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import os
import numpy as np
import torch
import torch.distributed as dist
from torch.cuda.amp import GradScaler
from abc import ABC, abstractmethod
from utils.iteration.iterator import MetricMeter
from utils.ddp_utils import gather_object_across_processes
class BaseSegmentationModel(ABC):
"""
This class is an abstract base class (ABC) for segmentation models.
To create a subclass, you need to implement the following four methods:
-- <__init__>: initialize the class.
-- <set_input>: unpack data from dataset.
-- <optimize_parameters>: calculate losses, gradients, and update network weights.
-- <evaluate_one_step>: performance evaluation.
"""
def __init__(self, cfg, num_classes, amp=False):
# initialize training CUDA devices
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# training configuration
self.cfg = cfg
self.num_classes = num_classes
self.is_mixed = amp
self.scaler = GradScaler()
self.start_epoch = -1
# initialize networks, criterion, optimizer and scheduler
self.network = None
self.criterion = None
self.optimizer = None
self.scheduler = None
# visualization
self.visual_names = []
self.loss_names = []
def train(self):
self.network.train()
return self
def eval(self):
self.network.eval()
return self
def training(self):
return self.network.training
def initialize_metric_meter(self, class_list):
self.class_list = class_list
self.metric_meter = MetricMeter(metrics=['dice', 'hd95', 'asd'], class_names=class_list, subject_names=['name'])
self.train_loss = MetricMeter(metrics=self.loss_names, class_names=['train'])
self.val_loss = MetricMeter(metrics=['loss'], class_names=['val'])
def update_loss_meter(self, print=False):
loss_dict = {}
for loss_name in self.loss_names:
try:
loss_value = float(getattr(self, loss_name))
loss_list = gather_object_across_processes(loss_value)
loss_value = np.mean(loss_list)
except:
continue
loss_dict['train_{}'.format(loss_name)] = loss_value
self.train_loss.update(loss_dict)
stats = self.train_loss.report(print_stats=print, mean_only=True)
return stats
@abstractmethod
def set_input(self, *args, **kwargs):
raise NotImplementedError
@abstractmethod
def optimize_parameters(self, *args, **kwargs):
raise NotImplementedError
@abstractmethod
def evaluate_one_step(self, *args, **kwargs):
raise NotImplementedError
def load_networks(self, ckpt_path, resume_training=False):
checkpoint = torch.load(ckpt_path, map_location=self.device)
print('Load ckpt weight: {}'.format(ckpt_path))
self.network.load_state_dict(checkpoint['net'])
if resume_training:
print('Load training config for breakpoint continuation')
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.scheduler.load_state_dict(checkpoint['scheduler'])
self.scaler.load_state_dict(checkpoint['scaler'])
self.start_epoch = checkpoint['epoch']
def save_networks(self, epoch_index, save_dir):
if dist.get_rank() == 0:
checkpoint = {
"net": self.network.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict(),
'scaler': self.scaler.state_dict(),
"epoch": epoch_index
}
torch.save(checkpoint,
os.path.join(save_dir, 'Epoch_{}.pkl'.format(epoch_index + 1)))
class MultiNetworkSegmentationModel(ABC):
"""
This class is an abstract base class (ABC) for segmentation models.
To create a subclass, you need to implement the following four methods:
-- <__init__>: initialize the class.
-- <set_input>: unpack data from dataset.
-- <optimize_parameters>: calculate losses, gradients, and update network weights.
-- <evaluate_one_step>: performance evaluation.
