Ccw4057 (#4592)

theano/tensor/nnet/tests/__init__.py

+from __future__ import absolute_import, print_function, division

theano/tensor/nnet/tests/test_conv3d2d.py

@@ -11,7 +11,7 @@ from six.moves import xrange
 
 import theano
 from theano.gof.opt import check_stack_trace
-from theano.tensor.nnet.conv3d2d import *
+from theano.tensor.nnet.conv3d2d import conv3d, get_diagonal_subtensor_view, DiagonalSubtensor, IncDiagonalSubtensor
 import theano.tests.unittest_tools as utt
 
 
@@ -57,11 +57,11 @@ def pyconv3d(signals, filters):
     Ns, Ts, C, Hs, Ws = signals.shape
     Nf, Tf, C, Hf, Wf = filters.shape
 
-    Tf2 = Tf//2
-    Hf2 = Hf//2
-    Wf2 = Wf//2
+    Tf2 = Tf // 2
+    Hf2 = Hf // 2
+    Wf2 = Wf // 2
 
-    rval = numpy.zeros((Ns, Ts-Tf+1, Nf, Hs-Hf+1, Ws-Wf+1))
+    rval = numpy.zeros((Ns, Ts - Tf + 1, Nf, Hs - Hf + 1, Ws - Wf + 1))
     for ns in xrange(Ns):
         for nf in xrange(Nf):
             for c in xrange(C):
@@ -71,7 +71,7 @@ def pyconv3d(signals, filters):
                 o_i = ndimage.convolve(s_i, f_i, mode='constant', cval=1)
                 o_i_sh0 = o_i.shape[0]
                 # print s_i.shape, f_i.shape, r_i.shape, o_i.shape
-                r_i += o_i[Tf2:o_i_sh0-Tf2, Hf2:-Hf2, Wf2:-Wf2]
+                r_i += o_i[Tf2:o_i_sh0 - Tf2, Hf2:-Hf2, Wf2:-Wf2]
     return rval
 
 
@@ -85,10 +85,10 @@ def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
         raise SkipTest("conv3d2d tests need SciPy")
 
     Ns, Ts, C, Hs, Ws = 3, 10, 3, 32, 32
-    Nf, Tf, C, Hf, Wf = 32, 5 , 3, 5 , 5
+    Nf, Tf, C, Hf, Wf = 32, 5, 3, 5, 5
 
-    signals = numpy.arange(Ns*Ts*C*Hs*Ws).reshape(Ns, Ts, C, Hs, Ws).astype('float32')
-    filters = numpy.arange(Nf*Tf*C*Hf*Wf).reshape(Nf, Tf, C, Hf, Wf).astype('float32')
+    signals = numpy.arange(Ns * Ts * C * Hs * Ws).reshape(Ns, Ts, C, Hs, Ws).astype('float32')
+    filters = numpy.arange(Nf * Tf * C * Hf * Wf).reshape(Nf, Tf, C, Hf, Wf).astype('float32')
 
     t0 = time.time()
     pyres = pyconv3d(signals, filters)
@@ -96,7 +96,7 @@ def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
 
     s_signals = shared(signals)
     s_filters = shared(filters)
-    s_output = shared(signals*0)
+    s_output = shared(signals * 0)
 
     out = conv3d(s_signals, s_filters,
                  signals_shape=signals.shape,
@@ -130,12 +130,12 @@ def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
     filters = numpy.random.rand(Nf, Tf, C, Hf, Wf).astype('float32')
     utt.verify_grad(conv3d, [signals, filters], eps=1e-1, mode=mode)
 
-    ### Additional Test that covers the case of patched implementation for filter with Tf=1
+    # Additional Test that covers the case of patched implementation for filter with Tf=1
     Ns, Ts, C, Hs, Ws = 3, 10, 3, 32, 32
-    Nf, Tf, C, Hf, Wf = 32, 1 , 3, 5 , 5
+    Nf, Tf, C, Hf, Wf = 32, 1, 3, 5, 5
 
-    signals = numpy.arange(Ns*Ts*C*Hs*Ws).reshape(Ns, Ts, C, Hs, Ws).astype('float32')
-    filters = numpy.arange(Nf*Tf*C*Hf*Wf).reshape(Nf, Tf, C, Hf, Wf).astype('float32')
+    signals = numpy.arange(Ns * Ts * C * Hs * Ws).reshape(Ns, Ts, C, Hs, Ws).astype('float32')
+    filters = numpy.arange(Nf * Tf * C * Hf * Wf).reshape(Nf, Tf, C, Hf, Wf).astype('float32')
 
     t0 = time.time()
     pyres = pyconv3d(signals, filters)
@@ -143,7 +143,7 @@ def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
 
