update

theano/tensor/nnet/tests/test_conv2d.py

+import unittest
+import numpy
+import copy
+
+import theano
+from theano.tests import unittest_tools as utt
+
+from nose.plugins.skip import SkipTest
+import theano.tensor.nnet.conv as conv_ref
+import theano.tensor.nnet.conv2d as conv
+from theano.sandbox.cuda import float32_shared_constructor as shared
+
+if theano.config.mode == 'FAST_COMPILE':
+    mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
+else:
+    mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
+
+from theano.sandbox.cuda.dnn import dnn_available
+
+class TestConv2d(unittest.TestCase):
+
+    def run_conv(self,
+                 inputs_shape,
+                 filters_shape,
+                 subsample=(1, 1),
+                 verify_grad=True,
+                 mode=mode_with_gpu):
+
+        inputs_val = numpy.random.random(inputs_shape).astype('float32')
+        filters_val = numpy.random.random(filters_shape).astype('float32')
+
+        inputs = shared(inputs_val)
+        filters = shared(filters_val)
+        c_ref = conv_ref.conv2d(inputs, filters,
+                                border_mode="valid", subsample=subsample)
+
+        c = conv.conv2d(inputs, filters,
+                        border_mode="valid", subsample=subsample)
+
+
+        f_ref = theano.function([], c_ref, mode=mode_with_gpu)
+        f = theano.function([], c, mode)
+
+        res_ref = f_ref()
+        res = f()
+        utt.assert_allclose(res_ref, res)
+        if verify_grad:
+            utt.verify_grad(conv.Conv2d(border_mode="valid",
+                                        subsample=subsample), [inputs_val, filters_val])
+
+
+
+    def test_valid(self):
+        mode = mode_with_gpu
+        if dnn_available():
+            self.run_conv(inputs_shape=(16, 1, 2, 2),
+                          filters_shape=(10, 1, 2, 2),
+                          verify_grad=False)
+            self.run_conv(inputs_shape=(16, 1, 8, 8),
+                          filters_shape=(10, 1, 2, 2),
+                          subsample=(2, 2),
+                          verify_grad=False)
+            # self.run_conv(inputs_shape=(16, 1, 2, 2),
+            #               filters_shape=(10, 1, 2, 2),
+            #               verify_grad=True)
+            # self.run_conv(inputs_shape=(16, 1, 8, 8),
+            #               filters_shape=(10, 1, 2, 2),
+            #               subsample=(2, 2),
+            #               verify_grad=True)
+
+        mode = mode.excluding('cudnn')
+        self.run_conv(inputs_shape=(16, 1, 2, 2),
+                      filters_shape=(10, 1, 2, 2),
+                      verify_grad=False, mode=mode)
+        self.run_conv(inputs_shape=(16, 1, 8, 8),
+                      filters_shape=(10, 1, 2, 2),
+                      subsample=(2, 2),
+                      verify_grad=False,mode=mode)
+        # self.run_conv(inputs_shape=(16, 1, 2, 2),
+        #               filters_shape=(10, 1, 2, 2),
+        #               verify_grad=True,mode=mode)
+        # self.run_conv(inputs_shape=(16, 1, 8, 8),
+        #               filters_shape=(10, 1, 2, 2),
+        #               subsample=(2, 2),
+        #               verify_grad=True,mode=mode)
+
+