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evaluate_mf.py
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import sys
sys.path.append('core')
import argparse
import numpy as np
import torch
from tqdm import tqdm
import os
import cv2
import datasets
import mf_datasets
from models import *
from utils import flow_viz
from utils import frame_utils
from utils.utils import InputPadder, forward_interpolate
from PIL import Image
import imageio
import torch.nn.functional as F
from matplotlib import pyplot as plt
@torch.no_grad()
def create_spring_submission_mf(args, model, iters, output_path='sintel_submission', nframes=4):
""" Create submission for the Sintel leaderboard """
model.eval()
test_dataset = mf_datasets.SpringSubmission(root=os.path.join(args.spring_root, 'test'), input_frames=nframes)
for test_id in tqdm(range(len(test_dataset))):
images, (scene, direction, cam, frame_ids) = test_dataset[test_id]
images = [image[None].cuda() for image in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).permute(1, 2, 0).cpu().numpy() for flow in flows]
output_dir = os.path.join(output_path, scene, f'flow_{direction}_{cam}')
os.makedirs(output_dir, exist_ok=True)
assert len(flows) == len(frame_ids) - 1
for i in range(len(flows)):
if frame_ids[i] != -1:
output_file = os.path.join(output_dir, f'flow_{direction}_{cam}_%04d.flo5' % (frame_ids[i]))
frame_utils.writeFlo5File(flows[i], output_file)
@torch.no_grad()
def validate_spring_mf(model, iters=6, root='/data/Sintel', tqdm_miniters=1, nframes=3, split=False):
""" Peform validation using the Sintel (train) split """
model.eval()
val_dataset = mf_datasets.SpringEval(input_frames=nframes, forward_warp=False, root=os.path.join(root, 'train'), subsample_groundtruth=True, split=split)
epe_list = []
epe_list_10 = []
epe_list_10_40 = []
epe_list_40 = []
print(len(val_dataset))
for val_id in tqdm(range(len(val_dataset))):
images, flows_gt, _, (folder_name, frame_ids) = val_dataset[val_id]
valid = [~torch.isnan(torch.sum(flows_gt[i], dim=0)) for i in range(len(flows_gt))]
valid_10 = [valid[i] & (torch.sum(flows_gt[i] ** 2, dim=0).sqrt() < 10) for i in range(len(flows_gt))]
valid_10_40 = [valid[i] & (torch.sum(flows_gt[i] ** 2, dim=0).sqrt() >= 10) & (torch.sum(flows_gt[i] ** 2, dim=0).sqrt() < 40) for i in range(len(flows_gt))]
valid_40 = [valid[i] & (torch.sum(flows_gt[i] ** 2, dim=0).sqrt() >= 40) for i in range(len(flows_gt))]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
for i in range(len(flows)):
epe = torch.sum((flows[i] - flows_gt[i])**2, dim=0).sqrt()
if frame_ids[i] != -1:
epe_list.append(epe.view(-1).numpy())
epe_list_10.append(epe.view(-1)[valid_10[i].view(-1)].numpy())
epe_list_10_40.append(epe.view(-1)[valid_10_40[i].view(-1)].numpy())
epe_list_40.append(epe.view(-1)[valid_40[i].view(-1)].numpy())
# print(val_id, frame_ids, epe.mean().item())
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)
px1_x = np.mean(epe_all > 1)
px1_10 = np.mean(np.concatenate(epe_list_10) > 1)
px1_10_40 = np.mean(np.concatenate(epe_list_10_40) > 1)
px1_40 = np.mean(np.concatenate(epe_list_40) > 1)
print("Validation EPE: %f, 1px: %f, 3px: %f, 5px: %f" % (epe, px1, px3, px5))
print("Spring 1px: %f, 1px(s0~10): %f, 1px(s10~40): %f, 1px(s40+): %f" % (px1_x, px1_10, px1_10_40, px1_40))
results = np.mean(epe_list)
return results
@torch.no_grad()
def validate_kitti_mf(model, iters=6, multi_root=None, nframes=3):
""" Peform validation using the KITTI-2015 (train) split """
model.eval()
val_dataset = mf_datasets.KITTIMultiFrameEval(split='training', multi_root=multi_root, nframes=nframes)
out_list, epe_list = [], []
for val_id in tqdm(range(len(val_dataset))):
images, flow_gts, valids, frame_names = val_dataset[val_id]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape, mode='kitti')
