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import numpy as np |
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import torch |
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import torch.utils.data as data |
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import torch.nn.functional as F |
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import os |
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import math |
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import random |
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from glob import glob |
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import os.path as osp |
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from utils import frame_utils |
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from utils.augmentor import FlowAugmentor, SparseFlowAugmentor |
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class FlowDataset(data.Dataset): |
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def __init__(self, aug_params=None, sparse=False): |
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self.augmentor = None |
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self.sparse = sparse |
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if aug_params is not None: |
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if sparse: |
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self.augmentor = SparseFlowAugmentor(**aug_params) |
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else: |
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self.augmentor = FlowAugmentor(**aug_params) |
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self.is_test = False |
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self.init_seed = False |
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self.flow_list = [] |
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self.image_list = [] |
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self.extra_info = [] |
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def __getitem__(self, index): |
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if self.is_test: |
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img1 = frame_utils.read_gen(self.image_list[index][0]) |
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img2 = frame_utils.read_gen(self.image_list[index][1]) |
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img1 = np.array(img1).astype(np.uint8)[..., :3] |
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img2 = np.array(img2).astype(np.uint8)[..., :3] |
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img1 = torch.from_numpy(img1).permute(2, 0, 1).float() |
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img2 = torch.from_numpy(img2).permute(2, 0, 1).float() |
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return img1, img2, self.extra_info[index] |
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if not self.init_seed: |
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worker_info = torch.utils.data.get_worker_info() |
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if worker_info is not None: |
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torch.manual_seed(worker_info.id) |
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np.random.seed(worker_info.id) |
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random.seed(worker_info.id) |
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self.init_seed = True |
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index = index % len(self.image_list) |
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valid = None |
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if self.sparse: |
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flow, valid = frame_utils.readFlowKITTI(self.flow_list[index]) |
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else: |
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flow = frame_utils.read_gen(self.flow_list[index]) |
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img1 = frame_utils.read_gen(self.image_list[index][0]) |
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img2 = frame_utils.read_gen(self.image_list[index][1]) |
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flow = np.array(flow).astype(np.float32) |
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img1 = np.array(img1).astype(np.uint8) |
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img2 = np.array(img2).astype(np.uint8) |
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if len(img1.shape) == 2: |
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img1 = np.tile(img1[...,None], (1, 1, 3)) |
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img2 = np.tile(img2[...,None], (1, 1, 3)) |
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else: |
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img1 = img1[..., :3] |
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img2 = img2[..., :3] |
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if self.augmentor is not None: |
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if self.sparse: |
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img1, img2, flow, valid = self.augmentor(img1, img2, flow, valid) |
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else: |
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img1, img2, flow = self.augmentor(img1, img2, flow) |
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img1 = torch.from_numpy(img1).permute(2, 0, 1).float() |
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img2 = torch.from_numpy(img2).permute(2, 0, 1).float() |
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flow = torch.from_numpy(flow).permute(2, 0, 1).float() |
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if valid is not None: |
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valid = torch.from_numpy(valid) |
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else: |
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valid = (flow[0].abs() < 1000) & (flow[1].abs() < 1000) |
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return img1, img2, flow, valid.float() |
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def __rmul__(self, v): |
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self.flow_list = v * self.flow_list |
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self.image_list = v * self.image_list |
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return self |
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def __len__(self): |
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return len(self.image_list) |
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class MpiSintel(FlowDataset): |
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def __init__(self, aug_params=None, split='training', root='datasets/Sintel', dstype='clean'): |
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super(MpiSintel, self).__init__(aug_params) |
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flow_root = osp.join(root, split, 'flow') |
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image_root = osp.join(root, split, dstype) |
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if split == 'test': |
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self.is_test = True |
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for scene in os.listdir(image_root): |
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image_list = sorted(glob(osp.join(image_root, scene, '*.png'))) |
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for i in range(len(image_list)-1): |
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self.image_list += [ [image_list[i], image_list[i+1]] ] |
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self.extra_info += [ (scene, i) ] |
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if split != 'test': |
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self.flow_list += sorted(glob(osp.join(flow_root, scene, '*.flo'))) |
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class FlyingChairs(FlowDataset): |
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def __init__(self, aug_params=None, split='train', root='datasets/FlyingChairs_release/data'): |
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super(FlyingChairs, self).__init__(aug_params) |
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images = sorted(glob(osp.join(root, '*.ppm'))) |
