diff --git a/.gitignore b/.gitignore index bbd4385..c74a94c 100644 --- a/.gitignore +++ b/.gitignore @@ -11,12 +11,8 @@ __pycache__/ .idea/ -# data & pretrain-model -backup/ -data/ -pretrained/ - # private files -utils_plot* -imgs/ 1.md +backup/ +all_data/ +pretrained/ diff --git a/EAS/run_search.py b/EAS/run_search.py index 8bf2fb0..b0f474d 100644 --- a/EAS/run_search.py +++ b/EAS/run_search.py @@ -47,7 +47,7 @@ def get_config(): parser.add_argument('-p_runs', default=1, type=int) # If batch_size is 1, set this to > 1 to do multiple runs for the instance in parallel parser.add_argument('-output_path', default="EAS_results", type=str) parser.add_argument('-norm', default="batch_no_track", choices=['instance', 'batch', 'batch_no_track', 'none'], type=str) - parser.add_argument('-gpu_id', default=2, type=int) + parser.add_argument('-gpu_id', default=0, type=int) parser.add_argument('-seed', default=2023, type=int, help="random seed") # EAS-Emb and EAS-Lay parameters @@ -247,7 +247,9 @@ def search(run_id, config): logging.info(f"Runtime: {runtime}s") logging.info("MEM: " + str(cutorch.max_memory_reserved(config.gpu_id) / 1024 / 1024) + "MB") logging.info(f"Num. instances: {len(perf)}") - print(">> Solving {}, with sol {}".format(config.instances_path, perf)) + print(">> Solved {}, with sol {}".format(config.instances_path, perf)) + + return np.mean(perf) def seed_everything(seed=2022): @@ -263,4 +265,15 @@ def seed_everything(seed=2022): seed_everything(config.seed) if torch.cuda.is_available(): torch.cuda.set_device(config.gpu_id) - search(run_id, config) + + sol_list, path_list = [], [] + if os.path.isdir(config.instances_path): + path_list = [os.path.join(config.instances_path, f) for f in sorted(os.listdir(config.instances_path))] + else: + path_list = config.instances_path + + for path in path_list: + config.instances_path = path + sol = search(run_id, config) + sol_list.append(sol) + print(len(sol_list), sol_list) diff --git a/POMO/CVRP/CVRPEnv.py b/POMO/CVRP/CVRPEnv.py index c3b788c..8ec6316 100644 --- a/POMO/CVRP/CVRPEnv.py +++ b/POMO/CVRP/CVRPEnv.py @@ -101,7 +101,8 @@ def load_problems(self, batch_size, problems=None, aug_factor=1): node_demand = self.saved_node_demand[self.saved_index:self.saved_index+batch_size] self.saved_index += batch_size else: - depot_xy, node_xy, node_demand = get_random_problems(batch_size, self.problem_size, distribution='uniform', problem="cvrp") + depot_xy, node_xy, node_demand, capacity = get_random_problems(batch_size, self.problem_size, distribution='uniform', problem="cvrp") + node_demand = node_demand / capacity.view(-1, 1) self.batch_size = depot_xy.size(0) if aug_factor > 1: diff --git a/POMO/CVRP/CVRPTrainer_meta_grad.py b/POMO/CVRP/CVRPTrainer_meta_grad.py new file mode 100644 index 0000000..18d8a63 --- /dev/null +++ b/POMO/CVRP/CVRPTrainer_meta_grad.py @@ -0,0 +1,634 @@ +import os +import copy +import math +import time +import random +import torch +from logging import getLogger +from collections import OrderedDict +from torch.optim import Adam as Optimizer + +from CVRPEnv import CVRPEnv as Env +from CVRPModel import CVRPModel as Model +from ProblemDef import get_random_problems, generate_task_set +from utils.utils import * +from utils.functions import * +from CVRP_baseline import * + + +class CVRPTrainer: + """ + Implementation of POMO with MAML / FOMAML / Reptile on CVRP. + For MAML & FOMAML, ref to "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"; + For Reptile, ref to "On First-Order Meta-Learning Algorithms" and "On the generalization of neural combinatorial optimization heuristics". + """ + def __init__(self, + env_params, + model_params, + optimizer_params, + trainer_params, + meta_params): + + # save arguments + self.env_params = env_params + self.model_params = model_params + self.optimizer_params = optimizer_params + self.trainer_params = trainer_params + self.meta_params = meta_params + + # result folder, logger + self.logger = getLogger(name='trainer') + self.result_folder = get_result_folder() + self.result_log = LogData() + + # cuda + USE_CUDA = self.trainer_params['use_cuda'] + if USE_CUDA: + cuda_device_num = self.trainer_params['cuda_device_num'] + torch.cuda.set_device(cuda_device_num) + self.device = torch.device('cuda', cuda_device_num) + torch.set_default_tensor_type('torch.cuda.FloatTensor') + else: + self.device = torch.device('cpu') + torch.set_default_tensor_type('torch.FloatTensor') + + # Main Components + self.model_params["norm"] = 'batch_no_track' + self.meta_model = Model(**self.model_params) + self.meta_optimizer = Optimizer(self.meta_model.parameters(), **self.optimizer_params['optimizer']) + self.alpha = self.meta_params['alpha'] # for reptile + self.task_set = generate_task_set(self.meta_params) + self.val_data, self.val_opt = {}, {} # for lkh3_offline + assert not (self.meta_params['curriculum'] and self.meta_params["data_type"] in ["size", "distribution"]), "Not Implemented!" + if self.meta_params["data_type"] == "size_distribution": + # hardcoded - task_set: range(self.min_n, self.max_n, self.task_interval) * self.num_dist + self.min_n, self.max_n, self.task_interval, self.num_dist = 50, 200, 5, 11 + self.task_w = torch.full(((self.max_n - self.min_n) // 5 + 1, self.num_dist), 1 / self.num_dist) + + # record grad + self.model_num = 86 + self.cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6) + self.ours_first, self.ours_last = [], [] + self.reptile1_first, self.reptile1_last, self.reptile2_first, self.reptile2_last = [], [], [], [] + + # Restore + self.start_epoch = 1 + model_load = trainer_params['model_load'] + if model_load['enable']: + checkpoint_fullname = '{path}/checkpoint-{epoch}.pt'.format(**model_load) + checkpoint = torch.load(checkpoint_fullname, map_location=self.device) + self.meta_model.load_state_dict(checkpoint['model_state_dict']) + self.start_epoch = 1 + model_load['epoch'] + self.result_log.set_raw_data(checkpoint['result_log']) + self.meta_optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + self.logger.info(">> Model loaded from {}".format(checkpoint_fullname)) + + # utility + self.time_estimator = TimeEstimator() + + def run(self): + + start_time, best_mean = time.time(), 1000 + self.time_estimator.reset(self.start_epoch) + for epoch in range(self.start_epoch, self.meta_params['epochs'] + 1): + self.logger.info('=================================================================') + + # lr decay (by 10) to speed up convergence at 90th and 95th iterations + if epoch in [int(self.meta_params['epochs'] * 0.9)]: + self.optimizer_params['optimizer']['lr'] /= 10 + for group in self.meta_optimizer.param_groups: + group["lr"] /= 10 + print(">> LR decay to {}".format(group["lr"])) + + # Train + train_score, train_loss = self._train_one_epoch(epoch) + self.result_log.append('train_score', epoch, train_score) + self.result_log.append('train_loss', epoch, train_loss) + model_save_interval = self.trainer_params['logging']['model_save_interval'] + img_save_interval = self.trainer_params['logging']['img_save_interval'] + # Val + no_aug_score_list = [] + if self.meta_params["data_type"] == "size": + dir = "../../data/CVRP/Size/" + paths = ["cvrp100_uniform.pkl", "cvrp200_uniform.pkl", "cvrp300_uniform.pkl"] + elif self.meta_params["data_type"] == "distribution": + dir = "../../data/CVRP/Distribution/" + paths = ["cvrp100_uniform.pkl", "cvrp100_gaussian.pkl", "cvrp100_cluster.pkl", "cvrp100_diagonal.pkl", "cvrp100_cvrplib.pkl"] + elif self.meta_params["data_type"] == "size_distribution": + dir = "../../data/CVRP/Size_Distribution/" + paths = ["cvrp200_uniform.pkl", "cvrp300_rotation.pkl"] + if epoch <= 1 or (epoch % img_save_interval) == 0: + for val_path in paths: + no_aug_score = self._fast_val(self.meta_model, path=os.path.join(dir, val_path), val_episodes=64, mode="eval") + no_aug_score_list.append(round(no_aug_score, 4)) + self.result_log.append('val_score', epoch, no_aug_score_list) + cur_mean = sum(no_aug_score_list) / len(no_aug_score_list) + # save best checkpoint (conditioned on the val datasets!) + if cur_mean < best_mean: + best_mean = cur_mean + self.best_meta_model = copy.deepcopy(self.meta_model) + self.logger.info("Saving (best) trained_model") + checkpoint_dict = { + 'epoch': epoch, + 'model_state_dict': self.meta_model.state_dict(), + 'optimizer_state_dict': self.meta_optimizer.state_dict(), + 'result_log': self.result_log.get_raw_data() + } + torch.save(checkpoint_dict, '{}/best_checkpoint.pt'.format(self.result_folder)) + + # Logs & Checkpoint + elapsed_time_str, remain_time_str = self.time_estimator.get_est_string(epoch, self.meta_params['epochs']) + self.logger.info("Epoch {:3d}/{:3d}({:.2f}%): Time Est.: Elapsed[{}], Remain[{}], Val Score: {}".format( + epoch, self.meta_params['epochs'], epoch / self.meta_params['epochs'] * 100, elapsed_time_str, remain_time_str, no_aug_score_list)) + + all_done = (epoch == self.meta_params['epochs']) + + if epoch > 1 and (epoch % img_save_interval) == 0: # save latest images, every X epoch + self.logger.info("Saving log_image") + image_prefix = '{}/latest'.format(self.result_folder) + util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'], self.result_log, labels=['train_score']) + util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'], self.result_log, labels=['val_score']) + util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_2'], self.result_log, labels=['train_loss']) + + # Save Model + if all_done or (epoch % model_save_interval) == 0: + self.logger.info("Saving trained_model") + checkpoint_dict = { + 'epoch': epoch, + 'model_state_dict': self.meta_model.state_dict(), + 'optimizer_state_dict': self.meta_optimizer.state_dict(), + 'result_log': self.result_log.get_raw_data() + } + torch.save(checkpoint_dict, '{}/checkpoint-{}.pt'.format(self.result_folder, epoch)) + + # self.fomaml_first, self.fomaml_last, self.ours_first, self.ours_last + # self.reptile1_first, self.reptile1_last, self.reptile2_first, self.reptile2_last + if epoch % 1000 == 0: + torch.save({ + 'ours_first': self.ours_first, + 'ours_last': self.ours_last, + 'reptile1_first': self.reptile1_first, + 'reptile1_last': self.reptile1_last, + 'reptile2_first': self.reptile2_first, + 'reptile2_last': self.reptile2_last, + }, "./grad_dir_cos.pt") + + if all_done: + self.logger.info(" *** Training Done *** ") + # self.logger.info("Now, printing log array...") + # util_print_log_array(self.logger, self.result_log) + + def _train_one_epoch(self, epoch): + """ + Meta-Learning framework: + 1. Sample B training tasks from task distribution P(T) + 2. Inner-loop: for a batch of tasks T_i, POMO training -> \theta_i + 3. Outer-loop: update meta-model -> \theta_0 + + Adaptive task scheduler: + for size: gradually increase the problem size (Curriculum learning); + for distribution: we compute the relative gaps (w.r.t. LKH3) or estimate the potential improvements of each distribution (i.e., bootstrap) every X iters; + for size_distribution: combine together. + """ + self.meta_optimizer.zero_grad() + score_AM, loss_AM = AverageMeter(), AverageMeter() + meta_batch_size = self.meta_params['meta_batch_size'] + + # Adaptive task scheduler - Not implemented for "size" and "distribution" + if self.meta_params['curriculum']: + if self.meta_params["data_type"] == "size_distribution": + start = self.min_n + int(min(epoch / self.meta_params['sch_epoch'], 1) * (self.max_n - self.min_n)) # linear + # start = self.min_n + int(1 / 2 * (1 - math.cos(math.pi * min(epoch / self.meta_params['sch_epoch'], 1))) * (self.max_n - self.min_n)) # cosine + n = start // 5 * 5 + idx = (n - self.min_n) // 5 + tasks, weights = self.task_set[idx * 11: (idx + 1) * 11], self.task_w[idx] + if epoch % self.meta_params['update_weight'] == 0: + self.task_w[idx] = self._update_task_weight(tasks, weights, epoch) + + self._alpha_scheduler(epoch) # for reptile + val_loss, meta_grad_dict = 0, {(i, j): 0 for i, group in enumerate(self.meta_optimizer.param_groups) for j, _ in enumerate(group['params'])} + fast_weights, fast_weights1 = [], [] + + for b in range(self.meta_params['B']): + # sample a task + task_params = tasks[torch.multinomial(self.task_w[idx], 1).item()] if self.meta_params['curriculum'] else random.sample(self.task_set, 1)[0] + batch_size = meta_batch_size if task_params[0] <= 150 else meta_batch_size // 2 + + # for fomaml + # task_model = Model(**self.model_params) + # task_model.load_state_dict(copy.deepcopy(self.meta_model.state_dict())) + # optimizer = Optimizer(task_model.parameters(), **self.optimizer_params['optimizer']) + # optimizer.load_state_dict(self.meta_optimizer.state_dict()) + + # for ours_fomaml + task_model1 = Model(**self.model_params) + task_model1.load_state_dict(copy.deepcopy(self.meta_model.state_dict())) + optimizer1 = Optimizer(task_model1.parameters(), **self.optimizer_params['optimizer']) + + # for reptile + task_model2 = Model(**self.model_params) + task_model2.load_state_dict(copy.deepcopy(self.meta_model.state_dict())) + optimizer2 = Optimizer(task_model2.parameters(), **self.optimizer_params['optimizer']) + # optimizer2.load_state_dict(self.meta_optimizer.state_dict()) + + # for maml + fast_weight = OrderedDict(self.meta_model.named_parameters()) + + # inner-loop optimization for maml + for step in range(1): + data = self._get_data(batch_size, task_params) + env_params = {'problem_size': data[-1].size(1), 'pomo_size': data[-1].size(1)} + self.meta_model.train() + # _, _ = self._train_one_batch(task_model, data, Env(**env_params), optimizer) # for fomaml + _, _ = self._train_one_batch(task_model1, data, Env(**env_params), optimizer1) # for ours_fomaml + _, _ = self._train_one_batch(task_model2, data, Env(**env_params), optimizer2) # for reptile + avg_score, avg_loss, fast_weight = self._train_one_batch_maml(fast_weight, data, Env(**env_params), create_graph=True) # for maml + # for reptile + fast_weights.append(task_model2.state_dict()) + for i, params_key in enumerate(self.meta_model.state_dict()): + if i == 0: + tmp = torch.mean(torch.stack([fast_w[params_key] - self.meta_model.state_dict()[params_key] for fast_w in fast_weights], dim=0), dim=0) + reptile_first_layer = -tmp.sign().cpu().detach().clone() + elif i == self.model_num: + tmp = torch.mean(torch.stack([fast_w[params_key] - self.meta_model.state_dict()[params_key] for fast_w in fast_weights], dim=0), dim=0) + reptile_last_layer = -tmp.sign().cpu().detach().clone() + score_AM.update(avg_score.item(), batch_size) + loss_AM.update(avg_loss.item(), batch_size) + + self.meta_model.train() + bootstrap_model = None + val_data = self._get_val_data(batch_size, task_params) + + # for reptile + _, _ = self._train_one_batch(task_model2, val_data, Env(**env_params), optimizer2) + fast_weights1.append(task_model2.state_dict()) + for i, params_key in enumerate(self.meta_model.state_dict()): + if i == 0: + tmp = torch.mean(torch.stack([fast_w[params_key] - self.meta_model.state_dict()[params_key] for fast_w in fast_weights1], dim=0), dim=0) + reptile1_first_layer = -tmp.sign().cpu().detach().clone() + elif i == self.model_num: + tmp = torch.mean(torch.stack([fast_w[params_key] - self.meta_model.state_dict()[params_key] for fast_w in fast_weights1], dim=0), dim=0) + reptile1_last_layer = -tmp.sign().cpu().detach().clone() + + # for fomaml + # val_loss1, kl_loss1 = self._fast_val(task_model, data=val_data, mode="fomaml", bootstrap_model=bootstrap_model) + # loss1 = (self.meta_params['beta'] * val_loss1 + (1 - self.meta_params['beta']) * kl_loss1) / self.meta_params['B'] + # optimizer.zero_grad() + # loss1.backward() + # for