######################################################################################################################## # IMPORT # ######################################################################################################################## import torch import sys import os import json import time import numpy as np import argparse from torch.utils.data import DataLoader from torch.utils.data import WeightedRandomSampler from umap.umap_ import find_ab_params from singleVis.custom_weighted_random_sampler import CustomWeightedRandomSampler from singleVis.SingleVisualizationModel import VisModel from singleVis.losses import UmapLoss, ReconstructionLoss, TemporalLoss, DVILoss, SingleVisLoss, DummyTemporalLoss from singleVis.edge_dataset import DVIDataHandler from singleVis.trainer import DVIALMODITrainer from singleVis.data import NormalDataProvider from singleVis.spatial_skeleton_edge_constructor import OriginSingleEpochSpatialEdgeConstructor, PredDistSingleEpochSpatialEdgeConstructor from singleVis.projector import DVIProjector from singleVis.eval.evaluator import Evaluator from singleVis.utils import find_neighbor_preserving_rate from singleVis.visualizer import visualizer from trustVis.skeleton_generator import CenterSkeletonGenerator ######################################################################################################################## # PARAMETERS # ######################################################################################################################## """This serve as an example of DeepVisualInsight implementation in pytorch.""" VIS_METHOD = "DVI" # DeepVisualInsight ######################################################################################################################## # LOAD PARAMETERS # ######################################################################################################################## parser = argparse.ArgumentParser(description='Process hyperparameters...') # get workspace dir current_path = os.getcwd() new_path = os.path.join(current_path, 'training_dynamic') parser.add_argument('--content_path', type=str,default=new_path) parser.add_argument('--base', type=str,default='proxy') parser.add_argument('--name', type=str,default='trustvis') parser.add_argument('--start', type=int,default=1) parser.add_argument('--end', type=int,default=3) parser.add_argument('--epoch', type=int,default=3) args = parser.parse_args() SAVED_NAME = args.name CONTENT_PATH = args.content_path sys.path.append(CONTENT_PATH) with open(os.path.join(CONTENT_PATH, "config.json"), "r") as f: config = json.load(f) config = config[VIS_METHOD] SETTING = config["SETTING"] CLASSES = config["CLASSES"] DATASET = config["DATASET"] PREPROCESS = config["VISUALIZATION"]["PREPROCESS"] GPU_ID = config["GPU"] EPOCH_START = args.epoch EPOCH_END = args.epoch EPOCH_PERIOD = 1 # Training parameter (subject model) TRAINING_PARAMETER = config["TRAINING"] NET = TRAINING_PARAMETER["NET"] LEN = TRAINING_PARAMETER["train_num"] # Training parameter (visualization model) VISUALIZATION_PARAMETER = config["VISUALIZATION"] LAMBDA1 = VISUALIZATION_PARAMETER["LAMBDA1"] LAMBDA2 = VISUALIZATION_PARAMETER["LAMBDA2"] B_N_EPOCHS = VISUALIZATION_PARAMETER["BOUNDARY"]["B_N_EPOCHS"] L_BOUND = VISUALIZATION_PARAMETER["BOUNDARY"]["L_BOUND"] ENCODER_DIMS = VISUALIZATION_PARAMETER["ENCODER_DIMS"] DECODER_DIMS = VISUALIZATION_PARAMETER["DECODER_DIMS"] S_N_EPOCHS = VISUALIZATION_PARAMETER["S_N_EPOCHS"] N_NEIGHBORS = VISUALIZATION_PARAMETER["N_NEIGHBORS"] PATIENT = VISUALIZATION_PARAMETER["PATIENT"] MAX_EPOCH = VISUALIZATION_PARAMETER["MAX_EPOCH"] VIS_MODEL_NAME = VISUALIZATION_PARAMETER["VIS_MODEL_NAME"] EVALUATION_NAME = VISUALIZATION_PARAMETER["EVALUATION_NAME"] # Define hyperparameters GPU_ID = 1 DEVICE = torch.device("cuda:{}".format(GPU_ID) if torch.cuda.is_available() else "cpu") import Model.model as subject_model net = eval("subject_model.{}()".format(NET)) ######################################################################################################################## # TRAINING SETTING # ######################################################################################################################## BASE_MODEL_NAME = args.base # PREPROCESS = 1 # Define data_provider data_provider = NormalDataProvider(CONTENT_PATH, net, EPOCH_START, EPOCH_END, EPOCH_PERIOD, device=DEVICE, classes=CLASSES, epoch_name='Epoch', verbose=1) # if PREPROCESS: # data_provider._meta_data() # if B_N_EPOCHS >0: # data_provider._estimate_boundary(LEN//10, l_bound=L_BOUND) # Define visualization models model = VisModel(ENCODER_DIMS, DECODER_DIMS) # Define Losses negative_sample_rate = 5 min_dist = .1 _a, _b = find_ab_params(1.0, min_dist) umap_loss_fn = UmapLoss(negative_sample_rate, DEVICE, _a, _b, repulsion_strength=1.0) recon_loss_fn = ReconstructionLoss(beta=1.0) single_loss_fn = SingleVisLoss(umap_loss_fn, recon_loss_fn, lambd=LAMBDA1) # Define Projector projector = DVIProjector(vis_model=model, content_path=CONTENT_PATH, vis_model_name=BASE_MODEL_NAME, device=DEVICE) # vis_model_name 一个初始的dvi start_flag = 1 prev_model = VisModel(ENCODER_DIMS, DECODER_DIMS) for iteration in range(EPOCH_START, EPOCH_END+EPOCH_PERIOD, EPOCH_PERIOD): # Define DVI Loss if start_flag: temporal_loss_fn = DummyTemporalLoss(DEVICE) criterion = DVILoss(umap_loss_fn, recon_loss_fn, temporal_loss_fn, lambd1=LAMBDA1, lambd2=0.0, device=DEVICE) start_flag = 0 else: # TODO AL mode, redefine train_representation prev_data = data_provider.train_representation(iteration-EPOCH_PERIOD) curr_data = data_provider.train_representation(iteration) npr = find_neighbor_preserving_rate(prev_data, curr_data, N_NEIGHBORS) temporal_loss_fn = TemporalLoss(w_prev, DEVICE) criterion = DVILoss(umap_loss_fn, recon_loss_fn, temporal_loss_fn, lambd1=LAMBDA1, lambd2=torch.from_numpy(LAMBDA2*npr), device=DEVICE) vis = visualizer(data_provider, projector, 200, "tab10") grid_high, grid_emd ,border = vis.get_epoch_decision_view(iteration,400,None, True) train_data_embedding = projector.batch_project(iteration, data_provider.train_representation(iteration)) from sklearn.neighbors import NearestNeighbors import numpy as np # 假设 train_data_embedding 和 grid_emd 都是 numpy arrays，每一行都是一个点 threshold = 5 # hyper-peremeter # use train_data_embedding initialize NearestNeighbors nbrs = NearestNeighbors(n_neighbors=1, algorithm='ball_tree').fit(train_data_embedding) # for each grid_emd，find train_data_embedding nearest sample distances, indices = nbrs.kneighbors(grid_emd) # filter by distance mask = distances.ravel() < threshold selected_indices = np.arange(grid_emd.shape[0])[mask] grid_high_mask = grid_high[selected_indices] skeleton_generator = CenterSkeletonGenerator(data_provider,iteration,0.5,500) high_bom, high_rad = skeleton_generator.center_skeleton_genertaion() print("number",len(high_bom)) # Define training parameters optimizer = torch.optim.Adam(model.parameters(), lr=.01, weight_decay=1e-5) lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=4, gamma=.1) # Define Edge dataset t0 = time.time() spatial_cons = OriginSingleEpochSpatialEdgeConstructor(data_provider, iteration, S_N_EPOCHS, B_N_EPOCHS, N_NEIGHBORS) edge_to, edge_from, probs, feature_vectors, attention = spatial_cons.construct() t1 = time.time() probs = probs / (probs.max()+1e-3) eliminate_zeros = probs> 1e-3 #1e-3 edge_to = edge_to[eliminate_zeros] edge_from = edge_from[eliminate_zeros] probs = probs[eliminate_zeros] dataset = DVIDataHandler(edge_to, edge_from, feature_vectors, attention) n_samples = int(np.sum(S_N_EPOCHS * probs) // 1) # chose sampler based on the number of dataset if len(edge_to) > pow(2,24): sampler = CustomWeightedRandomSampler(probs, n_samples, replacement=True) else: sampler = WeightedRandomSampler(probs, n_samples, replacement=True) edge_loader = DataLoader(dataset, batch_size=2000, sampler=sampler, num_workers=8, prefetch_factor=10) ######################################################################################################################## # TRAIN # ######################################################################################################################## file_path = os.path.join(data_provider.content_path, "Model", "Epoch_{}".format(iteration), "{}.pth".format(BASE_MODEL_NAME)) save_model = torch.load(file_path, map_location="cpu") model.load_state_dict(save_model["state_dict"]) trainer = DVIALMODITrainer(model, criterion, optimizer, lr_scheduler, edge_loader=edge_loader, DEVICE=DEVICE, grid_high_mask=grid_high_mask, high_bom=high_bom, high_rad=high_rad, iteration=iteration, data_provider=data_provider, prev_model=prev_model, S_N_EPOCHS=S_N_EPOCHS, B_N_EPOCHS=B_N_EPOCHS, N_NEIGHBORS=N_NEIGHBORS) t2=time.time() trainer.train(PATIENT, MAX_EPOCH) t3 = time.time() # save result save_dir = data_provider.model_path trainer.record_time(save_dir, "time_{}".format(VIS_MODEL_NAME), "complex_construction", str(iteration), t1-t0) trainer.record_time(save_dir, "time_{}".format(VIS_MODEL_NAME), "training", str(iteration), t3-t2) save_dir = os.path.join(data_provider.model_path, "Epoch_{}".format(iteration)) trainer.save(save_dir=save_dir, file_name="{}".format(SAVED_NAME)) print("Finish epoch {}...".format(iteration)) prev_model.load_state_dict(model.state_dict()) for param in prev_model.parameters(): param.requires_grad = False w_prev = dict(prev_model.named_parameters()) print('aaacccllll runtime', t3-t0) ######################################################################################################################## # VISUALIZATION # ######################################################################################################################## from singleVis.visualizer import visualizer vis = visualizer(data_provider, projector, 200, "tab10") save_dir = os.path.join(data_provider.content_path, "Trust_al") if not os.path.exists(save_dir): os.mkdir(save_dir) for i in range(EPOCH_START, EPOCH_END+1, EPOCH_PERIOD): vis.savefig(i, path=os.path.join(save_dir, "{}_{}_{}.png".format(VIS_MODEL_NAME, i, VIS_METHOD))) ######################################################################################################################## # EVALUATION # ######################################################################################################################## evaluator = Evaluator(data_provider, projector) Evaluation_NAME = 'trustvis_al_eval' for i in range(EPOCH_START, EPOCH_END+1, EPOCH_PERIOD): evaluator.save_epoch_eval(i, 15, temporal_k=5, file_name="{}".format(Evaluation_NAME))