198 lines
6.5 KiB
Python
198 lines
6.5 KiB
Python
import sys
|
|
sys.path.append('core')
|
|
|
|
from PIL import Image
|
|
import argparse
|
|
import os
|
|
import time
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import matplotlib.pyplot as plt
|
|
|
|
import datasets
|
|
from utils import flow_viz
|
|
from utils import frame_utils
|
|
|
|
from raft import RAFT
|
|
from utils.utils import InputPadder, forward_interpolate
|
|
|
|
|
|
@torch.no_grad()
|
|
def create_sintel_submission(model, iters=32, warm_start=False, output_path='sintel_submission'):
|
|
""" Create submission for the Sintel leaderboard """
|
|
model.eval()
|
|
for dstype in ['clean', 'final']:
|
|
test_dataset = datasets.MpiSintel(split='test', aug_params=None, dstype=dstype)
|
|
|
|
flow_prev, sequence_prev = None, None
|
|
for test_id in range(len(test_dataset)):
|
|
image1, image2, (sequence, frame) = test_dataset[test_id]
|
|
if sequence != sequence_prev:
|
|
flow_prev = None
|
|
|
|
padder = InputPadder(image1.shape)
|
|
image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
|
|
|
|
flow_low, flow_pr = model(image1, image2, iters=iters, flow_init=flow_prev, test_mode=True)
|
|
flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
|
|
|
|
if warm_start:
|
|
flow_prev = forward_interpolate(flow_low[0])[None].cuda()
|
|
|
|
output_dir = os.path.join(output_path, dstype, sequence)
|
|
output_file = os.path.join(output_dir, 'frame%04d.flo' % (frame+1))
|
|
|
|
if not os.path.exists(output_dir):
|
|
os.makedirs(output_dir)
|
|
|
|
frame_utils.writeFlow(output_file, flow)
|
|
sequence_prev = sequence
|
|
|
|
|
|
@torch.no_grad()
|
|
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].cuda(), image2[None].cuda())
|
|
|
|
_, 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)
|
|
|
|
|
|
@torch.no_grad()
|
|
def validate_chairs(model, iters=24):
|
|
""" Perform evaluation on the FlyingChairs (test) split """
|
|
model.eval()
|
|
epe_list = []
|
|
|
|
val_dataset = datasets.FlyingChairs(split='validation')
|
|
for val_id in range(len(val_dataset)):
|
|
image1, image2, flow_gt, _ = val_dataset[val_id]
|
|
image1 = image1[None].cuda()
|
|
image2 = image2[None].cuda()
|
|
|
|
_, flow_pr = model(image1, image2, iters=iters, test_mode=True)
|
|
epe = torch.sum((flow_pr[0].cpu() - flow_gt)**2, dim=0).sqrt()
|
|
epe_list.append(epe.view(-1).numpy())
|
|
|
|
epe = np.mean(np.concatenate(epe_list))
|
|
print("Validation Chairs EPE: %f" % epe)
|
|
return {'chairs': epe}
|
|
|
|
|
|
@torch.no_grad()
|
|
def validate_sintel(model, iters=32):
|
|
""" Peform validation using the Sintel (train) split """
|
|
model.eval()
|
|
results = {}
|
|
for dstype in ['clean', 'final']:
|
|
val_dataset = datasets.MpiSintel(split='training', dstype=dstype)
|
|
epe_list = []
|
|
|
|
for val_id in range(len(val_dataset)):
|
|
image1, image2, flow_gt, _ = val_dataset[val_id]
|
|
image1 = image1[None].cuda()
|
|
image2 = image2[None].cuda()
|
|
|
|
padder = InputPadder(image1.shape)
|
|
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()
|
|
|
|
epe = torch.sum((flow - flow_gt)**2, dim=0).sqrt()
|
|
epe_list.append(epe.view(-1).numpy())
|
|
|
|
epe_all = np.concatenate(epe_list)
|
|
epe = np.mean(epe_all)
|
|
px1 = np.mean(epe_all<1)
|
|
px3 = np.mean(epe_all<3)
|
|
px5 = np.mean(epe_all<5)
|
|
|
|
print("Validation (%s) EPE: %f, 1px: %f, 3px: %f, 5px: %f" % (dstype, epe, px1, px3, px5))
|
|
results[dstype] = np.mean(epe_list)
|
|
|
|
return results
|
|
|
|
|
|
@torch.no_grad()
|
|
def validate_kitti(model, iters=24):
|
|
""" Peform validation using the KITTI-2015 (train) split """
|
|
model.eval()
|
|
val_dataset = datasets.KITTI(split='training')
|
|
|
|
out_list, epe_list = [], []
|
|
for val_id in range(len(val_dataset)):
|
|
image1, image2, flow_gt, valid_gt = val_dataset[val_id]
|
|
image1 = image1[None].cuda()
|
|
image2 = image2[None].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()
|
|
|
|
epe = torch.sum((flow - flow_gt)**2, dim=0).sqrt()
|
|
mag = torch.sum(flow_gt**2, dim=0).sqrt()
|
|
|
|
epe = epe.view(-1)
|
|
mag = mag.view(-1)
|
|
val = valid_gt.view(-1) >= 0.5
|
|
|
|
out = ((epe > 3.0) & ((epe/mag) > 0.05)).float()
|
|
epe_list.append(epe[val].mean().item())
|
|
out_list.append(out[val].cpu().numpy())
|
|
|
|
epe_list = np.array(epe_list)
|
|
out_list = np.concatenate(out_list)
|
|
|
|
epe = np.mean(epe_list)
|
|
f1 = 100 * np.mean(out_list)
|
|
|
|
print("Validation KITTI: %f, %f" % (epe, f1))
|
|
return {'kitti-epe': epe, 'kitti-f1': f1}
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--model', help="restore checkpoint")
|
|
parser.add_argument('--dataset', help="dataset for evaluation")
|
|
parser.add_argument('--small', action='store_true', help='use small model')
|
|
parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
|
|
parser.add_argument('--alternate_corr', action='store_true', help='use efficent correlation implementation')
|
|
args = parser.parse_args()
|
|
|
|
model = torch.nn.DataParallel(RAFT(args))
|
|
model.load_state_dict(torch.load(args.model))
|
|
|
|
model.cuda()
|
|
model.eval()
|
|
|
|
# create_sintel_submission(model.module, warm_start=True)
|
|
# create_kitti_submission(model.module)
|
|
|
|
with torch.no_grad():
|
|
if args.dataset == 'chairs':
|
|
validate_chairs(model.module)
|
|
|
|
elif args.dataset == 'sintel':
|
|
validate_sintel(model.module)
|
|
|
|
elif args.dataset == 'kitti':
|
|
validate_kitti(model.module)
|
|
|
|
|