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train_and_test_sharan.py
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train_and_test_sharan.py
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#!/usr/bin/python
'''
Script for both training and evaluating the DLoc network
Automatically imports the parameters from params.py.
For further details onto which params file to load
read the README in `params_storage` folder.
'''
import torch
import random
import os
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=FutureWarning)
from utils import *
from modelADT import ModelADT
from Generators import *
from data_loader import load_data
from joint_model import Enc_2Dec_Network
from joint_model import Enc_Dec_Network
from params import *
import trainer
torch.manual_seed(0)
np.random.seed(0)
'''
Defining the paths from where to Load Data.
Assumes that the data is stored in a subfolder called data in the current data folder
'''
tpath = os.path.dirname(os.path.abspath("params.py"))
print(tpath)
#####################################Final Simple Space Results################################################
if "data" in opt_exp and opt_exp.data == "rw_to_rw_atk":
# Training and testing data loaded for the Final results For Env-1 (The smaller space) in the paper (Figure 10a)
trainpath = ['media/datadisk/Roshan/datasets/data_coll_July22_2_ref/204/data123.mat']
testpath = ['./data/dataset_non_fov_test_July18.mat',
'./data/dataset_fov_test_July18.mat']
print('Real World to Real World experiments started')
#####################################Final Complex Space Results################################################
elif "data" in opt_exp and opt_exp.data == "rw_to_rw":
# Training and testing data loaded for the Final results For Env-2 (The larger space) in the paper (Figure 10b)
trainpath = ['/datasets/dataset_non_fov_train_jacobs_July28_2.mat',
'/datasets/dataset_fov_train_jacobs_July28_2.mat']
# trainpath = ['/datasets/dataset_train_jacobs_July28.mat'
# ,'/datasets/dataset_train_jacobs_July28_2.mat']
testpath = ['/datasets/dataset_non_fov_test_jacobs_July28_2.mat',
'/datasets/dataset_fov_test_jacobs_July28_2.mat']
print('Real World to Real World experiments started')
#########################################Generalization across Scenarios###########################################
elif "data" in opt_exp and opt_exp.data == "rw_to_rw_env2":
# Training and testing data loaded for the Final results For Env-2
# for Generalization across scenarios (Table-1) train on 1/3/4 and test on 2
trainpath = ['./data/dataset_edit_jacobs_July28.mat',
'./data/dataset_non_fov_train_jacobs_July28_2.mat',
'./data/dataset_fov_train_jacobs_July28_2.mat',
'./data/dataset_train_jacobs_Aug16_3.mat',
'./data/dataset_train_jacobs_Aug16_4_ref.mat']
testpath = ['./data/dataset_train_jacobs_Aug16_1.mat']
print('Real World to Real World experiments started')
elif "data" in opt_exp and opt_exp.data == "rw_to_rw_env3":
# Training and testing data loaded for the Final results For Env-2
# for Generalization across scenarios (Table-1) train on 1/2/4 and test on 3
trainpath = ['./data/dataset_edit_jacobs_July28.mat',
'./data/dataset_non_fov_train_jacobs_July28_2.mat',
'./data/dataset_fov_train_jacobs_July28_2.mat',
'./data/dataset_train_jacobs_Aug16_1.mat',
'./data/dataset_train_jacobs_Aug16_4_ref.mat']
testpath = ['./data/dataset_train_jacobs_Aug16_3.mat']
print('Real World to Real World experiments started')
elif "data" in opt_exp and opt_exp.data == "rw_to_rw_env4":
# Training and testing data loaded for the Final results For Env-2
# for Generalization across scenarios (Table-1) train on 1/2/3 and test on 4
trainpath = ['./data/dataset_edit_jacobs_July28.mat',
'./data/dataset_non_fov_train_jacobs_July28_2.mat',
'./data/dataset_fov_train_jacobs_July28_2.mat',
'./data/dataset_train_jacobs_Aug16_1.mat',
'./data/dataset_train_jacobs_Aug16_3.mat']
testpath = ['./data/dataset_train_jacobs_Aug16_4_ref.mat']
print('Real World to Real World experiments started')
######################################Generalization Across Bandwidth##########################################
elif "data" in opt_exp and opt_exp.data == "rw_to_rw_40":
# Training and testing data loaded for the Generalization results For Env-2 (The larger space) in the paper (Figure 13a) at 40MHz
trainpath = ['./data/dataset40_edit_jacobs_July28.mat',
'./data/dataset40_non_fov_train_jacobs_July28_2.mat',
'./data/dataset40_fov_train_jacobs_July28_2.mat']
testpath = ['./data/dataset40_fov_test_jacobs_July28_2.mat',
'./data/dataset40_non_fov_test_jacobs_July28_2.mat']
print('Real World to Real World experiments started')
elif "data" in opt_exp and opt_exp.data == "rw_to_rw_20":
