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import numpy as np
import torch
import torch.utils.data as data
import torch.nn.functional as F
import os
import math
import random
from glob import glob
import os.path as osp
from core.utils import frame_utils
from core.utils.augmentor import FlowAugmentor, SparseFlowAugmentor
import sys
class FlowDataset(data.Dataset):
def __init__(self, aug_params=None, sparse=False):
self.augmentor = None
self.sparse = sparse
if aug_params is not None:
if sparse:
self.augmentor = SparseFlowAugmentor(**aug_params)
else:
self.augmentor = FlowAugmentor(**aug_params)
self.is_test = False
self.init_seed = False
self.flow_list = []
self.image_list = []
self.extra_info = []
self.occ_list = []
self.line_list = []
def __getitem__(self, index):
# print('Index is {}'.format(index))
# sys.stdout.flush()
index = index % len(self.image_list)
if self.is_test:
self.augmentor = None
valid = None
flow = frame_utils.read_gen(self.flow_list[index])
img1 = frame_utils.read_gen(self.image_list[index][0])
img2 = frame_utils.read_gen(self.image_list[index][1])
flow = np.array(flow).astype(np.float32)
img1 = np.array(img1).astype(np.uint8)
img2 = np.array(img2).astype(np.uint8)
# grayscale images
if len(img1.shape) == 2:
img1 = np.tile(img1[...,None], (1, 1, 3))
else:
img1 = img1[..., :3]
if len(img2.shape) == 2:
img2 = np.tile(img2[...,None], (1, 1, 3))
else:
img2 = img2[..., :3]
if self.augmentor is not None:
img1, img2, flow = self.augmentor(img1, img2, flow)
img1 = torch.from_numpy(img1).permute(2, 0, 1).float()
img2 = torch.from_numpy(img2).permute(2, 0, 1).float()
flow = torch.from_numpy(flow).permute(2, 0, 1).float()
if valid is not None:
valid = torch.from_numpy(valid)
else:
valid = (flow[0].abs() < 1000) & (flow[1].abs() < 1000)
if self.is_test:
occ = torch.from_numpy(np.load(self.occ_list[index]))
line = torch.from_numpy(np.load(self.line_list[index]))
return img1, img2, flow, occ, line, self.extra_info[index], valid.float()
else:
return img1, img2, flow, valid.float()
def __rmul__(self, v):
self.flow_list = v * self.flow_list
self.image_list = v * self.image_list
return self
def __len__(self):
return len(self.image_list)
class MpiSintel(FlowDataset):
def __init__(self, aug_params=None, split='training', root='/mnt/lustre/syli/AnimeRun/flow/data/Sintel', dstype='clean'):
super(MpiSintel, self).__init__(aug_params)
flow_root = osp.join(root, split, 'flow')
image_root = osp.join(root, split, dstype)
if split == 'test':
self.is_test = True
for scene in os.listdir(image_root):
image_list = sorted(glob(osp.join(image_root, scene, '*.png')))
for i in range(len(image_list)-1):
self.image_list += [ [image_list[i], image_list[i+1]] ]
self.extra_info += [ (scene, i) ] # scene and frame_id
if split != 'test':
self.flow_list += sorted(glob(osp.join(flow_root, scene, '*.flo')))
class AnimeRun(FlowDataset):
def __init__(self, aug_params=None, split='train', root='/mnt/lustre/syli/AnimeRun/flow/data/AnimeRun_v2', dstype='Frame_Anime/original'):
super(AnimeRun, self).__init__(aug_params)
# root = root + subset
flow_root = osp.join(root, split, 'Flow')
image_root = osp.join(root, split, dstype)
unmatch_root = osp.join(root, split, 'UnmatchedForward')
line_root = osp.join(root, split, 'LineArea')
if split == 'test':
self.is_test = True
for scene in os.listdir(image_root):
for color_pass in os.listdir(osp.join(image_root, scene)):
if color_pass != 'original':
continue
image_list = sorted(glob(osp.join(image_root, scene, color_pass, '*.png')))
for i in range(len(image_list)-1):
self.image_list += [ [image_list[i], image_list[i+1]] ]
self.extra_info += [ (scene, i) ] # scene and frame_id
self.occ_list += sorted(glob(osp.join(unmatch_root, scene, '*.npy')))
self.flow_list += sorted(glob(osp.join(flow_root, scene, 'forward', '*.flo')))
