From 218b139bf700766452bc568ba49d331056bd3923 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Fri, 4 Sep 2020 11:18:09 +0200 Subject: [PATCH 01/27] perf: change dense to sparse matrix --- models/layers/mesh_union.py | 52 +++++++++++++++++++++++++++++------- models/layers/mesh_unpool.py | 50 +++++++++++++++++++++++++++------- 2 files changed, 83 insertions(+), 19 deletions(-) diff --git a/models/layers/mesh_union.py b/models/layers/mesh_union.py index 325f29bf..f0635a48 100644 --- a/models/layers/mesh_union.py +++ b/models/layers/mesh_union.py @@ -1,15 +1,32 @@ import torch from torch.nn import ConstantPad2d +from util.util import myindexrowselect + class MeshUnion: def __init__(self, n, device=torch.device('cpu')): self.__size = n self.rebuild_features = self.rebuild_features_average - self.groups = torch.eye(n, device=device) + self.values = torch.ones(n, dtype= torch.float) + self.groups = torch.sparse_coo_tensor(indices= torch.stack((torch.arange(n), torch.arange(n)),dim=0), values= self.values, + size=(self.__size, self.__size), device=device) + + def union(self, source, target): - self.groups[target, :] += self.groups[source, :] + #Get source row + index = torch.tensor([source], dtype=torch.long) + row = myindexrowselect(self.groups, index) + + #Change to target row + row._indices()[0] = torch.tensor(target) + row = torch.sparse_coo_tensor(indices=row._indices(), values= row._values(), + size=(self.__size, self.__size)) + + #Add to target row + self.groups = self.groups.add(row) + self.groups = self.groups.coalesce() def remove_group(self, index): return @@ -18,16 +35,22 @@ def get_group(self, edge_key): return self.groups[edge_key, :] def get_occurrences(self): - return torch.sum(self.groups, 0) + return torch.sparse.sum(self.groups, 0).values() + def get_groups(self, tensor_mask): - self.groups = torch.clamp(self.groups, 0, 1) - return self.groups[tensor_mask, :] + ## Max comp + mask_index = torch.squeeze((tensor_mask == True).nonzero()) + groups = myindexrowselect(self.groups,mask_index) + return groups + def rebuild_features_average(self, features, mask, target_edges): self.prepare_groups(features, mask) - fe = torch.matmul(features.squeeze(-1), self.groups) - occurrences = torch.sum(self.groups, 0).expand(fe.shape) + + features = features.type(torch.FloatTensor) + fe = torch.matmul(self.groups.transpose(0,1),features.squeeze(-1).transpose(1,0)).transpose(0,1) + occurrences = torch.sparse.sum(self.groups, 0).to_dense() fe = fe / occurrences padding_b = target_edges - fe.shape[1] if padding_b > 0: @@ -35,10 +58,19 @@ def rebuild_features_average(self, features, mask, target_edges): fe = padding_b(fe) return fe + def prepare_groups(self, features, mask): tensor_mask = torch.from_numpy(mask) - self.groups = torch.clamp(self.groups[tensor_mask, :], 0, 1).transpose_(1, 0) + mask_index = torch.squeeze((tensor_mask == True).nonzero()) + + self.groups = myindexrowselect(self.groups, mask_index) + self.groups = self.groups.transpose(1,0) + padding_a = features.shape[1] - self.groups.shape[0] + if padding_a > 0: - padding_a = ConstantPad2d((0, 0, 0, padding_a), 0) - self.groups = padding_a(self.groups) + self.groups = torch.sparse_coo_tensor( + indices=self.groups._indices(), values=self.groups._values(), dtype=torch.float32, + size=(features.shape[1], self.groups.shape[1])) + + diff --git a/models/layers/mesh_unpool.py b/models/layers/mesh_unpool.py index 63a13e94..5cb53a2f 100644 --- a/models/layers/mesh_unpool.py +++ b/models/layers/mesh_unpool.py @@ -1,6 +1,7 @@ import torch import torch.nn as nn - +import sys +import time class MeshUnpool(nn.Module): @@ -16,8 +17,11 @@ def pad_groups(self, group, unroll_start): padding_rows = unroll_start - start padding_cols = self.unroll_target - end if padding_rows != 0 or padding_cols !=0: - padding = nn.ConstantPad2d((0, padding_cols, 0, padding_rows), 0) - group = padding(group) + size1 = group.shape[0] + padding_rows + size2 = group.shape[1] + padding_cols + group = torch.sparse_coo_tensor( + indices=group._indices(), values=group._values(), dtype=torch.float32, + size=(size1, size2)) return group def pad_occurrences(self, occurrences): @@ -30,12 +34,40 @@ def pad_occurrences(self, occurrences): def forward(self, features, meshes): batch_size, nf, edges = features.shape groups = [self.pad_groups(mesh.get_groups(), edges) for mesh in meshes] - unroll_mat = torch.cat(groups, dim=0).view(batch_size, edges, -1) + unroll_mat = torch.stack(groups) occurrences = [self.pad_occurrences(mesh.get_occurrences()) for mesh in meshes] - occurrences = torch.cat(occurrences, dim=0).view(batch_size, 1, -1) - occurrences = occurrences.expand(unroll_mat.shape) - unroll_mat = unroll_mat / occurrences - unroll_mat = unroll_mat.to(features.device) + occurrences = torch.unsqueeze(torch.stack(occurrences, dim=0), dim=1) + + imin = 0 + length = 500 + + while imin <= unroll_mat.size()[2]: + try: + slicesgroup = unroll_mat.narrow_copy(2, imin, length).to_dense() + occ = occurrences.narrow_copy(2, imin, length) + res = (slicesgroup / occ).to_sparse() + imin = imin + 500 + try: + allgroups = torch.cat((allgroups, res), -1) + except NameError: + allgroups = res + except Exception as e: + length = unroll_mat.size()[2] - imin + unroll_mat = allgroups.to(features.device) for mesh in meshes: mesh.unroll_gemm() - return torch.matmul(features, unroll_mat) + + one = unroll_mat.transpose(1,2) + two = features.transpose(1,2) + + mauz = torch.empty(size=(batch_size, features.shape[1], unroll_mat.shape[2])) + + for m in range(batch_size): + ml = torch.matmul(one[m], two[m]) + if m == 0: + mauz = torch.unsqueeze(ml, dim=0) + else: + mauz = torch.cat((mauz, torch.unsqueeze(ml, dim=0)), dim=0) + + mauz = mauz.transpose(1,2) + return mauz From aba692326d1e627d8fbc19f1260c9a0d827c36e4 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Fri, 4 Sep 2020 11:18:58 +0200 Subject: [PATCH 02/27] perf: faster index select function --- util/util.py | 109 ++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 108 insertions(+), 1 deletion(-) diff --git a/util/util.py b/util/util.py index 562c22f6..f8394df5 100644 --- a/util/util.py +++ b/util/util.py @@ -2,7 +2,7 @@ import torch import numpy as np import os - +import torch.nn.functional as F def mkdir(path): if not os.path.exists(path): @@ -31,6 +31,33 @@ def seg_accuracy(predicted, ssegs, meshes): correct += (correct_vec.float() * edge_areas).sum() return correct +def intersection_over_union(preds, target, num_classes, batch=1): + preds, target = F.one_hot(preds, num_classes), F.one_hot(target, num_classes) + if batch is 1: + i = (preds & target).sum(dim=0) + u = (preds | target).sum(dim=0) + iou = i.to(torch.float) / u.to(torch.float) + else: + iou = torch.zeros(num_classes, dtype=torch.float32) + for idx, pred in enumerate(preds): + i = (pred & target[idx]).sum(dim=0) + u = (pred | target[idx]).sum(dim=0) + iou = iou.add(i.to(torch.float) / u.to(torch.float)) + return iou + +def mean_iou_calc(pred, target, num_classes, batch=None): + pred = torch.squeeze(pred) + target = torch.squeeze(target) + if (target == -1).any(): + target = target[target!