     s_signals = shared(signals)
     s_filters = shared(filters)
-    s_output = shared(signals*0)
+    s_output = shared(signals * 0)
 
     out = conv3d(s_signals, s_filters,
                  signals_shape=signals.shape,

theano/tensor/nnet/tests/test_sigm.py

@@ -16,7 +16,7 @@ from theano.tensor.nnet.sigm import (
     compute_mul, is_1pexp, parse_mul_tree, perform_sigm_times_exp,
     register_local_1msigmoid, simplify_mul,
 )
-from theano.tensor.tests.test_basic import (makeBroadcastTester, rand,
+from theano.tensor.tests.test_basic import (makeBroadcastTester,
                                             check_floatX, upcast_int8_nfunc,
                                             _good_broadcast_unary_normal_no_complex)
 
@@ -45,7 +45,7 @@ UltraFastSigmoidTester = makeBroadcastTester(
     good=_good_broadcast_unary_normal_no_complex,
     # grad=_grad_broadcast_unary_normal,
     name='UltraFastSigmoidTester',
-# This is an approx of the sigmoid. That is why we raise eps
+    # This is an approx of the sigmoid. That is why we raise eps
     eps=5e-2)
 
 HardSigmoidTester = makeBroadcastTester(
@@ -55,7 +55,7 @@ HardSigmoidTester = makeBroadcastTester(
     good=_good_broadcast_unary_normal_no_complex,
     # grad=_grad_broadcast_unary_normal,
     name='HardSigmoidTester',
-# This is an approx of the sigmoid. That is why we raise eps
+    # This is an approx of the sigmoid. That is why we raise eps
     eps=1e-1)
 
 
@@ -146,20 +146,20 @@ class T_sigmoid_opts(unittest.TestCase):
             # todo: solve issue #4589 first
             # assert check_stack_trace(
             #     f, ops_to_check=[sigmoid, theano.tensor.inplace.neg_inplace])
-            assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid,
-                    theano.tensor.inplace.neg_inplace]
+            assert ([node.op for node in f.maker.fgraph.toposort()] ==
+                    [sigmoid, theano.tensor.inplace.neg_inplace])
             f(data)
             f = theano.function([x], T.fill(x, -1.0) / (1 - T.exp(-x)), mode=m)
-            assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
-                    theano.tensor.inplace.neg_inplace]
+            assert ([node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
+                    theano.tensor.inplace.neg_inplace])
             f(data)
             f = theano.function([x], T.fill(x, -1.0) / (2 + T.exp(-x)), mode=m)
-            assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
-                    theano.tensor.inplace.neg_inplace]
+            assert ([node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
+                    theano.tensor.inplace.neg_inplace])
             f(data)
             f = theano.function([x], T.fill(x, -1.1) / (1 + T.exp(-x)), mode=m)
-            assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
-                    theano.tensor.inplace.neg_inplace]
+            assert ([node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
+                    theano.tensor.inplace.neg_inplace])
             f(data)
 
             # tests double inv_1_plus_exp with neg
@@ -170,33 +170,33 @@ class T_sigmoid_opts(unittest.TestCase):
                                 ((1 + T.exp(x)) * (1 + T.exp(-x))), mode=m)
             # todo: solve issue #4589 first
             # assert check_stack_trace(f, ops_to_check=[sigmoid, T.mul])
-            assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid,
-                    T.mul]
+            assert ([node.op for node in f.maker.fgraph.toposort()] == [sigmoid,
+                    T.mul])
             f(data)
             f = theano.function([x], (T.fill(x, -1.1) * T.exp(x)) /
                                 ((1 + T.exp(x)) * (1 + T.exp(-x))), mode=m)
-            assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
-                    T.mul, theano.tensor.inplace.neg_inplace]
+            assert ([node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
+                    T.mul, theano.tensor.inplace.neg_inplace])
             f(data)
             f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
                                 ((2 + T.exp(x)) * (1 + T.exp(-x))), mode=m)
-            assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
-                    T.mul, theano.tensor.inplace.neg_inplace]
+            assert ([node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
+                    T.mul, theano.tensor.inplace.neg_inplace])
             f(data)
             f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
                                 ((1 + T.exp(x)) * (2 + T.exp(-x))), mode=m)
-            assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
-                    T.mul, theano.tensor.inplace.neg_inplace]
+            assert ([node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
+                    T.mul, theano.tensor.inplace.neg_inplace])
             f(data)
             f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
                                 ((1 + T.exp(x)) * (1 + T.exp(x))), mode=m)
-            assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
-                    T.mul, theano.tensor.inplace.neg_inplace]
+            assert ([node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
+                    T.mul, theano.tensor.inplace.neg_inplace])
             f(data)
             f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
                                 ((1 + T.exp(x)) * (2 + T.exp(-x))), mode=m)
-            assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
-                    T.mul, theano.tensor.inplace.neg_inplace]
+            assert ([node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
+                    T.mul, theano.tensor.inplace.neg_inplace])
             f(data)
 
