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GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/centripetal_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule, normal_init
from mmcv.ops import DeformConv2d
from mmdet.core import multi_apply
from ..builder import HEADS, build_loss
from .corner_head import CornerHead
@HEADS.register_module()
class CentripetalHead(CornerHead):
"""Head of CentripetalNet: Pursuing High-quality Keypoint Pairs for Object
Detection.
CentripetalHead inherits from :class:`CornerHead`. It removes the
embedding branch and adds guiding shift and centripetal shift branches.
More details can be found in the `paper
<https://arxiv.org/abs/2003.09119>`_ .
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
num_feat_levels (int): Levels of feature from the previous module. 2
for HourglassNet-104 and 1 for HourglassNet-52. HourglassNet-104
outputs the final feature and intermediate supervision feature and
HourglassNet-52 only outputs the final feature. Default: 2.
corner_emb_channels (int): Channel of embedding vector. Default: 1.
train_cfg (dict | None): Training config. Useless in CornerHead,
but we keep this variable for SingleStageDetector. Default: None.
test_cfg (dict | None): Testing config of CornerHead. Default: None.
loss_heatmap (dict | None): Config of corner heatmap loss. Default:
GaussianFocalLoss.
loss_embedding (dict | None): Config of corner embedding loss. Default:
AssociativeEmbeddingLoss.
loss_offset (dict | None): Config of corner offset loss. Default:
SmoothL1Loss.
loss_guiding_shift (dict): Config of guiding shift loss. Default:
SmoothL1Loss.
loss_centripetal_shift (dict): Config of centripetal shift loss.
Default: SmoothL1Loss.
"""
def __init__(self,
*args,
centripetal_shift_channels=2,
guiding_shift_channels=2,
feat_adaption_conv_kernel=3,
loss_guiding_shift=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=0.05),
loss_centripetal_shift=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=1),
**kwargs):
assert centripetal_shift_channels == 2, (
'CentripetalHead only support centripetal_shift_channels == 2')
self.centripetal_shift_channels = centripetal_shift_channels
assert guiding_shift_channels == 2, (
'CentripetalHead only support guiding_shift_channels == 2')
self.guiding_shift_channels = guiding_shift_channels
self.feat_adaption_conv_kernel = feat_adaption_conv_kernel
super(CentripetalHead, self).__init__(*args, **kwargs)
self.loss_guiding_shift = build_loss(loss_guiding_shift)
self.loss_centripetal_shift = build_loss(loss_centripetal_shift)
def _init_centripetal_layers(self):
"""Initialize centripetal layers.
Including feature adaption deform convs (feat_adaption), deform offset
prediction convs (dcn_off), guiding shift (guiding_shift) and
centripetal shift ( centripetal_shift). Each branch has two parts:
prefix `tl_` for top-left and `br_` for bottom-right.
"""
self.tl_feat_adaption = nn.ModuleList()
self.br_feat_adaption = nn.ModuleList()
self.tl_dcn_offset = nn.ModuleList()
self.br_dcn_offset = nn.ModuleList()
self.tl_guiding_shift = nn.ModuleList()
self.br_guiding_shift = nn.ModuleList()
self.tl_centripetal_shift = nn.ModuleList()
self.br_centripetal_shift = nn.ModuleList()
for _ in range(self.num_feat_levels):
self.tl_feat_adaption.append(
DeformConv2d(self.in_channels, self.in_channels,
self.feat_adaption_conv_kernel, 1, 1))
self.br_feat_adaption.append(
DeformConv2d(self.in_channels, self.in_channels,
self.feat_adaption_conv_kernel, 1, 1))
self.tl_guiding_shift.append(
self._make_layers(
out_channels=self.guiding_shift_channels,
in_channels=self.in_channels))
self.br_guiding_shift.append(
self._make_layers(
out_channels=self.guiding_shift_channels,
in_channels=self.in_channels))
self.tl_dcn_offset.append(
ConvModule(
self.guiding_shift_channels,
self.feat_adaption_conv_kernel**2 *
self.guiding_shift_channels,
1,
bias=False,
act_cfg=None))
self.br_dcn_offset.append(
ConvModule(
self.guiding_shift_channels,
self.feat_adaption_conv_kernel**2 *
self.guiding_shift_channels,
1,
bias=False,
act_cfg=None))
self.tl_centripetal_shift.append(
self._make_layers(
out_channels=self.centripetal_shift_channels,
in_channels=self.in_channels))
self.br_centripetal_shift.append(
self._make_layers(
out_channels=self.centripetal_shift_channels,
in_channels=self.in_channels))
def _init_layers(self):
"""Initialize layers for CentripetalHead.
Including two parts: CornerHead layers and CentripetalHead layers
"""
super()._init_layers() # using _init_layers in CornerHead
self._init_centripetal_layers()
def init_weights(self):
"""Initialize weights of the head."""
super().init_weights()
for i in range(self.num_feat_levels):
normal_init(self.tl_feat_adaption[i], std=0.01)
normal_init(self.br_feat_adaption[i], std=0.01)
normal_init(self.tl_dcn_offset[i].conv, std=0.1)
normal_init(self.br_dcn_offset[i].conv, std=0.1)
_ = [x.conv.reset_parameters() for x in self.tl_guiding_shift[i]]
_ = [x.conv.reset_parameters() for x in self.br_guiding_shift[i]]
_ = [
x.conv.reset_parameters() for x in self.tl_centripetal_shift[i]
]
_ = [
x.conv.reset_parameters() for x in self.br_centripetal_shift[i]
]
def forward_single(self, x, lvl_ind):
"""Forward feature of a single level.
Args:
x (Tensor): Feature of a single level.
lvl_ind (int): Level index of current feature.
Returns:
tuple[Tensor]: A tuple of CentripetalHead's output for current
feature level. Containing the following Tensors:
- tl_heat (Tensor): Predicted top-left corner heatmap.
- br_heat (Tensor): Predicted bottom-right corner heatmap.
- tl_off (Tensor): Predicted top-left offset heatmap.
- br_off (Tensor): Predicted bottom-right offset heatmap.
- tl_guiding_shift (Tensor): Predicted top-left guiding shift
heatmap.
- br_guiding_shift (Tensor): Predicted bottom-right guiding
shift heatmap.
- tl_centripetal_shift (Tensor): Predicted top-left centripetal
shift heatmap.
- br_centripetal_shift (Tensor): Predicted bottom-right
centripetal shift heatmap.
"""
tl_heat, br_heat, _, _, tl_off, br_off, tl_pool, br_pool = super(
).forward_single(
x, lvl_ind, return_pool=True)
tl_guiding_shift = self.tl_guiding_shift[lvl_ind](tl_pool)
br_guiding_shift = self.br_guiding_shift[lvl_ind](br_pool)
tl_dcn_offset = self.tl_dcn_offset[lvl_ind](tl_guiding_shift.detach())
br_dcn_offset = self.br_dcn_offset[lvl_ind](br_guiding_shift.detach())
tl_feat_adaption = self.tl_feat_adaption[lvl_ind](tl_pool,
tl_dcn_offset)
br_feat_adaption = self.br_feat_adaption[lvl_ind](br_pool,
br_dcn_offset)
tl_centripetal_shift = self.tl_centripetal_shift[lvl_ind](
tl_feat_adaption)
br_centripetal_shift = self.br_centripetal_shift[lvl_ind](
br_feat_adaption)
result_list = [
tl_heat, br_heat, tl_off, br_off, tl_guiding_shift,
br_guiding_shift, tl_centripetal_shift, br_centripetal_shift
]
return result_list
def loss(self,
tl_heats,
br_heats,
tl_offs,
br_offs,
tl_guiding_shifts,
br_guiding_shifts,
tl_centripetal_shifts,
br_centripetal_shifts,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute losses of the head.
Args:
tl_heats (list[Tensor]): Top-left corner heatmaps for each level
with shape (N, num_classes, H, W).
br_heats (list[Tensor]): Bottom-right corner heatmaps for each
level with shape (N, num_classes, H, W).
tl_offs (list[Tensor]): Top-left corner offsets for each level
with shape (N, corner_offset_channels, H, W).
br_offs (list[Tensor]): Bottom-right corner offsets for each level
with shape (N, corner_offset_channels, H, W).
tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
level with shape (N, guiding_shift_channels, H, W).
br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
each level with shape (N, guiding_shift_channels, H, W).
tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
for each level with shape (N, centripetal_shift_channels, H,
W).
br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
shifts for each level with shape (N,
centripetal_shift_channels, H, W).
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [left, top, right, bottom] format.
gt_labels (list[Tensor]): Class indices corresponding to each box.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components. Containing the
following losses:
- det_loss (list[Tensor]): Corner keypoint losses of all
feature levels.
- off_loss (list[Tensor]): Corner offset losses of all feature
levels.
- guiding_loss (list[Tensor]): Guiding shift losses of all
feature levels.
- centripetal_loss (list[Tensor]): Centripetal shift losses of
all feature levels.
"""
targets = self.get_targets(
gt_bboxes,
gt_labels,
tl_heats[-1].shape,
img_metas[0]['pad_shape'],
with_corner_emb=self.with_corner_emb,
with_guiding_shift=True,
with_centripetal_shift=True)
mlvl_targets = [targets for _ in range(self.num_feat_levels)]
[det_losses, off_losses, guiding_losses, centripetal_losses
] = multi_apply(self.loss_single, tl_heats, br_heats, tl_offs,
br_offs, tl_guiding_shifts, br_guiding_shifts,
tl_centripetal_shifts, br_centripetal_shifts,
mlvl_targets)
loss_dict = dict(
det_loss=det_losses,
off_loss=off_losses,
guiding_loss=guiding_losses,
centripetal_loss=centripetal_losses)
return loss_dict
def loss_single(self, tl_hmp, br_hmp, tl_off, br_off, tl_guiding_shift,
br_guiding_shift, tl_centripetal_shift,
br_centripetal_shift, targets):
"""Compute losses for single level.
Args:
tl_hmp (Tensor): Top-left corner heatmap for current level with
shape (N, num_classes, H, W).
br_hmp (Tensor): Bottom-right corner heatmap for current level with
shape (N, num_classes, H, W).
tl_off (Tensor): Top-left corner offset for current level with
shape (N, corner_offset_channels, H, W).
br_off (Tensor): Bottom-right corner offset for current level with
shape (N, corner_offset_channels, H, W).
tl_guiding_shift (Tensor): Top-left guiding shift for current level
with shape (N, guiding_shift_channels, H, W).
br_guiding_shift (Tensor): Bottom-right guiding shift for current
level with shape (N, guiding_shift_channels, H, W).
tl_centripetal_shift (Tensor): Top-left centripetal shift for
current level with shape (N, centripetal_shift_channels, H, W).
br_centripetal_shift (Tensor): Bottom-right centripetal shift for
current level with shape (N, centripetal_shift_channels, H, W).
targets (dict): Corner target generated by `get_targets`.