=-1] + len = target.size() + pred = pred[:len[0]] + + iou = intersection_over_union(pred, target, num_classes, batch) + print(iou) + mean_iou = iou.mean(dim=-1) + return mean_iou, iou + def print_network(net): """Print the total number of parameters in the network Parameters: @@ -66,3 +93,83 @@ def calculate_entropy(np_array): entropy -= a * np.log(a) entropy /= np.log(np_array.shape[0]) return entropy + +def pad_with(vector, pad_width, iaxis, kwargs): + pad_value = kwargs.get('padder', 10) + vector[:pad_width[0]] = pad_value + vector[-pad_width[1]:] = pad_value + +#Select all rows by index +def myindexrowselect(groups, mask_index): + + index = groups._indices() + newrowindex = -1 + + print(groups, mask_index) + + for ind in mask_index: + try: + newrowindex = newrowindex + 1 + except NameError: + newrowindex = 0 + + keptindex = torch.squeeze((index[0] == ind).nonzero()) + + if len(keptindex.size()) == 0: + # Get column values from mask, create new row idx + try: + newidx = torch.cat((newidx, torch.tensor([newrowindex])), 0) + newcolval = torch.cat((newcolval, torch.tensor([index[1][keptindex.item()]])), 0) + except NameError: + newidx = torch.tensor([newrowindex]) + newcolval = torch.tensor([index[1][keptindex.item()]]) + + else: + # Get column values from mask, create new row idx + # Add newrowindex eee.size() time to list + for i in range(list(keptindex.size())[0]): + try: + newidx = torch.cat((newidx, torch.tensor([newrowindex])), 0) + newcolval = torch.cat((newcolval, torch.tensor([index[1][keptindex.tolist()[i]]])), 0) + except NameError: + newidx = torch.tensor([newrowindex]) + newcolval = torch.tensor([index[1][keptindex.tolist()[i]]]) + + groups = torch.sparse_coo_tensor(indices=torch.stack((newidx, newcolval), dim=0), + values=torch.ones(newidx.shape[0], dtype=torch.float), + size=(len(mask_index), groups.shape[1])) + return groups + + + +def getsparserow(groups, ind): + +#ind gibt index von row an + index = groups._indices() + keptindex = torch.squeeze((index[0] == ind).nonzero()) + + + if len(keptindex.size()) == 0: + # Get column values from mask, create new row idx + try: + newidx = torch.tensor([0]) + newcolval = torch.cat((newcolval, torch.tensor([index[1][keptindex.item()]])), 0) + except NameError: + newcolval = torch.tensor([index[1][keptindex.item()]]) + else: + # Get column values from mask, create new row idx + # Add newrowindex eee.size() time to list + for i in range(list(keptindex.size())[0]): + try: + newidx = torch.cat((newidx, torch.tensor([0])), 0) + newcolval = torch.cat((newcolval, torch.tensor([index[1][keptindex.tolist()[i]]])), 0) + except NameError: + newidx = torch.tensor([0]) + newcolval = torch.tensor([index[1][keptindex.tolist()[i]]]) + + + + groups = torch.sparse_coo_tensor(indices=torch.stack((newidx, newcolval), dim=0), + values=torch.ones(newcolval.shape[0], dtype=torch.float), + size=(len(newcolval), groups.shape[1])) + return groups \ No newline at end of file From 073c492510e940320bf9b0e526b4b0585bd36c5e Mon Sep 17 00:00:00 2001 From: LSnyd Date: Fri, 4 Sep 2020 11:23:00 +0200 Subject: [PATCH 03/27] feat: weighted loss --- models/networks.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/models/networks.py b/models/networks.py index c2a13e2e..d6c825e2 100644 --- a/models/networks.py +++ b/models/networks.py @@ -7,6 +7,7 @@ import torch.nn.functional as F from models.layers.mesh_pool import MeshPool from models.layers.mesh_unpool import MeshUnpool +import numpy as np ############################################################################### @@ -110,13 +111,16 @@ def define_classifier(input_nc, ncf, ninput_edges, nclasses, opt, gpu_ids, arch, raise NotImplementedError('Encoder model name [%s] is not recognized' % arch) return init_net(net, init_type, init_gain, gpu_ids) + def define_loss(opt): if opt.dataset_mode == 'classification': loss = torch.nn.CrossEntropyLoss() elif opt.dataset_mode == 'segmentation': - loss = torch.nn.CrossEntropyLoss(ignore_index=-1) + weights = torch.FloatTensor(opt.weighted_loss) + loss = torch.nn.CrossEntropyLoss(weights, ignore_index=-1) return loss + ############################################################################## # Classes For Classification / Segmentation Networks ############################################################################## @@ -183,6 +187,7 @@ class MeshEncoderDecoder(nn.Module): """Network for fully-convolutional tasks (segmentation) """ def __init__(self, pools, down_convs, up_convs, blocks=0, transfer_data=True): + print("pool",pools, "down_convs", down_convs, "upconvs", up_convs, "blocks", blocks) super(MeshEncoderDecoder, self).__init__() self.transfer_data = transfer_data self.encoder = MeshEncoder(pools, down_convs, blocks=blocks) From b0d7337bb805e99452b852c3d38d46a1098c8ff4 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Fri, 4 Sep 2020 11:23:42 +0200 Subject: [PATCH 04/27] feat: IoU --- models/mesh_classifier.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/models/mesh_classifier.py b/models/mesh_classifier.py index 9ce50cb3..733f3c82 100644 --- a/models/mesh_classifier.py +++ b/models/mesh_classifier.py @@ -1,7 +1,7 @@ import torch from . import networks from os.path import join -from util.util import seg_accuracy, print_network +from util.util import seg_accuracy, print_network, intersection_over_union, mean_iou_calc class ClassifierModel: @@ -32,7 +32,7 @@ def __init__(self, opt): self.net = networks.define_classifier(opt.input_nc, opt.ncf, opt.ninput_edges, opt.nclasses, opt, self.gpu_ids, opt.arch, opt.init_type, opt.init_gain) self.net.train(self.is_train) - self.criterion = networks.define_loss(opt).to(self.device) + self.criterion = networks.define_loss(opt, self.labels ).to(self.device) if self.is_train: self.optimizer = torch.optim.Adam(self.net.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) @@ -113,7 +113,10 @@ def test(self): label_class = self.labels self.export_segmentation(pred_class.cpu()) correct = self.get_accuracy(pred_class, label_class) - return correct, len(label_class) + print("COOR", correct.shape) + print(correct) + mean_iou, iou = self.get_iou(pred_class, label_class) + return correct, len(label_class), mean_iou, iou def get_accuracy(self, pred, labels): """computes accuracy for classification / segmentation """ @@ -123,6 +126,12 @@ def get_accuracy(self, pred, labels): correct = seg_accuracy(pred, self.soft_label, self.mesh) return correct + + def get_iou(self, pred): + if self.opt.dataset_mode == 'segmentation': + mean_iou, iou = mean_iou_calc(pred, self.labels, self.nclasses, self.opt.batch_size) + return mean_iou, iou + def export_segmentation(self, pred_seg): if self.opt.dataset_mode == 'segmentation': for meshi, mesh in enumerate(self.mesh): From 76e4b28513c9786a1e23694ea5e12439cb210484 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Fri, 4 Sep 2020 16:48:18 +0200 Subject: [PATCH 05/27] feat: allow cpu and gpu --- models/layers/mesh_union.py | 37 ++++++++++++++++++------------------ models/layers/mesh_unpool.py | 23 +++++++++++++--------- options/base_options.py | 21 +++++++++++++++++++- 3 files changed, 52 insertions(+), 29 deletions(-) diff --git a/models/layers/mesh_union.py b/models/layers/mesh_union.py index f0635a48..3b3412ae 100644 --- a/models/layers/mesh_union.py +++ b/models/layers/mesh_union.py @@ -1,32 +1,33 @@ import torch from torch.nn import ConstantPad2d -from util.util import myindexrowselect - +import time +from util.util import myindexrowselect +from options.base_options import BaseOptions class MeshUnion: - def __init__(self, n, device=torch.device('cpu')): + def __init__(self, n, device=torch.device('cpu')): + gpu_ids = BaseOptions().get_device() + self.device = torch.device('cuda:{}'.format(gpu_ids[0])) if len(gpu_ids)>0 else torch.device('cpu') + self.__size = n self.rebuild_features = self.rebuild_features_average self.values = torch.ones(n, dtype= torch.float) self.groups = torch.sparse_coo_tensor(indices= torch.stack((torch.arange(n), torch.arange(n)),dim=0), values= self.values, - size=(self.__size, self.__size), device=device) + size=(self.__size, self.__size), device=device).to(self.device) def union(self, source, target): - #Get source row index = torch.tensor([source], dtype=torch.long) - row = myindexrowselect(self.groups, index) - - #Change to target row + row = myindexrowselect(self.groups, index).to(self.device) row._indices()[0] = torch.tensor(target) row = torch.sparse_coo_tensor(indices=row._indices(), values= row._values(), size=(self.__size, self.