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提交 563b7086 authored 作者: bergstra@ip05.m's avatar bergstra@ip05.m
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initial port of convop from ledeepnet into sandbox

上级 d8323502
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theano/sandbox/conv.py 0 → 100644
浏览文件 @ 563b7086
import numpy as N
import theano
import theano.tensor as T
from theano import gof, Op, tensor
from scipy.signal.signaltools import _valfrommode, _bvalfromboundary
from scipy.signal.sigtools import _convolve2d
from theano.printing import Print
def getFilterOutShp(inshp, kshp, (dx,dy)=(1,1), mode='valid'):
s = -1 if mode=='valid' else 1
inshp, kshp = N.array(inshp), N.array(kshp)
return N.int64(N.ceil((inshp[1:] + s*kshp - s*1)/\
N.array([dy,dx], dtype='float')))
class ConvOp(Op):
"""
A convolution op that should mimic scipy.signal.convolve2d, but faster!
In development.
"""
def __init__(self, imshp, kshp, nkern, bsize, dx, dy, output_mode='valid'):
if len(imshp)==2:
self.imshp = (1,)+imshp
elif len(imshp)==3:
self.imshp = imshp
else:
raise Exception("bad len for imshp")
self.kshp = kshp
self.nkern = nkern
self.bsize=bsize
self.dx=dx
self.dy=dy
if self.dx!=1 or self.dy!=1:
print "Warning, dx!=1 or dy!=1 only supported in python mode!"
raise NotImplementedError()
self.out_mode = output_mode
if not self.out_mode in ["valid", "full"]:
raise Exception("Mode %s not implemented"%self.out_mode)
self.fulloutshp = N.array(self.imshp[1:]) - N.array(self.kshp) + 1 \
if self.out_mode=='valid'\
else N.array(self.imshp[1:]) + N.array(self.kshp) - 1
assert ((N.array(self.imshp[1:])-self.kshp)>=0).all()
assert N.prod(self.fulloutshp)>0
# def __eq__(self, other):
# raise Error("Not implemented")
# def __hash__(self):
# raise Error("Not implemented")
def make_node(self, inputs, kerns):
#all kernels must have the same shape!
#output_mode only valid and full are supported!
self.outshp = getFilterOutShp(self.imshp, self.kshp, (self.dx,self.dy), self.out_mode)
self.dtype = inputs.dtype
assert kerns.dtype==self.dtype
# TODO: find a way to make ConvOp work for N-D (after NIPS09)
outdim = kerns.ndim
output = tensor.tensor(dtype=self.dtype, broadcastable=[False]*outdim);
return gof.Apply(self, [inputs, kerns], [output])
def perform(self,node, (img2d, filtersflipped), (z,)):
"""
By default if len(img2d.shape)==3, we
"""
if z[0] is None:
z[0] = N.zeros((self.bsize,)+(self.nkern,)+tuple(self.fulloutshp))
zz=z[0]
val = _valfrommode(self.out_mode)
bval = _bvalfromboundary('fill')
if len(img2d.shape)==2 and self.imshp[0]==1 and self.bsize==1:
img2d = img2d.reshape((1,1)+img2d.shape)
elif len(img2d.shape)==3 and self.imshp[0]==1 and self.bsize!=1:
img2d = img2d.reshape((img2d.shape[0],)+(1,)+img2d.shape[1:])
elif len(img2d.shape)==3:
img2d = img2d.reshape((1,)+(img2d.shape[0],)+img2d.shape[1:])
elif len(img2d.shape)==3 and self.imshp[0]==1 and self.bsize==1:
img2d = img2d.reshape((1,1)+img2d.shape[1:])
elif len(img2d.shape)!=4: raise Exception("bad img2d shape.")
