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105 lines
3.6 KiB
105 lines
3.6 KiB
# Copyright (c) 2022 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 .generators.builder import build_generator |
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from ...models.ppgan.models.criterions.builder import build_criterion |
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from ...models.ppgan.models.base_model import BaseModel |
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from ...models.ppgan.models.builder import MODELS |
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from ...models.ppgan.utils.visual import tensor2img |
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from ...models.ppgan.modules.init import reset_parameters |
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@MODELS.register() |
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class RCANModel(BaseModel): |
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"""Base SR model for single image super-resolution. |
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""" |
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def __init__(self, generator, pixel_criterion=None, use_init_weight=False): |
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""" |
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Args: |
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generator (dict): config of generator. |
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pixel_criterion (dict): config of pixel criterion. |
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""" |
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super(RCANModel, self).__init__() |
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self.nets['generator'] = build_generator(generator) |
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self.error_last = 1e8 |
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self.batch = 0 |
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if pixel_criterion: |
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self.pixel_criterion = build_criterion(pixel_criterion) |
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if use_init_weight: |
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init_sr_weight(self.nets['generator']) |
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def setup_input(self, input): |
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self.lq = paddle.to_tensor(input['lq']) |
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self.visual_items['lq'] = self.lq |
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if 'gt' in input: |
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self.gt = paddle.to_tensor(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 forward(self): |
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pass |
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def train_iter(self, optims=None): |
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optims['optim'].clear_grad() |
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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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skip_threshold = 1e6 |
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if loss_pixel.item() < skip_threshold * self.error_last: |
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loss_pixel.backward() |
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optims['optim'].step() |
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else: |
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print('Skip this batch {}! (Loss: {})'.format(self.batch + 1, |
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loss_pixel.item())) |
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self.batch += 1 |
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if self.batch % 1000 == 0: |
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self.error_last = loss_pixel.item() / 1000 |
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print("update error_last：{}".format(self.error_last)) |
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def test_iter(self, metrics=None): |
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self.nets['generator'].eval() |
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with paddle.no_grad(): |
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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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self.nets['generator'].train() |
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out_img = [] |
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gt_img = [] |
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for out_tensor, gt_tensor in zip(self.output, self.gt): |
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out_img.append(tensor2img(out_tensor, (0., 255.))) |
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gt_img.append(tensor2img(gt_tensor, (0., 255.))) |
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if metrics is not None: |
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for metric in metrics.values(): |
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metric.update(out_img, gt_img) |
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def init_sr_weight(net): |
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def reset_func(m): |
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if hasattr(m, 'weight') and ( |
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not isinstance(m, (nn.BatchNorm, nn.BatchNorm2D))): |
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reset_parameters(m) |
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net.apply(reset_func)
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