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