__size)) - - #Add to target row self.groups = self.groups.add(row) self.groups = self.groups.coalesce() + del index, row + def remove_group(self, index): return @@ -40,15 +41,16 @@ def get_occurrences(self): def get_groups(self, tensor_mask): ## Max comp - mask_index = torch.squeeze((tensor_mask == True).nonzero()) - groups = myindexrowselect(self.groups,mask_index) - return groups + mask_index = torch.squeeze((tensor_mask == True).nonzero()).to(self.device) + + start_time = time.time() + return myindexrowselect(self.groups, mask_index)#.cuda() def rebuild_features_average(self, features, mask, target_edges): self.prepare_groups(features, mask) - features = features.type(torch.FloatTensor) + self.groups = self.groups.to(self.device) fe = torch.matmul(self.groups.transpose(0,1),features.squeeze(-1).transpose(1,0)).transpose(0,1) occurrences = torch.sparse.sum(self.groups, 0).to_dense() fe = fe / occurrences @@ -60,12 +62,9 @@ def rebuild_features_average(self, features, mask, target_edges): def prepare_groups(self, features, mask): - tensor_mask = torch.from_numpy(mask) - mask_index = torch.squeeze((tensor_mask == True).nonzero()) - - self.groups = myindexrowselect(self.groups, mask_index) - self.groups = self.groups.transpose(1,0) + mask_index = torch.squeeze((torch.from_numpy(mask) == True).nonzero()) + self.groups = myindexrowselect(self.groups, mask_index).transpose(1,0) padding_a = features.shape[1] - self.groups.shape[0] if padding_a > 0: diff --git a/models/layers/mesh_unpool.py b/models/layers/mesh_unpool.py index 5cb53a2f..4053c26f 100644 --- a/models/layers/mesh_unpool.py +++ b/models/layers/mesh_unpool.py @@ -1,13 +1,13 @@ import torch import torch.nn as nn -import sys -import time - +from options.base_options import BaseOptions class MeshUnpool(nn.Module): def __init__(self, unroll_target): super(MeshUnpool, self).__init__() self.unroll_target = unroll_target + gpu_ids = BaseOptions().get_device() + self.device = torch.device('cuda:{}'.format(gpu_ids[0])) if len(gpu_ids)>0 else torch.device('cpu') def __call__(self, features, meshes): return self.forward(features, meshes) @@ -33,34 +33,37 @@ def pad_occurrences(self, occurrences): def forward(self, features, meshes): batch_size, nf, edges = features.shape - groups = [self.pad_groups(mesh.get_groups(), edges) for mesh in meshes] + groups = [self.pad_groups(mesh.get_groups(), edges).to(self.device) for mesh in meshes] unroll_mat = torch.stack(groups) occurrences = [self.pad_occurrences(mesh.get_occurrences()) for mesh in meshes] occurrences = torch.unsqueeze(torch.stack(occurrences, dim=0), dim=1) - imin = 0 length = 500 while imin <= unroll_mat.size()[2]: try: - slicesgroup = unroll_mat.narrow_copy(2, imin, length).to_dense() - occ = occurrences.narrow_copy(2, imin, length) + slicesgroup = unroll_mat.narrow_copy(2, imin, length).to_dense().to(self.device) + occ = occurrences.narrow_copy(2, imin, length).to(self.device) res = (slicesgroup / occ).to_sparse() imin = imin + 500 + try: allgroups = torch.cat((allgroups, res), -1) except NameError: allgroups = res + except Exception as e: + print(e) length = unroll_mat.size()[2] - imin + unroll_mat = allgroups.to(features.device) + for mesh in meshes: mesh.unroll_gemm() - one = unroll_mat.transpose(1,2) two = features.transpose(1,2) - mauz = torch.empty(size=(batch_size, features.shape[1], unroll_mat.shape[2])) + mauz = torch.empty(size=(batch_size, features.shape[1], unroll_mat.shape[2])) for m in range(batch_size): ml = torch.matmul(one[m], two[m]) @@ -71,3 +74,5 @@ def forward(self, features, meshes): mauz = mauz.transpose(1,2) return mauz + + diff --git a/options/base_options.py b/options/base_options.py index 61f21ce0..43c11be2 100644 --- a/options/base_options.py +++ b/options/base_options.py @@ -2,7 +2,7 @@ import os from util import util import torch - +import sys class BaseOptions: def __init__(self): @@ -35,9 +35,26 @@ def initialize(self): self.parser.add_argument('--seed', type=int, help='if specified, uses seed') # visualization params self.parser.add_argument('--export_folder', type=str, default='', help='exports intermediate collapses to this folder') + # loss + self.parser.add_argument('--weighted_loss', nargs='+', default=[0.25, 0.25, 0.25, 0.25], type=float, help='Weights for loss') # self.initialized = True + def get_device(self): + if not self.initialized: + self.initialize() + self.opt, unknown = self.parser.parse_known_args() + + str_ids = self.opt.gpu_ids.split(',') + self.opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + self.opt.gpu_ids.append(id) + return self.opt.gpu_ids + + + def parse(self): if not self.initialized: self.initialize() @@ -45,6 +62,7 @@ def parse(self): self.opt.is_train = self.is_train # train or test str_ids = self.opt.gpu_ids.split(',') + print("strid", str_ids) self.opt.gpu_ids = [] for str_id in str_ids: id = int(str_id) @@ -84,3 +102,4 @@ def parse(self): opt_file.write('%s: %s\n' % (str(k), str(v))) opt_file.write('-------------- End ----------------\n') return self.opt + From bb01b81095e6c3d195ea1dacf8fee9a9405a5ef5 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Fri, 4 Sep 2020 16:49:54 +0200 Subject: [PATCH 06/27] feat: add IoU to writer --- util/writer.py | 38 ++++++++++++++++++++++++++++++++++---- 1 file changed, 34 insertions(+), 4 deletions(-) diff --git a/util/writer.py b/util/writer.py index 6c0b4759..9a416b90 100644 --- a/util/writer.py +++ b/util/writer.py @@ -1,5 +1,7 @@ import os import time +import torch +import numpy as np try: from tensorboardX import SummaryWriter @@ -7,6 +9,7 @@ print('tensorboard X not installed, visualizing wont be available') SummaryWriter = None + class Writer: def __init__(self, opt): self.name = opt.name @@ -17,7 +20,10 @@ def __init__(self, opt): self.start_logs() self.nexamples = 0 self.ncorrect = 0 - # + self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + self.iou = torch.zeros([opt.nclasses]).to(self.device) + self.avg_iou = 0 + if opt.is_train and not opt.no_vis and SummaryWriter is not None: self.display = SummaryWriter(comment=opt.name) else: @@ -47,6 +53,10 @@ def plot_loss(self, loss, epoch, i, n): if self.display: self.display.add_scalar('data/train_loss', loss, iters) + def plot_lr(self, lr, epoch): + if self.display: + self.display.add_scalar('data/lr', lr, epoch) + def plot_model_wts(self, model, epoch): if self.opt.is_train and self.display: for name, param in model.net.named_parameters(): @@ -54,31 +64,51 @@ def plot_model_wts(self, model, epoch): def print_acc(self, epoch, acc): """ prints test accuracy to terminal / file """ - message = 'epoch: {}, TEST ACC: [{:.5} %]\n' \ + message = 'epoch: {}, TEST ACC: [{:.5} %]' \ .format(epoch, acc * 100) print(message) with open(self.testacc_log, "a") as log_file: log_file.write('%s\n' % message) + def print_iou(self, epoch, avg_iou, iou): + """ prints test accuracy to terminal / file """ + message = 'epoch: {}, TEST MEAN_IOU: [{:.5} %] \nepoch: {}, IOU: {}\n' \ + .format(epoch, avg_iou * 100, epoch, iou.cpu().numpy() * 100) + print(message) + with open(self.testacc_log, "a") as log_file: + log_file.write('%s' % message) + def plot_acc(self, acc, epoch): if self.display: self.display.add_scalar('data/test_acc', acc, epoch) - def reset_counter(self): + def reset_counter(self, opt): """ counts # of correct examples """ self.ncorrect = 0 self.nexamples = 0 + self.iou = torch.zeros([opt.nclasses]) + self.avg_iou = 0 - def update_counter(self, ncorrect, nexamples): + def update_counter(self, ncorrect, nexamples, avg_iou, iou): self.ncorrect += ncorrect self.nexamples += nexamples + self.iou = torch.stack([self.iou.to(self.device), iou.to(self.device)]).sum(dim=0) + self.avg_iou += avg_iou @property def acc(self): return float(self.ncorrect) / self.nexamples + @property + def mean_iou(self): + return (self.avg_iou / self.nexamples) + + @property + def seg_iou(self): + return (self.iou / self.nexamples) + def close(self): if self.display is not None: self.display.close() From 7549bd36272b7b9bf8d01ceb941fb93e38e38282 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Fri, 