PointFlow/metrics/evaluation_metrics.py
2019-07-13 21:32:26 -07:00

337 lines
11 KiB
Python

import torch
import numpy as np
import warnings
from scipy.stats import entropy
from sklearn.neighbors import NearestNeighbors
from numpy.linalg import norm
# Import CUDA version of approximate EMD, from https://github.com/zekunhao1995/pcgan-pytorch/
from .StructuralLosses.match_cost import match_cost
from .StructuralLosses.nn_distance import nn_distance
# # Import CUDA version of CD, borrowed from https://github.com/ThibaultGROUEIX/AtlasNet
# try:
# from . chamfer_distance_ext.dist_chamfer import chamferDist
# CD = chamferDist()
# def distChamferCUDA(x,y):
# return CD(x,y,gpu)
# except:
def distChamferCUDA(x, y):
return nn_distance(x, y)
def emd_approx(sample, ref):
B, N, N_ref = sample.size(0), sample.size(1), ref.size(1)
assert N == N_ref, "Not sure what would EMD do in this case"
emd = match_cost(sample, ref) # (B,)
emd_norm = emd / float(N) # (B,)
return emd_norm
# Borrow from https://github.com/ThibaultGROUEIX/AtlasNet
def distChamfer(a, b):
x, y = a, b
bs, num_points, points_dim = x.size()
xx = torch.bmm(x, x.transpose(2, 1))
yy = torch.bmm(y, y.transpose(2, 1))
zz = torch.bmm(x, y.transpose(2, 1))
diag_ind = torch.arange(0, num_points).to(a).long()
rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
P = (rx.transpose(2, 1) + ry - 2 * zz)
return P.min(1)[0], P.min(2)[0]
def EMD_CD(sample_pcs, ref_pcs, batch_size, accelerated_cd=False, reduced=True):
N_sample = sample_pcs.shape[0]
N_ref = ref_pcs.shape[0]
assert N_sample == N_ref, "REF:%d SMP:%d" % (N_ref, N_sample)
cd_lst = []
emd_lst = []
iterator = range(0, N_sample, batch_size)
for b_start in iterator:
b_end = min(N_sample, b_start + batch_size)
sample_batch = sample_pcs[b_start:b_end]
ref_batch = ref_pcs[b_start:b_end]
if accelerated_cd:
dl, dr = distChamferCUDA(sample_batch, ref_batch)
else:
dl, dr = distChamfer(sample_batch, ref_batch)
cd_lst.append(dl.mean(dim=1) + dr.mean(dim=1))
emd_batch = emd_approx(sample_batch, ref_batch)
emd_lst.append(emd_batch)
if reduced:
cd = torch.cat(cd_lst).mean()
emd = torch.cat(emd_lst).mean()
else:
cd = torch.cat(cd_lst)
emd = torch.cat(emd_lst)
results = {
'MMD-CD': cd,
'MMD-EMD': emd,
}
return results
def _pairwise_EMD_CD_(sample_pcs, ref_pcs, batch_size, accelerated_cd=True):
N_sample = sample_pcs.shape[0]
N_ref = ref_pcs.shape[0]
all_cd = []
all_emd = []
iterator = range(N_sample)
for sample_b_start in iterator:
sample_batch = sample_pcs[sample_b_start]
cd_lst = []
emd_lst = []
for ref_b_start in range(0, N_ref, batch_size):
ref_b_end = min(N_ref, ref_b_start + batch_size)
ref_batch = ref_pcs[ref_b_start:ref_b_end]
batch_size_ref = ref_batch.size(0)
sample_batch_exp = sample_batch.view(1, -1, 3).expand(batch_size_ref, -1, -1)
sample_batch_exp = sample_batch_exp.contiguous()
if accelerated_cd:
dl, dr = distChamferCUDA(sample_batch_exp, ref_batch)
else:
dl, dr = distChamfer(sample_batch_exp, ref_batch)
cd_lst.append((dl.mean(dim=1) + dr.mean(dim=1)).view(1, -1))
emd_batch = emd_approx(sample_batch_exp, ref_batch)
