def evaluate_withpq(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)
test_data_pkl = h5py.File(
'resnet_imagenet_features/backbone.7.0_test_reconstructed.h5', 'r')
#test_data_pkl = h5py.File('resnet_imagenet_features/backbone.7.0_test.h5', 'r')
for images, targets in tqdm(data_loader, desc=header):
images = list(image for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
image_id = targets[0]['image_id'].item()
quantized_x = test_data_pkl[str(image_id)][()]
quantized_x = torch.from_numpy(quantized_x)
imagepq = quantized_x.to(device)
torch.cuda.synchronize()
model_time = time.time()
#print ("----",image_id,"----",imagepq.shape)
outputs = model(images, imagepq, targets)
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
# 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()
coco_evaluator.summarize_per_category()
torch.set_num_threads(n_threads)
test_data_pkl.close()
return coco_evaluator
def evaluate(model, data_loader, device):
with torch.no_grad():
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):
for images, targets in tqdm(data_loader, desc=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]
torch.cuda.synchronize()
model_time = time.time()
outputs = model(images, targets)
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()
coco_evaluator.summarize_per_category()
torch.set_num_threads(n_threads)
return coco_evaluator