self.line_list += [ path.replace(unmatch_root, line_root) for path in sorted(glob(osp.join(unmatch_root, scene, '*.npy')))]
print('Len of Flow is ', len(self.flow_list))
print('Len of Anime is ', len(self.image_list))
print('Len of Occlusion is ', len(self.occ_list))
print('Len of Line Area is ', len(self.line_list))
class FlyingThings3D(FlowDataset):
def __init__(self, aug_params=None, root='/mnt/lustre/syli/AnimeRun/flow/data/FlyingThings', dstype='frames_cleanpass'):
super(FlyingThings3D, self).__init__(aug_params)
for cam in ['left']:
for direction in ['into_future', 'into_past']:
image_dirs = sorted(glob(osp.join(root, dstype, 'TRAIN/*/*')))
image_dirs = sorted([osp.join(f, cam) for f in image_dirs])
flow_dirs = sorted(glob(osp.join(root, 'optical_flow/TRAIN/*/*')))
flow_dirs = sorted([osp.join(f, direction, cam) for f in flow_dirs])
for idir, fdir in zip(image_dirs, flow_dirs):
images = sorted(glob(osp.join(idir, '*.png')) )
flows = sorted(glob(osp.join(fdir, '*.pfm')) )
for i in range(len(flows)-1):
if direction == 'into_future':
self.image_list += [ [images[i], images[i+1]] ]
self.flow_list += [ flows[i] ]
elif direction == 'into_past':
self.image_list += [ [images[i+1], images[i]] ]
self.flow_list += [ flows[i+1] ]
class CreativeFlow(FlowDataset):
def __init__(self, aug_params=None, split='train', root='/mnt/lustre/syli/AnimeRun/flow/data/CreativeFlow/decompressed'):
super(CreativeFlow, self).__init__(aug_params)
flow_root = osp.join(root, split)
image_root = osp.join(root, split)
for scene in os.listdir(image_root):
if split == 'test':
render_folder = os.listdir(osp.join(image_root, scene, 'cam0', 'renders', 'composite'))[0]
image_list = sorted(glob(osp.join(image_root, scene, 'cam0', 'renders', 'composite', render_folder, '*.png')))
flow_list = sorted(glob(osp.join(flow_root, scene, 'cam0', 'metadata', 'flow', '*.flo')))
else:
render_folder = os.listdir(osp.join(image_root, scene, 'renders', 'composite'))[0]
image_list = sorted(glob(osp.join(image_root, scene, 'renders', 'composite', render_folder, '*.png')))
flow_list = sorted(glob(osp.join(flow_root, scene, 'metadata', 'flow', '*.flo')))
if len(image_list)-1 == len(flow_list):
for i in range(len(image_list)-1):
self.image_list += [ [image_list[i], image_list[i+1]] ]
self.extra_info += [ (scene, i) ] # scene and frame_id
self.flow_list += [ flow_list[i] ]
print(len(self.image_list), len(self.extra_info), len(self.flow_list))
def fetch_dataloader(args, TRAIN_DS='C+T+K/S'):
""" Create the data loader for the corresponding trainign set """
if args.stage == 'sintel':
print('Training Sintel Stage...')
sys.stdout.flush()
aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.6, 'do_flip': True}
things = FlyingThings3D(aug_params, dstype='frames_cleanpass')
sintel_clean = MpiSintel(aug_params, split='training', dstype='clean')
sintel_final = MpiSintel(aug_params, split='training', dstype='final')
elif args.stage == 'anime':
print('Training AnimeRun Stage...')
sys.stdout.flush()
aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.6, 'do_flip': True}
things = FlyingThings3D(aug_params, dstype='frames_cleanpass')
anime_run = 200 * AnimeRun(aug_params, split='train', dstype='Frame_Anime')
train_dataset = anime_run + things
elif args.stage == 'creative':
print('Training Using Creative Flow...')
sys.stdout.flush()
aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.6, 'do_flip': True}
things = FlyingThings3D(aug_params, dstype='frames_cleanpass')
creative = CreativeFlow(aug_params, split='test')
train_dataset = 20*creative + things
train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size,
pin_memory=False, shuffle=True, num_workers=4, drop_last=True)
print('Training with %d image pairs' % len(train_dataset))
return train_loader
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