added Conv3D tests

theano/tensor/nnet/tests/test_conv3d.py

+import unittest
+import theano
+import theano.tensor as T
+from theano import function, Mode
+from theano.tests import unittest_tools as utt
+from theano.tensor.nnet.ConvTransp3D import convTransp3D
+from theano.tensor.nnet.ConvGrad3D import convGrad3D
+from theano.tensor.nnet.Conv3D import conv3D
+import numpy as N
+import copy
+from scipy import sparse
+from theano import shared
+
+floatX = theano.config.floatX
+
+
+class DummyConv3D:
+    """A dummy version of Conv3D passed to verify_grad
+    Stores a fixed stride, since stride is not differentiable
+    Exposes only one scalar argument, which is used as the position
+    along a parametrically defined line, with 0 being at VwbVals
+    Direction of the line is chosen randomly at construction
+    The reason for locking the inputs to lie on this line is so that the
+    verify_grad will not need to test hundreds of variables. Disadvantage
+    is we can't be certain that all of them are correct, advantange is that
+    this random projection lets us test lots of variables very quickly """
+    def __init__(self, rng, VWbVals, d):
+        """
+        param: rng    Random number generator used to pick direction of the line
+        param: VWbVals    tuple containing values to test V,W,b around
+        param: d    shared variable for d, the stride
+        """
+
+        self.V, self.W, self.b = VWbVals
+        self.dV, self.dW, self.db = shared(rng.uniform(-1,1,self.V.value.shape)), shared(rng.uniform(-1,1,self.W.value.shape)), shared(rng.uniform(-1,1,self.b.value.shape))
+
+        self.d = d
+
+    def __call__(self, t):
+        output = conv3D(self.V+t*self.dV,self.W+t*self.dW,self.b+t*self.db,self.d)
+
+        return output
+
+class DummyConvGrad3D:
+    def __init__(self, rng, VdHvals, d, WShape):
+        """
+        param: rng    Random number generator used to pick direction of the line
+        param: VWbVals    tuple containing values to test V,W,b around
+        param: d    shared variable for d, the stride
+        """
+
+        self.V, self.dCdH = VdHvals
+        self.dV, self.ddCdH = shared(rng.uniform(-1,1,self.V.value.shape)), shared(rng.uniform(-1,1,self.dCdH.value.shape))
+
+        self.d = d
+        self.WShape = WShape
+
+    def __call__(self, t):
+
+        output = convGrad3D(self.V+t*self.dV,self.d,self.WShape,self.dCdH + t * self.ddCdH)
+        return output
+
+
+class DummyConvTransp3D:
+    def __init__(self, rng, WbHvals, d, RShape):
+        """
+        param: rng    Random number generator used to pick direction of the line
+        param: VWbVals    tuple containing values to test V,W,b around
+        param: d    shared variable for d, the stride
+        """
+
+        self.W, self.b, self.H = WbHvals
+        self.dW = rng.uniform(-1,1,self.W.value.shape)
+        self.db = rng.uniform(-1,1,self.b.value.shape)
+        self.dH = rng.uniform(-1,1,self.H.value.shape)
+        self.dW, self.db, self.dH = shared(self.dW), shared(self.db), shared(self.dH)
+
+
+        self.d = d
+        self.RShape = RShape
+
+    def __call__(self, t):
+        output = convTransp3D(self.W+t*self.dW,self.b+t*self.db,self.d,self.H+t*self.dH, self.RShape)
+
+        return output
+
+class TestConv3D(unittest.TestCase):
