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PaddleRS/paddlers/tasks/restorer.py

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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 os
import os.path as osp
from collections import OrderedDict
import numpy as np
import cv2
import paddle
import paddle.nn.functional as F
from paddle.static import InputSpec
import paddlers
import paddlers.models.ppgan as ppgan
import paddlers.rs_models.res as cmres
import paddlers.utils.logging as logging
from paddlers.models import res_losses
from paddlers.transforms import Resize, decode_image
from paddlers.transforms.functions import calc_hr_shape
from paddlers.utils import get_single_card_bs
from .base import BaseModel
from .utils.res_adapters import GANAdapter, OptimizerAdapter
__all__ = []
class BaseRestorer(BaseModel):
MIN_MAX = (0., 255.)
def __init__(self, model_name, losses=None, sr_factor=None, **params):
self.init_params = locals()
if 'with_net' in self.init_params:
del self.init_params['with_net']
super(BaseRestorer, self).__init__('restorer')
self.model_name = model_name
self.losses = losses
self.sr_factor = sr_factor
if params.get('with_net', True):
params.pop('with_net', None)
self.net = self.build_net(**params)
self.find_unused_parameters = True
def build_net(self, **params):
# Currently, only use models from cmres.
if not hasattr(cmres, model_name):
raise ValueError("ERROR: There is no model named {}.".format(
model_name))
net = dict(**cmres.__dict__)[self.model_name](**params)
return net
def _build_inference_net(self):
# For GAN models, only the generator will be used for inference.
if isinstance(self.net, GANAdapter):
infer_net = self.net.generator
else:
infer_net = self.net
infer_net.eval()
return infer_net
def _fix_transforms_shape(self, image_shape):
if hasattr(self, 'test_transforms'):
if self.test_transforms is not None:
has_resize_op = False
resize_op_idx = -1
normalize_op_idx = len(self.test_transforms.transforms)
for idx, op in enumerate(self.test_transforms.transforms):
name = op.__class__.__name__
if name == 'Normalize':
normalize_op_idx = idx
if 'Resize' in name:
has_resize_op = True
resize_op_idx = idx
if not has_resize_op:
self.test_transforms.transforms.insert(
normalize_op_idx, Resize(target_size=image_shape))
else:
self.test_transforms.transforms[resize_op_idx] = Resize(
target_size=image_shape)
def _get_test_inputs(self, image_shape):
if image_shape is not None:
if len(image_shape) == 2:
image_shape = [1, 3] + image_shape
self._fix_transforms_shape(image_shape[-2:])
else:
image_shape = [None, 3, -1, -1]
self.fixed_input_shape = image_shape
input_spec = [
InputSpec(
shape=image_shape, name='image', dtype='float32')
]
return input_spec
def run(self, net, inputs, mode):
outputs = OrderedDict()
if mode == 'test':
if isinstance(net, GANAdapter):
net_out = net.generator(inputs[0])
else:
net_out = net(inputs[0])
tar_shape = inputs[1]
if self.status == 'Infer':
res_map_list = self._postprocess(
net_out, tar_shape, transforms=inputs[2])
else:
pred = self._postprocess(
net_out, tar_shape, transforms=inputs[2])
res_map_list = []
for res_map in pred:
res_map = self._tensor_to_images(res_map)
res_map_list.append(res_map)
outputs['res_map'] = res_map_list
if mode == 'eval':
if isinstance(net, GANAdapter):
net_out = net.generator(inputs[0])
else:
net_out = net(inputs[0])
tar = inputs[1]
tar_shape = [tar.shape[-2:]]
pred = self._postprocess(
net_out, tar_shape, transforms=inputs[2])[0] # NCHW
pred = self._tensor_to_images(pred)
outputs['pred'] = pred
tar = self.tensor_to_images(tar)
outputs['tar'] = tar
if mode == 'train':
# This is used by non-GAN models.
# For GAN models, self.run_gan() should be used.
net_out = net(inputs[0])
loss = self.losses(net_out, inputs[1])
outputs['loss'] = loss
return outputs
def run_gan(self, net, inputs, mode, gan_mode):
raise NotImplementedError
def default_loss(self):
return res_losses.L1Loss()
def default_optimizer(self,
parameters,
learning_rate,
num_epochs,
num_steps_each_epoch,
lr_decay_power=0.9):
decay_step = num_epochs * num_steps_each_epoch
lr_scheduler = paddle.optimizer.lr.PolynomialDecay(
learning_rate, decay_step, end_lr=0, power=lr_decay_power)
optimizer = paddle.optimizer.Momentum(
learning_rate=lr_scheduler,
parameters=parameters,
momentum=0.9,
weight_decay=4e-5)
return optimizer
def train(self,
num_epochs,
train_dataset,
train_batch_size=2,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=2,
save_dir='output',
pretrain_weights=None,
learning_rate=0.01,
lr_decay_power=0.9,
early_stop=False,
early_stop_patience=5,
use_vdl=True,
resume_checkpoint=None):
"""
Train the model.
