def step(self, grad_scale=1): def bk(g): return g.backward() l2norm_square = parallel_apply( [bk for _ in self.sub_optimizers], self.sub_optimizers, devices=[g.device for g in self.sub_optimizers]) l2norm = sum(l2norm_square)**0.5 if str(l2norm) in ['inf', 'nan']: return False if grad_scale != 1: l2norm *= grad_scale coef = self.grad_clip_norm / (l2norm + 1e-6) if coef < 1: grad_scale = grad_scale * coef if grad_scale != 1: for n, p in self.named_parameters: if p.grad is not None: p.grad.mul_(grad_scale) def st(g): return g.step(l2norm) parallel_apply([st for _ in self.sub_optimizers], self.sub_optimizers, devices=[g.device for g in self.sub_optimizers]) return True
def forward(self, x_gate, x_experts): x = self.sparse_gate(x_gate) selected_experts = (x != 0).nonzero() # ret: tuple of x's, y's, z's (indices) where x is not 0 inputs_for_experts = [] batch_indices_for_experts = [] for i in range(len(self.experts)): expert_was_selected = selected_experts[:, 1] == i batch_index_for_expert = selected_experts[expert_was_selected, 0] batch_indices_for_experts.append(batch_index_for_expert) inputs = None if len(batch_index_for_expert) == 0 else x_experts[batch_index_for_expert] inputs_for_experts.append(inputs) experts_to_run = [] inputs_to_feed = [] batch_indices_to_scatter = [] expert_run_to_orig_index = [] for i, (expert, inputs, batch_index) in enumerate( zip(self.experts, inputs_for_experts, batch_indices_for_experts)): if len(batch_index) > 0: experts_to_run.append(expert) inputs_to_feed.append(inputs.unsqueeze(0)) batch_indices_to_scatter.append(batch_index) expert_run_to_orig_index.append(i) if self._parallel_apply: res = parallel_apply(experts_to_run, inputs_to_feed) # If the number of selected experts is very large then we can do it in parallel if self._parallel_sum: def scatter_batch(r, indices_to_scatter, i): output = x.new_full((x_gate.shape[0],) + r.shape[1:], 0) attention = x[(indices_to_scatter, expert_run_to_orig_index[i]) + (None,) * (len(r.shape) - 1)] output[indices_to_scatter] += attention * r return output output_ = parallel_apply([scatter_batch] * len(res), [(r, idx, i) for i, (r, idx) in enumerate(zip(res, batch_indices_to_scatter))]) output = torch.sum(torch.stack(output_, dim=0), dim=0) else: output = x.new_full((x_gate.shape[0],) + res[0].shape[1:], 0) for i, (indices_to_scatter, r) in enumerate(zip(batch_indices_to_scatter, res)): attention = x[(indices_to_scatter, expert_run_to_orig_index[i]) + (None,) * (len(r.shape) - 1)] output[indices_to_scatter] += attention * r else: res = [] for expert, inputs in zip(experts_to_run, inputs_to_feed): res.append(expert(inputs.squeeze(0))) output = x.new_full((x_gate.shape[0],) + res[0].shape[1:], 0) for i, (indices_to_scatter, r) in enumerate(zip(batch_indices_to_scatter, res)): attention = x[(indices_to_scatter, expert_run_to_orig_index[i]) + (None,) * (len(r.shape) - 1)] output[indices_to_scatter] += attention * r self.last_experts = x.cpu().detach() self.output_mean = output.mean().cpu().detach() # self.output_std = output.mean().cpu().detach() return output
def test_parallel_apply_passes_exception(self): # we define and instantiate a module that will throw a KeyError class TestModule(nn.Module): def forward(self, *args): return {}['wonderful'] l1 = TestModule().to("cuda", torch.float) # and check that parallel_apply passes on the exception # (we can use a single device twice for this test) with self.assertRaisesRegex( KeyError, 'Caught KeyError in replica \\d ' 'on device 0.\nOriginal Traceback' '[\\s\\S]+wonderful'): dp.parallel_apply(modules=(l1, l1), inputs=(None, None))
def data_parallel(f, input, params, stats, mode, device_ids, output_device=None): if output_device is None: output_device = device_ids[0] if len(device_ids) == 1: return f(input, params, stats, mode) def replicate(param_dict, g): replicas = [{} for d in device_ids] for k, v in param_dict.iteritems(): for i, u in enumerate(g(v)): replicas[i][k] = u return replicas params_replicas = replicate(params, lambda x: Broadcast(device_ids)(x)) stats_replicas = replicate(stats, lambda x: comm.broadcast(x, device_ids)) replicas = [ lambda x, p=p, s=s, mode=mode: f(x, p, s, mode) for i, (p, s) in enumerate(zip(params_replicas, stats_replicas)) ] inputs = scatter(input, device_ids) outputs = parallel_apply(replicas, inputs) return gather(outputs, output_device)
def calc_distill_loss(self): losses = [] for i, netA in enumerate(self.netAs): assert isinstance(netA, SuperConv2d) n = self.mapping_layers[i] netA_replicas = replicate(netA, self.gpu_ids) kwargs = tuple([{ 'config': { 'channel': netA.out_channels } } for idx in self.gpu_ids]) Sacts = parallel_apply( netA_replicas, tuple([ self.Sacts[key] for key in sorted(self.Sacts.keys()) if n in key ]), kwargs) Tacts = [ self.Tacts[key] for key in sorted(self.Tacts.keys()) if n in key ] loss = [F.mse_loss(Sact, Tact) for Sact, Tact in zip(Sacts, Tacts)] loss = gather(loss, self.gpu_ids[0]).sum() setattr(self, 'loss_G_distill%d' % i, loss) losses.append(loss) return sum(losses)
def forward(self, inputs, im_info, gt_boxes, num_boxes, Ms, Ns): #tensors,_=scatter_kwargs([inputs,im_info,gt_boxes,num_boxes], {}, self.device_ids) inputs_multi = comm.scatter(inputs, self.device_ids) im_info = comm.scatter(im_info, self.device_ids) gt_boxes = comm.scatter(gt_boxes, self.device_ids) num_boxes = comm.scatter(num_boxes, self.device_ids) #im_info, gt_boxes, num_boxes tensors = parallel_apply(self.modules, [(v, ) for v in inputs_multi], devices=self.device_ids) out = [] for i, tensor in enumerate(tensors): with torch.cuda.device(tensor.get_device()): tensors[i] = tensors[i].view( tensors[i].size(0), tensors[i].size(1) * tensors[i].size(2), tensors[i].size(3), tensors[i].size(4)) tensors[i] = tensors[i][:, :, :Ms, :Ns] tensors[i] = tensors[i].contiguous() tensors[i] = Variable(tensors[i]) out.append([ tensors[i], im_info[i].cuda(), gt_boxes[i].cuda(), num_boxes[i].cuda() ]) return out #tensors,im_info, gt_boxes, num_boxes
def data_parallel(batch_group: List[TensorDict], model: Model, cuda_devices: List) -> Dict[str, torch.Tensor]: """ Performs a forward pass using multiple GPUs. This is a simplification of torch.nn.parallel.data_parallel to support the allennlp model interface. """ assert len(batch_group) <= len(cuda_devices) moved = [ nn_util.move_to_device(batch, device) for batch, device in zip(batch_group, cuda_devices) ] used_device_ids = cuda_devices[:len(moved)] # Counterintuitively, it appears replicate expects the source device id to be the first element # in the device id list. See torch.cuda.comm.broadcast_coalesced, which is called indirectly. replicas = replicate(model, used_device_ids) # We pass all our arguments as kwargs. Create a list of empty tuples of the # correct shape to serve as (non-existent) positional arguments. inputs = [()] * len(batch_group) outputs = parallel_apply(replicas, inputs, moved, used_device_ids) # Only the 'loss' is needed. # a (num_gpu, ) tensor with loss on each GPU losses = gather([output['loss'].unsqueeze(0) for output in outputs], used_device_ids[0], 0) return {'loss': losses.mean()}
