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提交 791d4871 authored 作者: Chiheb Trabelsi's avatar Chiheb Trabelsi
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test_conv_cuda_ndarray.py has been modified in order to respect the flake8 style.

上级 186b90a0
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正在显示 包含 136 行增加 和 140 行删除
theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py
浏览文件 @ 791d4871
...@@ -2,14 +2,16 @@ ...@@ -2,14 +2,16 @@
Tests for GPU convolution Tests for GPU convolution
""" """
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import sys
import time import time
import unittest import unittest
import traceback import theano
from theano import tensor
from theano.tests.unittest_tools import seed_rng, assert_allclose
from theano.sandbox import cuda
import numpy import numpy
from six.moves import xrange from six.moves import xrange
from theano.sandbox.cuda.dnn import GpuDnnConv, DnnBase, dnn_conv
from nose.plugins.skip import SkipTest from nose.plugins.skip import SkipTest
from nose.tools import assert_raises from nose.tools import assert_raises
imported_scipy_convolve2d = False imported_scipy_convolve2d = False
...@@ -19,16 +21,10 @@ try: ...@@ -19,16 +21,10 @@ try:
except ImportError: except ImportError:
pass pass
import theano
from theano import tensor
from theano.tests.unittest_tools import seed_rng, assert_allclose
# Skip test if cuda is not available. # Skip test if cuda is not available.
from theano.sandbox import cuda if cuda.cuda_available is False:
if cuda.cuda_available == False:
raise SkipTest('Optional package cuda disabled') raise SkipTest('Optional package cuda disabled')
from theano.sandbox.cuda.dnn import GpuDnnConv, DnnBase, dnn_conv
# needed as the gpu conv don't have a perform implementation. # needed as the gpu conv don't have a perform implementation.
if theano.config.mode == 'FAST_COMPILE': if theano.config.mode == 'FAST_COMPILE':
...@@ -56,8 +52,8 @@ device_prop = cuda_ndarray.device_properties(device_id) ...@@ -56,8 +52,8 @@ device_prop = cuda_ndarray.device_properties(device_id)
def py_conv_valid_numpy(img, kern): def py_conv_valid_numpy(img, kern):
assert img.shape[1] == kern.shape[1] assert img.shape[1] == kern.shape[1]
outshp = (img.shape[0], kern.shape[0], outshp = (img.shape[0], kern.shape[0],
img.shape[2] - kern.shape[2] + 1, img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1) img.shape[3] - kern.shape[3] + 1)
out = numpy.zeros(outshp, dtype='float32') out = numpy.zeros(outshp, dtype='float32')
for b in xrange(out.shape[0]): for b in xrange(out.shape[0]):
for k in xrange(out.shape[1]): for k in xrange(out.shape[1]):
...@@ -106,11 +102,11 @@ def py_conv(img, kern, mode, subsample): ...@@ -106,11 +102,11 @@ def py_conv(img, kern, mode, subsample):
if imported_scipy_convolve2d: if imported_scipy_convolve2d:
return py_conv_scipy(img, kern, mode, subsample) return py_conv_scipy(img, kern, mode, subsample)
elif mode == 'valid': elif mode == 'valid':
return py_conv_valid_numpy(img, kern)[:, :, ::subsample[0], return py_conv_valid_numpy(img, kern)[
::subsample[1]] :, :, ::subsample[0], ::subsample[1]]
elif mode == 'full': elif mode == 'full':
return py_conv_full_numpy(img, kern)[:, :, ::subsample[0], return py_conv_full_numpy(img, kern)[
::subsample[1]] :, :, ::subsample[0], ::subsample[1]]
else: else:
raise Exception("Can't execute this kernel.") raise Exception("Can't execute this kernel.")
