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test_on_crohd.py
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import time
import numpy as np
import timeit
import saverloader
from nets.raftnet import Raftnet
from nets.pips import Pips
import random
from utils.basic import print_, print_stats
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from tensorboardX import SummaryWriter
import torch.nn.functional as F
from crohddataset import CrohdDataset
import utils.basic
import utils.improc
import utils.test
from fire import Fire
device = 'cuda'
random.seed(125)
np.random.seed(125)
torch.manual_seed(125)
def prep_sample(sample, N_max, S_stride=3, req_occlusion=False):
rgbs = sample['rgbs'].permute(0,1,4,2,S_stride).float()[:,::S_stride] # (1, S, C, H, W) in 0-255
boxlist = sample['boxlist'][0].float()[::S_stride] # (S, N, 4), N = n heads
xylist = sample['xylist'][0].float()[::S_stride] # (S, N, 2)
scorelist = sample['scorelist'][0].float()[::S_stride] # (S, N)
vislist = sample['vislist'][0].float()[::S_stride] # (S, N)
S, N, _ = xylist.shape
# collect valid heads
scorelist_sum = scorelist.sum(0) # (N)
seq_present = scorelist_sum == S
motion = torch.sqrt(torch.sum((xylist[1:] - xylist[:1])**2, dim=2)).sum(0) # (N)
seq_moving = motion > 150
seq_vis_init = vislist[:2].sum(0) == 2
seq_occlusion = vislist.sum(0) < 8
seq_visible = vislist.sum(0) == 8
if req_occlusion:
seq_valid = seq_present * seq_vis_init * seq_moving * seq_occlusion
else:
seq_valid = seq_present * seq_vis_init * seq_moving * seq_visible
if seq_valid.sum() == 0:
return None, True
kp_xys = xylist[:, seq_valid> 0].unsqueeze(0)
vis = vislist[:, seq_valid > 0].unsqueeze(0)
N = kp_xys.shape[2]
# print('N', N)
if N > N_max:
kp_xys = kp_xys[:,:,:N_max]
vis = vis[:,:,:N_max]
d = {
'rgbs': rgbs, # B, S, C, H, W
'trajs_g': kp_xys, # B, S, 2
'vis_g': vis, # B, S
}
return d, False
def run_dino(dino, d, sw):
rgbs = d['rgbs'].cuda()
trajs_g = d['trajs_g'].cuda() # B,S,N,2
vis_g = d['vis_g'].cuda() # B,S,N
valids = torch.ones_like(vis_g) # B,S,N
B, S, C, H, W = rgbs.shape
B, S1, N, D = trajs_g.shape
rgbs_ = rgbs.reshape(B*S, C, H, W)
H_, W_ = 512, 768
sy = H_/H
sx = W_/W
rgbs_ = F.interpolate(rgbs_, (H_, W_), mode='bilinear')
H, W = H_, W_
rgbs = rgbs_.reshape(B, S, C, H, W)
trajs_g[:,:,:,0] *= sx
trajs_g[:,:,:,1] *= sy
_, S, C, H, W = rgbs.shape
trajs_e = utils.test.get_dino_output(dino, rgbs, trajs_g, vis_g)
ate = torch.norm(trajs_e - trajs_g, dim=-1) # B, S, N
ate_all = utils.basic.reduce_masked_mean(ate, valids)
ate_vis = utils.basic.reduce_masked_mean(ate, valids*vis_g)
ate_occ = utils.basic.reduce_masked_mean(ate, valids*(1.0-vis_g))
metrics = {
'ate_all': ate_all.item(),
'ate_vis': ate_vis.item(),
'ate_occ': ate_occ.item(),
}
if sw is not None and sw.save_this:
sw.summ_traj2ds_on_rgbs('inputs_0/orig_trajs_on_rgbs', trajs_g, utils.improc.preprocess_color(rgbs), cmap='winter', linewidth=2)
