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# set global seed
import random
import numpy as np
import torch
seed = SEED = 20
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
np.random.seed(seed)
random.seed(seed)
try: # relative import
from model import Model
from dataset import BinaryClassifierDataset as Dataset
from dataset import get_optimize_class
except ImportError:
from .model import Model
from .dataset import BinaryClassifierDataset as Dataset
from .dataset import get_optimize_class
# import
import torch.nn as nn
from torch import optim
from torch.optim import lr_scheduler
from torch.utils.data import DataLoader
from torch.nn import functional as F
import os
import sys
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
# load additional config
import json
config_file = os.path.join(os.path.dirname(os.path.dirname(__file__)), "config.json")
with open(config_file, "r") as f:
additional_config = json.load(f)
# config
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = {
"dataset_root": "from_additional_config",
"batch_size": 500 if __name__ == "__main__" else 50,
"num_workers": 16,
"pre_learning_rate": 0.01,
"learning_rate": 1e-4,
"pre_epochs": 2,
"epochs": 13,
"weight_decay": 0.1,
"save_learning_rate": 2e-5,
"total_save_number": 5,
"tag": os.path.basename(os.path.dirname(__file__)),
"optimize_class": get_optimize_class()[0],
"optimize_class_int": get_optimize_class()[1],
}
config.update(additional_config)
print("Training:", config["optimize_class"])
# Data
dataset = Dataset(
root=config["small_dataset_root"],
train=True,
optimize_class=config["optimize_class"],
)
train_loader = DataLoader(
dataset=dataset,
batch_size=config["batch_size"],
num_workers=config["num_workers"],
shuffle=True,
drop_last=True,
pin_memory=True,
persistent_workers=True,
)
test_loader = DataLoader(
dataset=Dataset(
root=config["small_dataset_root"],
train=False,
optimize_class=config["optimize_class"],
),
batch_size=config["batch_size"],
num_workers=config["num_workers"],
shuffle=False,
)
# Model
model, head = Model()
model = model.to(device)
class FocalLoss(nn.Module):
def __init__(self, weight=None, gamma=2):
super(FocalLoss, self).__init__()
self.weight = weight
self.gamma = gamma
def forward(self, input, target):
ce_loss = F.cross_entropy(input, target, reduction='none', weight=self.weight)
pt = torch.exp(-ce_loss)
focal_loss = (1 - pt) ** self.gamma * ce_loss
return focal_loss.mean()
criterion = FocalLoss()
# Optimizer
head_optimizer = optim.AdamW(
head.parameters(),
lr=config["pre_learning_rate"],
weight_decay=config["weight_decay"],
)
optimizer = optim.AdamW(
model.parameters(),
lr=config["learning_rate"],
weight_decay=config["weight_decay"],
)
scheduler = lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=config["epochs"],
eta_min=config["save_learning_rate"],
)
# Training
def train(model=model, optimizer=optimizer, scheduler=scheduler):
model.train()
for batch_idx, (inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
with torch.amp.autocast("cuda", enabled=False, dtype=torch.bfloat16):
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
if scheduler is not None:
scheduler.step()
# test
@torch.no_grad()
def test(model=model):
model.eval()
all_targets = []
all_predicts = []
test_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(test_loader):
inputs, targets = inputs.to(device), targets.to(device)
with torch.amp.autocast("cuda", enabled=False, dtype=torch.bfloat16):
outputs = model(inputs)
loss = criterion(outputs, targets)
# to logging losses
all_targets.extend(targets.flatten().tolist())
test_loss += loss.item()
_, predicts = outputs.max(1)
all_predicts.extend(predicts.flatten().tolist())
total += targets.size(0)
correct += predicts.eq(targets).sum().item()
loss = test_loss / (batch_idx + 1)
acc = correct / total
print(f"Loss: {loss:.4f} | Acc: {acc:.4f}\n")
model.train()
return loss, acc, all_targets, all_predicts
# save train
def save_train(model=model, optimizer=optimizer):
data_loader = DataLoader(
dataset=dataset,
batch_size=min(len(dataset) // config["total_save_number"], config["batch_size"]),
num_workers=config["num_workers"],
shuffle=True,
drop_last=True,
)
model.train()
for batch_idx, (inputs, targets) in enumerate(data_loader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
with torch.amp.autocast("cuda", enabled=False, dtype=torch.bfloat16):
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
# Save checkpoint
_, acc, _, _ = test(model=model)
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
save_state = {key: value.cpu().to(torch.float32) for key, value in model.state_dict().items()}
torch.save(save_state, f"checkpoint/{str(batch_idx).zfill(4)}_acc{acc:.4f}_class{config['optimize_class_int']}_{config['tag']}.pth")
print("save:", f"checkpoint/{str(batch_idx).zfill(4)}_acc{acc:.4f}_class{config['optimize_class_int']}_{config['tag']}.pth")
# exit loop
if batch_idx+1 == config["total_save_number"]:
break
# main
if __name__ == '__main__':
for epoch in range(config["pre_epochs"]):
train(model=model, optimizer=head_optimizer, scheduler=None)
# test(model=model)
for epoch in range(config["epochs"]):
train(model=model, optimizer=optimizer, scheduler=scheduler)
# test(model=model)
save_train(model=model, optimizer=optimizer)
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