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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from collections import defaultdict
import os
from typing import Optional
import torch
from tqdm import tqdm
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from cotracker.datasets.utils import dataclass_to_cuda_
from cotracker.utils.visualizer import Visualizer
from cotracker.models.core.model_utils import reduce_masked_mean
from cotracker.evaluation.core.eval_utils import compute_tapvid_metrics
import logging
class Evaluator:
"""
A class defining the CoTracker evaluator.
"""
def __init__(self, exp_dir) -> None:
# Visualization
self.exp_dir = exp_dir
os.makedirs(exp_dir, exist_ok=True)
self.visualization_filepaths = defaultdict(lambda: defaultdict(list))
self.visualize_dir = os.path.join(exp_dir, "visualisations")
def compute_metrics(self, metrics, sample, pred_trajectory, dataset_name):
if isinstance(pred_trajectory, tuple):
pred_trajectory, pred_visibility = pred_trajectory
else:
pred_visibility = None
if "tapvid" in dataset_name:
B, T, N, D = sample.trajectory.shape
traj = sample.trajectory.clone()
thr = 0.9
if pred_visibility is None:
logging.warning("visibility is NONE")
pred_visibility = torch.zeros_like(sample.visibility)
if not pred_visibility.dtype == torch.bool:
pred_visibility = pred_visibility > thr
query_points = sample.query_points.clone().cpu().numpy()
pred_visibility = pred_visibility[:, :, :N]
pred_trajectory = pred_trajectory[:, :, :N]
gt_tracks = traj.permute(0, 2, 1, 3).cpu().numpy()
gt_occluded = (
torch.logical_not(sample.visibility.clone().permute(0, 2, 1)).cpu().numpy()
)
pred_occluded = (
torch.logical_not(pred_visibility.clone().permute(0, 2, 1)).cpu().numpy()
)
pred_tracks = pred_trajectory.permute(0, 2, 1, 3).cpu().numpy()
out_metrics = compute_tapvid_metrics(
query_points,
gt_occluded,
gt_tracks,
pred_occluded,
pred_tracks,
query_mode="strided" if "strided" in dataset_name else "first",
)
metrics[sample.seq_name[0]] = out_metrics
for metric_name in out_metrics.keys():
if "avg" not in metrics:
metrics["avg"] = {}
metrics["avg"][metric_name] = np.mean(
[v[metric_name] for k, v in metrics.items() if k != "avg"]
)
logging.info(f"Metrics: {out_metrics}")
logging.info(f"avg: {metrics['avg']}")
print("metrics", out_metrics)
print("avg", metrics["avg"])
elif dataset_name == "dynamic_replica" or dataset_name == "pointodyssey":
*_, N, _ = sample.trajectory.shape
B, T, N = sample.visibility.shape
H, W = sample.video.shape[-2:]
device = sample.video.device
out_metrics = {}
d_vis_sum = d_occ_sum = d_sum_all = 0.0
thrs = [1, 2, 4, 8, 16]
sx_ = (W - 1) / 255.0
sy_ = (H - 1) / 255.0
sc_py = np.array([sx_, sy_]).reshape([1, 1, 2])
sc_pt = torch.from_numpy(sc_py).float().to(device)
__, first_visible_inds = torch.max(sample.visibility, dim=1)
frame_ids_tensor = torch.arange(T, device=device)[None, :, None].repeat(B, 1, N)
start_tracking_mask = frame_ids_tensor > (first_visible_inds.unsqueeze(1))
for thr in thrs:
d_ = (
torch.norm(
pred_trajectory[..., :2] / sc_pt - sample.trajectory[..., :2] / sc_pt,
dim=-1,
)
< thr
).float() # B,S-1,N
d_occ = (
reduce_masked_mean(d_, (1 - sample.visibility) * start_tracking_mask).item()
* 100.0
)
d_occ_sum += d_occ