if len(filtersflipped.shape)==3 and self.imshp[0]==1:
assert self.imshp[0]==1
filtersflipped = filtersflipped.reshape((filtersflipped.shape[0],)+(1,)+filtersflipped.shape[1:])
elif len(filtersflipped.shape)!=4: raise Exception("Bad filtersflipped shape")
for b in range(self.bsize):
for n in range(self.nkern):
zz[b,n,...].fill(0)
for im0 in range(self.imshp[0]):
zz[b,n,...] += _convolve2d(\
img2d[b,im0,...], filtersflipped[n,im0,...],1,val, bval, 0)
zz = zz[:,:,0::self.dx,0::self.dy]
z[0]=zz
def grad(self, (inputs, kerns), (gz,)):
"""
In development. Works for test cases in test_sp.py
A few known issues:
* doesn't work for rectangular images or filters
* inputs needs to be a 4D tensor. Couldn't get 3D to work
* will crash if filter the same size as input image
"""
print '************GRAD**************'
print 'self.outshp = ', self.outshp
####### Determine gradient on kernels ########
if inputs.ndim == 3:
print 'xxxxxx self.imshp = ', self.imshp
print 'inputs.broadcastable = ', inputs.broadcastable
print 'inputs.ndim = ', inputs.ndim
img = tensor.shape_padleft(inputs,1)
img = tensor.DimShuffle(inputs.broadcastable, (1,0,2,3))(inputs)
imshp = N.hstack((self.bsize, self.imshp[1:]))
bsize = self.imshp[0]
nkern = self.nkern
filters = tensor.DimShuffle(gz.broadcastable, (1,0,2,3))(gz)
filters = filters[:,:,::-1,::-1]
kshp = self.outshp[::-1]
print kshp, imshp
mode = self.out_mode
dw = ConvOp(imshp, kshp, nkern, bsize, 1,1, output_mode=mode)(img,filters)
dw = tensor.DimShuffle(dw.broadcastable, (1,0,2,3))(dw)
dw = dw[:,:,::-1,::-1]
####### Determine gradient on inputs ########
mode = 'valid' if self.out_mode == 'full' else 'full'
filters = tensor.DimShuffle(gz.broadcastable, (1,0,2,3))(kerns)
filters = filters[:,:,::-1,::-1]
nkern = self.imshp[0]
imshp = N.hstack((self.nkern,self.outshp))
din = ConvOp(imshp, self.kshp, nkern, self.bsize,
1,1, output_mode=mode)(gz,filters)
return [din, dw]
#def c():
def c_headers(self):
return ['"Python.h"', '"numpy/noprefix.h"']
def c_code_cleanup(self, node, name, input_names, output_names, sub):
"""
TODO: implement from c_code()???
"""
return ""
def c_support_code(self):
return """
#define STRIDES(arr) ((arr)->strides)
#define FULL 2
#define SAME 1
#define VALID 0
#include <iostream>
using namespace std;
"""
def c_code(self, node, name, (img2d, filtersflipped), (z, ), sub):
if node.inputs[0].type != node.inputs[1].type:
raise NotImplementedError()
code="""
int mode=-1,typenum;
PyArrayObject *ain1=NULL, *ain2=NULL, *aout=NULL;
const %(type)s fill_value = 0;
int type_im=PyArray_TYPE(%(img2d)s);
int type_ker=PyArray_TYPE(%(filtersflipped)s);
npy_intp dim_zz[2]={%(self_outshp0)s,%(self_outshp1)s};
npy_intp dim_im[2]={%(self_imshp1)s,%(self_imshp2)s};
npy_intp dim_ker[2]={%(self_kshp0)s,%(self_kshp1)s};