sw.summ_traj2ds_on_rgbs('outputs/trajs_on_rgbs', trajs_e[0:1], utils.improc.preprocess_color(rgbs[0:1]), cmap='spring', linewidth=2)
gt_rgb = utils.improc.preprocess_color(sw.summ_traj2ds_on_rgb('inputs_0_all/single_trajs_on_rgb', trajs_g[0:1], torch.mean(utils.improc.preprocess_color(rgbs[0:1]), dim=1), cmap='winter', frame_id=metrics['ate_all'], only_return=True, linewidth=2))
gt_black = utils.improc.preprocess_color(sw.summ_traj2ds_on_rgb('inputs_0_all/single_trajs_on_rgb', trajs_g[0:1], torch.ones_like(rgbs[0:1,0])*-0.5, cmap='winter', frame_id=metrics['ate_all'], only_return=True, linewidth=2))
sw.summ_traj2ds_on_rgb('outputs/single_trajs_on_gt_rgb', trajs_e[0:1], gt_rgb[0:1], cmap='spring', linewidth=2)
sw.summ_traj2ds_on_rgb('outputs/single_trajs_on_gt_black', trajs_e[0:1], gt_black[0:1], cmap='spring', linewidth=2)
return metrics
def run_pips(model, d, sw):
rgbs = d['rgbs'].cuda()
trajs_g = d['trajs_g'].cuda() # B,S,N,2
vis_g = d['vis_g'].cuda() # B,S,N
valids = torch.ones_like(vis_g) # B,S,N
B, S, C, H, W = rgbs.shape
B, S1, N, D = trajs_g.shape
rgbs_ = rgbs.reshape(B*S, C, H, W)
H_, W_ = 768, 1280
sy = H_/H
sx = W_/W
rgbs_ = F.interpolate(rgbs_, (H_, W_), mode='bilinear')
H, W = H_, W_
rgbs = rgbs_.reshape(B, S, C, H, W)
trajs_g[:,:,:,0] *= sx
trajs_g[:,:,:,1] *= sy
_, S, C, H, W = rgbs.shape
preds, preds_anim, vis_e, stats = model(trajs_g[:,0], rgbs, iters=6, trajs_g=trajs_g, vis_g=vis_g, valids=valids, sw=sw)
ate = torch.norm(preds[-1] - trajs_g, dim=-1) # B, S, N
ate_all = utils.basic.reduce_masked_mean(ate, valids)
ate_vis = utils.basic.reduce_masked_mean(ate, valids*vis_g)
ate_occ = utils.basic.reduce_masked_mean(ate, valids*(1.0-vis_g))
metrics = {
'ate_all': ate_all.item(),
'ate_vis': ate_vis.item(),
'ate_occ': ate_occ.item(),
}
trajs_e = preds[-1]
if sw is not None and sw.save_this:
sw.summ_traj2ds_on_rgbs('inputs_0/orig_trajs_on_rgbs', trajs_g, utils.improc.preprocess_color(rgbs), cmap='winter', linewidth=2)
sw.summ_traj2ds_on_rgbs('outputs/trajs_on_rgbs', trajs_e[0:1], utils.improc.preprocess_color(rgbs[0:1]), cmap='spring', linewidth=2)
gt_rgb = utils.improc.preprocess_color(sw.summ_traj2ds_on_rgb('inputs_0_all/single_trajs_on_rgb', trajs_g[0:1], torch.mean(utils.improc.preprocess_color(rgbs[0:1]), dim=1), cmap='winter', frame_id=metrics['ate_all'], only_return=True, linewidth=2))
gt_black = utils.improc.preprocess_color(sw.summ_traj2ds_on_rgb('inputs_0_all/single_trajs_on_rgb', trajs_g[0:1], torch.ones_like(rgbs[0:1,0])*-0.5, cmap='winter', frame_id=metrics['ate_all'], only_return=True, linewidth=2))
sw.summ_traj2ds_on_rgb('outputs/single_trajs_on_gt_rgb', trajs_e[0:1], gt_rgb[0:1], cmap='spring', linewidth=2)
sw.summ_traj2ds_on_rgb('outputs/single_trajs_on_gt_black', trajs_e[0:1], gt_black[0:1], cmap='spring', linewidth=2)
return metrics