i, group in enumerate(optimizer.param_groups): + # for j, p in enumerate(group['params']): + # # meta_grad_dict[(i, j)] += p.grad + # if j == 0: + # fomaml_first_layer = p.grad.sign().cpu().detach().clone() + # elif j == self.model_num: + # fomaml_last_layer = p.grad.sign().cpu().detach().clone() + + # for ours_fomaml + val_loss2, kl_loss2 = self._fast_val(task_model1, data=val_data, mode="fomaml", bootstrap_model=bootstrap_model) + loss2 = (self.meta_params['beta'] * val_loss2 + (1 - self.meta_params['beta']) * kl_loss2) / self.meta_params['B'] + optimizer1.zero_grad() + loss2.backward() + for i, group in enumerate(optimizer1.param_groups): + for j, p in enumerate(group['params']): + # meta_grad_dict[(i, j)] += p.grad + if j == 0: + ours_fomaml_first_layer = p.grad.sign().cpu().detach().clone() + elif j == self.model_num: + ours_fomaml_last_layer = p.grad.sign().cpu().detach().clone() + + # for maml + val_loss, kl_loss = self._fast_val(fast_weight, data=val_data, mode="maml", bootstrap_model=bootstrap_model) + # print(val_loss, kl_loss) + loss = (self.meta_params['beta'] * val_loss + (1 - self.meta_params['beta']) * kl_loss) / self.meta_params['B'] + self.meta_optimizer.zero_grad() + loss.backward() + for i, group in enumerate(self.meta_optimizer.param_groups): + for j, p in enumerate(group['params']): + meta_grad_dict[(i, j)] += p.grad + if j == 0: + maml_first_layer = p.grad.sign().cpu().detach().clone() + elif j == self.model_num: + maml_last_layer = p.grad.sign().cpu().detach().clone() + + # compute cos sim + # cos1 = self.cos(maml_first_layer.view(-1), fomaml_first_layer.view(-1)) + # cos2 = self.cos(maml_last_layer.view(-1), fomaml_last_layer.view(-1)) + cos3 = self.cos(maml_first_layer.view(-1), ours_fomaml_first_layer.view(-1)) + cos4 = self.cos(maml_last_layer.view(-1), ours_fomaml_last_layer.view(-1)) + cos5 = self.cos(maml_first_layer.view(-1), reptile_first_layer.view(-1)) + cos6 = self.cos(maml_last_layer.view(-1), reptile_last_layer.view(-1)) + cos7 = self.cos(maml_first_layer.view(-1), reptile1_first_layer.view(-1)) + cos8 = self.cos(maml_last_layer.view(-1), reptile1_last_layer.view(-1)) + # self.fomaml_first.append(cos1.item()) + # self.fomaml_last.append(cos2.item()) + self.ours_first.append(cos3.item()) + self.ours_last.append(cos4.item()) + self.reptile1_first.append(cos5.item()) + self.reptile1_last.append(cos6.item()) + self.reptile2_first.append(cos7.item()) + self.reptile2_last.append(cos8.item()) + + # outer-loop optimization (update meta-model) + self.meta_optimizer.zero_grad() + for i, group in enumerate(self.meta_optimizer.param_groups): + for j, p in enumerate(group['params']): + p.grad = meta_grad_dict[(i, j)] + self.meta_optimizer.step() + + # Log Once, for each epoch + self.logger.info('Meta Iteration {:3d}: alpha: {:6f}, Score: {:.4f}, Loss: {:.4f}'.format(epoch, self.alpha, score_AM.avg, loss_AM.avg)) + + return score_AM.avg, loss_AM.avg + + def _train_one_batch(self, task_model, data, env, optimizer=None): + + task_model.train() + batch_size = data[-1].size(0) + env.load_problems(batch_size, problems=data, aug_factor=1) + reset_state, _, _ = env.reset() + task_model.pre_forward(reset_state) + prob_list = torch.zeros(size=(batch_size, env.pomo_size, 0)) + # shape: (batch, pomo, 0~problem) + + # POMO Rollout + state, reward, done = env.pre_step() + while not done: + selected, prob = task_model(state) + # shape: (batch, pomo) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + + # Loss + advantage = reward - reward.float().mean(dim=1, keepdims=True) + # shape: (batch, pomo) + log_prob = prob_list.log().sum(dim=2) # for the first/last node, p=1 -> log_p=0 + # size = (batch, pomo) + loss = -advantage * log_prob # Minus Sign: To Increase REWARD + # shape: (batch, pomo) + loss_mean = loss.mean() + + # update model + optimizer.zero_grad() + loss_mean.backward() + optimizer.step() + + # Score + max_pomo_reward, _ = reward.max(dim=1) # get best results from pomo + score_mean = -max_pomo_reward.float().mean() # negative sign to make positive value + print(score_mean) + + return score_mean, loss_mean + + def _train_one_batch_maml(self, fast_weight, data, env, optimizer=None, create_graph=True): + + batch_size = data[-1].size(0) + env.load_problems(batch_size, problems=data, aug_factor=1) + reset_state, _, _ = env.reset() + self.meta_model.pre_forward(reset_state, weights=fast_weight) + prob_list = torch.zeros(size=(batch_size, env.pomo_size, 0)) + # shape: (batch, pomo, 0~problem) + + # POMO Rollout + state, reward, done = env.pre_step() + while not done: + selected, prob = self.meta_model(state, weights=fast_weight) + # shape: (batch, pomo) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + + # Loss + advantage = reward - reward.float().mean(dim=1, keepdims=True) + log_prob = prob_list.log().sum(dim=2) # for the first/last node, p=1 -> log_p=0 + loss = -advantage * log_prob # Minus Sign: To Increase REWARD + # shape: (batch, pomo) + loss_mean = loss.mean() + + # 1. update model - in SGD way + # gradients = torch.autograd.grad(loss_mean, fast_weight.values(), create_graph=create_graph) # allow_unused=True + # fast_weight = OrderedDict( + # (name, param - self.optimizer_params['optimizer']['lr'] * grad) + # for ((name, param), grad) in zip(fast_weight.items(), gradients) + # ) + # 2. update model - in Adam way + gradients = torch.autograd.grad(loss_mean, fast_weight.values(), create_graph=create_graph) # allow_unused=True + w_t, (beta1, beta2), eps = [], self.meta_optimizer.param_groups[0]['betas'], self.meta_optimizer.param_groups[0]['eps'] + lr, weight_decay = self.optimizer_params['optimizer']['lr'], self.optimizer_params['optimizer']['weight_decay'] + for i, ((name, param), grad) in enumerate(zip(fast_weight.items(), gradients)): + # print(i, name) + if self.meta_optimizer.state_dict()['state'] != {}: + # (with batch/instance norm layer): i \in [0, 86], where encoder \in [0, 81] + decoder \in [82, 86] + # (with rezero norm layer): i \in [0, 74], where encoder \in [0, 69] + decoder \in [70, 74] + # (without norm layer): i \in [0, 62], where encoder \in [0, 57] + decoder \in [58, 62] + i = i if self.model_params['meta_update_encoder'] else i + 82 + state = self.meta_optimizer.state_dict()['state'][i] + step, exp_avg, exp_avg_sq = state['step'], state['exp_avg'], state['exp_avg_sq'] + step += 1 + step = step.item() if isinstance(step, torch.Tensor) else step + # compute grad based on Adam source code using in-place operation + # update Adam stat (step, exp_avg and exp_avg_sq have already been updated by in-place operation) + # may encounter RuntimeError: (a leaf Variable that requires grad) / (the tensor used during grad computation) cannot use in-place operation. + grad = grad.add(param, alpha=weight_decay) + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) + exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2) + bias_correction1 = 1 - beta1 ** step + bias_correction2 = 1 - beta2 ** step + step_size = lr / bias_correction1 + bias_correction2_sqrt = math.sqrt(bias_correction2) + denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps) + # param.addcdiv_(exp_avg, denom, value=-step_size) + param = param - step_size * exp_avg / denom + self.meta_optimizer.state_dict()['state'][i]['exp_avg'] = exp_avg.clone().detach() + self.meta_optimizer.state_dict()['state'][i]['exp_avg_sq'] = exp_avg_sq.clone().detach() + else: + param = param - lr * grad + w_t.append((name, param)) + fast_weight = OrderedDict(w_t) + + # Score + max_pomo_reward, _ = reward.max(dim=1) # get best results from pomo + score_mean = -max_pomo_reward.float().mean() # negative sign to make positive value + print(score_mean) + + return score_mean, loss_mean, fast_weight + + def _fast_val(self, model, data=None, path=None, offset=0, val_episodes=32, mode="eval", return_all=False, bootstrap_model=None): + aug_factor = 1 + if data is None: + data = load_dataset(path)[offset: offset + val_episodes] # load dataset from file + depot_xy, node_xy, node_demand, capacity = [i[0] for i in data], [i[1] for i in data], [i[2] for i in data], [i[3] for i in data] + depot_xy, node_xy, node_demand, capacity = torch.Tensor(depot_xy), torch.Tensor(node_xy), torch.Tensor(node_demand), torch.Tensor(capacity) + node_demand = node_demand / capacity.view(-1, 1) + data = (depot_xy, node_xy, node_demand) + env = Env(**{'problem_size': data[-1].size(1), 'pomo_size': data[-1].size(1)}) + + batch_size = data[-1].size(0) + if mode == "eval": + model.eval() + with torch.no_grad(): + env.load_problems(batch_size, problems=data, aug_factor=aug_factor) + reset_state, _, _ = env.reset() + model.pre_forward(reset_state) + state, reward, done = env.pre_step() + while not done: + selected, _ = model(state) + # shape: (batch, pomo) + state, reward, done = env.step(selected) + + elif mode in ["maml", "fomaml"]: + fast_weight, kl_loss = model, 0 + env.load_problems(batch_size, problems=data, aug_factor=aug_factor) + reset_state, _, _ = env.reset() + if mode == "maml": + self.meta_model.pre_forward(reset_state, weights=fast_weight) + if bootstrap_model is not None: + with torch.no_grad(): + self.meta_model.pre_forward(reset_state, weights=bootstrap_model) + else: + model.pre_forward(reset_state) + if bootstrap_model is not None: + with torch.no_grad(): + bootstrap_model.pre_forward(reset_state) + + prob_list = torch.zeros(size=(batch_size, env.pomo_size, 0)) + state, reward, done = env.pre_step() + while not done: + if mode == "maml": + selected, prob, probs = self.meta_model(state, weights=fast_weight, return_probs=True) + if bootstrap_model is not None: + probs1 = torch.where(probs > 0, probs, torch.tensor(0.00001)) + with torch.no_grad(): + _, _, bs_probs = self.meta_model(state, weights=bootstrap_model, selected=selected, return_probs=True) + bs_probs = torch.where(bs_probs > 0, bs_probs, torch.tensor(0.00001)) + else: + selected, prob, probs = model(state, return_probs=True) + if bootstrap_model is not None: + probs1 = torch.where(probs > 0, probs, torch.tensor(0.00001)) + with torch.no_grad(): + _, _, bs_probs = bootstrap_model(state, selected=selected, return_probs=True) + bs_probs = torch.where(bs_probs > 0, bs_probs, torch.tensor(0.00001)) + + # shape: (batch, pomo) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + kl_loss += (bs_probs * (bs_probs.log() - probs1.log())).reshape(batch_size * data[-1].size(1), -1).sum(dim=-1).mean() if bootstrap_model is not None else 0 + + advantage = reward - reward.float().mean(dim=1, keepdims=True) + log_prob = prob_list.log().sum(dim=2) # for the first/last node, p=1 -> log_p=0 + loss = -advantage * log_prob # Minus Sign: To Increase REWARD + loss_mean = loss.mean() + else: + raise NotImplementedError + + # Return + aug_reward = reward.reshape(aug_factor, batch_size, env.pomo_size) + # shape: (augmentation, batch, pomo) + max_pomo_reward, _ = aug_reward.max(dim=2) # get best results from pomo + # shape: (augmentation, batch) + no_aug_score = -max_pomo_reward[0, :].float().mean() # negative sign to make positive value + print(no_aug_score) + + if mode == "eval": + if return_all: + return -max_pomo_reward[0, :].float() + else: + return no_aug_score.detach().item() + else: + return loss_mean, kl_loss + + def _get_data(self, batch_size, task_params, return_capacity=False): + """ + Return CVRP data with the form of: + depot_xy: [batch_size, 1, 2] + node_xy: [batch_size, problem_size, 2] + node_demand (unnormalized): [batch_size, problem_size] + capacity: [batch_size] + """ + if self.meta_params['data_type'] == 'distribution': + assert len(task_params) == 2 + data = get_random_problems(batch_size, self.env_params['problem_size'], num_modes=task_params[0], cdist=task_params[1], distribution='gaussian_mixture', problem="cvrp") + elif self.meta_params['data_type'] == 'size': + assert len(task_params) == 1 + data = get_random_problems(batch_size, task_params[0], num_modes=0, cdist=0, distribution='uniform', problem="cvrp") + elif self.meta_params['data_type'] == "size_distribution": + assert len(task_params) == 3 + data = get_random_problems(batch_size, task_params[0], num_modes=task_params[1], cdist=task_params[2], distribution='gaussian_mixture', problem="cvrp") + else: + raise NotImplementedError + + # normalized node_demand by capacity & only return (depot_xy, node_xy, node_demand) + if len(data) == 4 and not return_capacity: + depot_xy, node_xy, node_demand, capacity = data + node_demand = node_demand / capacity.view(-1, 1) + data = (depot_xy, node_xy, node_demand) + + return data + + def _get_val_data(self, batch_size, task_params): + if self.meta_params["data_type"] == "size": + # start1, end1 = min(task_params[0] + 10, self.max_n), min(task_params[0] + 20, self.max_n) + # val_size = random.sample(range(start1, end1 + 1), 1)[0] + # val_data = self._get_data(batch_size, (val_size,)) + val_data = self._get_data(batch_size, task_params) + elif self.meta_params["data_type"] == "distribution": + val_data = self._get_data(batch_size, task_params) + elif self.meta_params["data_type"] == "size_distribution": + val_data = self._get_data(batch_size, task_params) + else: + raise NotImplementedError + + return val_data + + def _alpha_scheduler(self, epoch): + """ + Update param for Reptile. + """ + self.alpha = max(self.alpha * self.meta_params['alpha_decay'], 0.0001) + + def _update_task_weight(self, tasks, weights, epoch): + """ + Update the weights of tasks. + For LKH3, set MAX_TRIALS = 100 to reduce time. + """ + global run_func + start_t, gap = time.time(), torch.zeros(weights.size(0)) + batch_size = 200 if self.meta_params["solver"] == "lkh3_offline" else 50 + idx = torch.randperm(batch_size)[:50] + for i in range(gap.size(0)): + selected = tasks[i] + data = self._get_data(batch_size=batch_size, task_params=selected, return_capacity=True) + + # only use lkh3 at the first iteration of updating task weights + if self.meta_params["solver"] == "lkh3_offline": + if selected not in self.val_data.keys(): + self.val_data[selected] = data # (depot, loc, demand, capacity) + opts = argparse.ArgumentParser() + opts.cpus, opts.n, opts.progress_bar_mininterval = None, None, 0.1 + dataset = [attr.cpu().tolist() for attr in data] + dataset = [(dataset[0][i][0], dataset[1][i], [int(d) for d in dataset[2][i]], int(dataset[3][i])) for i in range(data[0].size(0))] + executable = get_lkh_executable() + def run_func(args): + return solve_lkh_log(executable, *args, runs=1, disable_cache=True, MAX_TRIALS=100) # otherwise it directly loads data from dir + results, _ = run_all_in_pool(run_func, "./LKH3_result", dataset, opts, use_multiprocessing=False) + self.val_opt[selected] = [j[0] for j in results] + data = [attr[idx] for attr in self.val_data[selected]] + data = (data[0], data[1], data[2] / data[3].view(-1, 1)) + + model_score = self._fast_val(self.meta_model, data=data, mode="eval", return_all=True) + model_score = model_score.tolist() + + if self.meta_params["solver"] == "lkh3_offline": + lkh_score = [self.val_opt[selected][j] for j in idx.tolist()] + gap_list = [(model_score[j] - lkh_score[j]) / lkh_score[j] * 100 for j in range(len(lkh_score))] + gap[i] = sum(gap_list) / len(gap_list) + else: + raise NotImplementedError + print(">> Finish updating task weights within {}s".format(round(time.time() - start_t, 2))) + + temp = 1.0 + gap_temp = torch.Tensor([i / temp for i in gap.tolist()]) + print(gap, temp) + print(">> Old task weights: {}".format(weights)) + weights = torch.softmax(gap_temp, dim=0) + print(">> New task weights: {}".format(weights)) + + return weights diff --git a/POMO/CVRP/CVRPTrainer_meta_grad_reptile.py