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""" Object detection loader/collate
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.utils.data
from .transforms import *
from .transforms_albumentation import get_transform
from .random_erasing import RandomErasing
from effdet.anchors import AnchorLabeler
from timm.data.distributed_sampler import OrderedDistributedSampler
import os
MAX_NUM_INSTANCES = 100
class DetectionFastCollate:
""" A detection specific, optimized collate function w/ a bit of state.
Optionally performs anchor labelling. Doing this here offloads some work from the
GPU and the main training process thread and increases the load on the dataloader
threads.
"""
def __init__(
self,
instance_keys=None,
instance_shapes=None,
instance_fill=-1,
max_instances=MAX_NUM_INSTANCES,
anchor_labeler=None,
):
instance_keys = instance_keys or {'bbox', 'bbox_ignore', 'cls'}
instance_shapes = instance_shapes or dict(
bbox=(max_instances, 4), bbox_ignore=(max_instances, 4), cls=(max_instances,))
self.instance_info = {k: dict(fill=instance_fill, shape=instance_shapes[k]) for k in instance_keys}
self.max_instances = max_instances
self.anchor_labeler = anchor_labeler
def __call__(self, batch):
batch_size = len(batch)
target = dict()
labeler_outputs = dict()
img_tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
for i in range(batch_size):
img_tensor[i] += torch.from_numpy(batch[i][0])
labeler_inputs = {}
for tk, tv in batch[i][1].items():
instance_info = self.instance_info.get(tk, None)
if instance_info is not None:
# target tensor is associated with a detection instance
tv = torch.from_numpy(tv).to(dtype=torch.float32)
if self.anchor_labeler is None:
if i == 0:
shape = (batch_size,) + instance_info['shape']
target_tensor = torch.full(shape, instance_info['fill'], dtype=torch.float32)
target[tk] = target_tensor
else:
target_tensor = target[tk]
num_elem = min(tv.shape[0], self.max_instances)
target_tensor[i, 0:num_elem] = tv[0:num_elem]
else:
# no need to pass gt tensors through when labeler in use
if tk in ('bbox', 'cls'):
labeler_inputs[tk] = tv
else:
# target tensor is an image-level annotation / metadata
if i == 0:
# first batch elem, create destination tensors
if isinstance(tv, (tuple, list)):
# per batch elem sequence
shape = (batch_size, len(tv))
dtype = torch.float32 if isinstance(tv[0], (float, np.floating)) else torch.int32
else:
# per batch elem scalar
shape = batch_size,
dtype = torch.float32 if isinstance(tv, (float, np.floating)) else torch.int64
target_tensor = torch.zeros(shape, dtype=dtype)
target[tk] = target_tensor
else:
target_tensor = target[tk]
target_tensor[i] = torch.tensor(tv, dtype=target_tensor.dtype)
if self.anchor_labeler is not None:
cls_targets, box_targets, num_positives = self.anchor_labeler.label_anchors(
labeler_inputs['bbox'], labeler_inputs['cls'], filter_valid=False)
if i == 0:
# first batch elem, create destination tensors, separate key per level
for j, (ct, bt) in enumerate(zip(cls_targets, box_targets)):
labeler_outputs[f'label_cls_{j}'] = torch.zeros(
(batch_size,) + ct.shape, dtype=torch.int64)
labeler_outputs[f'label_bbox_{j}'] = torch.zeros(
(batch_size,) + bt.shape, dtype=torch.float32)
labeler_outputs['label_num_positives'] = torch.zeros(batch_size)
for j, (ct, bt) in enumerate(zip(cls_targets, box_targets)):
labeler_outputs[f'label_cls_{j}'][i] = ct
labeler_outputs[f'label_bbox_{j}'][i] = bt
labeler_outputs['label_num_positives'][i] = num_positives
if labeler_outputs:
target.update(labeler_outputs)
return img_tensor, target
class PrefetchLoader:
def __init__(self,
loader,
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
re_prob=0.,
re_mode='pixel',
re_count=1,
):
self.loader = loader
self.mean = torch.tensor([x * 255 for x in mean]).cuda().view(1, 3, 1, 1)
self.std = torch.tensor([x * 255 for x in std]).cuda().view(1, 3, 1, 1)
if re_prob > 0.:
self.random_erasing = RandomErasing(probability=re_prob, mode=re_mode, max_count=re_count)
else:
self.random_erasing = None
def __iter__(self):
stream = torch.cuda.Stream()
first = True
for next_input, next_target in self.loader:
with torch.cuda.stream(stream):
next_input = next_input.cuda(non_blocking=True)
next_input = next_input.float().sub_(self.mean).div_(self.std)
next_target = {k: v.cuda(non_blocking=True) for k, v in next_target.items()}
if self.random_erasing is not None:
next_input = self.random_erasing(next_input, next_target)
if not first:
yield input, target
else:
first = False
torch.cuda.current_stream().wait_stream(stream)
input = next_input
target = next_target
yield input, target
def __len__(self):
return len(self.loader)
@property
def sampler(self):
return self.loader.sampler
@property
def dataset(self):
return self.loader.dataset
def create_loader(
dataset,
input_size,
batch_size,
is_training=False,
use_prefetcher=True,
re_prob=0.,
re_mode='pixel',
re_count=1,
interpolation='bilinear',
fill_color='mean',
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
num_workers=1,
distributed=False,
pin_mem=False,
anchor_labeler=None,
):
if isinstance(input_size, tuple):
img_size = input_size[-2:]
else:
img_size = input_size
if is_training:
transforms = get_transform()
transform = transforms_coco_train(
img_size,
interpolation=interpolation,
use_prefetcher=use_prefetcher,
fill_color=fill_color,
mean=mean,
std=std)
else:
transforms = None
transform = transforms_coco_eval(
img_size,
interpolation=interpolation,
use_prefetcher=use_prefetcher,
fill_color=fill_color,
mean=mean,
std=std)
dataset.transforms = transforms
dataset.transform = transform
sampler = None
if distributed:
if is_training:
sampler = torch.utils.data.distributed.DistributedSampler(dataset)
else:
# This will add extra duplicate entries to result in equal num
# of samples per-process, will slightly alter validation results
sampler = OrderedDistributedSampler(dataset)
collate_fn = DetectionFastCollate(anchor_labeler=anchor_labeler)
loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=sampler is None and is_training,
num_workers=num_workers,
sampler=sampler,
pin_memory=pin_mem,
collate_fn=collate_fn,
)
if use_prefetcher:
if is_training:
loader = PrefetchLoader(loader, mean=mean, std=std, re_prob=re_prob, re_mode=re_mode, re_count=re_count)
else:
loader = PrefetchLoader(loader, mean=mean, std=std)
return loader
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