Returns:
tuple[torch.Tensor]: Losses of the head's differnet branches
containing the following losses:
- det_loss (Tensor): Corner keypoint loss.
- off_loss (Tensor): Corner offset loss.
- guiding_loss (Tensor): Guiding shift loss.
- centripetal_loss (Tensor): Centripetal shift loss.
"""
targets['corner_embedding'] = None
det_loss, _, _, off_loss = super().loss_single(tl_hmp, br_hmp, None,
None, tl_off, br_off,
targets)
gt_tl_guiding_shift = targets['topleft_guiding_shift']
gt_br_guiding_shift = targets['bottomright_guiding_shift']
gt_tl_centripetal_shift = targets['topleft_centripetal_shift']
gt_br_centripetal_shift = targets['bottomright_centripetal_shift']
gt_tl_heatmap = targets['topleft_heatmap']
gt_br_heatmap = targets['bottomright_heatmap']
# We only compute the offset loss at the real corner position.
# The value of real corner would be 1 in heatmap ground truth.
# The mask is computed in class agnostic mode and its shape is
# batch * 1 * width * height.
tl_mask = gt_tl_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
gt_tl_heatmap)
br_mask = gt_br_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
gt_br_heatmap)
# Guiding shift loss
tl_guiding_loss = self.loss_guiding_shift(
tl_guiding_shift,
gt_tl_guiding_shift,
tl_mask,
avg_factor=tl_mask.sum())
br_guiding_loss = self.loss_guiding_shift(
br_guiding_shift,
gt_br_guiding_shift,
br_mask,
avg_factor=br_mask.sum())
guiding_loss = (tl_guiding_loss + br_guiding_loss) / 2.0
# Centripetal shift loss
tl_centripetal_loss = self.loss_centripetal_shift(
tl_centripetal_shift,
gt_tl_centripetal_shift,
tl_mask,
avg_factor=tl_mask.sum())
br_centripetal_loss = self.loss_centripetal_shift(
br_centripetal_shift,
gt_br_centripetal_shift,
br_mask,
avg_factor=br_mask.sum())
centripetal_loss = (tl_centripetal_loss + br_centripetal_loss) / 2.0
return det_loss, off_loss, guiding_loss, centripetal_loss
def get_bboxes(self,
tl_heats,
br_heats,
tl_offs,
br_offs,
tl_guiding_shifts,
br_guiding_shifts,
tl_centripetal_shifts,
br_centripetal_shifts,
img_metas,
rescale=False,
with_nms=True):
"""Transform network output for a batch into bbox predictions.
Args:
tl_heats (list[Tensor]): Top-left corner heatmaps for each level
with shape (N, num_classes, H, W).
br_heats (list[Tensor]): Bottom-right corner heatmaps for each
level with shape (N, num_classes, H, W).
tl_offs (list[Tensor]): Top-left corner offsets for each level
with shape (N, corner_offset_channels, H, W).
br_offs (list[Tensor]): Bottom-right corner offsets for each level
with shape (N, corner_offset_channels, H, W).
tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
level with shape (N, guiding_shift_channels, H, W). Useless in
this function, we keep this arg because it's the raw output
from CentripetalHead.
br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
each level with shape (N, guiding_shift_channels, H, W).
Useless in this function, we keep this arg because it's the
raw output from CentripetalHead.
tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
for each level with shape (N, centripetal_shift_channels, H,
W).
br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
shifts for each level with shape (N,
centripetal_shift_channels, H, W).
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
"""
assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas)
result_list = []
for img_id in range(len(img_metas)):
result_list.append(
self._get_bboxes_single(
tl_heats[-1][img_id:img_id + 1, :],
br_heats[-1][img_id:img_id + 1, :],
tl_offs[-1][img_id:img_id + 1, :],
br_offs[-1][img_id:img_id + 1, :],
img_metas[img_id],
tl_emb=None,
br_emb=None,
tl_centripetal_shift=tl_centripetal_shifts[-1][
img_id:img_id + 1, :],
br_centripetal_shift=br_centripetal_shifts[-1][
img_id:img_id + 1, :],
rescale=rescale,
with_nms=with_nms))
return result_list
| 19,469 | 45.137441 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/paa_head.py | import numpy as np
import torch
from mmcv.runner import force_fp32
from mmdet.core import multi_apply, multiclass_nms
from mmdet.core.bbox.iou_calculators import bbox_overlaps
from mmdet.models import HEADS
from mmdet.models.dense_heads import ATSSHead
EPS = 1e-12
try:
import sklearn.mixture as skm
except ImportError:
skm = None
def levels_to_images(mlvl_tensor):
"""Concat multi-level feature maps by image.
[feature_level0, feature_level1...] -> [feature_image0, feature_image1...]
Convert the shape of each element in mlvl_tensor from (N, C, H, W) to
(N, H*W , C), then split the element to N elements with shape (H*W, C), and
concat elements in same image of all level along first dimension.
Args:
mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from
corresponding level. Each element is of shape (N, C, H, W)
Returns:
list[torch.Tensor]: A list that contains N tensors and each tensor is
of shape (num_elements, C)
"""
batch_size = mlvl_tensor[0].size(0)
batch_list = [[] for _ in range(batch_size)]
channels = mlvl_tensor[0].size(1)
for t in mlvl_tensor:
t = t.permute(0, 2, 3, 1)
t = t.view(batch_size, -1, channels).contiguous()
for img in range(batch_size):
batch_list[img].append(t[img])
return [torch.cat(item, 0) for item in batch_list]
@HEADS.register_module()
class PAAHead(ATSSHead):
"""Head of PAAAssignment: Probabilistic Anchor Assignment with IoU
Prediction for Object Detection.
Code is modified from the `official github repo
<https://github.com/kkhoot/PAA/blob/master/paa_core
/modeling/rpn/paa/loss.py>`_.
More details can be found in the `paper
<https://arxiv.org/abs/2007.08103>`_ .
Args:
topk (int): Select topk samples with smallest loss in
each level.
score_voting (bool): Whether to use score voting in post-process.
covariance_type : String describing the type of covariance parameters
to be used in :class:`sklearn.mixture.GaussianMixture`.
It must be one of:
- 'full': each component has its own general covariance matrix
- 'tied': all components share the same general covariance matrix
- 'diag': each component has its own diagonal covariance matrix
- 'spherical': each component has its own single variance
Default: 'diag'. From 'full' to 'spherical', the gmm fitting
process is faster yet the performance could be influenced. For most
cases, 'diag' should be a good choice.
"""
def __init__(self,
*args,
topk=9,
score_voting=True,
covariance_type='diag',
**kwargs):
# topk used in paa reassign process
self.topk = topk
self.with_score_voting = score_voting
self.covariance_type = covariance_type
super(PAAHead, self).__init__(*args, **kwargs)
@force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
def loss(self,
cls_scores,
bbox_preds,
iou_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute losses of the head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level
Has shape (N, num_anchors * num_classes, H, W)
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W)
iou_preds (list[Tensor]): iou_preds for each scale
level with shape (N, num_anchors * 1, H, W)
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
boxes can be ignored when are computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss gmm_assignment.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.anchor_generator.num_levels
device = cls_scores[0].device
anchor_list, valid_flag_list = self.get_anchors(
featmap_sizes, img_metas, device=device)
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = self.get_targets(
anchor_list,
valid_flag_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
)
(labels, labels_weight, bboxes_target, bboxes_weight, pos_inds,
pos_gt_index) = cls_reg_targets
cls_scores = levels_to_images(cls_scores)
cls_scores = [
item.reshape(-1, self.cls_out_channels) for item in cls_scores
]
bbox_preds = levels_to_images(bbox_preds)
bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
iou_preds = levels_to_images(iou_preds)
iou_preds = [item.reshape(-1, 1) for item in iou_preds]
pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list,
cls_scores, bbox_preds, labels,
labels_weight, bboxes_target,
bboxes_weight, pos_inds)
with torch.no_grad():
labels, label_weights, bbox_weights, num_pos = multi_apply(
self.paa_reassign,
pos_losses_list,
labels,
labels_weight,
bboxes_weight,
pos_inds,
pos_gt_index,
anchor_list,
)
num_pos = sum(num_pos)
# convert all tensor list to a flatten tensor
cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1))
bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1))
iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1))
labels = torch.cat(labels, 0).view(-1)
flatten_anchors = torch.cat(
[torch.cat(item, 0) for item in anchor_list])
labels_weight = torch.cat(labels_weight, 0).view(-1)
bboxes_target = torch.cat(bboxes_target,
0).view(-1, bboxes_target[0].size(-1))
pos_inds_flatten = ((labels >= 0)
&
(labels < self.num_classes)).nonzero().reshape(-1)
losses_cls = self.loss_cls(
cls_scores,
labels,
labels_weight,
avg_factor=max(num_pos, len(img_metas))) # avoid num_pos=0
if num_pos:
pos_bbox_pred = self.bbox_coder.decode(
flatten_anchors[pos_inds_flatten],
bbox_preds[pos_inds_flatten])
pos_bbox_target = bboxes_target[pos_inds_flatten]
iou_target = bbox_overlaps(
pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True)
losses_iou = self.loss_centerness(
iou_preds[pos_inds_flatten],
iou_target.unsqueeze(-1),
avg_factor=num_pos)
losses_bbox = self.loss_bbox(
pos_bbox_pred,
pos_bbox_target,
iou_target.clamp(min=EPS),
avg_factor=iou_target.sum())
else:
losses_iou = iou_preds.sum() * 0
losses_bbox = bbox_preds.sum() * 0
return dict(
loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou)
def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight,
bbox_target, bbox_weight, pos_inds):
"""Calculate loss of all potential positive samples obtained from first
match process.