b/POMO/CVRP/CVRPTrainer_meta_grad_reptile.py new file mode 100644 index 0000000..0a33f1e --- /dev/null +++ b/POMO/CVRP/CVRPTrainer_meta_grad_reptile.py @@ -0,0 +1,572 @@ +import os +import copy +import math +import time +import random +import torch +from logging import getLogger +from collections import OrderedDict +from torch.optim import Adam as Optimizer + +from CVRPEnv import CVRPEnv as Env +from CVRPModel import CVRPModel as Model +from ProblemDef import get_random_problems, generate_task_set +from utils.utils import * +from utils.functions import * +from CVRP_baseline import * + + +class CVRPTrainer: + """ + Implementation of POMO with MAML / FOMAML / Reptile on CVRP. + For MAML & FOMAML, ref to "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"; + For Reptile, ref to "On First-Order Meta-Learning Algorithms" and "On the generalization of neural combinatorial optimization heuristics". + """ + def __init__(self, + env_params, + model_params, + optimizer_params, + trainer_params, + meta_params): + + # save arguments + self.env_params = env_params + self.model_params = model_params + self.optimizer_params = optimizer_params + self.trainer_params = trainer_params + self.meta_params = meta_params + + # result folder, logger + self.logger = getLogger(name='trainer') + self.result_folder = get_result_folder() + self.result_log = LogData() + + # cuda + USE_CUDA = self.trainer_params['use_cuda'] + if USE_CUDA: + cuda_device_num = self.trainer_params['cuda_device_num'] + torch.cuda.set_device(cuda_device_num) + self.device = torch.device('cuda', cuda_device_num) + torch.set_default_tensor_type('torch.cuda.FloatTensor') + else: + self.device = torch.device('cpu') + torch.set_default_tensor_type('torch.FloatTensor') + + # Main Components + self.model_params["norm"] = 'batch_no_track' + self.meta_model = Model(**self.model_params) + self.meta_optimizer = Optimizer(self.meta_model.parameters(), **self.optimizer_params['optimizer']) + self.alpha = self.meta_params['alpha'] # for reptile + self.task_set = generate_task_set(self.meta_params) + self.val_data, self.val_opt = {}, {} # for lkh3_offline + assert not (self.meta_params['curriculum'] and self.meta_params["data_type"] in ["size", "distribution"]), "Not Implemented!" + if self.meta_params["data_type"] == "size_distribution": + # hardcoded - task_set: range(self.min_n, self.max_n, self.task_interval) * self.num_dist + self.min_n, self.max_n, self.task_interval, self.num_dist = 50, 200, 5, 11 + self.task_w = torch.full(((self.max_n - self.min_n) // 5 + 1, self.num_dist), 1 / self.num_dist) + + # record grad + self.model_num = 86 + self.cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6) + self.reptile_first, self.reptile_last = [], [] + # self.reptile2_first, self.reptile2_last, self.reptile4_first, self.reptile4_last = [], [], [], [] + # self.reptile6_first, self.reptile6_last, self.reptile8_first, self.reptile8_last = [], [], [], [] + # self.reptile10_first, self.reptile10_last = [], [] + + # Restore + self.start_epoch = 1 + model_load = trainer_params['model_load'] + if model_load['enable']: + checkpoint_fullname = '{path}/checkpoint-{epoch}.pt'.format(**model_load) + checkpoint = torch.load(checkpoint_fullname, map_location=self.device) + self.meta_model.load_state_dict(checkpoint['model_state_dict']) + self.start_epoch = 1 + model_load['epoch'] + self.result_log.set_raw_data(checkpoint['result_log']) + self.meta_optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + self.logger.info(">> Model loaded from {}".format(checkpoint_fullname)) + + # utility + self.time_estimator = TimeEstimator() + + def run(self): + + start_time, best_mean = time.time(), 1000 + self.time_estimator.reset(self.start_epoch) + for epoch in range(self.start_epoch, self.meta_params['epochs'] + 1): + self.logger.info('=================================================================') + + # lr decay (by 10) to speed up convergence at 90th and 95th iterations + if epoch in [int(self.meta_params['epochs'] * 0.9)]: + self.optimizer_params['optimizer']['lr'] /= 10 + for group in self.meta_optimizer.param_groups: + group["lr"] /= 10 + print(">> LR decay to {}".format(group["lr"])) + + # Train + train_score, train_loss = self._train_one_epoch(epoch) + self.result_log.append('train_score', epoch, train_score) + self.result_log.append('train_loss', epoch, train_loss) + model_save_interval = self.trainer_params['logging']['model_save_interval'] + img_save_interval = self.trainer_params['logging']['img_save_interval'] + # Val + no_aug_score_list = [] + if self.meta_params["data_type"] == "size": + dir = "../../data/CVRP/Size/" + paths = ["cvrp100_uniform.pkl", "cvrp200_uniform.pkl", "cvrp300_uniform.pkl"] + elif self.meta_params["data_type"] == "distribution": + dir = "../../data/CVRP/Distribution/" + paths = ["cvrp100_uniform.pkl", "cvrp100_gaussian.pkl", "cvrp100_cluster.pkl", "cvrp100_diagonal.pkl", "cvrp100_cvrplib.pkl"] + elif self.meta_params["data_type"] == "size_distribution": + dir = "../../data/CVRP/Size_Distribution/" + paths = ["cvrp200_uniform.pkl", "cvrp300_rotation.pkl"] + if epoch <= 1 or (epoch % img_save_interval) == 0: + for val_path in paths: + no_aug_score = self._fast_val(self.meta_model, path=os.path.join(dir, val_path), val_episodes=64, mode="eval") + no_aug_score_list.append(round(no_aug_score, 4)) + self.result_log.append('val_score', epoch, no_aug_score_list) + cur_mean = sum(no_aug_score_list) / len(no_aug_score_list) + # save best checkpoint (conditioned on the val datasets!) + if cur_mean < best_mean: + best_mean = cur_mean + self.best_meta_model = copy.deepcopy(self.meta_model) + self.logger.info("Saving (best) trained_model") + checkpoint_dict = { + 'epoch': epoch, + 'model_state_dict': self.meta_model.state_dict(), + 'optimizer_state_dict': self.meta_optimizer.state_dict(), + 'result_log': self.result_log.get_raw_data() + } + torch.save(checkpoint_dict, '{}/best_checkpoint.pt'.format(self.result_folder)) + + # Logs & Checkpoint + elapsed_time_str, remain_time_str = self.time_estimator.get_est_string(epoch, self.meta_params['epochs']) + self.logger.info("Epoch {:3d}/{:3d}({:.2f}%): Time Est.: Elapsed[{}], Remain[{}], Val Score: {}".format( + epoch, self.meta_params['epochs'], epoch / self.meta_params['epochs'] * 100, elapsed_time_str, remain_time_str, no_aug_score_list)) + + all_done = (epoch == self.meta_params['epochs']) + + if epoch > 1 and (epoch % img_save_interval) == 0: # save latest images, every X epoch + self.logger.info("Saving log_image") + image_prefix = '{}/latest'.format(self.result_folder) + util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'], self.result_log, labels=['train_score']) + util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'], self.result_log, labels=['val_score']) + util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_2'], self.result_log, labels=['train_loss']) + + # Save Model + if all_done or (epoch % model_save_interval) == 0: + self.logger.info("Saving trained_model") + checkpoint_dict = { + 'epoch': epoch, + 'model_state_dict': self.meta_model.state_dict(), + 'optimizer_state_dict': self.meta_optimizer.state_dict(), + 'result_log': self.result_log.get_raw_data() + } + torch.save(checkpoint_dict, '{}/checkpoint-{}.pt'.format(self.result_folder, epoch)) + + # self.fomaml_first, self.fomaml_last, self.ours_first, self.ours_last + # self.reptile1_first, self.reptile1_last, self.reptile2_first, self.reptile2_last + if epoch % 1000 == 0: + torch.save({ + 'reptile_first': self.reptile_first, + 'reptile_last': self.reptile_last, + }, "./grad_dir_cos.pt") + + if all_done: + self.logger.info(" *** Training Done *** ") + # self.logger.info("Now, printing log array...") + # util_print_log_array(self.logger, self.result_log) + + def _train_one_epoch(self, epoch): + """ + Meta-Learning framework: + 1. Sample B training tasks from task distribution P(T) + 2. Inner-loop: for a batch of tasks T_i, POMO training -> \theta_i + 3. Outer-loop: update meta-model -> \theta_0 + + Adaptive task scheduler: + for size: gradually increase the problem size (Curriculum learning); + for distribution: we compute the relative gaps (w.r.t. LKH3) or estimate the potential improvements of each distribution (i.e., bootstrap) every X iters; + for size_distribution: combine together. + """ + self.meta_optimizer.zero_grad() + score_AM, loss_AM = AverageMeter(), AverageMeter() + meta_batch_size = self.meta_params['meta_batch_size'] + + # Adaptive task scheduler - Not implemented for "size" and "distribution" + if self.meta_params['curriculum']: + if self.meta_params["data_type"] == "size_distribution": + start = self.min_n + int(min(epoch / self.meta_params['sch_epoch'], 1) * (self.max_n - self.min_n)) # linear + # start = self.min_n + int(1 / 2 * (1 - math.cos(math.pi * min(epoch / self.meta_params['sch_epoch'], 1))) * (self.max_n - self.min_n)) # cosine + n = start // 5 * 5 + idx = (n - self.min_n) // 5 + tasks, weights = self.task_set[idx * 11: (idx + 1) * 11], self.task_w[idx] + if epoch % self.meta_params['update_weight'] == 0: + self.task_w[idx] = self._update_task_weight(tasks, weights, epoch) + + self._alpha_scheduler(epoch) # for reptile + val_loss, meta_grad_dict = 0, {(i, j): 0 for i, group in enumerate(self.meta_optimizer.param_groups) for j, _ in enumerate(group['params'])} + fast_weights = [] + + for b in range(self.meta_params['B']): + # sample a task + task_params = tasks[torch.multinomial(self.task_w[idx], 1).item()] if self.meta_params['curriculum'] else random.sample(self.task_set, 1)[0] + batch_size = meta_batch_size if task_params[0] <= 150 else meta_batch_size // 2 + + # for reptile + task_model2 = Model(**self.model_params) + task_model2.load_state_dict(copy.deepcopy(self.meta_model.state_dict())) + optimizer2 = Optimizer(task_model2.parameters(), **self.optimizer_params['optimizer']) + # optimizer2.load_state_dict(self.meta_optimizer.state_dict()) + + # for maml + fast_weight = OrderedDict(self.meta_model.named_parameters()) + + # inner-loop optimization for maml + for step in range(3): + data = self._get_data(batch_size, task_params) + env_params = {'problem_size': data[-1].size(1), 'pomo_size': data[-1].size(1)} + self.meta_model.train() + _, _ = self._train_one_batch(task_model2, data, Env(**env_params), optimizer2) # for reptile + avg_score, avg_loss, fast_weight = self._train_one_batch_maml(fast_weight, data, Env(**env_params), create_graph=True) # for maml + score_AM.update(avg_score.item(), batch_size) + loss_AM.update(avg_loss.item(), batch_size) + + self.meta_model.train() + bootstrap_model = None + val_data = self._get_val_data(batch_size, task_params) + + # for reptile + _, _ = self._train_one_batch(task_model2, val_data, Env(**env_params), optimizer2) + fast_weights.append(task_model2.state_dict()) + for i, params_key in enumerate(self.meta_model.state_dict()): + if i == 0: + tmp = torch.mean(torch.stack([fast_w[params_key] - self.meta_model.state_dict()[params_key] for fast_w in fast_weights], dim=0), dim=0) + reptile_first_layer = -tmp.sign().cpu().detach().clone() + elif i == self.model_num: + tmp = torch.mean(torch.stack([fast_w[params_key] - self.meta_model.state_dict()[params_key] for fast_w in fast_weights], dim=0), dim=0) + reptile_last_layer = -tmp.sign().cpu().detach().clone() + + # for maml + val_loss, kl_loss = self._fast_val(fast_weight, data=val_data, mode="maml", bootstrap_model=bootstrap_model) + # print(val_loss, kl_loss) + loss = (self.meta_params['beta'] * val_loss + (1 - self.meta_params['beta']) * kl_loss) / self.meta_params['B'] + self.meta_optimizer.zero_grad() + loss.backward() + for i, group in enumerate(self.meta_optimizer.param_groups): + for j, p in enumerate(group['params']): + meta_grad_dict[(i, j)] += p.grad + if j == 0: + maml_first_layer = p.grad.sign().cpu().detach().clone() + elif j == self.model_num: + maml_last_layer = p.grad.sign().cpu().detach().clone() + + # compute cos sim + cos1 = self.cos(maml_first_layer.view(-1), reptile_first_layer.view(-1)) + cos2 = self.cos(maml_last_layer.view(-1), reptile_last_layer.view(-1)) + self.reptile_first.append(cos1.item()) + self.reptile_last.append(cos2.item()) + + # outer-loop optimization (update meta-model) + self.meta_optimizer.zero_grad() + for i, group in enumerate(self.meta_optimizer.param_groups): + for j, p in enumerate(group['params']): + p.grad = meta_grad_dict[(i, j)] + self.meta_optimizer.step() + + # Log Once, for each epoch + self.logger.info('Meta Iteration {:3d}: alpha: {:6f}, Score: {:.4f}, Loss: {:.4f}'.format(epoch, self.alpha, score_AM.avg, loss_AM.avg)) + + return score_AM.avg, loss_AM.avg + + def _train_one_batch(self, task_model, data, env, optimizer=None): + + task_model.train() + batch_size = data[-1].size(0) + env.load_problems(batch_size, problems=data, aug_factor=1) + reset_state, _, _ = env.reset() + task_model.pre_forward(reset_state) + prob_list = torch.zeros(size=(batch_size, env.pomo_size, 0)) + # shape: (batch, pomo, 0~problem) + + # POMO Rollout + state, reward, done = env.pre_step() + while not done: + selected, prob = task_model(state) + # shape: (batch, pomo) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + + # Loss + advantage = reward - reward.float().mean(dim=1, keepdims=True) + # shape: (batch, pomo) + log_prob = prob_list.log().sum(dim=2) # for the first/last node, p=1 -> log_p=0 + # size = (batch, pomo) + loss = -advantage * log_prob # Minus Sign: To Increase REWARD + # shape: (batch, pomo) + loss_mean = loss.mean() + + # update model + optimizer.zero_grad() + loss_mean.backward() + optimizer.step() + + # Score + max_pomo_reward, _ = reward.max(dim=1) # get best results from pomo + score_mean = -max_pomo_reward.float().mean() # negative sign to make positive value + print(score_mean) + + return score_mean, loss_mean + + def _train_one_batch_maml(self, fast_weight, data, env, optimizer=None, create_graph=True): + + batch_size = data[-1].size(0) + env.load_problems(batch_size, problems=data, aug_factor=1) + reset_state, _, _ = env.reset() + self.meta_model.pre_forward(reset_state, weights=fast_weight) + prob_list = torch.zeros(size=(batch_size, env.pomo_size, 0)) + # shape: (batch, pomo, 0~problem) + + # POMO Rollout + state, reward, done = env.pre_step() + while not done: + selected, prob = self.meta_model(state, weights=fast_weight) + # shape: (batch, pomo) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + + # Loss + advantage = reward - reward.float().mean(dim=1, keepdims=True) + log_prob = prob_list.log().sum(dim=2) # for the first/last node, p=1 -> log_p=0 + loss = -advantage * log_prob # Minus Sign: To Increase REWARD + # shape: (batch, pomo) + loss_mean = loss.mean() + + # 1. update model - in SGD way + # gradients = torch.autograd.grad(loss_mean, fast_weight.values(), create_graph=create_graph) # allow_unused=True + # fast_weight = OrderedDict( + # (name, param - self.optimizer_params['optimizer']['lr'] * grad) + # for ((name, param), grad) in zip(fast_weight.items(), gradients) + # ) + # 2. update model - in Adam way + gradients = torch.autograd.grad(loss_mean, fast_weight.values(), create_graph=create_graph) # allow_unused=True + w_t, (beta1, beta2), eps = [], self.meta_optimizer.param_groups[0]['betas'], self.meta_optimizer.param_groups[0]['eps'] + lr, weight_decay = self.optimizer_params['optimizer']['lr'], self.optimizer_params['optimizer']['weight_decay'] + for i, ((name, param), grad) in enumerate(zip(fast_weight.items(), gradients)): + # print(i, name) + if self.meta_optimizer.state_dict()['state'] != {}: + # (with