def data_parallel(f, input, params, stats, mode, device_ids, output_device=None): if output_device is None: output_device = device_ids[0] if len(device_ids) == 1: # only 1 device return f(input, params, stats, mode) # function inside data_parallel def replicate(param_dict, g): replicas = [{} for d in device_ids] # replicas, list of n_devices dict for k,v in param_dict.iteritems(): # v is parameter for i,u in enumerate(g(v)): replicas[i][k] = u return replicas # broadcast parameters params_replicas = replicate(params, lambda x: Broadcast(device_ids)(x)) # broadcast stats stats_replicas = replicate(stats, lambda x: comm.broadcast(x, device_ids)) replicas = [lambda x,p=p,s=s,mode=mode: f(x,p,s,mode) for i,(p,s) in enumerate(zip(params_replicas, stats_replicas))] inputs = scatter(input, device_ids) outputs = parallel_apply(replicas, inputs) return gather(outputs, output_device)
def data_parallel(f, input, params, stats, mode, device_ids, output_device=None): assert isinstance(device_ids, list) if output_device is None: output_device = device_ids[0] if len(device_ids) == 1: return f(input, params, stats, mode) params_all = Broadcast.apply(device_ids, *params.values()) params_replicas = [{ k: params_all[i + j * len(params)] for i, k in enumerate(params.keys()) } for j in range(len(device_ids))] stats_replicas = [ dict(zip(stats.keys(), p)) for p in comm.broadcast_coalesced(list(stats.values()), device_ids) ] replicas = [ partial(f, params=p, stats=s, mode=mode) for p, s in zip(params_replicas, stats_replicas) ] inputs = scatter([input], device_ids) outputs = parallel_apply(replicas, inputs) return gather(outputs, output_device)
def forward(self, inputs): inputs_multi = comm.scatter(inputs, self.device_ids) tensors = parallel_apply(self.modules, [(v, ) for v in inputs_multi], devices=self.device_ids) out = [] for i, tensor in enumerate(tensors): with torch.cuda.device(tensor.get_device()): tensors[i] = torch.autograd.Variable(tensors[i]) out.append([tensors[i]]) return out
def forward(self, inputs, **kwargs): outputs = parallel_apply(self.nets, [torch.cat(tup, dim=1) for tup in inputs], devices=list(range(len(self.nets)))) #outputs = [] #for net in self.nets: #out = net.forward(flat_inputs, **kwargs) #outputs.append(out) flat_outputs = torch.cat(outputs, dim=1) return flat_outputs
def _data_parallel(self, batch): """ Do the forward pass using multiple GPUs. This is a simplification of torch.nn.parallel.data_parallel to support the allennlp model interface. """ inputs, module_kwargs = scatter_kwargs((), batch, self._cuda_devices, 0) used_device_ids = self._cuda_devices[:len(inputs)] replicas = replicate(self._model, used_device_ids) outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids) # Only the 'loss' is needed. # a (num_gpu, ) tensor with loss on each GPU losses = gather([output['loss'].unsqueeze(0) for output in outputs], used_device_ids[0], 0) return {'loss': losses.mean()}
def _data_parallel(self, batch): """ Do the forward pass using multiple GPUs. This is a simplification of torch.nn.parallel.data_parallel to support the allennlp model interface. """ inputs, module_kwargs = scatter_kwargs((), batch, self._cuda_devices, 0) used_device_ids = self._cuda_devices[:len(inputs)] replicas = replicate(self._model, used_device_ids) outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids) # Only the 'loss' is needed. # a (num_gpu, ) tensor with loss on each GPU losses = gather([output['loss'] for output in outputs], used_device_ids[0], 0) return {'loss': losses.mean()}
def test_parallel_apply(self): l1 = nn.Linear(10, 5).float().cuda(0) l2 = nn.Linear(10, 5).float().cuda(1) i1 = Variable(torch.randn(2, 10).float().cuda(0)) i2 = Variable(torch.randn(2, 10).float().cuda(1)) expected1 = l1(i1).data expected2 = l2(i2).data inputs = (i1, i2) modules = (l1, l2) expected_outputs = (expected1, expected2) outputs = dp.parallel_apply(modules, inputs) for out, expected in zip(outputs, expected_outputs): self.assertEqual(out.data, expected) inputs = (i1, Variable(i2.data.new())) expected_outputs = (expected1, expected2.new())
def test_parallel_apply(self): l1 = nn.Linear(10, 5).to("cuda:0", torch.float) l2 = nn.Linear(10, 5).to("cuda:1", torch.float) i1 = torch.randn(2, 10, device="cuda:0", dtype=torch.float) i2 = torch.randn(2, 10, device="cuda:1", dtype=torch.float) expected1 = l1(i1) expected2 = l2(i2) modules = (l1, l2) expected_outputs = (expected1, expected2) # each input can be either a collection of positional arguments # or an object representing the single argument for inputs in [((i1, ), (i2, )), (i1, i2)]: outputs = dp.parallel_apply(modules, inputs, None) for out, expected in zip(outputs, expected_outputs): self.assertEqual(out, expected)
def data_parallel(batch, model: Model, cuda_devices: List) -> Dict[str, torch.Tensor]: """ Performs a forward pass using multiple GPUs. This is a simplification of torch.nn.parallel.data_parallel to support the allennlp model interface. """ inputs, module_kwargs = scatter_kwargs((), batch, cuda_devices, 0) used_device_ids = cuda_devices[:len(inputs)] replicas = replicate(model, used_device_ids) outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids) # Only the 'loss' is needed. # a (num_gpu, ) tensor with loss on each GPU losses = gather([output['loss'].unsqueeze(0) for output in outputs], used_device_ids[0], 0) return {'loss': losses.mean()}
def allen_data_parallel(batch_group: List[TensorDict], model: Model, cuda_devices: List) -> Dict[str, torch.Tensor]: """ Performs a forward pass using multiple GPUs. This is a simplification of torch.nn.parallel.data_parallel to support the allennlp model interface. """ assert len(batch_group) <= len(cuda_devices) moved = [ move_to_device(batch, device) for batch, device in zip(batch_group, cuda_devices) ] used_device_ids = cuda_devices[:len(moved)] # Counterintuitively, it appears replicate expects the source device id to be the first element # in the device id list. See torch.cuda.comm.broadcast_coalesced, which is called indirectly. replicas = nnP.replicate(model, used_device_ids) # We pass all our arguments as kwargs. Create a list of empty tuples of the # correct shape to serve as (non-existent) positional arguments. inputs = [()] * len(batch_group) outputs = nnP.parallel_apply(replicas, inputs, moved, used_device_ids) # Only the 'loss' is needed. # a (num_gpu, ) tensor with loss on each GPU if LOSS_KEY in outputs[0]: result = { LOSS_KEY: nnP.gather([output[LOSS_KEY].unsqueeze(0) for output in outputs], target_device=used_device_ids[0], dim=0).mean() } else: result = {} for key in outputs[0]: if key == 'tags': result[key] = list(chain([output[key] for output in outputs])) elif key != LOSS_KEY: result[key] = [ nnP.gather([output[key]], target_device=used_device_ids[0], dim=0) for output in outputs ] return result