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
# print(flow_gts)
for i in range(len(flow_gts)):
if flow_gts[i] is not None:
epe = torch.sum((flows[i] - flow_gts[i])**2, dim=0).sqrt()
mag = torch.sum(flow_gts[i]**2, dim=0).sqrt()
epe = epe.view(-1)
mag = mag.view(-1)
val = valids[i].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}
def EPE(flow, flow_gt):
return torch.sum((flow - flow_gt)**2, dim=0).sqrt()
def patch_EPE(flow, flow_gt):
return torch.sum(flow - flow_gt)
def vis_worst_flow_img(model, iters=6, dstype='final', val_id=0, save_root='./vis/model_name/val_id', nframes=2):
""" Peform validation using the Sintel (train) split """
model.eval()
val_dataset = mf_datasets.SintelMultiframeEval(split='training', dstype=dstype, nframes=nframes)
images, flows_gt, _, _ = val_dataset[val_id]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
for i in range(len(flows)):
epe = torch.sum((flows[i] - flows_gt[i])**2, dim=0).sqrt()
print(epe.mean().item())
@torch.no_grad()
def get_worst_case(model, iters=12, root='/data/Sintel', tqdm_miniters=1, nframes=3, save_root='/share/sunshangkun/worst_cases_mf', ):
model.eval()
results = {}
for dstype in ['clean', 'final']:
val_dataset = mf_datasets.SintelMultiframeEval(split='training', dstype=dstype, root=root, nframes=nframes)
epe_list = []
result_list = []
for val_id in tqdm(range(len(val_dataset))):
images, flows_gt, _, (_, frame_ids) = val_dataset[val_id]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
for i in range(len(flows)):
# print(len(flows), flows[i].shape, flows_gt[i].shape)
epe = torch.sum((flows[i] - flows_gt[i])**2, dim=0).sqrt()
if frame_ids[i] != -1:
epe_list.append(epe.view(-1).numpy())
# print(val_id, frame_ids, epe.mean().item())
result_list.append(epe.numpy().mean())
result_list = np.array(result_list)
worst_index = np.argsort(result_list)[-30:]
worst_index = worst_index[::-1]
if not os.path.exists(save_root): os.makedirs(save_root)
print(worst_index)
for i in range(len(worst_index)):
images, flows_gt, _, (_, frame_ids) = val_dataset[worst_index[i]]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
dir_path = os.path.join(save_root, str(i))
# print('ploting :', i, worst_index[i], 'EPE:', result_list[worst_index[i]])
if not os.path.exists(dir_path):
os.makedirs(dir_path)
for j, img in enumerate(images):
out_path = os.path.join(dir_path, f'{worst_index[i]}-image{j}.png')
Image.fromarray(np.uint8(img.squeeze(0).permute(1,2,0).cpu().numpy())).save(out_path)
for j, img in enumerate(flows):
out_path = os.path.join(dir_path, f'{worst_index[i]}-flowdiff{j}.png')
flow_diff = abs(flows[j] - flows_gt[j]).squeeze(0).mean(0).cpu().numpy()
plt.imshow(flow_diff, cmap='Greens')
plt.savefig(out_path)
epe = np.mean(torch.sum((flows[j] - flows_gt[j])**2, dim=0).sqrt().numpy())
print(f'{worst_index[i]}-{j}: {epe}')
img = img.permute(1, 2, 0).numpy()
out_path = os.path.join(dir_path, f'{worst_index[i]}-pred{j}.png')
Image.fromarray(flow_viz.flow_to_image(img)).save(out_path)
for j, img in enumerate(flows_gt):
out_path = os.path.join(dir_path, f'{worst_index[i]}-gt{j}.png')
img = img.permute(1, 2, 0).cpu().numpy()
Image.fromarray(flow_viz.flow_to_image(img)).save(out_path)
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 create_sintel_submission_mf(args, model, iters, output_path='sintel_submission', nframes=3):
""" Create submission for the Sintel leaderboard """
model.eval()
for dstype in ['clean', 'final']:
test_dataset = mf_datasets.SintelMultiframeEval(root=args.sintel_root, split='test', aug_params=None, dstype=dstype, nframes=nframes)
for test_id in tqdm(range(len(test_dataset))):
images, (scene, frame_ids) = test_dataset[test_id]
images = [image[None].cuda() for image in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).permute(1, 2, 0).cpu().numpy() for flow in flows]
