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timevis_main.py
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import torch
import sys
import os
import time
import json
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, SingleVisLoss
from singleVis.edge_dataset import DataHandler
from singleVis.trainer import SingleVisTrainer
from singleVis.data import NormalDataProvider
from singleVis.spatial_edge_constructor import kcSpatialEdgeConstructor
from singleVis.temporal_edge_constructor import GlobalTemporalEdgeConstructor
from singleVis.projector import TimeVisProjector
from singleVis.eval.evaluator import Evaluator
from config import load_cfg
########################################################################################################################
# VISUALIZATION SETTING #
########################################################################################################################
VIS_METHOD= "timevis"
########################################################################################################################
# LOAD PARAMETERS #
########################################################################################################################
parser = argparse.ArgumentParser(description='Process hyperparameters...')
parser.add_argument('--content_path', '-c', type=str)
args = parser.parse_args()
CONTENT_PATH = args.content_path
sys.path.append(CONTENT_PATH)
config = load_cfg(os.path.join(CONTENT_PATH, "config", f"{VIS_METHOD}.yaml"))
SETTING = config.SETTING
CLASSES = config.CLASSES
DATASET = config.DATASET
PREPROCESS = config.VISUALIZATION.PREPROCESS
GPU_ID = config.GPU
EPOCH_START = config.EPOCH_START
EPOCH_END = config.EPOCH_END
EPOCH_PERIOD = config.EPOCH_PERIOD
EPOCH_NAME = config.EPOCH_NAME
# 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
SAVE_BATCH_SIZE = VISUALIZATION_PARAMETER.SAVE_BATCH_SIZE
LAMBDA = VISUALIZATION_PARAMETER.LAMBDA
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
T_N_EPOCHS = VISUALIZATION_PARAMETER.T_N_EPOCHS
N_NEIGHBORS = VISUALIZATION_PARAMETER.N_NEIGHBORS
PATIENT = VISUALIZATION_PARAMETER.PATIENT
MAX_EPOCH = VISUALIZATION_PARAMETER.MAX_EPOCH
METRIC = VISUALIZATION_PARAMETER.METRIC
INIT_NUM = VISUALIZATION_PARAMETER.INIT_NUM
ALPHA = VISUALIZATION_PARAMETER.ALPHA
BETA = VISUALIZATION_PARAMETER.BETA
VIS_MODEL_NAME = f"{VIS_METHOD}"
EVALUATION_NAME = f"evaluation_{VIS_MODEL_NAME}"
SEGMENTS = [(EPOCH_START, EPOCH_END)]
# define hyperparameters
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 #
########################################################################################################################
data_provider = NormalDataProvider(CONTENT_PATH, net, EPOCH_START, EPOCH_END, EPOCH_PERIOD, device=DEVICE, classes=CLASSES, epoch_name=EPOCH_NAME, verbose=1)
if PREPROCESS:
data_provider._meta_data(batch_size=SAVE_BATCH_SIZE)
if B_N_EPOCHS >0:
data_provider._estimate_boundary(LEN//10, l_bound=L_BOUND, batch_size=SAVE_BATCH_SIZE)
model = VisModel(ENCODER_DIMS, DECODER_DIMS)
projector = TimeVisProjector(vis_model=model, content_path=CONTENT_PATH, vis_model_name=VIS_MODEL_NAME, device=DEVICE)
########################################################################################################################
# EDGE DATASET #
########################################################################################################################
negative_sample_rate = 5
min_dist = .1
_a, _b = find_ab_params(1.0, min_dist)
umap_loss_fn = UmapLoss(negative_sample_rate, _a, _b, repulsion_strength=1.0)
recon_loss_fn = ReconstructionLoss(beta=1.0)
criterion = SingleVisLoss(umap_loss_fn, recon_loss_fn, lambd=LAMBDA)
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)
t0 = time.time()
spatial_cons = kcSpatialEdgeConstructor(data_provider=data_provider, init_num=INIT_NUM, s_n_epochs=S_N_EPOCHS, b_n_epochs=B_N_EPOCHS, n_neighbors=N_NEIGHBORS,metric=METRIC, MAX_HAUSDORFF=None, ALPHA=ALPHA, BETA=BETA)
s_edge_to, s_edge_from, s_probs, feature_vectors, time_step_nums, time_step_idxs_list, knn_indices, sigmas, rhos, attention = spatial_cons.construct()
temporal_cons = GlobalTemporalEdgeConstructor(X=feature_vectors, time_step_nums=time_step_nums, sigmas=sigmas, rhos=rhos, n_neighbors=N_NEIGHBORS, n_epochs=T_N_EPOCHS)
t_edge_to, t_edge_from, t_probs = temporal_cons.construct()
t1 = time.time()
edge_to = np.concatenate((s_edge_to, t_edge_to),axis=0)
edge_from = np.concatenate((s_edge_from, t_edge_from), axis=0)
probs = np.concatenate((s_probs, t_probs), axis=0)
# probs = probs / (probs.max()+1e-3)
# eliminate_zeros = probs>1e-3
# edge_to = edge_to[eliminate_zeros]
# edge_from = edge_from[eliminate_zeros]
# probs = probs[eliminate_zeros]
dataset = DataHandler(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=1000, sampler=sampler, num_workers=4, prefetch_factor=10)
########################################################################################################################
# TRAIN #
########################################################################################################################
trainer = SingleVisTrainer(model, criterion, optimizer, lr_scheduler, edge_loader=edge_loader, DEVICE=DEVICE)
t2=time.time()
trainer.train(PATIENT, MAX_EPOCH)
t3 = time.time()
save_dir = data_provider.model_path
trainer.record_time(save_dir, "time_{}.json".format(VIS_MODEL_NAME), "complex_construction", "all", t1-t0)
trainer.record_time(save_dir, "time_{}.json".format(VIS_MODEL_NAME), "training", "all", t3-t2)
trainer.save(save_dir=save_dir, file_name="{}".format(VIS_MODEL_NAME))
########################################################################################################################
# VISUALIZATION #
########################################################################################################################
from singleVis.visualizer import visualizer
vis = visualizer(data_provider, projector, 200)
save_dir = os.path.join(data_provider.content_path, "img")
os.makedirs(save_dir, exist_ok=True)
for i in range(EPOCH_START, EPOCH_END+1, EPOCH_PERIOD):
# vis.savefig(i, path=os.path.join(save_dir, "{}_{}.png".format(VIS_METHOD, i)))
vis.savefig(i, "{}_{}.png".format(VIS_METHOD, i))
########################################################################################################################
# EVALUATION #
########################################################################################################################
eval_epochs = range(EPOCH_START, EPOCH_END+1, EPOCH_PERIOD)
evaluator = Evaluator(data_provider, projector, METRIC)
for eval_epoch in eval_epochs:
evaluator.save_epoch_eval(eval_epoch, 15, temporal_k=5, file_name="{}".format(EVALUATION_NAME))