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a6b4c35 af2f07c a6b4c35 d0742c0 a6b4c35 d0742c0 a6b4c35 af2f07c a6b4c35 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 | #!/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 os
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)
if lbl.dim() == 3:
lbl = lbl.unsqueeze(1)
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)
if lbl.dim() == 3:
lbl = lbl.unsqueeze(1)
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
# Check if "best" checkpoint exists; fall back to "latest" if not
if model_type == 'mamba':
best_path = os.path.join(eval_name, 'mamba_dloc', f'best_net_mamba_dloc.pth')
else:
best_path = os.path.join(eval_name, 'encoder', f'best_net_encoder.pth')
if not os.path.exists(best_path):
print(f'WARNING: "best" checkpoint not found at {best_path}, falling back to "latest"')
epoch = "latest"
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}")
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