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Commit bf5034d5 authored by Sanghyun Son's avatar Sanghyun Son
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support PyTorch 1.2.0 

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README.md
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**About PyTorch 1.0.0** **About PyTorch 1.2.0**
* We support PyTorch 1.0.0. If you prefer the previous versions of PyTorch, use legacy branches. * Now the master branch supports PyTorch 1.2.0 by default.
* ``--ext bin`` is not supported. Also, please erase your bin files with ``--ext sep-reset``. Once you successfully build those bin files, you can remove ``-reset`` from the argument. * Due to the serious version problem (especially torch.utils.data.dataloader), MDSR functions are temporarily disabled. If you have to train/evaluate the MDSR model, please use legacy branches.
# EDSR-PyTorch # EDSR-PyTorch
![](/figs/main.png) ![](/figs/main.png)
...@@ -20,7 +20,7 @@ If you find our work useful in your research or publication, please cite our wor ...@@ -20,7 +20,7 @@ If you find our work useful in your research or publication, please cite our wor
year = {2017} year = {2017}
} }
``` ```
We provide scripts for reproducing all the results from our paper. You can train your own model from scratch, or use pre-trained model to enlarge your images. We provide scripts for reproducing all the results from our paper. You can train your model from scratch, or use a pre-trained model to enlarge your images.
**Differences between Torch version** **Differences between Torch version**
* Codes are much more compact. (Removed all unnecessary parts.) * Codes are much more compact. (Removed all unnecessary parts.)
...@@ -47,7 +47,7 @@ cd EDSR-PyTorch ...@@ -47,7 +47,7 @@ cd EDSR-PyTorch
``` ```
## Quickstart (Demo) ## Quickstart (Demo)
You can test our super-resolution algorithm with your own images. Place your images in ``test`` folder. (like ``test/<your_image>``) We support **png** and **jpeg** files. You can test our super-resolution algorithm with your images. Place your images in ``test`` folder. (like ``test/<your_image>``) We support **png** and **jpeg** files.
Run the script in ``src`` folder. Before you run the demo, please uncomment the appropriate line in ```demo.sh``` that you want to execute. Run the script in ``src`` folder. Before you run the demo, please uncomment the appropriate line in ```demo.sh``` that you want to execute.
```bash ```bash
...@@ -123,17 +123,17 @@ sh demo.sh ...@@ -123,17 +123,17 @@ sh demo.sh
* Basically, this function first split a large image to small patches. Those images are merged after super-resolution. I checked this function with 12GB memory, 4000 x 2000 input image in scale 4. (Therefore, the output will be 16000 x 8000.) * Basically, this function first split a large image to small patches. Those images are merged after super-resolution. I checked this function with 12GB memory, 4000 x 2000 input image in scale 4. (Therefore, the output will be 16000 x 8000.)
* Feb 21, 2018 * Feb 21, 2018
* Fixed the problem when loading pre-trained multi-gpu model. * Fixed the problem when loading pre-trained multi-GPU model.
* Added pre-trained scale 2 baseline model. * Added pre-trained scale 2 baseline model.
* This code now only saves the best-performing model by default. For MDSR, 'the best' can be ambiguous. Use --save_models argument to save all the intermediate models. * This code now only saves the best-performing model by default. For MDSR, 'the best' can be ambiguous. Use --save_models argument to keep all the intermediate models.
* PyTorch 0.3.1 changed their implementation of DataLoader function. Therefore, I also changed my implementation of MSDataLoader. You can find it on feature/dataloader branch. * PyTorch 0.3.1 changed their implementation of DataLoader function. Therefore, I also changed my implementation of MSDataLoader. You can find it on feature/dataloader branch.
* Feb 23, 2018 * Feb 23, 2018
* Now PyTorch 0.3.1 is default. Use legacy/0.3.0 branch if you use the old version. * Now PyTorch 0.3.1 is a default. Use legacy/0.3.0 branch if you use the old version.
* With a new ``src/data/DIV2K.py`` code, one can easily create new data class for super-resolution. * With a new ``src/data/DIV2K.py`` code, one can easily create new data class for super-resolution.
