Rerender / gmflow_module /evaluate.py
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from PIL import Image
import os
import time
import numpy as np
import torch
import torch.nn.functional as F
import data
from utils import frame_utils
from utils.flow_viz import save_vis_flow_tofile
from utils.utils import InputPadder, compute_out_of_boundary_mask
from glob import glob
from gmflow.geometry import forward_backward_consistency_check
@torch.no_grad()
def create_sintel_submission(model,
output_path='sintel_submission',
padding_factor=8,
save_vis_flow=False,
no_save_flo=False,
attn_splits_list=None,
corr_radius_list=None,
prop_radius_list=None,
):
""" Create submission for the Sintel leaderboard """
model.eval()
for dstype in ['clean', 'final']:
test_dataset = data.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, padding_factor=padding_factor)
image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
results_dict = model(image1, image2,
attn_splits_list=attn_splits_list,
corr_radius_list=corr_radius_list,
prop_radius_list=prop_radius_list,
)
flow_pr = results_dict['flow_preds'][-1] # [B, 2, H, W]
flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
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)
if not no_save_flo:
frame_utils.writeFlow(output_file, flow)
sequence_prev = sequence
# Save vis flow
if save_vis_flow:
vis_flow_file = output_file.replace('.flo', '.png')
save_vis_flow_tofile(flow, vis_flow_file)
@torch.no_grad()
def create_kitti_submission(model,
output_path='kitti_submission',
padding_factor=8,
save_vis_flow=False,
attn_splits_list=None,
corr_radius_list=None,
prop_radius_list=None,
):
""" Create submission for the Sintel leaderboard """
model.eval()
test_dataset = data.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', padding_factor=padding_factor)
image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
results_dict = model(image1, image2,
attn_splits_list=attn_splits_list,
corr_radius_list=corr_radius_list,
prop_radius_list=prop_radius_list,
)
flow_pr = results_dict['flow_preds'][-1]
flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
output_filename = os.path.join(output_path, frame_id)
if save_vis_flow:
vis_flow_file = output_filename
save_vis_flow_tofile(flow, vis_flow_file)
else:
frame_utils.writeFlowKITTI(output_filename, flow)
@torch.no_grad()
def validate_chairs(model,
with_speed_metric=False,
attn_splits_list=False,
corr_radius_list=False,
prop_radius_list=False,
):
""" Perform evaluation on the FlyingChairs (test) split """
model.eval()
epe_list = []
results = {}
if with_speed_metric:
s0_10_list = []
s10_40_list = []
s40plus_list = []
val_dataset = data.FlyingChairs(split='validation')
print('Number of validation image pairs: %d' % 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()
results_dict = model(image1, image2,
attn_splits_list=attn_splits_list,
corr_radius_list=corr_radius_list,
prop_radius_list=prop_radius_list,
)
flow_pr = results_dict['flow_preds'][-1] # [B, 2, H, W]
assert flow_pr.size()[-2:] == flow_gt.size()[-2:]
epe = torch.sum((flow_pr[0].cpu() - flow_gt) ** 2, dim=0).sqrt()
epe_list.append(epe.view(-1).numpy())
if with_speed_metric:
flow_gt_speed = torch.sum(flow_gt ** 2, dim=0).sqrt()
valid_mask = (flow_gt_speed < 10)
if valid_mask.max() > 0:
s0_10_list.append(epe[valid_mask].cpu().numpy())
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40)
if valid_mask.max() > 0:
s10_40_list.append(epe[valid_mask].cpu().numpy())
