(self, featuremaps, size_pooling, pooling_type="average_pool")
| 143 | return focus_list, data_featuremap |
| 144 | |
| 145 | def pooling(self, featuremaps, size_pooling, pooling_type="average_pool"): |
| 146 | # pooling process |
| 147 | size_map = len(featuremaps[0]) |
| 148 | size_pooled = int(size_map / size_pooling) |
| 149 | featuremap_pooled = [] |
| 150 | for i_map in range(len(featuremaps)): |
| 151 | feature_map = featuremaps[i_map] |
| 152 | map_pooled = [] |
| 153 | for i_focus in range(0, size_map, size_pooling): |
| 154 | for j_focus in range(0, size_map, size_pooling): |
| 155 | focus = feature_map[ |
| 156 | i_focus : i_focus + size_pooling, |
| 157 | j_focus : j_focus + size_pooling, |
| 158 | ] |
| 159 | if pooling_type == "average_pool": |
| 160 | # average pooling |
| 161 | map_pooled.append(np.average(focus)) |
| 162 | elif pooling_type == "max_pooling": |
| 163 | # max pooling |
| 164 | map_pooled.append(np.max(focus)) |
| 165 | map_pooled = np.asmatrix(map_pooled).reshape(size_pooled, size_pooled) |
| 166 | featuremap_pooled.append(map_pooled) |
| 167 | return featuremap_pooled |
| 168 | |
| 169 | def _expand(self, data): |
| 170 | # expanding three dimension data to one dimension list |
no test coverage detected