def run_raft(raft, d, sw):
rgbs = d['rgbs'].cuda()
trajs_g = d['trajs_g'].cuda() # B,S,N,2
vis_g = d['vis_g'].cuda() # B,S,N
valids = torch.ones_like(vis_g) # B,S,N
B, S, C, H, W = rgbs.shape
B, S1, N, D = trajs_g.shape
rgbs_ = rgbs.reshape(B*S, C, H, W)
H_, W_ = 768, 1280
sy = H_/H
sx = W_/W
rgbs_ = F.interpolate(rgbs_, (H_, W_), mode='bilinear')
H, W = H_, W_
rgbs = rgbs_.reshape(B, S, C, H, W)
trajs_g[:,:,:,0] *= sx
trajs_g[:,:,:,1] *= sy
_, S, C, H, W = rgbs.shape
prep_rgbs = utils.improc.preprocess_color(rgbs)
flows_e = []
for s in range(S-1):
rgb0 = prep_rgbs[:,s]
rgb1 = prep_rgbs[:,s+1]
flow, _ = raft(rgb0, rgb1, iters=32)
flows_e.append(flow)
flows_e = torch.stack(flows_e, dim=1) # B, S-1, 2, H, W
coords = []
coord0 = trajs_g[:,0] # B, N, 2
coords.append(coord0)
coord = coord0.clone()
for s in range(S-1):
delta = utils.samp.bilinear_sample2d(
flows_e[:,s], coord[:,:,0], coord[:,:,1]).permute(0,2,1) # B, N, 2, forward flow at the discrete points
coord = coord + delta
coords.append(coord)
trajs_e = torch.stack(coords, dim=1) # B, S, N, 2
ate = torch.norm(trajs_e - trajs_g, dim=-1) # B, S, N
ate_all = utils.basic.reduce_masked_mean(ate, valids)
ate_vis = utils.basic.reduce_masked_mean(ate, valids*vis_g)
ate_occ = utils.basic.reduce_masked_mean(ate, valids*(1.0-vis_g))
metrics = {
'ate_all': ate_all.item(),
'ate_vis': ate_vis.item(),
'ate_occ': ate_occ.item(),
}
if sw is not None and sw.save_this:
sw.summ_traj2ds_on_rgbs('inputs_0/orig_trajs_on_rgbs', trajs_g, utils.improc.preprocess_color(rgbs), cmap='winter', linewidth=2)
sw.summ_traj2ds_on_rgbs('outputs/trajs_on_rgbs', trajs_e[0:1], utils.improc.preprocess_color(rgbs[0:1]), cmap='spring', linewidth=2)
gt_rgb = utils.improc.preprocess_color(sw.summ_traj2ds_on_rgb('inputs_0_all/single_trajs_on_rgb', trajs_g[0:1], torch.mean(utils.improc.preprocess_color(rgbs[0:1]), dim=1), cmap='winter', frame_id=metrics['ate_all'], only_return=True, linewidth=2))
gt_black = utils.improc.preprocess_color(sw.summ_traj2ds_on_rgb('inputs_0_all/single_trajs_on_rgb', trajs_g[0:1], torch.ones_like(rgbs[0:1,0])*-0.5, cmap='winter', frame_id=metrics['ate_all'], only_return=True, linewidth=2))
sw.summ_traj2ds_on_rgb('outputs/single_trajs_on_gt_rgb', trajs_e[0:1], gt_rgb[0:1], cmap='spring', linewidth=2)
sw.summ_traj2ds_on_rgb('outputs/single_trajs_on_gt_black', trajs_e[0:1], gt_black[0:1], cmap='spring', linewidth=2)
return metrics
def main(
exp_name='crohd',
B=1,
S=8,
N=16,
modeltype='pips',
init_dir='./data/my_pips/checkpoints/',
req_occlusion=True,
stride=4,
log_dir='logs_test_on_crohd',
dataset_location='./data/HT21',
max_iters=0, # auto-select based on dataset
log_freq=100,
shuffle=False,
subset='all',
use_augs=False,
):
# the idea in this file is to evaluate on head tracking in croHD
# pips vis: 4.57
# pips occ: 7.71
assert(modeltype=='pips' or modeltype=='raft' or modeltype=='dino')