4 Sep 2020 17:07:27 +0200 Subject: [PATCH 07/27] style: clean up --- models/layers/mesh_unpool.py | 3 +-- models/mesh_classifier.py | 12 +++++------- models/networks.py | 4 ---- options/base_options.py | 1 - test.py | 19 +++++++++++++------ util/util.py | 2 -- 6 files changed, 19 insertions(+), 22 deletions(-) diff --git a/models/layers/mesh_unpool.py b/models/layers/mesh_unpool.py index 4053c26f..518f0ddf 100644 --- a/models/layers/mesh_unpool.py +++ b/models/layers/mesh_unpool.py @@ -52,8 +52,7 @@ def forward(self, features, meshes): except NameError: allgroups = res - except Exception as e: - print(e) + except Exception: length = unroll_mat.size()[2] - imin unroll_mat = allgroups.to(features.device) diff --git a/models/mesh_classifier.py b/models/mesh_classifier.py index 733f3c82..d51f2302 100644 --- a/models/mesh_classifier.py +++ b/models/mesh_classifier.py @@ -32,7 +32,7 @@ def __init__(self, opt): self.net = networks.define_classifier(opt.input_nc, opt.ncf, opt.ninput_edges, opt.nclasses, opt, self.gpu_ids, opt.arch, opt.init_type, opt.init_gain) self.net.train(self.is_train) - self.criterion = networks.define_loss(opt, self.labels ).to(self.device) + self.criterion = networks.define_loss(opt).to(self.device) if self.is_train: self.optimizer = torch.optim.Adam(self.net.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) @@ -113,9 +113,7 @@ def test(self): label_class = self.labels self.export_segmentation(pred_class.cpu()) correct = self.get_accuracy(pred_class, label_class) - print("COOR", correct.shape) - print(correct) - mean_iou, iou = self.get_iou(pred_class, label_class) + mean_iou, iou =self.get_iou(pred_class, label_class) return correct, len(label_class), mean_iou, iou def get_accuracy(self, pred, labels): @@ -126,10 +124,10 @@ def get_accuracy(self, pred, labels): correct = seg_accuracy(pred, self.soft_label, self.mesh) return correct - - def get_iou(self, pred): + def get_iou(self, pred, labels): + """computes IoU for segmentation """ if self.opt.dataset_mode == 'segmentation': - mean_iou, iou = mean_iou_calc(pred, self.labels, self.nclasses, self.opt.batch_size) + mean_iou, iou = mean_iou_calc(pred, self.labels, self.nclasses) return mean_iou, iou def export_segmentation(self, pred_seg): diff --git a/models/networks.py b/models/networks.py index d6c825e2..6b266c72 100644 --- a/models/networks.py +++ b/models/networks.py @@ -7,7 +7,6 @@ import torch.nn.functional as F from models.layers.mesh_pool import MeshPool from models.layers.mesh_unpool import MeshUnpool -import numpy as np ############################################################################### @@ -111,7 +110,6 @@ def define_classifier(input_nc, ncf, ninput_edges, nclasses, opt, gpu_ids, arch, raise NotImplementedError('Encoder model name [%s] is not recognized' % arch) return init_net(net, init_type, init_gain, gpu_ids) - def define_loss(opt): if opt.dataset_mode == 'classification': loss = torch.nn.CrossEntropyLoss() @@ -120,7 +118,6 @@ def define_loss(opt): loss = torch.nn.CrossEntropyLoss(weights, ignore_index=-1) return loss - ############################################################################## # Classes For Classification / Segmentation Networks ############################################################################## @@ -187,7 +184,6 @@ class MeshEncoderDecoder(nn.Module): """Network for fully-convolutional tasks (segmentation) """ def __init__(self, pools, down_convs, up_convs, blocks=0, transfer_data=True): - print("pool",pools, "down_convs", down_convs, "upconvs", up_convs, "blocks", blocks) super(MeshEncoderDecoder, self).__init__() self.transfer_data = transfer_data self.encoder = MeshEncoder(pools, down_convs, blocks=blocks) diff --git a/options/base_options.py b/options/base_options.py index 43c11be2..ea1febd9 100644 --- a/options/base_options.py +++ b/options/base_options.py @@ -62,7 +62,6 @@ def parse(self): self.opt.is_train = self.is_train # train or test str_ids = self.opt.gpu_ids.split(',') - print("strid", str_ids) self.opt.gpu_ids = [] for str_id in str_ids: id = int(str_id) diff --git a/test.py b/test.py index 15492f5b..0b08aedd 100644 --- a/test.py +++ b/test.py @@ -1,8 +1,9 @@ + from options.test_options import TestOptions from data import DataLoader from models import create_model from util.writer import Writer - +import os def run_test(epoch=-1): print('Running Test') @@ -12,14 +13,20 @@ def run_test(epoch=-1): model = create_model(opt) writer = Writer(opt) # test - writer.reset_counter() + writer.reset_counter(opt) for i, data in enumerate(dataset): model.set_input(data) - ncorrect, nexamples = model.test() - writer.update_counter(ncorrect, nexamples) + ncorrect, nexamples, mean_iou, iou = model.test() + writer.update_counter(ncorrect, nexamples, mean_iou, iou) writer.print_acc(epoch, writer.acc) - return writer.acc + writer.print_iou(epoch, writer.mean_iou, writer.seg_iou) + return writer.acc, writer.mean_iou, writer.iou if __name__ == '__main__': - run_test() + run_test() + + + + + diff --git a/util/util.py b/util/util.py index f8394df5..3bf8e587 100644 --- a/util/util.py +++ b/util/util.py @@ -54,7 +54,6 @@ def mean_iou_calc(pred, target, num_classes, batch=None): pred = pred[:len[0]] iou = intersection_over_union(pred, target, num_classes, batch) - print(iou) mean_iou = iou.mean(dim=-1) return mean_iou, iou @@ -105,7 +104,6 @@ def myindexrowselect(groups, mask_index): index = groups._indices() newrowindex = -1 - print(groups, mask_index) for ind in mask_index: try: From a14c3bda7f7a45e0b70355d21db4efadcf387521 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Fri, 4 Sep 2020 17:21:01 +0200 Subject: [PATCH 08/27] fix: IoU computation --- util/util.py | 86 +++++++++++++++++++--------------------------------- 1 file changed, 32 insertions(+), 54 deletions(-) diff --git a/util/util.py b/util/util.py index 3bf8e587..d8b5d926 100644 --- a/util/util.py +++ b/util/util.py @@ -2,25 +2,31 @@ import torch import numpy as np import os -import torch.nn.functional as F + + +# from torch_scatter import scatter_add def mkdir(path): if not os.path.exists(path): os.makedirs(path) + MESH_EXTENSIONS = [ '.obj', ] + def is_mesh_file(filename): return any(filename.endswith(extension) for extension in MESH_EXTENSIONS) + def pad(input_arr, target_length, val=0, dim=1): shp = input_arr.shape npad = [(0, 0) for _ in range(len(shp))] npad[dim] = (0, target_length - shp[dim]) return np.pad(input_arr, pad_width=npad, mode='constant', constant_values=val) + def seg_accuracy(predicted, ssegs, meshes): correct = 0 ssegs = ssegs.squeeze(-1) @@ -31,32 +37,36 @@ def seg_accuracy(predicted, ssegs, meshes): correct += (correct_vec.float() * edge_areas).sum() return correct -def intersection_over_union(preds, target, num_classes, batch=1): - preds, target = F.one_hot(preds, num_classes), F.one_hot(target, num_classes) - if batch is 1: - i = (preds & target).sum(dim=0) - u = (preds | target).sum(dim=0) - iou = i.to(torch.float) / u.to(torch.float) - else: - iou = torch.zeros(num_classes, dtype=torch.float32) - for idx, pred in enumerate(preds): - i = (pred & target[idx]).sum(dim=0) - u = (pred | target[idx]).sum(dim=0) - iou = iou.add(i.to(torch.float) / u.to(torch.float)) - return iou + +def intersection_over_union(preds, target, num_classes, batch=None): + preds, target = torch.nn.functional.one_hot(preds, num_classes), torch.nn.functional.one_hot(target, num_classes) + preds.cpu() + target.cpu() + if batch == None: + i = (preds & target).sum(dim=0) + u = (preds | target).sum(dim=0) + iou = i.to(torch.float) / u.to(torch.float) + else: + iou = torch.zeros(num_classes, dtype=torch.float32) + for idx, pred in enumerate(preds): + i = (pred & target[idx]).sum(dim=0) + u = (pred | target[idx]).sum(dim=0) + iou = iou.add(i.cpu().to(torch.float) / u.cpu().to(torch.float)) + return iou + def mean_iou_calc(pred, target, num_classes, batch=None): pred = torch.squeeze(pred) target = torch.squeeze(target) if (target == -1).any(): - target = target[target!