PyArray_Dims img2d_shape;
npy_intp img2d_dim[4]={1,1,0,0};
img2d_shape.ptr=img2d_dim;
img2d_shape.len=4;
PyArray_Dims kerns_shape;
npy_intp kerns_dim[4]={1,1,0,0};
kerns_shape.ptr=kerns_dim;
kerns_shape.len=4;
PyObject *img2d, *contig, *filtersflipped;
string s="%(self_out_mode)s";
if(%(img2d)s->nd==2){
img2d_dim[3]=%(img2d)s->dimensions[1];
img2d_dim[2]=%(img2d)s->dimensions[0];
}else if(%(img2d)s->nd==3){
img2d_dim[3]=%(img2d)s->dimensions[2];
img2d_dim[2]=%(img2d)s->dimensions[1];
img2d_dim[0]=%(img2d)s->dimensions[0];
}else if(%(img2d)s->nd==4){
img2d_dim[3]=%(img2d)s->dimensions[3];
img2d_dim[2]=%(img2d)s->dimensions[2];
img2d_dim[1]=%(img2d)s->dimensions[1];
img2d_dim[0]=%(img2d)s->dimensions[0];
}else {
PyErr_SetString(PyExc_ValueError, "img don't have a good shape");
%(fail)s;
}
if(%(filtersflipped)s->nd==3){
kerns_dim[3]=%(filtersflipped)s->dimensions[2];
kerns_dim[2]=%(filtersflipped)s->dimensions[1];
kerns_dim[0]=%(filtersflipped)s->dimensions[0];
}else if(%(filtersflipped)s->nd==4){
kerns_dim[3]=%(filtersflipped)s->dimensions[3];
kerns_dim[2]=%(filtersflipped)s->dimensions[2];
kerns_dim[1]=%(filtersflipped)s->dimensions[1];
kerns_dim[0]=%(filtersflipped)s->dimensions[0];
}else{
PyErr_SetString(PyExc_ValueError, "kernel don't have a good shape");
%(fail)s;
}
img2d = PyArray_Newshape(%(img2d)s,&img2d_shape, PyArray_CORDER);
if (!PyArray_ISCONTIGUOUS(img2d)){
contig = (PyObject*)(PyArray_GETCONTIGUOUS((PyArrayObject*)img2d));
Py_DECREF(img2d);
img2d = contig;
}
if (!PyArray_ISCONTIGUOUS(img2d)){
PyErr_SetString(PyExc_ValueError, "img2d isn't contiguous");
%(fail)s;
}
filtersflipped = PyArray_Newshape(%(filtersflipped)s,&kerns_shape, PyArray_CORDER);
if (!PyArray_ISCONTIGUOUS(filtersflipped)){
contig = (PyObject*)(PyArray_GETCONTIGUOUS((PyArrayObject*)filtersflipped));
Py_DECREF(filtersflipped);
filtersflipped = contig;
}
if (!PyArray_ISCONTIGUOUS(filtersflipped)){
PyErr_SetString(PyExc_ValueError, "filtersflipped isn't contiguous");
%(fail)s;
}
if(s=="valid") mode=0;
else if(s=="full") mode=2;
else {PyErr_SetString(PyExc_ValueError, "invalid mode, only full and valid are supported"); %(fail)s;};
typenum = PyArray_ObjectType((PyObject*)%(img2d)s, 0);
typenum = PyArray_ObjectType((PyObject*)%(filtersflipped)s, 0);
if (typenum < 0) {PyErr_SetString(PyExc_ValueError, "Invalid type"); %(fail)s;}
if (!img2d) %(fail)s;
if (!filtersflipped) %(fail)s;
if ((!%(z)s)
|| *PyArray_DIMS(%(z)s)!=4
||(%(z)s->dimensions[0] != %(self_bsize)s)
||(%(z)s->dimensions[1] != %(self_nkern)s)
||(%(z)s->dimensions[2] != dim_zz[0])
|| (%(z)s->dimensions[3] != dim_zz[1])
)
{
if (%(z)s) Py_DECREF(%(z)s);
npy_intp dims[4] = {0,0,0,0}; //(npy_intp *)malloc(4*sizeof(%(type)s));
if(!dims) %(fail)s;
dims[0]=%(self_bsize)s;
