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Add test cases for fwd and bwd 3d convolution

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theano/sandbox/cuda/tests/test_dnn.py
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......@@ -2,7 +2,7 @@ import logging
from nose.plugins.skip import SkipTest
import numpy
from itertools import product
from itertools import chain, product
import theano
from six import StringIO
......@@ -922,13 +922,49 @@ def test_dnn_conv_grad():
utt.verify_grad(dconvw, [img_val, kern_val, out_val])
def get_conv3d_test_cases():
# Every element of test_shapes follows the format
# [input_shape, filter_shape, subsample]
test_shapes = [# Test with standard size inputs and kernels
[(128, 3, 5, 5, 5), (64, 3, 1, 2, 4), (1, 1, 1)],
[(8, 4, 20, 12, 15), (5, 4, 6, 12, 4), (2, 2, 2)],
[(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 3, 3)],
[(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 2, 1)],
[(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 2, 1)],
# Test with 1x1x1 filters
[(8, 1, 10, 10, 10), (10, 1, 1, 1, 1), (1, 1, 1)],
# Test with dimensions larger than 1024 (thread block dim)
[(1025, 1, 2, 3, 4), (5, 1, 1, 2, 3), (1, 1, 1)],
[(8, 1, 2, 3, 4), (1025, 1, 1, 2, 3), (1, 1, 1)],
[(8, 1025, 2, 3, 4), (5, 1025, 1, 2, 3), (1, 1, 1)],
[(8, 1, 1030, 3, 4), (5, 1, 1025, 2, 3), (1, 1, 1)],
[(8, 1, 2, 1030, 4), (5, 1, 1, 1025, 3), (1, 1, 1)],
[(8, 1, 2, 3, 1030), (5, 1, 1, 2, 1025), (1, 1, 1)],
# The equivalent of this caused a crash with conv2d
[(1, 1, 1, 44800, 1), (6, 1, 1, 1, 1), (1, 1, 1)]]
# With border mode 'full', test with kernel bigger than image in some/all
# dimensions
test_shapes_full = [[(6, 2, 2, 2, 2), (4, 2, 3, 1, 1), (1, 1, 1)],
[(6, 2, 2, 2, 2), (4, 2, 1, 3, 1), (1, 1, 1)],
[(6, 2, 2, 2, 2), (4, 2, 1, 1, 3), (1, 1, 1)],
[(6, 2, 2, 2, 2), (4, 2, 5, 5, 5), (1, 1, 1)],
]
border_modes = ['valid', 'full', (1, 2, 3), (3, 2, 1)]
conv_modes = ['conv', 'cross']
itt = chain(product(test_shapes, border_modes, conv_modes),
product(test_shapes_full, ['full'], conv_modes))
return itt
def test_conv3d_fwd():
if not cuda.dnn.dnn_available() and dnn.version()[0] >= 3000:
raise SkipTest('"3D conv not supported in cudnn v1')
raise SkipTest('"CuDNN 3D convolution requires CuDNN v3')
def run_conv3d_fwd(inputs_shape, filters_shape,
subsample=(1, 1, 1)):
def run_conv3d_fwd(inputs_shape, filters_shape, subsample,
border_mode, conv_mode):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
......@@ -936,132 +972,129 @@ def test_conv3d_fwd():
inputs = shared(inputs_val)
filters = shared(filters_val)
bias = shared(numpy.zeros(filters_shape[0]).astype('float32'))
conv_ref = theano.tensor.nnet.conv3D(V=inputs.dimshuffle(0, 2, 3, 4, 1),
W=filters.dimshuffle(0, 2, 3, 4, 1),
b=bias, d=subsample)
# Compile a theano function for the CuDNN implementation
conv = dnn.dnn_conv3d(img=inputs, kerns=filters,
border_mode="valid", subsample=subsample, conv_mode='cross')
f_ref = theano.function([], conv_ref.dimshuffle(0, 4, 1, 2, 3))
border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode)
f = theano.function([], conv, mode=mode_with_gpu)
res_ref = f_ref()
res = f()
utt.assert_allclose(res_ref, res)
run_conv3d_fwd(inputs_shape=(128, 3, 5, 5, 5),
filters_shape=(64, 3, 1, 2, 4))
run_conv3d_fwd(inputs_shape=(16, 4, 20, 12, 15),
filters_shape=(10, 4, 6, 12, 4),
subsample=(2, 2, 2))
run_conv3d_fwd(inputs_shape=(16, 4, 20, 12, 15),
filters_shape=(10, 4, 6, 12, 4),