Args:
anchors (list[Tensor]): Anchors of each scale.
cls_score (Tensor): Box scores of single image with shape
(num_anchors, num_classes)
bbox_pred (Tensor): Box energies / deltas of single image
with shape (num_anchors, 4)
label (Tensor): classification target of each anchor with
shape (num_anchors,)
label_weight (Tensor): Classification loss weight of each
anchor with shape (num_anchors).
bbox_target (dict): Regression target of each anchor with
shape (num_anchors, 4).
bbox_weight (Tensor): Bbox weight of each anchor with shape
(num_anchors, 4).
pos_inds (Tensor): Index of all positive samples got from
first assign process.
Returns:
Tensor: Losses of all positive samples in single image.
"""
if not len(pos_inds):
return cls_score.new([]),
anchors_all_level = torch.cat(anchors, 0)
pos_scores = cls_score[pos_inds]
pos_bbox_pred = bbox_pred[pos_inds]
pos_label = label[pos_inds]
pos_label_weight = label_weight[pos_inds]
pos_bbox_target = bbox_target[pos_inds]
pos_bbox_weight = bbox_weight[pos_inds]
pos_anchors = anchors_all_level[pos_inds]
pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred)
# to keep loss dimension
loss_cls = self.loss_cls(
pos_scores,
pos_label,
pos_label_weight,
avg_factor=self.loss_cls.loss_weight,
reduction_override='none')
loss_bbox = self.loss_bbox(
pos_bbox_pred,
pos_bbox_target,
pos_bbox_weight,
avg_factor=self.loss_cls.loss_weight,
reduction_override='none')
loss_cls = loss_cls.sum(-1)
pos_loss = loss_bbox + loss_cls
return pos_loss,
def paa_reassign(self, pos_losses, label, label_weight, bbox_weight,
pos_inds, pos_gt_inds, anchors):
"""Fit loss to GMM distribution and separate positive, ignore, negative
samples again with GMM model.
Args:
pos_losses (Tensor): Losses of all positive samples in
single image.
label (Tensor): classification target of each anchor with
shape (num_anchors,)
label_weight (Tensor): Classification loss weight of each
anchor with shape (num_anchors).
bbox_weight (Tensor): Bbox weight of each anchor with shape
(num_anchors, 4).
pos_inds (Tensor): Index of all positive samples got from
first assign process.
pos_gt_inds (Tensor): Gt_index of all positive samples got
from first assign process.
anchors (list[Tensor]): Anchors of each scale.
Returns:
tuple: Usually returns a tuple containing learning targets.
- label (Tensor): classification target of each anchor after
paa assign, with shape (num_anchors,)
- label_weight (Tensor): Classification loss weight of each
anchor after paa assign, with shape (num_anchors).
- bbox_weight (Tensor): Bbox weight of each anchor with shape
(num_anchors, 4).
- num_pos (int): The number of positive samples after paa
assign.
"""
if not len(pos_inds):
return label, label_weight, bbox_weight, 0
num_gt = pos_gt_inds.max() + 1
num_level = len(anchors)
num_anchors_each_level = [item.size(0) for item in anchors]
num_anchors_each_level.insert(0, 0)
inds_level_interval = np.cumsum(num_anchors_each_level)
pos_level_mask = []
for i in range(num_level):
mask = (pos_inds >= inds_level_interval[i]) & (
pos_inds < inds_level_interval[i + 1])
pos_level_mask.append(mask)
pos_inds_after_paa = [label.new_tensor([])]
ignore_inds_after_paa = [label.new_tensor([])]
for gt_ind in range(num_gt):
pos_inds_gmm = []
pos_loss_gmm = []
gt_mask = pos_gt_inds == gt_ind
for level in range(num_level):
level_mask = pos_level_mask[level]
level_gt_mask = level_mask & gt_mask
value, topk_inds = pos_losses[level_gt_mask].topk(
min(level_gt_mask.sum(), self.topk), largest=False)
pos_inds_gmm.append(pos_inds[level_gt_mask][topk_inds])
pos_loss_gmm.append(value)
pos_inds_gmm = torch.cat(pos_inds_gmm)
pos_loss_gmm = torch.cat(pos_loss_gmm)
# fix gmm need at least two sample
if len(pos_inds_gmm) < 2:
continue
device = pos_inds_gmm.device
pos_loss_gmm, sort_inds = pos_loss_gmm.sort()
pos_inds_gmm = pos_inds_gmm[sort_inds]
pos_loss_gmm = pos_loss_gmm.view(-1, 1).cpu().numpy()
min_loss, max_loss = pos_loss_gmm.min(), pos_loss_gmm.max()
means_init = np.array([min_loss, max_loss]).reshape(2, 1)
weights_init = np.array([0.5, 0.5])
precisions_init = np.array([1.0, 1.0]).reshape(2, 1, 1) # full
if self.covariance_type == 'spherical':
precisions_init = precisions_init.reshape(2)
elif self.covariance_type == 'diag':
precisions_init = precisions_init.reshape(2, 1)
elif self.covariance_type == 'tied':
precisions_init = np.array([[1.0]])
if skm is None:
raise ImportError('Please run "pip install sklearn" '
'to install sklearn first.')
gmm = skm.GaussianMixture(
2,
weights_init=weights_init,
means_init=means_init,
precisions_init=precisions_init,
covariance_type=self.covariance_type)
gmm.fit(pos_loss_gmm)
gmm_assignment = gmm.predict(pos_loss_gmm)
scores = gmm.score_samples(pos_loss_gmm)
gmm_assignment = torch.from_numpy(gmm_assignment).to(device)
scores = torch.from_numpy(scores).to(device)
pos_inds_temp, ignore_inds_temp = self.gmm_separation_scheme(
gmm_assignment, scores, pos_inds_gmm)
pos_inds_after_paa.append(pos_inds_temp)
ignore_inds_after_paa.append(ignore_inds_temp)
pos_inds_after_paa = torch.cat(pos_inds_after_paa)
ignore_inds_after_paa = torch.cat(ignore_inds_after_paa)
reassign_mask = (pos_inds.unsqueeze(1) != pos_inds_after_paa).all(1)
reassign_ids = pos_inds[reassign_mask]
label[reassign_ids] = self.num_classes
label_weight[ignore_inds_after_paa] = 0
bbox_weight[reassign_ids] = 0
num_pos = len(pos_inds_after_paa)
return label, label_weight, bbox_weight, num_pos
def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm):
"""A general separation scheme for gmm model.
It separates a GMM distribution of candidate samples into three
parts, 0 1 and uncertain areas, and you can implement other
separation schemes by rewriting this function.
Args:
gmm_assignment (Tensor): The prediction of GMM which is of shape
(num_samples,). The 0/1 value indicates the distribution
that each sample comes from.
scores (Tensor): The probability of sample coming from the
fit GMM distribution. The tensor is of shape (num_samples,).
pos_inds_gmm (Tensor): All the indexes of samples which are used
to fit GMM model. The tensor is of shape (num_samples,)
Returns:
tuple[Tensor]: The indices of positive and ignored samples.
- pos_inds_temp (Tensor): Indices of positive samples.
- ignore_inds_temp (Tensor): Indices of ignore samples.
"""
# The implementation is (c) in Fig.3 in origin paper intead of (b).
# You can refer to issues such as
# https://github.com/kkhoot/PAA/issues/8 and
# https://github.com/kkhoot/PAA/issues/9.
fgs = gmm_assignment == 0
pos_inds_temp = fgs.new_tensor([], dtype=torch.long)
ignore_inds_temp = fgs.new_tensor([], dtype=torch.long)
if fgs.nonzero().numel():
_, pos_thr_ind = scores[fgs].topk(1)
pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1]
ignore_inds_temp = pos_inds_gmm.new_tensor([])
return pos_inds_temp, ignore_inds_temp
def get_targets(
self,
anchor_list,
valid_flag_list,
gt_bboxes_list,
img_metas,
gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=1,
unmap_outputs=True,
):
"""Get targets for PAA head.
This method is almost the same as `AnchorHead.get_targets()`. We direct
return the results from _get_targets_single instead map it to levels
by images_to_levels function.
Args:
anchor_list (list[list[Tensor]]): Multi level anchors of each
image. The outer list indicates images, and the inner list
corresponds to feature levels of the image. Each element of
the inner list is a tensor of shape (num_anchors, 4).
valid_flag_list (list[list[Tensor]]): Multi level valid flags of
each image. The outer list indicates images, and the inner list
corresponds to feature levels of the image. Each element of
the inner list is a tensor of shape (num_anchors, )
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta info of each image.
gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
ignored.
gt_labels_list (list[Tensor]): Ground truth labels of each box.
label_channels (int): Channel of label.
unmap_outputs (bool): Whether to map outputs back to the original
set of anchors.
Returns:
tuple: Usually returns a tuple containing learning targets.
- labels (list[Tensor]): Labels of all anchors, each with
shape (num_anchors,).
- label_weights (list[Tensor]): Label weights of all anchor.
each with shape (num_anchors,).
- bbox_targets (list[Tensor]): BBox targets of all anchors.
each with shape (num_anchors, 4).
- bbox_weights (list[Tensor]): BBox weights of all anchors.
each with shape (num_anchors, 4).
- pos_inds (list[Tensor]): Contains all index of positive
sample in all anchor.
- gt_inds (list[Tensor]): Contains all gt_index of positive
sample in all anchor.
"""
num_imgs = len(img_metas)
assert len(anchor_list) == len(valid_flag_list) == num_imgs
concat_anchor_list = []
concat_valid_flag_list = []
for i in range(num_imgs):
assert len(anchor_list[i]) == len(valid_flag_list[i])
concat_anchor_list.append(torch.cat(anchor_list[i]))
concat_valid_flag_list.append(torch.cat(valid_flag_list[i]))
# compute targets for each image
if gt_bboxes_ignore_list is None:
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
if gt_labels_list is None:
gt_labels_list = [None for _ in range(num_imgs)]
results = multi_apply(
self._get_targets_single,
concat_anchor_list,
concat_valid_flag_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
gt_labels_list,
img_metas,
label_channels=label_channels,
unmap_outputs=unmap_outputs)
(labels, label_weights, bbox_targets, bbox_weights, valid_pos_inds,
valid_neg_inds, sampling_result) = results
# Due to valid flag of anchors, we have to calculate the real pos_inds
# in origin anchor set.
pos_inds = []
for i, single_labels in enumerate(labels):
pos_mask = (0 <= single_labels) & (
single_labels < self.num_classes)
pos_inds.append(pos_mask.nonzero().view(-1))
gt_inds = [item.pos_assigned_gt_inds for item in sampling_result]
return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
gt_inds)
def _get_targets_single(self,
flat_anchors,
valid_flags,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
img_meta,
label_channels=1,
unmap_outputs=True):
"""Compute regression and classification targets for anchors in a
single image.