valid_mask = (flow_gt_speed > 40)
if valid_mask.max() > 0:
s40plus_list.append(epe[valid_mask].cpu().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 Chairs EPE: %.3f, 1px: %.3f, 3px: %.3f, 5px: %.3f" % (epe, px1, px3, px5))
results['chairs_epe'] = epe
results['chairs_1px'] = px1
results['chairs_3px'] = px3
results['chairs_5px'] = px5
if with_speed_metric:
s0_10 = np.mean(np.concatenate(s0_10_list))
s10_40 = np.mean(np.concatenate(s10_40_list))
s40plus = np.mean(np.concatenate(s40plus_list))
print("Validation Chairs s0_10: %.3f, s10_40: %.3f, s40+: %.3f" % (
s0_10,
s10_40,
s40plus))
results['chairs_s0_10'] = s0_10
results['chairs_s10_40'] = s10_40
results['chairs_s40+'] = s40plus
return results
@torch.no_grad()
def validate_things(model,
padding_factor=8,
with_speed_metric=False,
max_val_flow=400,
val_things_clean_only=True,
attn_splits_list=False,
corr_radius_list=False,
prop_radius_list=False,
):
""" Peform validation using the Things (test) split """
model.eval()
results = {}
for dstype in ['frames_cleanpass', 'frames_finalpass']:
if val_things_clean_only:
if dstype == 'frames_finalpass':
continue
val_dataset = data.FlyingThings3D(dstype=dstype, test_set=True, validate_subset=True,
)
print('Number of validation image pairs: %d' % len(val_dataset))
epe_list = []
if with_speed_metric:
s0_10_list = []
s10_40_list = []
s40plus_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, padding_factor=padding_factor)
image1, image2 = padder.pad(image1, image2)
results_dict = model(image1, image2,
attn_splits_list=attn_splits_list,
corr_radius_list=corr_radius_list,
prop_radius_list=prop_radius_list,
)
flow_pr = results_dict['flow_preds'][-1]
flow = padder.unpad(flow_pr[0]).cpu()
# Evaluation on flow <= max_val_flow
flow_gt_speed = torch.sum(flow_gt ** 2, dim=0).sqrt()
valid_gt = valid_gt * (flow_gt_speed < max_val_flow)
valid_gt = valid_gt.contiguous()
epe = torch.sum((flow - flow_gt) ** 2, dim=0).sqrt()
val = valid_gt >= 0.5
epe_list.append(epe[val].cpu().numpy())
if with_speed_metric:
valid_mask = (flow_gt_speed < 10) * (valid_gt >= 0.5)
if valid_mask.max() > 0:
s0_10_list.append(epe[valid_mask].cpu().numpy())
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40) * (valid_gt >= 0.5)
if valid_mask.max() > 0:
s10_40_list.append(epe[valid_mask].cpu().numpy())
valid_mask = (flow_gt_speed > 40) * (valid_gt >= 0.5)
if valid_mask.max() > 0:
s40plus_list.append(epe[valid_mask].cpu().numpy())
epe_list = np.mean(np.concatenate(epe_list))
epe = np.mean(epe_list)
if dstype == 'frames_cleanpass':
dstype = 'things_clean'
if dstype == 'frames_finalpass':
dstype = 'things_final'
print("Validation Things test set (%s) EPE: %.3f" % (dstype, epe))
results[dstype + '_epe'] = epe
if with_speed_metric:
s0_10 = np.mean(np.concatenate(s0_10_list))
s10_40 = np.mean(np.concatenate(s10_40_list))
s40plus = np.mean(np.concatenate(s40plus_list))
print("Validation Things test (%s) s0_10: %.3f, s10_40: %.3f, s40+: %.3f" % (
dstype, s0_10,
s10_40,
s40plus))
results[dstype + '_s0_10'] = s0_10
results[dstype + '_s10_40'] = s10_40
results[dstype + '_s40+'] = s40plus
return results
@torch.no_grad()
def validate_sintel(model,
count_time=False,
padding_factor=8,
with_speed_metric=False,
evaluate_matched_unmatched=False,
attn_splits_list=False,
corr_radius_list=False,
prop_radius_list=False,
):
""" Peform validation using the Sintel (train) split """
model.eval()
results = {}
if count_time:
total_time = 0
num_runs = 100
for dstype in ['clean', 'final']:
