert grad_input.type == input.type, (
grad_input, grad_input.type, input, input.type)
assert grad_filters.type == filters.type, (
grad_filters, grad_filters.type, filters, filters.type)
# Check the gradient of gradweight
for input, topgrad in itertools.product(
(cpu_input, gpu_input),
(cpu_topgrad, gpu_topgrad)):
grad_filters = conv.AbstractConv2d_gradWeights()(
input, topgrad, out_shape)
grad_input, grad_topgrad = theano.grad(grad_filters.sum(),
wrt=(input, topgrad))
assert grad_input.type == input.type, (
grad_input, grad_input.type, input, input.type)
assert grad_topgrad.type == topgrad.type, (
grad_topgrad, grad_topgrad.type, topgrad, topgrad.type)
# Check the gradient of gradinputs
for filters, topgrad in itertools.product(
(cpu_filters, gpu_filters),
(cpu_topgrad, gpu_topgrad)):
grad_input = conv.AbstractConv2d_gradInputs()(
filters, topgrad, out_shape)
grad_filters, grad_topgrad = theano.grad(grad_input.sum(),
wrt=(filters, topgrad))
assert grad_filters.type == filters.type, (
grad_filters, grad_filters.type, filters, filters.type)
assert grad_topgrad.type == topgrad.type, (
grad_topgrad, grad_topgrad.type, topgrad, topgrad.type)
class TestDnnConvTypes(test_abstract_conv.TestConvTypes):
def setUp(self):
self.input = cuda.ftensor4()
self.filters = cuda.ftensor4()
self.topgrad = cuda.ftensor4()
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