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TAL min_memory argument, precommit, Docker fixes (#836)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jaap van de Loosdrecht <jaap@vdlmv.nl>
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Glenn Jocher 2023-02-07 02:37:13 +04:00 committed by GitHub
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commit 3633d4c06b
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6 changed files with 75 additions and 30 deletions
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4
.pre-commit-config.yaml
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@ -37,7 +37,7 @@ repos:
# - id: isort
# name: Sort imports
- repo: https://github.com/pre-commit/mirrors-yapf
- repo: https://github.com/google/yapf
rev: v0.32.0
hooks:
- id: yapf
@ -54,7 +54,7 @@ repos:
# exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"
- repo: https://github.com/PyCQA/flake8
rev: 5.0.4
rev: 6.0.0
hooks:
- id: flake8
name: PEP8

2
docker/Dockerfile-arm64
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@ -27,7 +27,7 @@ RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
RUN pip install --no-cache ultralytics albumentations gsutil notebook \
coremltools onnx onnx-simplifier onnxruntime openvino-dev>=2022.3
coremltools onnx onnxruntime
# tensorflow-aarch64 tensorflowjs \
# Cleanup

1
ultralytics/yolo/cfg/default.yaml
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@ -29,6 +29,7 @@ rect: False # support rectangular training if mode='train', support rectangular
cos_lr: False # use cosine learning rate scheduler
close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
resume: False # resume training from last checkpoint
min_memory: False # minimize memory footprint loss function, choices=[False, True, <roll_out_thr>]
# Segmentation
overlap_mask: True # masks should overlap during training (segment train only)
mask_ratio: 4 # mask downsample ratio (segment train only)

25
ultralytics/yolo/engine/exporter.py
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@ -82,13 +82,19 @@ MACOS = platform.system() == 'Darwin' # macOS environment
def export_formats():
# YOLOv8 export formats
x = [['PyTorch', '-', '.pt', True, True], ['TorchScript', 'torchscript', '.torchscript', True, True],
['ONNX', 'onnx', '.onnx', True, True], ['OpenVINO', 'openvino', '_openvino_model', True, False],
['TensorRT', 'engine', '.engine', False, True], ['CoreML', 'coreml', '.mlmodel', True, False],
['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
['TensorFlow GraphDef', 'pb', '.pb', True, True], ['TensorFlow Lite', 'tflite', '.tflite', True, False],
['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
['TensorFlow.js', 'tfjs', '_web_model', False, False], ['PaddlePaddle', 'paddle', '_paddle_model', True, True]]
x = [
['PyTorch', '-', '.pt', True, True],
['TorchScript', 'torchscript', '.torchscript', True, True],
['ONNX', 'onnx', '.onnx', True, True],
['OpenVINO', 'openvino', '_openvino_model', True, False],
['TensorRT', 'engine', '.engine', False, True],
['CoreML', 'coreml', '.mlmodel', True, False],
['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
['TensorFlow GraphDef', 'pb', '.pb', True, True],
['TensorFlow Lite', 'tflite', '.tflite', True, False],
['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
['TensorFlow.js', 'tfjs', '_web_model', False, False],
['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
@ -138,9 +144,12 @@ class Exporter:
self.run_callbacks("on_export_start")
t = time.time()
format = self.args.format.lower() # to lowercase
if format in {'tensorrt', 'trt'}: # engine aliases
format = 'engine'
fmts = tuple(export_formats()['Argument'][1:]) # available export formats
flags = [x == format for x in fmts]
assert sum(flags), f'ERROR: Invalid format={format}, valid formats are {fmts}'
if sum(flags) != 1:
raise ValueError(f"Invalid export format='{format}'. Valid formats are {fmts}")
jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans
# Load PyTorch model