def data_parallel(f, input, params, mode, device_ids, output_device=None): assert isinstance(device_ids, list) if output_device is None: output_device = device_ids[0] if len(device_ids) == 1: return f(input, params, mode) params_all = Broadcast.apply(device_ids, *params.values()) params_replicas = [{k: params_all[i + j*len(params)] for i, k in enumerate(params.keys())} for j in range(len(device_ids))] replicas = [partial(f, params=p, mode=mode) for p in params_replicas] inputs = scatter([input], device_ids) outputs = parallel_apply(replicas, inputs) return gather(outputs, output_device)
def data_parallel(f, input, params, mode, device_ids, output_device=None): device_ids = list(device_ids) if output_device is None: output_device = device_ids[0] if len(device_ids) == 1: return f(input, params, mode) params_all = Broadcast.apply(device_ids, *params.values()) params_replicas = [{ k: params_all[i + j * len(params)] for i, k in enumerate(params.keys()) } for j in range(len(device_ids))] replicas = [partial(f, params=p, mode=mode) for p in params_replicas] inputs = scatter([input], device_ids) outputs = parallel_apply(replicas, inputs) return gather(outputs, output_device)
def forward(self, *inputs, **kwargs): inputs = scatter(inputs, self.device_ids, dim=0) kwargs = scatter(kwargs, self.device_ids, dim=0) replicas = replicate(self.network, self.device_ids[:len(inputs)]) outputs = parallel_apply(replicas, inputs, kwargs) outputs = list(zip(*outputs)) res = [] for i in range(len(outputs)): buf = [] for j in range(len(outputs[i])): if isinstance(outputs[i][j], int): if outputs[i][j]<0: buf.append(outputs[i][j]) else: buf.append(outputs[i][j].to(self.device_ids[0])) res.append(buf) return res
def parallel_chain_loss(model, inputs, den_graph): """ inputs: list of input tuple ((mfcc, inputs), supervision) on different gpus """ from torch.nn.parallel import replicate, parallel_apply, gather model = ForwardParallelChain(model, den_graph, args) device_ids = list(range(torch.cuda.device_count())) assert len(inputs) == len(device_ids) output_device = device_ids[0] used_device_ids = device_ids[:len(inputs)] replicas = replicate(model, used_device_ids) model_kwargs = None outputs = parallel_apply(replicas, inputs, model_kwargs, used_device_ids) dim = 0 ret = gather(outputs, output_device, dim) loss = ret[:, 0] weights = ret[:, -1] nummerator = loss * weights results = ChainResults() results.data = ret[:, 1:].sum(dim=0) return numerator.sum() / weights.sum(), results
def main(args): def log_string(str): # logger.info(str) print(str) '''HYPER PARAMETER''' os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu '''CREATE DIR''' timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')) experiment_dir = Path('./log/') experiment_dir.mkdir(exist_ok=True) experiment_dir = experiment_dir.joinpath('part_seg') experiment_dir.mkdir(exist_ok=True) if args.log_dir is None: experiment_dir = experiment_dir.joinpath(timestr) else: experiment_dir = experiment_dir.joinpath(args.log_dir) experiment_dir.mkdir(exist_ok=True) checkpoints_dir = experiment_dir.joinpath('checkpoints/') checkpoints_dir.mkdir(exist_ok=True) log_dir = experiment_dir.joinpath('logs/') log_dir.mkdir(exist_ok=True) '''LOG''' args = parse_args() logger = logging.getLogger("Model") logger.setLevel(logging.INFO) formatter = logging.Formatter( '%(asctime)s - %(name)s - %(levelname)s - %(message)s') file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model)) file_handler.setLevel(logging.INFO) file_handler.setFormatter(formatter) logger.addHandler(file_handler) log_string('PARAMETER ...') log_string(args) root = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/' # file_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/train2.list' val_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/val2.list' # TRAIN_DATASET = KittiDataset(root = root, file_list=file_list, npoints=args.npoint, training=True, augment=True) # trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=2) TEST_DATASET = KittiDataset(root=root, file_list=val_list, npoints=args.npoint, training=False, augment=False) testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, drop_last=True, num_workers=2) # log_string("The number of training data is: %d" % len(TRAIN_DATASET)) log_string("The number of test data is: %d" % len(TEST_DATASET)) # num_classes = 16 num_devices = args.num_gpus #torch.cuda.device_count() # assert num_devices > 1, "Cannot detect more than 1 GPU." # print(num_devices) devices = list(range(num_devices)) target_device = devices[0] # MODEL = importlib.import_module(args.model) net = UNet(4, 20, nPlanes) # net = MODEL.get_model(num_classes, normal_channel=args.normal) net = net.to(target_device) try: checkpoint = torch.load( str(experiment_dir) + '/checkpoints/best_model.pth') start_epoch = checkpoint['epoch'] net.load_state_dict(checkpoint['model_state_dict']) log_string('Use pretrain model') except: log_string('No existing model, starting training from scratch...') quit() if 1: with torch.no_grad(): net.eval() evaluator = iouEval(num_classes, ignore) evaluator.reset() # for iteration, (points, target, ins, mask) in tqdm(enumerate(testDataLoader), total=len(testDataLoader), smoothing=0.9): for iteration, (points, target, ins, mask) in enumerate(testDataLoader): evaone = iouEval(num_classes, ignore) evaone.reset() cur_batch_size, NUM_POINT, _ = points.size() if iteration > 128: break inputs, targets, masks = [], [], [] coords = [] for i in range(num_devices): start = int(i * (cur_batch_size / num_devices)) end = int((i + 1) * (cur_batch_size / num_devices)) with torch.cuda.device(devices[i]): pc = points[start:end, :, :].to(devices[i]) #feas = points[start:end,:,3:].to(devices[i]) targeti = target[start:end, :].to(devices[i]) maski = mask[start:end, :].to(devices[i]) locs, feas, label, maski, offsets = input_layer( pc, targeti, maski, scale.to(devices[i]), spatialSize.to(devices[i]), True) # print(locs.size(), feas.size(), label.size(), maski.size(), offsets.size()) org_coords = locs[1] label = Variable(label, requires_grad=False) inputi = ME.SparseTensor(feas.cpu(), locs[0].cpu()) inputs.append([inputi.to(devices[i]), org_coords]) targets.append(label) masks.append(maski) replicas = parallel.replicate(net, devices) outputs = parallel.parallel_apply(replicas, inputs, devices=devices) seg_pred = outputs[0].cpu() mask = masks[0].cpu() target = targets[0].cpu() loc = locs[0].cpu() for i in range(1, num_devices): seg_pred = torch.cat((seg_pred, outputs[i].cpu()), 0) mask = torch.cat((mask, masks[i].cpu()), 0) target = torch.cat((target, targets[i].cpu()), 0) seg_pred = seg_pred[target > 0, :] target = target[target > 0] _, seg_pred = seg_pred.data.max(1) #[1] target = target.data.numpy() evaluator.addBatch(seg_pred, target) evaone.addBatch(seg_pred, target) cur_accuracy = evaone.getacc() cur_jaccard, class_jaccard = evaone.getIoU() print('%.4f %.4f' % (cur_accuracy, cur_jaccard)) m_accuracy = evaluator.getacc() m_jaccard, class_jaccard = evaluator.getIoU() log_string('Validation set:\n' 'Acc avg {m_accuracy:.3f}\n' 'IoU avg {m_jaccard:.3f}'.format(m_accuracy=m_accuracy, m_jaccard=m_jaccard)) # print also classwise for i, jacc in enumerate(class_jaccard): if i not in ignore: log_string( 'IoU class {i:} [{class_str:}] = {jacc:.3f}'.format( i=i, class_str=class_strings[class_inv_remap[i]], jacc=jacc))
def forward(self, x, label, **kwargs): if self.gpus is None: # cpu mode, normal fc layer x = classify(x, self.weight, label, simple_output=True, **kwargs) with torch.no_grad(): acc = accuracy(x, label) x = F.log_softmax(x, dim=1) label = label.unsqueeze(-1) loss = torch.gather(x, 1, label) loss = -loss.mean() return loss, acc else: weight_scattered = (w.to(i) for w, i in zip(self.weights, self.gpus)) feat_copies = [x.to(i) for i in self.gpus] labels_scattered = [] for i in range(len(self.weights)): labels_new = label.clone() labels_new[(labels_new >= self.weight_idx[i + 1]) | (labels_new < self.weight_idx[i])] = -1 labels_new = labels_new - self.weight_idx[i] labels_scattered.append(labels_new) kwargs_scattered = scatter(kwargs, self.gpus) input_scattered = list( zip(feat_copies, weight_scattered, labels_scattered)) modules = [classify] * len(self.weights) results_scattered = parallel_apply(modules, input_scattered, kwargs_scattered, self.gpus) logits = [i[0] for i in results_scattered] xexps = [i[1] for i in results_scattered] sums = [i[2] for i in results_scattered] argmaxs = [i[3] for i in results_scattered] maxs = [i[4] for i in results_scattered] sums = gather(sums, 0, dim=1) sums = sums.sum(dim=1, keepdim=True) sums_scattered = [sums.to(i) for i in self.gpus] loss_input_scattered = list( zip(logits, xexps, labels_scattered, sums_scattered)) loss_results_scattered = parallel_apply( [nllDistributed] * len(self.gpus), loss_input_scattered, None, self.gpus) loss_results_scattered = [i.sum() for i in loss_results_scattered] loss_results_scattered = [i.to(0) for i in loss_results_scattered] loss = sum(loss_results_scattered) loss = loss / x.shape[0] for i in range(len(argmaxs)): argmaxs[i] = argmaxs[i] + self.weight_idx[i] maxs = [i.to(0) for i in maxs] maxs = torch.stack(maxs, dim=1) _, max_split = torch.max(maxs, dim=1) idx = torch.arange(0, maxs.size(0), dtype=torch.long) argmaxs = [i.to(0) for i in argmaxs] argmaxs = torch.stack(argmaxs, dim=1) predicted = argmaxs[idx, max_split] total = label.size(0) predicted = predicted.cpu() label = label.cpu() correct = (predicted == label).sum().item() acc = correct / total return loss, acc
def main(args): def log_string(str): logger.info(str) print(str) '''HYPER PARAMETER''' os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu '''CREATE DIR''' timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')) experiment_dir = Path('./log/') experiment_dir.mkdir(exist_ok=True) experiment_dir = experiment_dir.joinpath('part_seg') experiment_dir.mkdir(exist_ok=True) if args.log_dir is None: experiment_dir = experiment_dir.joinpath(timestr) else: experiment_dir = experiment_dir.joinpath(args.log_dir) experiment_dir.mkdir(exist_ok=True) checkpoints_dir = experiment_dir.joinpath('checkpoints/') checkpoints_dir.mkdir(exist_ok=True) log_dir = experiment_dir.joinpath('logs/') log_dir.mkdir(exist_ok=True) '''LOG''' args = parse_args() logger = logging.getLogger("Model") logger.setLevel(logging.INFO) formatter = logging.Formatter( '%(asctime)s - %(name)s - %(levelname)s - %(message)s') file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model)) file_handler.setLevel(logging.INFO) file_handler.setFormatter(formatter) logger.addHandler(file_handler) log_string('PARAMETER ...') log_string(args) root = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/' file_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/train2.list' val_list = '/media/feihu/Storage/kitti_point_cloud/semantic_kitti/val2.list' TRAIN_DATASET = KittiDataset(root=root, file_list=file_list, npoints=args.npoint, training=True, augment=True) trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=2) TEST_DATASET = KittiDataset(root=root, file_list=val_list, npoints=args.npoint, training=False, augment=False) testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, drop_last=True, num_workers=2) log_string("The number of training data is: %d" % len(TRAIN_DATASET)) log_string("The number of test data is: %d" % len(TEST_DATASET)) # num_classes = 16 '''MODEL LOADING''' shutil.copy('models/%s.py' % args.model, str(experiment_dir)) shutil.copy('models/pointnet_util.py', str(experiment_dir)) num_devices = args.num_gpus #torch.cuda.device_count() # assert num_devices > 1, "Cannot detect more than 1 GPU." # print(num_devices) devices = list(range(num_devices)) target_device = devices[0] # MODEL = importlib.import_module(args.model) net = FusionNet(args.npoint, 4, 20, nPlanes) # net = MODEL.get_model(num_classes, normal_channel=args.normal) net = net.to(target_device) def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv2d') != -1: if m.weight is not None: torch.nn.init.xavier_normal_(m.weight.data) if m.bias is not None: torch.nn.init.constant_(m.bias.data, 0.0) elif classname.find('Linear') != -1: if m.weight is not None: torch.nn.init.xavier_normal_(m.weight.data) if m.bias is not None: torch.nn.init.constant_(m.bias.data, 0.0) try: checkpoint = torch.load( str(experiment_dir) + '/checkpoints/best_model.pth') start_epoch = checkpoint['epoch'] net.load_state_dict(checkpoint['model_state_dict']) log_string('Use pretrain model') except: log_string('No existing model, starting training from scratch...') start_epoch = 0 net = net.apply(weights_init) if