+
+    def setUp(self):
+
+        utt.seed_rng()
+
+        self.rng = N.random.RandomState(utt.fetch_seed())
+
+        mode = copy.copy(theano.compile.mode.get_default_mode())
+        mode.check_py_code = False
+
+        self.W  = shared(N.ndarray(shape=(1,1,1,1,1), dtype=floatX))
+        self.b  = shared(N.zeros(1,dtype=floatX))
+        self.rb = shared(N.zeros(1,dtype=floatX))
+        self.V  = shared(N.ndarray(shape=(1,1,1,1,1), dtype=floatX))
+        self.d  = shared(N.ndarray(shape=(3,),dtype=int))
+
+        self.H = conv3D(self.V, self.W, self.b, self.d)
+
+        self.H_func = function([], self.H, mode = mode)
+
+        self.H_shape_func = function( [], self.H.shape, mode = mode)
+
+        self.RShape = T.vector(dtype='int64')
+
+        self.otherH = T.TensorType(floatX,(False,False,False,False,False))(name='otherH')
+        self.transp = convTransp3D(self.W, self.rb, self.d, self.otherH, self.RShape)
+        self.transp_func = function([self.otherH,self.RShape],self.transp, mode=mode)
+
+        self.R = convTransp3D(self.W, self.rb, self.d, self.H, self.RShape)
+        self.R_func = function([self.RShape], self.R, mode = mode)
+        self.R_shape_func = function([self.RShape], self.R.shape)
+
+        self.reconsObj = T.sum(T.sqr(self.V-self.R))
+        self.reconsObjFunc = function([self.RShape], self.reconsObj, mode=mode)
+
+        self.gradientsFunc = function([self.RShape], [ T.grad(self.reconsObj, self.W), T.grad(self.reconsObj, self.H), T.grad(self.reconsObj, self.V), T.grad(self.reconsObj,self.b) ] , mode=mode)
+        self.check_c_against_python = function([self.RShape], [ T.grad(self.reconsObj, self.W), T.grad(self.reconsObj, self.H), T.grad(self.reconsObj, self.V), T.grad(self.reconsObj,self.b) ] , mode='DEBUG_MODE')
+
+
+        self.dCdW_shape_func = function([self.RShape],  T.grad(self.reconsObj, self.W).shape, mode=mode)
+
+
+    def random_tensor(self,*dims):
+        return N.asarray(self.rng.uniform(-.05,.05,dims),dtype=floatX)
+
+    def randomize(self):
+        batchSize    = self.rng.randint(1,4)
+        videoDur     = self.rng.randint(8,30)
+        filterWidth = self.rng.randint(1,8)
+        filterHeight = self.rng.randint(1,8)
+        filterDur    = self.rng.randint(1,8)
+
+        tsteps = self.rng.randint(1,4)
+        rsteps = self.rng.randint(1,4)
+        csteps = self.rng.randint(1,4)
+
+        videoDur    = tsteps * filterDur    + self.rng.randint(0,3)
+        videoWidth  = csteps * filterWidth  + self.rng.randint(0,3)
+        videoHeight = rsteps * filterHeight + self.rng.randint(0,3)
+
+        numFilters = self.rng.randint(1,3)
+        inputChannels = self.rng.randint(1,3)
+        self.d.value[0] = self.rng.randint(1,15)
+        self.d.value[1] = self.rng.randint(1,15)
+        self.d.value[2] = self.rng.randint(1,15)
+
+        outputHeight = int( (videoHeight - filterHeight) / self.d.value[0] )+1
+        outputWidth  = int( (videoWidth - filterWidth) / self.d.value[1] )+1
+        outputDur    = int( (videoDur - filterDur) / self.d.value[2] ) +1
+
+        self.W.value  = self.random_tensor(numFilters,filterHeight,filterWidth,filterDur,inputChannels)
+        self.b.value  = self.random_tensor(numFilters)
+        self.rb.value = self.random_tensor(inputChannels)