This method is same as `AnchorHead._get_targets_single()`.
"""
assert unmap_outputs, 'We must map outputs back to the original' \
'set of anchors in PAAhead'
return super(ATSSHead, self)._get_targets_single(
flat_anchors,
valid_flags,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
img_meta,
label_channels=1,
unmap_outputs=True)
def _get_bboxes_single(self,
cls_scores,
bbox_preds,
iou_preds,
mlvl_anchors,
img_shape,
scale_factor,
cfg,
rescale=False,
with_nms=True):
"""Transform outputs for a single batch item into labeled boxes.
This method is almost same as `ATSSHead._get_bboxes_single()`.
We use sqrt(iou_preds * cls_scores) in NMS process instead of just
cls_scores. Besides, score voting is used when `` score_voting``
is set to True.
"""
assert with_nms, 'PAA only supports "with_nms=True" now'
assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
mlvl_bboxes = []
mlvl_scores = []
mlvl_iou_preds = []
for cls_score, bbox_pred, iou_preds, anchors in zip(
cls_scores, bbox_preds, iou_preds, mlvl_anchors):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).sigmoid()
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
iou_preds = iou_preds.permute(1, 2, 0).reshape(-1).sigmoid()
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
max_scores, _ = (scores * iou_preds[:, None]).sqrt().max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
anchors = anchors[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
iou_preds = iou_preds[topk_inds]
bboxes = self.bbox_coder.decode(
anchors, bbox_pred, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_iou_preds.append(iou_preds)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
# Add a dummy background class to the backend when using sigmoid
# remind that we set FG labels to [0, num_class-1] since mmdet v2.0
# BG cat_id: num_class
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
mlvl_iou_preds = torch.cat(mlvl_iou_preds)
mlvl_nms_scores = (mlvl_scores * mlvl_iou_preds[:, None]).sqrt()
det_bboxes, det_labels = multiclass_nms(
mlvl_bboxes,
mlvl_nms_scores,
cfg.score_thr,
cfg.nms,
cfg.max_per_img,
score_factors=None)
if self.with_score_voting:
det_bboxes, det_labels = self.score_voting(det_bboxes, det_labels,
mlvl_bboxes,
mlvl_nms_scores,
cfg.score_thr)
return det_bboxes, det_labels
def score_voting(self, det_bboxes, det_labels, mlvl_bboxes,
mlvl_nms_scores, score_thr):
"""Implementation of score voting method works on each remaining boxes
after NMS procedure.
Args:
det_bboxes (Tensor): Remaining boxes after NMS procedure,
with shape (k, 5), each dimension means
(x1, y1, x2, y2, score).
det_labels (Tensor): The label of remaining boxes, with shape
(k, 1),Labels are 0-based.
mlvl_bboxes (Tensor): All boxes before the NMS procedure,
with shape (num_anchors,4).
mlvl_nms_scores (Tensor): The scores of all boxes which is used
in the NMS procedure, with shape (num_anchors, num_class)
mlvl_iou_preds (Tensot): The predictions of IOU of all boxes
before the NMS procedure, with shape (num_anchors, 1)
score_thr (float): The score threshold of bboxes.
Returns:
tuple: Usually returns a tuple containing voting results.
- det_bboxes_voted (Tensor): Remaining boxes after
score voting procedure, with shape (k, 5), each
dimension means (x1, y1, x2, y2, score).
- det_labels_voted (Tensor): Label of remaining bboxes
after voting, with shape (num_anchors,).
"""
candidate_mask = mlvl_nms_scores > score_thr
candidate_mask_nozeros = candidate_mask.nonzero()
candidate_inds = candidate_mask_nozeros[:, 0]
candidate_labels = candidate_mask_nozeros[:, 1]
candidate_bboxes = mlvl_bboxes[candidate_inds]
candidate_scores = mlvl_nms_scores[candidate_mask]
det_bboxes_voted = []
det_labels_voted = []
for cls in range(self.cls_out_channels):
candidate_cls_mask = candidate_labels == cls
if not candidate_cls_mask.any():
continue
candidate_cls_scores = candidate_scores[candidate_cls_mask]
candidate_cls_bboxes = candidate_bboxes[candidate_cls_mask]
det_cls_mask = det_labels == cls
det_cls_bboxes = det_bboxes[det_cls_mask].view(
-1, det_bboxes.size(-1))
det_candidate_ious = bbox_overlaps(det_cls_bboxes[:, :4],
candidate_cls_bboxes)
for det_ind in range(len(det_cls_bboxes)):
single_det_ious = det_candidate_ious[det_ind]
pos_ious_mask = single_det_ious > 0.01
pos_ious = single_det_ious[pos_ious_mask]
pos_bboxes = candidate_cls_bboxes[pos_ious_mask]
pos_scores = candidate_cls_scores[pos_ious_mask]
pis = (torch.exp(-(1 - pos_ious)**2 / 0.025) *
pos_scores)[:, None]
voted_box = torch.sum(
pis * pos_bboxes, dim=0) / torch.sum(
pis, dim=0)
voted_score = det_cls_bboxes[det_ind][-1:][None, :]
det_bboxes_voted.append(
torch.cat((voted_box[None, :], voted_score), dim=1))
det_labels_voted.append(cls)
det_bboxes_voted = torch.cat(det_bboxes_voted, dim=0)
det_labels_voted = det_labels.new_tensor(det_labels_voted)
return det_bboxes_voted, det_labels_voted
| 28,818 | 43.065749 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/retina_sepbn_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from ..builder import HEADS
from .anchor_head import AnchorHead
@HEADS.register_module()
class RetinaSepBNHead(AnchorHead):
""""RetinaHead with separate BN.
In RetinaHead, conv/norm layers are shared across different FPN levels,
while in RetinaSepBNHead, conv layers are shared across different FPN
levels, but BN layers are separated.
"""
def __init__(self,
num_classes,
num_ins,
in_channels,
stacked_convs=4,
conv_cfg=None,
norm_cfg=None,
**kwargs):
self.stacked_convs = stacked_convs
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.num_ins = num_ins
super(RetinaSepBNHead, self).__init__(num_classes, in_channels,
**kwargs)
def _init_layers(self):
"""Initialize layers of the head."""
self.relu = nn.ReLU(inplace=True)
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i in range(self.num_ins):
cls_convs = nn.ModuleList()
reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
cls_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
reg_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.cls_convs.append(cls_convs)
self.reg_convs.append(reg_convs)
for i in range(self.stacked_convs):
for j in range(1, self.num_ins):
self.cls_convs[j][i].conv = self.cls_convs[0][i].conv
self.reg_convs[j][i].conv = self.reg_convs[0][i].conv
self.retina_cls = nn.Conv2d(
self.feat_channels,
self.num_anchors * self.cls_out_channels,
3,
padding=1)
self.retina_reg = nn.Conv2d(
self.feat_channels, self.num_anchors * 4, 3, padding=1)
def init_weights(self):
"""Initialize weights of the head."""
for m in self.cls_convs[0]:
normal_init(m.conv, std=0.01)
for m in self.reg_convs[0]:
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.retina_cls, std=0.01, bias=bias_cls)
normal_init(self.retina_reg, std=0.01)
def forward(self, feats):
"""Forward features from the upstream network.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple: Usually a tuple of classification scores and bbox prediction
cls_scores (list[Tensor]): Classification scores for all scale
levels, each is a 4D-tensor, the channels number is
num_anchors * num_classes.
bbox_preds (list[Tensor]): Box energies / deltas for all scale
levels, each is a 4D-tensor, the channels number is
num_anchors * 4.
"""
cls_scores = []
bbox_preds = []
for i, x in enumerate(feats):
cls_feat = feats[i]
reg_feat = feats[i]
for cls_conv in self.cls_convs[i]:
cls_feat = cls_conv(cls_feat)
for reg_conv in self.reg_convs[i]:
reg_feat = reg_conv(reg_feat)
cls_score = self.retina_cls(cls_feat)
bbox_pred = self.retina_reg(reg_feat)
cls_scores.append(cls_score)
bbox_preds.append(bbox_pred)
return cls_scores, bbox_preds
| 4,288 | 36.622807 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/anchor_free_head.py | from abc import abstractmethod
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from mmcv.runner import force_fp32
from mmdet.core import multi_apply
from ..builder import HEADS, build_loss
from .base_dense_head import BaseDenseHead
from .dense_test_mixins import BBoxTestMixin
@HEADS.register_module()
class AnchorFreeHead(BaseDenseHead, BBoxTestMixin):
"""Anchor-free head (FCOS, Fovea, RepPoints, etc.).
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
feat_channels (int): Number of hidden channels. Used in child classes.
stacked_convs (int): Number of stacking convs of the head.
strides (tuple): Downsample factor of each feature map.
dcn_on_last_conv (bool): If true, use dcn in the last layer of
towers. Default: False.
conv_bias (bool | str): If specified as `auto`, it will be decided by
the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
None, otherwise False. Default: "auto".
loss_cls (dict): Config of classification loss.
loss_bbox (dict): Config of localization loss.
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Config dict for normalization layer. Default: None.
train_cfg (dict): Training config of anchor head.
test_cfg (dict): Testing config of anchor head.