output_dir = os.path.join(output_path, dstype, scene)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
vis_dir = f'{output_path}/{dstype}/'
if not os.path.exists(vis_dir):
os.makedirs(vis_dir)
assert len(flows) == len(frame_ids) - 1
for i in range(len(flows)):
if frame_ids[i] != -1:
output_file = os.path.join(output_dir, 'frame%04d.flo' % (frame_ids[i]+1))
frame_utils.writeFlow(output_file, flows[i])
flow_img = Image.fromarray(flow_viz.flow_to_image(flows[i]))
flow_img.save(os.path.join(vis_dir, f'{scene}-{frame_ids[i]+1}.png'))
@torch.no_grad()
def create_sintel_submission_mf_warmup(args, model, iters, output_path='sintel_submission', nframes=3):
""" Create submission for the Sintel leaderboard """
model.eval()
for dstype in ['clean', 'final']:
test_dataset = mf_datasets.SintelMultiframeEval(root=args.sintel_root, split='test', aug_params=None, dstype=dstype, nframes=nframes)
flow_prev, scene_prev = None, None
for test_id in tqdm(range(len(test_dataset))):
images, (scene, frame_ids) = test_dataset[test_id]
images = [image[None].cuda() for image in images]
if scene != scene_prev:
flow_prev = None
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows, flows_lowres = model(images, iters=iters, flow_init=flow_prev, test_mode=True)
flow_prev = [forward_interpolate(flows_lowres[i][0])[None].cuda() for i in range(len(flows_lowres))]
flows = [padder.unpad(flow[0]).permute(1, 2, 0).cpu().numpy() for flow in flows]
output_dir = os.path.join(output_path, dstype, scene)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
vis_dir = f'{output_path}/{dstype}/'
if not os.path.exists(vis_dir):
os.makedirs(vis_dir)
assert len(flows) == len(frame_ids) - 1
for i in range(len(flows)):
if frame_ids[i] != -1:
output_file = os.path.join(output_dir, 'frame%04d.flo' % (frame_ids[i]+1))
frame_utils.writeFlow(output_file, flows[i])
flow_img = Image.fromarray(flow_viz.flow_to_image(flows[i]))
flow_img.save(os.path.join(vis_dir, f'{scene}-{frame_ids[i]+1}.png'))
@torch.no_grad()
def create_sintel_submission_vis(model, iters, warm_start=False, output_path='sintel_submission'):
""" Create submission for the Sintel leaderboard """
model.eval()
vis_path = output_path.split('/')[-1]
for dstype in ['clean', 'final']:
test_dataset = datasets.MpiSintel(split='test', aug_params=None, dstype=dstype, root=args.sintel_root)
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].to(f'cuda:{model.device_ids[0]}'), image2[None].to(f'cuda:{model.device_ids[0]}'))
flow_low, flow_pr = model.module(image1, image2, iters=iters, flow_init=flow_prev, test_mode=True)
flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
# Visualizations
flow_img = flow_viz.flow_to_image(flow)
image = Image.fromarray(flow_img)
if not os.path.exists(f'vis_test/RAFT/{dstype}/'):
os.makedirs(f'vis_test/RAFT/{dstype}/flow')
if not os.path.exists(f'vis_test/{vis_path}/{dstype}/'):
os.makedirs(f'vis_test/{vis_path}/{dstype}/flow')
if not os.path.exists(f'vis_test/gt/{dstype}/'):
os.makedirs(f'vis_test/gt/{dstype}/image')
# image.save(f'vis_test/ours/{dstype}/flow/{test_id}.png')
# image.save(f'vis_test/RAFT/{dstype}/flow/{test_id}.png')
image.save(f'vis_test/{vis_path}/{dstype}/flow/{test_id}.png')
# imageio.imwrite(f'vis_test/gt/{dstype}/image/{test_id}.png', image1[0].cpu().permute(1, 2, 0).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 validate_chairs(model, iters=6, root='/data/FlyingChairs_release/data'):
""" Perform evaluation on the FlyingChairs (test) split """
model.eval()
epe_list = []
val_dataset = datasets.FlyingChairs(split='validation', root=root)
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': epe}
@torch.no_grad()
def validate_things(model, iters=6, root='/data/FlyingThings3D'):
""" Perform evaluation on the FlyingThings (test) split """
model.eval()
results = {}
