def sample_embs(self, emb0, emb1, valid, B, Z, Y, X, mod='', do_vis=False, summ_writer=None): if hyp.emb_3D_mindist == 0.0: # pure random perm = torch.randperm(B * Z * Y * X) emb0 = emb0.reshape(B * Z * Y * X, -1) emb1 = emb1.reshape(B * Z * Y * X, -1) valid = valid.reshape(B * Z * Y * X, -1) emb0 = emb0[perm[:self.num_samples * B]] emb1 = emb1[perm[:self.num_samples * B]] valid = valid[perm[:self.num_samples * B]] return emb0, emb1, valid else: emb0_all = [] emb1_all = [] valid_all = [] for b in list(range(B)): sample_indices, sample_locs, sample_valids = utils_misc.get_safe_samples( valid[b], (Z, Y, X), self.num_samples, mode='3D', tol=hyp.emb_3D_mindist) emb0_s_ = emb0[b, sample_indices] emb1_s_ = emb1[b, sample_indices] # these are N x D emb0_all.append(emb0_s_) emb1_all.append(emb1_s_) valid_all.append(sample_valids) if do_vis and (summ_writer is not None): sample_occ = utils_vox.voxelize_xyz(torch.unsqueeze( sample_locs, dim=0), Z, Y, X, already_mem=True) summ_writer.summ_occ('emb3D/samples_%s/sample_occ' % mod, sample_occ, reduce_axes=[2, 3]) summ_writer.summ_occ('emb3D/samples_%s/valid' % mod, torch.reshape(valid, [B, 1, Z, Y, X]), reduce_axes=[2, 3]) emb0_all = torch.cat(emb0_all, axis=0) emb1_all = torch.cat(emb1_all, axis=0) valid_all = torch.cat(valid_all, axis=0) return emb0_all, emb1_all, valid_all
def forward(self, feats, xyzlist_cam, scorelist, vislist, occs, summ_writer, suffix=''): total_loss = torch.tensor(0.0).cuda() B, S, C, Z2, Y2, X2 = list(feats.shape) B, S, C, Z, Y, X = list(occs.shape) B2, S2, D = list(xyzlist_cam.shape) assert (B == B2, S == S2) assert (D == 3) xyzlist_mem = utils_vox.Ref2Mem(xyzlist_cam, Z, Y, X) # these are B x S x 3 scorelist = scorelist.unsqueeze(2) # this is B x S x 1 vislist = vislist[:, 0].reshape(B, 1, 1) # we only care that the object was visible in frame0 scorelist = scorelist * vislist if self.use_cost_vols: if summ_writer.save_this: summ_writer.summ_traj_on_occ('forecast/actual_traj', xyzlist_mem * scorelist, torch.max(occs, dim=1)[0], already_mem=True, sigma=2) Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2) Z4, Y4, X4 = int(Z / 4), int(Y / 4), int(X / 4) occ_hint0 = utils_vox.voxelize_xyz(xyzlist_cam[:, 0:1], Z4, Y4, X4) occ_hint1 = utils_vox.voxelize_xyz(xyzlist_cam[:, 1:2], Z4, Y4, X4) occ_hint0 = occ_hint0 * scorelist[:, 0].reshape(B, 1, 1, 1, 1) occ_hint1 = occ_hint1 * scorelist[:, 1].reshape(B, 1, 1, 1, 1) occ_hint = torch.cat([occ_hint0, occ_hint1], dim=1) occ_hint = F.interpolate(occ_hint, scale_factor=4, mode='nearest') # this is B x 1 x Z x Y x X summ_writer.summ_occ('forecast/occ_hint', (occ_hint0 + occ_hint1).clamp(0, 1)) crops = [] for s in list(range(S)): crop = utils_vox.center_mem_on_xyz(occs_highres[:, s], xyzlist_cam[:, s], Z2, Y2, X2) crops.append(crop) crops = torch.stack(crops, dim=0) summ_writer.summ_occs('forecast/crops', crops) # condition on the occ_hint feat = torch.cat([feat, occ_hint], dim=1) N = hyp.forecast_num_negs sampled_trajs_mem = self.sample_trajs_from_library(N, xyzlist_mem) if summ_writer.save_this: for n in list(range(np.min([N, 10]))): xyzlist_mem = sampled_trajs_mem[0, n].unsqueeze(0) # this is 1 x S x 3 summ_writer.summ_traj_on_occ( 'forecast/lib%d_xyzlist' % n, xyzlist_mem, torch.zeros([1, 1, Z, Y, X]).float().cuda(), already_mem=True) cost_vols = self.cost_forecaster(feat) # cost_vols = F.sigmoid(cost_vols) cost_vols = F.interpolate(cost_vols, scale_factor=2, mode='trilinear') # cost_vols is B x S x Z x Y x X summ_writer.summ_histogram('forecast/cost_vols_hist', cost_vols) cost_vols = cost_vols.clamp( -1000, 1000) # raquel says this adds stability summ_writer.summ_histogram('forecast/cost_vols_clamped_hist', cost_vols) cost_vols_vis = torch.mean(cost_vols, dim=3).unsqueeze(2) # cost_vols_vis is B x S x 1 x Z x X summ_writer.summ_oneds('forecast/cost_vols_vis', torch.unbind(cost_vols_vis, dim=1)) # smooth loss cost_vols_ = cost_vols.reshape(B * S, 1, Z, Y, X) dz, dy, dx = gradient3D(cost_vols_, absolute=True) dt = torch.abs(cost_vols[:, 1:] - cost_vols[:, 0:-1]) smooth_vox_spatial = torch.mean(dx + dy + dz, dim=1, keepdims=True) smooth_vox_time = torch.mean(dt, dim=1, keepdims=True) summ_writer.summ_oned('forecast/smooth_loss_spatial', torch.mean(smooth_vox_spatial, dim=3)) summ_writer.summ_oned('forecast/smooth_loss_time', torch.mean(smooth_vox_time, dim=3)) smooth_loss = torch.mean(smooth_vox_spatial) + torch.mean( smooth_vox_time) total_loss = utils_misc.add_loss('forecast/smooth_loss', total_loss, smooth_loss, hyp.forecast_smooth_coeff, summ_writer) def clamp_xyz(xyz, X, Y, Z): x, y, z = torch.unbind(xyz, dim=-1) x = x.clamp(0, X) y = x.clamp(0, Y) z = x.clamp(0, Z) xyz = torch.stack([x, y, z], dim=-1) return xyz # obj_xyzlist_mem is K x B x S x 3 # xyzlist_mem is B x S x 3 # sampled_trajs_mem is B x N x S x 3 xyz_pos_ = xyzlist_mem.reshape(B * S, 1, 3) xyz_neg_ = sampled_trajs_mem.permute(0, 2, 1, 3).reshape(B * S, N, 3) # xyz_pos_ = clamp_xyz(xyz_pos_, X, Y, Z) # xyz_neg_ = clamp_xyz(xyz_neg_, X, Y, Z) xyz_ = torch.cat([xyz_pos_, xyz_neg_], dim=1) xyz_ = clamp_xyz(xyz_, X, Y, Z) cost_vols_ = cost_vols.reshape(B * S, 1, Z, Y, X) x, y, z = torch.unbind(xyz_, dim=2) # x = x.clamp(0, X) # y = x.clamp(0, Y) # z = x.clamp(0, Z) cost_ = utils_samp.bilinear_sample3D(cost_vols_, x, y, z).squeeze(1) # cost is B*S x 1+N cost_pos = cost_[:, 0:1] # B*S x 1 cost_neg = cost_[:, 1:] # B*S x N cost_pos = cost_pos.unsqueeze(2) # B*S x 1 x 1 cost_neg = cost_neg.unsqueeze(1) # B*S x 1 x N utils_misc.add_loss('forecast/mean_cost_pos', 0, torch.mean(cost_pos), 0, summ_writer) utils_misc.add_loss('forecast/mean_cost_neg', 0, torch.mean(cost_neg), 0, summ_writer) utils_misc.add_loss('forecast/mean_margin', 0, torch.mean(cost_neg - cost_pos), 0, summ_writer) xyz_pos = xyz_pos_.unsqueeze(2) # B*S x 1 x 1 x 3 xyz_neg = xyz_neg_.unsqueeze(1) # B*S x 1 x N x 3 dist = torch.norm(xyz_pos - xyz_neg, dim=3) # B*S x 1 x N dist = dist / float( Z) * 5.0 # normalize for resolution, but upweight it a bit margin = F.relu(cost_pos - cost_neg + dist) margin = margin.reshape(B, S, N) # mean over time (in the paper this is a sum) margin = utils_basic.reduce_masked_mean(margin, scorelist.repeat(1, 1, N), dim=1) # max over the negatives maxmargin = torch.max(margin, dim=1)[0] # B maxmargin_loss = torch.mean(maxmargin) total_loss = utils_misc.add_loss('forecast/maxmargin_loss', total_loss, maxmargin_loss, hyp.forecast_maxmargin_coeff, summ_writer) cost_neg = cost_neg.reshape(B, S, N)[0].detach().cpu().numpy() sampled_trajs_mem = sampled_trajs_mem.reshape(B, N, S, 3)[0:1] cost_neg = np.reshape(cost_neg, [S, N]) cost_neg = np.sum(cost_neg, axis=0) inds = np.argsort(cost_neg, axis=0) for n in list(range(2)): xyzlist_e_mem = sampled_trajs_mem[0:1, inds[n]] xyzlist_e_cam = utils_vox.Mem2Ref(xyzlist_e_mem, Z, Y, X) # this is B x S x 3 # if summ_writer.save_this and n==0: # print('xyzlist_e_cam', xyzlist_e_cam[0:1]) # print('xyzlist_g_cam', xyzlist_cam[0:1]) # print('scorelist', scorelist[0:1]) dist = torch.norm(xyzlist_cam[0:1] - xyzlist_e_cam[0:1], dim=2) # this is B x S meandist = utils_basic.reduce_masked_mean( dist, scorelist[0:1].squeeze(2)) utils_misc.add_loss('forecast/xyz_dist_%d' % n, 0, meandist, 0, summ_writer) # dist = torch.mean(torch.sum(torch.norm(xyzlist_cam[0:1] - xyzlist_e_cam[0:1], dim=2), dim=1)) # mpe = torch.mean(torch.norm(xyzlist_cam[0:1,int(S/2)] - xyzlist_e_cam[0:1,int(S/2)], dim=1)) # mpe = utils_basic.reduce_masked_mean(dist, scorelist[0:1]) # utils_misc.add_loss('forecast/xyz_mpe_%d' % n, 0, dist, 0, summ_writer) # epe = torch.mean(torch.norm(xyzlist_cam[0:1,-1] - xyzlist_e_cam[0:1,-1], dim=1)) # utils_misc.add_loss('forecast/xyz_epe_%d' % n, 0, dist, 0, summ_writer) if summ_writer.save_this: # plot the best and worst trajs # print('sorted costs:', cost_neg[inds]) for n in list(range(2)): ind = inds[n] # print('plotting good traj with cost %.2f' % (cost_neg[ind])) xyzlist_e_mem = sampled_trajs_mem[:, ind] # this is 1 x S x 3 summ_writer.summ_traj_on_occ( 'forecast/best_sampled_traj%d' % n, xyzlist_e_mem, torch.max(occs[0:1], dim=1)[0], # torch.zeros([1, 1, Z, Y, X]).float().cuda(), already_mem=True, sigma=1) for n in list(range(2)): ind = inds[-(n + 1)] # print('plotting bad traj with cost %.2f' % (cost_neg[ind])) xyzlist_e_mem = sampled_trajs_mem[:, ind] # this is 1 x S x 3 summ_writer.summ_traj_on_occ( 'forecast/worst_sampled_traj%d' % n, xyzlist_e_mem, torch.max(occs[0:1], dim=1)[0], # torch.zeros([1, 1, Z, Y, X]).float().cuda(), already_mem=True, sigma=1) else: # use some timesteps as input feat_input = feats[:, :self.num_given].squeeze(2) # feat_input is B x self.num_given x ZZ x ZY x ZX ## regular bottle3D # vel_e = self.regressor(feat_input) ## sparse-invar bottle3D comp_mask = 1.0 - (feat_input == 0).all(dim=1, keepdim=True).float() summ_writer.summ_feat('forecast/feat_input', feat_input, pca=False) summ_writer.summ_feat('forecast/feat_comp_mask', comp_mask, pca=False) vel_e = self.regressor(feat_input, comp_mask) vel_e = vel_e.reshape(B, self.num_need, 3) vel_g = xyzlist_cam[:, self.num_given:] - xyzlist_cam[:, self.num_given - 1:-1] xyzlist_e = torch.zeros_like(xyzlist_cam) xyzlist_g = torch.zeros_like(xyzlist_cam) for s in list(range(S)): # print('s = %d' % s) if s < self.num_given: # print('grabbing from gt ind %s' % s) xyzlist_e[:, s] = xyzlist_cam[:, s] xyzlist_g[:, s] = xyzlist_cam[:, s] else: # print('grabbing from s-self.num_given, which is ind %d' % (s-self.num_given)) xyzlist_e[:, s] = xyzlist_e[:, s - 1] + vel_e[:, s - self.num_given] xyzlist_g[:, s] = xyzlist_g[:, s - 1] + vel_g[:, s - self.num_given] xyzlist_e_mem = utils_vox.Ref2Mem(xyzlist_e, Z, Y, X) xyzlist_g_mem = utils_vox.Ref2Mem(xyzlist_g, Z, Y, X) summ_writer.summ_traj_on_occ('forecast/traj_e', xyzlist_e_mem, torch.max(occs, dim=1)[0], already_mem=True, sigma=2) summ_writer.summ_traj_on_occ('forecast/traj_g', xyzlist_g_mem, torch.max(occs, dim=1)[0], already_mem=True, sigma=2) scorelist_here = scorelist[:, self.num_given:, 0] sql2 = torch.sum((vel_g - vel_e)**2, dim=2) ## yes weightmask weightmask = torch.arange(0, self.num_need, dtype=torch.float32, device=torch.device('cuda')) weightmask = torch.exp(-weightmask**(1. / 4)) # 1.0000, 0.3679, 0.3045, 0.2682, 0.2431, 0.2242, 0.2091, 0.1966, 0.1860, # 0.1769, 0.1689, 0.1618, 0.1555, 0.1497, 0.1445, 0.1397, 0.1353 weightmask = weightmask.reshape(1, self.num_need) l2_loss = utils_basic.reduce_masked_mean(sql2, scorelist_here * weightmask) utils_misc.add_loss('forecast/l2_loss', 0, l2_loss, 0, summ_writer) # # no weightmask: # l2_loss = utils_basic.reduce_masked_mean(sql2, scorelist_here) # total_loss = utils_misc.add_loss('forecast/l2_loss', total_loss, l2_loss, hyp.forecast_l2_coeff, summ_writer) dist = torch.norm(xyzlist_e - xyzlist_g, dim=2) meandist = utils_basic.reduce_masked_mean(dist, scorelist[:, :, 0]) utils_misc.add_loss('forecast/xyz_dist_0', 0, meandist, 0, summ_writer) l2_loss_noexp = utils_basic.reduce_masked_mean(sql2, scorelist_here) # utils_misc.add_loss('forecast/vel_dist_noexp', 0, l2_loss, 0, summ_writer) total_loss = utils_misc.add_loss('forecast/l2_loss_noexp', total_loss, l2_loss_noexp, hyp.forecast_l2_coeff, summ_writer) return total_loss