dims[1]=%(self_nkern)s;
dims[2]=dim_zz[0];
dims[3]=dim_zz[1];
%(z)s = (PyArrayObject*) PyArray_ZEROS(4, dims, typenum,0);
}else{
//PyArray_FILLWBYTE((PyObject*)%(z)s,0);
}
int Os[2];
if (mode == FULL) {Os[0] = dim_im[0]+dim_ker[0]-1; Os[1] = dim_im[1]+dim_ker[1]-1;}
else {Os[0] = dim_im[0]-dim_ker[0]+1; Os[1] = dim_im[1]-dim_ker[1]+1;}
for(int b=0;b< %(self_bsize)s;b++){
for(int n_kern=0;n_kern<%(self_nkern)s;n_kern++){
//assertions
if (%(z)s->strides[0] != %(z)s->dimensions[1] *%(z)s->dimensions[2] *%(z)s->dimensions[3] * sizeof(%(type)s)) %(fail)s;
if (%(z)s->strides[1] != %(z)s->dimensions[2] * %(z)s->dimensions[3] * sizeof(%(type)s)) %(fail)s;
if (%(z)s->strides[2] != %(z)s->dimensions[3] * sizeof(%(type)s)) %(fail)s;
if (%(z)s->strides[3] != sizeof(%(type)s)) %(fail)s;
aout = (PyArrayObject *)PyArray_SimpleNewFromData(2,dim_zz,
typenum,PyArray_GETPTR2(%(z)s,b,n_kern));
if (aout == NULL) %(fail)s;
%(type)s *out=(%(type)s *)(aout->data);
for (int i = 0; i < dim_zz[0]*dim_zz[1]; ++i) out[i] = 0;
for(int stack_size=0;stack_size<%(self_imshp0)s;stack_size++){
ain1 = (PyArrayObject *)PyArray_SimpleNewFromData(2,dim_im,
type_im,PyArray_GETPTR2(img2d,b,stack_size));
ain2 = (PyArrayObject *)PyArray_SimpleNewFromData(2,dim_ker,
type_ker,PyArray_GETPTR2(filtersflipped,n_kern,stack_size));
if (ain1 == NULL) %(fail)s;
if (ain2 == NULL) %(fail)s;
if (dim_im[0] != ((PyArrayObject*)img2d)->dimensions[2]) %(fail)s;
if (dim_im[1] != ((PyArrayObject*)img2d)->dimensions[3]) %(fail)s;
if (dim_ker[0] !=((PyArrayObject*)filtersflipped)->dimensions[2]) %(fail)s;
if (dim_ker[1] !=((PyArrayObject*)filtersflipped)->dimensions[3]) %(fail)s;
if (ain1->strides[0] != ain1->dimensions[1] * sizeof(%(type)s)) %(fail)s;
if (ain2->strides[0] != ain2->dimensions[1] * sizeof(%(type)s)) %(fail)s;
if (aout->strides[0] != aout->dimensions[1] * sizeof(%(type)s)) %(fail)s;
if (ain1->strides[1] != sizeof(%(type)s)) %(fail)s;
if (ain2->strides[1] != sizeof(%(type)s)) %(fail)s;
if (aout->strides[1] != sizeof(%(type)s)) %(fail)s;
%(type)s *in=(%(type)s *)(ain1->data);
%(type)s *hvals=(%(type)s *)(ain2->data);
int new_m;
for (int m=0; m < Os[0]; m++) {
// Reposition index into input image based on requested output size
if (mode == FULL) new_m = m ;
else new_m = (m+dim_ker[0]-1);
for (int n=0; n < Os[1]; n++) { // loop over columns
%(type)s sum=0;
// Sum over kernel, if index into image is out of bounds
// fill with the value
for (int j=0; j < dim_ker[0]; j++) {
int ind0 = (new_m-j);
if(mode==FULL){
%(type)s * idx2=&hvals[j*dim_ker[1]];
if(ind0 < 0 || ind0 >= dim_im[0]){
if(fill_value!=0)
for (int k=0; k < dim_ker[1]; k++) {
sum+= idx2[k] * fill_value;
}
}else{
//do the part where kernel is to the right of the img
int k=0,max_k=max((int)(n-dim_im[1])+1,0);