val_dataset = data.MpiSintel(split='training', dstype=dstype,
load_occlusion=evaluate_matched_unmatched,
)
print('Number of validation image pairs: %d' % len(val_dataset))
epe_list = []
if evaluate_matched_unmatched:
matched_epe_list = []
unmatched_epe_list = []
if with_speed_metric:
s0_10_list = []
s10_40_list = []
s40plus_list = []
for val_id in range(len(val_dataset)):
if evaluate_matched_unmatched:
image1, image2, flow_gt, valid, noc_valid = val_dataset[val_id]
# compuate in-image-plane valid mask
in_image_valid = compute_out_of_boundary_mask(flow_gt.unsqueeze(0)).squeeze(0) # [H, W]
else:
image1, image2, flow_gt, _ = val_dataset[val_id]
image1 = image1[None].cuda()
image2 = image2[None].cuda()
padder = InputPadder(image1.shape, padding_factor=padding_factor)
image1, image2 = padder.pad(image1, image2)
if count_time and val_id >= 5: # 5 warmup
torch.cuda.synchronize()
time_start = time.perf_counter()
results_dict = model(image1, image2,
attn_splits_list=attn_splits_list,
corr_radius_list=corr_radius_list,
prop_radius_list=prop_radius_list,
)
# useful when using parallel branches
flow_pr = results_dict['flow_preds'][-1]
if count_time and val_id >= 5:
torch.cuda.synchronize()
total_time += time.perf_counter() - time_start
if val_id >= num_runs + 4:
break
flow = padder.unpad(flow_pr[0]).cpu()
epe = torch.sum((flow - flow_gt) ** 2, dim=0).sqrt()
epe_list.append(epe.view(-1).numpy())
if evaluate_matched_unmatched:
matched_valid_mask = (noc_valid > 0.5) & (in_image_valid > 0.5)
if matched_valid_mask.max() > 0:
matched_epe_list.append(epe[matched_valid_mask].cpu().numpy())
unmatched_epe_list.append(epe[~matched_valid_mask].cpu().numpy())
if with_speed_metric:
flow_gt_speed = torch.sum(flow_gt ** 2, dim=0).sqrt()
valid_mask = (flow_gt_speed < 10)
if valid_mask.max() > 0:
s0_10_list.append(epe[valid_mask].cpu().numpy())
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40)
if valid_mask.max() > 0:
s10_40_list.append(epe[valid_mask].cpu().numpy())
valid_mask = (flow_gt_speed > 40)
if valid_mask.max() > 0:
s40plus_list.append(epe[valid_mask].cpu().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)
dstype_ori = dstype
print("Validation Sintel (%s) EPE: %.3f, 1px: %.3f, 3px: %.3f, 5px: %.3f" % (dstype_ori, epe, px1, px3, px5))
dstype = 'sintel_' + dstype
results[dstype + '_epe'] = np.mean(epe_list)
results[dstype + '_1px'] = px1
results[dstype + '_3px'] = px3
results[dstype + '_5px'] = px5
if with_speed_metric:
s0_10 = np.mean(np.concatenate(s0_10_list))
s10_40 = np.mean(np.concatenate(s10_40_list))
s40plus = np.mean(np.concatenate(s40plus_list))
print("Validation Sintel (%s) s0_10: %.3f, s10_40: %.3f, s40+: %.3f" % (
dstype_ori, s0_10,
s10_40,
s40plus))
results[dstype + '_s0_10'] = s0_10
results[dstype + '_s10_40'] = s10_40
results[dstype + '_s40+'] = s40plus
if count_time:
print('Time: %.6fs' % (total_time / num_runs))
break # only the clean pass when counting time
if evaluate_matched_unmatched:
matched_epe = np.mean(np.concatenate(matched_epe_list))
unmatched_epe = np.mean(np.concatenate(unmatched_epe_list))
print('Validatation Sintel (%s) matched epe: %.3f, unmatched epe: %.3f' % (
dstype_ori, matched_epe, unmatched_epe))
results[dstype + '_matched'] = matched_epe
results[dstype + '_unmatched'] = unmatched_epe
return results
@torch.no_grad()
def validate_kitti(model,
padding_factor=8,
with_speed_metric=False,
average_over_pixels=True,
attn_splits_list=False,
corr_radius_list=False,