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flows = sorted(glob(osp.join(root, '*.flo'))) |
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assert (len(images)//2 == len(flows)) |
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split_list = np.loadtxt('chairs_split.txt', dtype=np.int32) |
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for i in range(len(flows)): |
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xid = split_list[i] |
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if (split=='training' and xid==1) or (split=='validation' and xid==2): |
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self.flow_list += [ flows[i] ] |
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self.image_list += [ [images[2*i], images[2*i+1]] ] |
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class FlyingThings3D(FlowDataset): |
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def __init__(self, aug_params=None, root='datasets/FlyingThings3D', dstype='frames_cleanpass'): |
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super(FlyingThings3D, self).__init__(aug_params) |
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for cam in ['left']: |
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for direction in ['into_future', 'into_past']: |
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image_dirs = sorted(glob(osp.join(root, dstype, 'TRAIN/*/*'))) |
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image_dirs = sorted([osp.join(f, cam) for f in image_dirs]) |
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flow_dirs = sorted(glob(osp.join(root, 'optical_flow/TRAIN/*/*'))) |
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flow_dirs = sorted([osp.join(f, direction, cam) for f in flow_dirs]) |
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for idir, fdir in zip(image_dirs, flow_dirs): |
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images = sorted(glob(osp.join(idir, '*.png')) ) |
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flows = sorted(glob(osp.join(fdir, '*.pfm')) ) |
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for i in range(len(flows)-1): |
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if direction == 'into_future': |
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self.image_list += [ [images[i], images[i+1]] ] |
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self.flow_list += [ flows[i] ] |
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elif direction == 'into_past': |
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self.image_list += [ [images[i+1], images[i]] ] |
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self.flow_list += [ flows[i+1] ] |
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class KITTI(FlowDataset): |
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def __init__(self, aug_params=None, split='training', root='datasets/KITTI'): |
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super(KITTI, self).__init__(aug_params, sparse=True) |
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if split == 'testing': |
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self.is_test = True |
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root = osp.join(root, split) |
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images1 = sorted(glob(osp.join(root, 'image_2/*_10.png'))) |
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images2 = sorted(glob(osp.join(root, 'image_2/*_11.png'))) |
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for img1, img2 in zip(images1, images2): |
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frame_id = img1.split('/')[-1] |
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self.extra_info += [ [frame_id] ] |
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self.image_list += [ [img1, img2] ] |
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if split == 'training': |
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self.flow_list = sorted(glob(osp.join(root, 'flow_occ/*_10.png'))) |
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class HD1K(FlowDataset): |
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def __init__(self, aug_params=None, root='datasets/HD1k'): |
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super(HD1K, self).__init__(aug_params, sparse=True) |
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seq_ix = 0 |
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while 1: |
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flows = sorted(glob(os.path.join(root, 'hd1k_flow_gt', 'flow_occ/%06d_*.png' % seq_ix))) |
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images = sorted(glob(os.path.join(root, 'hd1k_input', 'image_2/%06d_*.png' % seq_ix))) |
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if len(flows) == 0: |
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break |
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for i in range(len(flows)-1): |
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self.flow_list += [flows[i]] |
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self.image_list += [ [images[i], images[i+1]] ] |
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seq_ix += 1 |
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def fetch_dataloader(args, TRAIN_DS='C+T+K+S+H'): |
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""" Create the data loader for the corresponding trainign set """ |
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if args.stage == 'chairs': |
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aug_params = {'crop_size': args.image_size, 'min_scale': -0.1, 'max_scale': 1.0, 'do_flip': True} |
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train_dataset = FlyingChairs(aug_params, split='training') |
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elif args.stage == 'things': |
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aug_params = {'crop_size': args.image_size, 'min_scale': -0.4, 'max_scale': 0.8, 'do_flip': True} |
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clean_dataset = FlyingThings3D(aug_params, dstype='frames_cleanpass') |
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final_dataset = FlyingThings3D(aug_params, dstype='frames_finalpass') |
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train_dataset = clean_dataset + final_dataset |
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elif args.stage == 'sintel': |
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aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.6, 'do_flip': True} |
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things = FlyingThings3D(aug_params, dstype='frames_cleanpass') |
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sintel_clean = MpiSintel(aug_params, split='training', dstype='clean') |
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sintel_final = MpiSintel(aug_params, split='training', dstype='final') |
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if TRAIN_DS == 'C+T+K+S+H': |
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kitti = KITTI({'crop_size': args.image_size, 'min_scale': -0.3, 'max_scale': 0.5, 'do_flip': True}) |
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hd1k = HD1K({'crop_size': args.image_size, 'min_scale': -0.5, 'max_scale': 0.2, 'do_flip': True}) |
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train_dataset = 100*sintel_clean + 100*sintel_final + 200*kitti + 5*hd1k + things |
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elif TRAIN_DS == 'C+T+K/S': |
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train_dataset = 100*sintel_clean + 100*sintel_final + things |
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elif args.stage == 'kitti': |
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aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.4, 'do_flip': False} |
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train_dataset = KITTI(aug_params, split='training') |
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train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size, |
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pin_memory=False, shuffle=True, num_workers=4, drop_last=True) |
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print('Training with %d image pairs' % len(train_dataset)) |
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return train_loader |
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