...@@ -119,20 +115,20 @@ def py_conv_scipy(img, kern, mode, subsample): ...@@ -119,20 +115,20 @@ def py_conv_scipy(img, kern, mode, subsample):
assert img.shape[1] == kern.shape[1] assert img.shape[1] == kern.shape[1]
if mode == 'valid': if mode == 'valid':
outshp = (img.shape[0], kern.shape[0], outshp = (img.shape[0], kern.shape[0],
img.shape[2] - kern.shape[2] + 1, img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1) img.shape[3] - kern.shape[3] + 1)
else: else:
outshp = (img.shape[0], kern.shape[0], outshp = (img.shape[0], kern.shape[0],
img.shape[2] + kern.shape[2] - 1, img.shape[2] + kern.shape[2] - 1,
img.shape[3] + kern.shape[3] - 1) img.shape[3] + kern.shape[3] - 1)
out = numpy.zeros(outshp, dtype='float32') out = numpy.zeros(outshp, dtype='float32')
for b in xrange(out.shape[0]): for b in xrange(out.shape[0]):
for k in xrange(out.shape[1]): for k in xrange(out.shape[1]):
for s in xrange(img.shape[1]): for s in xrange(img.shape[1]):
#convolve2d or correlate # convolve2d or correlate
out[b, k, :, :] += convolve2d(img[b, s, :, :], out[b, k, :, :] += convolve2d(img[b, s, :, :],
kern[k, s, :, :], kern[k, s, :, :],
mode) mode)
return out[:, :, ::subsample[0], ::subsample[1]] return out[:, :, ::subsample[0], ::subsample[1]]
...@@ -168,10 +164,12 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1), ...@@ -168,10 +164,12 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
npy_kern = theano._asarray(numpy.random.rand(*kshape) - 2, npy_kern = theano._asarray(numpy.random.rand(*kshape) - 2,
dtype='float32') dtype='float32')
else: else:
npy_img = theano._asarray(numpy.arange( npy_img = theano._asarray(
numpy.prod(ishape)).reshape(ishape), dtype='float32') + 1 numpy.arange(numpy.prod(ishape)).reshape(ishape),
npy_kern = -(theano._asarray(numpy.arange( dtype='float32') + 1
numpy.prod(kshape)).reshape(kshape), dtype='float32') + 1) npy_kern = -(theano._asarray(
numpy.arange(numpy.prod(kshape)).reshape(kshape),
dtype='float32') + 1)
img = cuda_ndarray.CudaNdarray(npy_img) img = cuda_ndarray.CudaNdarray(npy_img)
kern = cuda_ndarray.CudaNdarray(npy_kern) kern = cuda_ndarray.CudaNdarray(npy_kern)
...@@ -239,7 +237,7 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1), ...@@ -239,7 +237,7 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
div = float('inf') div = float('inf')
print('%15s' % str(ishape), '%15s' % str(kshape), end=' ') print('%15s' % str(ishape), '%15s' % str(kshape), end=' ')
print('%12.5f %7.2f %7.2f %7.1f' % ( print('%12.5f %7.2f %7.2f %7.1f' % (
approx_fp, cpu_mflops, gpu_mflops, div)) approx_fp, cpu_mflops, gpu_mflops, div))
def exec_conv(version, shapes, verbose, random, mode, def exec_conv(version, shapes, verbose, random, mode,
...@@ -261,7 +259,7 @@ def get_basic_shapes(): ...@@ -261,7 +259,7 @@ def get_basic_shapes():
return [((1, 1, 1, 1), (1, 1, 1, 1), (1, 1), (1, 1), (1, 1)), return [((1, 1, 1, 1), (1, 1, 1, 1), (1, 1), (1, 1), (1, 1)),
((1, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)), ((1, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 3, 3), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)), ((1, 1, 3, 3), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
# basic test for unsquare kernel and image # basic test for unsquare kernel and image
((1, 1, 2, 4), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)), ((1, 1, 2, 4), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 3, 4), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)), ((1, 1, 3, 4), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 4, 3), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)), ((1, 1, 4, 3), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
...@@ -281,17 +279,17 @@ def get_shapes(imshp=(1, 1), kshp=(1, 1), subsample=(1, 1), ...@@ -281,17 +279,17 @@ def get_shapes(imshp=(1, 1), kshp=(1, 1), subsample=(1, 1),
((3, 1) + imshp, (1, 1) + kshp, subsample, img_stride, kern_stride), ((3, 1) + imshp, (1, 1) + kshp, subsample, img_stride, kern_stride),
# nkern only # nkern only
((1, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride), ((1, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride),
#batch and nkern # batch and nkern
((3, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride), ((3, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride),
#batch and stack # batch and stack
((3, 2) + imshp, (1, 2) + kshp, subsample, img_stride, kern_stride), ((3, 2) + imshp, (1, 2) + kshp, subsample, img_stride, kern_stride),
#stack and nkern # stack and nkern
((1, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride), ((1, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride),
#batch, nkern and stack # batch, nkern and stack
((2, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride), ((2, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride),
#batch, nkern and stack # batch, nkern and stack
((3, 2) + imshp, (4, 2) + kshp, subsample, img_stride, kern_stride) ((3, 2) + imshp, (4, 2) + kshp, subsample, img_stride, kern_stride)
] ]