if(fill_value!=0){
for(k=0;k<max_k;k++){
sum+= idx2[k]*fill_value;
}
}else {k=max_k;}
//do the part where the kernel is on the img
max_k=min(n+1,(int)dim_ker[1]);
%(type)s * idx1=&in[ind0*dim_im[1]];
for (int ind1=n-k; k<max_k; k++,ind1--) {
sum+= idx2[k] * idx1[ind1];
}
//do the part to the left of the img
if(fill_value!=0)
for(;k<dim_ker[1];k++) sum+= idx2[k]*fill_value;
}
}else{
%(type)s* idx1=&in[ind0*dim_im[1]]; //JB: should be dim_im[1] right? (was dim_im[0])
%(type)s* idx2=&hvals[j*dim_ker[1]];
int new_n = (n+dim_ker[1]-1);
for (int k=0,last=new_n; k < dim_ker[1]; k++,last--) {
sum+=idx2[k]*idx1[last];
}
}
}//for j
out[m*dim_zz[1]+n] %(affectation)s sum;
}//for n
}//for m
Py_DECREF(ain1);
Py_DECREF(ain2);
}//for stack_size
if (0 && (mode==FULL)){
for (int i = 0; i < dim_zz[0]*dim_zz[1]; ++i)
std::cout << " " << out[i];
std::cout << "\\n";
}
Py_DECREF(aout);
}//for n_kern
}//for b
Py_XDECREF(img2d);
Py_XDECREF(filtersflipped);
fail:
"""
d=locals()
d.update(sub)
d["self_out_mode"]=self.out_mode
d["self_bsize"]=self.bsize
d["self_nkern"]=self.nkern
d["self_dx"]=self.dx
d["self_dy"]=self.dy
d["self_outshp0"]=self.outshp[0]
d["self_outshp1"]=self.outshp[1]
d["self_imshp0"]=self.imshp[0]
d["self_imshp1"]=self.imshp[1]
d["self_imshp2"]=self.imshp[2]
d["self_kshp0"]=self.kshp[0]
d["self_kshp1"]=self.kshp[1]
d["affectation"]="=" if self.imshp[0]==1 else "+="
if self.dtype=="float32": d["type"]="float"
elif self.dtype=="float64": d["type"]="double"
else: raise Exception("Type %s not implemented"%self.dtype)
return code % d
def convolve2(kerns, kshp, nkern, images, imshp, bsize, step=(1,1),
bias=None, mode='valid'):
# if imshp, is a tuple, images contains one input dimension
nvis_dim = 1 if len(imshp)!=3 else imshp[0]
# all these reshapes should happen in place
imrshp = tensor.as_tensor([bsize] + list(imshp))
imtensor = tensor.reshape(images, imrshp)
kernrshp = tensor.as_tensor([nkern, nvis_dim] + list(kshp))
kerntensor = tensor.reshape(kerns, kernrshp)
print '***** convolve2 *****'
print 'imrshp = ', imrshp
print 'kernrshp = ', kernrshp
convop = ConvOp(imshp, kshp, nkern, bsize, 1, 1, output_mode=mode)
convout = convop(imtensor, kerntensor)
if bias:
biastensor = tensor.DimShuffle((False,), ('x',0,'x','x'), inplace=True)(bias)
convout = convout + biastensor
rval = tensor.flatten(convout, 2)
return rval, N.hstack((nkern, convop.outshp))
theano/sandbox/test_conv.py 0 → 100644
浏览文件 @ 563b7086
import sys, time, unittest
import numpy
import numpy as N
from scipy.signal import convolve2d
from theano.tests import unittest_tools as utt
from theano import function, Mode
import theano.tensor as T
from conv import ConvOp, convolve2, getFilterOutShp
def flip(kern, kshp):
"flip the kernel as scipy.convolv2d do it flipped."