+
+        self.V.value  = self.random_tensor(batchSize,videoHeight,videoWidth,videoDur,inputChannels)
+        self.rb.value = self.random_tensor(inputChannels)
+
+    def test_c_against_python(self):
+        self.randomize()
+        self.check_c_against_python(self.V.value.shape[1:4])
+
+    def test_c_against_mat_mul(self):
+        #Use a filter of the same size as the image, so the convolution is just a dense matrix multiply
+        #Check that dense matrix multiplication gives the same result as convolution
+
+        batchSize   = self.rng.randint(1,10)
+        videoDur    = self.rng.randint(3,10)
+        videoWidth  = self.rng.randint(1,5)
+        videoHeight = self.rng.randint(1,5)
+
+        filterWidth   = videoWidth
+        filterHeight  = videoHeight
+        filterDur     = videoDur
+        numFilters    = self.rng.randint(1,3)
+        inputChannels = self.rng.randint(1,4)
+
+        self.d.value[0] = self.rng.randint(1,15)
+        self.d.value[1] = self.rng.randint(1,15)
+        self.d.value[2] = self.rng.randint(1,15)
+
+
+        self.W.value = self.random_tensor(numFilters,filterHeight,filterWidth,filterDur,inputChannels)
+
+        self.W.value *= (self.W.value < 1e-5)
+
+        self.b.value = self.random_tensor(numFilters)
+        self.V.value = self.random_tensor(batchSize,videoHeight,videoWidth,videoDur,inputChannels)
+
+        Hv = self.H_func()
+
+        assert Hv.shape[1] == 1
+        assert Hv.shape[2] == 1
+        assert Hv.shape[3] == 1
+
+        n = inputChannels * videoHeight * videoWidth * videoDur
+        W_mat = N.zeros((n, numFilters))
+        V_mat = N.zeros((batchSize,n))
+        Hv_mat = N.zeros((batchSize, numFilters))
+        for qi in xrange(0,numFilters):
+            W_mat[:,qi] = self.W.value[qi,:,:,:,:].reshape((n))
+            Hv_mat[:,qi] = Hv[:,0,0,0,qi]
+        for qi in xrange(0,batchSize):
+            V_mat[qi,:] = self.V.value[qi,:,:,:,:].reshape((n))
+
+        H_mat = N.dot(V_mat,W_mat) + self.b.value
+
+        tol = 1e-5
+        if floatX == 'float32':
+            tol = 1e-4
+
+        if N.abs(H_mat-Hv_mat).max() > tol and not N.allclose(H_mat,Hv_mat):
+            print H_mat
+            print Hv_mat
+            print 'max error: '+str(N.abs(H_mat-Hv_mat).max())
+            W.value[W.value != 0] += 1.0
+            print 'min non-zero kernel mag: '+str(N.abs(W.value).min())
+
+            assert False
+
+    def test_c_against_mat_transp_mul(self):
+    #Use a filter of the same size as the image, so the convolution is just a dense matrix multiply
+    #Check that dense matrix multiplication by the transpose of the matrix gives the same result as ConvTransp
+        batchSize = self.rng.randint(1,10)
+        videoDur = self.rng.randint(3,15)
+        videoWidth = self.rng.randint(3,15)
+        videoHeight = self.rng.randint(3,15)
+
+
+        filterWidth = videoWidth
+        filterHeight = videoHeight
+        filterDur = videoDur
+        numFilters = self.rng.randint(1,15)
+        inputChannels = self.rng.randint(1,15)
+        self.d.value[0] = self.rng.randint(1,15)
+        self.d.value[1] = self.rng.randint(1,15)
+        self.d.value[2] = self.rng.randint(1,15)
+
+
+
+        self.W.value = self.random_tensor(numFilters,filterHeight,filterWidth,filterDur,inputChannels)