def __getitem__(self, index): if hyp.dataset_name == 'kitti' or hyp.dataset_name == 'clevr' or hyp.dataset_name == 'real' or hyp.dataset_name == "bigbird" or hyp.dataset_name == "carla" or hyp.dataset_name == "carla_mix" or hyp.dataset_name == "replica" or hyp.dataset_name == "clevr_vqa" or hyp.dataset_name == "carla_det": # print(index) # st() filename = self.records[index] d = pickle.load(open(filename, "rb")) d = dict(d) d_empty = pickle.load(open(self.empty_scene, "rb")) d_empty = dict(d_empty) # st() # elif hyp.dataset_name=="carla": # filename = self.records[index] # d = np.load(filename) # d = dict(d) # d['rgb_camXs_raw'] = d['rgb_camXs'] # d['pix_T_cams_raw'] = d['pix_T_cams'] # d['tree_seq_filename'] = "dummy_tree_filename" # d['origin_T_camXs_raw'] = d['origin_T_camXs'] # d['camR_T_origin_raw'] = utils_geom.safe_inverse(torch.from_numpy(d['origin_T_camRs'])).numpy() # d['xyz_camXs_raw'] = d['xyz_camXs'] else: assert (False) # reader not ready yet if hyp.do_empty: item_names = [ 'pix_T_cams_raw', 'origin_T_camXs_raw', 'camR_T_origin_raw', 'rgb_camXs_raw', 'xyz_camXs_raw', 'empty_rgb_camXs_raw', 'empty_xyz_camXs_raw', ] else: item_names = [ 'pix_T_cams_raw', 'origin_T_camXs_raw', 'camR_T_origin_raw', 'rgb_camXs_raw', 'xyz_camXs_raw', ] if hyp.use_gt_occs: __p = lambda x: utils_basic.pack_seqdim(x, 1) __u = lambda x: utils_basic.unpack_seqdim(x, 1) B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N PH, PW = hyp.PH, hyp.PW K = hyp.K BOX_SIZE = hyp.BOX_SIZE Z, Y, X = hyp.Z, hyp.Y, hyp.X Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2) Z4, Y4, X4 = int(Z / 4), int(Y / 4), int(X / 4) D = 9 pix_T_cams = torch.from_numpy( d["pix_T_cams_raw"]).unsqueeze(0).cuda().to(torch.float) camRs_T_origin = torch.from_numpy( d["camR_T_origin_raw"]).unsqueeze(0).cuda().to(torch.float) origin_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_origin))) origin_T_camXs = torch.from_numpy( d["origin_T_camXs_raw"]).unsqueeze(0).cuda().to(torch.float) camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0) camRs_T_camXs = __u( torch.matmul(utils_geom.safe_inverse(__p(origin_T_camRs)), __p(origin_T_camXs))) camXs_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_camXs))) camX0_T_camRs = camXs_T_camRs[:, 0] camX1_T_camRs = camXs_T_camRs[:, 1] camR_T_camX0 = utils_geom.safe_inverse(camX0_T_camRs) xyz_camXs = torch.from_numpy( d["xyz_camXs_raw"]).unsqueeze(0).cuda().to(torch.float) xyz_camRs = __u( utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs))) depth_camXs_, valid_camXs_ = utils_geom.create_depth_image( __p(pix_T_cams), __p(xyz_camXs), H, W) dense_xyz_camXs_ = utils_geom.depth2pointcloud( depth_camXs_, __p(pix_T_cams)) occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X)) occRs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z2, Y2, X2)) occRs_half = torch.max(occRs_half, dim=1).values.squeeze(0) occ_complete = occRs_half.cpu().numpy() # if hyp.do_time_flip: # d = random_time_flip_single(d,item_names) # if the sequence length > 2, select S frames # filename = d['raw_seq_filename'] original_filename = filename original_filename_empty = self.empty_scene # st() if hyp.dataset_name == "clevr_vqa": d['tree_seq_filename'] = "temp" pix_T_cams = d['pix_T_cams_raw'] num_cams = pix_T_cams.shape[0] # padding_1 = torch.zeros([num_cams,1,3]) # padding_2 = torch.zeros([num_cams,4,1]) # padding_2[:,3] = 1.0 # st() # pix_T_cams = torch.cat([pix_T_cams,padding_1],dim=1) # pix_T_cams = torch.cat([pix_T_cams,padding_2],dim=2) # st() shape_name = d['shape_list'] color_name = d['color_list'] material_name = d['material_list'] all_name = [] all_style = [] for index in range(len(shape_name)): name = shape_name[index] + "/" + color_name[ index] + "_" + material_name[index] style_name = color_name[index] + "_" + material_name[index] all_name.append(name) all_style.append(style_name) # st() if hyp.do_shape: class_name = shape_name elif hyp.do_color: class_name = color_name elif hyp.do_material: class_name = material_name elif hyp.do_style: class_name = all_style else: class_name = all_name object_category = class_name bbox_origin = d['bbox_origin'] # bbox_origin = torch.cat([bbox_origin],dim=0) # object_category = object_category bbox_origin_empty = np.zeros_like(bbox_origin) object_category_empty = ['0'] # st() if not hyp.dataset_name == "clevr_vqa": filename = d['tree_seq_filename'] filename_empty = d_empty['tree_seq_filename'] if hyp.fixed_view: d, indexes = non_random_select_single(d, item_names, num_samples=hyp.S) d_empty, indexes_empty = specific_select_single_empty( d_empty, item_names, d['origin_T_camXs_raw'], num_samples=hyp.S) filename_g = "/".join([original_filename, str(indexes[0])]) filename_e = "/".join([original_filename, str(indexes[1])]) filename_g_empty = "/".join([original_filename_empty, str(indexes[0])]) filename_e_empty = "/".join([original_filename_empty, str(indexes[1])]) rgb_camXs = d['rgb_camXs_raw'] rgb_camXs_empty = d_empty['rgb_camXs_raw'] # move channel dim inward, like pytorch wants # rgb_camRs = np.transpose(rgb_camRs, axes=[0, 3, 1, 2]) rgb_camXs = np.transpose(rgb_camXs, axes=[0, 3, 1, 2]) rgb_camXs = rgb_camXs[:, :3] rgb_camXs = utils_improc.preprocess_color(rgb_camXs) rgb_camXs_empty = np.transpose(rgb_camXs_empty, axes=[0, 3, 1, 2]) rgb_camXs_empty = rgb_camXs_empty[:, :3] rgb_camXs_empty = utils_improc.preprocess_color(rgb_camXs_empty) if hyp.dataset_name == "clevr_vqa": num_boxes = bbox_origin.shape[0] bbox_origin = np.array(bbox_origin) score = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes]) bbox_origin = np.pad(bbox_origin, [[0, hyp.N - num_boxes], [0, 0], [0, 0]]) object_category = np.pad(object_category, [[0, hyp.N - num_boxes]], lambda x, y, z, m: "0") object_category_empty = np.pad(object_category_empty, [[0, hyp.N - 1]], lambda x, y, z, m: "0") # st() score_empty = np.zeros_like(score) bbox_origin_empty = np.zeros_like(bbox_origin) d['gt_box'] = np.stack( [bbox_origin.astype(np.float32), bbox_origin_empty]) d['gt_scores'] = np.stack([score.astype(np.float32), score_empty]) try: d['classes'] = np.stack( [object_category, object_category_empty]).tolist() except Exception as e: st() d['rgb_camXs_raw'] = np.stack([rgb_camXs, rgb_camXs_empty]) d['pix_T_cams_raw'] = np.stack( [d["pix_T_cams_raw"], d_empty["pix_T_cams_raw"]]) d['origin_T_camXs_raw'] = np.stack( [d["origin_T_camXs_raw"], d_empty["origin_T_camXs_raw"]]) d['camR_T_origin_raw'] = np.stack( [d["camR_T_origin_raw"], d_empty["camR_T_origin_raw"]]) d['xyz_camXs_raw'] = np.stack( [d["xyz_camXs_raw"], d_empty["xyz_camXs_raw"]]) # d['rgb_camXs_raw'] = rgb_camXs # d['tree_seq_filename'] = filename if not hyp.dataset_name == "clevr_vqa": d['tree_seq_filename'] = [filename, "invalid_tree"] else: d['tree_seq_filename'] = ["temp"] # st() d['filename_e'] = ["temp"] d['filename_g'] = ["temp"] if hyp.use_gt_occs: d['occR_complete'] = np.expand_dims(occ_complete, axis=0) return d
def __getitem__(self, index): if hyp.dataset_name == 'kitti' or hyp.dataset_name == 'clevr' or hyp.dataset_name == 'real' or hyp.dataset_name == "bigbird" or hyp.dataset_name == "carla" or hyp.dataset_name == "carla_mix" or hyp.dataset_name == "carla_det" or hyp.dataset_name == "replica" or hyp.dataset_name == "clevr_vqa": # print(index) filename = self.records[index] d = pickle.load(open(filename, "rb")) d = dict(d) # elif hyp.dataset_name=="carla": # filename = self.records[index] # d = np.load(filename) # d = dict(d) # d['rgb_camXs_raw'] = d['rgb_camXs'] # d['pix_T_cams_raw'] = d['pix_T_cams'] # d['tree_seq_filename'] = "dummy_tree_filename" # d['origin_T_camXs_raw'] = d['origin_T_camXs'] # d['camR_T_origin_raw'] = utils_geom.safe_inverse(torch.from_numpy(d['origin_T_camRs'])).numpy() # d['xyz_camXs_raw'] = d['xyz_camXs'] else: assert (False) # reader not ready yet # st() # if hyp.save_gt_occs: # pickle.dump(d,open(filename, "wb")) # st() # st() if hyp.use_gt_occs: __p = lambda x: utils_basic.pack_seqdim(x, 1) __u = lambda x: utils_basic.unpack_seqdim(x, 1) B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N PH, PW = hyp.PH, hyp.PW K = hyp.K BOX_SIZE = hyp.BOX_SIZE Z, Y, X = hyp.Z, hyp.Y, hyp.X Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2) Z4, Y4, X4 = int(Z / 4), int(Y / 4), int(X / 4) D = 9 pix_T_cams = torch.from_numpy( d["pix_T_cams_raw"]).unsqueeze(0).cuda().to(torch.float) camRs_T_origin = torch.from_numpy( d["camR_T_origin_raw"]).unsqueeze(0).cuda().to(torch.float) origin_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_origin))) origin_T_camXs = torch.from_numpy( d["origin_T_camXs_raw"]).unsqueeze(0).cuda().to(torch.float) camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0) camRs_T_camXs = __u( torch.matmul(utils_geom.safe_inverse(__p(origin_T_camRs)), __p(origin_T_camXs))) camXs_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_camXs))) camX0_T_camRs = camXs_T_camRs[:, 0] camX1_T_camRs = camXs_T_camRs[:, 1] camR_T_camX0 = utils_geom.safe_inverse(camX0_T_camRs) xyz_camXs = torch.from_numpy( d["xyz_camXs_raw"]).unsqueeze(0).cuda().to(torch.float) xyz_camRs = __u( utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs))) depth_camXs_, valid_camXs_ = utils_geom.create_depth_image( __p(pix_T_cams), __p(xyz_camXs), H, W) dense_xyz_camXs_ = utils_geom.depth2pointcloud( depth_camXs_, __p(pix_T_cams)) occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X)) occRs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z2, Y2, X2)) occRs_half = torch.max(occRs_half, dim=1).values.squeeze(0) occ_complete = occRs_half.cpu().numpy() # st() if hyp.do_empty: item_names = [ 'pix_T_cams_raw', 'origin_T_camXs_raw', 'camR_T_origin_raw', 'rgb_camXs_raw', 'xyz_camXs_raw', 'empty_rgb_camXs_raw', 'empty_xyz_camXs_raw', ] else: item_names = [ 'pix_T_cams_raw', 'origin_T_camXs_raw', 'camR_T_origin_raw', 'rgb_camXs_raw', 'xyz_camXs_raw', ] # if hyp.do_time_flip: # d = random_time_flip_single(d,item_names) # if the sequence length > 2, select S frames # filename = d['raw_seq_filename'] original_filename = filename if hyp.dataset_name == "carla_mix" or hyp.dataset_name == "carla_det": bbox_origin_gt = d['bbox_origin'] if 'bbox_origin_predicted' in d: bbox_origin_predicted = d['bbox_origin_predicted'] else: bbox_origin_predicted = [] classes = d['obj_name'] if isinstance(classes, str): classes = [classes] # st() d['tree_seq_filename'] = "temp" if hyp.dataset_name == "replica": d['tree_seq_filename'] = "temp" object_category = d['object_category_names'] bbox_origin = d['bbox_origin'] if hyp.dataset_name == "clevr_vqa": d['tree_seq_filename'] = "temp" pix_T_cams = d['pix_T_cams_raw'] num_cams = pix_T_cams.shape[0] # padding_1 = torch.zeros([num_cams,1,3]) # padding_2 = torch.zeros([num_cams,4,1]) # padding_2[:,3] = 1.0 # st() # pix_T_cams = torch.cat([pix_T_cams,padding_1],dim=1) # pix_T_cams = torch.cat([pix_T_cams,padding_2],dim=2) # st() shape_name = d['shape_list'] color_name = d['color_list'] material_name = d['material_list'] all_name = [] all_style = [] for index in range(len(shape_name)): name = shape_name[index] + "/" + color_name[ index] + "_" + material_name[index] style_name = color_name[index] + "_" + material_name[index] all_name.append(name) all_style.append(style_name) # st() if hyp.do_shape: class_name = shape_name elif hyp.do_color: class_name = color_name elif hyp.do_material: class_name = material_name elif hyp.do_style: class_name = all_style else: class_name = all_name object_category = class_name bbox_origin = d['bbox_origin'] # st() if hyp.dataset_name == "carla": camR_index = d['camR_index'] rgb_camtop = d['rgb_camXs_raw'][camR_index:camR_index + 1] origin_T_camXs_top = d['origin_T_camXs_raw'][ camR_index:camR_index + 1] # predicted_box = d['bbox_origin_predicted'] predicted_box = [] filename = d['tree_seq_filename'] if hyp.do_2d_style_munit: d, indexes = non_random_select_single(d, item_names, num_samples=hyp.S) # st() if hyp.fixed_view: d, indexes = non_random_select_single(d, item_names, num_samples=hyp.S) elif self.shuffle or hyp.randomly_select_views: d, indexes = random_select_single(d, item_names, num_samples=hyp.S) else: d, indexes = non_random_select_single(d, item_names, num_samples=hyp.S) filename_g = "/".join([original_filename, str(indexes[0])]) filename_e = "/".join([original_filename, str(indexes[1])]) rgb_camXs = d['rgb_camXs_raw'] # move channel dim inward, like pytorch wants # rgb_camRs = np.transpose(rgb_camRs, axes=[0, 3, 1, 2]) rgb_camXs = np.transpose(rgb_camXs, axes=[0, 3, 1, 2]) rgb_camXs = rgb_camXs[:, :3] rgb_camXs = utils_improc.preprocess_color(rgb_camXs) if hyp.dataset_name == "carla": rgb_camtop = np.transpose(rgb_camtop, axes=[0, 3, 1, 2]) rgb_camtop = rgb_camtop[:, :3] rgb_camtop = utils_improc.preprocess_color(rgb_camtop) d['rgb_camtop'] = rgb_camtop d['origin_T_camXs_top'] = origin_T_camXs_top if len(predicted_box) == 0: predicted_box = np.zeros([hyp.N, 6]) score = np.zeros([hyp.N]).astype(np.float32) else: num_boxes = predicted_box.shape[0] score = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes]) predicted_box = np.pad(predicted_box, [[0, hyp.N - num_boxes], [0, 0]]) d['predicted_box'] = predicted_box.astype(np.float32) d['predicted_scores'] = score.astype(np.float32) if hyp.dataset_name == "clevr_vqa": num_boxes = bbox_origin.shape[0] bbox_origin = np.array(bbox_origin) score = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes]) bbox_origin = np.pad(bbox_origin, [[0, hyp.N - num_boxes], [0, 0], [0, 0]]) object_category = np.pad(object_category, [[0, hyp.N - num_boxes]], lambda x, y, z, m: "0") d['gt_box'] = bbox_origin.astype(np.float32) d['gt_scores'] = score.astype(np.float32) d['classes'] = list(object_category) if hyp.dataset_name == "replica": if len(bbox_origin) == 0: score = np.zeros([hyp.N]) bbox_origin = np.zeros([hyp.N, 6]) object_category = ["0"] * hyp.N object_category = np.array(object_category) else: num_boxes = len(bbox_origin) bbox_origin = torch.stack(bbox_origin).numpy().squeeze( 1).squeeze(1).reshape([num_boxes, 6]) bbox_origin = np.array(bbox_origin) score = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes]) bbox_origin = np.pad(bbox_origin, [[0, hyp.N - num_boxes], [0, 0]]) object_category = np.pad(object_category, [[0, hyp.N - num_boxes]], lambda x, y, z, m: "0") d['gt_box'] = bbox_origin.astype(np.float32) d['gt_scores'] = score.astype(np.float32) d['classes'] = list(object_category) # st() if hyp.dataset_name == "carla_mix" or hyp.dataset_name == "carla_det": bbox_origin_predicted = bbox_origin_predicted[:3] if len(bbox_origin_gt.shape) == 1: bbox_origin_gt = np.expand_dims(bbox_origin_gt, 0) num_boxes = bbox_origin_gt.shape[0] # st() score_gt = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes]) bbox_origin_gt = np.pad(bbox_origin_gt, [[0, hyp.N - num_boxes], [0, 0]]) # st() classes = np.pad(classes, [[0, hyp.N - num_boxes]], lambda x, y, z, m: "0") if len(bbox_origin_predicted) == 0: bbox_origin_predicted = np.zeros([hyp.N, 6]) score_pred = np.zeros([hyp.N]).astype(np.float32) else: num_boxes = bbox_origin_predicted.shape[0] score_pred = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes]) bbox_origin_predicted = np.pad( bbox_origin_predicted, [[0, hyp.N - num_boxes], [0, 0]]) d['predicted_box'] = bbox_origin_predicted.astype(np.float32) d['predicted_scores'] = score_pred.astype(np.float32) d['gt_box'] = bbox_origin_gt.astype(np.float32) d['gt_scores'] = score_gt.astype(np.float32) d['classes'] = list(classes) d['rgb_camXs_raw'] = rgb_camXs if hyp.dataset_name != "carla" and hyp.do_empty: empty_rgb_camXs = d['empty_rgb_camXs_raw'] # move channel dim inward, like pytorch wants empty_rgb_camXs = np.transpose(empty_rgb_camXs, axes=[0, 3, 1, 2]) empty_rgb_camXs = empty_rgb_camXs[:, :3] empty_rgb_camXs = utils_improc.preprocess_color(empty_rgb_camXs) d['empty_rgb_camXs_raw'] = empty_rgb_camXs # st() if hyp.use_gt_occs: d['occR_complete'] = occ_complete d['tree_seq_filename'] = filename d['filename_e'] = filename_e d['filename_g'] = filename_g return d
def forward(self, feed, moc_init_done=False, debug=False): summ_writer = utils_improc.Summ_writer( writer = feed['writer'], global_step = feed['global_step'], set_name= feed['set_name'], fps=8) writer = feed['writer'] global_step = feed['global_step'] total_loss = torch.tensor(0.0).cuda() ### ... All things sensor ... ### sensor_rgbs = feed['sensor_imgs'] sensor_depths = feed['sensor_depths'] center_sensor_H, center_sensor_W = sensor_depths[0][0].shape[-1] // 2, sensor_depths[0][0].shape[-2] // 2 ### ... All things sensor end ... ### # 1. Form the memory tensor using the feat net and visual images. # check what all do you need for this and create only those things ## .... Input images .... ## rgb_camRs = feed['rgb_camRs'] rgb_camXs = feed['rgb_camXs'] ## .... Input images end .... ## ## ... Hyperparams ... ## B, H, W, V, S = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S __p = lambda x: pack_seqdim(x, B) __u = lambda x: unpack_seqdim(x, B) PH, PW = hyp.PH, hyp.PW Z, Y, X = hyp.Z, hyp.Y, hyp.X Z2, Y2, X2 = int(Z/2), int(Y/2), int(X/2) ## ... Hyperparams end ... ## ## .... VISUAL TRANSFORMS BEGIN .... ## pix_T_cams = feed['pix_T_cams'] pix_T_cams_ = __p(pix_T_cams) origin_T_camRs = feed['origin_T_camRs'] origin_T_camRs_ = __p(origin_T_camRs) origin_T_camXs = feed['origin_T_camXs'] origin_T_camXs_ = __p(origin_T_camXs) camRs_T_camXs_ = torch.matmul(utils_geom.safe_inverse( origin_T_camRs_), origin_T_camXs_) camXs_T_camRs_ = utils_geom.safe_inverse(camRs_T_camXs_) camRs_T_camXs = __u(camRs_T_camXs_) camXs_T_camRs = __u(camXs_T_camRs_) pix_T_cams_ = utils_geom.pack_intrinsics(pix_T_cams_[:, 0, 0], pix_T_cams_[:, 1, 1], pix_T_cams_[:, 0, 2], pix_T_cams_[:, 1, 2]) pix_T_camRs_ = torch.matmul(pix_T_cams_, camXs_T_camRs_) pix_T_camRs = __u(pix_T_camRs_) ## ... VISUAL TRANSFORMS END ... ## ## ... SENSOR TRANSFORMS BEGIN ... ## sensor_origin_T_camXs = feed['sensor_extrinsics'] sensor_origin_T_camXs_ = __p(sensor_origin_T_camXs) sensor_origin_T_camRs = feed['sensor_origin_T_camRs'] sensor_origin_T_camRs_ = __p(sensor_origin_T_camRs) sensor_camRs_T_origin_ = utils_geom.safe_inverse(sensor_origin_T_camRs_) sensor_camRs_T_camXs_ = torch.matmul(utils_geom.safe_inverse( sensor_origin_T_camRs_), sensor_origin_T_camXs_) sensor_camXs_T_camRs_ = utils_geom.safe_inverse(sensor_camRs_T_camXs_) sensor_camRs_T_camXs = __u(sensor_camRs_T_camXs_) sensor_camXs_T_camRs = __u(sensor_camXs_T_camRs_) sensor_pix_T_cams = feed['sensor_intrinsics'] sensor_pix_T_cams_ = __p(sensor_pix_T_cams) sensor_pix_T_cams_ = utils_geom.pack_intrinsics(sensor_pix_T_cams_[:, 0, 0], sensor_pix_T_cams_[:, 1, 1], sensor_pix_T_cams_[:, 0, 2], sensor_pix_T_cams_[:, 1, 2]) sensor_pix_T_camRs_ = torch.matmul(sensor_pix_T_cams_, sensor_camXs_T_camRs_) sensor_pix_T_camRs = __u(sensor_pix_T_camRs_) ## .... SENSOR TRANSFORMS END .... ## ## .... Visual Input point clouds .... ## xyz_camXs = feed['xyz_camXs'] xyz_camXs_ = __p(xyz_camXs) xyz_camRs_ = utils_geom.apply_4x4(camRs_T_camXs_, xyz_camXs_) # (40, 4, 4) (B*S, N, 3) xyz_camRs = __u(xyz_camRs_) assert all([torch.allclose(xyz_camR, inp_xyz_camR) for xyz_camR, inp_xyz_camR in zip( xyz_camRs, feed['xyz_camRs'] )]), "computation of xyz_camR here and those computed in input do not match" ## .... Visual Input point clouds end .... ## ## ... Sensor input point clouds ... ## sensor_xyz_camXs = feed['sensor_xyz_camXs'] sensor_xyz_camXs_ = __p(sensor_xyz_camXs) sensor_xyz_camRs_ = utils_geom.apply_4x4(sensor_camRs_T_camXs_, sensor_xyz_camXs_) sensor_xyz_camRs = __u(sensor_xyz_camRs_) assert all([torch.allclose(sensor_xyz, inp_sensor_xyz) for sensor_xyz, inp_sensor_xyz in zip( sensor_xyz_camRs, feed['sensor_xyz_camRs'] )]), "the sensor_xyz_camRs computed in forward do not match those computed in input" ## ... visual occupancy computation voxelize the pointcloud from above ... ## occRs_ = utils_vox.voxelize_xyz(xyz_camRs_, Z, Y, X) occXs_ = utils_vox.voxelize_xyz(xyz_camXs_, Z, Y, X) occRs_half_ = utils_vox.voxelize_xyz(xyz_camRs_, Z2, Y2, X2) occXs_half_ = utils_vox.voxelize_xyz(xyz_camXs_, Z2, Y2, X2) ## ... visual occupancy computation end ... NOTE: no unpacking ## ## .. visual occupancy computation for sensor inputs .. ## sensor_occRs_ = utils_vox.voxelize_xyz(sensor_xyz_camRs_, Z, Y, X) sensor_occXs_ = utils_vox.voxelize_xyz(sensor_xyz_camXs_, Z, Y, X) sensor_occRs_half_ = utils_vox.voxelize_xyz(sensor_xyz_camRs_, Z2, Y2, X2) sensor_occXs_half_ = utils_vox.voxelize_xyz(sensor_xyz_camXs_, Z2, Y2, X2) ## ... unproject rgb images ... ## unpRs_ = utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z, Y, X, pix_T_camRs_) unpXs_ = utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z, Y, X, pix_T_cams_) ## ... unproject rgb finish ... NOTE: no unpacking ## ## ... Make depth images ... ## depth_camXs_, valid_camXs_ = utils_geom.create_depth_image(pix_T_cams_, xyz_camXs_, H, W) dense_xyz_camXs_ = utils_geom.depth2pointcloud(depth_camXs_, pix_T_cams_) dense_xyz_camRs_ = utils_geom.apply_4x4(camRs_T_camXs_, dense_xyz_camXs_) inbound_camXs_ = utils_vox.get_inbounds(dense_xyz_camRs_, Z, Y, X).float() inbound_camXs_ = torch.reshape(inbound_camXs_, [B*S, 1, H, W]) valid_camXs = __u(valid_camXs_) * __u(inbound_camXs_) ## ... Make depth images ... ## ## ... Make sensor depth images ... ## sensor_depth_camXs_, sensor_valid_camXs_ = utils_geom.create_depth_image(sensor_pix_T_cams_, sensor_xyz_camXs_, H, W) sensor_dense_xyz_camXs_ = utils_geom.depth2pointcloud(sensor_depth_camXs_, sensor_pix_T_cams_) sensor_dense_xyz_camRs_ = utils_geom.apply_4x4(sensor_camRs_T_camXs_, sensor_dense_xyz_camXs_) sensor_inbound_camXs_ = utils_vox.get_inbounds(sensor_dense_xyz_camRs_, Z, Y, X).float() sensor_inbound_camXs_ = torch.reshape(sensor_inbound_camXs_, [B*hyp.sensor_S, 1, H, W]) sensor_valid_camXs = __u(sensor_valid_camXs_) * __u(sensor_inbound_camXs_) ### .. Done making sensor depth images .. ## ### ... Sanity check ... Write to tensorboard ... ### summ_writer.summ_oneds('2D_inputs/depth_camXs', torch.unbind(__u(depth_camXs_), dim=1)) summ_writer.summ_oneds('2D_inputs/valid_camXs', torch.unbind(valid_camXs, dim=1)) summ_writer.summ_rgbs('2D_inputs/rgb_camXs', torch.unbind(rgb_camXs, dim=1)) summ_writer.summ_rgbs('2D_inputs/rgb_camRs', torch.unbind(rgb_camRs, dim=1)) summ_writer.summ_occs('3d_inputs/occXs', torch.unbind(__u(occXs_), dim=1), reduce_axes=[2]) summ_writer.summ_unps('3d_inputs/unpXs', torch.unbind(__u(unpXs_), dim=1),\ torch.unbind(__u(occXs_), dim=1)) # A different approach for viewing occRs of sensors sensor_occRs = __u(sensor_occRs_) vis_sensor_occRs = torch.max(sensor_occRs, dim=1, keepdim=True)[0] # summ_writer.summ_occs('3d_inputs/sensor_occXs', torch.unbind(__u(sensor_occXs_), dim=1), # reduce_axes=[2]) summ_writer.summ_occs('3d_inputs/sensor_occRs', torch.unbind(vis_sensor_occRs, dim=1), reduce_axes=[2]) ### ... code for visualizing sensor depths and sensor rgbs ... ### # summ_writer.summ_oneds('2D_inputs/depths_sensor', torch.unbind(sensor_depths, dim=1)) # summ_writer.summ_rgbs('2D_inputs/rgbs_sensor', torch.unbind(sensor_rgbs, dim=1)) # summ_writer.summ_oneds('2D_inputs/validXs_sensor', torch.unbind(sensor_valid_camXs, dim=1)) if summ_writer.save_this: unpRs_ = utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z, Y, X, matmul2(pix_T_cams_, camXs_T_camRs_)) unpRs = __u(unpRs_) occRs_ = utils_vox.voxelize_xyz(xyz_camRs_, Z, Y, X) summ_writer.summ_occs('3d_inputs/occRs', torch.unbind(__u(occRs_), dim=1), reduce_axes=[2]) summ_writer.summ_unps('3d_inputs/unpRs', torch.unbind(unpRs, dim=1),\ torch.unbind(__u(occRs_), dim=1)) ### ... Sanity check ... Writing to tensoboard complete ... ### results = list() mask_ = None ### ... Visual featnet part .... ### if hyp.do_feat: featXs_input = torch.cat([__u(occXs_), __u(occXs_)*__u(unpXs_)], dim=2) # B, S, 4, H, W, D featXs_input_ = __p(featXs_input) freeXs_ = utils_vox.get_freespace(__p(xyz_camXs), occXs_half_) freeXs = __u(freeXs_) visXs = torch.clamp(__u(occXs_half_) + freeXs, 0.0, 1.0) if type(mask_) != type(None): assert(list(mask_.shape)[2:5] == list(featXs_input.shape)[2:5]) featXs_, validXs_, _ = self.featnet(featXs_input_, summ_writer, mask=occXs_) # total_loss += feat_loss # Note no need of loss validXs, featXs = __u(validXs_), __u(featXs_) # unpacked into B, S, C, D, H, W # bring everything to ref_frame validRs = utils_vox.apply_4x4_to_voxs(camRs_T_camXs, validXs) visRs = utils_vox.apply_4x4_to_voxs(camRs_T_camXs, visXs) featRs = utils_vox.apply_4x4_to_voxs(camRs_T_camXs, featXs) # This is now in memory coordinates emb3D_e = torch.mean(featRs[:, 1:], dim=1) # context, or the features of the scene emb3D_g = featRs[:, 0] # this is to predict, basically I will pass emb3D_e as input and hope to predict emb3D_g vis3D_e = torch.max(validRs[:, 1:], dim=1)[0] * torch.max(visRs[:, 1:], dim=1)[0] vis3D_g = validRs[:, 0] * visRs[:, 0] #### ... I do not think I need this ... #### results = {} # # if hyp.do_eval_recall: # # results['emb3D_e'] = emb3D_e # # results['emb3D_g'] = emb3D_g # #### ... Check if you need the above