def validate_kitti_colorjitter(gpu, model, args, ngpus_per_node, eval_entries, iters=24):
""" Peform validation using the KITTI-2015 (train) split """
interval = np.floor(len(eval_entries) / ngpus_per_node).astype(np.int).item()
if gpu == ngpus_per_node - 1:
stidx = int(interval * gpu)
edidx = len(eval_entries)
else:
stidx = int(interval * gpu)
edidx = int(interval * (gpu + 1))
print("Initialize Instance on Gpu %d, from %d to %d, total %d" % (gpu, stidx, edidx, len(eval_entries)))
from tqdm import tqdm
model.eval()
model.cuda(gpu)
with torch.no_grad():
for val_id, entry in enumerate(tqdm(remove_dup(eval_entries[stidx : edidx]))):
seq, index = entry.split(' ')
index = int(index)
if os.path.exists(os.path.join(args.dataset, seq, 'image_02', 'data', "{}.png".format(str(index).zfill(10)))):
tmproot = args.dataset
else:
tmproot = args.odom_root
img1path = os.path.join(tmproot, seq, 'image_02', 'data', "{}.png".format(str(index).zfill(10)))
img2path = os.path.join(tmproot, seq, 'image_02', 'data', "{}.png".format(str(index + 1).zfill(10)))
if not os.path.exists(img2path):
img2path = img1path
image1 = frame_utils.read_gen(img1path)
image2 = frame_utils.read_gen(img2path)
image1 = np.array(image1).astype(np.uint8)
image2 = np.array(image2).astype(np.uint8)
image1 = torch.from_numpy(image1).permute([2, 0, 1]).float()
image2 = torch.from_numpy(image2).permute([2, 0, 1]).float()
svfold = os.path.join(args.exportroot, seq, 'image_02')
svpath = os.path.join(args.exportroot, seq, 'image_02', "{}.png".format(str(index).zfill(10)))
os.makedirs(svfold, exist_ok=True)
image1 = image1[None].cuda(gpu)
image2 = image2[None].cuda(gpu)
padder = InputPadder(image1.shape, mode='kitti')
image1, image2 = padder.pad(image1, image2)
flow_low, flow_pr = model(image1, image2, iters=iters, test_mode=True)
flow = padder.unpad(flow_pr[0]).cpu()
frame_utils.writeFlowKITTI(svpath, flow.permute(1, 2, 0).numpy())
# Image.fromarray(flow_viz.flow_to_image(flow.permute(1, 2, 0).numpy())).show()
# tensor2rgb(image1 / 255.0, viewind=0).show()
return
def create_kitti_submission(model, iters=24, output_path='kitti_submission'):
""" Create submission for the Sintel leaderboard """
model.eval()
test_dataset = datasets.KITTI(split='testing', aug_params=None)
if not os.path.exists(output_path):
os.makedirs(output_path)
for test_id in range(len(test_dataset)):
image1, image2, (frame_id, ) = test_dataset[test_id]
padder = InputPadder(image1.shape, mode='kitti')
image1, image2 = padder.pad(image1[None].to(DEVICE),
image2[None].to(DEVICE))
_, flow_pr = model(image1, image2, iters=iters, test_mode=True)
flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
output_filename = os.path.join(output_path, frame_id)
frame_utils.writeFlowKITTI(output_filename, flow)
def validate_kitti_colorjitter(model, args, iters=24):
""" Peform validation using the KITTI-2015 (train) split """
from tqdm import tqdm
model.eval()
expandentries = read_splits_mapping(args)
for val_id, entry in enumerate(tqdm(expandentries)):
seq, idx, _ = entry.split(' ')
idx = int(idx)
img1path = os.path.join(args.dataset, seq, 'image_02', 'data', "{}.png".format(str(idx).zfill(10)))
img2path = os.path.join(args.dataset, seq, 'image_02', 'data', "{}.png".format(str(idx + 1).zfill(10)))
if not os.path.exists(img2path):
img2path = img1path
img1 = np.array(Image.open(img1path)).astype(np.float32)
img2 = np.array(Image.open(img2path)).astype(np.float32)
img1 = torch.from_numpy(img1).permute([2, 0, 1]).unsqueeze(0)
img2 = torch.from_numpy(img2).permute([2, 0, 1]).unsqueeze(0)