args.optimizer == 'Adam': optimizer = torch.optim.Adam(net.parameters(), lr=args.learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=args.decay_rate) else: optimizer = torch.optim.SGD(net.parameters(), lr=1e-1, momentum=0.9, weight_decay=1e-4, nesterov=True) # optimizer = torch.optim.SGD(net.parameters(), lr=args.learning_rate, momentum=0.9) def bn_momentum_adjust(m, momentum): if isinstance(m, torch.nn.BatchNorm2d) or isinstance( m, torch.nn.BatchNorm1d): m.momentum = momentum LEARNING_RATE_CLIP = 1e-5 MOMENTUM_ORIGINAL = 0.1 MOMENTUM_DECCAY = 0.5 MOMENTUM_DECCAY_STEP = 20 / 2 # args.step_size best_acc = 0 global_epoch = 0 best_class_avg_iou = 0 best_inctance_avg_iou = 0 # criterion = MODEL.get_loss() criterion = nn.CrossEntropyLoss() criterions = parallel.replicate(criterion, devices) # The raw version of the parallel_apply # replicas = parallel.replicate(net, devices) # input_coding = scn.InputLayer(dimension, torch.LongTensor(spatialSize), mode=4) for epoch in range(start_epoch, args.epoch): log_string('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch)) '''Adjust learning rate and BN momentum''' # lr = max(args.learning_rate * (args.lr_decay ** (epoch // args.step_size)), LEARNING_RATE_CLIP) # lr = args.learning_rate * \ # math.exp((1 - epoch) * args.lr_decay) # log_string('Learning rate:%f' % lr) # for param_group in optimizer.param_groups: # param_group['lr'] = lr # for param_group in optimizer.param_groups: # param_group['lr'] = lr mean_correct = [] if 1: momentum = MOMENTUM_ORIGINAL * (MOMENTUM_DECCAY **(epoch // MOMENTUM_DECCAY_STEP)) if momentum < 0.01: momentum = 0.01 print('BN momentum updated to: %f' % momentum) net = net.apply(lambda x: bn_momentum_adjust(x, momentum)) '''learning one epoch''' net.train() # for iteration, data in tqdm(enumerate(trainDataLoader), total=len(trainDataLoader), smoothing=0.9): for iteration, data in enumerate(trainDataLoader): #adjust learing rate. if (iteration) % 320 == 0: lr_count = epoch * 6 + (iteration) / 320 lr = args.learning_rate * math.exp( (1 - lr_count) * args.lr_decay) for param_group in optimizer.param_groups: param_group['lr'] = lr log_string('Learning rate:%f' % lr) optimizer.zero_grad() if iteration > 1920: break points, target, ins, mask = data # print(torch.max(points[:, :, :3], 1)[0]) # print(torch.min(points[:, :, :3], 1)[0]) valid = mask > 0 total_points = valid.sum() orgs = points points = points.data.numpy() # print(total_points) inputs, targets, masks = [], [], [] coords = [] for i in range(num_devices): start = int(i * (args.batch_size / num_devices)) end = int((i + 1) * (args.batch_size / num_devices)) batch = provider.transform_for_sparse( points[start:end, :, :3], points[start:end, :, 3:], target[start:end, :].data.numpy(), mask[start:end, :].data.numpy(), scale, spatialSize) batch['x'][1] = batch['x'][1].type(torch.FloatTensor) batch['x'][0] = batch['x'][0].type(torch.IntTensor) batch['y'] = batch['y'].type(torch.LongTensor) org_xyz = orgs[start:end, :, :3].transpose(1, 2).contiguous() org_feas = orgs[start:end, :, 3:].transpose(1, 2).contiguous() label = Variable(batch['y'], requires_grad=False) maski = batch['mask'].type(torch.IntTensor) # print(torch.max(batch['x'][0], 0)[0]) # print(torch.min(batch['x'][0], 0)[0]) # locs, feas = input_layer(batch['x'][0].to(devices[i]), batch['x'][1].to(devices[i])) locs, feas = input_layer(batch['x'][0].cuda(), batch['x'][1].cuda()) # print(locs.size(), feas.size(), batch['x'][0].size()) # print(inputi.size(), batch['x'][1].size()) with torch.cuda.device(devices[i]): org_coords = batch['x'][0].to(devices[i]) inputi = ME.SparseTensor(feas.cpu(), locs).to( devices[i]) #input_coding(batch['x']) org_xyz = org_xyz.to(devices[i]) org_feas = org_feas.to(devices[i]) maski = maski.to(devices[i]) inputs.append( [inputi, org_coords, org_xyz, org_feas, maski]) targets.append(label.to(devices[i])) # masks.append(maski.contiguous().to(devices[i])) replicas = parallel.replicate(net, devices) predictions = parallel.parallel_apply(replicas, inputs, devices=devices) count = 0 # print("end ...") results = [] labels = [] match = 0 for i in range(num_devices): # temp = predictions[i]['output1'].F#.view(-1, num_classes) temp = predictions[i] # temp = output_layer(locs, predictions[i]['output1'].F, coords[i]) temp = temp[targets[i] > 0, :] results.append(temp) temp = targets[i] temp = temp[targets[i] > 0] labels.append(temp) # print(prediction2[i].size(), prediction1[i].size(), targets[i].size()) outputi = results[ i] #prediction2[i].contiguous().view(-1, num_classes) num_points = labels[i].size(0) count += num_points _, pred_choice = outputi.data.max(1) #[1] # print(pred_choice) correct = pred_choice.eq(labels[i].data).cpu().sum() match += correct.item() mean_correct.append(correct.item() / num_points) # print(prediction2, labels) losses = parallel.parallel_apply(criterions, tuple(zip(results, labels)), devices=devices) loss = parallel.gather(losses, target_device, dim=0).mean() loss.backward() optimizer.step() # assert(count1 == count2 and total_points == count1) log_string( "===> Epoch[{}]({}/{}) Valid points:{}/{} Loss: {:.4f} Accuracy: {:.4f}" .format(epoch, iteration, len(trainDataLoader), count, total_points, loss.item(), match / count)) # sys.stdout.flush() train_instance_acc = np.mean(mean_correct) log_string('Train accuracy is: %.5f' % train_instance_acc) # continue with torch.no_grad(): net.eval() evaluator = iouEval(num_classes, ignore) evaluator.reset() for iteration, (points, target, ins, mask) in tqdm(enumerate(testDataLoader), total=len(testDataLoader), smoothing=0.9): cur_batch_size, NUM_POINT, _ = points.size() # points, label, target, mask = points.float().cuda(), label.long().cuda(), target.long().cuda(), mask.float().cuda() if iteration > 192: break if 0: points = points.data.numpy() points[:, :, 0:3], norm = provider.pc_normalize( points[:, :, :3], mask.data.numpy()) points = torch.Tensor(points) orgs = points points = points.data.numpy() inputs, targets, masks = [], [], [] coords = [] for i in range(num_devices): start = int(i * (cur_batch_size / num_devices)) end = int((i + 1) * (cur_batch_size / num_devices)) batch = provider.transform_for_test( points[start:end, :, :3], points[start:end, :, 3:], target[start:end, :].data.numpy(), mask[start:end, :].data.numpy(), scale, spatialSize) batch['x'][1] = batch['x'][1].type(torch.FloatTensor) batch['x'][0] = batch['x'][0].type(torch.IntTensor) batch['y'] = batch['y'].type(torch.LongTensor) org_xyz = orgs[start:end, :, :3].transpose(1, 2).contiguous() org_feas = orgs[start:end, :, 3:].transpose(1, 2).contiguous() label = Variable(batch['y'], requires_grad=False) maski = batch['mask'].type(torch.IntTensor) locs, feas = input_layer(batch['x'][0].cuda(), batch['x'][1].cuda()) # print(locs.size(), feas.size(), batch['x'][0].size()) # print(inputi.size(), batch['x'][1].size()) with torch.cuda.device(devices[i]): org_coords = batch['x'][0].to(devices[i]) inputi = ME.SparseTensor(feas.cpu(), locs).to( devices[i]) #input_coding(batch['x']) org_xyz = org_xyz.to(devices[i]) org_feas = org_feas.to(devices[i]) maski = maski.to(devices[i]) inputs.append( [inputi, org_coords, org_xyz, org_feas, maski]) targets.append(label.to(devices[i])) # masks.append(maski.contiguous().to(devices[i])) replicas = parallel.replicate(net, devices) outputs = parallel.parallel_apply(replicas, inputs, devices=devices) # net = net.eval() # seg_pred = classifier(points, to_categorical(label, num_classes)) seg_pred = outputs[0].cpu() # mask = masks[0].cpu() target = targets[0].cpu() loc = locs[0].cpu() for i in range(1, num_devices): seg_pred = torch.cat((seg_pred, outputs[i].cpu()), 0) # mask = torch.cat((mask, masks[i].cpu()), 0) target = torch.cat((target, targets[i].cpu()), 0) seg_pred = seg_pred[target > 0, :] target = target[target > 0] _, seg_pred = seg_pred.data.max(1) #[1] target = target.data.numpy() evaluator.addBatch(seg_pred, target) # when I am done, print the evaluation m_accuracy = evaluator.getacc() m_jaccard, class_jaccard = evaluator.getIoU() log_string('Validation set:\n' 'Acc avg {m_accuracy:.3f}\n' 'IoU avg {m_jaccard:.3f}'.format(m_accuracy=m_accuracy, m_jaccard=m_jaccard)) # print also classwise for i, jacc in enumerate(class_jaccard): if i not in ignore: log_string( 'IoU class {i:} [{class_str:}] = {jacc:.3f}'.format( i=i, class_str=class_strings[class_inv_remap[i]], jacc=jacc)) log_string('Epoch %d test Accuracy: %f mean avg mIOU: %f' % (epoch + 1, m_accuracy, m_jaccard)) if (m_jaccard >= best_class_avg_iou): # logger.info('Save model...') log_string('Saveing model...') savepath = str(checkpoints_dir) + '/best_model.pth' log_string('Saving at %s' % savepath) state = { 'epoch': epoch, 'train_acc': train_instance_acc, 'test_acc': m_accuracy, 'class_avg_iou': m_jaccard, 'model_state_dict': net.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), } torch.save(state, savepath) # log_string('Saving model....') if m_accuracy > best_acc: best_acc = m_accuracy if m_jaccard > best_class_avg_iou: best_class_avg_iou = m_jaccard log_string('Best accuracy is: %.5f' % best_acc) log_string('Best class avg mIOU is: %.5f' % best_class_avg_iou) global_epoch += 1
def train(pipeline_model, data_loader, val_data_loader, config): # Set up the train flag for batch normalization pipeline_model.train() num_devices = torch.cuda.device_count() num_devices = min(config.max_ngpu, num_devices) devices = list(range(num_devices)) target_device = devices[0] pipeline_model.to(target_device) if num_devices > 1: pipeline_model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm( pipeline_model, devices) # Configuration writer = SummaryWriter(logdir=config.log_dir) data_timer, iter_timer = Timer(), Timer() data_time_avg, iter_time_avg = AverageMeter(), AverageMeter() meters = collections.defaultdict(AverageMeter) hists = pipeline_model.initialize_hists() optimizer = pipeline_model.initialize_optimizer(config) scheduler = pipeline_model.initialize_scheduler(optimizer, config) writer = SummaryWriter(logdir=config.log_dir) # Train the network logging.info('===> Start training') best_val, best_val_iter, curr_iter, epoch, is_training = 0, 0, 1, 1, True if config.resume: if osp.isfile(config.resume): logging.info("=> loading checkpoint '{}'".format(config.resume)) state = torch.load(config.resume) curr_iter = state['iteration'] + 1 epoch = state['epoch'] pipeline_model.load_state_dict(state['state_dict']) if config.resume_optimizer: curr_iter = state['iteration'] + 1 scheduler = pipeline_model.initialize_scheduler( optimizer, config, last_step=curr_iter) pipeline_model.load_optimizer(optimizer, state['optimizer']) if 'best_val' in state: best_val = state['best_val'] best_val_iter = state['best_val_iter'] logging.info("=> loaded checkpoint '{}' (epoch {})".format( config.resume, state['epoch'])) else: logging.info("=> no checkpoint found at '{}'".format( config.resume)) data_iter = data_loader.__iter__() while is_training: for iteration in range(len(data_loader)): pipeline_model.reset_gradient(optimizer) iter_timer.tic() pipelines = parallel.replicate(pipeline_model, devices) # Get training data data_timer.tic() inputs = [] for pipeline, device in zip(pipelines, devices): with torch.cuda.device(device): while True: datum = pipeline.load_datum(data_iter, has_gt=True) num_boxes = sum(box.shape[0] for box in datum['bboxes_coords']) if config.skip_empty_boxes and num_boxes == 0: continue break inputs.append(datum) data_time_avg.update(data_timer.toc(False)) outputs = parallel.parallel_apply(pipelines, [(x, True) for x in inputs], devices=devices) losses = parallel.parallel_apply( [pipeline.loss for pipeline in pipelines], tuple(zip(inputs, outputs)), devices=devices) losses = parallel.gather(losses, target_device) losses = dict([(k, v.mean()) for k, v in losses.items()]) meters, hists = pipeline_model.update_meters(meters, hists, losses) # Compute and accumulate gradient losses['loss'].backward() # Update number of steps pipeline_model.step_optimizer(losses, optimizer, scheduler, iteration) iter_time_avg.update(iter_timer.toc(False)) if curr_iter >= config.max_iter: is_training = False break if curr_iter % config.stat_freq == 0 or curr_iter == 1: lrs = ', '.join([ '{:.3e}'.format(x) for x in scheduler['default'].get_lr() ]) debug_str = "===> Epoch[{}]({}/{}): LR: {}\n".format( epoch, curr_iter, len(data_loader), lrs) debug_str += log_meters(meters, log_perclass_meters=False) debug_str += f"\n data time: {data_time_avg.avg:.3f}" debug_str += f" iter time: {iter_time_avg.avg:.3f}" logging.info(debug_str) # Reset timers data_time_avg.reset() iter_time_avg.reset() # Write logs update_writer(writer, meters, curr_iter, 'training') writer.add_scalar('training/learning_rate', scheduler['default'].get_lr()[0], curr_iter) # Reset meters reset_meters(meters, hists) # Save current status, save before val to prevent occational mem overflow if curr_iter % config.save_freq == 0: checkpoint(pipeline_model, optimizer, epoch, curr_iter, config, best_val, best_val_iter) if config.heldout_save_freq > 0 and curr_iter % config.heldout_save_freq == 0: checkpoint(pipeline_model, optimizer, epoch, curr_iter, config, best_val, best_val_iter, heldout_save=True) # Validation if curr_iter % config.val_freq == 0: if num_devices > 1: unconvert_sync_batchnorm(pipeline_model) best_val, best_val_iter = validate(pipeline_model, val_data_loader, config, writer, curr_iter, best_val, best_val_iter, optimizer, epoch) if num_devices > 1: pipeline_model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm( pipeline_model, devices) if curr_iter % config.empty_cache_freq == 0: # Clear cache torch.cuda.empty_cache() # End of iteration curr_iter += 1 epoch += 1 # Explicit memory cleanup if hasattr(data_iter, 'cleanup'): data_iter.cleanup() # Save the final model if num_devices > 1: unconvert_sync_batchnorm(pipeline_model) validate(pipeline_model, val_data_loader, config, writer, curr_iter, best_val, best_val_iter, optimizer, epoch) if num_devices > 1: pipeline_model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm( pipeline_model, devices) checkpoint(pipeline_model, optimizer, epoch, curr_iter, config, best_val, best_val_iter)