Args:
num_epochs (int): Number of epochs.
train_dataset (paddlers.datasets.ResDataset): Training dataset.
train_batch_size (int, optional): Total batch size among all cards used in
training. Defaults to 2.
eval_dataset (paddlers.datasets.ResDataset|None, optional): Evaluation dataset.
If None, the model will not be evaluated during training process.
Defaults to None.
optimizer (paddle.optimizer.Optimizer|None, optional): Optimizer used in
training. If None, a default optimizer will be used. Defaults to None.
save_interval_epochs (int, optional): Epoch interval for saving the model.
Defaults to 1.
log_interval_steps (int, optional): Step interval for printing training
information. Defaults to 2.
save_dir (str, optional): Directory to save the model. Defaults to 'output'.
pretrain_weights (str|None, optional): None or name/path of pretrained
weights. If None, no pretrained weights will be loaded.
Defaults to None.
learning_rate (float, optional): Learning rate for training. Defaults to .01.
lr_decay_power (float, optional): Learning decay power. Defaults to .9.
early_stop (bool, optional): Whether to adopt early stop strategy. Defaults
to False.
early_stop_patience (int, optional): Early stop patience. Defaults to 5.
use_vdl (bool, optional): Whether to use VisualDL to monitor the training
process. Defaults to True.
resume_checkpoint (str|None, optional): Path of the checkpoint to resume
training from. If None, no training checkpoint will be resumed. At most
Aone of `resume_checkpoint` and `pretrain_weights` can be set simultaneously.
Defaults to None.
"""
if self.status == 'Infer':
logging.error(
"Exported inference model does not support training.",
exit=True)
if pretrain_weights is not None and resume_checkpoint is not None:
logging.error(
"pretrain_weights and resume_checkpoint cannot be set simultaneously.",
exit=True)
if self.losses is None:
self.losses = self.default_loss()
if optimizer is None:
num_steps_each_epoch = train_dataset.num_samples // train_batch_size
if isinstance(self.net, GANAdapter):
parameters = {'params_g': [], 'params_d': []}
for net_g in self.net.generators:
parameters['params_g'].append(net_g.parameters())
for net_d in self.net.discriminators:
parameters['params_d'].append(net_d.parameters())
else:
parameters = self.net.parameters()
self.optimizer = self.default_optimizer(
parameters, learning_rate, num_epochs, num_steps_each_epoch,
lr_decay_power)
else:
self.optimizer = optimizer
if pretrain_weights is not None and not osp.exists(pretrain_weights):
logging.warning("Path of pretrain_weights('{}') does not exist!".
format(pretrain_weights))
elif pretrain_weights is not None and osp.exists(pretrain_weights):
if osp.splitext(pretrain_weights)[-1] != '.pdparams':
logging.error(
"Invalid pretrain weights. Please specify a '.pdparams' file.",
exit=True)
pretrained_dir = osp.join(save_dir, 'pretrain')
is_backbone_weights = pretrain_weights == 'IMAGENET'
self.net_initialize(
pretrain_weights=pretrain_weights,
save_dir=pretrained_dir,
resume_checkpoint=resume_checkpoint,
is_backbone_weights=is_backbone_weights)
self.train_loop(
num_epochs=num_epochs,
train_dataset=train_dataset,
train_batch_size=train_batch_size,
eval_dataset=eval_dataset,
save_interval_epochs=save_interval_epochs,
log_interval_steps=log_interval_steps,
save_dir=save_dir,
early_stop=early_stop,
early_stop_patience=early_stop_patience,
use_vdl=use_vdl)
def quant_aware_train(self,
num_epochs,
train_dataset,
train_batch_size=2,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=2,
save_dir='output',
learning_rate=0.0001,
lr_decay_power=0.9,
early_stop=False,
early_stop_patience=5,
use_vdl=True,
resume_checkpoint=None,
quant_config=None):
"""
Quantization-aware training.
