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engine.py
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import math
import sys
import random
import time
import datetime
from typing import Iterable
import torch
from models.BayeSeg import BayeSeg
import util.misc as utils
import logging
def augment(x, l, device, beta=0.5):
mixs = []
try:
x=x.tensors
except:
pass
mix = torch.distributions.beta.Beta(beta, beta).sample([x.shape[0], 1, 1, 1])
mix = torch.maximum(mix, 1 - mix)
mix = mix.to(device)
mixs.append(mix)
xmix = x * mix + torch.flip(x,(0,)) * (1 - mix)
lmix = l * mix + torch.flip(l,(0,)) * (1 - mix)
return xmix, lmix, mixs
def mix_targets(samples, targets, device):
masks = [t["masks"] for t in targets]
target_masks = torch.stack(masks)
shp_y = target_masks.shape
target_masks = target_masks.long()
y_onehot = torch.zeros((shp_y[0], 4, shp_y[2], shp_y[3]))
if target_masks.device.type == "cuda":
y_onehot = y_onehot.cuda(target_masks.device.index)
y_onehot.scatter_(1, target_masks, 1).float()
target_masks = y_onehot
aug_samples, aug_targets, rates = augment(samples, target_masks, device)
return aug_samples, aug_targets, rates
def convert_targets(targets, device):
masks = [t["masks"] for t in targets]
target_masks = torch.stack(masks)
shp_y = target_masks.shape
target_masks = target_masks.long()
y_onehot = torch.zeros((shp_y[0], 4, shp_y[2], shp_y[3]))
if target_masks.device.type == "cuda":
y_onehot = y_onehot.cuda(target_masks.device.index)
y_onehot.scatter_(1, target_masks, 1).float()
target_masks = y_onehot
return target_masks
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
dataloader_dict: dict, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, max_norm: float = 0):
logger = logging.getLogger('base')
model.train()
criterion.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
numbers = { k : len(v) for k, v in dataloader_dict.items() }
iterats = { k : iter(v) for k, v in dataloader_dict.items() }
tasks = dataloader_dict.keys()
counts = { k : 0 for k in tasks }
total_steps = sum(numbers.values())
start_time = time.time()
for step in range(total_steps):
start = time.time()
tasks = [ t for t in tasks if counts[t] < numbers[t] ]
task = random.sample(tasks, 1)[0]
samples, targets = next(iterats[task])
counts.update({task : counts[task] + 1 })
datatime = time.time() - start
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items() if not isinstance(v, str)} for t in targets]
## original
targets_onehot= convert_targets(targets, device)
##
second = task if model.args.model in ['Unet', 'BayeSeg', 'Baseline'] else targets_onehot
outputs = model(samples.tensors, second)
loss_dict = criterion(outputs, targets_onehot)
weight_dict = criterion.weight_dict
losses = sum([loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict.keys()])
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_unscaled = { f'{k}_unscaled': v for k, v in loss_dict_reduced.items() }
loss_dict_reduced_scaled = {k: v * weight_dict[k] for k, v in loss_dict_reduced.items() if k in weight_dict.keys()}
losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
loss_value = losses_reduced_scaled.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
optimizer.zero_grad()
losses.backward()
optimizer.step()
metric_logger.update(loss=loss_value, **loss_dict_reduced)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
itertime = time.time() - start
metric_logger.log_every(step, total_steps, datatime, itertime, print_freq, header)
# gather the stats from all processes
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('{} Total time: {} ({:.4f} s / it)'.format(header, total_time_str, total_time / total_steps))
metric_logger.synchronize_between_processes()
logger.info("Averaged stats:")
logger.info(metric_logger)
stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
return stats
@torch.no_grad()
def evaluate(model, criterion, postprocessors, dataloader_dict, device, output_dir, visualizer, epoch, writer):
model.eval()
criterion.eval()
logger = logging.getLogger('base')
metric_logger = utils.MetricLogger(delimiter=" ")
#metric_logger.add_meter('loss_multiDice', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Test:'
print_freq = 10
numbers = { k : len(v) for k, v in dataloader_dict.items() }
iterats = { k : iter(v) for k, v in dataloader_dict.items() }
tasks = dataloader_dict.keys()
counts = { k : 0 for k in tasks }
total_steps = sum(numbers.values())
start_time = time.time()
sample_list, output_list, target_list = [], [], []
for step in range(total_steps):
start = time.time()
tasks = [ t for t in tasks if counts[t] < numbers[t] ]
task = random.sample(tasks, 1)[0]
samples, targets = next(iterats[task])
counts.update({task : counts[task] + 1 })
datatime = time.time() - start
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items() if not isinstance(v, str)} for t in targets]
targets_onehot= convert_targets(targets, device)
second = task if model.args.model in ['Unet', 'BayeSeg', 'Baseline'] else targets_onehot
outputs = model(samples.tensors, second)
loss_dict = criterion(outputs, targets_onehot)
weight_dict = criterion.weight_dict
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_scaled = {k: v * weight_dict[k] for k, v in loss_dict_reduced.items() if k in weight_dict.keys()}
loss_dict_reduced_unscaled = {f'{k}_unscaled': v for k, v in loss_dict_reduced.items()}
losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
loss_value = losses_reduced_scaled.item()
metric_logger.update(loss=loss_value, **loss_dict_reduced)
itertime = time.time() - start
metric_logger.log_every(step, total_steps, datatime, itertime, print_freq, header)
if step % round(total_steps / 16.) == 0:
##original
sample_list.append(samples.tensors[0])
##
_, pre_masks = torch.max(outputs['pred_masks'][0], 0, keepdims=True)
output_list.append(pre_masks)
##original
target_list.append(targets[0]['masks'])
##
# gather the stats from all processes
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('{} Total time: {} ({:.4f} s / it)'.format(header, total_time_str, total_time / total_steps))
metric_logger.synchronize_between_processes()
logger.info("Averaged stats:")
logger.info(metric_logger)
stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
writer.add_scalar('avg_loss_total', stats['loss'], epoch)
writer.add_scalar('avg_loss_AvgDice', stats['loss_AvgDice'], epoch)
if model.args.model in ['BayeSeg', 'Baseline']:
writer.add_scalar('avg_loss_CrossEntropy', stats['loss_CrossEntropy'], epoch)
writer.add_scalar('avg_loss_Bayes', stats['loss_Bayes'], epoch)
visualizer(torch.stack(sample_list), torch.stack(output_list), torch.stack(target_list), outputs['visualize'], epoch, writer)
return stats