def get_shapes2(scales_img=(1, 1), scales_kern=(1, 1), subsample=(1, 1), def get_shapes2(scales_img=(1, 1), scales_kern=(1, 1), subsample=(1, 1),
...@@ -344,39 +342,39 @@ def get_valid_shapes(): ...@@ -344,39 +342,39 @@ def get_valid_shapes():
# test subsample done in a separate fct # test subsample done in a separate fct
shapes += [ shapes += [
# other test # other test
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)) ((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
, ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),
, ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)) ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
, ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)) ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
, ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)) ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
, ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)) ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
, ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)) ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
, ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize,
, ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image,
, ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image, non-square kern ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image, non-square kern,
, ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim,
, ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)) # a big one ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)), # a big one
, ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # MNIST LeNET layer 1 ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # MNIST LeNET layer 1
, ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)) # layer 1 backprop to weights ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)), # layer 1 backprop to weights
, ((60, 20, 28, 28), (10, 20, 5, 5), (1, 1), (2, 2), (1, 1)) # added a test case that fail from test_nnet.py.test_conv_nnet2 ((60, 20, 28, 28), (10, 20, 5, 5), (1, 1), (2, 2), (1, 1)), # added a test case that fail from test_nnet.py.test_conv_nnet2
, ((10, 5, 28, 28), (10, 5, 5, 5), (1, 1), (2, 2), (1, 1)) # test precedent but reduced that triger the error ((10, 5, 28, 28), (10, 5, 5, 5), (1, 1), (2, 2), (1, 1)), # test precedent but reduced that triger the error
# Test more than maxThreadsDim0 # Test more than maxThreadsDim0
, ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)) ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
, ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)) ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
] ]
shapes += [ ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 1 layers shapes += [((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 1 layers
, ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 2 layers ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 2 layers
, ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 bprop 1 full ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 1 full
, ((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)) # test_lenet_28 bprop 2 valid ((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 2 valid
# , ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid # ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 2 valid
, ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 1 layers ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 1 layers
, ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 2 layers ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 2 layers
, ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 full ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 full
# , ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 1 # ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1)), # test_lenet_64 bprop 1
# , ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 2 # ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1)) # test_lenet_64 bprop 2
] ]
return shapes return shapes
...@@ -466,48 +464,47 @@ def _test_full(cls, mode=None, version=[-1], extra_shapes=[], ...@@ -466,48 +464,47 @@ def _test_full(cls, mode=None, version=[-1], extra_shapes=[],
shapes += [ shapes += [
# other test # other test
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)) ((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
, ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),
, ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)) ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
, ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)) ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
, ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)) ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
, ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)) ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
, ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)) ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
, ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize
, ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image
, ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image, non-square kern ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image, non-square kern
, ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
, ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)) # a big one ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)), # a big one
, ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # MNIST LeNET layer 1 ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # MNIST LeNET layer 1
, ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)) # layer 1 backprop to weights ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)) # layer 1 backprop to weights
] ]
if test_bigger_kernels: if test_bigger_kernels:
# Shapes where the kernel is larger than the image in some dimension # Shapes where the kernel is larger than the image in some dimension
shapes += [ shapes += [
((3, 1, 1, 1), (2, 1, 5, 3), (1, 1), (1, 1), (1, 1)) ((3, 1, 1, 1), (2, 1, 5, 3), (1, 1), (1, 1), (1, 1)),
, ((3, 2, 1, 1), (4, 2, 1, 1), (1, 1), (1, 1), (1, 1)) ((3, 2, 1, 1), (4, 2, 1, 1), (1, 1), (1, 1), (1, 1)),
, ((3, 2, 4, 4), (4, 2, 2, 6), (1, 1), (1, 1), (1, 1)) ((3, 2, 4, 4), (4, 2, 2, 6), (1, 1), (1, 1), (1, 1)),
, ((3, 2, 4, 4), (4, 2, 8, 6), (1, 1), (1, 1), (1, 1)) ((3, 2, 4, 4), (4, 2, 8, 6), (1, 1), (1, 1), (1, 1)),
, ((4, 2, 10, 10), (3, 2, 2, 12), (1, 1), (1, 1), (1, 1)) ((4, 2, 10, 10), (3, 2, 2, 12), (1, 1), (1, 1), (1, 1))
] ]
shapes += [ shapes += [((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 1 layers
# ((60,1,28,28),(20,1,5,5), (1, 1), (1, 1), (1, 1))#test_lenet_28 1 layers # ((60, 20, 12, 12),(30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 2 layers
# , ((60,20,12,12),(30,20,5,5), (1, 1), (1, 1), (1, 1))#test_lenet_28 2 layers ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 1 full
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 bprop 1 full # ((20,60,12,12),(30,60,8,8), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 2 valid
# , ((20,60,12,12),(30,60,8,8), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid # ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 2 valid
# , ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid # ((10,1,64,64),(20,1,7,7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 1 layers
# , ((10,1,64,64),(20,1,7,7), (1, 1), (1, 1), (1, 1))#test_lenet_64 1 layers # ((10,20,29,29),(30,20,7,7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 2 layers
# , ((10,20,29,29),(30,20,7,7), (1, 1), (1, 1), (1, 1))#test_lenet_64 2 layers ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 full
, ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 full # ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1)), # test_lenet_64 bprop 1
# , ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 1 # ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1)), # test_lenet_64 bprop 2
# , ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 2 # Test more than maxThreadsDim0
# Test more than maxThreadsDim0 ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
, ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)) ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
, ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)) ((1, 1, 44800, 1), (6, 1, 1, 1), (1, 1), (1, 1), (1, 1)) # This caused crash
, ((1, 1, 44800, 1), (6, 1, 1, 1), (1, 1), (1, 1), (1, 1)) # This caused crash ]
]
verbose = 0 verbose = 0
random = True random = True
...@@ -561,7 +558,7 @@ def _test_subsample(cls, mode, version_valid=[-1], version_full=[-1]): ...@@ -561,7 +558,7 @@ def _test_subsample(cls, mode, version_valid=[-1], version_full=[-1]):
((4, 2, 10, 10), (3, 2, 2, 2), (1, 3), (1, 1), (1, 1)), ((4, 2, 10, 10), (3, 2, 2, 2), (1, 3), (1, 1), (1, 1)),
((4, 2, 10, 10), (3, 2, 2, 2), (3, 3), (1, 1), (1, 1)), ((4, 2, 10, 10), (3, 2, 2, 2), (3, 3), (1, 1), (1, 1)),
((4, 2, 10, 10), (3, 2, 2, 2), (3, 1), (1, 1), (1, 1)) ((4, 2, 10, 10), (3, 2, 2, 2), (3, 1), (1, 1), (1, 1))
] ]
shapes += get_shapes2(scales_img=(2, 2), subsample=(1, 1)) shapes += get_shapes2(scales_img=(2, 2), subsample=(1, 1))
shapes += get_shapes2(scales_img=(2, 2), subsample=(1, 2)) shapes += get_shapes2(scales_img=(2, 2), subsample=(1, 2))
shapes += get_shapes2(scales_img=(2, 2), subsample=(2, 1)) shapes += get_shapes2(scales_img=(2, 2), subsample=(2, 1))
...@@ -636,7 +633,6 @@ class TestConv2DGPU(unittest.TestCase): ...@@ -636,7 +633,6 @@ class TestConv2DGPU(unittest.TestCase):
imshp_logical=featshp_logical[1:], imshp_logical=featshp_logical[1:],
kshp_logical=kshp[2:]) kshp_logical=kshp[2:])
def test_invalid_input_shape(self): def test_invalid_input_shape(self):
""" """
Tests that when the shape gived at build time is not the same as Tests that when the shape gived at build time is not the same as
...@@ -659,7 +655,7 @@ class TestConv2DGPU(unittest.TestCase): ...@@ -659,7 +655,7 @@ class TestConv2DGPU(unittest.TestCase):
for mode in ['valid', 'full']: for mode in ['valid', 'full']:
for shapes in [((3, 2, 8, 8), (4, 2, 5, 5), (8, 8)), for shapes in [((3, 2, 8, 8), (4, 2, 5, 5), (8, 8)),