=-1] + target = target[target != -1] len = target.size() pred = pred[:len[0]] - iou = intersection_over_union(pred, target, num_classes, batch) mean_iou = iou.mean(dim=-1) return mean_iou, iou + def print_network(net): """Print the total number of parameters in the network Parameters: @@ -69,11 +79,12 @@ def print_network(net): print('[Network] Total number of parameters : %.3f M' % (num_params / 1e6)) print('-----------------------------------------------') + def get_heatmap_color(value, minimum=0, maximum=1): minimum, maximum = float(minimum), float(maximum) - ratio = 2 * (value-minimum) / (maximum - minimum) - b = int(max(0, 255*(1 - ratio))) - r = int(max(0, 255*(ratio - 1))) + ratio = 2 * (value - minimum) / (maximum - minimum) + b = int(max(0, 255 * (1 - ratio))) + r = int(max(0, 255 * (ratio - 1))) g = 255 - b - r return r, g, b @@ -93,18 +104,17 @@ def calculate_entropy(np_array): entropy /= np.log(np_array.shape[0]) return entropy + def pad_with(vector, pad_width, iaxis, kwargs): pad_value = kwargs.get('padder', 10) vector[:pad_width[0]] = pad_value vector[-pad_width[1]:] = pad_value -#Select all rows by index -def myindexrowselect(groups, mask_index): +def myindexrowselect(groups, mask_index): index = groups._indices() newrowindex = -1 - for ind in mask_index: try: newrowindex = newrowindex + 1 @@ -139,35 +149,3 @@ def myindexrowselect(groups, mask_index): return groups - -def getsparserow(groups, ind): - -#ind gibt index von row an - index = groups._indices() - keptindex = torch.squeeze((index[0] == ind).nonzero()) - - - if len(keptindex.size()) == 0: - # Get column values from mask, create new row idx - try: - newidx = torch.tensor([0]) - newcolval = torch.cat((newcolval, torch.tensor([index[1][keptindex.item()]])), 0) - except NameError: - newcolval = torch.tensor([index[1][keptindex.item()]]) - else: - # Get column values from mask, create new row idx - # Add newrowindex eee.size() time to list - for i in range(list(keptindex.size())[0]): - try: - newidx = torch.cat((newidx, torch.tensor([0])), 0) - newcolval = torch.cat((newcolval, torch.tensor([index[1][keptindex.tolist()[i]]])), 0) - except NameError: - newidx = torch.tensor([0]) - newcolval = torch.tensor([index[1][keptindex.tolist()[i]]]) - - - - groups = torch.sparse_coo_tensor(indices=torch.stack((newidx, newcolval), dim=0), - values=torch.ones(newcolval.shape[0], dtype=torch.float), - size=(len(newcolval), groups.shape[1])) - return groups \ No newline at end of file From fdc03677b6c93565e43b11c6360189333d42fed2 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Sun, 6 Sep 2020 18:10:22 +0200 Subject: [PATCH 09/27] fix: select row by index --- util/util.py | 59 +++++++++++++++++++++------------------------------- 1 file changed, 24 insertions(+), 35 deletions(-) diff --git a/util/util.py b/util/util.py index d8b5d926..530f837a 100644 --- a/util/util.py +++ b/util/util.py @@ -111,41 +111,30 @@ def pad_with(vector, pad_width, iaxis, kwargs): vector[-pad_width[1]:] = pad_value + + + def myindexrowselect(groups, mask_index): - index = groups._indices() - newrowindex = -1 - - for ind in mask_index: - try: - newrowindex = newrowindex + 1 - except NameError: - newrowindex = 0 - - keptindex = torch.squeeze((index[0] == ind).nonzero()) - - if len(keptindex.size()) == 0: - # Get column values from mask, create new row idx - try: - newidx = torch.cat((newidx, torch.tensor([newrowindex])), 0) - newcolval = torch.cat((newcolval, torch.tensor([index[1][keptindex.item()]])), 0) - except NameError: - newidx = torch.tensor([newrowindex]) - newcolval = torch.tensor([index[1][keptindex.item()]]) - - else: - # Get column values from mask, create new row idx - # Add newrowindex eee.size() time to list - for i in range(list(keptindex.size())[0]): - try: - newidx = torch.cat((newidx, torch.tensor([newrowindex])), 0) - newcolval = torch.cat((newcolval, torch.tensor([index[1][keptindex.tolist()[i]]])), 0) - except NameError: - newidx = torch.tensor([newrowindex]) - newcolval = torch.tensor([index[1][keptindex.tolist()[i]]]) - - groups = torch.sparse_coo_tensor(indices=torch.stack((newidx, newcolval), dim=0), - values=torch.ones(newidx.shape[0], dtype=torch.float), - size=(len(mask_index), groups.shape[1])) - return groups + sparseIndices = groups._indices() + newIndices = [] + + for i, value in enumerate(mask_index): + #Get index from relevant indices + index = (sparseIndices[0] == value).nonzero() + #Get rows by index + sparseRow = [sparseIndices[:, value] for value in index] + sparseRow = torch.cat(sparseRow,1)[1] + singleRowIndices = torch.squeeze(torch.full((1,len(sparseRow)),i, dtype=torch.long),0) + indices = torch.stack((singleRowIndices,sparseRow)) + newIndices.append(indices) + + allNewIndices = torch.cat(newIndices,1) + + #Create new tensor + groups = torch.sparse_coo_tensor(indices=allNewIndices, + values=torch.ones(allNewIndices.shape[1], dtype=torch.float), + size=(len(mask_index), groups.shape[1])) + + return groups From c2e392408b0f1c84ac21ed8f56877db02f42a459 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Mon, 7 Sep 2020 09:29:28 +0200 Subject: [PATCH 10/27] feat: allow CPU and GPU --- models/layers/mesh_union.py | 8 +++----- util/util.py | 4 ++-- 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/models/layers/mesh_union.py b/models/layers/mesh_union.py index 3b3412ae..5b8f2f89 100644 --- a/models/layers/mesh_union.py +++ b/models/layers/mesh_union.py @@ -20,7 +20,7 @@ def __init__(self, n, device=torch.device('cpu')): def union(self, source, target): index = torch.tensor([source], dtype=torch.long) - row = myindexrowselect(self.groups, index).to(self.device) + row = myindexrowselect(self.groups, index, self.device).to(self.device) row._indices()[0] = torch.tensor(target) row = torch.sparse_coo_tensor(indices=row._indices(), values= row._values(), size=(self.__size, self.__size)) @@ -42,9 +42,7 @@ def get_occurrences(self): def get_groups(self, tensor_mask): ## Max comp mask_index = torch.squeeze((tensor_mask == True).nonzero()).to(self.device) - - start_time = time.time() - return myindexrowselect(self.groups, mask_index)#.cuda() + return myindexrowselect(self.groups, mask_index, self.device) def rebuild_features_average(self, features, mask, target_edges): @@ -64,7 +62,7 @@ def rebuild_features_average(self, features, mask, target_edges): def prepare_groups(self, features, mask): mask_index = torch.squeeze((torch.from_numpy(mask) == True).nonzero()) - self.groups = myindexrowselect(self.groups, mask_index).transpose(1,0) + self.groups = myindexrowselect(self.groups, mask_index, self.device).transpose(1,0) padding_a = features.shape[1] - self.groups.shape[0] if padding_a > 0: diff --git a/util/util.py b/util/util.py index 530f837a..fabfec9a 100644 --- a/util/util.py +++ b/util/util.py @@ -114,7 +114,7 @@ def pad_with(vector, pad_width, iaxis, kwargs): -def myindexrowselect(groups, mask_index): +def myindexrowselect(groups, mask_index, device): sparseIndices = groups._indices() newIndices = [] @@ -126,7 +126,7 @@ def myindexrowselect(groups, mask_index): #Get rows by index sparseRow = [sparseIndices[:, value] for value in index] sparseRow = torch.cat(sparseRow,1)[1] - singleRowIndices = torch.squeeze(torch.full((1,len(sparseRow)),i, dtype=torch.long),0) + singleRowIndices = torch.squeeze(torch.full((1,len(sparseRow)),i, dtype=torch.long),0).to(device) indices = torch.stack((singleRowIndices,sparseRow)) newIndices.append(indices) From 8ae3f2def6374aad1eaf9ac7a8c127275e06ac89 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Mon, 7 Sep 2020 10:29:16 +0200 Subject: [PATCH 11/27] style: mesh_unpool for sparse representations --- models/layers/mesh_unpool.py | 41 ++++++++++++++++++------------------ 1 file changed, 20 insertions(+), 21 deletions(-) diff --git a/models/layers/mesh_unpool.py b/models/layers/mesh_unpool.py