* New binary data pack. (Please remove the ``DIV2K_decoded`` folder from your dataset if you have.) * New binary data pack. (Please remove the ``DIV2K_decoded`` folder from your dataset if you have.)
* With ``--ext bin``, this code will automatically generates and saves the binary data pack that corresponds to previous ``DIV2K_decoded``. (This requires huge RAM (~45GB, Swap can be used.), so please be careful.) * With ``--ext bin``, this code will automatically generate and saves the binary data pack that corresponds to previous ``DIV2K_decoded``. (This requires huge RAM (~45GB, Swap can be used.), so please be careful.)
* If you cannot make the binary pack, just use the default setting (``--ext img``). * If you cannot make the binary pack, use the default setting (``--ext img``).
* Fixed a bug that PSNR in the log and PSNR calculated from the saved images does not match. * Fixed a bug that PSNR in the log and PSNR calculated from the saved images does not match.
* Now saved images have better quality! (PSNR is ~0.1dB higher than the original code.) * Now saved images have better quality! (PSNR is ~0.1dB higher than the original code.)
...@@ -146,23 +146,23 @@ sh demo.sh ...@@ -146,23 +146,23 @@ sh demo.sh
* Mar 11, 2018 * Mar 11, 2018
* Fixed some typos in the code and script. * Fixed some typos in the code and script.
* Now --ext img is default setting. Although we recommend you to use --ext bin when training, please use --ext img when you use --test_only. * Now --ext img is default setting. Although we recommend you to use --ext bin when training, please use --ext img when you use --test_only.
* Skip_batch operation is implemented. Use --skip_threshold argument to skip the batch that you want to ignore. Although this function is not exactly same with that of Torch7 version, it will work as you expected. * Skip_batch operation is implemented. Use --skip_threshold argument to skip the batch that you want to ignore. Although this function is not exactly the same with that of Torch7 version, it will work as you expected.
* Mar 20, 2018 * Mar 20, 2018
* Use ``--ext sep_reset`` to pre-decode large png files. Those decoded files will be saved to the same directory with DIV2K png files. After the first run, you can use ``--ext sep`` to save time. * Use ``--ext sep-reset`` to pre-decode large png files. Those decoded files will be saved to the same directory with DIV2K png files. After the first run, you can use ``--ext sep`` to save time.
* Now supports various benchmark datasets. For example, try ``--data_test Set5`` to test your model on the Set5 images. * Now supports various benchmark datasets. For example, try ``--data_test Set5`` to test your model on the Set5 images.
* Changed the behavior of skip_batch. * Changed the behavior of skip_batch.
* Mar 29, 2018 * Mar 29, 2018
* We now provide all models from our paper. * We now provide all models from our paper.
* We also provide ``MDSR_baseline_jpeg`` model that suppresses JPEG artifacts in original low-resolution image. Please use it if you have any trouble. * We also provide ``MDSR_baseline_jpeg`` model that suppresses JPEG artifacts in the original low-resolution image. Please use it if you have any trouble.
* ``MyImage`` dataset is changed to ``Demo`` dataset. Also, it works more efficient than before. * ``MyImage`` dataset is changed to ``Demo`` dataset. Also, it works more efficient than before.
* Some codes and script are re-written. * Some codes and script are re-written.
* Apr 9, 2018 * Apr 9, 2018
* VGG and Adversarial loss is implemented based on [SRGAN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf). [WGAN](https://arxiv.org/abs/1701.07875) and [gradient penalty](https://arxiv.org/abs/1704.00028) are also implemented, but they are not tested yet. * VGG and Adversarial loss is implemented based on [SRGAN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf). [WGAN](https://arxiv.org/abs/1701.07875) and [gradient penalty](https://arxiv.org/abs/1704.00028) are also implemented, but they are not tested yet.
* Many codes are refactored. If there exists a bug, please report it. * Many codes are refactored. If there exists a bug, please report it.
