make test less verbose.

theano/sandbox/cuda/tests/test_cuda_ndarray.py

@@ -30,7 +30,7 @@ def advantage(cpu_dt, gpu_dt):
         return cpu_dt / gpu_dt
 
 def test_host_to_device():
-    print >>sys.stdout, 'starting test_host_to_dev'
+    #print >>sys.stdout, 'starting test_host_to_dev'
     for shape in ((), (3,), (2,3), (3,4,5,6)):
         a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
         b = cuda_ndarray.CudaNdarray(a)
@@ -84,7 +84,7 @@ def test_add_iadd_idiv():
             asum = a0 + a1
             t1 = time.time()
             cpu_dt = t1 - t0
-            print shape, 'adding ', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
+            #print shape, 'adding ', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
             assert numpy.allclose(asum,  numpy.asarray(bsum))
 
         #test not contiguous version.
@@ -122,7 +122,7 @@ def test_add_iadd_idiv():
         a0 += a1
         t1 = time.time()
         cpu_dt = t1 - t0
-        print shape, 'adding inplace', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
+        #print shape, 'adding inplace', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
         assert numpy.allclose(a0, numpy.asarray(b0))
         assert numpy.allclose(a0, a0_orig + a1)
 
@@ -144,7 +144,7 @@ def test_add_iadd_idiv():
         assert numpy.allclose(a0, ((a0_orig+a1)/a1+a1[..., ::-1])/a1[..., ::-1])
 
 def test_exp():
-    print >>sys.stdout, 'starting test_exp'
+    #print >>sys.stdout, 'starting test_exp'
     for shape in ((), (3,), (2,3), (1,10000000),(10,1000000), (100,100000),(1000,10000),(10000,1000)):
         a0 = theano._asarray(numpy.random.rand(*shape), dtype='float32')
         a1 = a0.copy()
@@ -158,26 +158,26 @@ def test_exp():
         asum = numpy.exp(a1)
         t1 = time.time()
         cpu_dt = t1 - t0
-        print shape, 'adding ', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
+        #print shape, 'adding ', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
         #c = numpy.asarray(b0+b1)
         if asum.shape:
             assert numpy.allclose(asum, numpy.asarray(bsum))
 
 
 def test_copy():
-    print >>sys.stdout, 'starting test_copy'
+    #print >>sys.stdout, 'starting test_copy'
     shape = (500,499)
     a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
 
-    print >>sys.stdout, '.. creating device object'
+    #print >>sys.stdout, '.. creating device object'
     b = cuda_ndarray.CudaNdarray(a)
 
-    print >>sys.stdout, '.. copy'
+    #print >>sys.stdout, '.. copy'
     c = copy.copy(b)
-    print >>sys.stdout, '.. deepcopy'
+    #print >>sys.stdout, '.. deepcopy'
     d = copy.deepcopy(b)
 
-    print >>sys.stdout, '.. comparisons'
+    #print >>sys.stdout, '.. comparisons'
     assert numpy.allclose(a, numpy.asarray(b))
     assert numpy.allclose(a, numpy.asarray(c))
     assert numpy.allclose(a, numpy.asarray(d))
@@ -268,7 +268,7 @@ class test_DimShuffle(unittest.TestCase):
 
 
 def test_dot():
-    print >>sys.stdout, 'starting test_dot'
+    #print >>sys.stdout, 'starting test_dot'
 
     utt.seed_rng()
     rng = numpy.random.RandomState(utt.fetch_seed())
@@ -320,8 +320,8 @@ def test_sum():
     a0sum = a0.sum(axis=0)
     b0sum = b0.reduce_sum([1,0])
 