svfold = os.path.join(args.exportroot, seq, 'image_02')
svpath = os.path.join(args.exportroot, seq, 'image_02', "{}.png".format(str(idx).zfill(10)))
os.makedirs(svfold, exist_ok=True)
image1 = img1.cuda()
image2 = img2.cuda()
padder = InputPadder(image1.shape, mode='kitti')
image1, image2 = padder.pad(image1, image2)
flow_low, flow_pr = model(image1, image2, iters=iters, test_mode=True)
flow = padder.unpad(flow_pr[0]).cpu()
frame_utils.writeFlowKITTI(svpath, flow.permute(1, 2, 0).numpy())
return
def validate_kitti_colorjitter(gpu,
model,
args,
ngpus_per_node,
eval_entries,
iters=24):
""" Peform validation using the KITTI-2015 (train) split """
interval = np.floor(len(eval_entries) / ngpus_per_node).astype(
np.int).item()
if gpu == ngpus_per_node - 1:
stidx = int(interval * gpu)
edidx = len(eval_entries)
else:
stidx = int(interval * gpu)
edidx = int(interval * (gpu + 1))
print("Initialize Instance on Gpu %d, from %d to %d, total %d" %
(gpu, stidx, edidx, len(eval_entries)))
from tqdm import tqdm
model.eval()
model.cuda(gpu)
with torch.no_grad():
for val_id, entry in enumerate(
tqdm(remove_dup(eval_entries[stidx:edidx]))):
seq, index = entry.split(' ')
index = int(index)
img1path = os.path.join(args.dataset, seq,
'rgb_{}.png'.format(str(index).zfill(5)))
img2path = os.path.join(
args.dataset, seq,
'rgb_{}.png'.format(str(index + 1).zfill(5)))
if not os.path.exists(img2path) and not os.path.exists(
img2path.replace('.png', '.jpg')):
img2path = img1path
if not os.path.exists(img1path):
img1path = img1path.replace('.png', '.jpg')
img2path = img2path.replace('.png', '.jpg')
image1 = frame_utils.read_gen(img1path)
image2 = frame_utils.read_gen(img2path)
image1 = np.array(image1).astype(np.uint8)
image2 = np.array(image2).astype(np.uint8)
image1 = torch.from_numpy(image1).permute([2, 0, 1]).float()
image2 = torch.from_numpy(image2).permute([2, 0, 1]).float()
svfold = os.path.join(
args.exportroot,
seq,
)
svpath = os.path.join(args.exportroot, seq,
'{}.png'.format(str(index).zfill(5)))
os.makedirs(svfold, exist_ok=True)
image1 = image1[None].cuda(gpu)
image2 = image2[None].cuda(gpu)
flow_low, flow_pr = model(image1,
image2,
iters=iters,
test_mode=True)
flow = flow_pr.squeeze(0)
flow_numpy = flow.cpu().permute(1, 2, 0).numpy()
frame_utils.writeFlowKITTI(svpath, flow_numpy)
if args.dovls:
vlsflow = Image.fromarray(flow_viz.flow_to_image(flow_numpy))
vlsrgb1 = tensor2rgb(image1 / 255.0, viewind=0)
vlsrgb2 = tensor2rgb(image2 / 255.0, viewind=0)
w, h = vlsrgb2.size
xx, yy = np.meshgrid(range(w), range(h), indexing='xy')
xxf = xx.flatten()
yyf = yy.flatten()
rndidx = np.random.randint(0, xxf.shape[0], 100)
xxf_rnd = xxf[rndidx]
yyf_rnd = yyf[rndidx]
flowx = flow_numpy[yyf_rnd, xxf_rnd, 0]
flowy = flow_numpy[yyf_rnd, xxf_rnd, 1]
fig = plt.figure(figsize=(12, 9))
plt.subplot(2, 1, 1)
plt.scatter(xxf_rnd, yyf_rnd, 1, 'r')
plt.imshow(vlsrgb1)
plt.subplot(2, 1, 2)
plt.scatter(flowx + xxf_rnd, flowy + yyf_rnd, 1, 'r')
plt.imshow(vlsrgb2)
plt.savefig(
os.path.join(
"/media/shengjie/disk1/Prediction/nyuv2_flow_vls",
"{}_{}.jpg".format(seq,
str(index).zfill(5))))
plt.close()
# combined_left = np.concatenate([np.array(vlsrgb1), np.array(vlsrgb2)], axis=0)
# combined_right = np.concatenate([np.array(vlsflow), np.array(vlsflow)], axis=0)
# combined = np.concatenate([combined_left, combined_right], axis=1)
# vls_sv_root = os.makedirs("/media/shengjie/disk1/Prediction/nyuv2_flow_vls", exist_ok=True)
# Image.fromarray(combined).save(os.path.join("/media/shengjie/disk1/Prediction/nyuv2_flow_vls", "{}_{}.jpg".format(seq, str(index).zfill(5))))
return