RAFT/evaluate.py
2020-03-26 23:19:08 -04:00

101 lines
3.1 KiB
Python

import sys
sys.path.append('core')
from PIL import Image
import cv2
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 raft import RAFT
def validate_sintel(args, model, iters=50):
""" Evaluate trained model on Sintel(train) clean + final passes """
model.eval()
pad = 2
for dstype in ['clean', 'final']:
val_dataset = datasets.MpiSintel(args, do_augument=False, dstype=dstype)
epe_list = []
for i in range(len(val_dataset)):
image1, image2, flow_gt, _ = val_dataset[i]
image1 = image1[None].cuda()
image2 = image2[None].cuda()
image1 = F.pad(image1, [0, 0, pad, pad], mode='replicate')
image2 = F.pad(image2, [0, 0, pad, pad], mode='replicate')
with torch.no_grad():
flow_predictions = model.module(image1, image2, iters=iters)
flow_pr = flow_predictions[-1][0,:,pad:-pad]
epe = torch.sum((flow_pr - flow_gt.cuda())**2, dim=0)
epe = torch.sqrt(epe).mean()
epe_list.append(epe.item())
print("Validation (%s) EPE: %f" % (dstype, np.mean(epe_list)))
def validate_kitti(args, model, iters=32):
""" Evaluate trained model on KITTI (train) """
model.eval()
val_dataset = datasets.KITTI(args, do_augument=False, is_val=True, do_pad=True)
with torch.no_grad():
epe_list, out_list = [], []
for i in range(len(val_dataset)):
image1, image2, flow_gt, valid_gt = val_dataset[i]
image1 = image1[None].cuda()
image2 = image2[None].cuda()
flow_gt = flow_gt.cuda()
valid_gt = valid_gt.cuda()
flow_predictions = model.module(image1, image2, iters=iters)
flow_pr = flow_predictions[-1][0]
epe = torch.sum((flow_pr - 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)
print("Validation KITTI: %f, %f" % (np.mean(epe_list), 100*np.mean(out_list)))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model', help="restore checkpoint")
parser.add_argument('--small', action='store_true', help='use small model')
parser.add_argument('--sintel_iters', type=int, default=50)
parser.add_argument('--kitti_iters', type=int, default=32)
args = parser.parse_args()
model = RAFT(args)
model = torch.nn.DataParallel(model)
model.load_state_dict(torch.load(args.model))
model.to('cuda')
model.eval()
validate_sintel(args, model, args.sintel_iters)
validate_kitti(args, model, args.kitti_iters)