summ_writer.summ_feats('3D_feats/featXs_input', torch.unbind(featXs_input, dim=1), pca=True) summ_writer.summ_feats('3D_feats/featXs_output', torch.unbind(featXs, dim=1), pca=True) summ_writer.summ_feats('3D_feats/featRs_output', torch.unbind(featRs, dim=1), pca=True) summ_writer.summ_feats('3D_feats/validRs', torch.unbind(validRs, dim=1), pca=False) summ_writer.summ_feat('3D_feats/vis3D_e', vis3D_e, pca=False) summ_writer.summ_feat('3D_feats/vis3D_g', vis3D_g, pca=False) # I need to aggregate the features and detach to prevent the backward pass on featnet featRs = torch.mean(featRs, dim=1) featRs = featRs.detach() # ... HERE I HAVE THE VISUAL FEATURE TENSOR ... WHICH IS MADE USING 5 EVENLY SPACED VIEWS # # FOR THE TOUCH PART, I HAVE THE OCC and THE AIM IS TO PREDICT FEATURES FROM THEM # if hyp.do_touch_feat: # 1. Pass all the sensor depth images through the backbone network input_sensor_depths = __p(sensor_depths) sensor_features_ = self.backbone_2D(input_sensor_depths) # should normalize these feature tensors sensor_features_ = l2_normalize(sensor_features_, dim=1) sensor_features = __u(sensor_features_) assert torch.allclose(torch.norm(sensor_features_, dim=1), torch.Tensor([1.0]).cuda()),\ "normalization has no effect on you huh." if hyp.do_eval_recall: results['sensor_features'] = sensor_features_ results['sensor_depths'] = input_sensor_depths results['object_img'] = rgb_camRs results['sensor_imgs'] = __p(sensor_rgbs) # if moco is used do the same procedure as above but with a different network # if hyp.do_moc or hyp.do_eval_recall: # 1. Pass all the sensor depth images through the key network key_input_sensor_depths = copy.deepcopy(__p(sensor_depths)) # bx1024x1x16x16->(2048x1x16x16) self.key_touch_featnet.eval() with torch.no_grad(): key_sensor_features_ = self.key_touch_featnet(key_input_sensor_depths) key_sensor_features_ = l2_normalize(key_sensor_features_, dim=1) key_sensor_features = __u(key_sensor_features_) assert torch.allclose(torch.norm(key_sensor_features_, dim=1), torch.Tensor([1.0]).cuda()),\ "normalization has no effect on you huh." # doing the same procedure for moco but with a different network end # # do you want to do metric learning voxel point based using visual features and sensor features if hyp.do_touch_embML and not hyp.do_touch_forward: # trial 1: I do not pass the above obtained features through some encoder decoder in 3d # So compute the location is ref_frame which the center of these depth images will occupy # at all of these locations I will sample the from the visual tensor. It forms the positive pairs # negatives are simply everything except the positive sensor_depths_centers_x = center_sensor_W * torch.ones((hyp.B, hyp.sensor_S)) sensor_depths_centers_x = sensor_depths_centers_x.cuda() sensor_depths_centers_y = center_sensor_H * torch.ones((hyp.B, hyp.sensor_S)) sensor_depths_centers_y = sensor_depths_centers_y.cuda() sensor_depths_centers_z = sensor_depths[:, :, 0, center_sensor_H, center_sensor_W] # Next use Pixels2Camera to unproject all of these together. # merge the batch and the sequence dimension sensor_depths_centers_x = sensor_depths_centers_x.reshape(-1, 1, 1) # BxHxW as required by Pixels2Camera sensor_depths_centers_y = sensor_depths_centers_y.reshape(-1, 1, 1) sensor_depths_centers_z = sensor_depths_centers_z.reshape(-1, 1, 1) fx, fy, x0, y0 = utils_geom.split_intrinsics(sensor_pix_T_cams_) sensor_depths_centers_in_camXs_ = utils_geom.Pixels2Camera(sensor_depths_centers_x, sensor_depths_centers_y, sensor_depths_centers_z, fx, fy, x0, y0) # finally use apply4x4 to get the locations in ref_cam sensor_depths_centers_in_ref_cam_ = utils_geom.apply_4x4(sensor_camRs_T_camXs_, sensor_depths_centers_in_camXs_) # NOTE: convert them to memory coordinates, the name is xyz so I presume it returns xyz but talk to ADAM sensor_depths_centers_in_mem_ = utils_vox.Ref2Mem(sensor_depths_centers_in_ref_cam_, Z2, Y2, X2) sensor_depths_centers_in_mem = sensor_depths_centers_in_mem_.reshape(hyp.B, hyp.sensor_S, -1) if debug: print('assert that you are not entering here') from IPython import embed; embed() # form a (0, 1) volume here at these locations and see if it resembles a cup dim1 = X2 * Y2 * Z2 dim2 = X2 * Y2 dim3 = X2 binary_voxel_grid = torch.zeros((hyp.B, X2, Y2, Z2)) # NOTE: Z is the leading dimension rounded_idxs = torch.round(sensor_depths_centers_in_mem) flat_idxs = dim2 * rounded_idxs[0, :, 0] + dim3 * rounded_idxs[0, :, 1] + rounded_idxs[0, :, 2] flat_idxs1 = dim2 * rounded_idxs[1, :, 0] + dim3 * rounded_idxs[1, :, 1] + rounded_idxs[1, :, 2] flat_idxs1 = flat_idxs1 + dim1 flat_idxs1 = flat_idxs1.long() flat_idxs = flat_idxs.long() flattened_grid = binary_voxel_grid.flatten() flattened_grid[flat_idxs] = 1. flattened_grid[flat_idxs1] = 1. binary_voxel_grid = flattened_grid.view(B, X2, Y2, Z2) assert binary_voxel_grid[0].sum() == len(torch.unique(flat_idxs)), "some indexes are missed here" assert binary_voxel_grid[1].sum() == len(torch.unique(flat_idxs1)), "some indexes are missed here" # o3d.io.write_voxel_grid("forward_pass_save/grid0.ply", binary_voxel_grid[0]) # o3d.io.write_voxel_grid("forward_pass_save/grid1.ply", binary_voxel_grid[0]) # need to save these voxels save_voxel(binary_voxel_grid[0].cpu().numpy(), "forward_pass_save/grid0.binvox") save_voxel(binary_voxel_grid[1].cpu().numpy(), "forward_pass_save/grid1.binvox") from IPython import embed; embed() # use grid sample to get the visual touch tensor at these locations, NOTE: visual tensor features shape is (B, C, N) visual_tensor_features = utils_samp.bilinear_sample3D(featRs, sensor_depths_centers_in_mem[:, :, 0], sensor_depths_centers_in_mem[:, :, 1], sensor_depths_centers_in_mem[:, :, 2]) visual_feature_tensor = visual_tensor_features.permute(0, 2, 1) # pack it visual_feature_tensor_ = __p(visual_feature_tensor) C = list(visual_feature_tensor.shape)[-1] print('C=', C) # do the metric learning this is the same as before. # the code is basically copied from embnet3d.py but some changes are being made very minor emb_vec = torch.stack((sensor_features_, visual_feature_tensor_), dim=1).view(B*self.num_samples*self.batch_k, C) y = torch.stack([torch.range(0,self.num_samples*B-1), torch.range(0,self.num_samples*B-1)], dim=1).view(self.num_samples*B*self.batch_k) a_indices, anchors, positives, negatives, _ = self.sampler(emb_vec) # I need to write my own version of margin loss since the negatives and anchors may not be same dim d_ap = torch.sqrt(torch.sum((positives - anchors)**2, dim=1) + 1e-8) pos_loss = torch.clamp(d_ap - beta + self._margin, min=0.0) # TODO: expand the dims of anchors and tile them and compute the negative loss # do the pair count where you average by contributors only # this is your total loss # Further idea is to check what volumetric locations do each of the depth images corresponds to # unproject the entire depth image and convert to ref. and then sample. if hyp.do_touch_forward: ## ... Begin code for getting crops from visual memory ... ## sensor_depths_centers_x = center_sensor_W * torch.ones((hyp.B, hyp.sensor_S)) sensor_depths_centers_x = sensor_depths_centers_x.cuda() sensor_depths_centers_y = center_sensor_H * torch.ones((hyp.B, hyp.sensor_S)) sensor_depths_centers_y = sensor_depths_centers_y.cuda() sensor_depths_centers_z = sensor_depths[:, :, 0, center_sensor_H, center_sensor_W] # Next use Pixels2Camera to unproject all of these together. # merge the batch and the sequence dimension sensor_depths_centers_x = sensor_depths_centers_x.reshape(-1, 1, 1) sensor_depths_centers_y = sensor_depths_centers_y.reshape(-1, 1, 1) sensor_depths_centers_z = sensor_depths_centers_z.reshape(-1, 1, 1) fx, fy, x0, y0 = utils_geom.split_intrinsics(sensor_pix_T_cams_) sensor_depths_centers_in_camXs_ = utils_geom.Pixels2Camera(sensor_depths_centers_x, sensor_depths_centers_y, sensor_depths_centers_z, fx, fy, x0, y0) sensor_depths_centers_in_world_ = utils_geom.apply_4x4(sensor_origin_T_camXs_, sensor_depths_centers_in_camXs_) # not used by the algorithm ## this will be later used for visualization hence saving it here for now sensor_depths_centers_in_ref_cam_ = utils_geom.apply_4x4(sensor_camRs_T_camXs_, sensor_depths_centers_in_camXs_) # not used by the algorithm sensor_depths_centers_in_camXs = __u(sensor_depths_centers_in_camXs_).squeeze(2) # There has to be a better way to do this, for each of the cameras in the batch I want a box of size (ch, cw, cd) # TODO: rotation is the deviation of the box from the axis aligned do I want this tB, tN, _ = list(sensor_depths_centers_in_camXs.shape) # 2, 512, _ boxlist = torch.zeros(tB, tN, 9) # 2, 512, 9 boxlist[:, :, :3] = sensor_depths_centers_in_camXs # this lies on the object boxlist[:, :, 3:6] = torch.FloatTensor([hyp.contextW, hyp.contextH, hyp.contextD]) # convert the boxlist to lrtlist and to cuda # the rt here transforms the from box coordinates to camera coordinates box_lrtlist = utils_geom.convert_boxlist_to_lrtlist(boxlist) # Now I will use crop_zoom_from_mem functionality to get the features in each of the boxes # I will do it for each of the box separately as required by the api context_grid_list = list() for m in range(box_lrtlist.shape[1]): curr_box = box_lrtlist[:, m, :] context_grid = utils_vox.crop_zoom_from_mem(featRs, curr_box, 8, 8, 8, sensor_camRs_T_camXs[:, m, :, :]) context_grid_list.append(context_grid) context_grid_list = torch.stack(context_grid_list, dim=1) context_grid_list_ = __p(context_grid_list) ## ... till here I believe I have not introduced any randomness, so the points are still in ## ... End code for getting crops around this center of certain height, width and depth ... ## ## ... Begin code for passing the context grid through 3D CNN to obtain a vector ... ## sensor_cam_locs = feed['sensor_locs'] # these are in origin coordinates sensor_cam_quats = feed['sensor_quats'] # this too in in world_coordinates sensor_cam_locs_ = __p(sensor_cam_locs) sensor_cam_quats_ = __p(sensor_cam_quats) sensor_cam_locs_in_R_ = utils_geom.apply_4x4(sensor_camRs_T_origin_, sensor_cam_locs_.unsqueeze(1)).squeeze(1) # TODO TODO TODO confirm that this is right? TODO TODO TODO get_r_mat = lambda cam_quat: transformations.quaternion_matrix_py(cam_quat) rot_mat_Xs_ = torch.from_numpy(np.stack(list(map(get_r_mat, sensor_cam_quats_.cpu().numpy())))).to(sensor_cam_locs_.device).float() rot_mat_Rs_ = torch.bmm(sensor_camRs_T_origin_, rot_mat_Xs_) get_quat = lambda r_mat: transformations.quaternion_from_matrix_py(r_mat) sensor_quats_in_R_ = torch.from_numpy(np.stack(list(map(get_quat, rot_mat_Rs_.cpu().numpy())))).to(sensor_cam_locs_.device).float() pred_features_ = self.context_net(context_grid_list_,\ sensor_cam_locs_in_R_, sensor_quats_in_R_) # normalize pred_features_ = l2_normalize(pred_features_, dim=1) pred_features = __u(pred_features_) # if doing moco I have to pass the inputs through the key(slow) network as well # if hyp.do_moc or hyp.do_eval_recall: key_context_grid_list_ = copy.deepcopy(context_grid_list_) key_sensor_cam_locs_in_R_ = copy.deepcopy(sensor_cam_locs_in_R_) key_sensor_quats_in_R_ = copy.deepcopy(sensor_quats_in_R_) self.key_context_net.eval() with torch.no_grad(): key_pred_features_ = self.key_context_net(key_context_grid_list_,\ key_sensor_cam_locs_in_R_, key_sensor_quats_in_R_) # normalize, normalization is very important why though key_pred_features_ = l2_normalize(key_pred_features_, dim=1) key_pred_features = __u(key_pred_features_) # end passing of the input through the slow network this is necessary for moco # ## ... End code for passing the context grid through 3D CNN to obtain a vector ... ## ## ... Begin code for doing metric learning between pred_features and sensor features ... ## # 1. Subsample both based on the number of positive samples if hyp.do_touch_embML: assert(hyp.do_touch_forward) assert(hyp.do_touch_feat) perm = torch.randperm(len(pred_features_)) ## 1024 chosen_sensor_feats_ = sensor_features_[perm[:self.num_pos_samples*hyp.B]] chosen_pred_feats_ = pred_features_[perm[:self.num_pos_samples*B]] # 2. form the emb_vec and get pos and negative samples for the batch emb_vec = torch.stack((chosen_sensor_feats_, chosen_pred_feats_), dim=1).view(hyp.B*self.num_pos_samples*self.batch_k, -1) y = torch.stack([torch.range(0, self.num_pos_samples*B-1), torch.range(0, self.num_pos_samples*B-1)],\ dim=1).view(B*self.num_pos_samples*self.batch_k) # (0, 0, 1, 1, ..., 255, 255) a_indices, anchors, positives, negatives, _ = self.sampler(emb_vec) # 3. Compute the loss, ML loss and the l2 distance betwee the embeddings margin_loss, _ = self.criterion(anchors, positives, negatives, self.beta, y[a_indices]) total_loss = utils_misc.add_loss('embtouch/emb_touch_ml_loss', total_loss, margin_loss, hyp.emb_3D_ml_coeff, summ_writer) # the l2 loss between the embeddings l2_loss = torch.nn.functional.mse_loss(chosen_sensor_feats_, chosen_pred_feats_) total_loss = utils_misc.add_loss('embtouch/emb_l2_loss', total_loss, l2_loss, hyp.emb_3D_l2_coeff, summ_writer) ## ... End code for doing metric learning between pred_features and sensor_features ... ## ## ... Begin code for doing moc inspired ML between pred_features and sensor_features ... ## if hyp.do_moc and moc_init_done: moc_loss = self.moc_ml_net(sensor_features_, key_sensor_features_,\ pred_features_, key_pred_features_, summ_writer) total_loss += moc_loss ## ... End code for doing moc inspired ML between pred_features and sensor_feature ... ## ## ... add code for filling up results needed for eval recall ... ## if hyp.do_eval_recall and moc_init_done: results['context_features'] = pred_features_ results['sensor_depth_centers_in_world'] = sensor_depths_centers_in_world_ results['sensor_depths_centers_in_ref_cam'] = sensor_depths_centers_in_ref_cam_ results['object_name'] = feed['object_name'] # I will do precision recall here at different recall values and summarize it using tensorboard recalls = [1, 5, 10, 50, 100, 200] # also should not include any gradients because of this # fast_sensor_emb_e = sensor_features_ # fast_context_emb_e = pred_features_ # slow_sensor_emb_g = key_sensor_features_ # slow_context_emb_g = key_context_features_ fast_sensor_emb_e = sensor_features_.clone().detach() fast_context_emb_e = pred_features_.clone().detach() # I will do multiple eval recalls here slow_sensor_emb_g = key_sensor_features_.clone().detach() slow_context_emb_g = key_pred_features_.clone().detach() # assuming the above thing goes well fast_sensor_emb_e = fast_sensor_emb_e.cpu().numpy() fast_context_emb_e = fast_context_emb_e.cpu().numpy() slow_sensor_emb_g = slow_sensor_emb_g.cpu().numpy() slow_context_emb_g = slow_context_emb_g.cpu().numpy() # now also move the vis to numpy and plot it using matplotlib vis_e = __p(sensor_rgbs) vis_g = __p(sensor_rgbs) np_vis_e = vis_e.cpu().detach().numpy() np_vis_e = np.transpose(np_vis_e, [0, 2, 3, 1]) np_vis_g = vis_g.cpu().detach().numpy() np_vis_g = np.transpose(np_vis_g, [0, 2, 3, 1]) # bring it back to original color np_vis_g = ((np_vis_g+0.5) * 255).astype(np.uint8) np_vis_e = ((np_vis_e+0.5) * 255).astype(np.uint8) # now compare fast_sensor_emb_e with slow_context_emb_g # since I am doing positive against this fast_sensor_emb_e_list = [fast_sensor_emb_e, np_vis_e] slow_context_emb_g_list = [slow_context_emb_g, np_vis_g] prec, vis, chosen_inds_and_neighbors_inds = compute_precision( fast_sensor_emb_e_list, slow_context_emb_g_list, recalls=recalls ) # finally plot the nearest neighbour retrieval and move ahead if feed['global_step'] % 1 == 0: plot_nearest_neighbours(vis, step=feed['global_step'], save_dir='/home/gauravp/eval_results', name='fast_sensor_slow_context') # plot the precisions at different recalls for pr, re in enumerate(recalls): summ_writer.summ_scalar(f'evrefast_sensor_slow_context/recall@{re}',\ prec[pr]) # now compare fast_context_emb_e with slow_sensor_emb_g fast_context_emb_e_list = [fast_context_emb_e, np_vis_e] slow_sensor_emb_g_list = [slow_sensor_emb_g, np_vis_g] prec, vis, chosen_inds_and_neighbors_inds = compute_precision( fast_context_emb_e_list, slow_sensor_emb_g_list, recalls=recalls ) if feed['global_step'] % 1 == 0: plot_nearest_neighbours(vis, step=feed['global_step'], save_dir='/home/gauravp/eval_results', name='fast_context_slow_sensor') # plot the precisions at different recalls for pr, re in enumerate(recalls): summ_writer.summ_scalar(f'evrefast_context_slow_sensor/recall@{re}',\ prec[pr]) # now finally compare both the fast, I presume we want them to go closer too fast_sensor_list = [fast_sensor_emb_e, np_vis_e] fast_context_list = [fast_context_emb_e, np_vis_g] prec, vis, chosen_inds_and_neighbors_inds = compute_precision( fast_sensor_list, fast_context_list, recalls=recalls ) if feed['global_step'] % 1 == 0: plot_nearest_neighbours(vis, step=feed['global_step'], save_dir='/home/gauravp/eval_results', name='fast_sensor_fast_context') for pr, re in enumerate(recalls): summ_writer.summ_scalar(f'evrefast_sensor_fast_context/recall@{re}',\ prec[pr]) ## ... done code for filling up results needed for eval recall ... ## summ_writer.summ_scalar('loss', total_loss.cpu().item()) return total_loss, results, [key_sensor_features_, key_pred_features_]
def forward(self, feed): results = dict() summ_writer = utils_improc.Summ_writer(writer=feed['writer'], global_step=feed['global_step'], set_name=feed['set_name'], fps=8) writer = feed['writer'] global_step = feed['global_step'] total_loss = torch.tensor(0.0) __p = lambda x: pack_seqdim(x, B) __u = lambda x: unpack_seqdim(x, B) B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N PH, PW = hyp.PH, hyp.PW K = hyp.K Z, Y, X = hyp.Z, hyp.Y, hyp.X Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2) D = 9 rgb_camRs = feed["rgb_camRs"] rgb_camXs = feed["rgb_camXs"] pix_T_cams = feed["pix_T_cams"] cam_T_velos = feed["cam_T_velos"] boxlist_camRs = feed["boxes3D"] tidlist_s = feed["tids"] # coordinate-less and plural scorelist_s = feed["scores"] # coordinate-less and plural # # postproc the boxes: # scorelist_s = __u(utils_misc.rescore_boxlist_with_inbound(__p(boxlist_camRs), __p(tidlist_s), Z, Y, X)) boxlist_camRs_, tidlist_s_, scorelist_s_ = __p(boxlist_camRs), __p( tidlist_s), __p(scorelist_s) boxlist_camRs_, tidlist_s_, scorelist_s_ = utils_misc.shuffle_valid_and_sink_invalid_boxes( boxlist_camRs_, tidlist_s_, scorelist_s_) boxlist_camRs = __u(boxlist_camRs_) tidlist_s = __u(tidlist_s_) scorelist_s = __u(scorelist_s_) origin_T_camRs = feed["origin_T_camRs"] origin_T_camRs_ = __p(origin_T_camRs) origin_T_camXs = feed["origin_T_camXs"] origin_T_camXs_ = __p(origin_T_camXs) camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0) camX0_T_camXs_ = __p(camX0_T_camXs) camRs_T_camXs_ = torch.matmul(origin_T_camRs_.inverse(), origin_T_camXs_) camXs_T_camRs_ = camRs_T_camXs_.inverse() camRs_T_camXs = __u(camRs_T_camXs_) camXs_T_camRs = __u(camXs_T_camRs_) xyz_veloXs = feed["xyz_veloXs"] xyz_camXs = __u(utils_geom.apply_4x4(__p(cam_T_velos), __p(xyz_veloXs))) xyz_camRs = __u( utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs))) xyz_camX0s = __u( utils_geom.apply_4x4(__p(camX0_T_camXs), __p(xyz_camXs))) occRs = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z, Y, X)) occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X)) occX0s = __u(utils_vox.voxelize_xyz(__p(xyz_camX0s), Z, Y, X)) occRs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z2, Y2, X2)) occXs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z2, Y2, X2)) occX0s_half = __u(utils_vox.voxelize_xyz(__p(xyz_camX0s), Z2, Y2, X2)) unpRs = __u( utils_vox.unproject_rgb_to_mem( __p(rgb_camXs), Z, Y, X, __p(torch.matmul(pix_T_cams, camXs_T_camRs)))) unpXs = __u( utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z, Y, X, __p(pix_T_cams))) unpX0s = utils_vox.apply_4x4_to_voxs(camX0_T_camXs, unpXs) unpRs_half = __u( utils_vox.unproject_rgb_to_mem( __p(rgb_camXs), Z2, Y2, X2, __p(torch.matmul(pix_T_cams, camXs_T_camRs)))) ##################### ## visualize what we got ##################### summ_writer.summ_rgbs('2D_inputs/rgb_camRs', torch.unbind(rgb_camRs, dim=1)) summ_writer.summ_rgbs('2D_inputs/rgb_camXs', torch.unbind(rgb_camXs, dim=1)) summ_writer.summ_occs('3D_inputs/occRs', torch.unbind(occRs, dim=1)) summ_writer.summ_occs('3D_inputs/occXs', torch.unbind(occXs, dim=1)) summ_writer.summ_unps('3D_inputs/unpRs', torch.unbind(unpRs, dim=1), torch.unbind(occRs, dim=1)) summ_writer.summ_unps('3D_inputs/unpXs', torch.unbind(unpXs, dim=1), torch.unbind(occXs, dim=1)) summ_writer.summ_unps('3D_inputs/unpX0s', torch.unbind(unpX0s, dim=1), torch.unbind(occX0s, dim=1)) lrtlist_camRs = __u( utils_geom.convert_boxlist_to_lrtlist(boxlist_camRs_)).reshape( B, S, N, 19) lrtlist_camXs = __u( utils_geom.apply_4x4_to_lrtlist(__p(camXs_T_camRs), __p(lrtlist_camRs))) # stabilize boxes for ego/cam motion lrtlist_camX0s = __u( utils_geom.apply_4x4_to_lrtlist(__p(camX0_T_camXs), __p(lrtlist_camXs))) # these are is B x S x N x 19 summ_writer.summ_lrtlist('lrtlist_camR0', rgb_camRs[:, 0], lrtlist_camRs[:, 0], scorelist_s[:, 0], tidlist_s[:, 0], pix_T_cams[:, 0]) summ_writer.summ_lrtlist('lrtlist_camR1', rgb_camRs[:, 1], lrtlist_camRs[:, 1], scorelist_s[:, 1], tidlist_s[:, 1], pix_T_cams[:, 1]) summ_writer.summ_lrtlist('lrtlist_camX0', rgb_camXs[:, 0], lrtlist_camXs[:, 0], scorelist_s[:, 0], tidlist_s[:, 0], pix_T_cams[:, 0]) summ_writer.summ_lrtlist('lrtlist_camX1', rgb_camXs[:, 1], lrtlist_camXs[:, 1], scorelist_s[:, 1], tidlist_s[:, 1], pix_T_cams[:, 1]) ( obj_lrtlist_camXs, obj_scorelist_s, ) = utils_misc.collect_object_info(lrtlist_camXs, tidlist_s, scorelist_s, pix_T_cams, K, mod='X', do_vis=True, summ_writer=summ_writer) ( obj_lrtlist_camRs, obj_scorelist_s, ) = utils_misc.collect_object_info(lrtlist_camRs, tidlist_s, scorelist_s, pix_T_cams, K, mod='R', do_vis=True, summ_writer=summ_writer) ( obj_lrtlist_camX0s, obj_scorelist_s, ) = utils_misc.collect_object_info(lrtlist_camX0s, tidlist_s, scorelist_s, pix_T_cams, K, mod='X0', do_vis=False) masklist_memR = utils_vox.assemble_padded_obj_masklist( lrtlist_camRs[:, 0], scorelist_s[:, 0], Z, Y, X, coeff=1.0) masklist_memX = utils_vox.assemble_padded_obj_masklist( lrtlist_camXs[:, 0], scorelist_s[:, 0], Z, Y, X, coeff=1.0) # obj_mask_memR is B x N x 1 x Z x Y x X summ_writer.summ_occ('obj/masklist_memR', torch.sum(masklist_memR, dim=1)) summ_writer.summ_occ('obj/masklist_memX', torch.sum(masklist_memX, dim=1)) # to do tracking or whatever, i need to be able to extract a 3d object crop cropX0_obj0 = utils_vox.crop_zoom_from_mem(occXs[:, 0], lrtlist_camXs[:, 0, 0], Z2, Y2, X2) cropX0_obj1 = utils_vox.crop_zoom_from_mem(occXs[:, 0], lrtlist_camXs[:, 0, 1], Z2, Y2, X2) cropR0_obj0 = utils_vox.crop_zoom_from_mem(occRs[:, 0], lrtlist_camRs[:, 0, 0], Z2, Y2, X2) cropR0_obj1 = utils_vox.crop_zoom_from_mem(occRs[:, 0], lrtlist_camRs[:, 0, 1], Z2, Y2, X2) # print('got it:') # print(cropX00.shape) # summ_writer.summ_occ('crops/cropX0_obj0', cropX0_obj0) # summ_writer.summ_occ('crops/cropX0_obj1', cropX0_obj1) summ_writer.summ_feat('crops/cropX0_obj0', cropX0_obj0, pca=False) summ_writer.summ_feat('crops/cropX0_obj1', cropX0_obj1, pca=False) summ_writer.summ_feat('crops/cropR0_obj0', cropR0_obj0, pca=False) summ_writer.summ_feat('crops/cropR0_obj1', cropR0_obj1, pca=False) if hyp.do_feat: if hyp.flow_do_synth_rt: result = utils_misc.get_synth_flow(unpRs_half, occRs_half, obj_lrtlist_camX0s, obj_scorelist_s, occXs_half, feed['set_name'], K=K, summ_writer=summ_writer, sometimes_zero=True, sometimes_real=False) occXs, unpXs, flowX0, camX1_T_camX0, is_synth = result else: # ego-stabilized flow from X00 to X01 flowX0 = utils_misc.get_gt_flow( obj_lrtlist_camX0s, obj_scorelist_s, utils_geom.eye_4x4s(B, S), occXs_half[:, 0], K=K, occ_only=False, # get the dense flow mod='X0', summ_writer=summ_writer) # occXs is B x S x 1 x H x W x D # unpXs is B x S x 3 x H x W x D # featXs_input = torch.cat([occXs, occXs*unpXs], dim=2) featX0s_input = torch.cat([occX0s, occX0s * unpX0s], dim=2) featX0s_input_ = __p(featX0s_input) featX0s_, validX0s_, feat_loss = self.featnet( featX0s_input_, summ_writer) total_loss += feat_loss featX0s = __u(featX0s_) # _featX00 = featXs[:,0:1] # _featX01 = utils_vox.apply_4x4_to_voxs(camX0_T_camXs[:,1:], featXs[:,1:]) # featX0s = torch.cat([_featX00, _featX01], dim=1) validX0s = 1.0 - (featX0s == 0).all( dim=2, keepdim=True).float() #this shall be B x S x 1 x H x W x D summ_writer.summ_feats('3D_feats/featX0s_input', torch.unbind(featX0s_input, dim=1), pca=True) # summ_writer.summ_feats('3D_feats/featXs_output', torch.unbind(featXs, dim=1), pca=True) summ_writer.summ_feats('3D_feats/featX0s_output', torch.unbind(featX0s, dim=1), pca=True) if hyp.do_flow: # total flow from X0 to X1 flowX = utils_misc.get_gt_flow( obj_lrtlist_camXs, obj_scorelist_s, camX0_T_camXs, occXs_half[:, 0], K=K, occ_only=False, # get the dense flow mod='X', vis=False, summ_writer=None) # # vis this to confirm it's ok (it is) # unpX0_e = utils_samp.backwarp_using_3D_flow(unpXs[:,1], flowX) # occX0_e = utils_samp.backwarp_using_3D_flow(occXs[:,1], flowX) # summ_writer.summ_unps('flow/backwarpX', [unpX0s[:,0], unpX0_e], [occXs[:,0], occX0_e]) # unpX0_e = utils_samp.backwarp_using_3D_flow(unpX0s[:,1], flowX0) # occX0_e = utils_samp.backwarp_using_3D_flow(occX0s[:,1], flowX0, binary_feat=True) # summ_writer.summ_unps('flow/backwarpX0', [unpX0s[:,0], unpX0_e], [occXs[:,0], occX0_e]) flow_loss, flowX0_pred = self.flownet( featX0s[:, 0], featX0s[:, 1], flowX0, # gt flow torch.max(validX0s[:, 1:], dim=1)[0], is_synth, summ_writer) total_loss += flow_loss # g = flowX.reshape(-1) # summ_writer.summ_histogram('flowX_g_nonzero_hist', g[torch.abs(g)>0.01]) # g = flowX0.reshape(-1) # e = flowX0_pred.reshape(-1) # summ_writer.summ_histogram('flowX0_g_nonzero_hist', g[torch.abs(g)>0.01]) # summ_writer.summ_histogram('flowX0_e_nonzero_hist', e[torch.abs(g)>0.01]) summ_writer.summ_scalar('loss', total_loss.cpu().item()) return total_loss, results
