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| # Copyright 2022 Google LLC | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # https://www.apache.org/licenses/LICENSE-2.0 | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Options for the film_net video frame interpolator.""" | |
| import gin.tf | |
| class Options(object): | |
| """Options for the film_net video frame interpolator. | |
| To further understand these options, see the paper here: | |
| https://augmentedperception.github.io/pixelfusion/. | |
| The default values are suitable for up to 64 pixel motions. For larger motions | |
| the number of flow convolutions and/or pyramid levels can be increased, but | |
| usually with the cost of accuracy on solving the smaller motions. | |
| The maximum motion in pixels that the system can resolve is equivalent to | |
| 2^(pyramid_levels-1) * flow_convs[-1]. I.e. the downsampling factor times | |
| the receptive field radius on the coarsest pyramid level. This, of course, | |
| assumes that the training data contains such motions. | |
| Note that to avoid a run-time error, the input image width and height have to | |
| be divisible by 2^(pyramid_levels-1). | |
| Attributes: | |
| pyramid_levels: How many pyramid levels to use for the feature pyramid and | |
| the flow prediction. | |
| fusion_pyramid_levels: How many pyramid levels to use for the fusion module | |
| this must be less or equal to 'pyramid_levels'. | |
| specialized_levels: How many fine levels of the pyramid shouldn't share the | |
| weights. If specialized_levels = 3, it means that two finest levels are | |
| independently learned, whereas the third will be learned together with the | |
| rest of the pyramid. Valid range [1, pyramid_levels]. | |
| flow_convs: Convolutions per residual flow predictor. This array should have | |
| specialized_levels+1 items on it, the last item representing the number of | |
| convs used by any pyramid level that uses shared weights. | |
| flow_filters: Base number of filters in residual flow predictors. This array | |
| should have specialized_levels+1 items on it, the last item representing | |
| the number of filters used by any pyramid level that uses shared weights. | |
| sub_levels: The depth of the cascaded feature tree each pyramid level | |
| concatenates together to compute the flow. This must be within range [1, | |
| specialized_level+1]. It is recommended to set this to specialized_levels | |
| + 1 | |
| filters: Base number of features to extract. On each pyramid level the | |
| number doubles. This is used by both feature extraction and fusion stages. | |
| use_aux_outputs: Set to True to include auxiliary outputs along with the | |
| predicted image. | |
| """ | |
| def __init__(self, | |
| pyramid_levels=5, | |
| fusion_pyramid_levels=5, | |
| specialized_levels=3, | |
| flow_convs=None, | |
| flow_filters=None, | |
| sub_levels=4, | |
| filters=16, | |
| use_aux_outputs=True): | |
| self.pyramid_levels = pyramid_levels | |
| self.fusion_pyramid_levels = fusion_pyramid_levels | |
| self.specialized_levels = specialized_levels | |
| self.flow_convs = flow_convs or [4, 4, 4, 4] | |
| self.flow_filters = flow_filters or [64, 128, 256, 256] | |
| self.sub_levels = sub_levels | |
| self.filters = filters | |
| self.use_aux_outputs = use_aux_outputs | |