out_metrics[f"accuracy_occ_{thr}"] = d_occ
d_vis = (
reduce_masked_mean(d_, sample.visibility * start_tracking_mask).item() * 100.0
)
d_vis_sum += d_vis
out_metrics[f"accuracy_vis_{thr}"] = d_vis
d_all = reduce_masked_mean(d_, start_tracking_mask).item() * 100.0
d_sum_all += d_all
out_metrics[f"accuracy_{thr}"] = d_all
d_occ_avg = d_occ_sum / len(thrs)
d_vis_avg = d_vis_sum / len(thrs)
d_all_avg = d_sum_all / len(thrs)
sur_thr = 50
dists = torch.norm(
pred_trajectory[..., :2] / sc_pt - sample.trajectory[..., :2] / sc_pt,
dim=-1,
) # B,S,N
dist_ok = 1 - (dists > sur_thr).float() * sample.visibility # B,S,N
survival = torch.cumprod(dist_ok, dim=1) # B,S,N
out_metrics["survival"] = torch.mean(survival).item() * 100.0
out_metrics["accuracy_occ"] = d_occ_avg
out_metrics["accuracy_vis"] = d_vis_avg
out_metrics["accuracy"] = d_all_avg
metrics[sample.seq_name[0]] = out_metrics
for metric_name in out_metrics.keys():
if "avg" not in metrics:
metrics["avg"] = {}
metrics["avg"][metric_name] = float(
np.mean([v[metric_name] for k, v in metrics.items() if k != "avg"])
)
logging.info(f"Metrics: {out_metrics}")
logging.info(f"avg: {metrics['avg']}")
print("metrics", out_metrics)
print("avg", metrics["avg"])
@torch.no_grad()
def evaluate_sequence(
self,
model,
test_dataloader: torch.utils.data.DataLoader,
dataset_name: str,
train_mode=False,
visualize_every: int = 1,
writer: Optional[SummaryWriter] = None,
step: Optional[int] = 0,
):
metrics = {}
vis = Visualizer(
save_dir=self.exp_dir,
fps=7,
)
for ind, sample in enumerate(tqdm(test_dataloader)):
if isinstance(sample, tuple):
sample, gotit = sample
if not all(gotit):
print("batch is None")
continue
if torch.cuda.is_available():
dataclass_to_cuda_(sample)
device = torch.device("cuda")
else:
device = torch.device("cpu")
if (
not train_mode
and hasattr(model, "sequence_len")
and (sample.visibility[:, : model.sequence_len].sum() == 0)
):
print(f"skipping batch {ind}")
continue
if "tapvid" in dataset_name:
queries = sample.query_points.clone().float()
queries = torch.stack(
[
queries[:, :, 0],
queries[:, :, 2],
queries[:, :, 1],
],
dim=2,
).to(device)
else:
queries = torch.cat(
[
torch.zeros_like(sample.trajectory[:, 0, :, :1]),
sample.trajectory[:, 0],
],
dim=2,
).to(device)
pred_tracks = model(sample.video, queries)
if "strided" in dataset_name:
inv_video = sample.video.flip(1).clone()
inv_queries = queries.clone()
inv_queries[:, :, 0] = inv_video.shape[1] - inv_queries[:, :, 0] - 1
pred_trj, pred_vsb = pred_tracks
inv_pred_trj, inv_pred_vsb = model(inv_video, inv_queries)
inv_pred_trj = inv_pred_trj.flip(1)
inv_pred_vsb = inv_pred_vsb.flip(1)
mask = pred_trj == 0
pred_trj[mask] = inv_pred_trj[mask]
pred_vsb[mask[:, :, :, 0]] = inv_pred_vsb[mask[:, :, :, 0]]
pred_tracks = pred_trj, pred_vsb
if dataset_name == "badja" or dataset_name == "fastcapture":
seq_name = sample.seq_name[0]
else:
seq_name = str(ind)
if ind % visualize_every == 0:
vis.visualize(
sample.video,
pred_tracks[0] if isinstance(pred_tracks, tuple) else pred_tracks,
filename=dataset_name + "_" + seq_name,
writer=writer,
step=step,
)
self.compute_metrics(metrics, sample, pred_tracks, dataset_name)
return metrics
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