111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
import math
|
|
import sys
|
|
import time
|
|
import torch
|
|
|
|
import torchvision.models.detection.mask_rcnn
|
|
|
|
from coco_utils import get_coco_api_from_dataset
|
|
from coco_eval import CocoEvaluator
|
|
import utils
|
|
|
|
|
|
def train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq):
|
|
model.train()
|
|
metric_logger = utils.MetricLogger(delimiter=" ")
|
|
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
|
|
header = 'Epoch: [{}]'.format(epoch)
|
|
|
|
lr_scheduler = None
|
|
if epoch == 0:
|
|
warmup_factor = 1. / 1000
|
|
warmup_iters = min(1000, len(data_loader) - 1)
|
|
lr_scheduler = utils.warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor)
|
|
|
|
|
|
for images, targets in metric_logger.log_every(data_loader, print_freq, header):
|
|
images = list(image.to(device) for image in images)
|
|
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
|
|
|
|
loss_dict = model(images, targets)
|
|
print('Hey I''m here')
|
|
losses = sum(loss for loss in loss_dict.values())
|
|
|
|
# reduce losses over all GPUs for logging purposes
|
|
loss_dict_reduced = utils.reduce_dict(loss_dict)
|
|
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
|
|
|
|
loss_value = losses_reduced.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()
|
|
|
|
if lr_scheduler is not None:
|
|
lr_scheduler.step()
|
|
|
|
metric_logger.update(loss=losses_reduced, **loss_dict_reduced)
|
|
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
|
|
|
|
return metric_logger
|
|
|
|
|
|
def _get_iou_types(model):
|
|
model_without_ddp = model
|
|
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
|
|
model_without_ddp = model.module
|
|
iou_types = ["bbox"]
|
|
if isinstance(model_without_ddp, torchvision.models.detection.MaskRCNN):
|
|
iou_types.append("segm")
|
|
if isinstance(model_without_ddp, torchvision.models.detection.KeypointRCNN):
|
|
iou_types.append("keypoints")
|
|
return iou_types
|
|
|
|
|
|
@torch.no_grad()
|
|
def evaluate(model, data_loader, device):
|
|
n_threads = torch.get_num_threads()
|
|
# FIXME remove this and make paste_masks_in_image run on the GPU
|
|
torch.set_num_threads(1)
|
|
cpu_device = torch.device("cpu")
|
|
model.eval()
|
|
metric_logger = utils.MetricLogger(delimiter=" ")
|
|
header = 'Test:'
|
|
|
|
coco = get_coco_api_from_dataset(data_loader.dataset)
|
|
iou_types = _get_iou_types(model)
|
|
coco_evaluator = CocoEvaluator(coco, iou_types)
|
|
|
|
for images, targets in metric_logger.log_every(data_loader, 100, header):
|
|
images = list(img.to(device) for img in images)
|
|
|
|
if torch.cuda.is_available():
|
|
torch.cuda.synchronize()
|
|
model_time = time.time()
|
|
outputs = model(images)
|
|
|
|
outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]
|
|
model_time = time.time() - model_time
|
|
|
|
res = {target["image_id"].item(): output for target, output in zip(targets, outputs)}
|
|
evaluator_time = time.time()
|
|
coco_evaluator.update(res)
|
|
evaluator_time = time.time() - evaluator_time
|
|
metric_logger.update(model_time=model_time, evaluator_time=evaluator_time)
|
|
|
|
# gather the stats from all processes
|
|
metric_logger.synchronize_between_processes()
|
|
print("Averaged stats:", metric_logger)
|
|
coco_evaluator.synchronize_between_processes()
|
|
|
|
# accumulate predictions from all images
|
|
coco_evaluator.accumulate()
|
|
coco_evaluator.summarize()
|
|
torch.set_num_threads(n_threads)
|
|
return coco_evaluator
|