batch/instance norm layer): i \in [0, 86], where encoder \in [0, 81] + decoder \in [82, 86] + # (with rezero norm layer): i \in [0, 74], where encoder \in [0, 69] + decoder \in [70, 74] + # (without norm layer): i \in [0, 62], where encoder \in [0, 57] + decoder \in [58, 62] + i = i if self.model_params['meta_update_encoder'] else i + 82 + state = self.meta_optimizer.state_dict()['state'][i] + step, exp_avg, exp_avg_sq = state['step'], state['exp_avg'], state['exp_avg_sq'] + step += 1 + step = step.item() if isinstance(step, torch.Tensor) else step + # compute grad based on Adam source code using in-place operation + # update Adam stat (step, exp_avg and exp_avg_sq have already been updated by in-place operation) + # may encounter RuntimeError: (a leaf Variable that requires grad) / (the tensor used during grad computation) cannot use in-place operation. + grad = grad.add(param, alpha=weight_decay) + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) + exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2) + bias_correction1 = 1 - beta1 ** step + bias_correction2 = 1 - beta2 ** step + step_size = lr / bias_correction1 + bias_correction2_sqrt = math.sqrt(bias_correction2) + denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps) + # param.addcdiv_(exp_avg, denom, value=-step_size) + param = param - step_size * exp_avg / denom + self.meta_optimizer.state_dict()['state'][i]['exp_avg'] = exp_avg.clone().detach() + self.meta_optimizer.state_dict()['state'][i]['exp_avg_sq'] = exp_avg_sq.clone().detach() + else: + param = param - lr * grad + w_t.append((name, param)) + fast_weight = OrderedDict(w_t) + + # Score + max_pomo_reward, _ = reward.max(dim=1) # get best results from pomo + score_mean = -max_pomo_reward.float().mean() # negative sign to make positive value + print(score_mean) + + return score_mean, loss_mean, fast_weight + + def _fast_val(self, model, data=None, path=None, offset=0, val_episodes=32, mode="eval", return_all=False, bootstrap_model=None): + aug_factor = 1 + if data is None: + data = load_dataset(path)[offset: offset + val_episodes] # load dataset from file + depot_xy, node_xy, node_demand, capacity = [i[0] for i in data], [i[1] for i in data], [i[2] for i in data], [i[3] for i in data] + depot_xy, node_xy, node_demand, capacity = torch.Tensor(depot_xy), torch.Tensor(node_xy), torch.Tensor(node_demand), torch.Tensor(capacity) + node_demand = node_demand / capacity.view(-1, 1) + data = (depot_xy, node_xy, node_demand) + env = Env(**{'problem_size': data[-1].size(1), 'pomo_size': data[-1].size(1)}) + + batch_size = data[-1].size(0) + if mode == "eval": + model.eval() + with torch.no_grad(): + env.load_problems(batch_size, problems=data, aug_factor=aug_factor) + reset_state, _, _ = env.reset() + model.pre_forward(reset_state) + state, reward, done = env.pre_step() + while not done: + selected, _ = model(state) + # shape: (batch, pomo) + state, reward, done = env.step(selected) + + elif mode in ["maml", "fomaml"]: + fast_weight, kl_loss = model, 0 + env.load_problems(batch_size, problems=data, aug_factor=aug_factor) + reset_state, _, _ = env.reset() + if mode == "maml": + self.meta_model.pre_forward(reset_state, weights=fast_weight) + if bootstrap_model is not None: + with torch.no_grad(): + self.meta_model.pre_forward(reset_state, weights=bootstrap_model) + else: + model.pre_forward(reset_state) + if bootstrap_model is not None: + with torch.no_grad(): + bootstrap_model.pre_forward(reset_state) + + prob_list = torch.zeros(size=(batch_size, env.pomo_size, 0)) + state, reward, done = env.pre_step() + while not done: + if mode == "maml": + selected, prob, probs = self.meta_model(state, weights=fast_weight, return_probs=True) + if bootstrap_model is not None: + probs1 = torch.where(probs > 0, probs, torch.tensor(0.00001)) + with torch.no_grad(): + _, _, bs_probs = self.meta_model(state, weights=bootstrap_model, selected=selected, return_probs=True) + bs_probs = torch.where(bs_probs > 0, bs_probs, torch.tensor(0.00001)) + else: + selected, prob, probs = model(state, return_probs=True) + if bootstrap_model is not None: + probs1 = torch.where(probs > 0, probs, torch.tensor(0.00001)) + with torch.no_grad(): + _, _, bs_probs = bootstrap_model(state, selected=selected, return_probs=True) + bs_probs = torch.where(bs_probs > 0, bs_probs, torch.tensor(0.00001)) + + # shape: (batch, pomo) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + kl_loss += (bs_probs * (bs_probs.log() - probs1.log())).reshape(batch_size * data[-1].size(1), -1).sum(dim=-1).mean() if bootstrap_model is not None else 0 + + advantage = reward - reward.float().mean(dim=1, keepdims=True) + log_prob = prob_list.log().sum(dim=2) # for the first/last node, p=1 -> log_p=0 + loss = -advantage * log_prob # Minus Sign: To Increase REWARD + loss_mean = loss.mean() + else: + raise NotImplementedError + + # Return + aug_reward = reward.reshape(aug_factor, batch_size, env.pomo_size) + # shape: (augmentation, batch, pomo) + max_pomo_reward, _ = aug_reward.max(dim=2) # get best results from pomo + # shape: (augmentation, batch) + no_aug_score = -max_pomo_reward[0, :].float().mean() # negative sign to make positive value + print(no_aug_score) + + if mode == "eval": + if return_all: + return -max_pomo_reward[0, :].float() + else: + return no_aug_score.detach().item() + else: + return loss_mean, kl_loss + + def _get_data(self, batch_size, task_params, return_capacity=False): + """ + Return CVRP data with the form of: + depot_xy: [batch_size, 1, 2] + node_xy: [batch_size, problem_size, 2] + node_demand (unnormalized): [batch_size, problem_size] + capacity: [batch_size] + """ + if self.meta_params['data_type'] == 'distribution': + assert len(task_params) == 2 + data = get_random_problems(batch_size, self.env_params['problem_size'], num_modes=task_params[0], cdist=task_params[1], distribution='gaussian_mixture', problem="cvrp") + elif self.meta_params['data_type'] == 'size': + assert len(task_params) == 1 + data = get_random_problems(batch_size, task_params[0], num_modes=0, cdist=0, distribution='uniform', problem="cvrp") + elif self.meta_params['data_type'] == "size_distribution": + assert len(task_params) == 3 + data = get_random_problems(batch_size, task_params[0], num_modes=task_params[1], cdist=task_params[2], distribution='gaussian_mixture', problem="cvrp") + else: + raise NotImplementedError + + # normalized node_demand by capacity & only return (depot_xy, node_xy, node_demand) + if len(data) == 4 and not return_capacity: + depot_xy, node_xy, node_demand, capacity = data + node_demand = node_demand / capacity.view(-1, 1) + data = (depot_xy, node_xy, node_demand) + + return data + + def _get_val_data(self, batch_size, task_params): + if self.meta_params["data_type"] == "size": + # start1, end1 = min(task_params[0] + 10, self.max_n), min(task_params[0] + 20, self.max_n) + # val_size = random.sample(range(start1, end1 + 1), 1)[0] + # val_data = self._get_data(batch_size, (val_size,)) + val_data = self._get_data(batch_size, task_params) + elif self.meta_params["data_type"] == "distribution": + val_data = self._get_data(batch_size, task_params) + elif self.meta_params["data_type"] == "size_distribution": + val_data = self._get_data(batch_size, task_params) + else: + raise NotImplementedError + + return val_data + + def _alpha_scheduler(self, epoch): + """ + Update param for Reptile. + """ + self.alpha = max(self.alpha * self.meta_params['alpha_decay'], 0.0001) + + def _update_task_weight(self, tasks, weights, epoch): + """ + Update the weights of tasks. + For LKH3, set MAX_TRIALS = 100 to reduce time. + """ + global run_func + start_t, gap = time.time(), torch.zeros(weights.size(0)) + batch_size = 200 if self.meta_params["solver"] == "lkh3_offline" else 50 + idx = torch.randperm(batch_size)[:50] + for i in range(gap.size(0)): + selected = tasks[i] + data = self._get_data(batch_size=batch_size, task_params=selected, return_capacity=True) + + # only use lkh3 at the first iteration of updating task weights + if self.meta_params["solver"] == "lkh3_offline": + if selected not in self.val_data.keys(): + self.val_data[selected] = data # (depot, loc, demand, capacity) + opts = argparse.ArgumentParser() + opts.cpus, opts.n, opts.progress_bar_mininterval = None, None, 0.1 + dataset = [attr.cpu().tolist() for attr in data] + dataset = [(dataset[0][i][0], dataset[1][i], [int(d) for d in dataset[2][i]], int(dataset[3][i])) for i in range(data[0].size(0))] + executable = get_lkh_executable() + def run_func(args): + return solve_lkh_log(executable, *args, runs=1, disable_cache=True, MAX_TRIALS=100) # otherwise it directly loads data from dir + results, _ = run_all_in_pool(run_func, "./LKH3_result", dataset, opts, use_multiprocessing=False) + self.val_opt[selected] = [j[0] for j in results] + data = [attr[idx] for attr in self.val_data[selected]] + data = (data[0], data[1], data[2] / data[3].view(-1, 1)) + + model_score = self._fast_val(self.meta_model, data=data, mode="eval", return_all=True) + model_score = model_score.tolist() + + if self.meta_params["solver"] == "lkh3_offline": + lkh_score = [self.val_opt[selected][j] for j in idx.tolist()] + gap_list = [(model_score[j] - lkh_score[j]) / lkh_score[j] * 100 for j in range(len(lkh_score))] + gap[i] = sum(gap_list) / len(gap_list) + else: + raise NotImplementedError + print(">> Finish updating task weights within {}s".format(round(time.time() - start_t, 2))) + + temp = 1.0 + gap_temp = torch.Tensor([i / temp for i in gap.tolist()]) + print(gap, temp) + print(">> Old task weights: {}".format(weights)) + weights = torch.softmax(gap_temp, dim=0) + print(">> New task weights: {}".format(weights)) + + return weights diff --git a/POMO/CVRP/CVRP_baseline.py b/POMO/CVRP/CVRP_baseline.py index e403c5b..60267ba 100644 --- a/POMO/CVRP/CVRP_baseline.py +++ b/POMO/CVRP/CVRP_baseline.py @@ -12,7 +12,7 @@ from utils.functions import check_extension, load_dataset, save_dataset, run_all_in_pool, move_to -def get_lkh_executable(url="http://www.akira.ruc.dk/~keld/research/LKH-3/LKH-3.0.7.tgz"): +def get_lkh_executable(url="http://www.akira.ruc.dk/~keld/research/LKH-3/LKH-3.0.8.tgz"): cwd = os.path.abspath("lkh") os.makedirs(cwd, exist_ok=True) diff --git a/POMO/TSP/TSPTrainer_Meta.py b/POMO/TSP/TSPTrainer_Meta.py index a206f05..291a1e8 100644 --- a/POMO/TSP/TSPTrainer_Meta.py +++ b/POMO/TSP/TSPTrainer_Meta.py @@ -229,7 +229,7 @@ def _train_one_epoch(self, epoch): w[b] = self.task_w[idx][selected].item() selected_tasks.append(task_params) w = torch.softmax(torch.Tensor(w), dim=0) - + for b in range(self.meta_params['B']): task_params, task_w = selected_tasks[b], w[b].item() # preparation diff --git a/POMO/TSP/TSPTrainer_meta_grad.py b/POMO/TSP/TSPTrainer_meta_grad.py new file mode 100644 index 0000000..7e2c8b3 --- /dev/null +++ b/POMO/TSP/TSPTrainer_meta_grad.py @@ -0,0 +1,754 @@ +import os +import copy +import math +import time +import random +import torch +from logging import getLogger +from collections import OrderedDict +from torch.optim import Adam as Optimizer +# from torch.optim import SGD as Optimizer + +from TSPEnv import TSPEnv as Env +from TSPModel import TSPModel as Model +from ProblemDef import get_random_problems, generate_task_set +from utils.utils import * +from utils.functions import * +from TSP_baseline import * + + +class TSPTrainer: + """ + Implementation of POMO with MAML / FOMAML / Reptile on TSP. + For MAML & FOMAML, ref to "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"; + For Reptile, ref to "On First-Order Meta-Learning Algorithms" and "On the generalization of neural combinatorial optimization heuristics". + """ + def __init__(self, + env_params, + model_params, + optimizer_params, + trainer_params, + meta_params): + + # save arguments + self.env_params = env_params + self.model_params = model_params + self.optimizer_params = optimizer_params + self.trainer_params = trainer_params + self.meta_params = meta_params + + # result folder, logger + self.logger = getLogger(name='trainer') + self.result_folder = get_result_folder() + self.result_log = LogData() + + # cuda + USE_CUDA = self.trainer_params['use_cuda'] + if USE_CUDA: + cuda_device_num = self.trainer_params['cuda_device_num'] + torch.cuda.set_device(cuda_device_num) + self.device = torch.device('cuda', cuda_device_num) + torch.set_default_tensor_type('torch.cuda.FloatTensor') + else: + self.device = torch.device('cpu') + torch.set_default_tensor_type('torch.FloatTensor') + + # Main Components + self.model_params["norm"] = "rezero" # instance norm seems to be better than batch norm when dealing with OOD, no norm will destabilize the training + self.model_num = 73 # j - batch/instnace: [0, 85] rezero: [0, 73] without: [0, 61] + self.meta_model = Model(**self.model_params) + # print(self.meta_model) + self.cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6) + self.fomaml_first_cos, self.fomaml_last_cos, self.reptile_first_cos1, self.reptile_last_cos1, self.reptile_first_cos2, self.reptile_last_cos2 = [], [], [], [], [], [] + self.meta_optimizer = Optimizer(self.meta_model.parameters(), **self.optimizer_params['optimizer']) + self.alpha = self.meta_params['alpha'] # for reptile + self.task_set = generate_task_set(self.meta_params) + self.val_data, self.val_opt = {}, {} # for lkh3_offline + if self.meta_params["data_type"] == "size": + self.min_n, self.max_n, self.task_interval = self.task_set[0][0], self.task_set[-1][0], 5 + # self.task_w = {start: 1/(len(self.task_set)//5) for start in range(self.min_n, self.max_n, self.task_interval)} + # self.task_w = torch.full((len(self.task_set)//self.task_interval,), 1/(len(self.task_set)//self.task_interval)) + elif self.meta_params["data_type"] == "distribution": + self.task_w = torch.full((len(self.task_set),), 1 / len(self.task_set)) + elif self.meta_params["data_type"] == "size_distribution": + # hardcoded - task_set: range(self.min_n, self.max_n, self.task_interval) * self.num_dist + self.min_n, self.max_n, self.task_interval, self.num_dist = 50, 200, 5, 11 + self.task_w = torch.full(((self.max_n - self.min_n) // 5 + 1, self.num_dist), 1 / self.num_dist) + + # Restore + self.start_epoch = 1 + model_load = trainer_params['model_load'] + if model_load['enable']: + checkpoint_fullname = '{path}/checkpoint-{epoch}.pt'.format(**model_load) + checkpoint = torch.load(checkpoint_fullname, map_location=self.device) + self.meta_model.load_state_dict(checkpoint['model_state_dict']) + self.start_epoch = 1 + model_load['epoch'] + self.result_log.set_raw_data(checkpoint['result_log']) + self.meta_optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + self.logger.info(">> Model loaded from {}".format(checkpoint_fullname)) + + # utility + self.time_estimator = TimeEstimator() + + def run(self): + + assert self.model_params['meta_update_encoder'] + assert self.meta_params['B'] == 1 and self.meta_params['k'] == 1 and self.meta_params['L'] == 0 + + start_time, best_mean = time.time(), 1000 + self.time_estimator.reset(self.start_epoch) + for epoch in range(self.start_epoch, self.meta_params['epochs']+1): + self.logger.info('=================================================================') + + # lr decay (by 10) to speed up convergence at 90th and 95th iterations + if epoch in [int(self.meta_params['epochs'] * 0.9)]: + self.optimizer_params['optimizer']['lr'] /= 10 + for group in self.meta_optimizer.param_groups: + group["lr"] /= 10 + print(">> LR decay to {}".format(group["lr"])) + + # Train + train_score, train_loss = self._train_one_epoch(epoch) + self.result_log.append('train_score', epoch, train_score) + self.result_log.append('train_loss', epoch, train_loss) + model_save_interval = self.trainer_params['logging']['model_save_interval'] + img_save_interval = self.trainer_params['logging']['img_save_interval'] + # Val + no_aug_score_list = [] + if self.meta_params["data_type"] == "size": + dir = "../../data/TSP/Size/" + paths = ["tsp100_uniform.pkl", "tsp200_uniform.pkl", "tsp300_uniform.pkl"] + elif self.meta_params["data_type"] == "distribution": + dir = "../../data/TSP/Distribution/" + paths = ["tsp100_uniform.pkl", "tsp100_gaussian.pkl", "tsp100_cluster.pkl", "tsp100_diagonal.pkl", "tsp100_tsplib.pkl"] + elif self.meta_params["data_type"] == "size_distribution": + dir = "../../data/TSP/Size_Distribution/" + paths = ["tsp200_uniform.pkl", "tsp200_gaussian.pkl", "tsp300_rotation.pkl"] + if epoch <= 1 or (epoch % img_save_interval) == 0: + for val_path in paths: + no_aug_score = self._fast_val(self.meta_model, path=os.path.join(dir, val_path), val_episodes=64, mode="eval") + no_aug_score_list.append(round(no_aug_score, 4)) + self.result_log.append('val_score', epoch, no_aug_score_list) + cur_mean = sum(no_aug_score_list) / len(no_aug_score_list) + # save best checkpoint (conditioned on the val datasets!) + if cur_mean < best_mean: + best_mean = cur_mean + self.best_meta_model = copy.deepcopy(self.meta_model) + self.logger.info("Saving (best) trained_model") + checkpoint_dict = { + 'epoch': epoch, + 'model_state_dict': self.meta_model.state_dict(), + 'optimizer_state_dict': self.meta_optimizer.state_dict(), + 'result_log': self.result_log.get_raw_data() + } + torch.save(checkpoint_dict, '{}/best_checkpoint.pt'.format(self.result_folder)) + + # Logs & Checkpoint + elapsed_time_str, remain_time_str = self.time_estimator.get_est_string(epoch, self.meta_params['epochs']) + self.logger.info("Epoch {:3d}/{:3d}({:.2f}%): Time Est.: Elapsed[{}], Remain[{}], Val Score: {}".format( + epoch, self.meta_params['epochs'], epoch/self.meta_params['epochs']*100, elapsed_time_str, remain_time_str, no_aug_score_list)) + + all_done = (epoch == self.meta_params['epochs']) + + if epoch > 1 and (epoch % img_save_interval) == 0: # save latest images, every X epoch + self.logger.info("Saving log_image") + image_prefix = '{}/latest'.format(self.result_folder) + util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'], self.result_log, labels=['train_score']) + util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'], self.result_log, labels=['val_score']) + util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_2'], self.result_log, labels=['train_loss']) + + # Save Model + if all_done or (epoch % model_save_interval) == 0: + self.logger.info("Saving trained_model") + checkpoint_dict = { + 'epoch': epoch, + 'model_state_dict': self.meta_model.state_dict(), + 'optimizer_state_dict': self.meta_optimizer.state_dict(), + 'result_log': self.result_log.get_raw_data() + } + torch.save(checkpoint_dict, '{}/checkpoint-{}.pt'.format(self.result_folder, epoch)) + + # if all_done or (epoch % img_save_interval) == 0: + # image_prefix = '{}/img/checkpoint-{}'.format(self.result_folder, epoch) + # util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'], self.result_log, labels=['train_score']) + # util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'], self.result_log, labels=['val_score']) + # util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_2'], self.result_log, labels=['train_loss']) + + if all_done: + self.logger.info(" *** Training Done *** ") + # self.logger.info("Now, printing log array...") + # util_print_log_array(self.logger, self.result_log) + + if epoch > 0 and epoch % 1000 == 0: + torch.save({ + 'fomaml_first_cos': self.fomaml_first_cos, + 'fomaml_last_cos': self.fomaml_last_cos, + 'reptile_first_cos1': self.reptile_first_cos1, + 'reptile_last_cos1': self.reptile_last_cos1, + 'reptile_first_cos2': self.reptile_first_cos2, + 'reptile_last_cos2': self.reptile_last_cos2, + }, "./grad_dir_cos.pt") + + def _train_one_epoch(self, epoch): + """ + Meta-Learning framework: + 1. Sample B training tasks from task distribution P(T) + 2. Inner-loop: for a batch of tasks T_i, POMO training -> \theta_i + 3. Outer-loop: update meta-model -> \theta_0 + + Adaptive task scheduler: + for size: gradually increase the problem size (Curriculum learning); + for distribution: we compute the relative gaps (w.r.t. LKH3) or estimate the potential improvements of each distribution (i.e., bootstrap) every X iters; + for size_distribution: combine together. + """ + + self.meta_optimizer.zero_grad() + score_AM, loss_AM = AverageMeter(), AverageMeter() + meta_batch_size = self.meta_params['meta_batch_size'] + + # Adaptive task scheduler: + if self.meta_params['curriculum']: + if self.meta_params["data_type"] == "size_distribution": + start = self.min_n + int(min(epoch / self.meta_params['sch_epoch'], 1) * (self.max_n - self.min_n)) # linear + # start = self.min_n + int(1 / 2 * (1 - math.cos(math.pi * min(epoch / self.meta_params['sch_epoch'], 1))) * (self.max_n - self.min_n)) # cosine + n = start // 5 * 5 + idx = (n - self.min_n) // 5 + tasks, weights = self.task_set[idx*11: (idx+1)*11], self.task_w[idx] + if epoch % self.meta_params['update_weight'] == 0: + self.task_w[idx] = self._update_task_weight(tasks, weights, epoch) + + self._alpha_scheduler(epoch) # for reptile + val_loss, meta_grad_dict = 0, {(i, j): 0 for i, group in enumerate(self.meta_optimizer.param_groups) for j, _ in enumerate(group['params'])} + fast_weights_1, fast_weights_2, fast_weights_5, fast_weights_10 = [], [], [], [] + + for b in range(self.meta_params['B']): + # sample a task + if self.meta_params["data_type"] == "size_distribution": + task_params = tasks[torch.multinomial(self.task_w[idx], 1).item()] if self.meta_params['curriculum'] else random.sample(self.task_set, 1)[0] + batch_size = meta_batch_size if (task_params[0] <= 150 and self.meta_params['k'] + self.meta_params['L'] == 1) else meta_batch_size // 2 + data = self._get_data(batch_size, task_params) + val_data = self._get_val_data(batch_size, task_params) + + # for fomaml + task_model = Model(**self.model_params) + task_model.load_state_dict(copy.deepcopy(self.meta_model.state_dict())) + # task_model = copy.deepcopy(self.meta_model) + optimizer = Optimizer(task_model.parameters(), **self.optimizer_params['optimizer']) + optimizer.load_state_dict(self.meta_optimizer.state_dict()) + # for reptile + task_model1 = Model(**self.model_params) + task_model1.load_state_dict(copy.deepcopy(self.meta_model.state_dict())) + optimizer1 = Optimizer(task_model1.parameters(), **self.optimizer_params['optimizer']) + optimizer1.load_state_dict(self.meta_optimizer.state_dict()) + # for maml + fast_weight = OrderedDict(self.meta_model.named_parameters()) + + # inner-loop optimization for maml + # for reptile: k \in [1, 2, 5, 10] + for step in range(2): + env_params = {'problem_size': data.size(1), 'pomo_size': data.size(1)} + self.meta_model.train() + if step < 1: + _, _ = self._train_one_batch(task_model, data, Env(**env_params), optimizer) # for fomaml + _, _ = self._train_one_batch(task_model1, data, Env(**env_params), optimizer1) # for reptile + avg_score, avg_loss, fast_weight = self._train_one_batch_maml(fast_weight, data, Env(**env_params), create_graph=True) # for maml + else: + data = val_data + avg_score, avg_loss = self._train_one_batch(task_model1, data, Env(**env_params), optimizer1) # for reptile + + if step == 0: + fast_weights_1.append(task_model1.state_dict()) + for i, params_key in enumerate(self.meta_model.state_dict()): + if i == 0: + tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights_1], dim=0), dim=0) + reptile_first_layer1 = -tmp.sign().cpu().detach().clone() + elif i == self.model_num: + tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights_1], dim=0), dim=0) + reptile_last_layer1 = -tmp.sign().cpu().detach().clone() + elif step == 1: + fast_weights_2.append(task_model1.state_dict()) + for i, params_key in enumerate(self.meta_model.state_dict()): + if i == 0: + tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights_2], dim=0), dim=0) + reptile_first_layer2 = -tmp.sign().cpu().detach().clone() + elif i == self.model_num: + tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights_2], dim=0), dim=0) + reptile_last_layer2 = -tmp.sign().cpu().detach().clone() + # elif step == 4: + # fast_weights_5.append(task_model1.state_dict()) + # for i, params_key in enumerate(self.meta_model.state_dict()): + # if i == 0: + # tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights_5], dim=0), dim=0) + # reptile_first_layer = -tmp.sign().cpu().detach().clone() + # elif i == self.model_num: + # tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights_5], dim=0), dim=0) + # reptile_last_layer = -tmp.sign().cpu().detach().clone() + # elif step == 9: + # fast_weights_10.append(task_model1.state_dict()) + # for i, params_key in enumerate(self.meta_model.state_dict()): + # if i == 0: + # tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights_10], dim=0), dim=0) + # reptile_first_layer = -tmp.sign().cpu().detach().clone() + # elif i == self.model_num: + # tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights_10], dim=0), dim=0) + # reptile_last_layer = -tmp.sign().cpu().detach().clone() + + score_AM.update(avg_score.item(), batch_size) + loss_AM.update(avg_loss.item(), batch_size) + + self.meta_model.train() + bootstrap_model = None + + # for maml + val_loss, kl_loss = self._fast_val(fast_weight, data=val_data, mode="maml", bootstrap_model=bootstrap_model) + loss = (self.meta_params['beta'] * val_loss + (1-self.meta_params['beta']) * kl_loss) / self.meta_params['B'] + self.meta_optimizer.zero_grad() + loss.backward() + for i, group in enumerate(self.meta_optimizer.param_groups): + for j, p in enumerate(group['params']): + meta_grad_dict[(i, j)] += p.grad + if j == 0: + maml_first_layer = p.grad.sign().cpu().detach().clone() + elif j == self.model_num: + maml_last_layer = p.grad.sign().cpu().detach().clone() + + # for fomaml + val_loss1, kl_loss1 = self._fast_val(task_model, data=val_data, mode="fomaml", bootstrap_model=bootstrap_model) + loss1 = (self.meta_params['beta'] * val_loss1 + (1-self.meta_params['beta']) * kl_loss1) / self.meta_params['B'] + optimizer.zero_grad() + loss1.backward() + for i, group in enumerate(optimizer.param_groups): + for j, p in enumerate(group['params']): + # meta_grad_dict[(i, j)] += p.grad + if j == 0: + fomaml_first_layer = p.grad.sign().cpu().detach().clone() + elif j == self.model_num: + fomaml_last_layer = p.grad.sign().cpu().detach().clone() + + # for reptile + # fast_weights.append(task_model.state_dict()) + # for i, params_key in enumerate(self.meta_model.state_dict()): + # if i == 0: + # tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights], dim=0), dim=0) + # reptile_first_layer = -tmp.sign().cpu().detach().clone() + # elif i == self.model_num: + # tmp = torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights], dim=0), dim=0) + # reptile_last_layer = -tmp.sign().cpu().detach().clone() + + # compute cos sim + cos1 = self.cos(maml_first_layer.view(-1), fomaml_first_layer.view(-1)) + cos2 = self.cos(maml_first_layer.view(-1), reptile_first_layer1.view(-1)) + cos3 = self.cos(maml_last_layer.view(-1), fomaml_last_layer.view(-1)) + cos4 = self.cos(maml_last_layer.view(-1), reptile_last_layer1.view(-1)) + cos5 = self.cos(maml_first_layer.view(-1), reptile_first_layer2.view(-1)) + cos6 = self.cos(maml_last_layer.view(-1), reptile_last_layer2.view(-1)) + self.fomaml_first_cos.append(cos1.item()) + self.fomaml_last_cos.append(cos3.item()) + self.reptile_first_cos1.append(cos2.item()) + self.reptile_last_cos1.append(cos4.item()) + self.reptile_first_cos2.append(cos5.item()) + self.reptile_last_cos2.append(cos6.item()) + + # outer-loop optimization (update meta-model) + if self.meta_params['meta_method'] == 'maml': + self.meta_optimizer.zero_grad() + for i, group in enumerate(self.meta_optimizer.param_groups): + for j, p in enumerate(group['params']): + p.grad = meta_grad_dict[(i, j)] + self.meta_optimizer.step() + else: + raise NotImplementedError + + # state_dict = {params_key: (self.meta_model.state_dict()[params_key] + self.alpha * torch.mean(torch.stack([fast_weight[params_key] - self.meta_model.state_dict()[params_key] for fast_weight in fast_weights], dim=0), dim=0)) for params_key in self.meta_model.state_dict()} + + # Log Once, for each epoch + self.logger.info('Meta Iteration {:3d}: alpha: {:6f}, Score: {:.4f}, Loss: {:.4f}'.format(epoch, self.alpha, score_AM.avg, loss_AM.avg)) + + return score_AM.avg, loss_AM.avg + + def _train_one_batch(self, task_model, data, env, optimizer=None): + + task_model.train() + batch_size = data.size(0) + env.load_problems(batch_size, problems=data, aug_factor=1) + reset_state, _, _ = env.reset() + task_model.pre_forward(reset_state) + prob_list = torch.zeros(size=(batch_size, env.pomo_size, 0)) + # shape: (batch, pomo, 0~problem) + + # POMO Rollout, please note that the reward is negative (i.e., -length of route). + state, reward, done = env.pre_step() + while not done: + selected, prob = task_model(state) + # shape: (batch, pomo) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + + # Loss + advantage = reward - reward.float().mean(dim=1, keepdims=True) + # shape: (batch, pomo) + log_prob = prob_list.log().sum(dim=2) # for the first/last node, p=1 -> log_p=0 + # size = (batch, pomo) + loss = -advantage * log_prob # Minus Sign: To Increase REWARD + # shape: (batch, pomo) + loss_mean = loss.mean() + + # update model + optimizer.zero_grad() + loss_mean.backward() + optimizer.step() + + # Score + max_pomo_reward, _ = reward.max(dim=1) # get best results from pomo + score_mean = -max_pomo_reward.float().mean() # negative sign to make positive value + print(score_mean) + + return score_mean, loss_mean + + def _train_one_batch_maml(self, fast_weight, data, env, optimizer=None, create_graph=True): + + # first_layer_grad_dir, last_layer_grad_dir = None, None + + batch_size = data.size(0) + env.load_problems(batch_size, problems=data, aug_factor=1) + reset_state, _, _ = env.reset() + self.meta_model.pre_forward(reset_state, weights=fast_weight) + prob_list = torch.zeros(size=(batch_size, env.pomo_size, 0)) + # shape: (batch, pomo, 0~problem) + + # POMO Rollout, please note that the reward is negative (i.e., -length of route). + state, reward, done = env.pre_step() + while not done: + selected, prob = self.meta_model(state, weights=fast_weight) + # shape: (batch, pomo) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + + # Loss + advantage = reward - reward.float().mean(dim=1, keepdims=True) + log_prob = prob_list.log().sum(dim=2) # for the first/last node, p=1 -> log_p=0 + loss = -advantage * log_prob # Minus Sign: To Increase REWARD + # shape: (batch, pomo) + loss_mean = loss.mean() + + # 1. update model - in SGD way + # gradients = torch.autograd.grad(loss_mean, fast_weight.values(), create_graph=create_graph) # allow_unused=True + # fast_weight = OrderedDict( + # (name, param - self.optimizer_params['optimizer']['lr'] * grad) + # for ((name, param), grad) in zip(fast_weight.items(), gradients) + # ) + # 2. update model - in Adam way + gradients = torch.autograd.grad(loss_mean, fast_weight.values(), create_graph=create_graph) # allow_unused=True + w_t, (beta1, beta2), eps = [], self.meta_optimizer.param_groups[0]['betas'], self.meta_optimizer.param_groups[0]['eps'] + lr, weight_decay = self.optimizer_params['optimizer']['lr'], self.optimizer_params['optimizer']['weight_decay'] + for