def forward(self, feed): results = dict() if 'log_freq' not in feed.keys(): feed['log_freq'] = None start_time = time.time() summ_writer = utils_improc.Summ_writer(writer=feed['writer'], global_step=feed['global_step'], set_name=feed['set_name'], log_freq=feed['log_freq'], fps=8) writer = feed['writer'] global_step = feed['global_step'] total_loss = torch.tensor(0.0).cuda() __p = lambda x: utils_basic.pack_seqdim(x, B) __u = lambda x: utils_basic.unpack_seqdim(x, B) __pb = lambda x: utils_basic.pack_boxdim(x, hyp.N) __ub = lambda x: utils_basic.unpack_boxdim(x, hyp.N) if hyp.aug_object_ent_dis: __pb_a = lambda x: utils_basic.pack_boxdim( x, hyp.max_obj_aug + hyp.max_obj_aug_dis) __ub_a = lambda x: utils_basic.unpack_boxdim( x, hyp.max_obj_aug + hyp.max_obj_aug_dis) else: __pb_a = lambda x: utils_basic.pack_boxdim(x, hyp.max_obj_aug) __ub_a = lambda x: utils_basic.unpack_boxdim(x, hyp.max_obj_aug) B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N PH, PW = hyp.PH, hyp.PW K = hyp.K BOX_SIZE = hyp.BOX_SIZE Z, Y, X = hyp.Z, hyp.Y, hyp.X Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2) Z4, Y4, X4 = int(Z / 4), int(Y / 4), int(X / 4) D = 9 tids = torch.from_numpy(np.reshape(np.arange(B * N), [B, N])) rgb_camXs = feed["rgb_camXs_raw"] pix_T_cams = feed["pix_T_cams_raw"] camRs_T_origin = feed["camR_T_origin_raw"] origin_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_origin))) origin_T_camXs = feed["origin_T_camXs_raw"] camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0) camRs_T_camXs = __u( torch.matmul(utils_geom.safe_inverse(__p(origin_T_camRs)), __p(origin_T_camXs))) camXs_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_camXs))) camX0_T_camRs = camXs_T_camRs[:, 0] camX1_T_camRs = camXs_T_camRs[:, 1] camR_T_camX0 = utils_geom.safe_inverse(camX0_T_camRs) xyz_camXs = feed["xyz_camXs_raw"] depth_camXs_, valid_camXs_ = utils_geom.create_depth_image( __p(pix_T_cams), __p(xyz_camXs), H, W) dense_xyz_camXs_ = utils_geom.depth2pointcloud(depth_camXs_, __p(pix_T_cams)) xyz_camRs = __u( utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs))) xyz_camX0s = __u( utils_geom.apply_4x4(__p(camX0_T_camXs), __p(xyz_camXs))) occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X)) occXs_to_Rs = utils_vox.apply_4x4s_to_voxs(camRs_T_camXs, occXs) occXs_to_Rs_45 = cross_corr.rotate_tensor_along_y_axis(occXs_to_Rs, 45) occXs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z2, Y2, X2)) occRs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z2, Y2, X2)) occX0s_half = __u(utils_vox.voxelize_xyz(__p(xyz_camX0s), Z2, Y2, X2)) unpXs = __u( utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z, Y, X, __p(pix_T_cams))) unpXs_half = __u( utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z2, Y2, X2, __p(pix_T_cams))) unpX0s_half = __u( utils_vox.unproject_rgb_to_mem( __p(rgb_camXs), Z2, Y2, X2, utils_basic.matmul2( __p(pix_T_cams), utils_geom.safe_inverse(__p(camX0_T_camXs))))) unpRs = __u( utils_vox.unproject_rgb_to_mem( __p(rgb_camXs), Z, Y, X, utils_basic.matmul2( __p(pix_T_cams), utils_geom.safe_inverse(__p(camRs_T_camXs))))) unpRs_half = __u( utils_vox.unproject_rgb_to_mem( __p(rgb_camXs), Z2, Y2, X2, utils_basic.matmul2( __p(pix_T_cams), utils_geom.safe_inverse(__p(camRs_T_camXs))))) dense_xyz_camRs_ = utils_geom.apply_4x4(__p(camRs_T_camXs), dense_xyz_camXs_) inbound_camXs_ = utils_vox.get_inbounds(dense_xyz_camRs_, Z, Y, X).float() inbound_camXs_ = torch.reshape(inbound_camXs_, [B * S, 1, H, W]) depth_camXs = __u(depth_camXs_) valid_camXs = __u(valid_camXs_) * __u(inbound_camXs_) summ_writer.summ_oneds('2D_inputs/depth_camXs', torch.unbind(depth_camXs, dim=1), maxdepth=21.0) summ_writer.summ_oneds('2D_inputs/valid_camXs', torch.unbind(valid_camXs, dim=1)) summ_writer.summ_rgbs('2D_inputs/rgb_camXs', torch.unbind(rgb_camXs, dim=1)) summ_writer.summ_occs('3D_inputs/occXs', torch.unbind(occXs, dim=1)) summ_writer.summ_unps('3D_inputs/unpXs', torch.unbind(unpXs, dim=1), torch.unbind(occXs, dim=1)) occRs = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z, Y, X)) if hyp.do_eval_boxes: if hyp.dataset_name == "clevr_vqa": gt_boxes_origin_corners = feed['gt_box'] gt_scores_origin = feed['gt_scores'].detach().cpu().numpy() classes = feed['classes'] scores = gt_scores_origin tree_seq_filename = feed['tree_seq_filename'] gt_boxes_origin = nlu.get_ends_of_corner( gt_boxes_origin_corners) gt_boxes_origin_end = torch.reshape(gt_boxes_origin, [hyp.B, hyp.N, 2, 3]) gt_boxes_origin_theta = nlu.get_alignedboxes2thetaformat( gt_boxes_origin_end) gt_boxes_origin_corners = utils_geom.transform_boxes_to_corners( gt_boxes_origin_theta) gt_boxesR_corners = __ub( utils_geom.apply_4x4(camRs_T_origin[:, 0], __pb(gt_boxes_origin_corners))) gt_boxesR_theta = utils_geom.transform_corners_to_boxes( gt_boxesR_corners) gt_boxesR_end = nlu.get_ends_of_corner(gt_boxesR_corners) else: tree_seq_filename = feed['tree_seq_filename'] tree_filenames = [ join(hyp.root_dataset, i) for i in tree_seq_filename if i != "invalid_tree" ] invalid_tree_filenames = [ join(hyp.root_dataset, i) for i in tree_seq_filename if i == "invalid_tree" ] num_empty = len(invalid_tree_filenames) trees = [pickle.load(open(i, "rb")) for i in tree_filenames] len_valid = len(trees) if len_valid > 0: gt_boxesR, scores, classes = nlu.trees_rearrange(trees) if num_empty > 0: gt_boxesR = np.concatenate([ gt_boxesR, empty_gt_boxesR ]) if len_valid > 0 else empty_gt_boxesR scores = np.concatenate([ scores, empty_scores ]) if len_valid > 0 else empty_scores classes = np.concatenate([ classes, empty_classes ]) if len_valid > 0 else empty_classes gt_boxesR = torch.from_numpy( gt_boxesR).cuda().float() # torch.Size([2, 3, 6]) gt_boxesR_end = torch.reshape(gt_boxesR, [hyp.B, hyp.N, 2, 3]) gt_boxesR_theta = nlu.get_alignedboxes2thetaformat( gt_boxesR_end) #torch.Size([2, 3, 9]) gt_boxesR_corners = utils_geom.transform_boxes_to_corners( gt_boxesR_theta) class_names_ex_1 = "_".join(classes[0]) summ_writer.summ_text('eval_boxes/class_names', class_names_ex_1) gt_boxesRMem_corners = __ub( utils_vox.Ref2Mem(__pb(gt_boxesR_corners), Z2, Y2, X2)) gt_boxesRMem_end = nlu.get_ends_of_corner(gt_boxesRMem_corners) gt_boxesRMem_theta = utils_geom.transform_corners_to_boxes( gt_boxesRMem_corners) gt_boxesRUnp_corners = __ub( utils_vox.Ref2Mem(__pb(gt_boxesR_corners), Z, Y, X)) gt_boxesRUnp_end = nlu.get_ends_of_corner(gt_boxesRUnp_corners) gt_boxesX0_corners = __ub( utils_geom.apply_4x4(camX0_T_camRs, __pb(gt_boxesR_corners))) gt_boxesX0Mem_corners = __ub( utils_vox.Ref2Mem(__pb(gt_boxesX0_corners), Z2, Y2, X2)) gt_boxesX0Mem_theta = utils_geom.transform_corners_to_boxes( gt_boxesX0Mem_corners) gt_boxesX0Mem_end = nlu.get_ends_of_corner(gt_boxesX0Mem_corners) gt_boxesX0_end = nlu.get_ends_of_corner(gt_boxesX0_corners) gt_cornersX0_pix = __ub( utils_geom.apply_pix_T_cam(pix_T_cams[:, 0], __pb(gt_boxesX0_corners))) rgb_camX0 = rgb_camXs[:, 0] rgb_camX1 = rgb_camXs[:, 1] summ_writer.summ_box_by_corners('eval_boxes/gt_boxescamX0', rgb_camX0, gt_boxesX0_corners, torch.from_numpy(scores), tids, pix_T_cams[:, 0]) unps_vis = utils_improc.get_unps_vis(unpX0s_half, occX0s_half) unp_vis = torch.mean(unps_vis, dim=1) unps_visRs = utils_improc.get_unps_vis(unpRs_half, occRs_half) unp_visRs = torch.mean(unps_visRs, dim=1) unps_visRs_full = utils_improc.get_unps_vis(unpRs, occRs) unp_visRs_full = torch.mean(unps_visRs_full, dim=1) summ_writer.summ_box_mem_on_unp('eval_boxes/gt_boxesR_mem', unp_visRs, gt_boxesRMem_end, scores, tids) unpX0s_half = torch.mean(unpX0s_half, dim=1) unpX0s_half = nlu.zero_out(unpX0s_half, gt_boxesX0Mem_end, scores) occX0s_half = torch.mean(occX0s_half, dim=1) occX0s_half = nlu.zero_out(occX0s_half, gt_boxesX0Mem_end, scores) summ_writer.summ_unp('3D_inputs/unpX0s', unpX0s_half, occX0s_half) if hyp.do_feat: featXs_input = torch.cat([occXs, occXs * unpXs], dim=2) featXs_input_ = __p(featXs_input) freeXs_ = utils_vox.get_freespace(__p(xyz_camXs), __p(occXs_half)) freeXs = __u(freeXs_) visXs = torch.clamp(occXs_half + freeXs, 0.0, 1.0) mask_ = None if (type(mask_) != type(None)): assert (list(mask_.shape)[2:5] == list( featXs_input_.shape)[2:5]) featXs_, feat_loss = self.featnet(featXs_input_, summ_writer, mask=__p(occXs)) #mask_) total_loss += feat_loss validXs = torch.ones_like(visXs) _validX00 = validXs[:, 0:1] _validX01 = utils_vox.apply_4x4s_to_voxs(camX0_T_camXs[:, 1:], validXs[:, 1:]) validX0s = torch.cat([_validX00, _validX01], dim=1) validRs = utils_vox.apply_4x4s_to_voxs(camRs_T_camXs, validXs) visRs = utils_vox.apply_4x4s_to_voxs(camRs_T_camXs, visXs) featXs = __u(featXs_) _featX00 = featXs[:, 0:1] _featX01 = utils_vox.apply_4x4s_to_voxs(camX0_T_camXs[:, 1:], featXs[:, 1:]) featX0s = torch.cat([_featX00, _featX01], dim=1) emb3D_e = torch.mean(featX0s[:, 1:], dim=1) vis3D_e_R = torch.max(visRs[:, 1:], dim=1)[0] emb3D_g = featX0s[:, 0] vis3D_g_R = visRs[:, 0] validR_combo = torch.min(validRs, dim=1).values summ_writer.summ_feats('3D_feats/featXs_input', torch.unbind(featXs_input, dim=1), pca=True) summ_writer.summ_feats('3D_feats/featXs_output', torch.unbind(featXs, dim=1), valids=torch.unbind(validXs, dim=1), pca=True) summ_writer.summ_feats('3D_feats/featX0s_output', torch.unbind(featX0s, dim=1), valids=torch.unbind( torch.ones_like(validRs), dim=1), pca=True) summ_writer.summ_feats('3D_feats/validRs', torch.unbind(validRs, dim=1), pca=False) summ_writer.summ_feat('3D_feats/vis3D_e_R', vis3D_e_R, pca=False) summ_writer.summ_feat('3D_feats/vis3D_g_R', vis3D_g_R, pca=False) if hyp.do_munit: object_classes, filenames = nlu.create_object_classes( classes, [tree_seq_filename, tree_seq_filename], scores) if hyp.do_munit_fewshot: emb3D_e_R = utils_vox.apply_4x4_to_vox(camR_T_camX0, emb3D_e) emb3D_g_R = utils_vox.apply_4x4_to_vox(camR_T_camX0, emb3D_g) emb3D_R = emb3D_e_R emb3D_e_R_object, emb3D_g_R_object, validR_combo_object = nlu.create_object_tensors( [emb3D_e_R, emb3D_g_R], [validR_combo], gt_boxesRMem_end, scores, [BOX_SIZE, BOX_SIZE, BOX_SIZE]) emb3D_R_object = (emb3D_e_R_object + emb3D_g_R_object) / 2 content, style = self.munitnet.net.gen_a.encode(emb3D_R_object) objects_taken, _ = self.munitnet.net.gen_a.decode( content, style) styles = style contents = content elif hyp.do_3d_style_munit: emb3D_e_R = utils_vox.apply_4x4_to_vox(camR_T_camX0, emb3D_e) emb3D_g_R = utils_vox.apply_4x4_to_vox(camR_T_camX0, emb3D_g) emb3D_R = emb3D_e_R # st() emb3D_e_R_object, emb3D_g_R_object, validR_combo_object = nlu.create_object_tensors( [emb3D_e_R, emb3D_g_R], [validR_combo], gt_boxesRMem_end, scores, [BOX_SIZE, BOX_SIZE, BOX_SIZE]) emb3D_R_object = (emb3D_e_R_object + emb3D_g_R_object) / 2 camX1_T_R = camXs_T_camRs[:, 1] camX0_T_R = camXs_T_camRs[:, 0] assert hyp.B == 2 assert emb3D_e_R_object.shape[0] == 2 munit_loss, sudo_input_0, sudo_input_1, recon_input_0, recon_input_1, sudo_input_0_cycle, sudo_input_1_cycle, styles, contents, adin = self.munitnet( emb3D_R_object[0:1], emb3D_R_object[1:2]) if hyp.store_content_style_range: if self.max_content == None: self.max_content = torch.zeros_like( contents[0][0]).cuda() - 100000000 if self.min_content == None: self.min_content = torch.zeros_like( contents[0][0]).cuda() + 100000000 if self.max_style == None: self.max_style = torch.zeros_like( styles[0][0]).cuda() - 100000000 if self.min_style == None: self.min_style = torch.zeros_like( styles[0][0]).cuda() + 100000000 self.max_content = torch.max( torch.max(self.max_content, contents[0][0]), contents[1][0]) self.min_content = torch.min( torch.min(self.min_content, contents[0][0]), contents[1][0]) self.max_style = torch.max( torch.max(self.max_style, styles[0][0]), styles[1][0]) self.min_style = torch.min( torch.min(self.min_style, styles[0][0]), styles[1][0]) data_to_save = { 'max_content': self.max_content.cpu().numpy(), 'min_content': self.min_content.cpu().numpy(), 'max_style': self.max_style.cpu().numpy(), 'min_style': self.min_style.cpu().numpy() } with open('content_style_range.p', 'wb') as f: pickle.dump(data_to_save, f) elif hyp.is_contrastive_examples: if hyp.normalize_contrast: content0 = (contents[0] - self.min_content) / ( self.max_content - self.min_content + 1e-5) content1 = (contents[1] - self.min_content) / ( self.max_content - self.min_content + 1e-5) style0 = (styles[0] - self.min_style) / ( self.max_style - self.min_style + 1e-5) style1 = (styles[1] - self.min_style) / ( self.max_style - self.min_style + 1e-5) else: content0 = contents[0] content1 = contents[1] style0 = styles[0] style1 = styles[1] # euclid_dist_content = torch.sum(torch.sqrt((content0 - content1)**2))/torch.prod(torch.tensor(content0.shape)) # euclid_dist_style = torch.sum(torch.sqrt((style0-style1)**2))/torch.prod(torch.tensor(style0.shape)) euclid_dist_content = (content0 - content1).norm(2) / ( content0.numel()) euclid_dist_style = (style0 - style1).norm(2) / (style0.numel()) content_0_pooled = torch.mean( content0.reshape(list(content0.shape[:2]) + [-1]), dim=-1) content_1_pooled = torch.mean( content1.reshape(list(content1.shape[:2]) + [-1]), dim=-1) euclid_dist_content_pooled = (content_0_pooled - content_1_pooled).norm(2) / ( content_0_pooled.numel()) content_0_normalized = content0 / content0.norm() content_1_normalized = content1 / content1.norm() style_0_normalized = style0 / style0.norm() style_1_normalized = style1 / style1.norm() content_0_pooled_normalized = content_0_pooled / content_0_pooled.norm( ) content_1_pooled_normalized = content_1_pooled / content_1_pooled.norm( ) cosine_dist_content = torch.sum(content_0_normalized * content_1_normalized) cosine_dist_style = torch.sum(style_0_normalized * style_1_normalized) cosine_dist_content_pooled = torch.sum( content_0_pooled_normalized * content_1_pooled_normalized) print("euclid dist [content, pooled-content, style]: ", euclid_dist_content, euclid_dist_content_pooled, euclid_dist_style) print("cosine sim [content, pooled-content, style]: ", cosine_dist_content, cosine_dist_content_pooled, cosine_dist_style) if hyp.run_few_shot_on_munit: if (global_step % 300) == 1 or (global_step % 300) == 0: wrong = False try: precision_style = float(self.tp_style) / self.all_style precision_content = float( self.tp_content) / self.all_content except ZeroDivisionError: wrong = True if not wrong: summ_writer.summ_scalar( 'precision/unsupervised_precision_style', precision_style) summ_writer.summ_scalar( 'precision/unsupervised_precision_content', precision_content) # st() self.embed_list_style = defaultdict(lambda: []) self.embed_list_content = defaultdict(lambda: []) self.tp_style = 0 self.all_style = 0 self.tp_content = 0 self.all_content = 0 self.check = False elif not self.check and not nlu.check_fill_dict( self.embed_list_content, self.embed_list_style): print("Filling \n") for index, class_val in enumerate(object_classes): if hyp.dataset_name == "clevr_vqa": class_val_content, class_val_style = class_val.split( "/") else: class_val_content, class_val_style = [ class_val.split("/")[0], class_val.split("/")[0] ] print(len(self.embed_list_style.keys()), "style class", len(self.embed_list_content), "content class", self.embed_list_content.keys()) if len(self.embed_list_style[class_val_style] ) < hyp.few_shot_nums: self.embed_list_style[class_val_style].append( styles[index].squeeze()) if len(self.embed_list_content[class_val_content] ) < hyp.few_shot_nums: if hyp.avg_3d: content_val = contents[index] content_val = torch.mean(content_val.reshape( [content_val.shape[1], -1]), dim=-1) # st() self.embed_list_content[ class_val_content].append(content_val) else: self.embed_list_content[ class_val_content].append( contents[index].reshape([-1])) else: self.check = True try: print(float(self.tp_content) / self.all_content) print(float(self.tp_style) / self.all_style) except Exception as e: pass average = True if average: for key, val in self.embed_list_style.items(): if isinstance(val, type([])): self.embed_list_style[key] = torch.mean( torch.stack(val, dim=0), dim=0) for key, val in self.embed_list_content.items(): if isinstance(val, type([])): self.embed_list_content[key] = torch.mean( torch.stack(val, dim=0), dim=0) else: for key, val in self.embed_list_style.items(): if isinstance(val, type([])): self.embed_list_style[key] = torch.stack(val, dim=0) for key, val in self.embed_list_content.items(): if isinstance(val, type([])): self.embed_list_content[key] = torch.stack( val, dim=0) for index, class_val in enumerate(object_classes): class_val = class_val if hyp.dataset_name == "clevr_vqa": class_val_content, class_val_style = class_val.split( "/") else: class_val_content, class_val_style = [ class_val.split("/")[0], class_val.split("/")[0] ] style_val = styles[index].squeeze().unsqueeze(0) if not average: embed_list_val_style = torch.cat(list( self.embed_list_style.values()), dim=0) embed_list_key_style = list( np.repeat( np.expand_dims( list(self.embed_list_style.keys()), 1), hyp.few_shot_nums, 1).reshape([-1])) else: embed_list_val_style = torch.stack(list( self.embed_list_style.values()), dim=0) embed_list_key_style = list( self.embed_list_style.keys()) embed_list_val_style = utils_basic.l2_normalize( embed_list_val_style, dim=1).permute(1, 0) style_val = utils_basic.l2_normalize(style_val, dim=1) scores_styles = torch.matmul(style_val, embed_list_val_style) index_key = torch.argmax(scores_styles, dim=1).squeeze() selected_class_style = embed_list_key_style[index_key] self.styles_prediction[class_val_style].append( selected_class_style) if class_val_style == selected_class_style: self.tp_style += 1 self.all_style += 1 if hyp.avg_3d: content_val = contents[index] content_val = torch.mean(content_val.reshape( [content_val.shape[1], -1]), dim=-1).unsqueeze(0) else: content_val = contents[index].reshape( [-1]).unsqueeze(0) if not average: embed_list_val_content = torch.cat(list( self.embed_list_content.values()), dim=0) embed_list_key_content = list( np.repeat( np.expand_dims( list(self.embed_list_content.keys()), 1), hyp.few_shot_nums, 1).reshape([-1])) else: embed_list_val_content = torch.stack(list( self.embed_list_content.values()), dim=0) embed_list_key_content = list( self.embed_list_content.keys()) embed_list_val_content = utils_basic.l2_normalize( embed_list_val_content, dim=1).permute(1, 0) content_val = utils_basic.l2_normalize(content_val, dim=1) scores_content = torch.matmul(content_val, embed_list_val_content) index_key = torch.argmax(scores_content, dim=1).squeeze() selected_class_content = embed_list_key_content[ index_key] self.content_prediction[class_val_content].append( selected_class_content) if class_val_content == selected_class_content: self.tp_content += 1 self.all_content += 1 # st() munit_loss = hyp.munit_loss_weight * munit_loss recon_input_obj = torch.cat([recon_input_0, recon_input_1], dim=0) recon_emb3D_R = nlu.update_scene_with_objects( emb3D_R, recon_input_obj, gt_boxesRMem_end, scores) sudo_input_obj = torch.cat([sudo_input_0, sudo_input_1], dim=0) styled_emb3D_R = nlu.update_scene_with_objects( emb3D_R, sudo_input_obj, gt_boxesRMem_end, scores) styled_emb3D_e_X1 = utils_vox.apply_4x4_to_vox( camX1_T_R, styled_emb3D_R) styled_emb3D_e_X0 = utils_vox.apply_4x4_to_vox( camX0_T_R, styled_emb3D_R) emb3D_e_X1 = utils_vox.apply_4x4_to_vox(camX1_T_R, recon_emb3D_R) emb3D_e_X0 = utils_vox.apply_4x4_to_vox(camX0_T_R, recon_emb3D_R) emb3D_e_X1_og = utils_vox.apply_4x4_to_vox(camX1_T_R, emb3D_R) emb3D_e_X0_og = utils_vox.apply_4x4_to_vox(camX0_T_R, emb3D_R) emb3D_R_aug_diff = torch.abs(emb3D_R - recon_emb3D_R) summ_writer.summ_feat(f'aug_feat/og', emb3D_R) summ_writer.summ_feat(f'aug_feat/og_gen', recon_emb3D_R) summ_writer.summ_feat(f'aug_feat/og_aug_diff', emb3D_R_aug_diff) if hyp.cycle_style_view_loss: sudo_input_obj_cycle = torch.cat( [sudo_input_0_cycle, sudo_input_1_cycle], dim=0) styled_emb3D_R_cycle = nlu.update_scene_with_objects( emb3D_R, sudo_input_obj_cycle, gt_boxesRMem_end, scores) styled_emb3D_e_X0_cycle = utils_vox.apply_4x4_to_vox( camX0_T_R, styled_emb3D_R_cycle) styled_emb3D_e_X1_cycle = utils_vox.apply_4x4_to_vox( camX1_T_R, styled_emb3D_R_cycle) summ_writer.summ_scalar('munit_loss', munit_loss.cpu().item()) total_loss += munit_loss if hyp.do_occ and hyp.occ_do_cheap: occX0_sup, freeX0_sup, _, freeXs = utils_vox.prep_occs_supervision( camX0_T_camXs, xyz_camXs, Z2, Y2, X2, agg=True) summ_writer.summ_occ('occ_sup/occ_sup', occX0_sup) summ_writer.summ_occ('occ_sup/free_sup', freeX0_sup) summ_writer.summ_occs('occ_sup/freeXs_sup', torch.unbind(freeXs, dim=1)) summ_writer.summ_occs('occ_sup/occXs_sup', torch.unbind(occXs_half, dim=1)) occ_loss, occX0s_pred_ = self.occnet( torch.mean(featX0s[:, 1:], dim=1), occX0_sup, freeX0_sup, torch.max(validX0s[:, 1:], dim=1)[0], summ_writer) occX0s_pred = __u(occX0s_pred_) total_loss += occ_loss if hyp.do_view: assert (hyp.do_feat) PH, PW = hyp.PH, hyp.PW sy = float(PH) / float(hyp.H) sx = float(PW) / float(hyp.W) assert (sx == 0.5) # else we need a fancier downsampler assert (sy == 0.5) projpix_T_cams = __u( utils_geom.scale_intrinsics(__p(pix_T_cams), sx, sy)) # st() if hyp.do_munit: feat_projX00 = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 0], camX0_T_camXs[:, 1], emb3D_e_X1, # use feat1 to predict rgb0 hyp.view_depth, PH, PW) feat_projX00_og = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 0], camX0_T_camXs[:, 1], emb3D_e_X1_og, # use feat1 to predict rgb0 hyp.view_depth, PH, PW) # only for checking the style styled_feat_projX00 = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 0], camX0_T_camXs[:, 1], styled_emb3D_e_X1, # use feat1 to predict rgb0 hyp.view_depth, PH, PW) if hyp.cycle_style_view_loss: styled_feat_projX00_cycle = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 0], camX0_T_camXs[:, 1], styled_emb3D_e_X1_cycle, # use feat1 to predict rgb0 hyp.view_depth, PH, PW) else: feat_projX00 = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 0], camX0_T_camXs[:, 1], featXs[:, 1], # use feat1 to predict rgb0 hyp.view_depth, PH, PW) rgb_X00 = utils_basic.downsample(rgb_camXs[:, 0], 2) rgb_X01 = utils_basic.downsample(rgb_camXs[:, 1], 2) valid_X00 = utils_basic.downsample(valid_camXs[:, 0], 2) view_loss, rgb_e, emb2D_e = self.viewnet(feat_projX00, rgb_X00, valid_X00, summ_writer, "rgb") if hyp.do_munit: _, rgb_e, emb2D_e = self.viewnet(feat_projX00_og, rgb_X00, valid_X00, summ_writer, "rgb_og") if hyp.do_munit: styled_view_loss, styled_rgb_e, styled_emb2D_e = self.viewnet( styled_feat_projX00, rgb_X00, valid_X00, summ_writer, "recon_style") if hyp.cycle_style_view_loss: styled_view_loss_cycle, styled_rgb_e_cycle, styled_emb2D_e_cycle = self.viewnet( styled_feat_projX00_cycle, rgb_X00, valid_X00, summ_writer, "recon_style_cycle") rgb_input_1 = torch.cat( [rgb_X01[1], rgb_X01[0], styled_rgb_e[0]], dim=2) rgb_input_2 = torch.cat( [rgb_X01[0], rgb_X01[1], styled_rgb_e[1]], dim=2) complete_vis = torch.cat([rgb_input_1, rgb_input_2], dim=1) summ_writer.summ_rgb('munit/munit_recons_vis', complete_vis.unsqueeze(0)) if not hyp.do_munit: total_loss += view_loss else: if hyp.basic_view_loss: total_loss += view_loss if hyp.style_view_loss: total_loss += styled_view_loss if hyp.cycle_style_view_loss: total_loss += styled_view_loss_cycle summ_writer.summ_scalar('loss', total_loss.cpu().item()) if hyp.save_embed_tsne: for index, class_val in enumerate(object_classes): class_val_content, class_val_style = class_val.split("/") style_val = styles[index].squeeze().unsqueeze(0) self.cluster_pool.update(style_val, [class_val_style]) print(self.cluster_pool.num) if self.cluster_pool.is_full(): embeds, classes = self.cluster_pool.fetch() with open("offline_cluster" + '/%st.txt' % 'classes', 'w') as f: for index, embed in enumerate(classes): class_val = classes[index] f.write("%s\n" % class_val) f.close() with open("offline_cluster" + '/%st.txt' % 'embeddings', 'w') as f: for index, embed in enumerate(embeds): # embed = utils_basic.l2_normalize(embed,dim=0) print("writing {} embed".format(index)) embed_l_s = [str(i) for i in embed.tolist()] embed_str = '\t'.join(embed_l_s) f.write("%s\n" % embed_str) f.close() st() return total_loss, results