Args:
num_epochs (int): Number of epochs.
train_dataset (paddlers.datasets.ResDataset): Training dataset.
train_batch_size (int, optional): Total batch size among all cards used in
training. Defaults to 2.
eval_dataset (paddlers.datasets.ResDataset|None, optional): Evaluation dataset.
If None, the model will not be evaluated during training process.
Defaults to None.
optimizer (paddle.optimizer.Optimizer|None, optional): Optimizer used in
training. If None, a default optimizer will be used. Defaults to None.
save_interval_epochs (int, optional): Epoch interval for saving the model.
Defaults to 1.
log_interval_steps (int, optional): Step interval for printing training
information. Defaults to 2.
save_dir (str, optional): Directory to save the model. Defaults to 'output'.
learning_rate (float, optional): Learning rate for training.
Defaults to .0001.
lr_decay_power (float, optional): Learning decay power. Defaults to .9.
early_stop (bool, optional): Whether to adopt early stop strategy.
Defaults to False.
early_stop_patience (int, optional): Early stop patience. Defaults to 5.
use_vdl (bool, optional): Whether to use VisualDL to monitor the training
process. Defaults to True.
quant_config (dict|None, optional): Quantization configuration. If None,
a default rule of thumb configuration will be used. Defaults to None.
resume_checkpoint (str|None, optional): Path of the checkpoint to resume
quantization-aware training from. If None, no training checkpoint will
be resumed. Defaults to None.
"""
self._prepare_qat(quant_config)
self.train(
num_epochs=num_epochs,
train_dataset=train_dataset,
train_batch_size=train_batch_size,
eval_dataset=eval_dataset,
optimizer=optimizer,
save_interval_epochs=save_interval_epochs,
log_interval_steps=log_interval_steps,
save_dir=save_dir,
pretrain_weights=None,
learning_rate=learning_rate,
lr_decay_power=lr_decay_power,
early_stop=early_stop,
early_stop_patience=early_stop_patience,
use_vdl=use_vdl,
resume_checkpoint=resume_checkpoint)
def evaluate(self, eval_dataset, batch_size=1, return_details=False):
"""
Evaluate the model.
Args:
eval_dataset (paddlers.datasets.ResDataset): Evaluation dataset.
batch_size (int, optional): Total batch size among all cards used for
evaluation. Defaults to 1.
return_details (bool, optional): Whether to return evaluation details.
Defaults to False.
Returns:
If `return_details` is False, return collections.OrderedDict with
key-value pairs:
{"psnr": `peak signal-to-noise ratio`,
"ssim": `structural similarity`}.
"""
self._check_transforms(eval_dataset.transforms, 'eval')
self.net.eval()
nranks = paddle.distributed.get_world_size()
local_rank = paddle.distributed.get_rank()
if nranks > 1:
# Initialize parallel environment if not done.
if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized(
):
paddle.distributed.init_parallel_env()
batch_size_each_card = get_single_card_bs(batch_size)
if batch_size_each_card > 1:
batch_size_each_card = 1
batch_size = batch_size_each_card * paddlers.env_info['num']
logging.warning(
"Restorer only supports batch_size=1 for each gpu/cpu card " \
"during evaluation, so batch_size " \
"is forcibly set to {}.".format(batch_size))
# TODO: Distributed evaluation
if nranks < 2 or local_rank == 0:
self.eval_data_loader = self.build_data_loader(
eval_dataset, batch_size=batch_size, mode='eval')
# XXX: Hard-code crop_border and test_y_channel
psnr = ppgan.metrics.PSNR(crop_border=4, test_y_channel=True)
ssim = ppgan.metrics.SSIM(crop_border=4, test_y_channel=True)
with paddle.no_grad():
for step, data in enumerate(self.eval_data_loader):
outputs = self.run(self.net, data, 'eval')
psnr.update(outputs['pred'], outputs['tar'])
ssim.update(outputs['pred'], outputs['tar'])
eval_metrics = OrderedDict(
zip(['psnr', 'ssim'], [psnr.accumulate(), ssim.accumulate()]))
if return_details:
# TODO: Add details
return eval_metrics, None
return eval_metrics
def predict(self, img_file, transforms=None):
"""
Do inference.
Args:
img_file (list[np.ndarray|str] | str | np.ndarray): Image path or decoded
image data, which also could constitute a list, meaning all images to be
predicted as a mini-batch.
transforms (paddlers.transforms.Compose|None, optional): Transforms for
inputs. If None, the transforms for evaluation process will be used.