""" # noqa: W605
_version = 1
def __init__(self,
num_classes,
in_channels,
feat_channels=256,
stacked_convs=4,
strides=(4, 8, 16, 32, 64),
dcn_on_last_conv=False,
conv_bias='auto',
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='IoULoss', loss_weight=1.0),
conv_cfg=None,
norm_cfg=None,
train_cfg=None,
test_cfg=None):
super(AnchorFreeHead, self).__init__()
self.num_classes = num_classes
self.cls_out_channels = num_classes
self.in_channels = in_channels
self.feat_channels = feat_channels
self.stacked_convs = stacked_convs
self.strides = strides
self.dcn_on_last_conv = dcn_on_last_conv
assert conv_bias == 'auto' or isinstance(conv_bias, bool)
self.conv_bias = conv_bias
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.fp16_enabled = False
self._init_layers()
def _init_layers(self):
"""Initialize layers of the head."""
self._init_cls_convs()
self._init_reg_convs()
self._init_predictor()
def _init_cls_convs(self):
"""Initialize classification conv layers of the head."""
self.cls_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
if self.dcn_on_last_conv and i == self.stacked_convs - 1:
conv_cfg = dict(type='DCNv2')
else:
conv_cfg = self.conv_cfg
self.cls_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=self.norm_cfg,
bias=self.conv_bias))
def _init_reg_convs(self):
"""Initialize bbox regression conv layers of the head."""
self.reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
if self.dcn_on_last_conv and i == self.stacked_convs - 1:
conv_cfg = dict(type='DCNv2')
else:
conv_cfg = self.conv_cfg
self.reg_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=self.norm_cfg,
bias=self.conv_bias))
def _init_predictor(self):
"""Initialize predictor layers of the head."""
self.conv_cls = nn.Conv2d(
self.feat_channels, self.cls_out_channels, 3, padding=1)
self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
def init_weights(self):
"""Initialize weights of the head."""
for m in self.cls_convs:
if isinstance(m.conv, nn.Conv2d):
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
if isinstance(m.conv, nn.Conv2d):
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.conv_cls, std=0.01, bias=bias_cls)
normal_init(self.conv_reg, std=0.01)
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
"""Hack some keys of the model state dict so that can load checkpoints
of previous version."""
version = local_metadata.get('version', None)
if version is None:
# the key is different in early versions
# for example, 'fcos_cls' become 'conv_cls' now
bbox_head_keys = [
k for k in state_dict.keys() if k.startswith(prefix)
]
ori_predictor_keys = []
new_predictor_keys = []
# e.g. 'fcos_cls' or 'fcos_reg'
for key in bbox_head_keys:
ori_predictor_keys.append(key)
key = key.split('.')
conv_name = None
if key[1].endswith('cls'):
conv_name = 'conv_cls'
elif key[1].endswith('reg'):
conv_name = 'conv_reg'
elif key[1].endswith('centerness'):
conv_name = 'conv_centerness'
else:
assert NotImplementedError
if conv_name is not None:
key[1] = conv_name
new_predictor_keys.append('.'.join(key))
else:
ori_predictor_keys.pop(-1)
for i in range(len(new_predictor_keys)):
state_dict[new_predictor_keys[i]] = state_dict.pop(
ori_predictor_keys[i])
super()._load_from_state_dict(state_dict, prefix, local_metadata,
strict, missing_keys, unexpected_keys,
error_msgs)
def forward(self, feats):
"""Forward features from the upstream network.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple: Usually contain classification scores and bbox predictions.
cls_scores (list[Tensor]): Box scores for each scale level,
each is a 4D-tensor, the channel number is
num_points * num_classes.
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level, each is a 4D-tensor, the channel number is
num_points * 4.
"""
return multi_apply(self.forward_single, feats)[:2]
def forward_single(self, x):
"""Forward features of a single scale levle.
Args:
x (Tensor): FPN feature maps of the specified stride.
Returns:
tuple: Scores for each class, bbox predictions, features
after classification and regression conv layers, some
models needs these features like FCOS.
"""
cls_feat = x
reg_feat = x
for cls_layer in self.cls_convs:
cls_feat = cls_layer(cls_feat)
cls_score = self.conv_cls(cls_feat)
for reg_layer in self.reg_convs:
reg_feat = reg_layer(reg_feat)
bbox_pred = self.conv_reg(reg_feat)
return cls_score, bbox_pred, cls_feat, reg_feat
@abstractmethod
@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def loss(self,
cls_scores,
bbox_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute loss of the head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level,
each is a 4D-tensor, the channel number is
num_points * num_classes.
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level, each is a 4D-tensor, the channel number is
num_points * 4.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
"""
raise NotImplementedError
@abstractmethod
@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def get_bboxes(self,
cls_scores,
bbox_preds,
img_metas,
cfg=None,
rescale=None):
"""Transform network output for a batch into bbox predictions.
Args:
cls_scores (list[Tensor]): Box scores for each scale level
Has shape (N, num_points * num_classes, H, W)
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level with shape (N, num_points * 4, H, W)
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used
rescale (bool): If True, return boxes in original image space
"""
raise NotImplementedError
@abstractmethod
def get_targets(self, points, gt_bboxes_list, gt_labels_list):
"""Compute regression, classification and centerss targets for points
in multiple images.
Args:
points (list[Tensor]): Points of each fpn level, each has shape
(num_points, 2).
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
each has shape (num_gt, 4).
gt_labels_list (list[Tensor]): Ground truth labels of each box,
each has shape (num_gt,).
"""
raise NotImplementedError
def _get_points_single(self,
featmap_size,
stride,
dtype,
device,
flatten=False):
"""Get points of a single scale level."""
h, w = featmap_size
x_range = torch.arange(w, dtype=dtype, device=device)
y_range = torch.arange(h, dtype=dtype, device=device)
y, x = torch.meshgrid(y_range, x_range)
if flatten:
y = y.flatten()
x = x.flatten()
return y, x
def get_points(self, featmap_sizes, dtype, device, flatten=False):
"""Get points according to feature map sizes.
Args:
featmap_sizes (list[tuple]): Multi-level feature map sizes.
dtype (torch.dtype): Type of points.
device (torch.device): Device of points.
Returns:
tuple: points of each image.
"""
mlvl_points = []
for i in range(len(featmap_sizes)):
mlvl_points.append(
self._get_points_single(featmap_sizes[i], self.strides[i],
dtype, device, flatten))
return mlvl_points
def aug_test(self, feats, img_metas, rescale=False):
"""Test function with test time augmentation.
Args:
feats (list[Tensor]): the outer list indicates test-time
augmentations and inner Tensor should have a shape NxCxHxW,
which contains features for all images in the batch.
img_metas (list[list[dict]]): the outer list indicates test-time
augs (multiscale, flip, etc.) and the inner list indicates
images in a batch. each dict has image information.
rescale (bool, optional): Whether to rescale the results.
Defaults to False.
Returns:
list[ndarray]: bbox results of each class
"""
return self.aug_test_bboxes(feats, img_metas, rescale=rescale)
| 13,494 | 38.57478 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/pisa_ssd_head.py | import torch
from mmdet.core import multi_apply
from ..builder import HEADS
from ..losses import CrossEntropyLoss, SmoothL1Loss, carl_loss, isr_p
from .ssd_head import SSDHead
# TODO: add loss evaluator for SSD
@HEADS.register_module()
class PISASSDHead(SSDHead):
def loss(self,
cls_scores,
bbox_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute losses of the head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level
Has shape (N, num_anchors * num_classes, H, W)
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W)
gt_bboxes (list[Tensor]): Ground truth bboxes of each image
with shape (num_obj, 4).
gt_labels (list[Tensor]): Ground truth labels of each image
with shape (num_obj, 4).
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image.
Default: None.
Returns:
dict: Loss dict, comprise classification loss regression loss and
carl loss.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.anchor_generator.num_levels
device = cls_scores[0].device
anchor_list, valid_flag_list = self.get_anchors(
featmap_sizes, img_metas, device=device)
cls_reg_targets = self.get_targets(
anchor_list,
valid_flag_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=1,
unmap_outputs=False,
return_sampling_results=True)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg, sampling_results_list) = cls_reg_targets
num_images = len(img_metas)
all_cls_scores = torch.cat([
s.permute(0, 2, 3, 1).reshape(
num_images, -1, self.cls_out_channels) for s in cls_scores
], 1)
all_labels = torch.cat(labels_list, -1).view(num_images, -1)
all_label_weights = torch.cat(label_weights_list,
-1).view(num_images, -1)
all_bbox_preds = torch.cat([
b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
for b in bbox_preds
], -2)
all_bbox_targets = torch.cat(bbox_targets_list,
-2).view(num_images, -1, 4)
all_bbox_weights = torch.cat(bbox_weights_list,
-2).view(num_images, -1, 4)
# concat all level anchors to a single tensor
all_anchors = []
for i in range(num_images):
all_anchors.append(torch.cat(anchor_list[i]))
isr_cfg = self.train_cfg.get('isr', None)
all_targets = (all_labels.view(-1), all_label_weights.view(-1),
all_bbox_targets.view(-1,
4), all_bbox_weights.view(-1, 4))
# apply ISR-P
if isr_cfg is not None:
all_targets = isr_p(
all_cls_scores.view(-1, all_cls_scores.size(-1)),
all_bbox_preds.view(-1, 4),
all_targets,
torch.cat(all_anchors),
sampling_results_list,
loss_cls=CrossEntropyLoss(),
bbox_coder=self.bbox_coder,
**self.train_cfg.isr,
num_class=self.num_classes)
(new_labels, new_label_weights, new_bbox_targets,
new_bbox_weights) = all_targets
all_labels = new_labels.view(all_labels.shape)
all_label_weights = new_label_weights.view(all_label_weights.shape)
all_bbox_targets = new_bbox_targets.view(all_bbox_targets.shape)
all_bbox_weights = new_bbox_weights.view(all_bbox_weights.shape)
# add CARL loss
carl_loss_cfg = self.train_cfg.get('carl', None)
if carl_loss_cfg is not None:
loss_carl = carl_loss(
all_cls_scores.view(-1, all_cls_scores.size(-1)),
all_targets[0],
all_bbox_preds.view(-1, 4),
all_targets[2],
SmoothL1Loss(beta=1.),
**self.train_cfg.carl,
avg_factor=num_total_pos,
num_class=self.num_classes)
# check NaN and Inf
assert torch.isfinite(all_cls_scores).all().item(), \
'classification scores become infinite or NaN!'
assert torch.isfinite(all_bbox_preds).all().item(), \
'bbox predications become infinite or NaN!'
losses_cls, losses_bbox = multi_apply(
self.loss_single,
all_cls_scores,
all_bbox_preds,
all_anchors,
all_labels,
all_label_weights,
all_bbox_targets,
all_bbox_weights,
num_total_samples=num_total_pos)
loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
if carl_loss_cfg is not None:
loss_dict.update(loss_carl)
return loss_dict
| 5,551 | 38.657143 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/pisa_retinanet_head.py | import torch
from mmcv.runner import force_fp32
from mmdet.core import images_to_levels
from ..builder import HEADS
from ..losses import carl_loss, isr_p
from .retina_head import RetinaHead
@HEADS.register_module()
class PISARetinaHead(RetinaHead):
"""PISA Retinanet Head.
The head owns the same structure with Retinanet Head, but differs in two
aspects:
1. Importance-based Sample Reweighting Positive (ISR-P) is applied to
change the positive loss weights.
