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Add docformatter to pre-commit (#5279)

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Glenn Jocher 2023-10-09 02:25:22 +02:00 • committed by GitHub
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32
ultralytics/models/utils/ops.py
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@ -11,8 +11,8 @@ from ultralytics.utils.ops import xywh2xyxy, xyxy2xywh
class HungarianMatcher(nn.Module):
"""
A module implementing the HungarianMatcher, which is a differentiable module to solve the assignment problem in
an end-to-end fashion.
A module implementing the HungarianMatcher, which is a differentiable module to solve the assignment problem in an
end-to-end fashion.
HungarianMatcher performs optimal assignment over the predicted and ground truth bounding boxes using a cost
function that considers classification scores, bounding box coordinates, and optionally, mask predictions.
@ -32,6 +32,9 @@ class HungarianMatcher(nn.Module):
"""
def __init__(self, cost_gain=None, use_fl=True, with_mask=False, num_sample_points=12544, alpha=0.25, gamma=2.0):
"""Initializes HungarianMatcher with cost coefficients, Focal Loss, mask prediction, sample points, and alpha
gamma factors.
"""
super().__init__()
if cost_gain is None:
cost_gain = {'class': 1, 'bbox': 5, 'giou': 2, 'mask': 1, 'dice': 1}
@ -45,8 +48,8 @@ class HungarianMatcher(nn.Module):
def forward(self, pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None):
"""
Forward pass for HungarianMatcher. This function computes costs based on prediction and ground truth
(classification cost, L1 cost between boxes and GIoU cost between boxes) and finds the optimal matching
between predictions and ground truth based on these costs.
(classification cost, L1 cost between boxes and GIoU cost between boxes) and finds the optimal matching between
predictions and ground truth based on these costs.
Args:
pred_bboxes (Tensor): Predicted bounding boxes with shape [batch_size, num_queries, 4].
@ -153,9 +156,9 @@ def get_cdn_group(batch,
box_noise_scale=1.0,
training=False):
"""
Get contrastive denoising training group. This function creates a contrastive denoising training group with
positive and negative samples from the ground truths (gt). It applies noise to the class labels and bounding
box coordinates, and returns the modified labels, bounding boxes, attention mask and meta information.
Get contrastive denoising training group. This function creates a contrastive denoising training group with positive
and negative samples from the ground truths (gt). It applies noise to the class labels and bounding box coordinates,
and returns the modified labels, bounding boxes, attention mask and meta information.
Args:
batch (dict): A dict that includes 'gt_cls' (torch.Tensor with shape [num_gts, ]), 'gt_bboxes'
@ -191,12 +194,12 @@ def get_cdn_group(batch,
gt_bbox = batch['bboxes'] # bs*num, 4
b_idx = batch['batch_idx']
# each group has positive and negative queries.
# Each group has positive and negative queries.
dn_cls = gt_cls.repeat(2 * num_group) # (2*num_group*bs*num, )
dn_bbox = gt_bbox.repeat(2 * num_group, 1) # 2*num_group*bs*num, 4
dn_b_idx = b_idx.repeat(2 * num_group).view(-1) # (2*num_group*bs*num, )
# positive and negative mask
# Positive and negative mask
# (bs*num*num_group, ), the second total_num*num_group part as negative samples
neg_idx = torch.arange(total_num * num_group, dtype=torch.long, device=gt_bbox.device) + num_group * total_num
@ -220,10 +223,9 @@ def get_cdn_group(batch,
known_bbox += rand_part * diff
known_bbox.clip_(min=0.0, max=1.0)
dn_bbox = xyxy2xywh(known_bbox)
dn_bbox = inverse_sigmoid(dn_bbox)
dn_bbox = torch.logit(dn_bbox, eps=1e-6) # inverse sigmoid
# total denoising queries
num_dn = int(max_nums * 2 * num_group)
num_dn = int(max_nums * 2 * num_group) # total denoising queries
# class_embed = torch.cat([class_embed, torch.zeros([1, class_embed.shape[-1]], device=class_embed.device)])
dn_cls_embed = class_embed[dn_cls] # bs*num * 2 * num_group, 256
padding_cls = torch.zeros(bs, num_dn, dn_cls_embed.shape[-1], device=gt_cls.device)
@ -256,9 +258,3 @@ def get_cdn_group(batch,
return padding_cls.to(class_embed.device), padding_bbox.to(class_embed.device), attn_mask.to(
class_embed.device), dn_meta
def inverse_sigmoid(x, eps=1e-6):
"""Inverse sigmoid function."""
x = x.clip(min=0., max=1.)
return torch.log(x / (1 - x + eps) + eps)