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