2. Classification-aware regression loss is adopted as a third loss.
"""
@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def loss(self,
cls_scores,
bbox_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute losses of the head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level
Has shape (N, num_anchors * num_classes, H, W)
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W)
gt_bboxes (list[Tensor]): Ground truth bboxes of each image
with shape (num_obj, 4).
gt_labels (list[Tensor]): Ground truth labels of each image
with shape (num_obj, 4).
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image.
Default: None.
Returns:
dict: Loss dict, comprise classification loss, regression loss and
carl loss.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.anchor_generator.num_levels
device = cls_scores[0].device
anchor_list, valid_flag_list = self.get_anchors(
featmap_sizes, img_metas, device=device)
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = self.get_targets(
anchor_list,
valid_flag_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
return_sampling_results=True)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg, sampling_results_list) = cls_reg_targets
num_total_samples = (
num_total_pos + num_total_neg if self.sampling else num_total_pos)
# anchor number of multi levels
num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
# concat all level anchors and flags to a single tensor
concat_anchor_list = []
for i in range(len(anchor_list)):
concat_anchor_list.append(torch.cat(anchor_list[i]))
all_anchor_list = images_to_levels(concat_anchor_list,
num_level_anchors)
num_imgs = len(img_metas)
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1, label_channels)
for cls_score in cls_scores
]
flatten_cls_scores = torch.cat(
flatten_cls_scores, dim=1).reshape(-1,
flatten_cls_scores[0].size(-1))
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
for bbox_pred in bbox_preds
]
flatten_bbox_preds = torch.cat(
flatten_bbox_preds, dim=1).view(-1, flatten_bbox_preds[0].size(-1))
flatten_labels = torch.cat(labels_list, dim=1).reshape(-1)
flatten_label_weights = torch.cat(
label_weights_list, dim=1).reshape(-1)
flatten_anchors = torch.cat(all_anchor_list, dim=1).reshape(-1, 4)
flatten_bbox_targets = torch.cat(
bbox_targets_list, dim=1).reshape(-1, 4)
flatten_bbox_weights = torch.cat(
bbox_weights_list, dim=1).reshape(-1, 4)
# Apply ISR-P
isr_cfg = self.train_cfg.get('isr', None)
if isr_cfg is not None:
all_targets = (flatten_labels, flatten_label_weights,
flatten_bbox_targets, flatten_bbox_weights)
with torch.no_grad():
all_targets = isr_p(
flatten_cls_scores,
flatten_bbox_preds,
all_targets,
flatten_anchors,
sampling_results_list,
bbox_coder=self.bbox_coder,
loss_cls=self.loss_cls,
num_class=self.num_classes,
**self.train_cfg.isr)
(flatten_labels, flatten_label_weights, flatten_bbox_targets,
flatten_bbox_weights) = all_targets
# For convenience we compute loss once instead separating by fpn level,
# so that we don't need to separate the weights by level again.
# The result should be the same
losses_cls = self.loss_cls(
flatten_cls_scores,
flatten_labels,
flatten_label_weights,
avg_factor=num_total_samples)
losses_bbox = self.loss_bbox(
flatten_bbox_preds,
flatten_bbox_targets,
flatten_bbox_weights,
avg_factor=num_total_samples)
loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
# CARL Loss
carl_cfg = self.train_cfg.get('carl', None)
if carl_cfg is not None:
loss_carl = carl_loss(
flatten_cls_scores,
flatten_labels,
flatten_bbox_preds,
flatten_bbox_targets,
self.loss_bbox,
**self.train_cfg.carl,
avg_factor=num_total_pos,
sigmoid=True,
num_class=self.num_classes)
loss_dict.update(loss_carl)
return loss_dict
| 6,220 | 39.135484 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/gfl_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
from mmcv.runner import force_fp32
from mmdet.core import (anchor_inside_flags, bbox2distance, bbox_overlaps,
build_assigner, build_sampler, distance2bbox,
images_to_levels, multi_apply, multiclass_nms,
reduce_mean, unmap)
from ..builder import HEADS, build_loss
from .anchor_head import AnchorHead
class Integral(nn.Module):
"""A fixed layer for calculating integral result from distribution.
This layer calculates the target location by :math: `sum{P(y_i) * y_i}`,
P(y_i) denotes the softmax vector that represents the discrete distribution
y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max}
Args:
reg_max (int): The maximal value of the discrete set. Default: 16. You
may want to reset it according to your new dataset or related
settings.
"""
def __init__(self, reg_max=16):
super(Integral, self).__init__()
self.reg_max = reg_max
self.register_buffer('project',
torch.linspace(0, self.reg_max, self.reg_max + 1))
def forward(self, x):
"""Forward feature from the regression head to get integral result of
bounding box location.
Args:
x (Tensor): Features of the regression head, shape (N, 4*(n+1)),
n is self.reg_max.
Returns:
x (Tensor): Integral result of box locations, i.e., distance
offsets from the box center in four directions, shape (N, 4).
"""
x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)
x = F.linear(x, self.project.type_as(x)).reshape(-1, 4)
return x
@HEADS.register_module()
class GFLHead(AnchorHead):
"""Generalized Focal Loss: Learning Qualified and Distributed Bounding
Boxes for Dense Object Detection.
GFL head structure is similar with ATSS, however GFL uses
1) joint representation for classification and localization quality, and
2) flexible General distribution for bounding box locations,
which are supervised by
Quality Focal Loss (QFL) and Distribution Focal Loss (DFL), respectively
https://arxiv.org/abs/2006.04388
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
stacked_convs (int): Number of conv layers in cls and reg tower.
Default: 4.
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None.
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='GN', num_groups=32, requires_grad=True).
loss_qfl (dict): Config of Quality Focal Loss (QFL).
reg_max (int): Max value of integral set :math: `{0, ..., reg_max}`
in QFL setting. Default: 16.
Example:
>>> self = GFLHead(11, 7)
>>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
>>> cls_quality_score, bbox_pred = self.forward(feats)
>>> assert len(cls_quality_score) == len(self.scales)
"""
def __init__(self,
num_classes,
in_channels,
stacked_convs=4,
conv_cfg=None,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
reg_max=16,
**kwargs):
self.stacked_convs = stacked_convs
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.reg_max = reg_max
super(GFLHead, self).__init__(num_classes, in_channels, **kwargs)
self.sampling = False
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# SSD sampling=False so use PseudoSampler
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.integral = Integral(self.reg_max)
self.loss_dfl = build_loss(loss_dfl)
def _init_layers(self):
"""Initialize layers of the head."""
self.relu = nn.ReLU(inplace=True)
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.reg_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
assert self.num_anchors == 1, 'anchor free version'
self.gfl_cls = nn.Conv2d(
self.feat_channels, self.cls_out_channels, 3, padding=1)
self.gfl_reg = nn.Conv2d(
self.feat_channels, 4 * (self.reg_max + 1), 3, padding=1)
self.scales = nn.ModuleList(
[Scale(1.0) for _ in self.anchor_generator.strides])
def init_weights(self):
"""Initialize weights of the head."""
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.gfl_cls, std=0.01, bias=bias_cls)
normal_init(self.gfl_reg, std=0.01)
def forward(self, feats):
"""Forward features from the upstream network.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple: Usually a tuple of classification scores and bbox prediction
cls_scores (list[Tensor]): Classification and quality (IoU)
joint scores for all scale levels, each is a 4D-tensor,
the channel number is num_classes.
bbox_preds (list[Tensor]): Box distribution logits for all
scale levels, each is a 4D-tensor, the channel number is
4*(n+1), n is max value of integral set.
"""
return multi_apply(self.forward_single, feats, self.scales)
def forward_single(self, x, scale):
"""Forward feature of a single scale level.
Args:
x (Tensor): Features of a single scale level.
scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
the bbox prediction.
Returns:
tuple:
cls_score (Tensor): Cls and quality joint scores for a single
scale level the channel number is num_classes.
bbox_pred (Tensor): Box distribution logits for a single scale
level, the channel number is 4*(n+1), n is max value of
integral set.
"""
cls_feat = x
reg_feat = x
for cls_conv in self.cls_convs:
cls_feat = cls_conv(cls_feat)
for reg_conv in self.reg_convs:
reg_feat = reg_conv(reg_feat)
cls_score = self.gfl_cls(cls_feat)
bbox_pred = scale(self.gfl_reg(reg_feat)).float()
return cls_score, bbox_pred
def anchor_center(self, anchors):
"""Get anchor centers from anchors.
Args:
anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format.
Returns:
Tensor: Anchor centers with shape (N, 2), "xy" format.
"""
anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
return torch.stack([anchors_cx, anchors_cy], dim=-1)
def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
bbox_targets, stride, num_total_samples):
"""Compute loss of a single scale level.
Args:
anchors (Tensor): Box reference for each scale level with shape
(N, num_total_anchors, 4).
cls_score (Tensor): Cls and quality joint scores for each scale
level has shape (N, num_classes, H, W).
bbox_pred (Tensor): Box distribution logits for each scale
level with shape (N, 4*(n+1), H, W), n is max value of integral
set.
labels (Tensor): Labels of each anchors with shape
(N, num_total_anchors).
label_weights (Tensor): Label weights of each anchor with shape
(N, num_total_anchors)
bbox_targets (Tensor): BBox regression targets of each anchor wight
shape (N, num_total_anchors, 4).
stride (tuple): Stride in this scale level.
num_total_samples (int): Number of positive samples that is
reduced over all GPUs.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
assert stride[0] == stride[1], 'h stride is not equal to w stride!'
anchors = anchors.reshape(-1, 4)
cls_score = cls_score.permute(0, 2, 3,
1).reshape(-1, self.cls_out_channels)
bbox_pred = bbox_pred.permute(0, 2, 3,
1).reshape(-1, 4 * (self.reg_max + 1))
bbox_targets = bbox_targets.reshape(-1, 4)
labels = labels.reshape(-1)
label_weights = label_weights.reshape(-1)
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
bg_class_ind = self.num_classes
pos_inds = ((labels >= 0)
& (labels < bg_class_ind)).nonzero().squeeze(1)
score = label_weights.new_zeros(labels.shape)
if len(pos_inds) > 0:
pos_bbox_targets = bbox_targets[pos_inds]
pos_bbox_pred = bbox_pred[pos_inds]
pos_anchors = anchors[pos_inds]
pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
weight_targets = cls_score.detach().sigmoid()
weight_targets = weight_targets.max(dim=1)[0][pos_inds]
pos_bbox_pred_corners = self.integral(pos_bbox_pred)
pos_decode_bbox_pred = distance2bbox(pos_anchor_centers,
pos_bbox_pred_corners)
pos_decode_bbox_targets = pos_bbox_targets / stride[0]
score[pos_inds] = bbox_overlaps(
pos_decode_bbox_pred.detach(),
pos_decode_bbox_targets,
is_aligned=True)
pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
target_corners = bbox2distance(pos_anchor_centers,
pos_decode_bbox_targets,
self.reg_max).reshape(-1)
# regression loss
loss_bbox = self.loss_bbox(
pos_decode_bbox_pred,
pos_decode_bbox_targets,
weight=weight_targets,
avg_factor=1.0)
# dfl loss
loss_dfl = self.loss_dfl(
pred_corners,
target_corners,
weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
avg_factor=4.0)
else:
loss_bbox = bbox_pred.sum() * 0
loss_dfl = bbox_pred.sum() * 0
weight_targets = torch.tensor(0).cuda()
# cls (qfl) loss
loss_cls = self.loss_cls(
cls_score, (labels, score),
weight=label_weights,
avg_factor=num_total_samples)
return loss_cls, loss_bbox, loss_dfl, weight_targets.sum()
@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def loss(self,
cls_scores,
bbox_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute losses of the head.
