Delete duplicate/old file: \/train_and_test.py

///train_and_test.py

@@ -1,255 +0,0 @@
-#!/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 sys
-import torch
-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, LazyMultiHDF5Dataset
-from joint_model import Enc_2Dec_Network
-from joint_model import Enc_Dec_Network
-from joint_model import Mamba_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
-'''
-
-#####################################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 = ['./data/dataset_non_fov_train_July18.mat',
-                './data/dataset_fov_train_July18.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 = ['./data/dataset_jacobs_July28.mat',
-                './data/dataset_non_fov_train_jacobs_July28_2.mat',
-                './data/dataset_fov_train_jacobs_July28_2.mat']
-    testpath = ['./data/dataset_fov_test_jacobs_July28_2.mat',
-                './data/dataset_non_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_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_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_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_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_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)
-    trainpath = ['./data/dataset_train_jacobs_July28.mat',
-                './data/dataset_train_jacobs_July28_2.mat']
-    testpath = ['./data/dataset_test_jacobs_July28.mat',
-                './data/dataset_test_jacobs_July28_2.mat']
-    print('non-FOV to non-FOV experiments started')
-
-######################################################################################################################
-'''
-Loading Training and Evaluation Data into RAM.
-'''
-print('Loading training files into RAM...')
-train_wo, train_w, train_lbl = [], [], []
-for fp in trainpath:
-    wo, w, lbl = load_data(fp)
-    train_wo.append(wo)
-    train_w.append(w)
-    train_lbl.append(lbl)
-train_wo = torch.cat(train_wo, dim=0)
-train_w = torch.cat(train_w, dim=0)
-train_lbl = torch.cat(train_lbl, dim=0)
-train_data = torch.utils.data.TensorDataset(train_wo, train_w, train_lbl)
-train_loader = torch.utils.data.DataLoader(
-    train_data,
-    batch_size=opt_exp.batch_size,
-    shuffle=True,
-    num_workers=4,
-    pin_memory=True,
-    persistent_workers=True,
-)
-print(f'# training samples = {len(train_data)}')
-print(f'# training mini batch = {len(train_loader)}')
-
-print('Loading testing files into RAM...')
-test_wo, test_w, test_lbl = [], [], []
-for fp in testpath:
-    wo, w, lbl = load_data(fp)
-    test_wo.append(wo)
-    test_w.append(w)
-    test_lbl.append(lbl)
-test_wo = torch.cat(test_wo, dim=0)
-test_w = torch.cat(test_w, dim=0)
-test_lbl = torch.cat(test_lbl, dim=0)
-test_data = torch.utils.data.TensorDataset(test_wo, test_w, test_lbl)
-test_loader = torch.utils.data.DataLoader(
-    test_data,
-    batch_size=opt_exp.batch_size,
-    shuffle=False,
-    num_workers=4,
-    pin_memory=True,
-    persistent_workers=True,
-)
-print(f'# testing samples = {len(test_data)}')
-print(f'# testing mini batch = {len(test_loader)}')
-print('Data Loaded (RAM)')
-
-'''
-Initiate the Network and build the graph
-'''
-
-model_type = getattr(opt_exp, 'model_type', 'dloc')
-
-if model_type == 'mamba':
-    # Mamba: single end-to-end model wrapped in one ModelADT
-    from params import opt_mamba
-    mamba_model = ModelADT()
-    mamba_model.initialize(opt_mamba)
-    mamba_model.setup(opt_mamba)
-
-    print('Making the Mamba joint model')
-    joint_model = Mamba_Network()
-    joint_model.initialize(opt_exp, mamba_model, gpu_ids=opt_exp.gpu_ids)
-
-else:
-    # Original DLoc pipeline
-    # 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')
-        sys.exit(-1)
-
-    if getattr(opt_exp, 'isFrozen', False):
-        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"
-eval_name = opt_exp.checkpoints_dir
-
-if model_type == 'mamba':
-    # Mamba: reload single model
-    mamba_model.load_networks(epoch, load_dir=eval_name)
-    joint_model.initialize(opt_exp, mamba_model, gpu_ids=opt_exp.gpu_ids)
-else:
-    # DLoc: reload encoder + decoder(s)
-    enc_model.load_networks(epoch, load_dir=eval_name)
-    dec_model.load_networks(epoch, load_dir=eval_name)
-    if opt_exp.n_decoders == 2:
-        offset_dec_model.load_networks(epoch, load_dir=eval_name)
-        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_dir=eval_name,
-    save_name=f"decoder_test_result_epoch_{epoch}",
-    log=False)
-print(f"total_loss: {total_loss}, median_error: {median_error}")