for dstype in ['frames_cleanpass', 'frames_finalpass']:
epe_list = []
val_dataset = datasets.FlyingThings3D(dstype=dstype, split='validation', root=root)
print(f'Dataset length {len(val_dataset)}')
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_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_sintel(model, iters=6, root='/data/Sintel', tqdm_miniters=1):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
for dstype in ['clean', 'final']:
val_dataset = datasets.MpiSintel(split='training', dstype=dstype, root=root)
epe_list = []
for val_id in tqdm(range(len(val_dataset)), miniters=tqdm_miniters):
image1, image2, flow_gt, _ = val_dataset[val_id]
image1 = image1[None].cuda()
image2 = image2[None].cuda()
padder = InputPadder(image1.shape, factor=8)
image1, image2 = padder.pad(image1, image2)
_, 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_sintel_mf(model, iters=6, root='/data/Sintel', tqdm_miniters=1, nframes=3):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
for dstype in ['clean', 'final']:
val_dataset = mf_datasets.SintelMultiframeEval(split='training', dstype=dstype, root=root, nframes=nframes)
epe_list = []
for val_id in tqdm(range(len(val_dataset)), miniters=tqdm_miniters):
images, flows_gt, _, (_, frame_ids) = val_dataset[val_id]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
for i in range(len(flows)):
# print(len(flows), flows[i].shape, flows_gt[i].shape)
epe = torch.sum((flows[i] - flows_gt[i])**2, dim=0).sqrt()
if frame_ids[i] != -1:
epe_list.append(epe.view(-1).numpy())
# print(val_id, frame_ids, epe.mean().item())
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_sintel_mf_stride2(model, iters=6, root='/data/Sintel', tqdm_miniters=1, nframes=3):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
for dstype in ['clean', 'final']:
val_dataset = mf_datasets.SintelMultiframeEval_stride2(split='training', dstype=dstype, root=root, nframes=nframes)
epe_list = []
predicted_frame = []
for val_id in tqdm(range(len(val_dataset)), miniters=tqdm_miniters):
images, flows_gt, _, (base_folder, frame_ids) = val_dataset[val_id]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
for i in range(len(flows)):
# print(len(flows), flows[i].shape, flows_gt[i].shape)
if (base_folder + str(frame_ids[i])) in predicted_frame: continue
else:
predicted_frame.append([base_folder + str(frame_ids[i])])
epe = torch.sum((flows[i] - flows_gt[i])**2, dim=0).sqrt()
epe_list.append(epe.view(-1).numpy())
# print(val_id, frame_ids, epe.mean().item())
print(predicted_frame)
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_sintel_occ_mf(model, iters=6, root='/data/Sintel', tqdm_miniters=1, nframes=3):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
for dstype in ['albedo', 'clean', 'final']:
val_dataset = mf_datasets.SintelMultiframeEval(split='training', dstype=dstype, root=root, nframes=nframes, occ_map=True)
epe_list = []
epe_occ_list = []
epe_noc_list = []
for val_id in tqdm(range(len(val_dataset)), miniters=tqdm_miniters):
images, flows_gt, _, (_, frame_ids), occs = val_dataset[val_id]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
for i in range(len(flows)):
epe = torch.sum((flows[i] - flows_gt[i])**2, dim=0).sqrt()
if frame_ids[i] != -1:
epe_list.append(epe.view(-1).numpy())
epe_noc_list.append(epe[~occs[i]].numpy())
epe_occ_list.append(epe[occs[i]].numpy())
epe_all = np.concatenate(epe_list)
epe_noc = np.concatenate(epe_noc_list)
epe_occ = np.concatenate(epe_occ_list)
epe = np.mean(epe_all)
px1 = np.mean(epe_all<1)
px3 = np.mean(epe_all<3)
px5 = np.mean(epe_all<5)
epe_occ_mean = np.mean(epe_occ)
epe_noc_mean = np.mean(epe_noc)
print("Validation (%s) EPE: %f, 1px: %f, 3px: %f, 5px: %f" % (dstype, epe, px1, px3, px5))
print("Occ epe: %f, Noc epe: %f" % (epe_occ_mean, epe_noc_mean))
results[dstype] = np.mean(epe_list)
return results