"""
def __init__(self, cfg, num_classes, amp=False):
# initialize training CUDA devices
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# training configuration
self.cfg = cfg
self.num_classes = num_classes
self.is_mixed = amp
self.scaler = GradScaler()
self.start_epoch = -1
# initialize networks, criterion, optimizer and scheduler
self.net_names = []
# visualization
self.visual_names = []
self.loss_names = []
def train(self):
for name in self.net_names:
net = getattr(self, name)
net.train()
return self
def eval(self):
for name in self.net_names:
net = getattr(self, name)
net.eval()
return self
def training(self):
return getattr(self, self.net_names[0]).training
def initialize_metric_meter(self, class_list):
self.class_list = class_list
self.metric_meter = MetricMeter(metrics=['dice', 'hd95', 'asd'], class_names=class_list, subject_names=['name'])
self.train_loss = MetricMeter(metrics=self.loss_names, class_names=['train'])
self.val_loss = MetricMeter(metrics=['loss'], class_names=['val'])
def update_loss_meter(self, print=False):
loss_dict = {}
for loss_name in self.loss_names:
try:
loss_value = float(getattr(self, loss_name))
loss_list = gather_object_across_processes(loss_value)
loss_value = np.mean(loss_list)
except:
continue
loss_dict['train_{}'.format(loss_name)] = loss_value
self.train_loss.update(loss_dict)
stats = self.train_loss.report(print_stats=print, mean_only=True)
return stats
@abstractmethod
def set_input(self, *args, **kwargs):
raise NotImplementedError
@abstractmethod
def optimize_parameters(self, *args, **kwargs):
raise NotImplementedError
@abstractmethod
def evaluate_one_step(self, *args, **kwargs):
raise NotImplementedError
def load_networks(self, ckpt_path, resume_training=False, strict=True):
checkpoint = torch.load(ckpt_path, map_location=self.device)
print('Load ckpt weight: {}'.format(ckpt_path))
if resume_training:
print('Load training config for breakpoint continuation')
self.scaler.load_state_dict(checkpoint['scaler'])
self.start_epoch = checkpoint['epoch']
for name in self.net_names:
try:
getattr(self, name).load_state_dict(checkpoint[name], strict=strict)
if resume_training:
getattr(self, '{}_optimizer'.format(name)).load_state_dict(checkpoint['{}_optimizer'.format(name)])
getattr(self, '{}_scheduler'.format(name)).load_state_dict(checkpoint['{}_scheduler'.format(name)])
except:
print('Failed to load network: {}'.format(name))
def load_single_network(self, ckpt_path, net_name, resume_training=False, strict=True):
checkpoint = torch.load(ckpt_path, map_location=self.device)
print('Load ckpt weight: {}'.format(ckpt_path))
if resume_training:
print('Load training config for breakpoint continuation')
self.scaler.load_state_dict(checkpoint['scaler'])
self.start_epoch = checkpoint['epoch']
getattr(self, net_name).load_state_dict(checkpoint[net_name], strict=strict)
if resume_training:
getattr(self, '{}_optimizer'.format(net_name)).load_state_dict(checkpoint['{}_optimizer'.format(net_name)])
getattr(self, '{}_scheduler'.format(net_name)).load_state_dict(checkpoint['{}_scheduler'.format(net_name)])
def save_networks(self, epoch_index, save_dir):
if dist.get_rank() == 0:
checkpoint = {}
for name in self.net_names:
checkpoint[name] = getattr(self, name).state_dict()
checkpoint['{}_optimizer'.format(name)] = getattr(self, '{}_optimizer'.format(name)).state_dict()
checkpoint['{}_scheduler'.format(name)] = getattr(self, '{}_scheduler'.format(name)).state_dict()
checkpoint['scaler'] = self.scaler.state_dict()
checkpoint['epoch'] = epoch_index
torch.save(checkpoint, os.path.join(save_dir, 'Epoch_{}.pkl'.format(epoch_index)))
def save_best_networks(self, epoch_index, save_dir):
if dist.get_rank() == 0:
checkpoint = {}
for name in self.net_names:
checkpoint[name] = getattr(self, name).state_dict()
checkpoint['{}_optimizer'.format(name)] = getattr(self, '{}_optimizer'.format(name)).state_dict()
checkpoint['{}_scheduler'.format(name)] = getattr(self, '{}_scheduler'.format(name)).state_dict()
checkpoint['scaler'] = self.scaler.state_dict()
checkpoint['epoch'] = epoch_index
torch.save(checkpoint, os.path.join(save_dir, 'Epoch_best.pkl'))
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