Apply pyupgrade to tests.gpuarray

tests/gpuarray/check_dnn_conv.py

@@ -319,7 +319,7 @@ class ConvCaseGeneratorChain:
         return chain(*[generator.get_cases(filter) for generator in self.generators])
 
 
-class CuDNNV51ConvCaseGenerator(object):
+class CuDNNV51ConvCaseGenerator:
     """
     Helper class to generate specific test cases for every algorithm supported by cuDNN V5.1.
     Same class exists for cuDNN V6.0 (see below).
@@ -486,9 +486,7 @@ class CuDNNV6ConvCaseGenerator(CuDNNV51ConvCaseGenerator):
             ]
             return ConvCaseGeneratorChain(*generators)
         if ndim == 3:
-            return super(CuDNNV6ConvCaseGenerator, self)._fwd_fft_tiling(
-                ndim, dtype, precision
-            )
+            return super()._fwd_fft_tiling(ndim, dtype, precision)
 
     def _gw_none(self, ndim):
         return self._fwd_none(ndim)
@@ -513,36 +511,34 @@ class CuDNNV6ConvCaseGenerator(CuDNNV51ConvCaseGenerator):
     def _fwd_runtime(self, ndim, dtype, precision):
         if ndim == 2 and dtype == precision == "float16":
             return ConvCaseGenerator(ndim=ndim, dilations=self._dilations(ndim))
-        return super(CuDNNV6ConvCaseGenerator, self)._fwd_runtime(
-            ndim, dtype, precision
-        )
+        return super()._fwd_runtime(ndim, dtype, precision)
 
     def _gw_runtime(self, ndim, dtype, precision):
         if ndim == 2 and dtype == precision == "float16":
             return ConvCaseGenerator(ndim=ndim, dilations=self._dilations(ndim))
-        return super(CuDNNV6ConvCaseGenerator, self)._gw_runtime(ndim, dtype, precision)
+        return super()._gw_runtime(ndim, dtype, precision)
 
     def _gi_runtime(self, ndim, dtype, precision):
         if ndim == 2 and dtype == precision == "float16":
             return ConvCaseGenerator(ndim=ndim, dilations=self._dilations(ndim))
-        return super(CuDNNV6ConvCaseGenerator, self)._gi_runtime(ndim, dtype, precision)
+        return super()._gi_runtime(ndim, dtype, precision)
 
     def fwd(self, algo, ndim, dtype, precision):
         if algo == self.NONE:
             return self._fwd_none(ndim)
-        return super(CuDNNV6ConvCaseGenerator, self).fwd(algo, ndim, dtype, precision)
+        return super().fwd(algo, ndim, dtype, precision)
 
     def gw(self, algo, ndim, dtype, precision):
         if algo == self.NONE:
             return self._gw_none(ndim)
         if algo == self.FFT_TILING:
             return self._gw_fft_tiling(ndim)
-        return super(CuDNNV6ConvCaseGenerator, self).gw(algo, ndim, dtype, precision)
+        return super().gw(algo, ndim, dtype, precision)
 
     def gi(self, algo, ndim, dtype, precision):
         if algo == self.NONE:
             return self._gi_none(ndim)
-        return super(CuDNNV6ConvCaseGenerator, self).gi(algo, ndim, dtype, precision)
+        return super().gi(algo, ndim, dtype, precision)
 
 
 cudnn_conv_case_generator = (
@@ -550,7 +546,7 @@ cudnn_conv_case_generator = (
 )
 
 
-class BaseTestDnnConv(object):
+class BaseTestDnnConv:
     """
     Base class for exhaustive tests. Use its subclasses
     to run actual tests.

tests/gpuarray/rnn_support.py

@@ -4,7 +4,7 @@ import theano
 import theano.tensor as tt
 
 
-class Model(object):
+class Model:
     def __init__(self, name=""):
         self.name = name
         self.layers = []
@@ -54,7 +54,7 @@ def bias_weights(length, param_list=None, name=""):
     return bias
 
