PaddleRS/paddlers/custom_models/gan/rcan_model.py

#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# 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
#
#     http://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.

import paddle
import paddle.nn as nn

from .generators.builder import build_generator
from ...models.ppgan.models.criterions.builder import build_criterion
from ...models.ppgan.models.base_model import BaseModel
from ...models.ppgan.models.builder import MODELS
from ...models.ppgan.utils.visual import tensor2img
from ...models.ppgan.modules.init import reset_parameters


@MODELS.register()
class RCANModel(BaseModel):
    """Base SR model for single image super-resolution.
    """
    def __init__(self, generator, pixel_criterion=None, use_init_weight=False):
        """
        Args:
            generator (dict): config of generator.
            pixel_criterion (dict): config of pixel criterion.
        """
        super(RCANModel, self).__init__()

        self.nets['generator'] = build_generator(generator)
        self.error_last = 1e8
        self.batch = 0
        if pixel_criterion:
            self.pixel_criterion = build_criterion(pixel_criterion)
        if use_init_weight:
            init_sr_weight(self.nets['generator'])

    def setup_input(self, input):
        self.lq = paddle.to_tensor(input['lq'])
        self.visual_items['lq'] = self.lq
        if 'gt' in input:
            self.gt = paddle.to_tensor(input['gt'])
            self.visual_items['gt'] = self.gt
        self.image_paths = input['lq_path']

    def forward(self):
        pass

    def train_iter(self, optims=None):
        optims['optim'].clear_grad()

        self.output = self.nets['generator'](self.lq)
        self.visual_items['output'] = self.output
        # pixel loss
        loss_pixel = self.pixel_criterion(self.output, self.gt)
        self.losses['loss_pixel'] = loss_pixel

        skip_threshold = 1e6

        if loss_pixel.item() < skip_threshold * self.error_last:
            loss_pixel.backward()
            optims['optim'].step()
        else:
            print('Skip this batch {}! (Loss: {})'.format(
                    self.batch + 1, loss_pixel.item()
                ))
        self.batch += 1

        if self.batch % 1000 == 0:
            self.error_last = loss_pixel.item()/1000
            print("update error_last：{}".format(self.error_last))


    def test_iter(self, metrics=None):
        self.nets['generator'].eval()
        with paddle.no_grad():
            self.output = self.nets['generator'](self.lq)
            self.visual_items['output'] = self.output
        self.nets['generator'].train()

        out_img = []
        gt_img = []
        for out_tensor, gt_tensor in zip(self.output, self.gt):
            out_img.append(tensor2img(out_tensor, (0., 255.)))
            gt_img.append(tensor2img(gt_tensor, (0., 255.)))

        if metrics is not None:
            for metric in metrics.values():
                metric.update(out_img, gt_img)


def init_sr_weight(net):
    def reset_func(m):
        if hasattr(m, 'weight') and (not isinstance(
                m, (nn.BatchNorm, nn.BatchNorm2D))):
            reset_parameters(m)

    net.apply(reset_func)