index 518f0ddf..3250fe1b 100644 --- a/models/layers/mesh_unpool.py +++ b/models/layers/mesh_unpool.py @@ -37,41 +37,40 @@ def forward(self, features, meshes): unroll_mat = torch.stack(groups) occurrences = [self.pad_occurrences(mesh.get_occurrences()) for mesh in meshes] occurrences = torch.unsqueeze(torch.stack(occurrences, dim=0), dim=1) + + #Sparse division only possible for scalars + #Iterate over dense batches + imin = 0 length = 500 - + result = [] while imin <= unroll_mat.size()[2]: try: - slicesgroup = unroll_mat.narrow_copy(2, imin, length).to_dense().to(self.device) - occ = occurrences.narrow_copy(2, imin, length).to(self.device) - res = (slicesgroup / occ).to_sparse() + sliceUnroll_mat = unroll_mat.narrow_copy(2, imin, length).to_dense().to(self.device) + sliceOcc = occurrences.narrow_copy(2, imin, length).to(self.device) + sliceResult = (sliceUnroll_mat / sliceOcc).to_sparse() imin = imin + 500 - try: - allgroups = torch.cat((allgroups, res), -1) - except NameError: - allgroups = res + result.append(sliceResult) except Exception: length = unroll_mat.size()[2] - imin - unroll_mat = allgroups.to(features.device) + unroll_mat = torch.cat(result, -1).to(features.device) for mesh in meshes: mesh.unroll_gemm() - one = unroll_mat.transpose(1,2) - two = features.transpose(1,2) - - mauz = torch.empty(size=(batch_size, features.shape[1], unroll_mat.shape[2])) - for m in range(batch_size): - ml = torch.matmul(one[m], two[m]) - if m == 0: - mauz = torch.unsqueeze(ml, dim=0) - else: - mauz = torch.cat((mauz, torch.unsqueeze(ml, dim=0)), dim=0) + #Fix Matmul, due to missing strides of sparse representation + result = [] + unroll_mat = unroll_mat.transpose(1,2) + features = features.transpose(1,2) - mauz = mauz.transpose(1,2) - return mauz + #iterate over batches + for batch in range(batch_size): + mat = torch.matmul(unroll_mat[batch], features[batch]) + mat = torch.unsqueeze(mat, dim=0) + result.append(mat) + return torch.cat(result, dim=0).transpose(1,2) From b2b41f197ccb4133cd72c7916f8f06111c43e1b9 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Mon, 7 Sep 2020 15:18:09 +0200 Subject: [PATCH 12/27] fix: IoU for batches > 1 --- util/util.py | 38 +++++++++++++++++++------------------- util/writer.py | 2 +- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/util/util.py b/util/util.py index fabfec9a..2e5c9cc0 100644 --- a/util/util.py +++ b/util/util.py @@ -38,31 +38,31 @@ def seg_accuracy(predicted, ssegs, meshes): return correct -def intersection_over_union(preds, target, num_classes, batch=None): +def intersection_over_union(preds, target, num_classes): preds, target = torch.nn.functional.one_hot(preds, num_classes), torch.nn.functional.one_hot(target, num_classes) - preds.cpu() - target.cpu() - if batch == None: - i = (preds & target).sum(dim=0) - u = (preds | target).sum(dim=0) - iou = i.to(torch.float) / u.to(torch.float) - else: - iou = torch.zeros(num_classes, dtype=torch.float32) - for idx, pred in enumerate(preds): + iou = torch.zeros(num_classes, dtype=torch.float32) + for idx, pred in enumerate(preds): i = (pred & target[idx]).sum(dim=0) u = (pred | target[idx]).sum(dim=0) iou = iou.add(i.cpu().to(torch.float) / u.cpu().to(torch.float)) return iou -def mean_iou_calc(pred, target, num_classes, batch=None): - pred = torch.squeeze(pred) - target = torch.squeeze(target) - if (target == -1).any(): - target = target[target != -1] - len = target.size() - pred = pred[:len[0]] - iou = intersection_over_union(pred, target, num_classes, batch) +def mean_iou_calc(pred, target, num_classes): + #Removal of padded labels marked with -1 + slimpred = [] + slimtarget = [] + + for batch in range(pred.shape[0]): + if (target[batch] == -1).any(): + slimLabels = target[batch][target[batch]!=-1] + slimtarget.append(slimLabels) + slimpred.append(pred[batch][:slimLabels.size()[0]]) + + pred = torch.stack(slimpred,0) + target = torch.stack(slimtarget, 0) + + iou = intersection_over_union(pred, target, num_classes) mean_iou = iou.mean(dim=-1) return mean_iou, iou @@ -126,7 +126,7 @@ def myindexrowselect(groups, mask_index, device): #Get rows by index sparseRow = [sparseIndices[:, value] for value in index] sparseRow = torch.cat(sparseRow,1)[1] - singleRowIndices = torch.squeeze(torch.full((1,len(sparseRow)),i, dtype=torch.long),0).to(device) + singleRowIndices = torch.squeeze(torch.full((1,len(sparseRow)),i, dtype=torch.long),0).to(sparseRow.device) indices = torch.stack((singleRowIndices,sparseRow)) newIndices.append(indices) diff --git a/util/writer.py b/util/writer.py index 9a416b90..74df714f 100644 --- a/util/writer.py +++ b/util/writer.py @@ -80,7 +80,7 @@ def print_iou(self, epoch, avg_iou, iou): def plot_acc(self, acc, epoch): if self.display: - self.display.add_scalar('data/test_acc', acc, epoch) + self.display.add_scalar('data/test_acc', acc[0], epoch) def reset_counter(self, opt): """ From efb16937acfec4e92769e20bfde732ca9065bd6f Mon Sep 17 00:00:00 2001 From: LSnyd Date: Mon, 7 Sep 2020 15:19:18 +0200 Subject: [PATCH 13/27] style: clean up --- models/mesh_classifier.py | 2 +- test.py | 11 +++-------- train.py | 2 +- 3 files changed, 5 insertions(+), 10 deletions(-) diff --git a/models/mesh_classifier.py b/models/mesh_classifier.py index d51f2302..a72e694e 100644 --- a/models/mesh_classifier.py +++ b/models/mesh_classifier.py @@ -1,7 +1,7 @@ import torch from . import networks from os.path import join -from util.util import seg_accuracy, print_network, intersection_over_union, mean_iou_calc +from util.util import seg_accuracy, print_network, mean_iou_calc class ClassifierModel: diff --git a/test.py b/test.py index 0b08aedd..eb5a8710 100644 --- a/test.py +++ b/test.py @@ -1,9 +1,8 @@ - from options.test_options import TestOptions from data import DataLoader from models import create_model from util.writer import Writer -import os + def run_test(epoch=-1): print('Running Test') @@ -15,6 +14,7 @@ def run_test(epoch=-1): # test writer.reset_counter(opt) for i, data in enumerate(dataset): + #print(i) model.set_input(data) ncorrect, nexamples, mean_iou, iou = model.test() writer.update_counter(ncorrect, nexamples, mean_iou, iou) @@ -24,9 +24,4 @@ def run_test(epoch=-1): if __name__ == '__main__': - run_test() - - - - - + run_test() diff --git a/train.py b/train.py index 41b326b7..1823cb55 100644 --- a/train.py +++ b/train.py @@ -5,6 +5,7 @@ from util.writer import Writer from test import run_test + if __name__ == '__main__': opt = TrainOptions().parse() dataset = DataLoader(opt) @@ -40,7 +41,6 @@ (epoch, total_steps)) model.save_network('latest') - iter_data_time = time.time() if epoch % opt.save_epoch_freq == 0: print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps)) From 3fcb53b1aec309d4dfa41668fc626f37e6daf72a Mon Sep 17 00:00:00 2001 From: LSnyd Date: Mon, 7 Sep 2020 15:21:18 +0200 Subject: [PATCH 14/27] feat: add new training script --- scripts/human_seg/train.sh | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) mode change 100755 => 100644 scripts/human_seg/train.sh diff --git a/scripts/human_seg/train.sh b/scripts/human_seg/train.sh old mode 100755 new mode 100644 index 5e735a37..ac9459f2 --- a/scripts/human_seg/train.sh +++ b/scripts/human_seg/train.sh @@ -1,16 +1,20 @@ #!