         finally:
@@ -219,8 +219,8 @@ class T_sigmoid_opts(unittest.TestCase):
         # tests inv_1_plus_exp
         f = theano.function([x], 1 - T.fill(x, 1.0) / (1 + T.exp(-x)), mode=m)
         assert check_stack_trace(f, ops_to_check=[tensor.neg, sigmoid_inplace])
-        assert [node.op for node in f.maker.fgraph.toposort()] == [tensor.neg,
-                sigmoid_inplace]
+        assert ([node.op for node in f.maker.fgraph.toposort()] == [tensor.neg,
+                sigmoid_inplace])
 
     def test_local_sigm_times_exp(self):
         """
@@ -249,8 +249,7 @@ class T_sigmoid_opts(unittest.TestCase):
         f = theano.function(
             [x, y],
             (sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *
-                 tensor.exp(x * y) * tensor.exp(y)),
-                mode=m)
+                tensor.exp(x * y) * tensor.exp(y)), mode=m)
         match(f, [sigmoid, tensor.mul, tensor.neg, tensor.exp, sigmoid,
                   tensor.mul])
         # assert check_stack_trace(f, ops_to_check=[sigmoid, tensor.mul,
@@ -286,11 +285,11 @@ class T_sigmoid_opts(unittest.TestCase):
            -x * sigmoid(-x) * (y * (-1 * z)))
         ok(-sigmoid(-x) *
            (exp(y) * (-exp(-z) * 3 * -exp(x)) *
-            (y * 2 * (-sigmoid(-y) * (z + t) * exp(z)) * sigmoid(z))) *
-           -sigmoid(x),
+            (y * 2 * (-sigmoid(-y) * (z + t) * exp(z)) * sigmoid(z))) * -
+           sigmoid(x),
            sigmoid(x) *
            (-sigmoid(y) * (-sigmoid(-z) * 3) * (y * 2 * ((z + t) * exp(z)))) *
-           -sigmoid(x))
+           (-sigmoid(x)))
         ok(exp(-x) * -exp(-x) * (-sigmoid(x) * -sigmoid(x)),
            -sigmoid(-x) * sigmoid(-x))
         ok(-exp(x) * -sigmoid(-x) * -exp(-x),
@@ -333,7 +332,7 @@ class T_sigmoid_opts(unittest.TestCase):
         topo = f.maker.fgraph.toposort()
         assert topo[0].op == ultra_fast_sigmoid
         assert len(topo) == 1
-        ux_v = f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
+        f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
 
     def test_local_hard_sigmoid(self):
         x = tensor.matrix('x')
@@ -350,7 +349,7 @@ class T_sigmoid_opts(unittest.TestCase):
         f = theano.function([x], s, mode=mode)
         topo = f.maker.fgraph.toposort()
         assert not any([n.op == sigmoid for n in topo])
-        ux_v = f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
+        f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
 
         mode2 = mode.excluding('fusion').excluding('inplace')
         f2 = theano.function([x], s, mode=mode2)
@@ -416,7 +415,7 @@ class T_softplus_opts(unittest.TestCase):
         out = T.log(1 - sigmoid(x).reshape([x.size]))
         f = theano.function([x], out, mode=self.m)
         topo = f.maker.fgraph.toposort()
-        #assert len(topo) == 3
+        # assert len(topo) == 3
         assert any(isinstance(node.op, T.Reshape) for node in topo)
         assert any(isinstance(getattr(node.op, 'scalar_op', None),
                               theano.tensor.nnet.sigm.ScalarSoftplus)

theano/tests/test_flake8.py

@@ -72,14 +72,6 @@ whitelist_flake8 = [
     "tensor/tests/test_blas_scipy.py",
     "tensor/tests/test_mpi.py",
     "tensor/nnet/__init__.py",
-    "tensor/nnet/tests/__init__.py",
-    "tensor/nnet/tests/test_conv.py",
-    "tensor/nnet/tests/test_neighbours.py",
-    "tensor/nnet/tests/test_nnet.py",
-    "tensor/nnet/tests/test_conv3d2d.py",
-    "tensor/nnet/tests/test_conv3d.py",
-    "tensor/nnet/tests/speed_test_conv.py",
-    "tensor/nnet/tests/test_sigm.py",
     "tensor/signal/__init__.py",
     "tensor/signal/tests/__init__.py",
     "scalar/__init__.py",