Defaults to None.
Returns:
If `img_file` is a tuple of string or np.array, the result is a dict with
the following key-value pairs:
res_map (np.ndarray): Restored image (HWC).
If `img_file` is a list, the result is a list composed of dicts with the
above keys.
"""
if transforms is None and not hasattr(self, 'test_transforms'):
raise ValueError("transforms need to be defined, now is None.")
if transforms is None:
transforms = self.test_transforms
if isinstance(img_file, (str, np.ndarray)):
images = [img_file]
else:
images = img_file
batch_im, batch_tar_shape = self._preprocess(images, transforms,
self.model_type)
self.net.eval()
data = (batch_im, batch_tar_shape, transforms.transforms)
outputs = self.run(self.net, data, 'test')
res_map_list = outputs['res_map']
if isinstance(img_file, list):
prediction = [{'res_map': m} for m in res_map_list]
else:
prediction = {'res_map': res_map_list[0]}
return prediction
def _preprocess(self, images, transforms, to_tensor=True):
self._check_transforms(transforms, 'test')
batch_im = list()
batch_tar_shape = list()
for im in images:
if isinstance(im, str):
im = decode_image(im, to_rgb=False)
ori_shape = im.shape[:2]
sample = {'image': im}
im = transforms(sample)[0]
batch_im.append(im)
batch_tar_shape.append(self._get_target_shape(ori_shape))
if to_tensor:
batch_im = paddle.to_tensor(batch_im)
else:
batch_im = np.asarray(batch_im)
return batch_im, batch_tar_shape
def _get_target_shape(self, ori_shape):
if self.sr_factor is None:
return ori_shape
else:
return calc_hr_shape(ori_shape, self.sr_factor)
@staticmethod
def get_transforms_shape_info(batch_tar_shape, transforms):
batch_restore_list = list()
for tar_shape in batch_tar_shape:
restore_list = list()
h, w = tar_shape[0], tar_shape[1]
for op in transforms:
if op.__class__.__name__ == 'Resize':
restore_list.append(('resize', (h, w)))
h, w = op.target_size
elif op.__class__.__name__ == 'ResizeByShort':
restore_list.append(('resize', (h, w)))
im_short_size = min(h, w)
im_long_size = max(h, w)
scale = float(op.short_size) / float(im_short_size)
if 0 < op.max_size < np.round(scale * im_long_size):
scale = float(op.max_size) / float(im_long_size)
h = int(round(h * scale))
w = int(round(w * scale))
elif op.__class__.__name__ == 'ResizeByLong':
restore_list.append(('resize', (h, w)))
im_long_size = max(h, w)
scale = float(op.long_size) / float(im_long_size)
h = int(round(h * scale))
w = int(round(w * scale))
elif op.__class__.__name__ == 'Pad':
if op.target_size:
target_h, target_w = op.target_size
else:
target_h = int(
(np.ceil(h / op.size_divisor) * op.size_divisor))
target_w = int(
(np.ceil(w / op.size_divisor) * op.size_divisor))
if op.pad_mode == -1:
offsets = op.offsets
elif op.pad_mode == 0:
offsets = [0, 0]
elif op.pad_mode == 1:
offsets = [(target_h - h) // 2, (target_w - w) // 2]
else:
offsets = [target_h - h, target_w - w]
restore_list.append(('padding', (h, w), offsets))
h, w = target_h, target_w
batch_restore_list.append(restore_list)
return batch_restore_list
def _postprocess(self, batch_pred, batch_tar_shape, transforms):
batch_restore_list = BaseRestorer.get_transforms_shape_info(
batch_tar_shape, transforms)
if isinstance(batch_pred, (tuple, list)) and self.status == 'Infer':
return self._infer_postprocess(
batch_res_map=batch_pred[0],
batch_restore_list=batch_restore_list)
results = []
if batch_pred.dtype == paddle.float32:
mode = 'bilinear'
else:
mode = 'nearest'
for pred, restore_list in zip(batch_pred, batch_restore_list):
pred = paddle.unsqueeze(pred, axis=0)
for item in restore_list[::-1]:
h, w = item[1][0], item[1][1]
if item[0] == 'resize':
pred = F.interpolate(
pred, (h, w), mode=mode, data_format='NCHW')
elif item[0] == 'padding':
x, y = item[2]
pred = pred[:, :, y:y + h, x:x + w]
else:
pass
results.append(pred)
return results
def _infer_postprocess(self, batch_res_map, batch_restore_list):
res_maps = []
for score_map, restore_list in zip(batch_res_map, batch_restore_list):
if not isinstance(res_map, np.ndarray):
res_map = paddle.unsqueeze(res_map, axis=0)
for item in restore_list[::-1]:
h, w = item[1][0], item[1][1]
if item[0] == 'resize':
if isinstance(res_map, np.ndarray):
res_map = cv2.resize(
res_map, (w, h), interpolation=cv2.INTER_LINEAR)
else:
res_map = F.interpolate(
score_map, (h, w),
mode='bilinear',
data_format='NHWC')
elif item[0] == 'padding':
x, y = item[2]
if isinstance(res_map, np.ndarray):
res_map = res_map[..., y:y + h, x:x + w]
else:
res_map = res_map[:, :, y:y + h, x:x + w]
else:
pass
res_map = res_map.squeeze()
if not isinstance(res_map, np.ndarray):
res_map = res_map.numpy()
res_map = self._normalize(res_map)
res_maps.append(res_map.squeeze())
return res_maps
def _check_transforms(self, transforms, mode):
super()._check_transforms(transforms, mode)
if not isinstance(transforms.arrange,
paddlers.transforms.ArrangeRestorer):
raise TypeError(
"`transforms.arrange` must be an ArrangeRestorer object.")