subsample=(2, 2, 2))
run_conv3d_fwd(inputs_shape=(16, 1, 20, 12, 15),
filters_shape=(10, 1, 6, 12, 4),
subsample=(3, 3, 3))
run_conv3d_fwd(inputs_shape=(16, 2, 20, 12, 15),
filters_shape=(10, 2, 6, 12, 4),
subsample=(3, 3, 3))
run_conv3d_fwd(inputs_shape=(16, 1, 20, 12, 15),
filters_shape=(10, 1, 6, 12, 4),
subsample=(3, 2, 1))
run_conv3d_fwd(inputs_shape=(16, 1, 20, 12, 15),
filters_shape=(10, 1, 6, 12, 4),
subsample=(1, 2, 3))
def test_conv3d_gradweight():
# If conv_mode is 'conv' the reference implementation should use
# filters filpped according to the width, height and time axis
if conv_mode == 'conv':
flipped_filters = filters[:,:,::-1,::-1,::-1]
else:
flipped_filters = filters
if not cuda.dnn.dnn_available() and dnn.version()[0] >= 3000:
raise SkipTest('"3D conv not supported in cudnn v1')
# If border mode is anything but 'valid', the reference implementation
# should operate on padded inputs
if border_mode == 'valid':
padded_inputs = inputs
else:
if border_mode == 'full':
pad_per_dim = [filters_shape[i] - 1 for i in range(2,5)]
else:
pad_per_dim = border_mode
pad_before_after = ([(0, 0), (0, 0)] +
[(p, p) for p in pad_per_dim])
padded_inputs_val = numpy.pad(inputs_val, pad_before_after,
'constant')
padded_inputs = shared(padded_inputs_val)
# Compile a theano function for the reference implementation
conv_ref = theano.tensor.nnet.conv3D(V=padded_inputs.dimshuffle(0, 2, 3, 4, 1),
W=flipped_filters.dimshuffle(0, 2, 3, 4, 1),
b=bias, d=subsample)
f_ref = theano.function([], conv_ref.dimshuffle(0, 4, 1, 2, 3))
def run_gradweight(inputs_shape, filters_shape, dCdH_shape,
subsample=(1, 1, 1)):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
dCdH_val = numpy.random.random(dCdH_shape).astype('float32')
kern_val = numpy.random.random(filters_shape).astype('float32')
inputs = shared(inputs_val)
dCdH = shared(dCdH_val)
kern = shared(kern_val)
filters_shape_s = (filters_shape[0], filters_shape[2],
filters_shape[3], filters_shape[4],
filters_shape[1])
conv = theano.tensor.nnet.convGrad3D(V=inputs.dimshuffle(0, 2, 3, 4, 1),
dCdH=dCdH.dimshuffle(0, 2, 3, 4, 1),
WShape=filters_shape_s,
d=subsample)
desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=subsample,
conv_mode='cross')(inputs.shape, kern.shape)
gradW = dnn.GpuDnnConv3dGradW()(inputs, dCdH, kern, desc)
f_ref = theano.function([], conv.dimshuffle(0, 4, 1, 2, 3))
f = theano.function([], gradW, mode=mode_with_gpu)
# Compare the results of the two implementations
res_ref = f_ref()
res = f()
utt.assert_allclose(res_ref, res)
run_gradweight(inputs_shape=(16, 1, 10, 12, 16),
filters_shape=(10, 1, 6, 12, 4),
dCdH_shape=(16, 10, 5, 1, 13),
subsample=(1, 1, 1))
run_gradweight(inputs_shape=(16, 1, 20, 10, 16),
filters_shape=(10, 1, 6, 4, 4),
dCdH_shape=(16, 10, 8, 4, 7),
subsample=(2, 2, 2))
run_gradweight(inputs_shape=(16, 1, 20, 10, 16),
filters_shape=(10, 1, 6, 3, 4),
dCdH_shape=(16, 10, 5, 3, 5),
subsample=(3, 3, 3))
run_gradweight(inputs_shape=(16, 1, 20, 12, 16),
filters_shape=(10, 1, 6, 12, 4),
dCdH_shape=(16, 10, 8, 1, 5),
subsample=(2, 1, 3))
def test_conv3d_gradinput():
test_cases = get_conv3d_test_cases()
for (i_shape, f_shape, subsample), border_mode, conv_mode in test_cases:
yield (run_conv3d_fwd, i_shape, f_shape, subsample, border_mode,
conv_mode)
def test_conv3d_bwd():
if not cuda.dnn.dnn_available() and dnn.version()[0] >= 3000:
raise SkipTest('"3D conv not supported in cudnn v1')
raise SkipTest('"CuDNN 3D convolution requires CuDNN v3')