-    print 'asum\n',a0sum
-    print 'bsum\n',numpy.asarray(b0sum)
+    #print 'asum\n',a0sum
+    #print 'bsum\n',numpy.asarray(b0sum)
 
     assert numpy.allclose(a0.sum(axis=0), numpy.asarray(b0.reduce_sum([1,0])))
     assert numpy.allclose(a0.sum(axis=1), numpy.asarray(b0.reduce_sum([0,1])))
@@ -932,7 +932,7 @@ def test_base():
 
     c = a[0]
     d = c[:,0]
-    print d.shape
+    #print d.shape
     assert c.base is a
     assert d.base is a
 

theano/sandbox/cuda/tests/test_mlp.py

@@ -103,7 +103,7 @@ def run_nnet(use_gpu, n_batch=60, n_in=1024, n_hid=2048, n_out=10,
 
     mode = get_mode(use_gpu)
 
-    print 'building pfunc ...'
+    #print 'building pfunc ...'
     train = pfunc([x, y, lr], [loss], mode=mode,
                   updates=[(p, p - g) for p, g in izip(params, gparams)])
 
@@ -138,9 +138,9 @@ def test_run_nnet():
                     theano.gradient.numeric_grad.abs_rel_err(rval_gpu,
                                                              rval_cpu)
             max_abs_diff = abs_diff.max()
-            print "max abs diff=%e max rel diff=%e n_in=%d n_hid=%d" % (
-                max_abs_diff, rel_diff.max(), n_in, n_hid)
-            print "time cpu: %f, time gpu: %f, speed up %f" % (tc, tg, tc / tg)
+            #print "max abs diff=%e max rel diff=%e n_in=%d n_hid=%d" % (
+            #    max_abs_diff, rel_diff.max(), n_in, n_hid)
+            #print "time cpu: %f, time gpu: %f, speed up %f" % (tc, tg, tc / tg)
             rtol = 1e-4
             if n_in * n_hid >= 2048 * 4096:
                 rtol = 7e-4
@@ -192,14 +192,14 @@ def run_conv_nnet1(use_gpu):
     hid_flat = hid.reshape((n_batch, n_hid))
     out = tensor.tanh(tensor.dot(hid_flat, v)+c)
     loss = tensor.sum(0.5 * (out-y)**2 * lr)
-    print 'loss type', loss.type
+    #print 'loss type', loss.type
 
     params = [w, b, v, c]
     gparams = tensor.grad(loss, params)
 
     mode = get_mode(use_gpu)
 
-    print 'building pfunc ...'
+    #print 'building pfunc ...'
     train = pfunc([x,y,lr], [loss], mode=mode, updates=[(p, p-g) for p,g in zip(params, gparams)])
 
 #    for i, n in enumerate(train.maker.env.toposort()):
@@ -211,7 +211,7 @@ def run_conv_nnet1(use_gpu):
 
     for i in xrange(n_train):
         rval = train(xval, yval, lr)
-    print 'training done'
+    #print 'training done'
     print_mode(mode)
     return rval
 
@@ -281,14 +281,14 @@ def run_conv_nnet2(use_gpu): # pretend we are training LeNet for MNIST
     hid_flat = hid1.reshape((n_batch, n_hid))
     out = tensor.tanh(tensor.dot(hid_flat, v)+c)
     loss = tensor.sum(0.5 * (out-y)**2 * lr)
-    print 'loss type', loss.type
+    #print 'loss type', loss.type
 
     params = [w0, b0, w1, b1, v, c]
     gparams = tensor.grad(loss, params)
 
     mode = get_mode(use_gpu)
 
-    print 'building pfunc ...'
+    #print 'building pfunc ...'
     train = pfunc([x,y,lr], [loss], mode=mode, updates=[(p, p-g) for p,g in zip(params, gparams)])
 
 #    for i, n in enumerate(train.maker.env.toposort()):
@@ -310,7 +310,7 @@ def test_conv_nnet2():
     if True:
         utt.seed_rng()
         rval_cpu = run_conv_nnet2(False)
-        print rval_cpu[0], rval_gpu[0],rval_cpu[0]-rval_gpu[0]
+        #print rval_cpu[0], rval_gpu[0],rval_cpu[0]-rval_gpu[0]
         assert numpy.allclose(rval_cpu, rval_gpu,rtol=1e-4,atol=1e-4)
 