* [D-DBPN](https://arxiv.org/abs/1803.02735) is implemented. Default setting is D-DBPN-L. * [D-DBPN](https://arxiv.org/abs/1803.02735) is implemented. The default setting is D-DBPN-L.
* Apr 26, 2018 * Apr 26, 2018
* Compatible with PyTorch 0.4.0 * Compatible with PyTorch 0.4.0
...@@ -171,9 +171,12 @@ sh demo.sh ...@@ -171,9 +171,12 @@ sh demo.sh
* July 22, 2018 * July 22, 2018
* Thanks for recent commits that contains RDN and RCAN. Please see ``code/demo.sh`` to train/test those models. * Thanks for recent commits that contains RDN and RCAN. Please see ``code/demo.sh`` to train/test those models.
* Now the dataloader is much stable than the previous version. Please erase ``DIV2K/bin`` folder that is created before this commit. Also, please avoid to use ``--ext bin`` argument. Our code will automatically pre-decode png images before training. If you do not have enough spaces(~10GB) in your disk, we recommend ``--ext img``(But SLOW!). * Now the dataloader is much stable than the previous version. Please erase ``DIV2K/bin`` folder that is created before this commit. Also, please avoid using ``--ext bin`` argument. Our code will automatically pre-decode png images before training. If you do not have enough spaces(~10GB) in your disk, we recommend ``--ext img``(But SLOW!).
* Oct 18, 2018 * Oct 18, 2018
* with ``--pre_train download``, pretrained models will be automatically downloaded from server. * with ``--pre_train download``, pretrained models will be automatically downloaded from the server.
* Supports video input/output (inference only). Try with ``--data_test video --dir_demo [video file directory]``. * Supports video input/output (inference only). Try with ``--data_test video --dir_demo [video file directory]``.
* About PyTorch 1.0.0
* We support PyTorch 1.0.0. If you prefer the previous versions of PyTorch, use legacy branches.
* ``--ext bin`` is not supported. Also, please erase your bin files with ``--ext sep-reset``. Once you successfully build those bin files, you can remove ``-reset`` from the argument.
src/data/__init__.py
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from importlib import import_module from importlib import import_module
from dataloader import MSDataLoader #from dataloader import MSDataLoader
from torch.utils.data import dataloader
from torch.utils.data import ConcatDataset from torch.utils.data import ConcatDataset
# This is a simple wrapper function for ConcatDataset # This is a simple wrapper function for ConcatDataset
...@@ -22,12 +23,12 @@ class Data: ...@@ -22,12 +23,12 @@ class Data:
m = import_module('data.' + module_name.lower()) m = import_module('data.' + module_name.lower())
datasets.append(getattr(m, module_name)(args, name=d)) datasets.append(getattr(m, module_name)(args, name=d))
self.loader_train = MSDataLoader( self.loader_train = dataloader.DataLoader(
args,
MyConcatDataset(datasets), MyConcatDataset(datasets),
batch_size=args.batch_size, batch_size=args.batch_size,
shuffle=True, shuffle=True,
pin_memory=not args.cpu pin_memory=not args.cpu,
num_workers=args.n_threads,
) )
self.loader_test = [] self.loader_test = []
...@@ -40,11 +41,12 @@ class Data: ...@@ -40,11 +41,12 @@ class Data:
m = import_module('data.' + module_name.lower()) m = import_module('data.' + module_name.lower())
testset = getattr(m, module_name)(args, train=False, name=d) testset = getattr(m, module_name)(args, train=False, name=d)
self.loader_test.append(MSDataLoader( self.loader_test.append(
args, dataloader.DataLoader(
testset, testset,
batch_size=1, batch_size=1,
shuffle=False, shuffle=False,
pin_memory=not args.cpu pin_memory=not args.cpu,
)) num_workers=args.n_threads,
)
)
src/dataloader.py deleted 100644 → 0
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import sys
import threading
import queue
import random
import collections
import torch
import torch.multiprocessing as multiprocessing
from torch._C import _set_worker_signal_handlers, _update_worker_pids, \
_remove_worker_pids, _error_if_any_worker_fails
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.dataloader import _DataLoaderIter
from torch.utils.data.dataloader import ManagerWatchdog
from torch.utils.data.dataloader import _pin_memory_loop
from torch.utils.data.dataloader import MP_STATUS_CHECK_INTERVAL
from torch.utils.data.dataloader import ExceptionWrapper
from torch.utils.data.dataloader import _use_shared_memory