def set_losses(self, losses):
self.losses = losses
def _tensor_to_images(self, tensor, squeeze=True, quantize=True):
tensor = paddle.transpose(tensor, perm=[0, 2, 3, 1]) # NHWC
if squeeze:
tensor = tensor.squeeze()
images = tensor.numpy().astype('float32')
images = np.clip(images, self.MIN_MAX[0], self.MIN_MAX[1])
images = self._normalize(images, copy=True, quantize=quantize)
return images
def _normalize(self, im, copy=False, quantize=True):
if copy:
im = im.copy()
im -= im.min()
im /= im.max() + 1e-32
if quantize:
im *= 255
im = im.astype('uint8')
return im
class RCAN(BaseRestorer):
def __init__(self,
losses=None,
sr_factor=4,
n_resgroups=10,
n_resblocks=20,
n_feats=64,
n_colors=3,
rgb_range=255,
kernel_size=3,
reduction=16,
**params):
params.update({
'factor': sr_factor,
'n_resgroups': n_resgroups,
'n_resblocks': n_resblocks,
'n_feats': n_feats,
'n_colors': n_colors,
'rgb_range': rgb_range,
'kernel_size': kernel_size,
'reduction': reduction
})
super(RCAN, self).__init__(
model_name='RCAN', losses=losses, sr_factor=sr_factor, **params)
class DRN(BaseRestorer):
def __init__(self,
losses=None,
sr_factor=4,
scale=(2, 4),
n_blocks=30,
n_feats=16,
n_colors=3,
rgb_range=255,
negval=0.2,
**params):
if sr_factor != max(scale):
raise ValueError(f"`sr_factor` must be equal to `max(scale)`.")
params.update({
'scale': scale,
'n_blocks': n_blocks,
'n_feats': n_feats,
'n_colors': n_colors,
'rgb_range': rgb_range,
'negval': negval
})
super(DRN, self).__init__(
model_name='DRN', losses=losses, sr_factor=sr_factor, **params)
def build_net(self, **params):
net = ppgan.models.generators.DRNGenerator(**params)
return net
class LESRCNN(BaseRestorer):
def __init__(self, losses=None, sr_factor=4, multi_scale=False, group=1):
params.update({'scale': sr_factor, 'multi_scale': False, 'group': 1})
super(LESRCNN, self).__init__(
model_name='LESRCNN', losses=losses, sr_factor=sr_factor, **params)
def build_net(self, **params):
net = ppgan.models.generators.LESRCNNGenerator(**params)
return net
class ESRGAN(BaseRestorer):
MIN_MAX = (0., 1.)