/usr/bin/env bash ## run the training -python train.py \ +python3 train.py \ --dataroot datasets/human_seg \ --name human_seg \ --arch meshunet \ --dataset_mode segmentation \ --ncf 32 64 128 256 \ ---ninput_edges 2280 \ ---pool_res 1800 1350 600 \ ---resblocks 3 \ ---batch_size 12 \ +--ninput_edges 3000 \ +--pool_res 2000 1000 500 \ +--num_threads 0 \ +--resblocks 1 \ +--batch_size 2 \ --lr 0.001 \ --num_aug 20 \ ---slide_verts 0.2 \ \ No newline at end of file +--slide_verts 0.2 \ +--gpu_ids -1 \ +--verbose_plot \ +--weighted_loss 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125 \ From d4ace2566103003f04d6494c020c834b70353934 Mon Sep 17 00:00:00 2001 From: LSnyd Date: Tue, 8 Sep 2020 13:23:48 +0200 Subject: [PATCH 15/27] style: update README --- README.md | 84 ++++++++++++----------------- scripts/coseg_seg/get_data.sh | 8 --- scripts/coseg_seg/get_pretrained.sh | 10 ---- scripts/coseg_seg/test.sh | 14 ----- scripts/coseg_seg/train.sh | 21 -------- scripts/coseg_seg/view.sh | 7 --- scripts/cubes/get_data.sh | 9 ---- scripts/cubes/get_pretrained.sh | 10 ---- scripts/cubes/test.sh | 11 ---- scripts/cubes/train.sh | 13 ----- scripts/cubes/view.sh | 7 --- scripts/shrec/get_data.sh | 9 ---- scripts/shrec/get_pretrained.sh | 9 ---- scripts/shrec/test.sh | 11 ---- scripts/shrec/train.sh | 14 ----- scripts/shrec/view.sh | 7 --- 16 files changed, 34 insertions(+), 210 deletions(-) delete mode 100755 scripts/coseg_seg/get_data.sh delete mode 100755 scripts/coseg_seg/get_pretrained.sh delete mode 100755 scripts/coseg_seg/test.sh delete mode 100755 scripts/coseg_seg/train.sh delete mode 100755 scripts/coseg_seg/view.sh delete mode 100755 scripts/cubes/get_data.sh delete mode 100755 scripts/cubes/get_pretrained.sh delete mode 100755 scripts/cubes/test.sh delete mode 100755 scripts/cubes/train.sh delete mode 100755 scripts/cubes/view.sh delete mode 100755 scripts/shrec/get_data.sh delete mode 100755 scripts/shrec/get_pretrained.sh delete mode 100755 scripts/shrec/test.sh delete mode 100755 scripts/shrec/train.sh delete mode 100755 scripts/shrec/view.sh diff --git a/README.md b/README.md index c24668c1..403bdb48 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,23 @@


+ +# MedMeshCNN + +MedMeshCNN is an expansion of [MeshCNN](https://ranahanocka.github.io/MeshCNN/) proposed by [Rana Hanocka](https://www.cs.tau.ac.il/~hanocka/) et al.
+It enables the use of [MeshCNN](https://ranahanocka.github.io/MeshCNN/) for medical surface meshes through an improved memory efficiency that allows to +to keep patient-specific properties and fine-grained patterns during segmentation. Furthermore, a weighted loss function improves the performance of MedMeshCNN on imbalanced datasets that are often caused by pathological appearances. + +MedMeshCNN may also be used beyond the medical domain for all applications that include imbalanced datasets and require fine-grained segmentation results. + +Advances of MedMeshCNN include: +* Processing meshes with 170.000 edges (NVIDIA GeForce GTX 1080 TiGPU with 12GB RAM) +* IoU metrics +* Weighted loss function to enable better performances on imbalanced class distributions + +Please check out the corresponding [PartSegmentationToolbox](https://github.com/LSnyd/PartSegmentationToolbox) to find further information on how to create a segmentation ground truth and helper scripts to scale segmentation results to different mesh resultions. + + # MeshCNN in PyTorch @@ -10,58 +27,47 @@ MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which
-The code was written by [Rana Hanocka](https://www.cs.tau.ac.il/~hanocka/) and [Amir Hertz](http://pxcm.org/) with support from [Noa Fish](http://www.cs.tau.ac.il/~noafish/). +The code of MeshCNN was written by [Rana Hanocka](https://www.cs.tau.ac.il/~hanocka/) and [Amir Hertz](http://pxcm.org/) with support from [Noa Fish](http://www.cs.tau.ac.il/~noafish/). # Getting Started +```bash +bash ./scripts/human_seg/get_data.sh +bash ./scripts/human_seg/train.sh +bash ./scripts/human_seg/get_pretrained.sh +bash ./scripts/human_seg/test.sh +bash ./scripts/human_seg/view.sh +``` + ### Installation - Clone this repo: ```bash -git clone https://github.com/ranahanocka/MeshCNN.git -cd MeshCNN +git clone https://github.com/LSnyd/MedMeshCNN.git +cd MedMeshCNN ``` -- Install dependencies: [PyTorch](https://pytorch.org/) version 1.2. Optional : [tensorboardX](https://github.com/lanpa/tensorboardX) for training plots. +- Install dependencies: [PyTorch](https://pytorch.org/) version 1.4. Optional : [tensorboardX](https://github.com/lanpa/tensorboardX) for training plots. - Via new conda environment `conda env create -f environment.yml` (creates an environment called meshcnn) -### 3D Shape Classification on SHREC +### 3D Shape Segmentation on Humans Download the dataset ```bash -bash ./scripts/shrec/get_data.sh +bash ./scripts/human_seg/get_data.sh ``` Run training (if using conda env first activate env e.g. ```source activate meshcnn```) ```bash -bash ./scripts/shrec/train.sh +bash ./scripts/human_seg/train.sh ``` To view the training loss plots, in another terminal run ```tensorboard --logdir runs``` and click [http://localhost:6006](http://localhost:6006). Run test and export the intermediate pooled meshes: ```bash -bash ./scripts/shrec/test.sh +bash /scripts/human_seg/test.sh ``` Visualize the network-learned edge collapses: ```bash -bash ./scripts/shrec/view.sh -``` - -An example of collapses for a mesh: - - - -Note, you can also get pre-trained weights using bash ```./scripts/shrec/get_pretrained.sh```. - -In order to use the pre-trained weights, run ```train.sh``` which will compute and save the mean / standard deviation of the training data. - - -### 3D Shape Segmentation on Humans -The same as above, to download the dataset / run train / get pretrained / run test / view -```bash -bash ./scripts/human_seg/get_data.sh -bash ./scripts/human_seg/train.sh -bash ./scripts/human_seg/get_pretrained.sh -bash ./scripts/human_seg/test.sh bash ./scripts/human_seg/view.sh ``` @@ -69,30 +75,8 @@ Some segmentation result examples: -### Additional Datasets -The same scripts also exist for COSEG segmentation in ```scripts/coseg_seg``` and cubes classification in ```scripts/cubes```. - # More Info Check out the [MeshCNN wiki](https://github.com/ranahanocka/MeshCNN/wiki) for more details. Specifically, see info on [segmentation](https://github.com/ranahanocka/MeshCNN/wiki/Segmentation) and [data processing](https://github.com/ranahanocka/MeshCNN/wiki/Data-Processing). -# Citation -If you find this code useful, please consider citing our paper -``` -@article{hanocka2019meshcnn, - title={MeshCNN: A Network with an Edge}, - author={Hanocka, Rana and Hertz, Amir and Fish, Noa and Giryes, Raja and Fleishman, Shachar and Cohen-Or, Daniel}, - journal={ACM Transactions on Graphics (TOG)}, - volume={38}, - number={4}, - pages = {90:1--90:12}, - year={2019}, - publisher={ACM} -} -``` - - # Questions / Issues -If you have questions or issues running this code, please open an issue so we can know to fix it. - -# Acknowledgments -This code design was adopted from [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). +If you have questions or issues running this code, please open an issue so we can know to fix it. \ No newline at end of file diff --git a/scripts/coseg_seg/get_data.sh b/scripts/coseg_seg/get_data.sh deleted file mode 100755 index b7673dbd..00000000 --- a/scripts/coseg_seg/get_data.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env bash - -DATADIR='datasets' #location where data gets downloaded to - -echo "downloading the data and putting it in: " $DATADIR -mkdir -p $DATADIR && cd $DATADIR -wget https://www.dropbox.com/s/34vy4o5fthhz77d/coseg.tar.gz -tar -xzvf coseg.tar.gz && rm coseg.tar.gz \ No newline at end of file diff --git a/scripts/coseg_seg/get_pretrained.sh b/scripts/coseg_seg/get_pretrained.sh deleted file mode 100755 index 562b7630..00000000 --- a/scripts/coseg_seg/get_pretrained.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/usr/bin/env bash - -CHECKPOINT=checkpoints/coseg_aliens -mkdir -p $CHECKPOINT - -#gets the pretrained weights -wget https://www.dropbox.com/s/er7my13k9dwg9ii/coseg_aliens_wts.tar.gz -tar -xzvf coseg_aliens_wts.tar.gz && rm coseg_aliens_wts.tar.gz -mv latest_net.pth $CHECKPOINT -echo "downloaded pretrained weights to" $CHECKPOINT \ No newline at end of file diff --git a/scripts/coseg_seg/test.sh b/scripts/coseg_seg/test.sh deleted file mode 100755 index 422cf071..00000000 --- a/scripts/coseg_seg/test.