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single_dvi_main.py
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########################################################################################################################
# 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, SingleVisLoss
from singleVis.edge_dataset import DataHandler
from singleVis.trainer import SingleVisTrainer
from singleVis.data import NormalDataProvider
from singleVis.spatial_edge_constructor import SingleEpochSpatialEdgeConstructor
from singleVis.projector import DVIProjector
from singleVis.eval.evaluator import Evaluator
from singleVis.utils import knn
########################################################################################################################
# DVI PARAMETERS #
########################################################################################################################
"""This serve as an example of DeepVisualInsight implementation in pytorch."""
VIS_METHOD = "DVI" # DeepVisualInsight
########################################################################################################################
# LOAD PARAMETERS #
########################################################################################################################
parser = argparse.ArgumentParser(description='Process hyperparameters...')
parser.add_argument('--content_path', type=str)
parser.add_argument('--iteration','-i', type=int)
parser.add_argument('-g', type=int)
args = parser.parse_args()
CONTENT_PATH = args.content_path
I = args.iteration
GPU_ID = args.g
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]
# record output information
# now = time.strftime("%Y-%m-%d-%H_%M_%S", time.localtime(time.time()))
# sys.stdout = open(os.path.join(CONTENT_PATH, now+".txt"), "w")
SETTING = config["SETTING"]
CLASSES = config["CLASSES"]
DATASET = config["DATASET"]
PREPROCESS = config["VISUALIZATION"]["PREPROCESS"]
# GPU_ID = config["GPU"]
# Training parameter (subject model)
TRAINING_PARAMETER = config["TRAINING"]
NET = TRAINING_PARAMETER["NET"]
LEN = TRAINING_PARAMETER["train_num"]
EPOCH_START = config["EPOCH_START"]
EPOCH_END = config["EPOCH_END"]
EPOCH_PERIOD = config["EPOCH_PERIOD"]
EPOCH_NAME = config["EPOCH_NAME"]
# Training parameter (visualization model)
VISUALIZATION_PARAMETER = config["VISUALIZATION"]
LAMBDA1 = VISUALIZATION_PARAMETER["LAMBDA1"]
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
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 #
########################################################################################################################
# Define data_provider
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()
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)
# Define Projector
projector = DVIProjector(vis_model=model, content_path=CONTENT_PATH, vis_model_name=VIS_MODEL_NAME, device=DEVICE)
# Define DVI Loss
criterion = SingleVisLoss(umap_loss_fn, recon_loss_fn, lambd=LAMBDA1)
# 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 = SingleEpochSpatialEdgeConstructor(data_provider, I, 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-2#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)
########################################################################################################################
# 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 time result
save_dir = data_provider.model_path
file_name = "time_{}".format(VIS_MODEL_NAME)
save_file = os.path.join(save_dir, file_name+".json")
if not os.path.exists(save_file):
evaluation = dict()
else:
f = open(save_file, "r")
evaluation = json.load(f)
f.close()
if "complex_construction" not in evaluation.keys():
evaluation["complex_construction"] = dict()
evaluation["complex_construction"][str(I)] = round(t1-t0, 3)
if "training" not in evaluation.keys():
evaluation["training"] = dict()
evaluation["training"][str(I)] = round(t3-t2, 3)
with open(save_file, 'w') as f:
json.dump(evaluation, f)
save_dir = os.path.join(data_provider.model_path, "Epoch_{}".format(I))
trainer.save(save_dir=save_dir, file_name="{}".format(VIS_MODEL_NAME))
########################################################################################################################
# VISUALIZATION #
########################################################################################################################
# from singleVis.visualizer import visualizer
# vis = visualizer(data_provider, projector, 200, "tab10")
# save_dir = os.path.join(data_provider.content_path, "img")
# if not os.path.exists(save_dir):
# os.mkdir(save_dir)
# vis.savefig(I, path=os.path.join(save_dir, "{}_{}_{}.png".format(DATASET, I, VIS_METHOD)))
########################################################################################################################
# EVALUATION #
########################################################################################################################
evaluator = Evaluator(data_provider, projector)
evaluator.save_epoch_eval(I, 15, temporal_k=5, file_name="{}".format(EVALUATION_NAME))