Args:
cls_scores (list[Tensor]): Cls and quality scores for each scale
level has shape (N, num_classes, H, W).
bbox_preds (list[Tensor]): Box distribution logits for each scale
level with shape (N, 4*(n+1), H, W), n is max value of integral
set.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (list[Tensor] | None): specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.anchor_generator.num_levels
device = cls_scores[0].device
anchor_list, valid_flag_list = self.get_anchors(
featmap_sizes, img_metas, device=device)
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = self.get_targets(
anchor_list,
valid_flag_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels)
if cls_reg_targets is None:
return None
(anchor_list, labels_list, label_weights_list, bbox_targets_list,
bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
num_total_samples = reduce_mean(
torch.tensor(num_total_pos).cuda()).item()
num_total_samples = max(num_total_samples, 1.0)
losses_cls, losses_bbox, losses_dfl,\
avg_factor = multi_apply(
self.loss_single,
anchor_list,
cls_scores,
bbox_preds,
labels_list,
label_weights_list,
bbox_targets_list,
self.anchor_generator.strides,
num_total_samples=num_total_samples)
avg_factor = sum(avg_factor)
avg_factor = reduce_mean(avg_factor).item()
losses_bbox = list(map(lambda x: x / avg_factor, losses_bbox))
losses_dfl = list(map(lambda x: x / avg_factor, losses_dfl))
return dict(
loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dfl=losses_dfl)
def _get_bboxes_single(self,
cls_scores,
bbox_preds,
mlvl_anchors,
img_shape,
scale_factor,
cfg,
rescale=False,
with_nms=True):
"""Transform outputs for a single batch item into labeled boxes.
Args:
cls_scores (list[Tensor]): Box scores for a single scale level
has shape (num_classes, H, W).
bbox_preds (list[Tensor]): Box distribution logits for a single
scale level with shape (4*(n+1), H, W), n is max value of
integral set.
mlvl_anchors (list[Tensor]): Box reference for a single scale level
with shape (num_total_anchors, 4).
img_shape (tuple[int]): Shape of the input image,
(height, width, 3).
scale_factor (ndarray): Scale factor of the image arange as
(w_scale, h_scale, w_scale, h_scale).
cfg (mmcv.Config | None): Test / postprocessing configuration,
if None, test_cfg would be used.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
Returns:
tuple(Tensor):
det_bboxes (Tensor): Bbox predictions in shape (N, 5), where
the first 4 columns are bounding box positions
(tl_x, tl_y, br_x, br_y) and the 5-th column is a score
between 0 and 1.
det_labels (Tensor): A (N,) tensor where each item is the
predicted class label of the corresponding box.
"""
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
mlvl_bboxes = []
mlvl_scores = []
for cls_score, bbox_pred, stride, anchors in zip(
cls_scores, bbox_preds, self.anchor_generator.strides,
mlvl_anchors):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
assert stride[0] == stride[1]
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).sigmoid()
bbox_pred = bbox_pred.permute(1, 2, 0)
bbox_pred = self.integral(bbox_pred) * stride[0]
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
max_scores, _ = scores.max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
anchors = anchors[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
bboxes = distance2bbox(
self.anchor_center(anchors), bbox_pred, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
# Add a dummy background class to the backend when using sigmoid
# remind that we set FG labels to [0, num_class-1] since mmdet v2.0
# BG cat_id: num_class
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
if with_nms:
det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
else:
return mlvl_bboxes, mlvl_scores
def get_targets(self,
anchor_list,
valid_flag_list,
gt_bboxes_list,
img_metas,
gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=1,
unmap_outputs=True):
"""Get targets for GFL head.
This method is almost the same as `AnchorHead.get_targets()`. Besides
returning the targets as the parent method does, it also returns the
anchors as the first element of the returned tuple.
"""
num_imgs = len(img_metas)
assert len(anchor_list) == len(valid_flag_list) == num_imgs
# anchor number of multi levels
num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
num_level_anchors_list = [num_level_anchors] * num_imgs
# concat all level anchors and flags to a single tensor
for i in range(num_imgs):
assert len(anchor_list[i]) == len(valid_flag_list[i])
anchor_list[i] = torch.cat(anchor_list[i])
valid_flag_list[i] = torch.cat(valid_flag_list[i])
# compute targets for each image
if gt_bboxes_ignore_list is None:
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
if gt_labels_list is None:
gt_labels_list = [None for _ in range(num_imgs)]
(all_anchors, all_labels, all_label_weights, all_bbox_targets,
all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
self._get_target_single,
anchor_list,
valid_flag_list,
num_level_anchors_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
gt_labels_list,
img_metas,
label_channels=label_channels,
unmap_outputs=unmap_outputs)
# no valid anchors
if any([labels is None for labels in all_labels]):
return None
# sampled anchors of all images
num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
# split targets to a list w.r.t. multiple levels
anchors_list = images_to_levels(all_anchors, num_level_anchors)
labels_list = images_to_levels(all_labels, num_level_anchors)
label_weights_list = images_to_levels(all_label_weights,
num_level_anchors)
bbox_targets_list = images_to_levels(all_bbox_targets,
num_level_anchors)
bbox_weights_list = images_to_levels(all_bbox_weights,
num_level_anchors)
return (anchors_list, labels_list, label_weights_list,
bbox_targets_list, bbox_weights_list, num_total_pos,
num_total_neg)
def _get_target_single(self,
flat_anchors,
valid_flags,
num_level_anchors,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
img_meta,
label_channels=1,
unmap_outputs=True):
"""Compute regression, classification targets for anchors in a single
image.
Args:
flat_anchors (Tensor): Multi-level anchors of the image, which are
concatenated into a single tensor of shape (num_anchors, 4)
valid_flags (Tensor): Multi level valid flags of the image,
which are concatenated into a single tensor of
shape (num_anchors,).
num_level_anchors Tensor): Number of anchors of each scale level.
gt_bboxes (Tensor): Ground truth bboxes of the image,
shape (num_gts, 4).
gt_bboxes_ignore (Tensor): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4).
gt_labels (Tensor): Ground truth labels of each box,
shape (num_gts,).
img_meta (dict): Meta info of the image.
label_channels (int): Channel of label.
unmap_outputs (bool): Whether to map outputs back to the original
set of anchors.
Returns:
tuple: N is the number of total anchors in the image.
anchors (Tensor): All anchors in the image with shape (N, 4).
labels (Tensor): Labels of all anchors in the image with shape
(N,).
label_weights (Tensor): Label weights of all anchor in the
image with shape (N,).
bbox_targets (Tensor): BBox targets of all anchors in the
image with shape (N, 4).
bbox_weights (Tensor): BBox weights of all anchors in the
image with shape (N, 4).
pos_inds (Tensor): Indices of postive anchor with shape
(num_pos,).
neg_inds (Tensor): Indices of negative anchor with shape
(num_neg,).
"""
inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
img_meta['img_shape'][:2],
self.train_cfg.allowed_border)
if not inside_flags.any():
return (None, ) * 7
# assign gt and sample anchors
anchors = flat_anchors[inside_flags, :]
num_level_anchors_inside = self.get_num_level_anchors_inside(
num_level_anchors, inside_flags)
assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
gt_bboxes, gt_bboxes_ignore,
gt_labels)
sampling_result = self.sampler.sample(assign_result, anchors,
gt_bboxes)
num_valid_anchors = anchors.shape[0]
bbox_targets = torch.zeros_like(anchors)
bbox_weights = torch.zeros_like(anchors)
labels = anchors.new_full((num_valid_anchors, ),
self.num_classes,
dtype=torch.long)
label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
if len(pos_inds) > 0:
pos_bbox_targets = sampling_result.pos_gt_bboxes
bbox_targets[pos_inds, :] = pos_bbox_targets
bbox_weights[pos_inds, :] = 1.0
if gt_labels is None:
# Only rpn gives gt_labels as None
# Foreground is the first class
labels[pos_inds] = 0
else:
labels[pos_inds] = gt_labels[
sampling_result.pos_assigned_gt_inds]
if self.train_cfg.pos_weight <= 0:
label_weights[pos_inds] = 1.0
else:
label_weights[pos_inds] = self.train_cfg.pos_weight
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
# map up to original set of anchors
if unmap_outputs:
num_total_anchors = flat_anchors.size(0)
anchors = unmap(anchors, num_total_anchors, inside_flags)
labels = unmap(
labels, num_total_anchors, inside_flags, fill=self.num_classes)
label_weights = unmap(label_weights, num_total_anchors,
inside_flags)
bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
return (anchors, labels, label_weights, bbox_targets, bbox_weights,
pos_inds, neg_inds)
def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
split_inside_flags = torch.split(inside_flags, num_level_anchors)
num_level_anchors_inside = [
int(flags.sum()) for flags in split_inside_flags
]
return num_level_anchors_inside
| 27,254 | 42.125 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/corner_head.py | from math import ceil, log
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, bias_init_with_prob
from mmcv.ops import CornerPool, batched_nms
from mmdet.core import multi_apply
from ..builder import HEADS, build_loss
from ..utils import gaussian_radius, gen_gaussian_target
from .base_dense_head import BaseDenseHead
class BiCornerPool(nn.Module):
"""Bidirectional Corner Pooling Module (TopLeft, BottomRight, etc.)