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/usr/bin/env bash - -## run the test and export collapses -python test.py \ ---dataroot datasets/coseg_aliens \ ---name coseg_aliens \ ---arch meshunet \ ---dataset_mode segmentation \ ---ncf 32 64 128 256 \ ---ninput_edges 2280 \ ---pool_res 1800 1350 600 \ ---resblocks 3 \ ---batch_size 12 \ ---export_folder meshes \ \ No newline at end of file diff --git a/scripts/coseg_seg/train.sh b/scripts/coseg_seg/train.sh deleted file mode 100755 index 143f18c7..00000000 --- a/scripts/coseg_seg/train.sh +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env bash - -## run the training -python train.py \ ---dataroot datasets/coseg_aliens \ ---name coseg_aliens \ ---arch meshunet \ ---dataset_mode segmentation \ ---ncf 32 64 128 256 \ ---ninput_edges 2280 \ ---pool_res 1800 1350 600 \ ---resblocks 3 \ ---lr 0.001 \ ---batch_size 12 \ ---num_aug 20 \ ---slide_verts 0.2 \ - - -# -# python train.py --dataroot datasets/coseg_vases --name coseg_vases --arch meshunet --dataset_mode -segmentation --ncf 32 64 128 256 --ninput_edges 1500 --pool_res 1050 600 300 --resblocks 3 --lr 0.001 --batch_size 12 --num_aug 20 \ No newline at end of file diff --git a/scripts/coseg_seg/view.sh b/scripts/coseg_seg/view.sh deleted file mode 100755 index 08c80675..00000000 --- a/scripts/coseg_seg/view.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/usr/bin/env bash - -python util/mesh_viewer.py \ ---files \ -checkpoints/coseg_aliens/meshes/142_0.obj \ -checkpoints/coseg_aliens/meshes/142_2.obj \ -checkpoints/coseg_aliens/meshes/142_3.obj \ \ No newline at end of file diff --git a/scripts/cubes/get_data.sh b/scripts/cubes/get_data.sh deleted file mode 100755 index ca594ce9..00000000 --- a/scripts/cubes/get_data.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/usr/bin/env bash - -DATADIR='datasets' #location where data gets downloaded to - -# get data -mkdir -p $DATADIR && cd $DATADIR -wget https://www.dropbox.com/s/2bxs5f9g60wa0wr/cubes.tar.gz -tar -xzvf cubes.tar.gz && rm cubes.tar.gz -echo "downloaded the data and put it in: " $DATADIR \ No newline at end of file diff --git a/scripts/cubes/get_pretrained.sh b/scripts/cubes/get_pretrained.sh deleted file mode 100755 index 2d3637e0..00000000 --- a/scripts/cubes/get_pretrained.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/usr/bin/env bash - -CHECKPOINT='checkpoints/cubes' - -# get pretrained model -mkdir -p $CHECKPOINT -wget https://www.dropbox.com/s/fg7wum39bmlxr7w/cubes_wts.tar.gz -tar -xzvf cubes_wts.tar.gz && rm cubes_wts.tar.gz -mv latest_net.pth $CHECKPOINT -echo "downloaded pretrained weights to" $CHECKPOINT \ No newline at end of file diff --git a/scripts/cubes/test.sh b/scripts/cubes/test.sh deleted file mode 100755 index c8e31596..00000000 --- a/scripts/cubes/test.sh +++ /dev/null @@ -1,11 +0,0 @@ -#!/usr/bin/env bash - -## run the test and export collapses -python test.py \ ---dataroot datasets/cubes \ ---name cubes \ ---ncf 64 128 256 256 \ ---pool_res 600 450 300 210 \ ---norm group \ ---resblocks 1 \ ---export_folder meshes \ \ No newline at end of file diff --git a/scripts/cubes/train.sh b/scripts/cubes/train.sh deleted file mode 100755 index 50f91f72..00000000 --- a/scripts/cubes/train.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/usr/bin/env bash - -## run the training -python train.py \ ---dataroot datasets/cubes \ ---name cubes \ ---ncf 64 128 256 256 \ ---pool_res 600 450 300 210 \ ---norm group \ ---resblocks 1 \ ---flip_edges 0.2 \ ---slide_verts 0.2 \ ---num_aug 20 \ \ No newline at end of file diff --git a/scripts/cubes/view.sh b/scripts/cubes/view.sh deleted file mode 100755 index 452c3bc6..00000000 --- a/scripts/cubes/view.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/usr/bin/env bash - -python util/mesh_viewer.py \ ---files checkpoints/cubes/meshes/horseshoe_4_0.obj \ -checkpoints/cubes/meshes/horseshoe_4_2.obj \ -checkpoints/cubes/meshes/horseshoe_4_3.obj \ -checkpoints/cubes/meshes/horseshoe_4_4.obj \ No newline at end of file diff --git a/scripts/shrec/get_data.sh b/scripts/shrec/get_data.sh deleted file mode 100755 index 4763e22a..00000000 --- a/scripts/shrec/get_data.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/usr/bin/env bash - -DATADIR='datasets' #location where data gets downloaded to - -# get data -mkdir -p $DATADIR && cd $DATADIR -wget https://www.dropbox.com/s/w16st84r6wc57u7/shrec_16.tar.gz -tar -xzvf shrec_16.tar.gz && rm shrec_16.tar.gz -echo "downloaded the data and putting it in: " $DATADIR diff --git a/scripts/shrec/get_pretrained.sh b/scripts/shrec/get_pretrained.sh deleted file mode 100755 index d5a229b9..00000000 --- a/scripts/shrec/get_pretrained.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/usr/bin/env bash - -CHECKPOINT='checkpoints/shrec16' - -mkdir -p $CHECKPOINT -wget https://www.dropbox.com/s/wqq1qxj4fjbpfas/shrec16_wts.tar.gz -tar -xzvf shrec16_wts.tar.gz && rm shrec16_wts.tar.gz -mv latest_net.pth $CHECKPOINT -echo "downloaded pretrained weights to" $CHECKPOINT \ No newline at end of file diff --git a/scripts/shrec/test.sh b/scripts/shrec/test.sh deleted file mode 100755 index 0d2c8b90..00000000 --- a/scripts/shrec/test.sh +++ /dev/null @@ -1,11 +0,0 @@ -#!/usr/bin/env bash - -## run the test and export collapses -python test.py \ ---dataroot datasets/shrec_16 \ ---name shrec16 \ ---ncf 64 128 256 256 \ ---pool_res 600 450 300 180 \ ---norm group \ ---resblocks 1 \ ---export_folder meshes \ \ No newline at end of file diff --git a/scripts/shrec/train.sh b/scripts/shrec/train.sh deleted file mode 100755 index cecb2e1a..00000000 --- a/scripts/shrec/train.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/usr/bin/env bash - -## run the training -python train.py \ ---dataroot datasets/shrec_16 \ ---name shrec16 \ ---ncf 64 128 256 256 \ ---pool_res 600 450 300 180 \ ---norm group \ ---resblocks 1 \ ---flip_edges 0.2 \ ---slide_verts 0.2 \ ---num_aug 20 \ ---niter_decay 100 \ \ No newline at end of file diff --git a/scripts/shrec/view.sh b/scripts/shrec/view.sh deleted file mode 100755 index 54bf19db..00000000 --- a/scripts/shrec/view.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/usr/bin/env bash - -python util/mesh_viewer.py \ ---files \ -checkpoints/shrec16/meshes/T74_0.obj \ -checkpoints/shrec16/meshes/T74_3.obj \ -checkpoints/shrec16/meshes/T74_4.obj \ No newline at end of file From 0cf9bff76af0406537ecb09763a7ffb9a80fc97a Mon Sep 17 00:00:00 2001 From: LSnyd Date: Tue, 8 Sep 2020 13:31:06 +0200 Subject: [PATCH 16/27] style: images --- docs/imgs/C60.png | Bin 0 -> 22539 bytes docs/imgs/T18.png | Bin 258821 -> 0 bytes docs/imgs/T252.png | Bin 175222 -> 0 bytes docs/imgs/T76.png | Bin 311291 -> 0 bytes docs/imgs/alien.gif | Bin 830076 -> 0 bytes docs/imgs/coseg_alien.png | Bin 190558 -> 0 bytes docs/imgs/coseg_chair.png | Bin 349189 -> 0 bytes docs/imgs/coseg_vase.png | Bin 253075 -> 0 bytes docs/imgs/cubes.png | Bin 126036 -> 0 bytes docs/imgs/cubes2.png | Bin 113965 -> 0 bytes docs/imgs/input_edge_features.png | Bin 18900 -> 0 bytes docs/imgs/mesh_conv.png | Bin 16958 -> 0 bytes docs/imgs/mesh_pool_unpool.png | Bin 55354 -> 0 bytes docs/imgs/meshcnn_overview.png | Bin 480067 -> 0 bytes 14 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/imgs/C60.png delete mode 100644 docs/imgs/T18.png delete mode 100644 docs/imgs/T252.png delete mode 100644 docs/imgs/T76.png delete mode 100644 docs/imgs/alien.gif delete mode 100644 docs/imgs/coseg_alien.png delete mode 100644 docs/imgs/coseg_chair.png delete mode 100644 docs/imgs/coseg_vase.png delete mode 100644 docs/imgs/cubes.png delete mode 100644 docs/imgs/cubes2.png delete mode 100644 docs/imgs/input_edge_features.png delete mode 100644 docs/imgs/mesh_conv.png delete mode 100644 docs/imgs/mesh_pool_unpool.png delete mode 100644 docs/imgs/meshcnn_overview.png diff --git a/docs/imgs/C60.png b/docs/imgs/C60.png new file mode 100644 index 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