def run_gradinput(inputs_shape, filters_shape,
subsample=(1, 1, 1)):
def run_conv3d_bwd(inputs_shape, filters_shape, subsample,
border_mode, conv_mode):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs = shared(inputs_val)
filters = shared(filters_val)
bias = shared(numpy.zeros(filters_shape[0]).astype('float32'))
bias = shared(numpy.zeros(filters_shape[1]).astype('float32'))
conv = theano.tensor.nnet.convTransp3D(W=filters.dimshuffle(0, 2, 3, 4, 1),
b=bias, d=subsample,
H=inputs.dimshuffle(0, 2, 3, 4, 1))
# Compile a theano function for the CuDNN implementation
conv = dnn.dnn_conv3d(img=inputs, kerns=filters,
border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode)
f_ref = theano.function([], conv.dimshuffle(0, 4, 1, 2, 3))
res_ref = f_ref()
grad_i, grad_w = theano.tensor.grad(conv.sum(), [inputs, filters])
bottom_shape = res_ref.shape
bottom_val = numpy.random.random(bottom_shape).astype('float32')
bottom = shared(bottom_val)
f = theano.function([], [grad_i, grad_w], mode=mode_with_gpu)
desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=subsample,
conv_mode='cross')(bottom.shape, filters.shape)
gradI = dnn.GpuDnnConv3dGradI()(filters, inputs, bottom, desc)
f = theano.function([], gradI, mode=mode_with_gpu)
res = f()
# If conv_mode is 'conv' the reference implementation should use
# filters filpped according to the width, height and time axis
if conv_mode == 'conv':
flipped_filters = filters[:,:,::-1,::-1,::-1]
else:
flipped_filters = filters
utt.assert_allclose(res_ref, res)
# If border mode is anything but 'valid', the reference implementation
# should operate on padded inputs
if border_mode == 'valid':
padded_inputs = inputs
else:
if border_mode == 'full':
pad_per_dim = [filters_shape[i] - 1 for i in range(2,5)]
else:
pad_per_dim = border_mode
pad_before_after = ([(0, 0), (0, 0)] +
[(p, p) for p in pad_per_dim])
padded_inputs_val = numpy.pad(inputs_val, pad_before_after,
'constant')
padded_inputs = shared(padded_inputs_val)
# Compile a theano function for the reference implementation
conv_ref = theano.tensor.nnet.conv3D(V=padded_inputs.dimshuffle(0, 2, 3, 4, 1),
W=flipped_filters.dimshuffle(0, 2, 3, 4, 1),
b=bias, d=subsample)
(grad_padded_i_ref,
grad_w_ref) = theano.tensor.grad(conv_ref.sum(), [padded_inputs, filters])
# Recover grad_i_ref from grad_padded_i_ref
if border_mode == 'valid':
grad_i_ref = grad_padded_i_ref
else:
shp = grad_padded_i_ref.shape
grad_i_ref = grad_padded_i_ref[:, :,
pad_per_dim[0]:shp[2] - pad_per_dim[0],
pad_per_dim[1]:shp[3] - pad_per_dim[1],
pad_per_dim[2]:shp[4] - pad_per_dim[2]]
f_ref = theano.function([], [grad_i_ref, grad_w_ref])
# Compare the results of the two implementations
res_ref = f_ref()
res = f()
utt.assert_allclose(res_ref[0], res[0])
utt.assert_allclose(res_ref[1], res[1])
run_gradinput(inputs_shape=(16, 10, 15, 12, 12),
filters_shape=(10, 1, 6, 12, 4))
run_gradinput(inputs_shape=(16, 10, 15, 12, 12),
filters_shape=(10, 1, 6, 12, 4),
subsample=(2, 2, 2))
run_gradinput(inputs_shape=(16, 10, 15, 12, 12),
filters_shape=(10, 1, 6, 12, 4),
subsample=(3, 3, 3))
run_gradinput(inputs_shape=(16, 10, 15, 12, 12),
filters_shape=(10, 1, 6, 12, 4),
subsample=(3, 1, 2))
test_cases = get_conv3d_test_cases()
for (i_shape, f_shape, subsample), border_mode, conv_mode in test_cases:
yield (run_conv3d_bwd, i_shape, f_shape, subsample, border_mode,
conv_mode)
def test_version():
......
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