def __init__(self,
losses=None,
sr_factor=4,
use_gan=True,
in_channels=3,
out_channels=3,
nf=64,
nb=23):
params.update({
'scale': sr_factor,
'in_nc': in_channels,
'out_nc': out_channels,
'nf': nf,
'nb': nb
})
self.use_gan = use_gan
super(ESRGAN, self).__init__(
model_name='ESRGAN', losses=losses, sr_factor=sr_factor, **params)
def build_net(self, **params):
generator = ppgan.models.generators.RRDBNet(**params)
if self.use_gan:
discriminator = ppgan.models.discriminators.VGGDiscrinimator128(
in_channels=params['out_nc'], num_feat=64)
net = GANAdapter(
generators=[generator], discriminators=[discriminator])
else:
net = generator
return net
def default_loss(self):
if self.use_gan:
self.losses = {
'pixel': res_losses.L1Loss(loss_weight=0.01),
'perceptual':
res_losses.PerceptualLoss(layer_weights={'34': 1.0}),
'gan': res_losses.GANLoss(
gan_mode='vanilla', loss_weight=0.005)
}
else:
return res_losses.L1Loss()
def default_optimizer(self, parameters, *args, **kwargs):
if self.use_gan:
optim_g = super(ESRGAN, self).default_optimizer(
parameters['optims_g'][0], *args, **kwargs)
optim_d = super(ESRGAN, self).default_optimizer(
parameters['optims_d'][0], *args, **kwargs)
return OptimizerAdapter(optim_g, optim_d)
else:
return super(ESRGAN, self).default_optimizer(params, *args,
**kwargs)
def run_gan(self, net, inputs, mode, gan_mode='forward_g'):
if mode != 'train':
raise ValueError("`mode` is not 'train'.")
outputs = OrderedDict()
if gan_mode == 'forward_g':
loss_g = 0
g_pred = net.generator(inputs[0])
loss_pix = self.losses['pixel'](g_pred, tar)
loss_perc, loss_sty = self.losses['perceptual'](g_pred, tar)
loss_g += loss_pix
if loss_perc is not None:
loss_g += loss_perc
if loss_sty is not None:
loss_g += loss_sty
self._set_requires_grad(net.discriminator, False)
real_d_pred = net.discriminator(inputs[1]).detach()
fake_g_pred = net.discriminator(g_pred)
loss_g_real = self.losses['gan'](
real_d_pred - paddle.mean(fake_g_pred), False,
is_disc=False) * 0.5
loss_g_fake = self.losses['gan'](
fake_g_pred - paddle.mean(real_d_pred), True,
is_disc=False) * 0.5
loss_g_gan = loss_g_real + loss_g_fake
outputs['g_pred'] = g_pred.detach()
outputs['loss_g_pps'] = loss_g
outputs['loss_g_gan'] = loss_g_gan
elif gan_mode == 'forward_d':
self._set_requires_grad(net.discriminator, True)
# Real
fake_d_pred = net.discriminator(data[0]).detach()
real_d_pred = net.discriminator(data[1])
loss_d_real = self.losses['gan'](
real_d_pred - paddle.mean(fake_d_pred), True,
is_disc=True) * 0.5
# Fake
fake_d_pred = self.nets['discriminator'](self.output.detach())
loss_d_fake = self.gan_criterion(
fake_d_pred - paddle.mean(real_d_pred.detach()),
False,
is_disc=True) * 0.5
outputs['loss_d'] = loss_d_real + loss_d_fake
else:
raise ValueError("Invalid `gan_mode`!")
return outputs
def train_step(self, step, data, net):
if self.use_gan:
optim_g, optim_d = self.optimizer
outputs = self.run_gan(net, data, gan_mode='forward_g')
optim_g.clear_grad()
(outputs['loss_g_pps'] + outputs['loss_g_gan']).backward()
optim_g.step()
outputs.update(
self.run_gan(
net, (outputs['g_pred'], data[1]), gan_mode='forward_d'))
optim_d.clear_grad()
outputs['loss_d'].backward()
optim_d.step()
outputs['loss'] = outupts['loss_g_pps'] + outputs[
'loss_g_gan'] + outputs['loss_d']
if isinstance(optim_g._learning_rate,
paddle.optimizer.lr.LRScheduler):
# If ReduceOnPlateau is used as the scheduler, use the loss value as the metric.
if isinstance(optim_g._learning_rate,
paddle.optimizer.lr.ReduceOnPlateau):
optim_g._learning_rate.step(loss.item())
else:
optim_g._learning_rate.step()
if isinstance(optim_d._learning_rate,
paddle.optimizer.lr.LRScheduler):
if isinstance(optim_d._learning_rate,
paddle.optimizer.lr.ReduceOnPlateau):
optim_d._learning_rate.step(loss.item())
else:
optim_d._learning_rate.step()
return outputs
else:
super(ESRGAN, self).train_step(step, data, net)
def _set_requires_grad(self, net, requires_grad):
for p in net.parameters():
p.trainable = requires_grad