 
@@ -350,9 +350,9 @@ def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
     v = shared_fn(0.01*my_randn(n_hid, n_out), 'v')
     c = shared_fn(my_zeros(n_out), 'c')
 
-    print 'ALLOCATING ARCH: w0 shape', w0.get_value(borrow=True).shape
-    print 'ALLOCATING ARCH: w1 shape', w1.get_value(borrow=True).shape
-    print 'ALLOCATING ARCH: v shape', v.get_value(borrow=True).shape
+    #print 'ALLOCATING ARCH: w0 shape', w0.get_value(borrow=True).shape
+    #print 'ALLOCATING ARCH: w1 shape', w1.get_value(borrow=True).shape
+    #print 'ALLOCATING ARCH: v shape', v.get_value(borrow=True).shape
 
     x = tensor.Tensor(dtype='float32', broadcastable=(0,1,0,0))('x')
     y = tensor.fmatrix('y')
@@ -375,14 +375,14 @@ def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
     hid_flat = hid1.reshape((n_batch, n_hid))
     out = tensor.nnet.softmax(tensor.dot(hid_flat, v)+c)
     loss = tensor.sum(tensor.nnet.crossentropy_categorical_1hot(out, tensor.argmax(y, axis=1)) * lr)
-    print 'loss type', loss.type
+    #print 'loss type', loss.type
 
     params = [w0, b0, w1, b1, v, c]
     gparams = tensor.grad(loss, params, warn_type=True)
 
     mode = get_mode(use_gpu, check_isfinite)
 
-    print 'building pfunc ...'
+    #print 'building pfunc ...'
     train = pfunc([x,y,lr], [loss], mode=mode, updates=[(p, p-g) for p,g in zip(params, gparams)])
 
     if verbose:
@@ -437,9 +437,9 @@ def run_conv_nnet2_classif(use_gpu, seed, isize, ksize, bsize,
         print pickle.dumps(mode)
         print "END %s profile mode dump" % device
 
-    print "%s time: %.3f" % (device, t1-t0)
-    print "estimated time for one pass through MNIST with %s: %f" % (
-            device, (t1-t0) * (60000.0 / (n_train*bsize)))
+    #print "%s time: %.3f" % (device, t1-t0)
+    #print "estimated time for one pass through MNIST with %s: %f" % (
+    #        device, (t1-t0) * (60000.0 / (n_train*bsize)))
 
 
 def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
@@ -465,7 +465,7 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
     orig_float32_atol = theano.tensor.basic.float32_atol
     try:
         if float_atol:
-            print "float_atol", float_atol
+            #print "float_atol", float_atol
             theano.tensor.basic.float32_atol = float_atol
 
         if gpu_only and cpu_only:
@@ -565,12 +565,12 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
             print pickle.dumps(gpu_mode)
             print "END GPU profile mode dump"
 
-    print "CPU time: %.3f, GPU time: %.3f, speed up %f" % (
-            (time_cpu, time_gpu, time_cpu/time_gpu))
-    print "Estimated time for one pass through MNIST with CPU: %f" % (
-            (time_cpu * (60000.0 / (n_train*bsize))))
-    print "Estimated time for one pass through MNIST with GPU: %f" % (
-            (time_gpu * (60000.0 / (n_train*bsize))))
+    #print "CPU time: %.3f, GPU time: %.3f, speed up %f" % (
+    #        (time_cpu, time_gpu, time_cpu/time_gpu))
+    #print "Estimated time for one pass through MNIST with CPU: %f" % (
+    #        (time_cpu * (60000.0 / (n_train*bsize))))
+    #print "Estimated time for one pass through MNIST with GPU: %f" % (
+    #        (time_gpu * (60000.0 / (n_train*bsize))))
 
 
 # Default parameters for all subsequent tests