(self)
| 2041 | rand_w, rand_b, s) |
| 2042 | |
| 2043 | def testConv(self): |
| 2044 | batch_size = 50 |
| 2045 | H = 1 |
| 2046 | W = 10 |
| 2047 | C = 50 |
| 2048 | output_dims = 32 |
| 2049 | kernel_h = 1 |
| 2050 | kernel_w = 3 |
| 2051 | stride_h = 1 |
| 2052 | stride_w = 1 |
| 2053 | pad_t = 0 |
| 2054 | pad_b = 0 |
| 2055 | pad_r = None |
| 2056 | pad_l = None |
| 2057 | |
| 2058 | input_record = self.new_record(schema.Scalar((np.float32, (H, W, C)))) |
| 2059 | X = np.random.random((batch_size, H, W, C)).astype(np.float32) |
| 2060 | schema.FeedRecord(input_record, [X]) |
| 2061 | conv = self.model.Conv( |
| 2062 | input_record, |
| 2063 | output_dims, |
| 2064 | kernel_h=kernel_h, |
| 2065 | kernel_w=kernel_w, |
| 2066 | stride_h=stride_h, |
| 2067 | stride_w=stride_w, |
| 2068 | pad_t=pad_t, |
| 2069 | pad_b=pad_b, |
| 2070 | pad_r=pad_r, |
| 2071 | pad_l=pad_l, |
| 2072 | order='NHWC' |
| 2073 | ) |
| 2074 | |
| 2075 | self.assertEqual( |
| 2076 | schema.Scalar((np.float32, (output_dims,))), |
| 2077 | conv |
| 2078 | ) |
| 2079 | |
| 2080 | self.run_train_net_forward_only() |
| 2081 | output_record = schema.FetchRecord(conv) |
| 2082 | # check the number of output channels is the same as input in this example |
| 2083 | assert output_record.field_types()[0].shape == (H, W, output_dims) |
| 2084 | assert output_record().shape == (batch_size, H, W, output_dims) |
| 2085 | |
| 2086 | train_init_net, train_net = self.get_training_nets() |
| 2087 | # Init net assertions |
| 2088 | init_ops = self.assertNetContainOps( |
| 2089 | train_init_net, |
| 2090 | [ |
| 2091 | OpSpec("XavierFill", None, None), |
| 2092 | OpSpec("ConstantFill", None, None), |
| 2093 | ] |
| 2094 | ) |
| 2095 | conv_spec = OpSpec( |
| 2096 | "Conv", |
| 2097 | [ |
| 2098 | input_record.field_blobs()[0], |
| 2099 | init_ops[0].output[0], |
| 2100 | init_ops[1].output[0], |
nothing calls this directly
no test coverage detected