def get_synth_flow_v2(xyz_cam0, occ0, unp0, summ_writer, sometimes_zero=False, do_vis=False): # this version re-voxlizes occ1, rather than warp B, C, Z, Y, X = list(unp0.shape) assert (C == 3) __p = lambda x: utils_basic.pack_seqdim(x, B) __u = lambda x: utils_basic.unpack_seqdim(x, B) # we do not sample any rotations here, to keep the distribution purely # uniform across all translations # (rotation ruins this, since the pivot point is at the camera) cam1_T_cam0 = [ utils_geom.get_random_rt(B, r_amount=0.0, t_amount=3.0), # large motion utils_geom.get_random_rt( B, r_amount=0.0, t_amount=0.1, # small motion sometimes_zero=sometimes_zero) ] cam1_T_cam0 = random.sample(cam1_T_cam0, k=1)[0] xyz_cam1 = utils_geom.apply_4x4(cam1_T_cam0, xyz_cam0) occ1 = utils_vox.voxelize_xyz(xyz_cam1, Z, Y, X) unp1 = utils_vox.apply_4x4_to_vox(cam1_T_cam0, unp0) occs = [occ0, occ1] unps = [unp0, unp1] if do_vis: summ_writer.summ_occs('synth/occs', occs) summ_writer.summ_unps('synth/unps', unps, occs) mem_T_cam = utils_vox.get_mem_T_ref(B, Z, Y, X) cam_T_mem = utils_vox.get_ref_T_mem(B, Z, Y, X) mem1_T_mem0 = utils_basic.matmul3(mem_T_cam, cam1_T_cam0, cam_T_mem) xyz_mem0 = utils_basic.gridcloud3D(B, Z, Y, X) xyz_mem1 = utils_geom.apply_4x4(mem1_T_mem0, xyz_mem0) xyz_mem0 = xyz_mem0.reshape(B, Z, Y, X, 3) xyz_mem1 = xyz_mem1.reshape(B, Z, Y, X, 3) flow = xyz_mem1 - xyz_mem0 # this is B x Z x Y x X x 3 flow = flow.permute(0, 4, 1, 2, 3) # this is B x 3 x Z x Y x X if do_vis: summ_writer.summ_3D_flow('synth/flow', flow, clip=2.0) if do_vis: occ0_e = utils_samp.backwarp_using_3D_flow(occ1, flow, binary_feat=True) unp0_e = utils_samp.backwarp_using_3D_flow(unp1, flow) summ_writer.summ_occs('synth/occs_stab', [occ0, occ0_e]) summ_writer.summ_unps('synth/unps_stab', [unp0, unp0_e], [occ0, occ0_e]) occs = torch.stack(occs, dim=1) unps = torch.stack(unps, dim=1) return occs, unps, flow, cam1_T_cam0
def forward(self, feed): results = dict() summ_writer = utils_improc.Summ_writer(writer=feed['writer'], global_step=feed['global_step'], set_name=feed['set_name'], fps=8) writer = feed['writer'] global_step = feed['global_step'] total_loss = torch.tensor(0.0).cuda() __p = lambda x: pack_seqdim(x, B) __u = lambda x: unpack_seqdim(x, B) B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N PH, PW = hyp.PH, hyp.PW K = hyp.K Z, Y, X = hyp.Z, hyp.Y, hyp.X Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2) D = 9 rgb_camRs = feed["rgb_camRs"] rgb_camXs = feed["rgb_camXs"] pix_T_cams = feed["pix_T_cams"] cam_T_velos = feed["cam_T_velos"] origin_T_camRs = feed["origin_T_camRs"] origin_T_camRs_ = __p(origin_T_camRs) origin_T_camXs = feed["origin_T_camXs"] origin_T_camXs_ = __p(origin_T_camXs) camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0) camX0_T_camXs_ = __p(camX0_T_camXs) camRs_T_camXs_ = torch.matmul(utils_geom.safe_inverse(origin_T_camRs_), origin_T_camXs_) camXs_T_camRs_ = utils_geom.safe_inverse(camRs_T_camXs_) camRs_T_camXs = __u(camRs_T_camXs_) camXs_T_camRs = __u(camXs_T_camRs_) xyz_veloXs = feed["xyz_veloXs"] xyz_camXs = __u(utils_geom.apply_4x4(__p(cam_T_velos), __p(xyz_veloXs))) xyz_camRs = __u( utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs))) xyz_camX0s = __u( utils_geom.apply_4x4(__p(camX0_T_camXs), __p(xyz_camXs))) occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X)) occXs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z2, Y2, X2)) occX0s_half = __u(utils_vox.voxelize_xyz(__p(xyz_camX0s), Z2, Y2, X2)) unpXs = __u( utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z, Y, X, __p(pix_T_cams))) ## projected depth, and inbound mask depth_camXs_, valid_camXs_ = utils_geom.create_depth_image( __p(pix_T_cams), __p(xyz_camXs), H, W) dense_xyz_camXs_ = utils_geom.depth2pointcloud(depth_camXs_, __p(pix_T_cams)) dense_xyz_camX0s_ = utils_geom.apply_4x4(__p(camX0_T_camXs), dense_xyz_camXs_) inbound_camXs_ = utils_vox.get_inbounds(dense_xyz_camX0s_, Z, Y, X).float() inbound_camXs_ = torch.reshape(inbound_camXs_, [B * S, 1, H, W]) depth_camXs = __u(depth_camXs_) valid_camXs = __u(valid_camXs_) * __u(inbound_camXs_) ##################### ## visualize what we got ##################### summ_writer.summ_oneds('2D_inputs/depth_camXs', torch.unbind(depth_camXs, dim=1)) summ_writer.summ_oneds('2D_inputs/valid_camXs', torch.unbind(valid_camXs, dim=1)) summ_writer.summ_oneds('2D_inputs/valid_camXs', torch.unbind(valid_camXs, dim=1)) summ_writer.summ_rgbs('2D_inputs/rgb_camRs', torch.unbind(rgb_camRs, dim=1)) summ_writer.summ_rgbs('2D_inputs/rgb_camXs', torch.unbind(rgb_camXs, dim=1)) summ_writer.summ_occs('3D_inputs/occXs', torch.unbind(occXs, dim=1)) summ_writer.summ_unps('3D_inputs/unpXs', torch.unbind(unpXs, dim=1), torch.unbind(occXs, dim=1)) if summ_writer.save_this: unpRs = __u( utils_vox.unproject_rgb_to_mem( __p(rgb_camXs), Z, Y, X, matmul2(__p(pix_T_cams), utils_geom.safe_inverse(__p(camRs_T_camXs))))) occRs = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z, Y, X)) summ_writer.summ_occs('3D_inputs/occRs', torch.unbind(occRs, dim=1)) summ_writer.summ_unps('3D_inputs/unpRs', torch.unbind(unpRs, dim=1), torch.unbind(occRs, dim=1)) ##################### ## run the nets ##################### mask_ = None if hyp.do_occ and (not hyp.occ_do_cheap): ''' occRs_sup, freeRs_sup, freeXs = utils_vox.prep_occs_supervision(xyz_camXs, occRs_half, occXs_half, camRs_T_camXs, agg=True) featRs_input = torch.cat([occRs, occRs*unpRs], dim=2) featRs_input_ = __p(featRs_input) occRs_sup_ = __p(occRs_sup) freeRs_sup_ = __p(freeRs_sup) occ_loss, occRs_pred_ = self.occnet(featRs_input_, occRs_sup_, freeRs_sup_, summ_writer ) occRs_pred = __u(occRs_pred_) total_loss += occ_loss mask_ = F.upsample(occRs_pred_, scale_factor=2) ''' occXs_ = __p(occXs) mask_ = occXs_ if hyp.do_feat: # occXs is B x S x 1 x H x W x D # unpXs is B x S x 3 x H x W x D featXs_input = torch.cat([occXs, occXs * unpXs], dim=2) featXs_input_ = __p(featXs_input) # it is useful to keep track of what was visible from each viewpoint freeXs_ = utils_vox.get_freespace(__p(xyz_camXs), __p(occXs_half)) freeXs = __u(freeXs_) visXs = torch.clamp(occXs_half + freeXs, 0.0, 1.0) if (type(mask_) != type(None)): assert (list(mask_.shape)[2:5] == list( featXs_input_.shape)[2:5]) featXs_, validXs_, feat_loss = self.featnet( featXs_input_, summ_writer, mask=__p(occXs)) #mask_) total_loss += feat_loss validXs = __u(validXs_) _validX00 = validXs[:, 0:1] _validX01 = utils_vox.apply_4x4_to_voxs(camX0_T_camXs[:, 1:], validXs[:, 1:]) validX0s = torch.cat([_validX00, _validX01], dim=1) _visX00 = visXs[:, 0:1] _visX01 = utils_vox.apply_4x4_to_voxs(camX0_T_camXs[:, 1:], visXs[:, 1:]) visX0s = torch.cat([_visX00, _visX01], dim=1) featXs = __u(featXs_) _featX00 = featXs[:, 0:1] _featX01 = utils_vox.apply_4x4_to_voxs(camX0_T_camXs[:, 1:], featXs[:, 1:]) featX0s = torch.cat([_featX00, _featX01], dim=1) emb3D_e = torch.mean(featX0s[:, 1:], dim=1) # context emb3D_g = featX0s[:, 0] # obs vis3D_e = torch.max(validX0s[:, 1:], dim=1)[0] * torch.max( visX0s[:, 1:], dim=1)[0] vis3D_g = validX0s[:, 0] * visX0s[:, 0] # obs if hyp.do_eval_recall: results['emb3D_e'] = emb3D_e results['emb3D_g'] = emb3D_g summ_writer.summ_feats('3D_feats/featXs_input', torch.unbind(featXs_input, dim=1), pca=True) summ_writer.summ_feats('3D_feats/featXs_output', torch.unbind(featXs, dim=1), pca=True) summ_writer.summ_feats('3D_feats/featX0s_output', torch.unbind(featX0s, dim=1), pca=True) summ_writer.summ_feats('3D_feats/validX0s', torch.unbind(validX0s, dim=1), pca=False) summ_writer.summ_feat('3D_feats/vis3D_e', vis3D_e, pca=False) summ_writer.summ_feat('3D_feats/vis3D_g', vis3D_g, pca=False) if hyp.do_occ and hyp.occ_do_cheap: occX0_sup, freeX0_sup, freeXs = utils_vox.prep_occs_supervision( xyz_camXs, occX0s_half, occXs_half, camX0_T_camXs, agg=True) summ_writer.summ_occ('occ_sup/occ_sup', occX0_sup) summ_writer.summ_occ('occ_sup/free_sup', freeX0_sup) summ_writer.summ_occs('occ_sup/freeXs_sup', torch.unbind(freeXs, dim=1)) summ_writer.summ_occs('occ_sup/occXs_sup', torch.unbind(occXs_half, dim=1)) occ_loss, occRs_pred_ = self.occnet( torch.mean(featX0s[:, 1:], dim=1), occX0_sup, freeX0_sup, torch.max(validX0s[:, 1:], dim=1)[0], summ_writer) occRs_pred = __u(occRs_pred_) total_loss += occ_loss if hyp.do_view: assert (hyp.do_feat) # we warped the features into the canonical view # now we resample to the target view and decode PH, PW = hyp.PH, hyp.PW sy = float(PH) / float(hyp.H) sx = float(PW) / float(hyp.W) assert (sx == 0.5) # else we need a fancier downsampler assert (sy == 0.5) projpix_T_cams = __u( utils_geom.scale_intrinsics(__p(pix_T_cams), sx, sy)) assert (S == 2) # else we should warp each feat in 1: feat_projX00 = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 0], camX0_T_camXs[:, 1], featXs[:, 1], hyp.view_depth, PH, PW) # feat_projX0 is B x hyp.feat_dim x hyp.view_depth x PH x PW rgb_X00 = downsample(rgb_camXs[:, 0], 2) if summ_writer.save_this: # for vis, let's also project some rgb rgb_projX00 = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 0], camXs_T_camRs[:, 0], unpRs[:, 0], hyp.view_depth, PH, PW) rgb_projX01 = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 1], camXs_T_camRs[:, 1], unpRs[:, 1], hyp.view_depth, PH, PW) occ_projX00 = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 0], camXs_T_camRs[:, 0], occRs[:, 0], hyp.view_depth, PH, PW) occ_projX01 = utils_vox.apply_pixX_T_memR_to_voxR( projpix_T_cams[:, 1], camXs_T_camRs[:, 1], occRs[:, 1], hyp.view_depth, PH, PW) rgb_projX00_vis = reduce_masked_mean(rgb_projX00, occ_projX00.repeat( [1, 3, 1, 1, 1]), dim=2) rgb_projX01_vis = reduce_masked_mean(rgb_projX01, occ_projX01.repeat( [1, 3, 1, 1, 1]), dim=2) summ_writer.summ_rgbs('projection/rgb_projX', [rgb_projX00_vis, rgb_projX01_vis]) rgb_X01 = downsample(rgb_camXs[:, 1], 2) summ_writer.summ_rgbs('projection/rgb_origX', [rgb_X00, rgb_X01]) # decode the perspective volume into an image view_loss, rgb_e, emb2D_e = self.viewnet(feat_projX00, rgb_X00, summ_writer) total_loss += view_loss if hyp.do_emb2D: assert (hyp.do_view) # create an embedding image, representing the bottom-up 2D feature tensor emb_loss_2D, emb2D_g = self.embnet2D(rgb_camXs[:, 0], emb2D_e, valid_camXs[:, 0], summ_writer) total_loss += emb_loss_2D if hyp.do_emb3D: occX0_sup, freeX0_sup, freeXs = utils_vox.prep_occs_supervision( xyz_camXs, occX0s_half, occXs_half, camX0_T_camXs, agg=True) emb_loss_3D = self.embnet3D(emb3D_e, emb3D_g, vis3D_e, vis3D_g, summ_writer) total_loss += emb_loss_3D if hyp.do_eval_recall: results['emb2D_e'] = None results['emb2D_g'] = None summ_writer.summ_scalar('loss', total_loss.cpu().item()) return total_loss, results