emd_lst.append(emd_batch.view(1, -1))
cd_lst = torch.cat(cd_lst, dim=1)
emd_lst = torch.cat(emd_lst, dim=1)
all_cd.append(cd_lst)
all_emd.append(emd_lst)
all_cd = torch.cat(all_cd, dim=0) # N_sample, N_ref
all_emd = torch.cat(all_emd, dim=0) # N_sample, N_ref
return all_cd, all_emd
# Adapted from https://github.com/xuqiantong/GAN-Metrics/blob/master/framework/metric.py
def knn(Mxx, Mxy, Myy, k, sqrt=False):
n0 = Mxx.size(0)
n1 = Myy.size(0)
label = torch.cat((torch.ones(n0), torch.zeros(n1))).to(Mxx)
M = torch.cat((torch.cat((Mxx, Mxy), 1), torch.cat((Mxy.transpose(0, 1), Myy), 1)), 0)
if sqrt:
M = M.abs().sqrt()
INFINITY = float('inf')
val, idx = (M + torch.diag(INFINITY * torch.ones(n0 + n1).to(Mxx))).topk(k, 0, False)
count = torch.zeros(n0 + n1).to(Mxx)
for i in range(0, k):
count = count + label.index_select(0, idx[i])
pred = torch.ge(count, (float(k) / 2) * torch.ones(n0 + n1).to(Mxx)).float()
s = {
'tp': (pred * label).sum(),
'fp': (pred * (1 - label)).sum(),
'fn': ((1 - pred) * label).sum(),
'tn': ((1 - pred) * (1 - label)).sum(),
}
s.update({
'precision': s['tp'] / (s['tp'] + s['fp'] + 1e-10),
'recall': s['tp'] / (s['tp'] + s['fn'] + 1e-10),
'acc_t': s['tp'] / (s['tp'] + s['fn'] + 1e-10),
'acc_f': s['tn'] / (s['tn'] + s['fp'] + 1e-10),
'acc': torch.eq(label, pred).float().mean(),
})
return s
def lgan_mmd_cov(all_dist):
N_sample, N_ref = all_dist.size(0), all_dist.size(1)
min_val_fromsmp, min_idx = torch.min(all_dist, dim=1)
min_val, _ = torch.min(all_dist, dim=0)
mmd = min_val.mean()
mmd_smp = min_val_fromsmp.mean()
cov = float(min_idx.unique().view(-1).size(0)) / float(N_ref)
cov = torch.tensor(cov).to(all_dist)
return {
'lgan_mmd': mmd,
'lgan_cov': cov,
'lgan_mmd_smp': mmd_smp,
}
def compute_all_metrics(sample_pcs, ref_pcs, batch_size, accelerated_cd=False):
results = {}
M_rs_cd, M_rs_emd = _pairwise_EMD_CD_(ref_pcs, sample_pcs, batch_size, accelerated_cd=accelerated_cd)
res_cd = lgan_mmd_cov(M_rs_cd.t())
results.update({
"%s-CD" % k: v for k, v in res_cd.items()
})
res_emd = lgan_mmd_cov(M_rs_emd.t())
results.update({
"%s-EMD" % k: v for k, v in res_emd.items()
})
M_rr_cd, M_rr_emd = _pairwise_EMD_CD_(ref_pcs, ref_pcs, batch_size, accelerated_cd=accelerated_cd)
M_ss_cd, M_ss_emd = _pairwise_EMD_CD_(sample_pcs, sample_pcs, batch_size, accelerated_cd=accelerated_cd)
# 1-NN results
one_nn_cd_res = knn(M_rr_cd, M_rs_cd, M_ss_cd, 1, sqrt=False)
results.update({
"1-NN-CD-%s" % k: v for k, v in one_nn_cd_res.items() if 'acc' in k
})
one_nn_emd_res = knn(M_rr_emd, M_rs_emd, M_ss_emd, 1, sqrt=False)
results.update({
"1-NN-EMD-%s" % k: v for k, v in one_nn_emd_res.items() if 'acc' in k
})
return results
#######################################################
# JSD : from https://github.com/optas/latent_3d_points
#######################################################
def unit_cube_grid_point_cloud(resolution, clip_sphere=False):
"""Returns the center coordinates of each cell of a 3D grid with resolution^3 cells,
that is placed in the unit-cube.
If clip_sphere it True it drops the "corner" cells that lie outside the unit-sphere.
