(self, dev, pad_mode, is_2d)
| 147 | self._conv2d_helper(gpu_dev) |
| 148 | |
| 149 | def _conv_same_pad(self, dev, pad_mode, is_2d): |
| 150 | if is_2d: |
| 151 | x_h, w_h, k_h, p_h = 32, 4, 4, 1 |
| 152 | else: |
| 153 | x_h, w_h, k_h, p_h = 1, 1, 1, 0 |
| 154 | |
| 155 | x = tensor.Tensor(shape=(3, 3, x_h, 32), device=dev) |
| 156 | x.gaussian(0.0, 1.0) |
| 157 | |
| 158 | # with the same padding, the padding should be 3 |
| 159 | # for SAME_UPPER, is (1, 1) + (0, 1) |
| 160 | # for SAME_LOWER, is (1, 1) + (1, 0) |
| 161 | |
| 162 | kernel = (k_h, 4) |
| 163 | padding = (p_h, 1) |
| 164 | stride = (1, 1) |
| 165 | group = 1 |
| 166 | bias = False |
| 167 | out_channels = 3 |
| 168 | |
| 169 | conv_0 = layer.Conv2d(out_channels, |
| 170 | kernel, |
| 171 | stride=stride, |
| 172 | group=group, |
| 173 | bias=bias, |
| 174 | pad_mode=pad_mode) |
| 175 | |
| 176 | y = conv_0(x) |
| 177 | dy = np.ones((3, 3, x_h, 32), dtype=np.float32) |
| 178 | dy = tensor.from_numpy(dy) |
| 179 | dy.to_device(dev) |
| 180 | |
| 181 | dx, dW = y.creator.backward(dy.data) |
| 182 | self.check_shape(y.shape, (3, 3, x_h, 32)) |
| 183 | self.check_shape(dx.shape(), (3, 3, x_h, 32)) |
| 184 | self.check_shape(dW.shape(), (3, 3, w_h, 4)) |
| 185 | |
| 186 | def test_conv2d_same_pad_cpu(self): |
| 187 | self._conv_same_pad(cpu_dev, "SAME_LOWER", True) |
no test coverage detected