i, ((name, param), grad) in enumerate(zip(fast_weight.items(), gradients)): + if self.meta_optimizer.state_dict()['state'] != {}: + i = i if self.model_params['meta_update_encoder'] else i + 80 # (with norm layer): i \in [0, 85], where encoder \in [0, 79] + decoder \in [80, 85] + state = self.meta_optimizer.state_dict()['state'][i] + step, exp_avg, exp_avg_sq = state['step'], state['exp_avg'], state['exp_avg_sq'] + step += 1 + step = step.item() if isinstance(step, torch.Tensor) else step + # compute grad based on Adam source code using in-place operation + # update Adam stat (step, exp_avg and exp_avg_sq have already been updated by in-place operation) + # may encounter RuntimeError: (a leaf Variable that requires grad) / (the tensor used during grad computation) cannot use in-place operation. + grad = grad.add(param, alpha=weight_decay) + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) + exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2) + bias_correction1 = 1 - beta1 ** step + bias_correction2 = 1 - beta2 ** step + step_size = lr / bias_correction1 + bias_correction2_sqrt = math.sqrt(bias_correction2) + denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps) + # param.addcdiv_(exp_avg, denom, value=-step_size) + param = param - step_size * exp_avg / denom + self.meta_optimizer.state_dict()['state'][i]['exp_avg'] = exp_avg.clone().detach() + self.meta_optimizer.state_dict()['state'][i]['exp_avg_sq'] = exp_avg_sq.clone().detach() + else: + param = param - lr * grad + + # if i == 0: + # first_layer_grad_dir = grad.sign().cpu().detach().clone() + # if i == self.model_num: + # last_layer_grad_dir = grad.sign().cpu().detach().clone() + + w_t.append((name, param)) + fast_weight = OrderedDict(w_t) + """ + # 3. update model using optimizer - this method can not work properly. + optimizer.zero_grad() + # torch.autograd.grad(loss_mean, fast_weight.values(), create_graph=create_graph) + # print(list(self.meta_model.parameters())[-1]) + loss_mean.backward(retain_graph=True, create_graph=True) + optimizer.step() # will update meta_model as well... + """ + + # Score + max_pomo_reward, _ = reward.max(dim=1) # get best results from pomo + score_mean = -max_pomo_reward.float().mean() # negative sign to make positive value + print(score_mean) + + return score_mean, loss_mean, fast_weight + + def _fast_val(self, model, data=None, path=None, offset=0, val_episodes=32, mode="eval", return_all=False, bootstrap_model=None): + aug_factor = 1 + data = torch.Tensor(load_dataset(path)[offset: offset+val_episodes]) if data is None else data + env = Env(**{'problem_size': data.size(1), 'pomo_size': data.size(1)}) + + batch_size = data.size(0) + if mode == "eval": + model.eval() + with torch.no_grad(): + env.load_problems(batch_size, problems=data, aug_factor=aug_factor) + reset_state, _, _ = env.reset() + model.pre_forward(reset_state) + state, reward, done = env.pre_step() + while not done: + selected, _ = model(state) + # shape: (batch, pomo) + state, reward, done = env.step(selected) + + elif mode in ["maml", "fomaml"]: + fast_weight, kl_loss = model, 0 + env.load_problems(batch_size, problems=data, aug_factor=aug_factor) + reset_state, _, _ = env.reset() + if mode == "maml": + self.meta_model.pre_forward(reset_state, weights=fast_weight) + if bootstrap_model is not None: + with torch.no_grad(): + self.meta_model.pre_forward(reset_state, weights=bootstrap_model) + else: + model.pre_forward(reset_state) + if bootstrap_model is not None: + with torch.no_grad(): + bootstrap_model.pre_forward(reset_state) + + prob_list = torch.zeros(size=(batch_size, env.pomo_size, 0)) + state, reward, done = env.pre_step() + while not done: + if mode == "maml": + selected, prob, probs = self.meta_model(state, weights=fast_weight, return_probs=True) + if bootstrap_model is not None: + probs1 = torch.where(probs > 0, probs, torch.tensor(0.00001)) + with torch.no_grad(): + _, _, bs_probs = self.meta_model(state, weights=bootstrap_model, selected=selected, return_probs=True) + bs_probs = torch.where(bs_probs > 0, bs_probs, torch.tensor(0.00001)) + else: + selected, prob, probs = model(state, return_probs=True) + if bootstrap_model is not None: + probs1 = torch.where(probs > 0, probs, torch.tensor(0.00001)) + with torch.no_grad(): + _, _, bs_probs = bootstrap_model(state, selected=selected, return_probs=True) + bs_probs = torch.where(bs_probs > 0, bs_probs, torch.tensor(0.00001)) + + # shape: (batch, pomo) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + kl_loss += (bs_probs * (bs_probs.log() - probs1.log())).reshape(batch_size * data.size(1), -1).sum(dim=-1).mean() if bootstrap_model is not None else 0 + + advantage = reward - reward.float().mean(dim=1, keepdims=True) + log_prob = prob_list.log().sum(dim=2) # for the first/last node, p=1 -> log_p=0 + loss = -advantage * log_prob # Minus Sign: To Increase REWARD + loss_mean = loss.mean() + else: + raise NotImplementedError + + # Return + aug_reward = reward.reshape(aug_factor, batch_size, env.pomo_size) + # shape: (augmentation, batch, pomo) + max_pomo_reward, _ = aug_reward.max(dim=2) # get best results from pomo + # shape: (augmentation, batch) + no_aug_score = -max_pomo_reward[0, :].float().mean() # negative sign to make positive value + print(no_aug_score) + + if mode == "eval": + if return_all: + return -max_pomo_reward[0, :].float() + else: + return no_aug_score.detach().item() + else: + return loss_mean, kl_loss + + def _get_data(self, batch_size, task_params): + if self.meta_params['data_type'] == 'size': + assert len(task_params) == 1 + data = get_random_problems(batch_size, task_params[0], num_modes=0, cdist=0, distribution='uniform', problem="tsp") + elif self.meta_params['data_type'] == 'distribution': + assert len(task_params) == 2 + data = get_random_problems(batch_size, self.env_params['problem_size'], num_modes=task_params[0], cdist=task_params[1], distribution='gaussian_mixture', problem="tsp") + elif self.meta_params['data_type'] == "size_distribution": + assert len(task_params) == 3 + data = get_random_problems(batch_size, task_params[0], num_modes=task_params[1], cdist=task_params[2], distribution='gaussian_mixture', problem="tsp") + else: + raise NotImplementedError + + return data + + def _get_val_data(self, batch_size, task_params): + if self.meta_params["data_type"] == "size": + # start1, end1 = min(task_params[0] + 10, self.max_n), min(task_params[0] + 20, self.max_n) + # val_size = random.sample(range(start1, end1 + 1), 1)[0] + # val_data = self._get_data(batch_size, (val_size,)) + val_data = self._get_data(batch_size, task_params) + elif self.meta_params["data_type"] == "distribution": + val_data = self._get_data(batch_size, task_params) + elif self.meta_params["data_type"] == "size_distribution": + val_data = self._get_data(batch_size, task_params) + else: + raise NotImplementedError + + return val_data + + def _alpha_scheduler(self, epoch): + """ + Update param for Reptile. + """ + self.alpha = max(self.alpha * self.meta_params['alpha_decay'], 0.0001) + + def _generate_x_adv(self, data, eps=10.0): + """ + Generate adversarial data based on the current model, also need to generate optimal sol for x_adv. + See also: "Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum" in AAAI 2022. + """ + from torch.autograd import Variable + def minmax(xy_): + # min_max normalization: [b,n,2] + xy_ = (xy_ - xy_.min(dim=1, keepdims=True)[0]) / (xy_.max(dim=1, keepdims=True)[0] - xy_.min(dim=1, keepdims=True)[0]) + return xy_ + + if eps == 0: return data + # generate x_adv + self.meta_model.eval() + aug_factor, batch_size = 1, data.size(0) + env = Env(**{'problem_size': data.size(1), 'pomo_size': data.size(1)}) + with torch.enable_grad(): + data.requires_grad_() + env.load_problems(batch_size, problems=data, aug_factor=aug_factor) + reset_state, _, _ = env.reset() + self.meta_model.pre_forward(reset_state) + prob_list = torch.zeros(size=(aug_factor * batch_size, env.pomo_size, 0)) + state, reward, done = env.pre_step() + while not done: + selected, prob = self.meta_model(state) + state, reward, done = env.step(selected) + prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2) + + aug_reward = reward.reshape(aug_factor, batch_size, env.pomo_size).permute(1, 0, 2).view(batch_size, -1) + baseline_reward = aug_reward.float().mean(dim=1, keepdims=True) + advantage = aug_reward - baseline_reward + log_prob = prob_list.log().sum(dim=2).reshape(aug_factor, batch_size, env.pomo_size).permute(1, 0, 2).view(batch_size, -1) + + # delta = torch.autograd.grad(eps * ((advantage / baseline_reward) * log_prob).mean(), data)[0] + delta = torch.autograd.grad(eps * ((-advantage) * log_prob).mean(), data)[0] + data = data.detach() + delta + data = minmax(data) + data = Variable(data, requires_grad=False) + + # generate opt sol + # opt_sol = solve_all_gurobi(data) + # return data, opt_sol + + return data + + def _update_task_weight(self, tasks, weights, epoch): + """ + Update the weights of tasks. + For LKH3, set MAX_TRIALS = 100 to reduce time. + """ + global run_func + start_t, gap = time.time(), torch.zeros(weights.size(0)) + batch_size = 200 if self.meta_params["solver"] == "lkh3_offline" else 50 + idx = torch.randperm(batch_size)[:50] + for i in range(gap.size(0)): + selected = tasks[i] + data = self._get_data(batch_size=batch_size, task_params=selected) + + # only use lkh3 at the first iteration of updating task weights + if self.meta_params["solver"] == "lkh3_offline": + if selected not in self.val_data.keys(): + self.val_data[selected] = data + opts = argparse.ArgumentParser() + opts.cpus, opts.n, opts.progress_bar_mininterval = None, None, 0.1 + dataset = [(instance.cpu().numpy(),) for instance in data] + executable = get_lkh_executable() + def run_func(args): + return solve_lkh_log(executable, *args, runs=1, disable_cache=True, MAX_TRIALS=100) # otherwise it directly loads data from dir + results, _ = run_all_in_pool(run_func, "./LKH3_result", dataset, opts, use_multiprocessing=False) + self.val_opt[selected] = [j[0] for j in results] + data = self.val_data[selected][idx] + + model_score = self._fast_val(self.meta_model, data=data, mode="eval", return_all=True) + model_score = model_score.tolist() + + if self.meta_params["solver"] == "lkh3_online": + # get results from LKH3 + opts = argparse.ArgumentParser() + opts.cpus, opts.n, opts.progress_bar_mininterval = None, None, 0.1 + dataset = [(instance.cpu().numpy(),) for instance in data] + executable = get_lkh_executable() + def run_func(args): + return solve_lkh_log(executable, *args, runs=1, disable_cache=True, MAX_TRIALS=100) # otherwise it directly loads data from dir + results, _ = run_all_in_pool(run_func, "./LKH3_result", dataset, opts, use_multiprocessing=False) + gap_list = [(model_score[j]-results[j][0])/results[j][0]*100 for j in range(len(results))] + gap[i] = sum(gap_list)/len(gap_list) + elif self.meta_params["solver"] == "lkh3_offline": + lkh_score = [self.val_opt[selected][j] for j in idx.tolist()] + gap_list = [(model_score[j] - lkh_score[j]) / lkh_score[j] * 100 for j in range(len(lkh_score))] + gap[i] = sum(gap_list) / len(gap_list) + elif self.meta_params["solver"] == "best_model": # not recommend: how to define the best model? (biased to the val dataset) + best_model_score = self._fast_val(self.best_meta_model, data=data, mode="eval", return_all=True) + best_model_score = best_model_score.tolist() + gap_list = [(model_score[j] - best_model_score[j]) / best_model_score[j] * 100 for j in range(len(best_model_score))] + gap[i] = sum(gap_list) / len(gap_list) + else: + raise NotImplementedError + print(">> Finish updating task weights within {}s".format(round(time.time()-start_t, 2))) + + temp = 1.0 + gap_temp = torch.Tensor([i/temp for i in gap.tolist()]) + print(gap, temp) + print(">> Old task weights: {}".format(weights)) + weights = torch.softmax(gap_temp, dim=0) + print(">> New task weights: {}".format(weights)) + + return weights + + def _get_kl_loss(self, bootstrap_model, val_data, slow_tour, slow_probs): + """ + Ref to "Bootstrap Meta-Learning", ICLR 2022; + This function is deprecated since + a. storing probs_list for large-scale COPs on GPU is extremely (memory) expensive (e.g., > 20GB on TSP200); + b. probs_list.cpu() at every step is also extremely (time) expensive. + Instead, we compute KL loss on the fly now, see self._fast_val() + """ + if isinstance(bootstrap_model, torch.nn.Module): + bootstrap_model.eval() + env = Env(**{'problem_size': val_data.size(1), 'pomo_size': val_data.size(1)}) + batch_size = val_data.size(0) + env.load_problems(batch_size, problems=val_data, aug_factor=1) + reset_state, _, _ = env.reset() + + with torch.no_grad(): + if self.meta_params['meta_method'] == 'maml': + self.meta_model.pre_forward(reset_state, weights=bootstrap_model) + else: + bootstrap_model.pre_forward(reset_state) + probs_list = torch.zeros(size=(batch_size, env.pomo_size, env.problem_size, 0)) + state, reward, done = env.pre_step() + selected_idx = 0 + while not done: + if self.meta_params['meta_method'] == 'maml': + selected, prob, probs = self.meta_model(state, weights=bootstrap_model, selected=slow_tour[:, :, selected_idx].reshape(batch_size, -1).long(), return_probs=True) + else: + selected, prob, probs = bootstrap_model(state, selected=slow_tour[:, :, selected_idx].reshape(batch_size, -1).long(), return_probs=True) + # shape: (batch, pomo) + selected_idx += 1 + state, reward, done = env.step(selected) + probs_list = torch.cat((probs_list, probs[:, :, :, None]), dim=3) + probs_list = torch.where(probs_list > 0, probs_list, torch.tensor(0.00001)) + + slow_probs = torch.where(slow_probs > 0, slow_probs, torch.tensor(0.00001)) # avoid log0 + # kl_loss = (probs_list * (probs_list.log() - slow_probs.log())).sum(dim=2).mean() + kl_loss = (probs_list * (probs_list.log() - slow_probs.log())).reshape(batch_size * val_data.size(1), -1).sum(dim=-1).mean() + # kl_loss = torch.nn.KLDivLoss(reduction="batchmean")(slow_probs.log().reshape(batch_size * val_data.size(1), -1), probs_list.reshape(batch_size * val_data.size(1), -1)) + + return kl_loss diff --git a/POMO/TSP/TSP_baseline.py b/POMO/TSP/TSP_baseline.py index 615c0ee..1f74052 100644 --- a/POMO/TSP/TSP_baseline.py +++ b/POMO/TSP/TSP_baseline.py @@ -98,7 +98,7 @@ def solve_concorde_log(executable, directory, name, loc, disable_cache=False): return None -def get_lkh_executable(url="http://www.akira.ruc.dk/~keld/research/LKH-3/LKH-3.0.7.tgz"): +def get_lkh_executable(url="http://www.akira.ruc.dk/~keld/research/LKH-3/LKH-3.0.8.tgz"): cwd = os.path.abspath("lkh") os.makedirs(cwd, exist_ok=True) diff --git a/data/CVRP/cvrplib-Set-X/X-n1001-k43.vrp b/data/CVRP/cvrplib-Set-X/X-n1001-k43.vrp new file mode 100644 index 0000000..1838606 --- /dev/null +++ b/data/CVRP/cvrplib-Set-X/X-n1001-k43.vrp @@ -0,0 +1,2014 @@ +NAME : X-n1001-k43 +COMMENT : "Generated by Uchoa, Pecin, Pessoa, Poggi, Subramanian, and Vidal (2013)" +TYPE : CVRP +DIMENSION : 1001 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 131 +NODE_COORD_SECTION +1 2 857 +2 407 102 +3 915 965 +4 797 45 +5 997 241 +6 650 683 +7 748 248 +8 99 174 +9 2 545 +10 277 689 +11 568 771 +12 76 105 +13 117 973 +14 171 971 +15 327 183 +16 277 697 +17 269 246 +18 670 18 +19 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The instance name format is XML100_ABCD_EF, such that ABCD identifies the group as follows: + A: Depot positioning (1. random, 2. centered, 3. cornered) + B: Customer positioning (1. random, 2. clustered, 3. random-clustered) + C: Demand distribution (1. unitary, 2. small values with large CV, 3. small values with small CV, 4. large values with large CV, 5. large values with small CV, 6. depending on quadrant, 7. many small values and few large values) + D: Average route size (1. very short, 2. short, 3. medium, 4. long, 5. very long, 6. ultra long) + +EF identifies the instance inside the group ABCD. For example, instance XML100_2216_01 is the first instance of the group with centered depot, clustered customers, unitary demands, and ultra long routes. There are 378 groups generated by the Cartesian product of {1,2,3}x{1,2,3}x{1,2,3,4,5,6,7}x{1,2,3,4,5,6} (possible values to ABCD), where the first 172 groups have 27 instances, whereas the other 206 groups have 26 instances. + +All the solutions in were proven to be optimal. The vast majority of these optimal solutions were found by VRPSolver (Pessoa et al., 2020; vrpsolver.math.u-bordeaux.fr), sometimes using customized parameterizations. Eighteen instances could only be solved using special branching rules and seventeen instances were solved by a Branch-and-Cut algorithm, as in Lysgaard et al. (2004) and Subramanian et al. (2011). Both VRPSolver and BC approaches take advantage of very good initial solutions provided by HGS-CVRP (Vidal, 2022; github.com/vidalt/HGS-CVRP). + +A Python script is available to produce new XML100-like instances. +Ex: "python generate.py 200 2 1 4 5 1 0" produces XML200_2145_01.vrp (call "python generator.py" to understand the arguments). + +Script genXML100.sh produces the original XML100 instances, if run with Python 2.7. Running with Python versions >= 3 produces different instances. + +References +Lysgaard, J. et al. 2004. A new branch-and-cut algorithm for the capacitated vehicle routing problem. Math. Program., 100(2): 423–445. +Pessoa, A. et al. 2020. A generic exact solver for vehicle routing and related problems. Math. Program., 183(1): 483–523. +Subramanian, A. et al. 2011. Branch-and-cut with lazy separation for the vehicle routing problem with simultaneous pickup and delivery. Operations Research Letters 39.5, pp. 338–341. +Vidal, T. 2022. Hybrid genetic search for the CVRP: Open-source implementation and SWAP* Neighborhood. 