S_stride = 3 # subsample the frames this much
## autogen a name
model_name = "%d_%d_%d_%s" % (B, S, N, modeltype)
if req_occlusion:
model_name += "_occ"
else:
model_name += "_vis"
model_name += "_%s" % exp_name
import datetime
model_date = datetime.datetime.now().strftime('%H:%M:%S')
model_name = model_name + '_' + model_date
print('model_name', model_name)
writer_t = SummaryWriter(log_dir + '/' + model_name + '/t', max_queue=10, flush_secs=60)
dataset = CrohdDataset(seqlen=S*S_stride, dataset_root=dataset_location)
test_dataloader = DataLoader(
dataset,
batch_size=B,
shuffle=shuffle,
num_workers=12)
test_iterloader = iter(test_dataloader)
global_step = 0
if modeltype=='pips':
model = Pips(S=S, stride=stride).cuda()
_ = saverloader.load(init_dir, model)
model.eval()
elif modeltype=='raft':
model = Raftnet(ckpt_name='../RAFT/models/raft-things.pth').cuda()
model.eval()
elif modeltype=='dino':
patch_size = 8
model = torch.hub.load('facebookresearch/dino:main', 'dino_vits%d' % patch_size).cuda()
model.eval()
else:
assert(False) # need to choose a valid modeltype
n_pool = 10000
ate_all_pool_t = utils.misc.SimplePool(n_pool, version='np')
ate_vis_pool_t = utils.misc.SimplePool(n_pool, version='np')
ate_occ_pool_t = utils.misc.SimplePool(n_pool, version='np')
if max_iters==0:
max_iters = len(test_dataloader)
print('setting max_iters', max_iters)
while global_step < max_iters:
read_start_time = time.time()
global_step += 1
sw_t = utils.improc.Summ_writer(
writer=writer_t,
global_step=global_step,
log_freq=log_freq,
fps=5,
scalar_freq=int(log_freq/2),
just_gif=True)
returned_early = True
while returned_early:
try:
sample = next(test_iterloader)
except StopIteration:
test_iterloader = iter(test_dataloader)
sample = next(test_iterloader)
sample, returned_early = prep_sample(sample, N, S_stride, req_occlusion)
read_time = time.time()-read_start_time
iter_start_time = time.time()
with torch.no_grad():
if modeltype=='pips':
metrics = run_pips(model, sample, sw_t)
elif modeltype=='raft':
metrics = run_raft(model, sample, sw_t)
elif modeltype=='dino':
metrics = run_dino(model, sample, sw_t)
else:
assert(False) # need to choose a valid modeltype
if metrics['ate_all'] > 0:
ate_all_pool_t.update([metrics['ate_all']])
if metrics['ate_vis'] > 0:
ate_vis_pool_t.update([metrics['ate_vis']])
if metrics['ate_occ'] > 0:
ate_occ_pool_t.update([metrics['ate_occ']])
sw_t.summ_scalar('pooled/ate_all', ate_all_pool_t.mean())
sw_t.summ_scalar('pooled/ate_vis', ate_vis_pool_t.mean())
sw_t.summ_scalar('pooled/ate_occ', ate_occ_pool_t.mean())
iter_time = time.time()-iter_start_time
print('%s; step %06d/%d; rtime %.2f; itime %.2f; ate = %.2f; ate_pooled = %.2f' % (
model_name, global_step, max_iters, read_time, iter_time,
metrics['ate_all'], ate_all_pool_t.mean()))
writer_t.close()
if __name__ == '__main__':
Fire(main)