prop_radius_list=False,
):
""" Peform validation using the KITTI-2015 (train) split """
model.eval()
val_dataset = data.KITTI(split='training')
print('Number of validation image pairs: %d' % len(val_dataset))
out_list, epe_list = [], []
results = {}
if with_speed_metric:
if average_over_pixels:
s0_10_list = []
s10_40_list = []
s40plus_list = []
else:
s0_10_epe_sum = 0
s0_10_valid_samples = 0
s10_40_epe_sum = 0
s10_40_valid_samples = 0
s40plus_epe_sum = 0
s40plus_valid_samples = 0
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', padding_factor=padding_factor)
image1, image2 = padder.pad(image1, image2)
results_dict = model(image1, image2,
attn_splits_list=attn_splits_list,
corr_radius_list=corr_radius_list,
prop_radius_list=prop_radius_list,
)
# useful when using parallel branches
flow_pr = results_dict['flow_preds'][-1]
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()
if with_speed_metric:
# flow_gt_speed = torch.sum(flow_gt ** 2, dim=0).sqrt()
flow_gt_speed = mag
if average_over_pixels:
valid_mask = (flow_gt_speed < 10) * (valid_gt >= 0.5) # note KITTI GT is sparse
if valid_mask.max() > 0:
s0_10_list.append(epe[valid_mask].cpu().numpy())
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40) * (valid_gt >= 0.5)
if valid_mask.max() > 0:
s10_40_list.append(epe[valid_mask].cpu().numpy())
valid_mask = (flow_gt_speed > 40) * (valid_gt >= 0.5)
if valid_mask.max() > 0:
s40plus_list.append(epe[valid_mask].cpu().numpy())
else:
valid_mask = (flow_gt_speed < 10) * (valid_gt >= 0.5) # note KITTI GT is sparse
if valid_mask.max() > 0:
s0_10_epe_sum += (epe * valid_mask).sum() / valid_mask.sum()
s0_10_valid_samples += 1
valid_mask = (flow_gt_speed >= 10) * (flow_gt_speed <= 40) * (valid_gt >= 0.5)
if valid_mask.max() > 0:
s10_40_epe_sum += (epe * valid_mask).sum() / valid_mask.sum()
s10_40_valid_samples += 1
valid_mask = (flow_gt_speed > 40) * (valid_gt >= 0.5)
if valid_mask.max() > 0:
s40plus_epe_sum += (epe * valid_mask).sum() / valid_mask.sum()
s40plus_valid_samples += 1
epe = epe.view(-1)
mag = mag.view(-1)
val = valid_gt.view(-1) >= 0.5
out = ((epe > 3.0) & ((epe / mag) > 0.05)).float()
if average_over_pixels:
epe_list.append(epe[val].cpu().numpy())
else:
epe_list.append(epe[val].mean().item())
out_list.append(out[val].cpu().numpy())
if average_over_pixels:
epe_list = np.concatenate(epe_list)
else:
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 EPE: %.3f, F1-all: %.3f" % (epe, f1))
results['kitti_epe'] = epe
results['kitti_f1'] = f1
if with_speed_metric:
if average_over_pixels:
s0_10 = np.mean(np.concatenate(s0_10_list))
s10_40 = np.mean(np.concatenate(s10_40_list))
s40plus = np.mean(np.concatenate(s40plus_list))
else:
s0_10 = s0_10_epe_sum / s0_10_valid_samples
s10_40 = s10_40_epe_sum / s10_40_valid_samples
s40plus = s40plus_epe_sum / s40plus_valid_samples
print("Validation KITTI s0_10: %.3f, s10_40: %.3f, s40+: %.3f" % (
s0_10,
s10_40,
s40plus))
results['kitti_s0_10'] = s0_10
results['kitti_s10_40'] = s10_40
results['kitti_s40+'] = s40plus
return results
@torch.no_grad()
def inference_on_dir(model,
inference_dir,
output_path='output',
padding_factor=8,
inference_size=None,
paired_data=False, # dir of paired testdata instead of a sequence
save_flo_flow=False, # save as .flo for quantative evaluation
attn_splits_list=None,
corr_radius_list=None,
prop_radius_list=None,
pred_bidir_flow=False,
fwd_bwd_consistency_check=False,
):
""" Inference on a directory """
model.eval()
if fwd_bwd_consistency_check:
