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86 lines
3.2 KiB
86 lines
3.2 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import paddle |
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import paddle.nn as nn |
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from .builder import MODELS |
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from .sr_model import BaseSRModel |
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from .generators.edvr import ResidualBlockNoBN, DCNPack |
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from ..modules.init import reset_parameters |
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@MODELS.register() |
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class EDVRModel(BaseSRModel): |
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"""EDVR Model. |
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Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks. |
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""" |
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def __init__(self, generator, tsa_iter, pixel_criterion=None): |
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"""Initialize the EDVR class. |
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Args: |
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generator (dict): config of generator. |
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tsa_iter (dict): config of tsa_iter. |
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pixel_criterion (dict): config of pixel criterion. |
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""" |
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super(EDVRModel, self).__init__(generator, pixel_criterion) |
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self.tsa_iter = tsa_iter |
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self.current_iter = 1 |
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init_edvr_weight(self.nets['generator']) |
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def setup_input(self, input): |
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self.lq = input['lq'] |
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self.visual_items['lq'] = self.lq[:, 2, :, :, :] |
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self.visual_items['lq-2'] = self.lq[:, 0, :, :, :] |
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self.visual_items['lq-1'] = self.lq[:, 1, :, :, :] |
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self.visual_items['lq+1'] = self.lq[:, 3, :, :, :] |
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self.visual_items['lq+2'] = self.lq[:, 4, :, :, :] |
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if 'gt' in input: |
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self.gt = input['gt'] |
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self.visual_items['gt'] = self.gt |
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self.image_paths = input['lq_path'] |
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def train_iter(self, optims=None): |
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optims['optim'].clear_grad() |
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if self.tsa_iter: |
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if self.current_iter == 1: |
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print('Only train TSA module for', self.tsa_iter, 'iters.') |
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for name, param in self.nets['generator'].named_parameters(): |
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if 'TSAModule' not in name: |
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param.trainable = False |
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elif self.current_iter == self.tsa_iter + 1: |
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print('Train all the parameters.') |
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for param in self.nets['generator'].parameters(): |
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param.trainable = True |
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self.output = self.nets['generator'](self.lq) |
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self.visual_items['output'] = self.output |
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# pixel loss |
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loss_pixel = self.pixel_criterion(self.output, self.gt) |
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self.losses['loss_pixel'] = loss_pixel |
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loss_pixel.backward() |
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optims['optim'].step() |
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self.current_iter += 1 |
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def init_edvr_weight(net): |
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def reset_func(m): |
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if hasattr(m, 'weight') and (not isinstance( |
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m, (nn.BatchNorm, nn.BatchNorm2D))) and ( |
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not isinstance(m, ResidualBlockNoBN) and |
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(not isinstance(m, DCNPack))): |
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reset_parameters(m) |
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net.apply(reset_func)
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