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torch.backends.cudnn.deterministic = True + # torch.cuda.manual_seed_all(seed) + + +seed_everything(2023) +size = [125, 150, 175, 200] +A = [1, 2, 3] +B = [1, 2, 3] +C = [1, 2, 3, 4, 5, 6, 7] +D = [1, 2, 3, 4, 5, 6] +for s in size: + for i in range(5): + a = random.sample(A, 1)[0] + b = random.sample(B, 1)[0] + c = random.sample(C, 1)[0] + d = random.sample(D, 1)[0] + os.system("python generator.py {} {} {} {} {} 1 1".format(s, a, b, c, d)) diff --git a/data/CVRP/cvrplib-Set-XML100/generated_instances/XML1000_1132_01.vrp b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML1000_1132_01.vrp new file mode 100644 index 0000000..7cd7444 --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML1000_1132_01.vrp @@ -0,0 +1,2014 @@ +NAME : XML1000_1132_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 1001 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 51 +NODE_COORD_SECTION +1 821 782 +2 549 62 +3 766 69 +4 918 283 +5 775 801 +6 897 498 +7 11 405 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML1000_1276_01.vrp @@ -0,0 +1,2014 @@ +NAME : XML1000_1276_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 1001 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 321 +NODE_COORD_SECTION +1 821 782 +2 869 418 +3 120 312 +4 183 261 +5 736 544 +6 78 306 +7 112 219 +8 55 313 +9 663 578 +10 46 530 +11 984 395 +12 18 282 +13 146 477 +14 244 365 +15 859 480 +16 954 650 +17 141 529 +18 205 392 +19 67 598 +20 706 405 +21 245 586 +22 943 488 +23 852 239 +24 871 499 +25 832 391 +26 757 486 +27 117 46 +28 715 480 +29 62 348 +30 740 564 +31 3 271 +32 794 392 +33 56 286 +34 55 470 +35 107 508 +36 111 469 +37 146 436 +38 263 365 +39 69 234 +40 246 532 +41 27 228 +42 799 497 +43 810 345 +44 36 304 +45 160 608 +46 150 573 +47 27 255 +48 712 456 +49 136 171 +50 133 511 +51 802 368 +52 295 671 +53 773 481 +54 222 421 +55 153 587 +56 60 697 +57 11 265 +58 189 253 +59 87 233 +60 125 525 +61 69 346 +62 1 202 +63 754 458 +64 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML100_1341_01.vrp new file mode 100644 index 0000000..f1d6bbf --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML100_1341_01.vrp @@ -0,0 +1,214 @@ +NAME : XML100_1341_01 +COMMENT : Generated by Queiroga, Sadykov, Uchoa, and Vidal (2021) +TYPE : CVRP +DIMENSION : 101 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 177 +NODE_COORD_SECTION +1 522 616 +2 883 433 +3 510 368 +4 814 546 +5 623 336 +6 536 42 +7 332 547 +8 512 284 +9 303 492 +10 669 561 +11 17 64 +12 959 620 +13 937 516 +14 901 22 +15 239 270 +16 551 763 +17 833 785 +18 374 206 +19 605 44 +20 263 437 +21 227 157 +22 627 430 +23 772 172 +24 559 771 +25 359 157 +26 919 254 +27 639 253 +28 289 604 +29 622 204 +30 107 283 +31 202 610 +32 374 408 +33 883 944 +34 732 834 +35 275 87 +36 573 411 +37 643 473 +38 184 402 +39 823 554 +40 364 615 +41 545 347 +42 225 581 +43 386 870 +44 849 501 +45 260 562 +46 551 184 +47 949 585 +48 12 822 +49 39 739 +50 742 330 +51 382 884 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML100_2271_01.vrp @@ -0,0 +1,214 @@ +NAME : XML100_2271_01 +COMMENT : Generated by Queiroga, Sadykov, Uchoa, and Vidal (2021) +TYPE : CVRP +DIMENSION : 101 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 125 +NODE_COORD_SECTION +1 500 500 +2 359 660 +3 269 772 +4 624 418 +5 383 951 +6 129 797 +7 582 922 +8 108 791 +9 523 849 +10 360 960 +11 708 401 +12 367 863 +13 413 387 +14 612 409 +15 651 786 +16 135 765 +17 397 900 +18 492 774 +19 179 879 +20 487 520 +21 454 562 +22 89 825 +23 527 618 +24 162 864 +25 593 355 +26 485 937 +27 636 289 +28 511 793 +29 428 893 +30 328 944 +31 386 955 +32 66 764 +33 373 943 +34 40 777 +35 530 371 +36 240 794 +37 487 571 +38 561 366 +39 126 731 +40 467 744 +41 558 336 +42 537 965 +43 555 533 +44 563 325 +45 660 349 +46 621 817 +47 453 517 +48 579 342 +49 154 872 +50 225 845 +51 176 744 +52 524 323 +53 397 236 +54 576 429 +55 327 967 +56 340 742 +57 554 353 +58 491 592 +59 703 605 +60 555 523 +61 290 914 +62 172 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML100_3223_01.vrp new file mode 100644 index 0000000..306e01a --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML100_3223_01.vrp @@ -0,0 +1,214 @@ +NAME : XML100_3223_01 +COMMENT : Generated by Queiroga, Sadykov, Uchoa, and Vidal (2021) +TYPE : CVRP +DIMENSION : 101 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 57 +NODE_COORD_SECTION +1 0 0 +2 137 257 +3 669 265 +4 501 318 +5 634 242 +6 645 383 +7 210 263 +8 140 324 +9 57 323 +10 682 286 +11 958 335 +12 276 168 +13 68 334 +14 741 270 +15 48 278 +16 627 237 +17 891 226 +18 773 222 +19 67 418 +20 9 498 +21 771 295 +22 660 241 +23 132 358 +24 48 446 +25 704 248 +26 98 289 +27 98 407 +28 707 137 +29 698 235 +30 90 487 +31 668 261 +32 21 318 +33 129 378 +34 696 291 +35 78 239 +36 52 467 +37 9 309 +38 497 260 +39 228 339 +40 807 300 +41 99 439 +42 76 396 +43 50 272 +44 749 278 +45 102 239 +46 711 333 +47 96 391 +48 720 212 +49 675 337 +50 775 343 +51 79 378 +52 834 233 +53 705 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML1500_1171_01.vrp new file mode 100644 index 0000000..d1473b6 --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML1500_1171_01.vrp @@ -0,0 +1,3014 @@ +NAME : XML1500_1171_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 1501 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 127 +NODE_COORD_SECTION +1 821 782 +2 549 62 +3 18 920 +4 46 530 +5 142 992 +6 382 70 +7 252 369 +8 408 64 +9 640 450 +10 365 776 +11 0 126 +12 442 93 +13 422 264 +14 5 878 +15 244 77 +16 341 275 +17 465 719 +18 472 615 +19 523 660 +20 418 415 +21 31 872 +22 698 140 +23 928 745 +24 537 860 +25 603 805 +26 258 133 +27 583 567 +28 740 959 +29 550 161 +30 650 248 +31 935 497 +32 640 957 +33 946 246 +34 664 942 +35 414 93 +36 108 575 +37 983 164 +38 312 719 +39 402 118 +40 722 474 +41 352 297 +42 57 349 +43 520 110 +44 790 937 +45 864 823 +46 258 231 +47 269 240 +48 717 516 +49 438 872 +50 819 829 +51 980 746 +52 356 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML1500_2114_01.vrp @@ -0,0 +1,3014 @@ +NAME : XML1500_2114_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 1501 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 14 +NODE_COORD_SECTION +1 500 500 +2 549 62 +3 18 920 +4 46 530 +5 142 992 +6 382 70 +7 252 369 +8 408 64 +9 640 450 +10 365 776 +11 0 126 +12 442 93 +13 422 264 +14 5 878 +15 244 77 +16 341 275 +17 465 719 +18 472 615 +19 523 660 +20 418 415 +21 31 872 +22 698 140 +23 928 745 +24 537 860 +25 603 805 +26 258 133 +27 583 567 +28 740 959 +29 550 161 +30 650 248 +31 935 497 +32 640 957 +33 946 246 +34 664 942 +35 414 93 +36 108 575 +37 983 164 +38 312 719 +39 402 118 +40 722 474 +41 352 297 +42 57 349 +43 520 110 +44 790 937 +45 864 823 +46 258 231 +47 269 240 +48 717 516 +49 438 872 +50 819 829 +51 980 746 +52 356 393 +53 594 710 +54 21 31 +55 687 758 +56 728 301 +57 75 496 +58 167 908 +59 945 644 +60 369 219 +61 795 556 +62 541 331 +63 31 80 +64 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100644 index 0000000..929a2ca --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML2000_1231_01.vrp @@ -0,0 +1,4014 @@ +NAME : XML2000_1231_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 2001 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 31 +NODE_COORD_SECTION +1 821 782 +2 869 418 +3 189 573 +4 55 313 +5 663 578 +6 87 382 +7 46 530 +8 18 282 +9 244 365 +10 902 222 +11 871 463 +12 730 524 +13 943 488 +14 51 235 +15 225 208 +16 820 417 +17 871 499 +18 832 391 +19 715 480 +20 56 286 +21 17 215 +22 55 470 +23 128 540 +24 246 532 +25 27 228 +26 810 345 +27 160 608 +28 136 171 +29 133 511 +30 770 537 +31 55 591 +32 69 346 +33 772 354 +34 104 569 +35 21 571 +36 236 230 +37 109 275 +38 162 430 +39 100 455 +40 736 287 +41 117 544 +42 211 322 +43 190 528 +44 365 236 +45 754 426 +46 174 525 +47 91 527 +48 4 230 +49 23 243 +50 64 244 +51 102 536 +52 303 526 +53 104 582 +54 237 437 +55 772 452 +56 165 677 +57 310 686 +58 79 265 +59 215 534 +60 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML2000_3263_01.vrp @@ -0,0 +1,4014 @@ +NAME : XML2000_3263_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 2001 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 679 +NODE_COORD_SECTION +1 0 0 +2 189 573 +3 912 788 +4 55 313 +5 87 382 +6 46 530 +7 763 665 +8 18 282 +9 800 812 +10 244 365 +11 792 728 +12 133 534 +13 787 878 +14 51 235 +15 678 742 +16 225 208 +17 873 954 +18 901 756 +19 56 286 +20 17 215 +21 55 470 +22 128 540 +23 246 532 +24 27 228 +25 880 818 +26 945 798 +27 160 608 +28 697 733 +29 136 171 +30 133 511 +31 55 591 +32 887 713 +33 69 346 +34 834 823 +35 104 569 +36 805 796 +37 21 571 +38 771 803 +39 236 230 +40 109 275 +41 162 430 +42 100 455 +43 767 769 +44 117 544 +45 211 322 +46 190 528 +47 862 777 +48 782 668 +49 365 236 +50 694 820 +51 174 525 +52 91 527 +53 794 764 +54 4 230 +55 64 244 +56 102 536 +57 303 526 +58 962 756 +59 104 582 +60 237 437 +61 778 904 +62 165 677 +63 788 805 +64 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML200_1131_01.vrp new file mode 100644 index 0000000..c806804 --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML200_1131_01.vrp @@ -0,0 +1,414 @@ +NAME : XML200_1131_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 201 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 31 +NODE_COORD_SECTION +1 821 782 +2 779 460 +3 574 210 +4 192 264 +5 64 261 +6 22 26 +7 899 870 +8 620 442 +9 399 443 +10 449 679 +11 432 519 +12 991 217 +13 601 996 +14 557 852 +15 646 436 +16 328 675 +17 177 375 +18 583 471 +19 773 287 +20 836 953 +21 354 1 +22 816 413 +23 275 675 +24 758 524 +25 888 890 +26 566 238 +27 975 867 +28 561 623 +29 690 755 +30 313 72 +31 592 403 +32 279 663 +33 465 719 +34 879 78 +35 861 966 +36 522 1000 +37 9 961 +38 712 456 +39 662 174 +40 235 650 +41 402 379 +42 365 424 +43 258 133 +44 665 554 +45 104 923 +46 583 567 +47 738 996 +48 728 512 +49 961 507 +50 938 569 +51 629 607 +52 552 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@@ +NAME : XML200_3161_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 201 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 207 +NODE_COORD_SECTION +1 0 0 +2 779 460 +3 574 210 +4 192 264 +5 64 261 +6 22 26 +7 899 870 +8 620 442 +9 399 443 +10 449 679 +11 432 519 +12 991 217 +13 601 996 +14 557 852 +15 646 436 +16 328 675 +17 177 375 +18 583 471 +19 773 287 +20 836 953 +21 354 1 +22 816 413 +23 275 675 +24 758 524 +25 888 890 +26 566 238 +27 975 867 +28 561 623 +29 690 755 +30 313 72 +31 592 403 +32 279 663 +33 465 719 +34 879 78 +35 861 966 +36 522 1000 +37 9 961 +38 712 456 +39 662 174 +40 235 650 +41 402 379 +42 365 424 +43 258 133 +44 665 554 +45 104 923 +46 583 567 +47 738 996 +48 728 512 +49 961 507 +50 938 569 +51 629 607 +52 552 942 +53 551 553 +54 828 132 +55 149 36 +56 395 351 +57 598 185 +58 261 33 +59 28 823 +60 237 414 +61 849 932 +62 272 738 +63 785 2 +64 999 645 +65 694 436 +66 992 432 +67 352 297 +68 736 982 +69 921 747 +70 520 110 +71 880 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90 +185 41 +186 49 +187 75 +188 43 +189 39 +190 29 +191 65 +192 91 +193 6 +194 78 +195 49 +196 39 +197 49 +198 7 +199 26 +200 29 +201 76 +DEPOT_SECTION +1 +-1 +EOF diff --git a/data/CVRP/cvrplib-Set-XML100/generated_instances/XML200_3171_01.vrp b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML200_3171_01.vrp new file mode 100644 index 0000000..4ce4773 --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML200_3171_01.vrp @@ -0,0 +1,414 @@ +NAME : XML200_3171_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 201 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 128 +NODE_COORD_SECTION +1 0 0 +2 779 460 +3 574 210 +4 192 264 +5 64 261 +6 22 26 +7 899 870 +8 620 442 +9 399 443 +10 449 679 +11 432 519 +12 991 217 +13 601 996 +14 557 852 +15 646 436 +16 328 675 +17 177 375 +18 583 471 +19 773 287 +20 836 953 +21 354 1 +22 816 413 +23 275 675 +24 758 524 +25 888 890 +26 566 238 +27 975 867 +28 561 623 +29 690 755 +30 313 72 +31 592 403 +32 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML2500_2325_01.vrp new file mode 100644 index 0000000..5b53cc9 --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML2500_2325_01.vrp @@ -0,0 +1,5014 @@ +NAME : XML2500_2325_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 2501 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 116 +NODE_COORD_SECTION +1 500 500 +2 549 62 +3 721 583 +4 869 418 +5 660 424 +6 18 920 +7 875 232 +8 663 578 +9 851 549 +10 46 530 +11 663 349 +12 576 430 +13 142 992 +14 382 70 +15 252 369 +16 512 445 +17 902 222 +18 834 316 +19 718 550 +20 823 572 +21 917 350 +22 943 488 +23 744 13 +24 408 64 +25 640 450 +26 678 742 +27 861 189 +28 871 499 +29 832 391 +30 623 626 +31 365 776 +32 0 126 +33 442 93 +34 422 264 +35 715 480 +36 5 878 +37 244 77 +38 871 355 +39 341 275 +40 789 551 +41 810 345 +42 824 243 +43 465 719 +44 472 615 +45 523 660 +46 418 415 +47 31 872 +48 629 449 +49 731 317 +50 928 745 +51 537 860 +52 843 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3000_2221_01.vrp new file mode 100644 index 0000000..9dbe5b6 --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3000_2221_01.vrp @@ -0,0 +1,6014 @@ +NAME : XML3000_2221_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 3001 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 23 +NODE_COORD_SECTION +1 500 500 +2 42 640 +3 189 573 +4 792 872 +5 912 788 +6 42 231 +7 55 313 +8 209 408 +9 87 382 +10 736 