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Main.py
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# -*- coding: utf-8 -*-
"""
@Time : 2019/04/16 17:26
@Author : Yuppie
"""
import os
import torch
import argparse
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import MultiStepLR
from Train import train, test
from Model_Size import model_size
from Transform_DataSet import Create_Dataset
from Process_DCRNN_Data import load_graph_data
from Sublayers import Graph2S
from Models import Prediction_Model
from Compare_Methods.LSTM import LSTM_Model
from Compare_Methods.ChebNet import Merge_ChebNet
from Compare_Methods.GCGRU import Merge_GCGRU
from Compare_Methods.ST_GCN import STGCN_Model
from collections import OrderedDict
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-file_name", type=str, default="DCRNN_Provide")
parser.add_argument("-beta", type=float, default=2)
parser.add_argument("-num_of_graph", type=int, default=1)
parser.add_argument("-num_of_neighbors", type=int, default=2)
parser.add_argument("-num_of_stations", type=int, default=325)
parser.add_argument("-temporal_in_channel", type=int, default=1)
parser.add_argument("-spatial_out_channel", type=int, default=4)
parser.add_argument("-switch", type=str, default="dot_product")
parser.add_argument("-epoch", type=int, default=600)
parser.add_argument("-batch_size", type=int, default=48)
parser.add_argument("-log", default=None)
parser.add_argument("-save_model", default=None)
parser.add_argument("-save_mode", type=str, choices=["all", "best"], default="best")
option = parser.parse_args()
option.save_model = "Result_Models/STGCN"
device = torch.device("cpu")
training_dataset = DataLoader(Create_Dataset(option.file_name,
mode="train",
dtype=torch.float,
device=device),
batch_size=option.batch_size, shuffle=True, num_workers=32)
validation_dataset = DataLoader(Create_Dataset(option.file_name,
mode="val",
dtype=torch.float,
device=device),
batch_size=option.batch_size, shuffle=True, num_workers=32)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
_, _, c_graph = load_graph_data("DCRNN_Provide/adj_mx_bay.pkl")
c_graph = torch.from_numpy(c_graph)
s_graph = Graph2S(c_graph, option.beta)
# S1
# c_graph = c_graph.unsqueeze(0).repeat(torch.cuda.device_count(), 1, 1).to(device=device, dtype=torch.float)
# s_graph = s_graph.unsqueeze(0).repeat(torch.cuda.device_count(), 1, 1).to(device=device, dtype=torch.float)
# spatial_graph = c_graph
# spatial_graph = [c_graph, s_graph]
# S2
graph_0 = torch.eye(option.num_of_stations).unsqueeze(0).to(device=device, dtype=torch.float)
graph_1 = c_graph.unsqueeze(0).to(device=device, dtype=torch.float)
spatial_graph = torch.cat([graph_0, graph_1 - graph_0], dim=0)
model = Prediction_Model(Ks=option.num_of_neighbors,
encoder_in_channel=option.temporal_in_channel,
encoder_out_channel=option.spatial_out_channel,
num_stations=option.num_of_stations,
switch=option.switch)
# model = LSTM_Model(option.num_of_stations, 1, 4, 1)
# model = Merge_ChebNet(2, 96, 6, 1)
# model = Merge_GCGRU(option.num_of_stations, 1, [8, 8, 1], 2, 3)
model = STGCN_Model(1, 1, [5, 2], option.num_of_stations, False)
model = model.to(device)
if torch.cuda.device_count() > 1:
print("{} GPU(s) will be used.".format(torch.cuda.device_count()))
model = nn.DataParallel(model)
model_size(model, 0)
cudnn.benchmark = True
criterion = nn.MSELoss(size_average=False)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
scheduler = MultiStepLR(optimizer, [10], gamma=1)
option.log = option.save_model
train(model, training_dataset, validation_dataset, spatial_graph, criterion, optimizer, scheduler, option, device)
test_main(option.save_model + ".pkl")
def test_main(model_file_name):
print("Loading Saved Model")
checkpoint = torch.load(model_file_name)
model_state_dict = checkpoint["model"]
option = checkpoint["setting"]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_structure = Prediction_Model(Ks=option.num_of_neighbors,
encoder_in_channel=option.temporal_in_channel,
encoder_out_channel=option.spatial_out_channel,
num_stations=option.num_of_stations,
switch=option.switch)
# model_structure = LSTM_Model(option.num_of_stations, 1, 4, 1)
# model_structure = Merge_ChebNet(2, 96, 6, 1)
# model_structure = Merge_GCGRU(option.num_of_stations, 1, [8, 8, 1], 2, 3)
# model_structure = STGCN_Model(1, 1, [5, 2], option.num_of_stations, False)
if torch.cuda.device_count() > 1:
model_structure = nn.DataParallel(model_structure).to(device)
model_structure.load_state_dict(model_state_dict)
else:
model_structure = model_structure.to(device)
model_state_dict = {k.replace("module.", ""): v for k, v in model_state_dict.item()}
model_structure.load_state_dict(model_state_dict)
device = torch.device("cpu")
print("Loading Testing Dataset")
testing_dataset = DataLoader(Create_Dataset(option.file_name,
mode="test",
dtype=torch.float,
device=device),
batch_size=option.batch_size, num_workers=32)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
_, _, c_graph = load_graph_data("DCRNN_Provide/adj_mx_bay.pkl")
c_graph = torch.from_numpy(c_graph)
s_graph = Graph2S(c_graph, option.beta)
# S1
c_graph = c_graph.unsqueeze(0).repeat(torch.cuda.device_count(), 1, 1).to(device=device, dtype=torch.float)
s_graph = s_graph.unsqueeze(0).repeat(torch.cuda.device_count(), 1, 1).to(device=device, dtype=torch.float)
# spatial_graph = c_graph
spatial_graph = [c_graph, s_graph]
# S2
graph_0 = torch.eye(option.num_of_stations).unsqueeze(0).to(device=device, dtype=torch.float)
graph_1 = c_graph.unsqueeze(0).to(device=device, dtype=torch.float)
# spatial_graph = torch.cat([graph_0, graph_1 - graph_0], dim=0)
criterion = nn.MSELoss(size_average=False)
mae, mape, rmse, loss = test(model_structure, testing_dataset, spatial_graph, criterion, device)
print(
"[ Results ]\n - loss: {:2.4f} - mae: {:2.2f} - mape: {:2.4f} - rmse: {:2.2f}".format(loss, mae, mape,
rmse))
if __name__ == "__main__":
os.environ["CUDA_VISIBLE_DEVICES"] = "0, 1"
print("GPU ID: ", os.environ["CUDA_VISIBLE_DEVICES"])
# main()
test_main("Result_Models/GSTNet.pkl")