Spaces:
Running
Running
File size: 13,196 Bytes
719d0db |
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 |
from tqdm.autonotebook import tqdm
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchmetrics.classification import MulticlassAccuracy, MulticlassF1Score
from models.classifiers.nn_classifiers.nn_classifier import NNClassifier
from models.loss_functions import GeneralCrossEntropy
from utils.data_utils.tsptw_dataset import TSPTWDataloader
from utils.data_utils.pctsp_dataset import PCTSPDataloader
from utils.data_utils.pctsptw_dataset import PCTSPTWDataloader
from utils.data_utils.cvrp_dataset import CVRPDataloader
from utils.utils import set_device, count_trainable_params, batched_bincount, fix_seed
def main(args):
#---------------
# seed settings
#---------------
fix_seed(args.seed)
#--------------
# gpu settings
#--------------
use_cuda, device = set_device(args.gpu)
#-------------------
# model & optimizer
#-------------------
num_classes = 3 if args.problem == "pctsptw" else 2
model = NNClassifier(problem=args.problem,
node_enc_type=args.node_enc_type,
edge_enc_type=args.edge_enc_type,
dec_type=args.dec_type,
emb_dim=args.emb_dim,
num_enc_mlp_layers=args.num_enc_mlp_layers,
num_dec_mlp_layers=args.num_dec_mlp_layers,
num_classes=num_classes,
dropout=args.dropout,
pos_encoder=args.pos_encoder)
is_sequential = model.is_sequential
if use_cuda:
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# count number of trainable parameters
num_trainable_params = count_trainable_params(model)
print(f"num_trainable_params: {num_trainable_params}")
with open(f"{args.model_checkpoint_path}/num_trainable_params.dat", "w") as f:
f.write(str(num_trainable_params))
# loss function
if not is_sequential:
assert args.loss_function != "seq_cbce", "Non-sequential model does not support the loss funtion: seq_cbce"
loss_func = GeneralCrossEntropy(weight_type=args.loss_function, beta=args.cb_beta, is_sequential=is_sequential)
#---------
# dataset
#---------
if args.problem == "tsptw":
train_dataset = TSPTWDataloader(args.train_dataset_path, sequential=is_sequential, parallel=args.parallel, num_cpus=args.num_cpus)
if args.valid_dataset_path is not None:
valid_dataset = TSPTWDataloader(args.valid_dataset_path, sequential=is_sequential, parallel=args.parallel, num_cpus=args.num_cpus)
elif args.problem == "pctsp":
train_dataset = PCTSPDataloader(args.train_dataset_path, sequential=is_sequential, parallel=args.parallel, num_cpus=args.num_cpus)
if args.valid_dataset_path is not None:
valid_dataset = PCTSPDataloader(args.valid_dataset_path, sequential=is_sequential, parallel=args.parallel, num_cpus=args.num_cpus)
elif args.problem == "pctsptw":
train_dataset = PCTSPTWDataloader(args.train_dataset_path, sequential=is_sequential, parallel=args.parallel, num_cpus=args.num_cpus)
if args.valid_dataset_path is not None:
valid_dataset = PCTSPTWDataloader(args.valid_dataset_path, sequential=is_sequential, parallel=args.parallel, num_cpus=args.num_cpus)
elif args.problem == "cvrp":
train_dataset = CVRPDataloader(args.train_dataset_path, sequential=is_sequential, parallel=args.parallel, num_cpus=args.num_cpus)
if args.valid_dataset_path is not None:
valid_dataset = CVRPDataloader(args.valid_dataset_path, sequential=is_sequential, parallel=args.parallel, num_cpus=args.num_cpus)
else:
raise NotImplementedError
#------------
# dataloader
#------------
if is_sequential:
def pad_seq_length(batch):
data = {}
for key in batch[0].keys():
padding_value = True if key == "mask" else 0.0
# post-padding
data[key] = torch.nn.utils.rnn.pad_sequence([d[key] for d in batch], batch_first=True, padding_value=padding_value)
pad_mask = torch.nn.utils.rnn.pad_sequence([torch.full((d["mask"].size(0), ), True) for d in batch], batch_first=True, padding_value=False)
data.update({"pad_mask": pad_mask})
return data
collate_fn = pad_seq_length
else:
collate_fn = None
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=collate_fn,
num_workers=args.num_workers)
if args.valid_dataset_path is not None:
valid_dataloader = DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
collate_fn=collate_fn,
num_workers=args.num_workers)
#---------
# metrics
#---------
macro_accuracy = MulticlassF1Score(num_classes=num_classes, average="macro")
if use_cuda:
macro_accuracy.to(device)
#---------------
# training loop
#---------------
best_valid_accuracy = 0.0
model.train()
with tqdm(range(args.epochs + 1)) as tq1:
for epoch in tq1:
#--------------------------
# save the current weights
#--------------------------
# print(f"Epoch {epoch}: saving a model to {args.model_checkpoint_path}/model_epoch{epoch}.pth...", end="", flush=True)
torch.save(model.cpu().state_dict(), f"{args.model_checkpoint_path}/model_epoch{epoch}.pth")
model.to(device)