((3, 2, 8, 8), (4, 2, 5, 5), (5, 8)), ((3, 2, 8, 8), (4, 2, 5, 5), (5, 8)),
#((3, 2, 8, 8), (4, 2, 5, 5), (8, 5)), # ((3, 2, 8, 8), (4, 2, 5, 5), (8, 5)),
# We use only the number of columns. # We use only the number of columns.
]: ]:
...@@ -700,11 +696,11 @@ class TestConvWithPadding(object): ...@@ -700,11 +696,11 @@ class TestConvWithPadding(object):
kern = theano._asarray(numpy.empty((1, 1, 1, 1)), dtype='float32') kern = theano._asarray(numpy.empty((1, 1, 1, 1)), dtype='float32')
for i in self.conv_ops: for i in self.conv_ops:
assert_raises(ValueError, i, img, kern, assert_raises(ValueError, i, img, kern,
border_mode=(-1, 0)) border_mode=(-1, 0))
assert_raises(ValueError, i, img, kern, assert_raises(ValueError, i, img, kern,
border_mode=(0, -1)) border_mode=(0, -1))
assert_raises(ValueError, i, img, kern, assert_raises(ValueError, i, img, kern,
border_mode='not border') border_mode='not border')
def _run_onecase(self, img_shape, kern_shape, padding, op): def _run_onecase(self, img_shape, kern_shape, padding, op):
npy_img = numpy.random.rand(*img_shape).astype('float32') npy_img = numpy.random.rand(*img_shape).astype('float32')
...@@ -776,9 +772,9 @@ def gemm_directly(bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsx, subsy, ...@@ -776,9 +772,9 @@ def gemm_directly(bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsx, subsy,
border_mode='valid', subsample=subsample)(i, k) border_mode='valid', subsample=subsample)(i, k)
f = theano.function([i, k], op, mode=theano_mode) f = theano.function([i, k], op, mode=theano_mode)
gpuval = numpy.array(f( gpuval = numpy.array(f(
npy_img.transpose(1, 0, 2, 3), npy_img.transpose(1, 0, 2, 3),
npy_kern.transpose(1, 0, 2, 3)[:, :, ::-1, ::-1])).transpose( npy_kern.transpose(1, 0, 2, 3)[:, :, ::-1, ::-1])
1, 0, 2, 3) ).transpose(1, 0, 2, 3)
assert_allclose(cpuval, gpuval, rtol=1e-4) assert_allclose(cpuval, gpuval, rtol=1e-4)
...@@ -892,44 +888,44 @@ def benchmark(): ...@@ -892,44 +888,44 @@ def benchmark():
shapes_valid = [ shapes_valid = [
# test_lenet_28 shape # test_lenet_28 shape
((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)) # valid ((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)), # valid
, ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)) # valid ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
, ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # valid ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
, ((1, 60, 28, 28), (20, 60, 24, 24), (1, 1), (1, 1), (1, 1)) # valid ((1, 60, 28, 28), (20, 60, 24, 24), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_32 shape # test_lenet_32 shape
, ((20, 60, 14, 14), (30, 60, 10, 10), (1, 1), (1, 1), (1, 1)) # valid ((20, 60, 14, 14), (30, 60, 10, 10), (1, 1), (1, 1), (1, 1)), # valid
, ((60, 20, 14, 14), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)) # valid ((60, 20, 14, 14), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
, ((60, 1, 32, 32), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # valid ((60, 1, 32, 32), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
, ((1, 60, 32, 32), (20, 60, 28, 28), (1, 1), (1, 1), (1, 1)) # valid ((1, 60, 32, 32), (20, 60, 28, 28), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_64 shape # test_lenet_64 shape
, ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)) # valid ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
, ((20, 10, 29, 29), (30, 10, 23, 23), (1, 1), (1, 1), (1, 1)) # valid ((20, 10, 29, 29), (30, 10, 23, 23), (1, 1), (1, 1), (1, 1)), # valid
, ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)) # valid ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
, ((1, 10, 64, 64), (20, 10, 58, 58), (1, 1), (1, 1), (1, 1)) # valid ((1, 10, 64, 64), (20, 10, 58, 58), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_108 shape # test_lenet_108 shape
, ((10, 20, 51, 51), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)) # valid ((10, 20, 51, 51), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
, ((20, 10, 51, 51), (30, 10, 45, 45), (1, 1), (1, 1), (1, 1)) # valid ((20, 10, 51, 51), (30, 10, 45, 45), (1, 1), (1, 1), (1, 1)), # valid
, ((10, 1, 108, 108), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)) # valid ((10, 1, 108, 108), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
, ((1, 10, 108, 108), (20, 10, 102, 102), (1, 1), (1, 1), (1, 1)) # valid ((1, 10, 108, 108), (20, 10, 102, 102), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_256 shape # test_lenet_256 shape
, ((2, 20, 124, 124), (30, 20, 9, 9), (1, 1), (1, 1), (1, 1)) # valid ((2, 20, 124, 124), (30, 20, 9, 9), (1, 1), (1, 1), (1, 1)), # valid
, ((20, 2, 124, 124), (30, 2, 116, 116), (1, 1), (1, 1), (1, 1)) # valid ((20, 2, 124, 124), (30, 2, 116, 116), (1, 1), (1, 1), (1, 1)), # valid
, ((2, 1, 256, 256), (20, 1, 9, 9), (1, 1), (1, 1), (1, 1)) # valid ((2, 1, 256, 256), (20, 1, 9, 9), (1, 1), (1, 1), (1, 1)), # valid
, ((1, 2, 256, 256), (20, 2, 248, 248), (1, 1), (1, 1), (1, 1)) # valid ((1, 2, 256, 256), (20, 2, 248, 248), (1, 1), (1, 1), (1, 1)) # valid
] ]
shapes_full = [ shapes_full = [
# test_lenet_28 shape # test_lenet_28 shape
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # full ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # full
# test_lenet_32 shape # test_lenet_32 shape
, ((60, 30, 10, 10), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # full conv_full_patch_stack_padded' N=1 ((60, 30, 10, 10), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # full conv_full_patch_stack_padded' N=1
# test_lenet_64 shape # test_lenet_64 shape
, ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # full conv_full_patch_stack_padded' N=3 ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # full conv_full_patch_stack_padded' N=3
# test_lenet_108 shape # test_lenet_108 shape
, ((10, 30, 45, 45), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # full 'conv_full_patch_stack_padded' N=9 ((10, 30, 45, 45), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # full 'conv_full_patch_stack_padded' N=9
# test_lenet_256 shape # test_lenet_256 shape
, ((2, 30, 116, 116), (20, 30, 9, 9), (1, 1), (1, 1), (1, 1)) # full conv_reference_full ((2, 30, 116, 116), (20, 30, 9, 9), (1, 1), (1, 1), (1, 1)) # full conv_reference_full
] ]
version = [-1] version = [-1]
verbose = 1 verbose = 1
...@@ -952,6 +948,6 @@ def test_stack_rows_segfault_070312(): ...@@ -952,6 +948,6 @@ def test_stack_rows_segfault_070312():
kern = theano.shared(numpy.random.rand(1, 80, 9, 9).astype('float32')) kern = theano.shared(numpy.random.rand(1, 80, 9, 9).astype('float32'))
out = theano.shared(numpy.random.rand(1, 2, 2, 3).astype('float32')) out = theano.shared(numpy.random.rand(1, 2, 2, 3).astype('float32'))
op = theano.tensor.nnet.conv.ConvOp(imshp=(80, 96, 96), kshp=(9, 9), op = theano.tensor.nnet.conv.ConvOp(imshp=(80, 96, 96), kshp=(9, 9),
nkern=1, bsize=1) nkern=1, bsize=1)
f = theano.function([], [], updates=[(out, op(img, kern))], mode=theano_mode) f = theano.function([], [], updates=[(out, op(img, kern))], mode=theano_mode)
f() f()
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