# Training and testing data loaded for the Generalization results For Env-2 (The larger space) in the paper (Figure 13a) at 20MHz
trainpath = ['./data/dataset20_edit_jacobs_July28.mat',
'./data/dataset20_non_fov_train_jacobs_July28_2.mat',
'./data/dataset20_fov_train_jacobs_July28_2.mat']
testpath = ['./data/dataset20_fov_test_jacobs_July28_2.mat',
'./data/dataset20_non_fov_test_jacobs_July28_2.mat']
print('Real World to Real World experiments started')
######################################Generalization Across Space##########################################
elif "data" in opt_exp and opt_exp.data == "data_segment":
# Training and testing data loaded for the Final results For Env-2
# for Disjoint Training and Testing(The larger space) in the paper (Figure 13b)
testpath = ['./data/dataset_test_jacobs_July28.mat',
'./data/dataset_test_jacobs_July28_2.mat']
trainpath = ['./data/dataset_train_jacobs_July28.mat',
'./data/dataset_train_jacobs_July28_2.mat']
print('non-FOV to non-FOV experiments started')
######################################################################################################################
'''
Loading Training and Evaluation Data into their respective Dataloaders
'''
# load traning data
B_train,A_train,labels_train = load_data(trainpath[0])
for i in range(len(trainpath)-1):
f,f1,l = load_data(trainpath[i+1])
B_train = torch.cat((B_train, f), 0)
A_train = torch.cat((A_train, f1), 0)
labels_train = torch.cat((labels_train, l), 0)
# labels_train = torch.unsqueeze(labels_train, 1)
train_data = torch.utils.data.TensorDataset(B_train, A_train, labels_train)
# randlist = random.sample(range(0,8000), 1500)
# train_data_subset = torch.utils.data.Subset(train_data, randlist)
train_loader =torch.utils.data.DataLoader(train_data, batch_size=opt_exp.batch_size, shuffle=True)
print(f"A_train.shape: {A_train.shape}")
print(f"B_train.shape: {B_train.shape}")
print(f"labels_train.shape: {labels_train.shape}")
print('# training mini batch = %d' % len(train_loader))
# load testing data
B_test,A_test,labels_test = load_data(testpath[0])
for i in range(len(testpath)-1):
f,f1,l = load_data(testpath[i+1])
B_test = torch.cat((B_test, f), 0)
A_test = torch.cat((A_test, f1), 0)
labels_test = torch.cat((labels_test, l), 0)
# labels_test = torch.unsqueeze(labels_test, 1)
# create data loader
test_data = torch.utils.data.TensorDataset(B_test, A_test, labels_test)
# test_data_subset = torch.utils.data.Subset(test_data, random.sample(range(0, 2000), 100))
test_loader =torch.utils.data.DataLoader(test_data, batch_size=opt_exp.batch_size, shuffle=False)
print(f"A_test.shape: {A_test.shape}")
print(f"B_test.shape: {B_test.shape}")
print(f"labels_test.shape: {labels_test.shape}")
print('# testing mini batch = %d' % len(test_loader))
print('Test Data Loaded')
'''
Initiate the Network and build the graph
'''
# init encoder
enc_model = ModelADT()
enc_model.initialize(opt_encoder)
enc_model.setup(opt_encoder)
# init decoder1
dec_model = ModelADT()
dec_model.initialize(opt_decoder)
dec_model.setup(opt_decoder)
if opt_exp.n_decoders == 2:
# init decoder2
offset_dec_model = ModelADT()
offset_dec_model.initialize(opt_offset_decoder)
offset_dec_model.setup(opt_offset_decoder)
# join all models
print('Making the joint_model')
joint_model = Enc_2Dec_Network()
joint_model.initialize(opt_exp, enc_model, dec_model, offset_dec_model, gpu_ids=opt_exp.gpu_ids)
elif opt_exp.n_decoders == 1:
# join all models
print('Making the joint_model')
joint_model = Enc_Dec_Network()
joint_model.initialize(opt_exp, enc_model, dec_model, gpu_ids=opt_exp.gpu_ids)
else:
print('Incorrect number of Decoders specified in the parameters')
# return -1
# if opt_exp.isFrozen:
# enc_model.load_networks(opt_encoder.starting_epoch_count)
# dec_model.load_networks(opt_decoder.starting_epoch_count)
# if opt_exp.n_decoders == 2:
# offset_dec_model.load_networks(opt_offset_decoder.starting_epoch_count)
# train the model
'''
Trainig the network
'''
trainer.train(joint_model, train_loader, test_loader)
'''
Model Evaluation at the best epoch
'''
epoch = "best" # int/"best"/"last"
# load network
enc_model.load_networks(epoch)
dec_model.load_networks(epoch)
if opt_exp.n_decoders == 2:
offset_dec_model.load_networks(epoch)
joint_model.initialize(opt_exp, enc_model, dec_model, offset_dec_model, gpu_ids = opt_exp.gpu_ids)
elif opt_exp.n_decoders == 1:
joint_model.initialize(opt_exp, enc_model, dec_model, gpu_ids = opt_exp.gpu_ids)
# pass data through model
total_loss, median_error = trainer.test(joint_model,
test_loader,
save_output=True,
save_name=f"decoder_test_result_epoch_{epoch}",
log=False)
print(f"total_loss: {total_loss}, median_error: {median_error}")