flip = N.zeros(kern.shape)
if len(kern.shape)==3:
kern=kern.reshape(kern.shape[0],-1)
for k in range(kern.shape[0]):
it = reversed(kern[k,:])
for i in range(kshp[0]):
for j in range(kshp[1]):
flip[k,i,j] = it.next()
elif len(kern.shape)==4:
kern=kern.reshape(kern.shape[0],kern.shape[1],-1)
for k in range(kern.shape[0]):
for m in range(kern.shape[1]):
it = reversed(kern[k,m,:])
for i in range(kshp[0]):
for j in range(kshp[1]):
flip[k,m,i,j] = it.next()
else:
raise NotImplementedError()
return flip
class TestConvOp(unittest.TestCase):
def setUp(self):
utt.seed_rng()
def test_convolution(self):
print '\n\n*************************************************'
print ' TEST CONVOLUTION'
print '*************************************************'
# fixed parameters
bsize = 10 # batch size
imshp = (28,28)# image shape
print >> sys.stderr, "WARNING: only square shape tested"
kshps = [(5,5),(6,7),(12,8)] # kernel shaped
nkern = 5 # nb kernel
ssizes = ((1,1),(2,2),(3,3),(4,4))#step size
convmodes = ('full','valid')
# TODO: ask Fred about this
# this combination trigered a bug.
# bsize=1
# imshp=(9,9)#fail with 9,9
# kshp=(2,2)
# nkern=5
# ssizes=((1,1),)
# this combination trigered a bug.
# bsize = 1 # batch size
# imshp = (3,3)# image shape
# kshp = (2,3)#(5,5) # kernel shaped
# nkern = 1 # nb kernel
# ssizes = ((1,1),)#(2,2),(3,3),(4,4))#step size
# convmodes = ('full','valid')
# symbolic stuff
bias = T.dvector()
kerns = T.dmatrix()
input = T.dmatrix()
rng = N.random.RandomState(3423489)
biasvals = rng.randn(nkern)
#profmode = wraplinker.ProfileMode(OpWiseCLinker(), 'fast_run')
tconvop, tscipy, tconv2 = [], [], []
for conv_mode in convmodes:
for kshp in kshps:
filters = rng.randn(nkern,N.prod(kshp))
for ss in ssizes:
# now test with real values
img2d = N.arange(bsize*N.prod(imshp)).reshape((bsize,)+imshp)
img1d = img2d.reshape(bsize,-1)
# create filters (need to be flipped to use convolve2d)
filtersflipped = flip(filters.reshape((nkern,)+kshp), kshp)
# compute with new convolve2 (no timing info)
output4, outshp4 = convolve2(kerns, kshp, nkern, input,\
imshp, bsize, (1,1), bias=bias, mode=conv_mode)
ttime1 = time.time()
f = function([kerns, bias, input], output4)
out4 = f(filtersflipped.reshape(nkern,-1), biasvals, img1d)
tconv2 += [time.time() - ttime1]
out4 = out4.reshape(bsize, nkern, outshp4[1], outshp4[2])
out4 = out4[:,:,0::ss[0],0::ss[1]]
out4 = out4.reshape(bsize, -1)
# compute with ConvOp
dmatrix3=T.TensorType('float64', (False, False, False))
inputs=dmatrix3()
kerns3=dmatrix3()
bia=T.dscalar()
conv_op = ConvOp(imshp, kshp, nkern, bsize, 1,1, conv_mode)(inputs, kerns3)
f2 = function([inputs, kerns3], conv_op, mode=Mode(linker="c"))
f3 = function([inputs, kerns3], conv_op, mode=Mode(linker="py"))
ttime1 = time.time()
out2_ = f2(img2d, filtersflipped)
out2__ = out2_[:,:,0::ss[0],0::ss[1]]