@torch.no_grad()
def validate_sintel_mf_warp(model, iters=6, root='/data/Sintel', tqdm_miniters=1, nframes=3):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
for dstype in ['clean', 'final']:
test_dataset = mf_datasets.SintelMultiframeEval(root=args.sintel_root, split='test', aug_params=None, dstype=dstype, nframes=nframes)
epe_list = []
for test_id in tqdm(range(len(test_dataset))):
images, (scene, frame_ids) = test_dataset[test_id]
images = [image[None].cuda() for image in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).permute(1, 2, 0).cpu().numpy() for flow in flows]
for i in range(len(flows)):
# print(len(flows), flows[i].shape, flows_gt[i].shape)
epe = torch.sum((flows[i] - flows_gt[i])**2, dim=0).sqrt()
if frame_ids[i] != -1:
epe_list.append(epe.view(-1).numpy())
# print(val_id, frame_ids, epe.mean().item())
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 vis_case_mf(model, imgs_filenames, flows_filenames, iters=6, vis_root='/share2/sunshangkun/vis_case'):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
imgs = [np.array(frame_utils.read_gen(filename)).astype(np.uint8) for filename in imgs_filenames]
flows = [np.array(frame_utils.read_gen(filename)).astype(np.float32) for filename in flows_filenames]
images = [torch.from_numpy(img).permute(2, 0, 1).float() for img in imgs]
flows_gt = [torch.from_numpy(flow).permute(2, 0, 1).float() for flow in flows]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
for i in range(len(flows)):
# print(len(flows), flows[i].shape, flows_gt[i].shape)
epe = torch.sum((flows[i] - flows_gt[i])**2, dim=0).sqrt()
flow_diff = abs(flows[i] - flows_gt[i]).squeeze(0).mean(0).cpu().numpy()
if not os.path.exists(vis_root): os.makedirs(vis_root)
plt.imshow(flow_diff, cmap='Greens')
plt.savefig(f'{vis_root}/flow_diff_{i}.png')
epe_item = np.mean(epe.numpy())
print(f'{i}: {epe_item}')
# flow1 = flow_viz.flow_to_image(flows[0].permute(1,2,0).cpu().numpy())
# flow2 = flow_viz.flow_to_image(flows[1].permute(1,2,0).cpu().numpy())
# cv2.imwrite('flow1.jpg', flow1)
# cv2.imwrite('flow2.jpg', flow2)
@torch.no_grad()
def validate_sintel_tri(model, iters=6, root='/data/Sintel', tqdm_miniters=1, nframes=3):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
for dstype in ['clean', 'final']:
val_dataset = mf_datasets.SintelMultiframe(split='training', dstype=dstype, root=root, nframes=nframes)
epe_list = []
for val_id in tqdm(range(len(val_dataset)), miniters=tqdm_miniters):
images, flows_gt, _, (_, frame_ids) = val_dataset[val_id]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape, factor=8)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow).cpu() for flow in flows]
epe = torch.sum((flows[-1] - flows_gt[-1])**2, dim=0).sqrt()
epe_list.append(epe.view(-1).numpy())
if frame_ids[0] == 0:
for i in range(len(flows) - 1):
epe = torch.sum((flows[i] - flows_gt[i])**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 vis_worst_case_mf(model, iters=6, root='/data/Sintel', tqdm_miniters=1, nframes=3):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
for dstype in ['final']:
val_dataset = mf_datasets.SintelMultiframeEval(split='training', dstype=dstype, root=root, nframes=nframes)
epe_list = []
for val_id in tqdm(range(len(val_dataset)), miniters=tqdm_miniters):
images, flows_gt, _, (_, frame_ids) = val_dataset[val_id]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape)
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).cpu() for flow in flows]
for i in range(len(flows)):
# print(len(flows), flows[i].shape, flows_gt[i].shape)
epe = torch.sum((flows[i] - flows_gt[i])**2, dim=0).sqrt()
if frame_ids[i] != -1:
flow_diff = abs(flows[i] - flows_gt[i]).squeeze(0).mean(0).cpu().numpy()
if not os.path.exists('./ours_real'): os.makedirs('./ours_real')
plt.imshow(flow_diff, cmap='Greens')