def forward(self, x, y=None, nsamples=1, sample_from='posterior', reduce='none', samples=None): if self.use_posterior: assert (y is not None) x_unet = x.to(self.devices['unet']) modules = [self.unet] inputs = [x_unet] devices = [self.devices['unet']] if self.training or sample_from == 'prior': x_prior = Variable(x.data.to(self.devices['prior_net'])) modules.append(self.prior_net) inputs.append(x_prior) devices.append(self.devices['prior_net']) if self.training or self.use_posterior: x_posterior = Variable(x.data.to(self.devices['posterior_net'])) y_posterior = Variable(y.data.to(self.devices['posterior_net'])) posterior_in = torch.cat([x_posterior, y_posterior], dim=1) modules.append(self.posterior_net) inputs.append(posterior_in) devices.append(self.devices['posterior_net']) if self.n_unique_devices > 1: output = parallel_apply(modules, inputs, devices=devices) else: output = [ module(module_input) for module, module_input in zip(modules, inputs) ] # Sample # Prior if sample_from == 'prior' or self.training: prior_params = output[1] if self.visualize: attn_blocks_prior = prior_params[2] prior_means = prior_params[0] prior_log_vars = prior_params[1] prior_samples = self.sample(means=prior_means, log_vars=prior_log_vars, nsamples=nsamples, sample_from='prior') # Posterior if sample_from == 'posterior' or self.training: # If training, a samples from prior has also been drawn if self.training: posterior_params = output[2] # If eval, sample from prior won't be drawn else: posterior_params = output[1] posterior_means = posterior_params[0] posterior_log_vars = posterior_params[1] posterior_samples = self.sample(means=posterior_means, log_vars=posterior_log_vars, nsamples=nsamples, sample_from='posterior') if samples is None: samples = posterior_samples if self.training else prior_samples if self.visualize: unet_features = output[0][0].to(self.devices['output']) attn_blocks_unet = output[0][1] else: unet_features = output[0].to(self.devices['output']) samples = samples.to(self.devices['output']) out = self.comb(unet_features, samples, reduce=reduce) if self.training or self.use_posterior: return out, prior_means, prior_log_vars, posterior_means, posterior_log_vars elif sample_from == 'prior': if self.visualize: return out, prior_means, prior_log_vars, attn_blocks_unet, attn_blocks_prior return out, prior_means, prior_log_vars else: return out, posterior_means, posterior_log_vars
def parallel_apply(self, replicas, inputs, kargs=None): if kargs is not None: kargs = tuple(kargs for _ in inputs) return parallel_apply(replicas, inputs, kargs, self.device_ids[:len(replicas)])
def parallel_apply(self, replicas, inputs, kwargs): return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
# Get new data inputs, all_labels = [], [] for i in range(num_devices): coordinates, features, labels = generate_input(config.file_name, voxel_size=0.05) with torch.cuda.device(devices[i]): inputs.append( ME.SparseTensor(features - 0.5, coords=coordinates).to(devices[i])) all_labels.append(labels.long().to(devices[i])) # The raw version of the parallel_apply st = time() replicas = parallel.replicate(net, devices) outputs = parallel.parallel_apply(replicas, inputs, devices=devices) # Extract features from the sparse tensors to use a pytorch criterion out_features = [output.F for output in outputs] losses = parallel.parallel_apply(criterions, tuple(zip(out_features, all_labels)), devices=devices) loss = parallel.gather(losses, target_device, dim=0).mean() t = time() - st min_time = min(t, min_time) print('Iteration: ', iteration, ', Loss: ', loss.item(), ', Time: ', t, ', Min time: ', min_time) # Gradient loss.backward() optimizer.step()
def train( self, train_dataset, *, progress_bar=True, resume=False, device=None, ): """ A simplified training loop:: for epoch in range(1, ...): for example in train_iterator: model_out = self.model(example) review = self.model.review(example, model_out) review = maybe_add_loss_from_losses(review) review['loss'].backward() self.optimizer.step() add_review_to_tensorboardX(review) The remaining code takes care about calling validation and save the result to tensorboard (if a validation_hook is registered), save checkpoints, cleanup checkpoints that are stale (not best according to metric and not last) and display a progessbar. The code is designed that many aspects can be customized. (e.g. see test_runtime_tests.py DictTrainer for multi model trainer) Args: train_iterator: The train_iterator is python iterable (e.g. tuple, list, ...) that can consumed multiple times (i.e. not generator). Usually it will be paderbox.database.BaseIterator that is returned from a database in paderbox.database. progress_bar: flag whether to show a progress bar or not. resume: Whether to resume a training or start a fresh one. device: Defines the device which shall be used ('cpu', 0, 1, ...). If None, it selects device 0 if CUDA is available and 'cpu' if CUDA is not available. """ if torch.cuda.is_available(): if device is None: device = 0 else: if device is None: warnings.warn( 'CUDA is not available in this environment! The training ' 'will run on the CPU! This might be caused by a damaged ' 'installation or a version mismatch between PyTorch and ' 'your CUDA installation.') device = 'cpu' elif device != 'cpu': raise RuntimeError( 'CUDA is not available in this environment, but you set ' 'device to use a GPU! This might be caused by a damaged ' 'installation or a version mismatch between PyTorch and ' 'your CUDA installation.') if resume: assert resume is True, resume self.load_checkpoint() else: assert not self.checkpoint_dir.exists(),\ f'A checkpoint directory already exists. If you want to ' \ f'restart the training set resume to True.' self.iteration = 0 self.epoch = 0 torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False # Change model to train mode (e.g. activate dropout) self.model.train() if isinstance(device, (tuple, list)): assert all([isinstance(d, int) for d in device]), device # multiple devises e.g. [0, 1], [0, 1, 2, 3], ... # torch.nn.parallel.DataParallel moves everything to the first gpu. # We do then the same thing. self.to(device[0]) device = list(device) else: self.to(device) device = [device] # Reset all gradients self.optimizer_zero_grad() self.writer = self.writer_cls(str(self.storage_dir)) hooks = [*self.hooks] if progress_bar: try: max_it_len = len(train_dataset) except TypeError: # TypeError: object of type '...' has no len() max_it_len = None hooks.append(ProgressBarHook(self._stop_trigger, max_it_len)) hooks = sorted(hooks, key=lambda h: h.priority, reverse=True) if len(device) >= 2: import textwrap print( 'WARNING: You called padertorch.Trainer.train with multiple\n' + textwrap.indent( 'devices. With this the trainer will use data parallel to\n' 'utilize the multiple GPUs to speedup your training.\n' 'We observed some problems with some versions of pytorch.\n' 'In 1.4 the performance on a NN was quite bad and accoring to\n' 'https://github.com/pytorch/pytorch/issues/33552\n' 'this was because the RNNs get no gradients.\n' 'In 1.5 the training got stuck, the reason is unclear in the' 'moment.\n' 'With Pytorch <= 1.3 we have not tested the code.\n' f'Your pytorch version is: {torch.__version__}', ' ' * len('WARNING: '))) assert self.virtual_minibatch_size % len(device) == 0, ( self.virtual_minibatch_size, device) assert len(device) > 0, (self.virtual_minibatch_size, device) # ================ MAIN TRAINING LOOP! =================== try: train_iterable = None while True: new_epoch = False if train_iterable is None: new_epoch = True # Call pre_step between the epochs. # We call it here, so it is done, before the iteration # over the train_dataset starts. for hook in hooks: hook.pre_step(self) train_iterable = iter(train_dataset) optimize = True with self.train_timer['time_per_iteration'] as timer: for minibatch_index in range(self.virtual_minibatch_size // len(device)): with self.train_timer['time_per_data_loading']: example = list( itertools.islice(train_iterable, len(device))) if len(example) == 0: train_iterable = None self.epoch += 1 if minibatch_index == 0: optimize = False break # end minibatch loop if new_epoch: new_epoch = False elif minibatch_index == 0: # Call pre_step after getting the next example, # to correctly detect the next epoch with timer.pause(): for hook in hooks: hook.pre_step(self) if len(device) == 1: assert len(example) == 1, (len(example), example) example = example[0] loss, example, model_output, review = \ self.train_step(self.model, example, device[0]) with timer.pause(): for hook in hooks: hook.post_step(self, example, model_output, review) # Release pytorch object to reduce memory footprint del example del model_output del review with self.train_timer['time_per_backward']: loss.backward(retain_graph=False) del loss else: # The data parallel idea here follows the idea from # torch.nn.parallel.DataParallel. # We also use the same functions # (i.e. replicate, parallel_apply and gather). # # The difference is, that we need no scatter, # because we simply use multiple examples and # the gather must only be applied on the loss. # Move copies of the model to each GPU with self.train_timer['time_per_replicate']: replicas = replicate(self.model, device[:len(example)]) # Use threads to call train_step. Each thread # processes one example on one GPU. with self.train_timer['time_per_parallel_apply']: outputs = parallel_apply( [self.train_step] * len(example), list( zip( replicas, example, device[:len(example)], )), ) del replicas # Take the sum of all losses. Since they are on # different GPUs, use gather. with self.train_timer['time_per_gather']: loss = gather([ loss.view(1) for loss, _, _, _ in outputs ], device[0]).sum() with timer.pause(): for _, example, model_output, review in outputs: for hook in hooks: hook.post_step(self, example, model_output, review) # Release pytorch object to reduce memory footprint del example del model_output del review with self.train_timer['time_per_backward']: loss.backward(retain_graph=False) del loss # Only the summary hook will use optimizer_review if optimize: with self.train_timer['time_per_optimize']: optimizer_summary = self.optimizer_step() for hook in hooks: hook.post_optimize(self, optimizer_summary) del optimizer_summary self.iteration += 1 except StopTraining: pass finally: try: for hook in hooks: hook.close(self) except Exception: print('Exception in finally. May hide actual exception!!!\n' 'You may comment this finally block for debugging.') raise self.writer.close() self.writer = None
def parallel_tensor_dict( tensor_dicts: List[Mapping], model: Model, device_ids: List, loss_key='loss', atom_types=(str, )) -> Dict[str, torch.Tensor]: """ Performs a forward pass using multiple GPUs. This is a simplification of torch.nn.parallel.data_parallel to support the allennlp model interface. """ if len(tensor_dicts) > len(device_ids): raise ValueError( "the number of tensor dicts must be the same as the number of device ids" ) # region 1 - copy data and model to multiple GPUS # NOTE, there can be fewer tensor dicts, # and in this case the number of used device ids might be less than the number of provided device ids moved = [ move_tensor_dict_to_device(tensor_dict, device_id) for tensor_dict, device_id in zip(tensor_dicts, device_ids) ] used_device_ids = device_ids[:len(moved)] # must replicate the model to the GPUs every time, because its parameters have been updated replicas = nnP.replicate(model, used_device_ids) # endregion # region 2 - get the outputs # the outputs must be a dictionary of results returned by each GPU outputs = nnP.parallel_apply( replicas, [()] * len(tensor_dicts), # no positional argument moved, # the tensor dict as named arguments used_device_ids) # endregion # region 3 - gather the results on the first GPU result = {} for k, v in outputs[0].items(): if k == loss_key: # special treatment for the loss key result[k] = nnP.gather( [output[k].unsqueeze(0) for output in outputs], target_device=used_device_ids[0], dim=0).mean() else: if isinstance(v, torch.Tensor): result[k] = [ nnP.gather([output[k]], target_device=used_device_ids[0], dim=0) for output in outputs ] elif gx.iterable__(v, atom_types=atom_types): result[k] = tuple(chain([output[k] for output in outputs])) else: result[k] = tuple(output[k] for output in outputs) # endregion return result