tconvop += [time.time() - ttime1]
out2___ = out2__.copy()
out2 = out2___ + biasvals.reshape(1,nkern,1,1)
out3_ = f3(img2d, filtersflipped)
out3__ = out3_[:,:,0::ss[0],0::ss[1]]
out3___ = out3__.copy()
out3 = out3___ + biasvals.reshape(1,nkern,1,1)
assert (N.abs(out2_-out3_)<1e-5).all()
# REFERENCE IMPLEMENTATION: compute output with convolve2d
fulloutshp = N.array(imshp) - N.array(kshp) + 1 if conv_mode=='valid'\
else N.array(imshp) + N.array(kshp) - 1
ntime1 = time.time()
refout = N.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
for b in range(bsize):
for n in range(nkern):
refout[b,...,n] = convolve2d(\
img2d[b,:,:], filtersflipped[n,...],conv_mode)
tscipy += [time.time() - ntime1]
# need to flatten images
bench1 = refout[:,0::ss[0],0::ss[1],:].reshape(bsize,-1,nkern)
bench1 += biasvals.reshape(1,1,nkern)
# swap the last two dimensions (output needs to be nkern x outshp)
bench1 = N.swapaxes(bench1,1,2)
# compare benchmark with ConvOp
temp = bench1.flatten() - out2.flatten()
assert (temp < 1e-5).all()
# compare benchmark with convolve2
temp = bench1.flatten() - out4.flatten()
assert (temp < 1e-5).all()
print '**** Convolution Profiling Results ****'
print 'Scipy convolve2d processing time: %.3fs'%sum(tscipy),tscipy
print 'ConvOp processing time: %.3fs'%sum(tconvop),tconvop
print 'convolve2 processing time: %.3fs'%sum(tconv2),tconv2
d=N.asarray(tscipy)/tconvop
print 'speed up ConvOp vs convolve2d: %.3f'%d.mean(),d
def test_ConvOpGrad(self):
nkern = 3
bsize = 2
imgs = T.dmatrix('imgs')
kerns = T.dmatrix('kerns')
for mode in 'valid',:#'full':
for imshp in (2,5,5),(2,10,10): # (12,10), (3,12,11):
visdim = 1 if len(imshp)!=3 else imshp[0]
for kshp in (3,3),:# (6,7):
imgvals = N.random.random(N.hstack((bsize,imshp)))
print 'imgvals.shape = ', imgvals.shape
imgvals = imgvals.reshape(bsize,-1)
kernvals = N.random.rand(nkern,visdim,kshp[0],kshp[1])
print 'kernvals.shape = ', kernvals.shape
kernvals = kernvals.reshape(nkern,-1)
def testf(imgs, kerns):
out, outshp = convolve2(kerns, kshp, nkern, imgs,
imshp, bsize, mode=mode)
return out
utt.verify_grad(testf, [imgvals, kernvals])
def test_ConvOpGrad32(self):
nkern = 4
bsize = 3
imgs = T.fmatrix('imgs')
kerns = T.fmatrix('kerns')
def testf(imgs, kerns):
out, outshp = convolve2(kerns, kshp, nkern, imgs,
imshp, bsize, mode='valid')
return out
for mode in 'valid',:# 'full':
for imshp in (1,5,5),(2,10,10): # (12,10), (3,12,11):
visdim = 1 if len(imshp)!=3 else imshp[0]
for kshp in (3,3),:# (6,7):
imgvals = N.random.random(N.hstack((bsize,imshp)))
print 'imgvals.shape = ', imgvals.shape
imgvals = imgvals.reshape(bsize,-1)
kernvals = N.random.rand(nkern,visdim,kshp[0],kshp[1])
print 'kernvals.shape = ', kernvals.shape
kernvals = kernvals.reshape(nkern,-1)
utt.verify_grad(testf, [imgvals, kernvals])
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