# plt.colorbar()
plt.savefig(f'./ours_real/flow_diff_{frame_ids[i]}.png')
epe_item = np.mean(epe.numpy())
print(f'{frame_ids[i]}: {epe_item}')
epe_list.append(epe.view(-1).numpy())
# print(val_id, frame_ids, epe.mean().item())
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_sintel_occ(model, iters=6, root='/data/Sintel', tqdm_miniters=1):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
for dstype in ['albedo', 'clean', 'final']:
# for dstype in ['clean', 'final']:
val_dataset = datasets.MpiSintel(split='training', dstype=dstype, occlusion=True)
epe_list = []
epe_occ_list = []
epe_noc_list = []
for val_id in tqdm(range(len(val_dataset)), miniters=tqdm_miniters):
image1, image2, flow_gt, _, occ, _ = val_dataset[val_id]
image1 = image1[None].cuda()
image2 = image2[None].cuda()
padder = InputPadder(image1.shape)
image1, image2 = padder.pad(image1, image2)
_, 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_noc_list.append(epe[~occ].numpy())
epe_occ_list.append(epe[occ].numpy())
epe_all = np.concatenate(epe_list)
epe_noc = np.concatenate(epe_noc_list)
epe_occ = np.concatenate(epe_occ_list)
epe = np.mean(epe_all)
px1 = np.mean(epe_all<1)
px3 = np.mean(epe_all<3)
px5 = np.mean(epe_all<5)
epe_occ_mean = np.mean(epe_occ)
epe_noc_mean = np.mean(epe_noc)
print("Validation (%s) EPE: %f, 1px: %f, 3px: %f, 5px: %f" % (dstype, epe, px1, px3, px5))
print("Occ epe: %f, Noc epe: %f" % (epe_occ_mean, epe_noc_mean))
results[dstype] = np.mean(epe_list)
return results
@torch.no_grad()
def create_kitti_submission(model, iters, 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(f'cuda:{model.device_ids[0]}'), image2[None].to(f'cuda:{model.device_ids[0]}'))
_, flow_pr = model.module(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 create_kitti_submission_mf(model, iters, output_path='kitti_submission'):
""" Create submission for the Sintel leaderboard """
model.eval()
test_dataset = datasets.KITTI(split='testing', aug_params=None) # 和sintel submission的dataset有何处不同
if not os.path.exists(output_path):
os.makedirs(output_path)
for test_id in range(len(test_dataset)):
images, frame_name = test_dataset[test_id]
images = [x[None].cuda() for x in images]
padder = InputPadder(images[0].shape, mode='kitti') # TODO: Mode?
images = padder.pad_list(images)
flows = model(images, iters=iters, test_mode=True)
flows = [padder.unpad(flow[0]).permute(1, 2, 0).cpu().numpy() for flow in flows]
output_filename = os.path.join(output_path, frame_name)
frame_utils.writeFlowKITTI(output_filename, flows[-1])
print(frame_name)
flow_img = flow_viz.flow_to_image(flows[-1])
image = Image.fromarray(flow_img)
if not os.path.exists(f'vis_kitti'):
os.makedirs(f'vis_kitti/flow')
image.save(f'vis_kitti/flow/{frame_name}')
TRAIN_SIZE = [432, 960]
class InputPadder2:
""" Pads images such that dimensions are divisible by 8 """
def __init__(self, dims, mode='sintel'):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
pad_wd = (((self.wd // 8) + 1) * 8 - self.wd) % 8
if mode == 'sintel':
self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2]
elif mode == 'kitti432':
self._pad = [0, 0, 0, 432 - self.ht]
elif mode == 'kitti400':
self._pad = [0, 0, 0, 400 - self.ht]
elif mode == 'kitti376':
self._pad = [0, 0, 0, 376 - self.ht]
else:
self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht]
def pad(self, *inputs):
return [F.pad(x, self._pad, mode='constant', value=0.0) for x in inputs]
def unpad(self,x):
ht, wd = x.shape[-2:]
c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]]
return x[..., c[0]:c[1], c[2]:c[3]]
def pad_list(self, inputs):
return [F.pad(x, self._pad, mode='replicate') for x in inputs]
def compute_grid_indices(image_shape, patch_size=TRAIN_SIZE, min_overlap=20):
if min_overlap >= patch_size[0] or min_overlap >= patch_size[1]:
raise ValueError("!!")