from torch.utils.data.dataloader import numpy_type_map
from torch.utils.data.dataloader import default_collate
from torch.utils.data.dataloader import pin_memory_batch
from torch.utils.data.dataloader import _SIGCHLD_handler_set
from torch.utils.data.dataloader import _set_SIGCHLD_handler
if sys.version_info[0] == 2:
import Queue as queue
else:
import queue
def _ms_loop(dataset, index_queue, data_queue, done_event, collate_fn, scale, seed, init_fn, worker_id):
try:
global _use_shared_memory
_use_shared_memory = True
_set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
data_queue.cancel_join_thread()
if init_fn is not None:
init_fn(worker_id)
watchdog = ManagerWatchdog()
while watchdog.is_alive():
try:
r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
except queue.Empty:
continue
if r is None:
assert done_event.is_set()
return
elif done_event.is_set():
continue
idx, batch_indices = r
try:
idx_scale = 0
if len(scale) > 1 and dataset.train:
idx_scale = random.randrange(0, len(scale))
dataset.set_scale(idx_scale)
samples = collate_fn([dataset[i] for i in batch_indices])
samples.append(idx_scale)
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
except KeyboardInterrupt:
pass
class _MSDataLoaderIter(_DataLoaderIter):
def __init__(self, loader):
self.dataset = loader.dataset
self.scale = loader.scale
self.collate_fn = loader.collate_fn
self.batch_sampler = loader.batch_sampler
self.num_workers = loader.num_workers
self.pin_memory = loader.pin_memory and torch.cuda.is_available()
self.timeout = loader.timeout
self.sample_iter = iter(self.batch_sampler)
base_seed = torch.LongTensor(1).random_().item()
if self.num_workers > 0:
self.worker_init_fn = loader.worker_init_fn
self.worker_queue_idx = 0
self.worker_result_queue = multiprocessing.Queue()
self.batches_outstanding = 0
self.worker_pids_set = False
self.shutdown = False
self.send_idx = 0
self.rcvd_idx = 0
self.reorder_dict = {}
self.done_event = multiprocessing.Event()
base_seed = torch.LongTensor(1).random_()[0]
self.index_queues = []
self.workers = []
for i in range(self.num_workers):
index_queue = multiprocessing.Queue()
index_queue.cancel_join_thread()
w = multiprocessing.Process(
target=_ms_loop,
args=(
self.dataset,
index_queue,
self.worker_result_queue,
self.done_event,
self.collate_fn,
self.scale,
base_seed + i,
self.worker_init_fn,
i
)
)
w.start()
self.index_queues.append(index_queue)
self.workers.append(w)
if self.pin_memory:
self.data_queue = queue.Queue()
pin_memory_thread = threading.Thread(
target=_pin_memory_loop,
args=(
self.worker_result_queue,
self.data_queue,
torch.cuda.current_device(),
self.done_event
)
)
pin_memory_thread.daemon = True
pin_memory_thread.start()
self.pin_memory_thread = pin_memory_thread
else:
self.data_queue = self.worker_result_queue
_update_worker_pids(id(self), tuple(w.pid for w in self.workers))
_set_SIGCHLD_handler()
self.worker_pids_set = True
for _ in range(2 * self.num_workers):
self._put_indices()
class MSDataLoader(DataLoader):
def __init__(
self, args, dataset, batch_size=1, shuffle=False,
sampler=None, batch_sampler=None,
collate_fn=default_collate, pin_memory=False, drop_last=False,
timeout=0, worker_init_fn=None):
super(MSDataLoader, self).__init__(
dataset,
batch_size=batch_size,
shuffle=shuffle,
sampler=sampler,
batch_sampler=batch_sampler,
num_workers=args.n_threads,
collate_fn=collate_fn,
pin_memory=pin_memory,
drop_last=drop_last,
timeout=timeout,
worker_init_fn=worker_init_fn
)
self.scale = args.scale
def __iter__(self):
return _MSDataLoaderIter(self)
src/demo.sh
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− 1
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# EDSR baseline model (x2) + JPEG augmentation # EDSR baseline model (x2) + JPEG augmentation
#python main.py --model EDSR --scale 2 --patch_size 96 --save edsr_baseline_x2 --reset python main.py --model EDSR --scale 2 --patch_size 96 --save test_edsr_baseline_x2 --reset
#python main.py --model EDSR --scale 2 --patch_size 96 --save edsr_baseline_x2 --reset --data_train DIV2K+DIV2K-Q75 --data_test DIV2K+DIV2K-Q75 #python main.py --model EDSR --scale 2 --patch_size 96 --save edsr_baseline_x2 --reset --data_train DIV2K+DIV2K-Q75 --data_test DIV2K+DIV2K-Q75
# EDSR baseline model (x3) - from EDSR baseline model (x2) # EDSR baseline model (x3) - from EDSR baseline model (x2)
......