562 +11 46 530 +12 763 665 +13 92 497 +14 18 282 +15 800 812 +16 244 365 +17 792 728 +18 54 241 +19 133 534 +20 120 415 +21 847 780 +22 787 878 +23 51 235 +24 678 742 +25 152 138 +26 225 208 +27 77 373 +28 873 954 +29 44 541 +30 901 756 +31 56 286 +32 17 215 +33 55 470 +34 128 540 +35 134 471 +36 246 532 +37 27 228 +38 880 818 +39 945 798 +40 160 608 +41 697 733 +42 136 171 +43 133 511 +44 71 536 +45 55 591 +46 887 713 +47 128 661 +48 69 346 +49 208 541 +50 834 823 +51 104 569 +52 805 796 +53 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a/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3000_3214_01.vrp b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3000_3214_01.vrp new file mode 100644 index 0000000..88921c1 --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3000_3214_01.vrp @@ -0,0 +1,6014 @@ +NAME : XML3000_3214_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 3001 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 14 +NODE_COORD_SECTION +1 0 0 +2 42 640 +3 189 573 +4 792 872 +5 912 788 +6 42 231 +7 55 313 +8 209 408 +9 87 382 +10 736 562 +11 46 530 +12 763 665 +13 92 497 +14 18 282 +15 800 812 +16 244 365 +17 792 728 +18 54 241 +19 133 534 +20 120 415 +21 847 780 +22 787 878 +23 51 235 +24 678 742 +25 152 138 +26 225 208 +27 77 373 +28 873 954 +29 44 541 +30 901 756 +31 56 286 +32 17 215 +33 55 470 +34 128 540 +35 134 471 +36 246 532 +37 27 228 +38 880 818 +39 945 798 +40 160 608 +41 697 733 +42 136 171 +43 133 511 +44 71 536 +45 55 591 +46 887 713 +47 128 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3500_3325_01.vrp new file mode 100644 index 0000000..2d0acdf --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3500_3325_01.vrp @@ -0,0 +1,7014 @@ +NAME : XML3500_3325_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 3501 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 117 +NODE_COORD_SECTION +1 0 0 +2 549 62 +3 18 920 +4 875 232 +5 55 313 +6 772 256 +7 46 530 +8 663 349 +9 142 992 +10 799 93 +11 382 70 +12 252 369 +13 48 719 +14 902 222 +15 681 223 +16 895 820 +17 737 383 +18 816 447 +19 744 13 +20 709 321 +21 755 288 +22 408 64 +23 640 450 +24 895 182 +25 861 189 +26 365 776 +27 0 126 +28 442 93 +29 422 264 +30 463 36 +31 837 97 +32 694 277 +33 5 878 +34 244 77 +35 921 176 +36 98 697 +37 703 362 +38 341 275 +39 857 191 +40 810 345 +41 730 236 +42 465 719 +43 472 615 +44 523 660 +45 418 415 +46 180 852 +47 31 872 +48 2 845 +49 698 140 +50 928 745 +51 123 726 +52 945 160 +53 537 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diff --git a/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3500_3372_01.vrp b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3500_3372_01.vrp new file mode 100644 index 0000000..1ab699e --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML3500_3372_01.vrp @@ -0,0 +1,7014 @@ +NAME : XML3500_3372_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 3501 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 141 +NODE_COORD_SECTION +1 0 0 +2 549 62 +3 18 920 +4 875 232 +5 55 313 +6 772 256 +7 46 530 +8 663 349 +9 142 992 +10 799 93 +11 382 70 +12 252 369 +13 48 719 +14 902 222 +15 681 223 +16 895 820 +17 737 383 +18 816 447 +19 744 13 +20 709 321 +21 755 288 +22 408 64 +23 640 450 +24 895 182 +25 861 189 +26 365 776 +27 0 126 +28 442 93 +29 422 264 +30 463 36 +31 837 97 +32 694 277 +33 5 878 +34 244 77 +35 921 176 +36 98 697 +37 703 362 +38 341 275 +39 857 191 +40 810 345 +41 730 236 +42 465 719 +43 472 615 +44 523 660 +45 418 415 +46 180 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file mode 100644 index 0000000..1bb4e30 --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML5000_1243_01.vrp @@ -0,0 +1,10014 @@ +NAME : XML5000_1243_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 5001 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 521 +NODE_COORD_SECTION +1 821 782 +2 767 550 +3 189 573 +4 51 235 +5 871 499 +6 768 492 +7 56 286 +8 61 630 +9 27 228 +10 113 268 +11 71 42 +12 136 171 +13 623 518 +14 125 507 +15 23 230 +16 69 346 +17 104 569 +18 21 571 +19 236 230 +20 958 629 +21 736 287 +22 190 528 +23 61 643 +24 854 508 +25 69 467 +26 974 344 +27 110 239 +28 811 57 +29 365 236 +30 122 249 +31 557 406 +32 548 357 +33 4 230 +34 9 574 +35 23 243 +36 64 244 +37 102 536 +38 303 526 +39 96 462 +40 187 580 +41 104 582 +42 164 367 +43 112 177 +44 772 452 +45 120 526 +46 69 480 +47 215 534 +48 154 295 +49 661 476 +50 834 404 +51 85 545 +52 53 778 +53 175 475 +54 698 516 +55 120 539 +56 703 594 +57 78 542 +58 449 714 +59 707 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML5000_2314_01.vrp @@ -0,0 +1,10014 @@ +NAME : XML5000_2314_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 5001 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 14 +NODE_COORD_SECTION +1 500 500 +2 18 920 +3 92 906 +4 163 786 +5 994 714 +6 43 869 +7 794 774 +8 388 86 +9 585 142 +10 442 93 +11 559 89 +12 225 738 +13 435 53 +14 472 615 +15 31 872 +16 698 140 +17 603 805 +18 367 40 +19 167 918 +20 583 567 +21 550 161 +22 935 497 +23 460 618 +24 983 164 +25 244 993 +26 402 118 +27 677 94 +28 202 996 +29 352 297 +30 57 349 +31 520 110 +32 365 236 +33 790 937 +34 557 406 +35 573 121 +36 526 46 +37 393 685 +38 68 888 +39 258 231 +40 717 516 +41 438 872 +42 469 185 +43 680 634 +44 192 816 +45 154 295 +46 687 758 +47 728 301 +48 129 881 +49 74 945 +50 148 894 +51 31 80 +52 65 896 +53 619 250 +54 76 725 +55 561 16 +56 552 196 +57 138 593 +58 47 963 +59 201 878 +60 614 293 +61 449 714 +62 704 186 +63 165 137 +64 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML500_1311_01.vrp new file mode 100644 index 0000000..9ebab3e --- /dev/null +++ b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML500_1311_01.vrp @@ -0,0 +1,1014 @@ +NAME : XML500_1311_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 501 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 4 +NODE_COORD_SECTION +1 821 782 +2 918 283 +3 105 854 +4 33 847 +5 245 586 +6 557 852 +7 104 745 +8 408 64 +9 173 170 +10 117 46 +11 658 858 +12 465 719 +13 861 966 +14 9 961 +15 712 456 +16 2 845 +17 402 379 +18 857 703 +19 78 584 +20 295 24 +21 304 2 +22 258 133 +23 583 567 +24 5 837 +25 254 19 +26 828 132 +27 138 347 +28 676 678 +29 777 837 +30 352 297 +31 74 847 +32 520 110 +33 365 236 +34 214 988 +35 496 832 +36 547 496 +37 619 664 +38 836 998 +39 695 632 +40 68 888 +41 233 49 +42 948 22 +43 719 687 +44 977 732 +45 55 945 +46 540 227 +47 880 561 +48 980 746 +49 657 650 +50 47 977 +51 567 604 +52 689 72 +53 43 845 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML500_1362_01.vrp @@ -0,0 +1,1014 @@ +NAME : XML500_1362_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 501 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 370 +NODE_COORD_SECTION +1 821 782 +2 918 283 +3 105 854 +4 33 847 +5 245 586 +6 557 852 +7 104 745 +8 408 64 +9 173 170 +10 117 46 +11 658 858 +12 465 719 +13 861 966 +14 9 961 +15 712 456 +16 2 845 +17 402 379 +18 857 703 +19 78 584 +20 295 24 +21 304 2 +22 258 133 +23 583 567 +24 5 837 +25 254 19 +26 828 132 +27 138 347 +28 676 678 +29 777 837 +30 352 297 +31 74 847 +32 520 110 +33 365 236 +34 214 988 +35 496 832 +36 547 496 +37 619 664 +38 836 998 +39 695 632 +40 68 888 +41 233 49 +42 948 22 +43 719 687 +44 977 732 +45 55 945 +46 540 227 +47 880 561 +48 980 746 +49 657 650 +50 47 977 +51 567 604 +52 689 72 +53 43 845 +54 608 474 +55 728 301 +56 610 691 +57 11 731 +58 117 898 +59 675 640 +60 721 518 +61 309 601 +62 541 331 +63 426 857 +64 558 738 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b/data/CVRP/cvrplib-Set-XML100/generated_instances/XML500_3134_01.vrp @@ -0,0 +1,1014 @@ +NAME : XML500_3134_01 +COMMENT : Generated as the XML100 dataset from the CVRPLIB +TYPE : CVRP +DIMENSION : 501 +EDGE_WEIGHT_TYPE : EUC_2D +CAPACITY : 110 +NODE_COORD_SECTION +1 0 0 +2 918 283 +3 775 801 +4 11 405 +5 142 992 +6 252 369 +7 105 445 +8 557 852 +9 615 546 +10 408 64 +11 173 170 +12 306 443 +13 365 776 +14 0 126 +15 704 480 +16 515 693 +17 408 347 +18 210 182 +19 465 719 +20 40 27 +21 861 966 +22 523 660 +23 9 961 +24 712 456 +25 279 318 +26 928 745 +27 402 379 +28 857 703 +29 78 584 +30 677 364 +31 258 133 +32 583 567 +33 550 161 +34 497 24 +35 400 320 +36 946 246 +37 828 132 +38 796 814 +39 664 942 +40 823 688 +41 138 347 +42 761 865 +43 352 297 +44 702 654 +45 520 110 +46 254 387 +47 214 988 +48 496 832 +49 547 496 +50 836 998 +51 948 22 +52 335 78 +53 746 76 +54 222 964 +55 747 41 +56 290 830 +57 977 732 +58 540 227 +59 607 128 +60 880 561 +61 980 746 +62 356 393 +63 21 31 +64 689 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b/data/CVRP/cvrplib-Set-XML100/generator.py @@ -0,0 +1,255 @@ +import sys, random, math, os + +# To plot the instance, uncomment the next five lines and the twelve last lines (it deteriorates performance) +# import matplotlib as mpl +# if os.environ.get('DISPLAY','') == '': +# print('no display found. Using non-interactive Agg backend') +# mpl.use('Agg') +# import matplotlib.pyplot as plt + +if len(sys.argv) < 8: + print('Missing arguments:\n\t python generate.py n depotPos custPos demandType avgRouteSize instanceID randSeed') + help=""" + + n (number of customers) + + Depot positioning + 1 = Random + 2 = Centered + 3 = Cornered + + Customer positioning + 1 = Random + 2 = Clustered + 3 = Random-clustered + + Demand distribution + 1 = Unitary + 2 = Small, large var + 3 = Small, small var + 4 = Large, large var + 5 = Large, small var + 6 = Large, depending on quadrant + 7 = Few large, many small + + Average route size + 1 = Very short + 2 = Short + 3 = Medium + 4 = Long + 5 = Very long + 6 = Ultra long + + Output: instance file XML__.vrp + + For more details about the generation process read: + Uchoa et al (2017). New benchmark instances for the Capacitated Vehicle Routing Problem. European Journal of Operational Research + Queiroga, Eduardo, et al. (2022). 10,000 optimal CVRP solutions for testing machine learning based heuristics. + """ + print(help) + exit(0) + + +def distance(x,y): + return math.sqrt((x[0] - y[0])**2 + (x[1] - y[1])**2) + +# constants +maxCoord = 1000 +decay = 40 + +# read input argmuments +n = int(sys.argv[1]) +rootPos = int(sys.argv[2]) +custPos = int(sys.argv[3]) +demandType = int(sys.argv[4]) +instanceID = int(sys.argv[6]) +randSeed = int(sys.argv[7]) # random seed for reproducibility +if demandType > 7: + print("Demant type out of range!") + exit(0) + +random.seed(randSeed) + +nSeeds = random.randint(2,6) + +In = {1:(3,5), 2:(5,8), 3:(8,12), 4:(12,16), 5:(16,25), 6:(25,50)} +avgRouteSize = int(sys.argv[5]) +if avgRouteSize > 6: + print("Average route size out of range!") + exit(0) +r = random.uniform(In[avgRouteSize][0], In[avgRouteSize][1]) + +# change '02d' if you need more than two digits (e.g. with '03d' you can index from 001 to 999) +instanceName = 'XML'+str(n)+'_'+str(rootPos)+str(custPos)+str(demandType)+str(avgRouteSize)+'_'+ format(instanceID, '02d') + +pathToWrite = instanceName+'.vrp' + +depot = (-1,-1) # depot position +S = set() # set of coordinates for the customers + +x_,y_ = (-1,-1) +#Root positioning +if rootPos == 1: + x_ = random.randint(0,maxCoord) + y_ = random.randint(0,maxCoord) +elif rootPos == 2: + x_ = y_ = int(maxCoord/2.0) +elif rootPos == 3: + x_ = y_ = 0 +else: + print("Depot Positioning out of range!") + exit(0) +depot = (x_,y_) + +#Customer positioning +nRandCust = -1 +if custPos == 3: + nRandCust = int(n/2.0) +elif custPos == 2: + nRandCust = 0 +elif custPos == 1: + nRandCust = n + nSeeds = 0 +else: + print("Costumer Positioning out of range!") + exit(0) + +nClustCust = n - nRandCust + +#Generating random customers +for i in range(1, nRandCust+1): + x_ = random.randint(0,maxCoord) + y_ = random.randint(0,maxCoord) + while (x_,y_) in S or (x_,y_) == depot: + x_ = random.randint(0,maxCoord) + y_ = random.randint(0,maxCoord) + S.add((x_,y_)) + +nS = nRandCust + +seeds = [] +# Generation of the clustered customers +if nClustCust > 0: + if nClustCust < nSeeds: + print("Too many seeds!") + exit(0) + + #Generate the seeds + for i in range(nSeeds): + x_ = random.randint(0,maxCoord) + y_ = random.randint(0,maxCoord) + while (x_,y_) in S or (x_,y_) == depot: + x_ = random.randint(0,maxCoord) + y_ = random.randint(0,maxCoord) + S.add((x_,y_)) + seeds.append((x_,y_)) + nS = nS + nSeeds + + # Determine the seed with maximum sum of weights (w.r.t. all seeds) + maxWeight = 0.0 + for i,j in seeds: + w_ij = 0.0 + for i_,j_ in seeds: + w_ij += 2**(-distance((i,j), (i_,j_)) / decay) + if w_ij > maxWeight: + maxWeight = w_ij + + norm_factor = 1.0/maxWeight + + # Generate the remaining customers using Accept-reject method + while nS < n: + x_ = random.randint(0,maxCoord) + y_ = random.randint(0,maxCoord) + while (x_,y_) in S or (x_,y_) == depot: + x_ = random.randint(0,maxCoord) + y_ = random.randint(0,maxCoord) + + weight = 0.0 + for i_,j_ in seeds: + weight += 2**(-distance((x_,y_), (i_,j_)) / decay) + weight *= norm_factor + rand = random.uniform(0,1) + + if rand <= weight: # Will we accept the customer? + S.add((x_,y_)) + nS = nS + 1 + +V = [depot] + list(S) # set of vertices (from now on, the ids are defined) + +# Demands +demandMinValues = [1,1,5,1,50,1,51,50,1] +demandMaxValues = [1,10,10,100,100,50,100,100,10] +demandMin = demandMinValues[demandType-1] +demandMax = demandMaxValues[demandType-1] +demandMinEvenQuadrant = 51 +demandMaxEvenQuadrant = 100 +demandMinLarge = 50 +demandMaxLarge = 100 +largePerRoute = 1.5 +demandMinSmall = 1 +demandMaxSmall = 10 + +D = [] # demands +sumDemands = 0 +maxDemand = 0 + +for i in range(2,n + 2): + j = int((demandMax - demandMin + 1) * random.uniform(0,1) + demandMin) + if demandType == 6: + if (V[i - 1][0] < maxCoord/2.0 and V[i - 1][1] < maxCoord/2.0) or (V[i - 1][0] >= maxCoord/2.0 and V[i - 1][1] >= maxCoord/2.0): + j = int((demandMaxEvenQuadrant - demandMinEvenQuadrant + 1) * random.uniform(0,1) + demandMinEvenQuadrant) + if demandType == 7: + if i < (n / r) * largePerRoute: + j = int((demandMaxLarge - demandMinLarge + 1) * random.uniform(0,1) + demandMinLarge) + else: + j = int((demandMaxSmall - demandMinSmall + 1) * random.uniform(0,1) + demandMinSmall) + D.append(j) + if j > maxDemand: + maxDemand = j + sumDemands = sumDemands + j + +# Generate capacity +capacity = -1 +if sumDemands == n: + capacity = math.floor(r) +else: + capacity = max(maxDemand, math.ceil(r * sumDemands / n)) + +k = math.ceil(sumDemands/float(capacity)) + +f = open(instanceName+'.vrp', 'w') +f.write('NAME : ' + instanceName + '\n') +f.write('COMMENT : Generated as the XML100 dataset from the CVRPLIB\n') +f.write('TYPE : CVRP\n') +f.write('DIMENSION : ' + str(n+1) + '\n') +f.write('EDGE_WEIGHT_TYPE : EUC_2D\n') +f.write('CAPACITY : ' + str(int(capacity)) + '\n') +f.write('NODE_COORD_SECTION\n') + +for i,v in enumerate(V): + f.write('{:<4}'.format(i+1)+' '+'{:<4}'.format(v[0])+' '+'{:<4}'.format(v[1])+'\n') + +f.write('DEMAND_SECTION\n') +if demandType != 6: + random.shuffle(D) +D = [0] + D +for i,d in enumerate(V): + f.write('{:<4}'.format(i+1)+' '+'{:<4}'.format(D[i])+'\n') + +f.write('DEPOT_SECTION\n1\n-1\nEOF\n') + +f.close() + +# x = [v[0] for v in V] +# y = [v[1] for v in V] +# x_s = [v[0] for v in seeds] +# y_s = [v[1] for v in seeds] +# plt.figure(figsize=(20, 20), dpi=80) +# plt.scatter(x, y, marker='o', color='blue',edgecolor='blue', s=40) +# plt.scatter(x_s, y_s, marker='o', color='magenta',edgecolor='magenta', s=40) +# plt.scatter([x[0]], [y[0]], marker='s', edgecolor='black', color='yellow', s=200) +# plt.xticks([]) # Turn off x labels +# plt.yticks([]) # Turn off y labels +# plt.savefig(instanceName+'.png') +# plt.close() +