+        
+        self.b.value = self.random_tensor(numFilters)
+        
+        self.V.value = self.random_tensor(batchSize,videoHeight,videoWidth,videoDur,inputChannels)
+        self.rb.value = self.random_tensor(inputChannels)
+
+        H_shape = self.H_shape_func()
+
+        assert H_shape[1] ==  1
+        assert H_shape[2] == 1
+        assert H_shape[3] == 1
+
+        Hv = self.random_tensor( * H_shape )
+
+        Vv = self.transp_func(Hv,[videoHeight,videoWidth,videoDur])
+        
+        n = inputChannels * videoHeight * videoWidth * videoDur
+        rbim = N.zeros((videoHeight,videoWidth,videoDur,inputChannels))
+        for qi in xrange(0,inputChannels):
+            rbim[:,:,:,qi] = self.rb.value[qi]
+        rbv = rbim.reshape((n))
+        W_mat = N.zeros((numFilters, n))
+        Vv_mat = N.zeros((n, batchSize))
+        Hv_mat = N.zeros((numFilters,batchSize))
+        for qi in xrange(0,numFilters):
+            W_mat[qi,:] = self.W.value[qi,:,:,:,:].reshape((n))
+            Hv_mat[qi,:] = Hv[:,0,0,0,qi]
+        for qi in xrange(0,batchSize):
+            Vv_mat[:,qi] = Vv[qi,:,:,:,:].reshape((n))
+
+
+        V_mat = (N.dot(W_mat.transpose(),Hv_mat).transpose() + rbv).transpose()
+
+        if N.abs(V_mat-Vv_mat).max() > 1e-5:
+            print V_mat
+            print Vv_mat
+
+            for qq in xrange(V_mat.shape[0]):
+                for qqq in xrange(Vv_mat.shape[1]):
+                    if abs(V_mat[qq,qqq]-Vv_mat[qq,qqq]) > 1e-5:
+                        print 'wrong at '+str((qq,qqq))+': '+str((V_mat[qq,qqq],Vv_mat[qq,qqq]))
+                        assert False
+
+    def test_c_against_sparse_mat_transp_mul(self):
+    #like test_c_against_mat_transp_mul but using a sparse matrix and a kernel that is smaller than the image
+
+        batchSize    = self.rng.randint(1,3)
+        filterWidth  = self.rng.randint(1,8)
+        filterHeight = self.rng.randint(1,8)
+        filterDur    = self.rng.randint(1,8)
+
+        self.d.value[0]   = self.rng.randint(1,3)
+        self.d.value[1]   = self.rng.randint(1,3)
+        self.d.value[2]   = self.rng.randint(1,3)
+
+        dr = self.d.value[0]
+        dc = self.d.value[1]
+        dt = self.d.value[2]
+
+
+        numFilters = self.rng.randint(1,3)
+        row_steps  = self.rng.randint(1,4)
+        col_steps  = self.rng.randint(1,4)
+        time_steps = self.rng.randint(1,4)
+
+        print (row_steps,col_steps,time_steps)
+
+        videoDur    = (time_steps-1)*dt+filterDur   + self.rng.randint(0,3)
+        videoWidth  = (col_steps-1)*dc+filterWidth  + self.rng.randint(0,3)
+        videoHeight = (row_steps-1)*dr+filterHeight + self.rng.randint(0,3)
+
+
+        inputChannels = self.rng.randint(1,15)
+
+        self.W.value = self.random_tensor(numFilters,filterHeight,filterWidth,filterDur,inputChannels)
+        self.b.value = self.random_tensor(numFilters)
+        #just needed so H_shape works
+        self.V.value = self.random_tensor(batchSize,videoHeight,videoWidth,videoDur,inputChannels)
+        self.rb.value = self.random_tensor(inputChannels)
+
+        H_shape = self.H_shape_func()
+
+        #make index maps
+        h = N.zeros(  H_shape[1:])
+        r = N.zeros(  H_shape[1:])
+        c = N.zeros(  H_shape[1:])