hs = list(range(0, image_shape[0], patch_size[0] - min_overlap))
ws = list(range(0, image_shape[1], patch_size[1] - min_overlap))
# Make sure the final patch is flush with the image boundary
hs[-1] = image_shape[0] - patch_size[0]
ws[-1] = image_shape[1] - patch_size[1]
return [(h, w) for h in hs for w in ws]
import math
def compute_weight(hws, image_shape, patch_size=TRAIN_SIZE, sigma=1.0, wtype='gaussian'):
patch_num = len(hws)
h, w = torch.meshgrid(torch.arange(patch_size[0]), torch.arange(patch_size[1]))
h, w = h / float(patch_size[0]), w / float(patch_size[1])
c_h, c_w = 0.5, 0.5
h, w = h - c_h, w - c_w
weights_hw = (h ** 2 + w ** 2) ** 0.5 / sigma
denorm = 1 / (sigma * math.sqrt(2 * math.pi))
weights_hw = denorm * torch.exp(-0.5 * (weights_hw) ** 2)
weights = torch.zeros(1, patch_num, *image_shape)
for idx, (h, w) in enumerate(hws):
weights[:, idx, h:h+patch_size[0], w:w+patch_size[1]] = weights_hw
weights = weights.cuda()
patch_weights = []
for idx, (h, w) in enumerate(hws):
patch_weights.append(weights[:, idx:idx+1, h:h+patch_size[0], w:w+patch_size[1]])
return patch_weights
@torch.no_grad()
def validate_kitti_tile(model, iters=6, root='/data/kitti15'):
sigma=0.05
IMAGE_SIZE = [376, 1242]
TRAIN_SIZE = [376, 720]
hws = compute_grid_indices(IMAGE_SIZE, TRAIN_SIZE)
weights = compute_weight(hws, IMAGE_SIZE, TRAIN_SIZE, sigma)
model.eval()
from core.datasets import KITTI
val_dataset = KITTI(split='training', root=root)
out_list, epe_list = [], []
for val_id in range(len(val_dataset)):
image1, image2, flow_gt, valid_gt = val_dataset[val_id]
# print(image1.shape, 'ooo')
new_shape = image1.shape[1:]
if new_shape[1] != IMAGE_SIZE[1]:
print(f"replace {IMAGE_SIZE} with {new_shape}")
IMAGE_SIZE[0] = 376
IMAGE_SIZE[1] = new_shape[1]
hws = compute_grid_indices(IMAGE_SIZE, TRAIN_SIZE)
weights = compute_weight(hws, IMAGE_SIZE, TRAIN_SIZE, sigma)
padder = InputPadder2(image1.shape, mode='kitti376')
image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
# print(image1.shape, 'xxx')
flows = 0
flow_count = 0
for idx, (h, w) in enumerate(hws):
image1_tile = image1[:, :, h:h+TRAIN_SIZE[0], w:w+TRAIN_SIZE[1]]
image2_tile = image2[:, :, h:h+TRAIN_SIZE[0], w:w+TRAIN_SIZE[1]]
print(image1_tile.shape, '=======', image2_tile.shape, h, w, TRAIN_SIZE[0], TRAIN_SIZE[1])
flow_s = model([image1_tile, image2_tile], test_mode=True)
flow_pre = flow_s[-1]
padding = (w, IMAGE_SIZE[1]-w-TRAIN_SIZE[1], h, IMAGE_SIZE[0]-h-TRAIN_SIZE[0], 0, 0)
flows += F.pad(flow_pre * weights[idx], padding)
flow_count += F.pad(weights[idx], padding)
flow_pre = flows / flow_count
flow = padder.unpad(flow_pre[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}
@torch.no_grad()
def validate_kitti_mf_tile(model, iters=6, multi_root=None, nframes=3):
""" Peform validation using the KITTI-2015 (train) split """
sigma=0.05
IMAGE_SIZE = [432, 1242]
TRAIN_SIZE = [432, 960]
hws = compute_grid_indices(IMAGE_SIZE, TRAIN_SIZE)
weights = compute_weight(hws, IMAGE_SIZE, TRAIN_SIZE, sigma)
model.eval()
val_dataset = mf_datasets.KITTIMultiFrameEval(split='training', multi_root=multi_root, nframes=nframes)
out_list, epe_list = [], []
for val_id in tqdm(range(len(val_dataset))):
images, flow_gts, valids, frame_names = val_dataset[val_id]
# print(images[0].shape, '===========xxxxx')
padder = InputPadder2(images[0].shape, mode='kitti432')