assert pred_bidir_flow
if not os.path.exists(output_path):
os.makedirs(output_path)
filenames = sorted(glob(inference_dir + '/*'))
print('%d images found' % len(filenames))
stride = 2 if paired_data else 1
if paired_data:
assert len(filenames) % 2 == 0
for test_id in range(0, len(filenames) - 1, stride):
image1 = frame_utils.read_gen(filenames[test_id])
image2 = frame_utils.read_gen(filenames[test_id + 1])
image1 = np.array(image1).astype(np.uint8)
image2 = np.array(image2).astype(np.uint8)
if len(image1.shape) == 2: # gray image, for example, HD1K
image1 = np.tile(image1[..., None], (1, 1, 3))
image2 = np.tile(image2[..., None], (1, 1, 3))
else:
image1 = image1[..., :3]
image2 = image2[..., :3]
image1 = torch.from_numpy(image1).permute(2, 0, 1).float()
image2 = torch.from_numpy(image2).permute(2, 0, 1).float()
if inference_size is None:
padder = InputPadder(image1.shape, padding_factor=padding_factor)
image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
else:
image1, image2 = image1[None].cuda(), image2[None].cuda()
# resize before inference
if inference_size is not None:
assert isinstance(inference_size, list) or isinstance(inference_size, tuple)
ori_size = image1.shape[-2:]
image1 = F.interpolate(image1, size=inference_size, mode='bilinear',
align_corners=True)
image2 = F.interpolate(image2, size=inference_size, mode='bilinear',
align_corners=True)
results_dict = model(image1, image2,
attn_splits_list=attn_splits_list,
corr_radius_list=corr_radius_list,
prop_radius_list=prop_radius_list,
pred_bidir_flow=pred_bidir_flow,
)
flow_pr = results_dict['flow_preds'][-1] # [B, 2, H, W]
# resize back
if inference_size is not None:
flow_pr = F.interpolate(flow_pr, size=ori_size, mode='bilinear',
align_corners=True)
flow_pr[:, 0] = flow_pr[:, 0] * ori_size[-1] / inference_size[-1]
flow_pr[:, 1] = flow_pr[:, 1] * ori_size[-2] / inference_size[-2]
if inference_size is None:
flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy() # [H, W, 2]
else:
flow = flow_pr[0].permute(1, 2, 0).cpu().numpy() # [H, W, 2]
output_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_flow.png')
# save vis flow
save_vis_flow_tofile(flow, output_file)
# also predict backward flow
if pred_bidir_flow:
assert flow_pr.size(0) == 2 # [2, H, W, 2]
if inference_size is None:
flow_bwd = padder.unpad(flow_pr[1]).permute(1, 2, 0).cpu().numpy() # [H, W, 2]
else:
flow_bwd = flow_pr[1].permute(1, 2, 0).cpu().numpy() # [H, W, 2]
output_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_flow_bwd.png')
# save vis flow
save_vis_flow_tofile(flow_bwd, output_file)
# forward-backward consistency check
# occlusion is 1
if fwd_bwd_consistency_check:
if inference_size is None:
fwd_flow = padder.unpad(flow_pr[0]).unsqueeze(0) # [1, 2, H, W]
bwd_flow = padder.unpad(flow_pr[1]).unsqueeze(0) # [1, 2, H, W]
else:
fwd_flow = flow_pr[0].unsqueeze(0)
bwd_flow = flow_pr[1].unsqueeze(0)
fwd_occ, bwd_occ = forward_backward_consistency_check(fwd_flow, bwd_flow) # [1, H, W] float
fwd_occ_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_occ.png')
bwd_occ_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_occ_bwd.png')
Image.fromarray((fwd_occ[0].cpu().numpy() * 255.).astype(np.uint8)).save(fwd_occ_file)
Image.fromarray((bwd_occ[0].cpu().numpy() * 255.).astype(np.uint8)).save(bwd_occ_file)
if save_flo_flow:
output_file = os.path.join(output_path, os.path.basename(filenames[test_id])[:-4] + '_pred.flo')
frame_utils.writeFlow(output_file, flow)