60
ultralytics/yolo/utils/tal.py
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@ -10,7 +10,7 @@ from .metrics import bbox_iou
TORCH_1_10 = check_version(torch.__version__, '1.10.0')
def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9, roll_out=False):
"""select the positive anchor center in gt
Args:
@ -21,10 +21,18 @@ def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
"""
n_anchors = xy_centers.shape[0]
bs, n_boxes, _ = gt_bboxes.shape
lt, rb = gt_bboxes.view(-1, 1, 4).chunk(2, 2) # left-top, right-bottom
bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1)
# return (bbox_deltas.min(3)[0] > eps).to(gt_bboxes.dtype)
return bbox_deltas.amin(3).gt_(eps)
if roll_out:
bbox_deltas = torch.empty((bs, n_boxes, n_anchors), device=gt_bboxes.device)
for b in range(bs):
lt, rb = gt_bboxes[b].view(-1, 1, 4).chunk(2, 2) # left-top, right-bottom
bbox_deltas[b] = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]),
dim=2).view(n_boxes, n_anchors, -1).amin(2).gt_(eps)
return bbox_deltas
else:
lt, rb = gt_bboxes.view(-1, 1, 4).chunk(2, 2) # left-top, right-bottom
bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1)
# return (bbox_deltas.min(3)[0] > eps).to(gt_bboxes.dtype)
return bbox_deltas.amin(3).gt_(eps)
def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
@ -55,7 +63,7 @@ def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
class TaskAlignedAssigner(nn.Module):
def __init__(self, topk=13, num_classes=80, alpha=1.0, beta=6.0, eps=1e-9):
def __init__(self, topk=13, num_classes=80, alpha=1.0, beta=6.0, eps=1e-9, roll_out_thr=0):
super().__init__()
self.topk = topk
self.num_classes = num_classes
@ -63,6 +71,7 @@ class TaskAlignedAssigner(nn.Module):
self.alpha = alpha
self.beta = beta
self.eps = eps
self.roll_out_thr = roll_out_thr
@torch.no_grad()
def forward(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt):
@ -84,6 +93,7 @@ class TaskAlignedAssigner(nn.Module):
"""
self.bs = pd_scores.size(0)
self.n_max_boxes = gt_bboxes.size(1)
self.roll_out = self.n_max_boxes > self.roll_out_thr if self.roll_out_thr else False
if self.n_max_boxes == 0:
device = gt_bboxes.device
@ -112,7 +122,7 @@ class TaskAlignedAssigner(nn.Module):
# get anchor_align metric, (b, max_num_obj, h*w)
align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes)
# get in_gts mask, (b, max_num_obj, h*w)
mask_in_gts = select_candidates_in_gts(anc_points, gt_bboxes)
mask_in_gts = select_candidates_in_gts(anc_points, gt_bboxes, roll_out=self.roll_out)
# get topk_metric mask, (b, max_num_obj, h*w)
mask_topk = self.select_topk_candidates(align_metric * mask_in_gts,
topk_mask=mask_gt.repeat([1, 1, self.topk]).bool())
@ -122,14 +132,27 @@ class TaskAlignedAssigner(nn.Module):
return mask_pos, align_metric, overlaps
def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes):
ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long) # 2, b, max_num_obj
ind[0] = torch.arange(end=self.bs).view(-1, 1).repeat(1, self.n_max_boxes) # b, max_num_obj
ind[1] = gt_labels.long().squeeze(-1) # b, max_num_obj
# get the scores of each grid for each gt cls
bbox_scores = pd_scores[ind[0], :, ind[1]] # b, max_num_obj, h*w
if self.roll_out:
align_metric = torch.empty((self.bs, self.n_max_boxes, pd_scores.shape[1]), device=pd_scores.device)
overlaps = torch.empty((self.bs, self.n_max_boxes, pd_scores.shape[1]), device=pd_scores.device)
ind_0 = torch.empty(self.n_max_boxes, dtype=torch.long)
for b in range(self.bs):
ind_0[:], ind_2 = b, gt_labels[b].squeeze(-1).long()
# get the scores of each grid for each gt cls
bbox_scores = pd_scores[ind_0, :, ind_2] # b, max_num_obj, h*w
overlaps[b] = bbox_iou(gt_bboxes[b].unsqueeze(1), pd_bboxes[b].unsqueeze(0), xywh=False,
CIoU=True).squeeze(2).clamp(0)
align_metric[b] = bbox_scores.pow(self.alpha) * overlaps[b].pow(self.beta)
else:
ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long) # 2, b, max_num_obj
ind[0] = torch.arange(end=self.bs).view(-1, 1).repeat(1, self.n_max_boxes) # b, max_num_obj
ind[1] = gt_labels.long().squeeze(-1) # b, max_num_obj
# get the scores of each grid for each gt cls
bbox_scores = pd_scores[ind[0], :, ind[1]] # b, max_num_obj, h*w
overlaps = bbox_iou(gt_bboxes.unsqueeze(2), pd_bboxes.unsqueeze(1), xywh=False, CIoU=True).squeeze(3).clamp(0)
align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
overlaps = bbox_iou(gt_bboxes.unsqueeze(2), pd_bboxes.unsqueeze(1), xywh=False,
CIoU=True).squeeze(3).clamp(0)
align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
return align_metric, overlaps
def select_topk_candidates(self, metrics, largest=True, topk_mask=None):
@ -145,9 +168,14 @@ class TaskAlignedAssigner(nn.Module):
if topk_mask is None:
topk_mask = (topk_metrics.max(-1, keepdim=True) > self.eps).tile([1, 1, self.topk])
# (b, max_num_obj, topk)
topk_idxs = torch.where(topk_mask, topk_idxs, 0)
topk_idxs[~topk_mask] = 0
# (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(-2)
if self.roll_out:
is_in_topk = torch.empty(metrics.shape, dtype=torch.long, device=metrics.device)
for b in range(len(topk_idxs)):
is_in_topk[b] = F.one_hot(topk_idxs[b], num_anchors).sum(-2)
else:
is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(-2)
# filter invalid bboxes
is_in_topk = torch.where(is_in_topk > 1, 0, is_in_topk)
return is_in_topk.to(metrics.dtype)

13
ultralytics/yolo/v8/detect/train.py
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@ -33,14 +33,15 @@ class DetectionTrainer(BaseTrainer):
augment=mode == "train",
cache=self.args.cache,
pad=0 if mode == "train" else 0.5,
rect=self.args.rect or mode=="val",
rect=self.args.rect or mode == "val",
rank=rank,
workers=self.args.workers,
close_mosaic=self.args.close_mosaic != 0,
prefix=colorstr(f'{mode}: '),
shuffle=mode == "train",
seed=self.args.seed)[0] if self.args.v5loader else \
build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode, rect=mode=="val")[0]
build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode,
rect=mode == "val")[0]
def preprocess_batch(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255
@ -121,7 +122,13 @@ class Loss:
self.device = device
self.use_dfl = m.reg_max > 1
self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
roll_out_thr = h.min_memory if h.min_memory > 1 else 64 if h.min_memory else 0 # 64 is default
self.assigner = TaskAlignedAssigner(topk=10,
num_classes=self.nc,
alpha=0.5,
beta=6.0,
roll_out_thr=roll_out_thr)
self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)