+        t = N.zeros(  H_shape[1:])
+
+        for qi in xrange(0,H_shape[4]):
+            h[:,:,:,qi] = qi
+        for qi in xrange(0,H_shape[1]):
+            r[qi,:,:,:] = qi
+        for qi in xrange(0,H_shape[2]):
+            c[:,qi,:,:] = qi
+        for qi in xrange(0,H_shape[3]):
+            t[:,:,qi,:] = qi
+
+        hn = H_shape[1] * H_shape[2] * H_shape[3] * H_shape[4]
+
+        h = h.reshape((hn))
+        r = r.reshape((hn))
+        c = c.reshape((hn))
+        t = t.reshape((hn))
+
+
+        Hv = self.random_tensor( * H_shape )
+
+        Vv = self.transp_func(Hv,[videoHeight,videoWidth,videoDur])
+
+        n = inputChannels * videoHeight * videoWidth * videoDur
+        rbim = N.zeros((videoHeight,videoWidth,videoDur,inputChannels))
+        for qi in xrange(0,inputChannels):
+            rbim[:,:,:,qi] = self.rb.value[qi]
+        rbv = rbim.reshape((n))
+
+        W_mat = N.zeros((hn,n))
+        Vv_mat = N.zeros((n, batchSize))
+        Hv_mat = N.zeros((hn,batchSize))
+        for qi in xrange(0,hn):
+            hi = h[qi]
+            ri = r[qi]
+            ci = c[qi]
+            ti = t[qi]
+
+            placed_filter = N.zeros(self.V.value.shape[1:])
+
+            placed_filter[ri*dr:ri*dr+self.W.value.shape[1],ci*dc:ci*dc+self.W.value.shape[2],ti*dt:ti*dt+self.W.value.shape[3],:] = self.W.value[hi,:,:,:,:]
+
+            W_mat[qi,:] = placed_filter.reshape((n))
+            Hv_mat[qi,:] = Hv[:,ri,ci,ti,hi]
+        for qi in xrange(0,batchSize):
+            Vv_mat[:,qi] = Vv[qi,:,:,:,:].reshape((n))
+
+
+        W_mat_T = sparse.csr_matrix(W_mat.transpose())
+
+        temp = W_mat_T.matmat(Hv_mat)
+        V_mat = (temp.transpose() + rbv).transpose()
+
+        if N.abs(V_mat-Vv_mat).max() > 1e-5:
+            print 'mul'
+            print V_mat
+            print 'conv'
+            print Vv_mat
+            for i in xrange(0,n):
+                for j in xrange(0,batchSize):
+                    if abs(V_mat[i,j] - Vv_mat[i,j]) > 1e-5:
+                        print 'wrong at %d,%d: %f mul versus %f conv' % (i,j,V_mat[i,j],Vv_mat[i,j])
+            assert False
+
+
+    def test_infer_shape(self):
+        self.randomize()
+        Hv = self.H_func()
+        H_shape = self.H_shape_func()
+        assert N.all(Hv.shape == H_shape) 
+            
+        gradients = self.gradientsFunc(self.V.value.shape[1:4])
+        dCdWv = gradients[0]
+        dCdW_shape = self.dCdW_shape_func(self.V.value.shape[1:4])
+
+        assert N.all(dCdWv.shape == dCdW_shape)
+
+        Rv = self.R_func(self.V.value.shape[1:4])
+        R_shape = self.R_shape_func(self.V.value.shape[1:4])
+
+        assert N.all(Rv.shape == R_shape)
+
+
+    def test_gradient(self):
+        self.randomize()
+        rng, V,W,b,d,rb = self.rng, self.V, self.W, self.b, self.d, self.rb
+        dCdH = shared(self.random_tensor( *self.H_shape_func() ))
+        testsPerDir = 2
+        theano.tests.unittest_tools.verify_grad(DummyConv3D(rng, (V,W,b), d), [0.0], n_tests=testsPerDir)
+        theano.tests.unittest_tools.verify_grad(DummyConvTransp3D(rng, (W,rb,dCdH), d,V.value.shape[1:4]), [0.0], n_tests=testsPerDir)
+        theano.tests.unittest_tools.verify_grad(DummyConvGrad3D(rng, (V,dCdH), d, W.value.shape), [0.0], n_tests=testsPerDir)