Update specific tests for runtime algorithms.

theano/gpuarray/tests/check_dnn_conv.py

@@ -832,25 +832,31 @@ class BaseTestDnnConv(object):
         def run_fwd_runtime_algorithm(algo):
             inputs = theano.tensor.TensorType(dtype, _broadcastable)()
             filters = theano.tensor.TensorType(dtype, _broadcastable)()
-            inputs /= 10
-            filters /= 10
-            conv = dnn_conv(img=inputs, kerns=filters, algo=algo, precision=dtype,
+            # Scale down the input values to prevent very large absolute errors
+            # due to float rounding
+            lower_inputs = inputs / 10
+            lower_filters = filters / 10
+            conv = dnn_conv(img=lower_inputs, kerns=lower_filters, algo=algo, precision=dtype,
                             subsample=unit_shape, dilation=unit_shape)
             f = theano.function([inputs, filters], conv, mode=mode_with_gpu)
             if self.ndim == 3:
-                flipped_filters = filters[:, :, ::-1, ::-1, ::-1]
+                flipped_filters = lower_filters[:, :, ::-1, ::-1, ::-1]
             else:
-                flipped_filters = filters[:, :, ::-1, ::-1]
-            conv_ref = self.cpu_conv_class(subsample=unit_shape)(ref_cast(inputs), flipped_filters)
+                flipped_filters = lower_filters[:, :, ::-1, ::-1]
+            conv_ref = self.cpu_conv_class(subsample=unit_shape)(ref_cast(lower_inputs), flipped_filters)
             f_ref = theano.function([inputs, filters], conv_ref, mode='FAST_RUN')
-            for ntimes, (inputs_shape, filters_shape) in self.runtime_shapes:
+            runtime_shapes = self.runtime_shapes
+            if algo in ('time_once', 'guess_once'):
+                runtime_shapes = [list(runtime_shapes[0])]
+                runtime_shapes[0][0] = 5
+            for ntimes, (inputs_shape, filters_shape) in runtime_shapes:
                 print('Shapes:', inputs_shape, filters_shape)
                 for i in range(ntimes):
                     inputs_val = np.random.random(inputs_shape).astype(dtype)
                     filters_val = np.random.random(filters_shape).astype(dtype)
                     gpu_res = f(inputs_val, filters_val)
                     cpu_res = f_ref(inputs_val, filters_val)
-                    utt.assert_allclose(cpu_res, gpu_res)
+                    utt.assert_allclose(cpu_res, np.asarray(gpu_res))
 
         for algo in SUPPORTED_DNN_CONV_ALGO_RUNTIME:
             yield (run_fwd_runtime_algorithm, algo)
@@ -864,8 +870,6 @@ class BaseTestDnnConv(object):
             theano.config.dnn.conv.algo_bwd_data = algo
             inputs = theano.tensor.TensorType(dtype, _broadcastable)()
             filters = theano.tensor.TensorType(dtype, _broadcastable)()
-            inputs /= 10
-            filters /= 10
             conv = dnn_conv(img=inputs, kerns=filters, algo=algo, precision=dtype,
                             subsample=unit_shape, dilation=unit_shape)
             grad_i = theano.tensor.grad(conv.sum(), [inputs])
@@ -880,14 +884,18 @@ class BaseTestDnnConv(object):
             conv_ref = self.cpu_conv_class(subsample=unit_shape)(ref_cast(inputs), flipped_filters)
             grad_i_ref = theano.tensor.grad(conv_ref.sum(), [inputs])
             f_ref = theano.function([inputs, filters], grad_i_ref, mode='FAST_RUN')
-            for ntimes, (inputs_shape, filters_shape) in self.runtime_shapes:
+            runtime_shapes = self.runtime_shapes
+            if algo in ('time_once', 'guess_once'):
+                runtime_shapes = [list(runtime_shapes[0])]
+                runtime_shapes[0][0] = 5
+            for ntimes, (inputs_shape, filters_shape) in runtime_shapes:
                 print('Shapes:', inputs_shape, filters_shape)
                 for i in range(ntimes):
                     inputs_val = np.random.random(inputs_shape).astype(dtype)
                     filters_val = np.random.random(filters_shape).astype(dtype)
                     gpu_res = f(inputs_val, filters_val)
                     cpu_res = f_ref(inputs_val, filters_val)
-                    utt.assert_allclose(cpu_res, gpu_res)
+                    utt.assert_allclose(cpu_res, np.asarray(gpu_res))
 
         for algo in SUPPORTED_DNN_CONV_ALGO_RUNTIME:
             yield (run_gradinput_runtime_algorithm, algo)
@@ -901,8 +909,6 @@ class BaseTestDnnConv(object):
             theano.config.dnn.conv.algo_bwd_filter = algo
             inputs = theano.tensor.TensorType(dtype, _broadcastable)()
             filters = theano.tensor.TensorType(dtype, _broadcastable)()
-            inputs /= 10
-            filters /= 10
             conv = dnn_conv(img=inputs, kerns=filters, algo=algo, precision=dtype,
                             subsample=unit_shape, dilation=unit_shape)
             grad_w = theano.tensor.grad(conv.sum(), [filters])
@@ -917,14 +923,18 @@ class BaseTestDnnConv(object):
             conv_ref = self.cpu_conv_class(subsample=unit_shape)(ref_cast(inputs), flipped_filters)
             grad_w_ref = theano.tensor.grad(conv_ref.sum(), [filters])
             f_ref = theano.function([inputs, filters], grad_w_ref, mode='FAST_RUN')
-            for ntimes, (inputs_shape, filters_shape) in self.runtime_shapes:
+            runtime_shapes = self.runtime_shapes
+            if algo in ('time_once', 'guess_once'):
+                runtime_shapes = [list(runtime_shapes[0])]
+                runtime_shapes[0][0] = 5
+            for ntimes, (inputs_shape, filters_shape) in runtime_shapes:
                 print('Shapes:', inputs_shape, filters_shape)
                 for i in range(ntimes):
                     inputs_val = np.random.random(inputs_shape).astype(dtype)
                     filters_val = np.random.random(filters_shape).astype(dtype)
                     gpu_res = f(inputs_val, filters_val)
                     cpu_res = f_ref(inputs_val, filters_val)
-                    utt.assert_allclose(cpu_res, gpu_res)
+                    utt.assert_allclose(cpu_res, np.asarray(gpu_res))
 
         for algo in SUPPORTED_DNN_CONV_ALGO_RUNTIME:
             yield (run_gradweight_runtime_algorithm, algo)