# print("done.")
#------------
# validation
#------------
model.eval()
with torch.no_grad():
tq1.set_description(f"Epoch {epoch}")
# check train accuracy
for data in train_dataloader:
if use_cuda:
data = {key: value.to(device) for key, value in data.items()}
probs = model(data)
if is_sequential:
mask = data["pad_mask"].view(-1) # [batch_size x max_seq_length] -> [(batch_size*max_seq_length)]
macro_accuracy(probs.argmax(-1).view(-1)[mask], data["labels"].view(-1)[mask])
else:
macro_accuracy(probs.argmax(-1).view(-1), data["labels"].view(-1))
train_macro_accuracy = macro_accuracy.compute()
# print(f"Epoch {epoch}: Train_accuracy={total_macro_accuracy}", flush=True)
macro_accuracy.reset()
# check valid accuracy
if args.valid_dataset_path is not None:
for data in valid_dataloader:
if use_cuda:
data = {key: value.to(device) for key, value in data.items()}
probs = model(data)
if is_sequential:
mask = data["pad_mask"].view(-1) # [batch_size x max_seq_length] -> [(batch_size*max_seq_length)]
macro_accuracy(probs.argmax(-1).view(-1)[mask], data["labels"].view(-1)[mask])
else:
macro_accuracy(probs.argmax(-1).view(-1), data["labels"].view(-1))
valid_macro_accuracy = macro_accuracy.compute()
# print(f"Epoch {epoch}: Valid_accuracy={total_macro_accuracy}", flush=True)
macro_accuracy.reset()
model.train()
tq1.set_postfix(Train_accuracy=train_macro_accuracy.item(), Valid_accuracy=valid_macro_accuracy.item())
# update the best epoch
if valid_macro_accuracy >= best_valid_accuracy:
best_valid_accuracy = valid_macro_accuracy
with open(f"{args.model_checkpoint_path}/best_epoch.dat", "w") as f:
f.write(str(epoch))
#--------------------
# update the weights
#--------------------
if epoch < args.epochs:
with tqdm(train_dataloader, leave=False) as tq:
tq.set_description(f"Epoch {epoch}")
for data in tq:
if use_cuda:
data = {key: value.to(device) for key, value in data.items()}
out = model(data)
if is_sequential:
loss = loss_func(out, data["labels"], data["pad_mask"])
else:
loss = loss_func(out, data["labels"])
# if is_sequential:
# # mask = data["pad_mask"].view(-1) # [batch_size x max_seq_length] -> [(batch_size*max_seq_length)]
# # # out = out.view(-1, out.size(-1))
# # # bincount = data["labels"].view(-1)[mask].bincount()
# # # weight = bincount.min() / bincount
# # # loss = F.nll_loss(out[mask], data["labels"].view(-1)[mask], weight=weight)
# # bin = batched_bincount(data["labels"].T, 1, out.size(-1)) # [max_seq_length x num_classes]
# # bin_max, _ = bin.max(-1)
# # weight = bin_max[:, None] / (bin + 1e-8)
# # weight = weight / weight.max(-1, keepdim=True)[0]
# # # weight = (1 - beta) / (1 - beta**bin)
# # # print(weight)
# # loss = 0.0 # torch.FloatTensor([0.0]).to(device)
# # for seq_no in range(weight.size(0)):
# # loss += F.nll_loss(out[:, seq_no], data["labels"][:, seq_no], weight=weight[seq_no])
# else:
# bincount = data["labels"].view(-1).bincount()
# weight = (1 - beta) / (1 - beta**bincount)
# loss = F.nll_loss(out, data["labels"].squeeze(-1), weight=weight)
optimizer.zero_grad()
loss.backward()
optimizer.step()
tq.set_postfix(Loss=loss.item())
if __name__ == "__main__":
import datetime
import json
import os
import argparse
now = datetime.datetime.now()
parser = argparse.ArgumentParser()
# general settings
parser.add_argument("-p", "--problem", default="tsptw", type=str, help="Problem type: [tsptw, cvrptw]")
parser.add_argument("--gpu", default=-1, type=int, help="Used GPU Number: gpu=-1 indicates using cpu")
parser.add_argument("--num_workers", default=4, type=int, help="Number of workers in dataloader")
parser.add_argument("-s", "--seed", type=int, default=1234, help="Random seed for reproductivity")
# data setting
parser.add_argument("-train", "--train_dataset_path", type=str, help="Path to a read file", required=True)
parser.add_argument("-valid", "--valid_dataset_path", type=str, default=None)
parser.add_argument("--parallel", action="store_true")
parser.add_argument("--num_cpus", type=int, default=4)
# training settings
parser.add_argument("-e", "--epochs", default=100, type=int, help="Number of epochs")
parser.add_argument("-b", "--batch_size", default=256, type=int, help="Batch size")
parser.add_argument("--lr", default=0.001, type=float, help="Learning rate")
parser.add_argument("--cb_beta", default=0.99)
# parser.add_argument("--valid_interval", default=1, type=int, help="interval outputting intermidiate test accuracy")
# parser.add_argument("--model_save_interval", type=int, default=1)
parser.add_argument("--model_checkpoint_path", type=str, default=f"checkpoints/model_{now.strftime('%Y%m%d_%H%M%S')}")
# model settings
parser.add_argument("-loss", "--loss_function", type=str, default="seq_cbce", help="[seq_cbce, cbce, wce, ce]")
parser.add_argument("-node_enc", "--node_enc_type", type=str, default="mlp")
parser.add_argument("-edge_enc", "--edge_enc_type", type=str, default="attn")
parser.add_argument("-dec", "--dec_type", type=str, default="lstm")
parser.add_argument("-pe", "--pos_encoder", type=str, default="sincos")
parser.add_argument("--emb_dim", type=int, default=128)
parser.add_argument("--num_enc_mlp_layers", type=int, default=2)
parser.add_argument("--num_dec_mlp_layers", type=int, default=3)
parser.add_argument("--dropout", default=0.0, type=float, help="Dropout probability")
args = parser.parse_args()
os.makedirs(args.model_checkpoint_path, exist_ok=True)
with open(f'{args.model_checkpoint_path}/cmd_args.dat', 'w') as f:
json.dump(args.__dict__, f, indent=2)
main(args) |