Added test_gemm_grads() test to test whether the gradients of GpuCorrMM are correct

theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py

@@ -842,7 +842,6 @@ class TestConv2DGPU(unittest.TestCase):
             theano_mode = theano_mode_orig
 
 
-
 def test_gemm_directly():
     for direction in ['fprop', 'bprop img', 'bprop kern']:
         print 'Testing direction: ' + direction
@@ -893,6 +892,56 @@ def test_gemm_directly():
                                                 print "subsample: ", subsample
                                                 assert False
 
+
+def test_gemm_grads():
+    for mode in 'valid', 'full':
+        for bs in range(1, 5):
+            for ch in range(1,4):
+                for nf in range(1,4):
+                    for rImg1 in range(5, 9):
+                        for rImg2 in range(5, 9):
+                            for rFlt1 in range(2, 4):
+                                for rFlt2 in range(2, 4):
+                                    for subsx in range(1, 3):
+                                        for subsy in range(1, 3):
+                                            ishape = (bs, ch, rImg1, rImg2)
+                                            kshape = (nf, ch, rFlt1, rFlt2)
+                                            subsample = (subsx, subsy)
+
+                                            npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
+                                            npy_kern = theano._asarray(numpy.random.rand(*kshape), dtype='float32')
+
+                                            i = cuda_tensor4()
+                                            k = cuda_tensor4()
+
+                                            pad = 'auto' if mode == 'full' else (0, 0)
+                                            # TODO: also test custom pad values
+                                            corr_op = theano.sandbox.cuda.blas.GpuCorrMM(
+                                                    'valid', subsample, pad)(i, k)
+                                            conv_op = tensor.nnet.conv2d(i, k[:,:,::-1,::-1],
+                                                    ishape, kshape, mode, subsample)
+
+                                            f = theano.function([i, k],
+                                                    [corr_op,
+                                                    theano.grad(corr_op.sum(), i),
+                                                    theano.grad(corr_op.sum(), k),
+                                                    conv_op,
+                                                    theano.grad(conv_op.sum(), i),
+                                                    theano.grad(conv_op.sum(), k)],
+                                                    mode=theano_mode)
+
+                                            allvals = f(npy_img, npy_kern)
+
+                                            for a, b, p in zip(allvals[:3], allvals[3:], ('fprop', 'bprop img', 'bprop kern')):
+                                                if (a.shape != b.shape) or not numpy.allclose(a, b, rtol=1e-4):
+                                                    print "Test failed for", p
+                                                    print "mode: ", mode
+                                                    print "ishape: ", ishape
+                                                    print "kshape: ", kshape
+                                                    print "subsample: ", subsample
+                                                    assert False
+
+
 def benchmark():
 
     shapes_valid = [