 
-class Layer(object):
+class Layer:
     """Generic Layer Template which all layers should inherit"""
 
     def __init__(self, name=""):

tests/gpuarray/run_dnn_conv.py

@@ -35,7 +35,7 @@ class BorderAction(TupleAction):
     # Border extractor for command line args parser.
     def __call__(self, parser, namespace, values, option_string=None):
         if values not in ("valid", "full", "half"):
-            super(BorderAction, self).__call__(parser, namespace, values, option_string)
+            super().__call__(parser, namespace, values, option_string)
         else:
             setattr(namespace, self.dest, values)
 
@@ -201,7 +201,7 @@ else:
     args.dtype, args.precision = data_type_configurations[args.dtype_config]
 if (args.dtype, args.precision) not in cudnn.get_supported_dtype_configs():
     raise ValueError(
-        "Unsupported data type configuration %s %s." % (args.dtype, args.precision)
+        "Unsupported data type configuration {} {}.".format(args.dtype, args.precision)
     )
 
 if args.algo not in SUPPORTED_DNN_CONV_ALGO_RUNTIME:

tests/gpuarray/test_blas.py

@@ -153,13 +153,11 @@ TestGpuGemm = makeTester(
 )
 
 
-gemm_batched_tests = dict(
-    (
-        "test_b%im%ik%in%i" % (b, m, k, n),
-        [rand(b, m, n), rand(), rand(b, m, k), rand(b, k, n), rand()],
-    )
+gemm_batched_tests = {
+    "test_b%im%ik%in%i"
+    % (b, m, k, n): [rand(b, m, n), rand(), rand(b, m, k), rand(b, k, n), rand()]
     for b, m, k, n in itertools.combinations([2, 3, 5, 7, 11, 13], 4)
-)
+}
 
 gemm_batched_tests["float16"] = [
     rand(3, 4, 7).astype("float16"),

tests/gpuarray/test_dnn.py

@@ -2768,7 +2768,7 @@ class Cudnn_grouped_conv(TestGroupedConvNoOptim):
     conv_gradi_op = dnn.GpuDnnConvGradI
 
     def __init__(self, *args, **kwargs):
-        super(Cudnn_grouped_conv, self).__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
 
 
 class Cudnn_grouped_conv3d(TestGroupedConv3dNoOptim):
@@ -2778,7 +2778,7 @@ class Cudnn_grouped_conv3d(TestGroupedConv3dNoOptim):
     conv_gradi_op = dnn.GpuDnnConvGradI
 
     def __init__(self, *args, **kwargs):
-        super(Cudnn_grouped_conv3d, self).__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
 
 
 def test_dnn_spatialtf():
@@ -3049,7 +3049,7 @@ def test_dnn_spatialtf_grad():
     )
 
 
-class TestDnnConv2DRuntimeAlgorithms(object):
+class TestDnnConv2DRuntimeAlgorithms:
     ndim = 2
     cpu_conv_class = CorrMM
     runtime_shapes = [

tests/gpuarray/test_multinomial.py

@@ -147,7 +147,7 @@ def test_gpu_opt_dtypes():
         pval = pval / pval.sum(axis=1)[:, None]
         uval = np.ones_like(pval[:, 0]) * 0.5
         samples = f(pval, uval)
-        assert samples.dtype == dtype, "%s != %s" % (samples.dtype, dtype)
+        assert samples.dtype == dtype, "{} != {}".format(samples.dtype, dtype)
 
 
 def test_gpu_opt():
@@ -380,7 +380,7 @@ def test_unpickle_legacy_op():
     fname = "test_gpuarray_multinomial_wo_replacement.pkl"
 
     if not PY3:
-        with open(os.path.join(testfile_dir, fname), "r") as fp:
+        with open(os.path.join(testfile_dir, fname)) as fp:
             u = Unpickler(fp)
             m = u.load()
             assert isinstance(m, GPUAChoiceFromUniform)