Merge pull request #3358 from nouiz/tests

theano/sandbox/cuda/cuda_ndarray.cu

@@ -3213,9 +3213,10 @@ CudaNdarray_gpu_init(PyObject* _unused, PyObject* args)
         if (cnmem > 1)
             mem = cnmem * 1024 * 1024;
         else{
-            // Clip to 98.5% to let memory for the driver.
-            if (cnmem > .985){
-                cnmem = .985;
+            // Clip to 98% to let memory for the driver.
+            // 98.5% didn't worked in some cases.
+            if (cnmem > .98){
+                cnmem = .98;
             }
             size_t free = 0, total = 0;
             cudaError_t err = cudaMemGetInfo(&free, &total);

theano/sandbox/cuda/tests/test_mlp.py

@@ -225,7 +225,7 @@ def test_conv_nnet1():
     rval_cpu = run_conv_nnet1(False)
     utt.seed_rng()
     rval_gpu = run_conv_nnet1(True)
-    assert numpy.allclose(rval_cpu, rval_gpu, rtol=1e-4, atol=1e-6)
+    utt.assert_allclose(rval_cpu, rval_gpu, rtol=1e-4, atol=1e-6)
 
 
 def run_conv_nnet2(use_gpu):  # pretend we are training LeNet for MNIST
@@ -318,7 +318,7 @@ def test_conv_nnet2():
         utt.seed_rng()
         rval_cpu = run_conv_nnet2(False)
         # 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)
+        utt.assert_allclose(rval_cpu, rval_gpu, rtol=1e-4, atol=1e-4)
 
 
 def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
@@ -559,8 +559,8 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
                     rval_gpu - rval_cpu) / rval_gpu))
 
             if not ignore_error:
-                assert numpy.allclose(rval_cpu, rval_gpu,
-                     rtol=1e-5, atol=float_atol)
+                utt.assert_allclose(rval_cpu, rval_gpu,
+                                    rtol=1e-5, atol=float_atol)
 
             # Synchronize parameters to start from the same point next time
             if i < n_train - 1:

theano/sandbox/tests/test_blocksparse.py

@@ -21,7 +21,10 @@ class BlockSparse_Gemv_and_Outer(unittest.TestCase):
 
     def setUp(self):
         utt.seed_rng()
-        self.mode = theano.compile.get_default_mode().excluding(
+        mode = None
+        if theano.config.mode == "FAST_COMPILE":
+            mode = "FAST_RUN"
+        self.mode = theano.compile.get_mode(mode).excluding(
             'constant_folding'
         )
         self.gemv_op = sparse_block_gemv
@@ -304,7 +307,7 @@ class BlockSparse_Gemv_and_Outer(unittest.TestCase):
         out = self.outer_op(o, x, y, xIdx, yIdx)
 
         f = theano.function([o, x, y, xIdx, yIdx], out,
-                            on_unused_input="warn")
+                            on_unused_input="warn", mode=self.mode)
 
         o_val, x_val, y_val, xIdx_val, yIdx_val = \
             BlockSparse_Gemv_and_Outer.outer_data()

theano/sandbox/tests/test_multinomial.py

@@ -7,6 +7,7 @@ from theano import config, function, tensor
 from theano.sandbox import multinomial
 from theano.compile.mode import get_default_mode, predefined_linkers
 import theano.sandbox.cuda as cuda
+import theano.tests.unittest_tools as utt
 
 
 def get_mode(gpu):
@@ -45,22 +46,22 @@ def test_multinomial_0():
                         for node in f.maker.fgraph.toposort()])
 
         # test that both first and second samples can be drawn
-        assert numpy.allclose(f([[1, 0], [0, 1]], [.1, .1]),
+        utt.assert_allclose(f([[1, 0], [0, 1]], [.1, .1]),
                               [[2, 0], [0, 2]])
 
         # test that both second labels can be drawn
         r = f([[.2, .8], [.3, .7]], [.31, .31])
-        assert numpy.allclose(r, [[0, 2], [0, 2]]), r
+        utt.assert_allclose(r, [[0, 2], [0, 2]])
 
         # test that both first labels can be drawn
         r = f([[.2, .8], [.3, .7]], [.21, .21])
-        assert numpy.allclose(r, [[0, 2], [2, 0]]), r
+        utt.assert_allclose(r, [[0, 2], [2, 0]])
 
         # change the size to make sure output gets reallocated ok
         # and also make sure that the GPU version doesn't screw up the
         # transposed-ness
         r = f([[.2, .8]], [.25])
-        assert numpy.allclose(r, [[0, 2]]), r
+        utt.assert_allclose(r, [[0, 2]])
 
     run_with_c(body)
     if cuda.cuda_available:
@@ -93,9 +94,9 @@ def test_multinomial_large():
             assert mval.dtype == 'float64'
         else:
             raise NotImplementedError(config.cast_policy)
-        assert numpy.allclose(mval.sum(axis=1), 2)
+        utt.assert_allclose(mval.sum(axis=1), 2)
         asdf = numpy.asarray([0, 0, 2, 0])+0*pval
-        assert numpy.allclose(mval, asdf)  # broadcast over all rows
+        utt.assert_allclose(mval, asdf)  # broadcast over all rows
     run_with_c(body)
     if cuda.cuda_available:
         run_with_c(body, True)

theano/tensor/basic.py

@@ -4064,8 +4064,11 @@ def get_vector_length(v):
         if ((isinstance(stop, numbers.Integral) and
              isinstance(start, numbers.Integral))):
             return stop - start
-
-    raise ValueError("length not known")
+    if isinstance(v, Variable):
+        msg = theano.printing.debugprint(v, file='str')
+    else:
+        msg = str(v)
+    raise ValueError("length not known: %s" % msg)
 
 
 @constructor