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import argparse
import math
import os, sys
import random
import datetime
import time
from typing import List
import json
import logging
import numpy as np
from copy import deepcopy
from .seg_tester_dev import DatasetEvaluator
import torch
import torch.nn as nn
import torch.nn.parallel
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
class SMPLMAEEvaluator(DatasetEvaluator):
def __init__(
self,
dataset_name,
config,
distributed=True,
output_dir=None,
):
self._logger = logging.getLogger(__name__)
self._dataset_name = dataset_name
self._distributed = distributed
self._output_dir = output_dir
self._cpu_device = torch.device("cpu")
def reset(self):
self.gt_3d_joints = []
self.has_3d_joints = []
self.has_smpl = []
self.gt_vertices_fine = []
self.pred_vertices = []
self.pred_3d_joints_from_smpl = []
def process(self, inputs, outputs):
self.gt_3d_joints.append(inputs['gt_3d_joints'].cpu())
self.has_3d_joints.append(inputs['has_3d_joints'].cpu())
self.has_smpl.append(inputs['has_smpl'].cpu())
self.gt_vertices_fine.append(inputs['gt_3d_vertices_fine'].cpu())
self.pred_vertices.append(outputs['pred']["pred_3d_vertices_fine"].cpu())
self.pred_3d_joints_from_smpl.append(outputs['pred']["pred_3d_joints_from_smpl"].cpu())
# gt_label = inputs['label']
# preds_probs = outputs['logit']
# self.gt_label.append(gt_label)
# self.preds_probs.append(preds_probs)
@staticmethod
def all_gather(data, group=0):
assert dist.get_world_size() == 1, \
f"distributed eval unsupported yet, \
uncertain if we can use torch.dist \
with link jointly"
if dist.get_world_size() ==1:
return [data]
world_size = dist.get_world_size()
tensors_gather = [torch.ones_like(data) for _ in range(world_size)]
dist.allgather(tensors_gather, data, group=group)
return tensors_gather
def evaluate(self):
gt_3d_joints = torch.cat(self.gt_3d_joints, dim=0)
has_3d_joints = torch.cat(self.has_3d_joints, dim=0)
has_smpl = torch.cat(self.has_smpl, dim=0)
gt_vertices_fine = torch.cat(self.gt_vertices_fine, dim=0)
pred_vertices = torch.cat(self.pred_vertices, dim=0)
pred_3d_joints_from_smpl = torch.cat(self.pred_3d_joints_from_smpl, dim=0)
if self._distributed:
torch.cuda.synchronize()
gt_3d_joints = self.all_gather(gt_3d_joints)
has_3d_joints = self.all_gather(has_3d_joints)
has_smpl = self.all_gather(has_smpl)
gt_vertices_fine = self.all_gather(gt_vertices_fine)
pred_vertices = self.all_gather(pred_vertices)
pred_3d_joints_from_smpl = self.all_gather(pred_3d_joints_from_smpl)
if dist.get_rank() != 0:
return
gt_vertices_fine = [x.cpu() for x in gt_vertices_fine]
pred_vertices = [x.cpu() for x in pred_vertices]
gt_3d_joints = torch.cat(gt_3d_joints, dim=0)
has_3d_joints = torch.cat(has_3d_joints, dim=0)
has_smpl = torch.cat(has_smpl, dim=0)
gt_vertices_fine = torch.cat(gt_vertices_fine, dim=0)
pred_vertices = torch.cat(pred_vertices, dim=0)
pred_3d_joints_from_smpl = torch.cat(pred_3d_joints_from_smpl, dim=0)
print('===')
print(pred_3d_joints_from_smpl.shape)
print(gt_3d_joints.shape)
# measure errors
np.save('pred_3d_joint_from_smpl.npy', pred_3d_joints_from_smpl.numpy())
np.save('gt_3d_joint.npy',gt_3d_joints.numpy())
error_vertices = mean_per_vertex_error(pred_vertices, gt_vertices_fine, has_smpl)
error_joints = mean_per_joint_position_error(pred_3d_joints_from_smpl, gt_3d_joints, has_3d_joints)
error_joints_pa = reconstruction_error(pred_3d_joints_from_smpl.cpu().numpy(), gt_3d_joints[:,:,:3].cpu().numpy(), reduction=None)
result = {}
result['@mPVE'] = np.mean(error_vertices) * 1000
result['@mPJPE'] = np.mean(error_joints) * 1000
result['@PAmPJPE'] = np.mean(error_joints_pa) * 1000
return result
def mean_per_joint_position_error(pred, gt, has_3d_joints):
"""
Compute mPJPE
"""
gt = gt[has_3d_joints == 1]
gt = gt[:, :, :-1]
pred = pred[has_3d_joints == 1]
with torch.no_grad():
gt_pelvis = (gt[:, 2,:] + gt[:, 3,:]) / 2
gt = gt - gt_pelvis[:, None, :]
pred_pelvis = (pred[:, 2,:] + pred[:, 3,:]) / 2
pred = pred - pred_pelvis[:, None, :]
error = torch.sqrt( ((pred - gt) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy()
return error
def mean_per_vertex_error(pred, gt, has_smpl):
"""
Compute mPVE
"""
pred = pred[has_smpl == 1]
gt = gt[has_smpl == 1]
with torch.no_grad():
error = torch.sqrt( ((pred - gt) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy()
return error
def reconstruction_error(S1, S2, reduction='mean'):
"""Do Procrustes alignment and compute reconstruction error."""
S1_hat = compute_similarity_transform_batch(S1, S2)
re = np.sqrt( ((S1_hat - S2)** 2).sum(axis=-1)).mean(axis=-1)
if reduction == 'mean':
re = re.mean()
elif reduction == 'sum':
re = re.sum()
return re
def compute_similarity_transform_batch(S1, S2):
"""Batched version of compute_similarity_transform."""
S1_hat = np.zeros_like(S1)
for i in range(S1.shape[0]):
S1_hat[i] = compute_similarity_transform(S1[i], S2[i])
return S1_hat
def compute_similarity_transform(S1, S2):
"""Computes a similarity transform (sR, t) that takes
a set of 3D points S1 (3 x N) closest to a set of 3D points S2,
where R is an 3x3 rotation matrix, t 3x1 translation, s scale.
i.e. solves the orthogonal Procrutes problem.
"""
transposed = False
if S1.shape[0] != 3 and S1.shape[0] != 2:
S1 = S1.T
S2 = S2.T
transposed = True
assert(S2.shape[1] == S1.shape[1])
# 1. Remove mean.
mu1 = S1.mean(axis=1, keepdims=True)
mu2 = S2.mean(axis=1, keepdims=True)
X1 = S1 - mu1
X2 = S2 - mu2
# 2. Compute variance of X1 used for scale.
var1 = np.sum(X1**2)
# 3. The outer product of X1 and X2.
K = X1.dot(X2.T)
# 4. Solution that Maximizes trace(R'K) is R=U*V', where U, V are
# singular vectors of K.
U, s, Vh = np.linalg.svd(K)
V = Vh.T
# Construct Z that fixes the orientation of R to get det(R)=1.
Z = np.eye(U.shape[0])
Z[-1, -1] *= np.sign(np.linalg.det(U.dot(V.T)))
# Construct R.
R = V.dot(Z.dot(U.T))
# 5. Recover scale.
scale = np.trace(R.dot(K)) / var1
# 6. Recover translation.
t = mu2 - scale*(R.dot(mu1))
# 7. Error:
S1_hat = scale*R.dot(S1) + t
if transposed:
S1_hat = S1_hat.T
return S1_hat
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