"""
grid = np.ndarray((resolution, resolution, resolution, 3), np.float32)
spacing = 1.0 / float(resolution - 1)
for i in range(resolution):
for j in range(resolution):
for k in range(resolution):
grid[i, j, k, 0] = i * spacing - 0.5
grid[i, j, k, 1] = j * spacing - 0.5
grid[i, j, k, 2] = k * spacing - 0.5
if clip_sphere:
grid = grid.reshape(-1, 3)
grid = grid[norm(grid, axis=1) <= 0.5]
return grid, spacing
def jsd_between_point_cloud_sets(sample_pcs, ref_pcs, resolution=28):
"""Computes the JSD between two sets of point-clouds, as introduced in the paper
```Learning Representations And Generative Models For 3D Point Clouds```.
Args:
sample_pcs: (np.ndarray S1xR2x3) S1 point-clouds, each of R1 points.
ref_pcs: (np.ndarray S2xR2x3) S2 point-clouds, each of R2 points.
resolution: (int) grid-resolution. Affects granularity of measurements.
"""
in_unit_sphere = True
sample_grid_var = entropy_of_occupancy_grid(sample_pcs, resolution, in_unit_sphere)[1]
ref_grid_var = entropy_of_occupancy_grid(ref_pcs, resolution, in_unit_sphere)[1]
return jensen_shannon_divergence(sample_grid_var, ref_grid_var)
def entropy_of_occupancy_grid(pclouds, grid_resolution, in_sphere=False, verbose=False):
"""Given a collection of point-clouds, estimate the entropy of the random variables
corresponding to occupancy-grid activation patterns.
Inputs:
pclouds: (numpy array) #point-clouds x points per point-cloud x 3
grid_resolution (int) size of occupancy grid that will be used.
"""
epsilon = 10e-4
bound = 0.5 + epsilon
if abs(np.max(pclouds)) > bound or abs(np.min(pclouds)) > bound:
if verbose:
warnings.warn('Point-clouds are not in unit cube.')
if in_sphere and np.max(np.sqrt(np.sum(pclouds ** 2, axis=2))) > bound:
if verbose:
warnings.warn('Point-clouds are not in unit sphere.')
grid_coordinates, _ = unit_cube_grid_point_cloud(grid_resolution, in_sphere)
grid_coordinates = grid_coordinates.reshape(-1, 3)
grid_counters = np.zeros(len(grid_coordinates))
grid_bernoulli_rvars = np.zeros(len(grid_coordinates))
nn = NearestNeighbors(n_neighbors=1).fit(grid_coordinates)
for pc in pclouds:
_, indices = nn.kneighbors(pc)
indices = np.squeeze(indices)
for i in indices:
grid_counters[i] += 1
indices = np.unique(indices)
for i in indices:
grid_bernoulli_rvars[i] += 1
acc_entropy = 0.0
n = float(len(pclouds))
for g in grid_bernoulli_rvars:
if g > 0:
p = float(g) / n
acc_entropy += entropy([p, 1.0 - p])
return acc_entropy / len(grid_counters), grid_counters
def jensen_shannon_divergence(P, Q):
if np.any(P < 0) or np.any(Q < 0):
raise ValueError('Negative values.')
if len(P) != len(Q):
raise ValueError('Non equal size.')
P_ = P / np.sum(P) # Ensure probabilities.
Q_ = Q / np.sum(Q)
e1 = entropy(P_, base=2)
e2 = entropy(Q_, base=2)
e_sum = entropy((P_ + Q_) / 2.0, base=2)
res = e_sum - ((e1 + e2) / 2.0)
res2 = _jsdiv(P_, Q_)
if not np.allclose(res, res2, atol=10e-5, rtol=0):
warnings.warn('Numerical values of two JSD methods don\'t agree.')
return res
def _jsdiv(P, Q):
"""another way of computing JSD"""
def _kldiv(A, B):
a = A.copy()
b = B.copy()
idx = np.logical_and(a > 0, b > 0)
a = a[idx]
b = b[idx]
return np.sum([v for v in a * np.log2(a / b)])
P_ = P / np.sum(P)
Q_ = Q / np.sum(Q)
M = 0.5 * (P_ + Q_)
return 0.5 * (_kldiv(P_, M) + _kldiv(Q_, M))
if __name__ == "__main__":
B, N = 2, 10
x = torch.rand(B, N, 3)
y = torch.rand(B, N, 3)
distChamfer = distChamferCUDA()
min_l, min_r = distChamfer(x.cuda(), y.cuda())
print(min_l.shape)
print(min_r.shape)
l_dist = min_l.mean().cpu().detach().item()
r_dist = min_r.mean().cpu().detach().item()
print(l_dist, r_dist)