src/trainer.py
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...@@ -26,7 +26,6 @@ class Trainer(): ...@@ -26,7 +26,6 @@ class Trainer():
self.error_last = 1e8 self.error_last = 1e8
def train(self): def train(self):
self.optimizer.schedule()
self.loss.step() self.loss.step()
epoch = self.optimizer.get_last_epoch() + 1 epoch = self.optimizer.get_last_epoch() + 1
lr = self.optimizer.get_lr() lr = self.optimizer.get_lr()
...@@ -38,13 +37,15 @@ class Trainer(): ...@@ -38,13 +37,15 @@ class Trainer():
self.model.train() self.model.train()
timer_data, timer_model = utility.timer(), utility.timer() timer_data, timer_model = utility.timer(), utility.timer()
for batch, (lr, hr, _, idx_scale) in enumerate(self.loader_train): # TEMP
self.loader_train.dataset.set_scale(0)
for batch, (lr, hr, _,) in enumerate(self.loader_train):
lr, hr = self.prepare(lr, hr) lr, hr = self.prepare(lr, hr)
timer_data.hold() timer_data.hold()
timer_model.tic() timer_model.tic()
self.optimizer.zero_grad() self.optimizer.zero_grad()
sr = self.model(lr, idx_scale) sr = self.model(lr, 0)
loss = self.loss(sr, hr) loss = self.loss(sr, hr)
loss.backward() loss.backward()
if self.args.gclip > 0: if self.args.gclip > 0:
...@@ -68,11 +69,12 @@ class Trainer(): ...@@ -68,11 +69,12 @@ class Trainer():
self.loss.end_log(len(self.loader_train)) self.loss.end_log(len(self.loader_train))
self.error_last = self.loss.log[-1, -1] self.error_last = self.loss.log[-1, -1]
self.optimizer.schedule()
def test(self): def test(self):
torch.set_grad_enabled(False) torch.set_grad_enabled(False)
epoch = self.optimizer.get_last_epoch() + 1 epoch = self.optimizer.get_last_epoch()
self.ckp.write_log('\nEvaluation:') self.ckp.write_log('\nEvaluation:')
self.ckp.add_log( self.ckp.add_log(
torch.zeros(1, len(self.loader_test), len(self.scale)) torch.zeros(1, len(self.loader_test), len(self.scale))
...@@ -84,7 +86,7 @@ class Trainer(): ...@@ -84,7 +86,7 @@ class Trainer():
for idx_data, d in enumerate(self.loader_test): for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale): for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale) d.dataset.set_scale(idx_scale)
for lr, hr, filename, _ in tqdm(d, ncols=80): for lr, hr, filename in tqdm(d, ncols=80):
lr, hr = self.prepare(lr, hr) lr, hr = self.prepare(lr, hr)
sr = self.model(lr, idx_scale) sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range) sr = utility.quantize(sr, self.args.rgb_range)
......
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