/
test_on_flt.py
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/
test_on_flt.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 flyingthingsdataset import FlyingThingsDataset
import utils.basic
import utils.improc
import utils.test
from fire import Fire
device = 'cuda'
random.seed(125)
np.random.seed(125)
def run_dino(dino, d, sw):
rgbs = d['rgbs'].cuda().float() # B, S, C, H, W
occs = d['occs'].cuda().float() # B, S, 1, H, W
masks = d['masks'].cuda().float() # B, S, 1, H, W
trajs_g = d['trajs'].cuda().float() # B, S, N, 2
vis_g = d['visibles'].cuda().float() # B, S, N
valids = d['valids'].cuda().float() # B, S, N
B, S, C, H, W = rgbs.shape
B, S1, N, D = trajs_g.shape
assert(torch.sum(valids)==B*S*N)
# compute per-sequence visibility labels
vis_g = (torch.sum(vis_g, dim=1, keepdim=True) >= 4).float().repeat(1, S, 1)
_, 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().float() # B, S, C, H, W
occs = d['occs'].cuda().float() # B, S, 1, H, W
masks = d['masks'].cuda().float() # B, S, 1, H, W
trajs_g = d['trajs'].cuda().float() # B, S, N, 2
vis_g = d['visibles'].cuda().float() # B, S, N
valids = d['valids'].cuda().float() # B, S, N
B, S, C, H, W = rgbs.shape
assert(C==3)
B, S, N, D = trajs_g.shape
assert(torch.sum(valids)==B*S*N)
# compute per-sequence visibility labels
vis_g = (torch.sum(vis_g, dim=1, keepdim=True) >= 4).float().repeat(1, S, 1)
_, 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)
# animate_traj2ds_on_rgbs
rgb_vis = []
black_vis = []
for trajs_e_ in preds_anim:
rgb_vis.append(sw.summ_traj2ds_on_rgb('', trajs_e_[0:1], gt_rgb, only_return=True, cmap='coolwarm'))
black_vis.append(sw.summ_traj2ds_on_rgb('', trajs_e_[0:1], gt_black, only_return=True, cmap='coolwarm'))
sw.summ_rgbs('outputs/animated_trajs_on_black', black_vis)
sw.summ_rgbs('outputs/animated_trajs_on_rgb', rgb_vis)
return metrics
def run_raft(raft, d, sw):
rgbs = d['rgbs'].cuda().float() # B, S, C, H, W
occs = d['occs'].cuda().float() # B, S, 1, H, W
masks = d['masks'].cuda().float() # B, S, 1, H, W
trajs_g = d['trajs'].cuda().float() # B, S, N, 2
vis_g = d['visibles'].cuda().float() # B, S, N
valids = d['valids'].cuda().float() # B, S, N
B, S, C, H, W = rgbs.shape
assert(C==3)
B, S, N, D = trajs_g.shape
assert(torch.sum(valids)==B*S*N)
# compute per-sequence visibility labels
vis_g = (torch.sum(vis_g, dim=1, keepdim=True) >= 4).float().repeat(1, S, 1)
_, 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='flt',
B=1,
S=8,
N=16,
modeltype='pips',
init_dir='reference_model',
stride=4,
log_dir='logs_test_on_flt',
dataset_location='/data/flyingthings',
max_iters=0, # auto-select based on dataset
log_freq=100,
shuffle=False,
subset='all',
crop_size=(384,512), # the raw data is 540,960
use_augs=False,
):
# the idea in this file is to evaluate on flyingthings++
assert(modeltype=='pips' or modeltype=='raft' or modeltype=='dino')
## autogen a name
model_name = "%d_%d_%d_%s" % (B, S, N, modeltype)
if use_augs:
model_name += "_A"
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)
def worker_init_fn(worker_id):
np.random.seed(np.random.get_state()[1][0] + worker_id)
test_dataset = FlyingThingsDataset(
dataset_location=dataset_location,
dset='TEST', subset=subset,
use_augs=use_augs,
N=N, S=S,
crop_size=crop_size)
test_dataloader = DataLoader(
test_dataset,
batch_size=B,
shuffle=shuffle,
num_workers=24,
worker_init_fn=worker_init_fn,
drop_last=True)
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)
gotit = (False,False)
while not all(gotit):
try:
sample, gotit = next(test_iterloader)
except StopIteration:
test_iterloader = iter(test_dataloader)
sample, gotit = next(test_iterloader)
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_vis = %.2f, ate_occ = %.2f' % (
model_name, global_step, max_iters, read_time, iter_time,
ate_vis_pool_t.mean(), ate_occ_pool_t.mean()))
writer_t.close()
if __name__ == '__main__':
Fire(main)