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import os
import os.path as osp
import numpy as np
import numpy.random as npr
import torch
import torch.distributed as dist
import torchvision.transforms as tvtrans
import PIL.Image
PIL.Image.MAX_IMAGE_PIXELS = None
import math
import json
import copy
import pickle
from multiprocessing import shared_memory
import time
from .common import *
from ..log_service import print_log
from lib import visual_service as vis
from .. import sync
import webdataset as wds
###################################################
# this is a special ds that use webdataset mainly #
###################################################
@regdataset()
class laion2b_dummy(ds_base):
def init_load_info(self):
self.load_info = []
@regdataset()
class laion2b_webdataset(ds_base):
def init_load_info(self):
self.load_info = []
def make_loader(self, batch_size, num_workers, train=True):
cfg = self.cfg
self.root_dir = cfg.root_dir
interpolation_mode = tvtrans.InterpolationMode.BICUBIC
if train:
trans = [
tvtrans.Resize(cfg.scale, interpolation=interpolation_mode),
tvtrans.RandomCrop(cfg.scale),
tvtrans.ToTensor(),]
else:
trans = [
tvtrans.Resize(cfg.scale, interpolation=interpolation_mode),
tvtrans.CenterCrop(cfg.scale),
tvtrans.ToTensor(),]
trans = tvtrans.Compose(trans)
trans_dict = {'jpg': trans}
postprocess = customized_postprocess
shuffle = cfg.get('shuffle', 10000)
shardshuffle = shuffle > 0
node_world_size = sync.get_world_size('node')
nodesplitter = wds.shardlists.split_by_node \
if node_world_size==1 else wds.shardlists.single_node_only
tars = [osp.join(self.root_dir, 'data', i) for i in os.listdir(osp.join(self.root_dir, 'data'))
if osp.splitext(i)[1]=='.tar']
tars = sorted(tars)
dset = wds.WebDataset(
tars,
nodesplitter=nodesplitter,
shardshuffle=shardshuffle,
handler=wds.warn_and_continue).repeat().shuffle(shuffle)
print_log(f'Loading webdataset with {len(dset.pipeline[0].urls)} shards.')
self.min_size = cfg.get('min_size', None)
self.max_pwatermark = cfg.get('max_pwatermark', None)
dset = (dset
.select(self.filter_keys)
.decode('pil', handler=wds.warn_and_continue)
.select(self.filter_size)
.map_dict(**trans_dict, handler=wds.warn_and_continue))
if postprocess is not None:
dset = dset.map(postprocess)
dset.batched(batch_size, partial=False)
loader = wds.WebLoader(
dset,
batch_size=None,
shuffle=False,
num_workers=num_workers, )
return loader
def filter_size(self, x):
try:
valid = True
if self.min_size is not None and self.min_size > 1:
try:
valid = valid and x['json']['original_width'] >= self.min_size and \
x['json']['original_height'] >= self.min_size
except Exception:
valid = False
if self.max_pwatermark is not None and self.max_pwatermark < 1.0:
try:
valid = valid and x['json']['pwatermark'] <= self.max_pwatermark
except Exception:
valid = False
return valid
except Exception:
return False
def filter_keys(self, x):
try:
return ("jpg" in x) and ("txt" in x)
except Exception:
return False
def train_dataloader(self):
return self.make_loader(self.train)
def val_dataloader(self):
return self.make_loader(self.validation, train=False)
def test_dataloader(self):
return self.make_loader(self.test, train=False)
def customized_postprocess(element):
return element['jpg']*2-1, element['txt'], element['__key__']
def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
keys = set.intersection(*[set(sample.keys()) for sample in samples])
batched = {key: [] for key in keys}
for s in samples:
[batched[key].append(s[key]) for key in batched]
result = {}
for key in batched:
if isinstance(batched[key][0], (int, float)):
if combine_scalars:
result[key] = np.array(list(batched[key]))
elif isinstance(batched[key][0], torch.Tensor):
if combine_tensors:
result[key] = torch.stack(list(batched[key]))
elif isinstance(batched[key][0], np.ndarray):
if combine_tensors:
result[key] = np.array(list(batched[key]))
else:
result[key] = list(batched[key])
return result
###################
# for sd official #
###################
def customized_postprocess_sdofficial(element):
return {
'jpg': element['jpg']*2-1,
'txt': element['txt'], }
@regdataset()
class laion2b_webdataset_sdofficial(laion2b_webdataset):
def make_loader(self, batch_size, num_workers, train=True):
cfg = self.cfg
self.root_dir = cfg.root_dir
interpolation_mode = tvtrans.InterpolationMode.BICUBIC
if train:
trans = [
tvtrans.Resize(cfg.scale, interpolation=interpolation_mode),
tvtrans.RandomCrop(cfg.scale),
tvtrans.ToTensor(),]
else:
trans = [
tvtrans.Resize(cfg.scale, interpolation=interpolation_mode),
tvtrans.CenterCrop(cfg.scale),
tvtrans.ToTensor(),]
trans = tvtrans.Compose(trans)
trans_dict = {'jpg': trans}
postprocess = customized_postprocess_sdofficial
shuffle = 10000
shardshuffle = shuffle > 0
node_world_size = 1
nodesplitter = wds.shardlists.split_by_node \
if node_world_size==1 else wds.shardlists.single_node_only
tars = [osp.join(self.root_dir, 'data', i) for i in os.listdir(osp.join(self.root_dir, 'data'))
if osp.splitext(i)[1]=='.tar']
tars = sorted(tars)
dset = wds.WebDataset(
tars,
nodesplitter=nodesplitter,
shardshuffle=shardshuffle,
handler=wds.warn_and_continue).repeat().shuffle(shuffle)
print(f'Loading webdataset with {len(dset.pipeline[0].urls)} shards.')
self.min_size = cfg.get('min_size', None)
self.max_pwatermark = cfg.get('max_pwatermark', None)
dset = (dset
.select(self.filter_keys)
.decode('pil', handler=wds.warn_and_continue)
.select(self.filter_size)
.map_dict(**trans_dict, handler=wds.warn_and_continue))
if postprocess is not None:
dset = dset.map(postprocess)
dset.batched(batch_size, partial=False, collation_fn=dict_collation_fn)
loader = wds.WebLoader(
dset,
batch_size=None,
shuffle=False,
num_workers=num_workers, )
return loader
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