Args:
in_channels (int): Input channels of module.
out_channels (int): Output channels of module.
feat_channels (int): Feature channels of module.
directions (list[str]): Directions of two CornerPools.
norm_cfg (dict): Dictionary to construct and config norm layer.
"""
def __init__(self,
in_channels,
directions,
feat_channels=128,
out_channels=128,
norm_cfg=dict(type='BN', requires_grad=True)):
super(BiCornerPool, self).__init__()
self.direction1_conv = ConvModule(
in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg)
self.direction2_conv = ConvModule(
in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg)
self.aftpool_conv = ConvModule(
feat_channels,
out_channels,
3,
padding=1,
norm_cfg=norm_cfg,
act_cfg=None)
self.conv1 = ConvModule(
in_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None)
self.conv2 = ConvModule(
in_channels, out_channels, 3, padding=1, norm_cfg=norm_cfg)
self.direction1_pool = CornerPool(directions[0])
self.direction2_pool = CornerPool(directions[1])
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
"""Forward features from the upstream network.
Args:
x (tensor): Input feature of BiCornerPool.
Returns:
conv2 (tensor): Output feature of BiCornerPool.
"""
direction1_conv = self.direction1_conv(x)
direction2_conv = self.direction2_conv(x)
direction1_feat = self.direction1_pool(direction1_conv)
direction2_feat = self.direction2_pool(direction2_conv)
aftpool_conv = self.aftpool_conv(direction1_feat + direction2_feat)
conv1 = self.conv1(x)
relu = self.relu(aftpool_conv + conv1)
conv2 = self.conv2(relu)
return conv2
@HEADS.register_module()
class CornerHead(BaseDenseHead):
"""Head of CornerNet: Detecting Objects as Paired Keypoints.
Code is modified from the `official github repo
<https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/
kp.py#L73>`_ .
More details can be found in the `paper
<https://arxiv.org/abs/1808.01244>`_ .
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
num_feat_levels (int): Levels of feature from the previous module. 2
for HourglassNet-104 and 1 for HourglassNet-52. Because
HourglassNet-104 outputs the final feature and intermediate
supervision feature and HourglassNet-52 only outputs the final
feature. Default: 2.
corner_emb_channels (int): Channel of embedding vector. Default: 1.
train_cfg (dict | None): Training config. Useless in CornerHead,
but we keep this variable for SingleStageDetector. Default: None.
test_cfg (dict | None): Testing config of CornerHead. Default: None.
loss_heatmap (dict | None): Config of corner heatmap loss. Default:
GaussianFocalLoss.
loss_embedding (dict | None): Config of corner embedding loss. Default:
AssociativeEmbeddingLoss.
loss_offset (dict | None): Config of corner offset loss. Default:
SmoothL1Loss.
"""
def __init__(self,
num_classes,
in_channels,
num_feat_levels=2,
corner_emb_channels=1,
train_cfg=None,
test_cfg=None,
loss_heatmap=dict(
type='GaussianFocalLoss',
alpha=2.0,
gamma=4.0,
loss_weight=1),
loss_embedding=dict(
type='AssociativeEmbeddingLoss',
pull_weight=0.25,
push_weight=0.25),
loss_offset=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=1)):
super(CornerHead, self).__init__()
self.num_classes = num_classes
self.in_channels = in_channels
self.corner_emb_channels = corner_emb_channels
self.with_corner_emb = self.corner_emb_channels > 0
self.corner_offset_channels = 2
self.num_feat_levels = num_feat_levels
self.loss_heatmap = build_loss(
loss_heatmap) if loss_heatmap is not None else None
self.loss_embedding = build_loss(
loss_embedding) if loss_embedding is not None else None
self.loss_offset = build_loss(
loss_offset) if loss_offset is not None else None
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self._init_layers()
def _make_layers(self, out_channels, in_channels=256, feat_channels=256):
"""Initialize conv sequential for CornerHead."""
return nn.Sequential(
ConvModule(in_channels, feat_channels, 3, padding=1),
ConvModule(
feat_channels, out_channels, 1, norm_cfg=None, act_cfg=None))
def _init_corner_kpt_layers(self):
"""Initialize corner keypoint layers.
Including corner heatmap branch and corner offset branch. Each branch
has two parts: prefix `tl_` for top-left and `br_` for bottom-right.
"""
self.tl_pool, self.br_pool = nn.ModuleList(), nn.ModuleList()
self.tl_heat, self.br_heat = nn.ModuleList(), nn.ModuleList()
self.tl_off, self.br_off = nn.ModuleList(), nn.ModuleList()
for _ in range(self.num_feat_levels):
self.tl_pool.append(
BiCornerPool(
self.in_channels, ['top', 'left'],
out_channels=self.in_channels))
self.br_pool.append(
BiCornerPool(
self.in_channels, ['bottom', 'right'],
out_channels=self.in_channels))
self.tl_heat.append(
self._make_layers(
out_channels=self.num_classes,
in_channels=self.in_channels))
self.br_heat.append(
self._make_layers(
out_channels=self.num_classes,
in_channels=self.in_channels))
self.tl_off.append(
self._make_layers(
out_channels=self.corner_offset_channels,
in_channels=self.in_channels))
self.br_off.append(
self._make_layers(
out_channels=self.corner_offset_channels,
in_channels=self.in_channels))
def _init_corner_emb_layers(self):
"""Initialize corner embedding layers.
Only include corner embedding branch with two parts: prefix `tl_` for
top-left and `br_` for bottom-right.
"""
self.tl_emb, self.br_emb = nn.ModuleList(), nn.ModuleList()
for _ in range(self.num_feat_levels):
self.tl_emb.append(
self._make_layers(
out_channels=self.corner_emb_channels,
in_channels=self.in_channels))
self.br_emb.append(
self._make_layers(
out_channels=self.corner_emb_channels,
in_channels=self.in_channels))
def _init_layers(self):
"""Initialize layers for CornerHead.
Including two parts: corner keypoint layers and corner embedding layers
"""
self._init_corner_kpt_layers()
if self.with_corner_emb:
self._init_corner_emb_layers()
def init_weights(self):
"""Initialize weights of the head."""
bias_init = bias_init_with_prob(0.1)
for i in range(self.num_feat_levels):
# The initialization of parameters are different between nn.Conv2d
# and ConvModule. Our experiments show that using the original
# initialization of nn.Conv2d increases the final mAP by about 0.2%
self.tl_heat[i][-1].conv.reset_parameters()
self.tl_heat[i][-1].conv.bias.data.fill_(bias_init)
self.br_heat[i][-1].conv.reset_parameters()
self.br_heat[i][-1].conv.bias.data.fill_(bias_init)
self.tl_off[i][-1].conv.reset_parameters()
self.br_off[i][-1].conv.reset_parameters()
if self.with_corner_emb:
self.tl_emb[i][-1].conv.reset_parameters()
self.br_emb[i][-1].conv.reset_parameters()
def forward(self, feats):
"""Forward features from the upstream network.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple: Usually a tuple of corner heatmaps, offset heatmaps and
embedding heatmaps.
- tl_heats (list[Tensor]): Top-left corner heatmaps for all
levels, each is a 4D-tensor, the channels number is
num_classes.
- br_heats (list[Tensor]): Bottom-right corner heatmaps for all
levels, each is a 4D-tensor, the channels number is
num_classes.
- tl_embs (list[Tensor] | list[None]): Top-left embedding
heatmaps for all levels, each is a 4D-tensor or None.
If not None, the channels number is corner_emb_channels.
- br_embs (list[Tensor] | list[None]): Bottom-right embedding
heatmaps for all levels, each is a 4D-tensor or None.
If not None, the channels number is corner_emb_channels.
- tl_offs (list[Tensor]): Top-left offset heatmaps for all
levels, each is a 4D-tensor. The channels number is
corner_offset_channels.
- br_offs (list[Tensor]): Bottom-right offset heatmaps for all
levels, each is a 4D-tensor. The channels number is
corner_offset_channels.
"""
lvl_ind = list(range(self.num_feat_levels))
return multi_apply(self.forward_single, feats, lvl_ind)
def forward_single(self, x, lvl_ind, return_pool=False):
"""Forward feature of a single level.
Args:
x (Tensor): Feature of a single level.
lvl_ind (int): Level index of current feature.
return_pool (bool): Return corner pool feature or not.
Returns:
tuple[Tensor]: A tuple of CornerHead's output for current feature
level. Containing the following Tensors:
- tl_heat (Tensor): Predicted top-left corner heatmap.
- br_heat (Tensor): Predicted bottom-right corner heatmap.
- tl_emb (Tensor | None): Predicted top-left embedding heatmap.
None for `self.with_corner_emb == False`.
- br_emb (Tensor | None): Predicted bottom-right embedding
heatmap. None for `self.with_corner_emb == False`.
- tl_off (Tensor): Predicted top-left offset heatmap.
- br_off (Tensor): Predicted bottom-right offset heatmap.
- tl_pool (Tensor): Top-left corner pool feature. Not must
have.
- br_pool (Tensor): Bottom-right corner pool feature. Not must
have.
"""
tl_pool = self.tl_pool[lvl_ind](x)
tl_heat = self.tl_heat[lvl_ind](tl_pool)
br_pool = self.br_pool[lvl_ind](x)
br_heat = self.br_heat[lvl_ind](br_pool)
tl_emb, br_emb = None, None
if self.with_corner_emb:
tl_emb = self.tl_emb[lvl_ind](tl_pool)
br_emb = self.br_emb[lvl_ind](br_pool)
tl_off = self.tl_off[lvl_ind](tl_pool)
br_off = self.br_off[lvl_ind](br_pool)
result_list = [tl_heat, br_heat, tl_emb, br_emb, tl_off, br_off]
if return_pool:
result_list.append(tl_pool)
result_list.append(br_pool)
return result_list
def get_targets(self,
gt_bboxes,
gt_labels,
feat_shape,
img_shape,
with_corner_emb=False,
with_guiding_shift=False,
with_centripetal_shift=False):
"""Generate corner targets.
Including corner heatmap, corner offset.
Optional: corner embedding, corner guiding shift, centripetal shift.
For CornerNet, we generate corner heatmap, corner offset and corner
embedding from this function.
For CentripetalNet, we generate corner heatmap, corner offset, guiding
shift and centripetal shift from this function.
Args:
gt_bboxes (list[Tensor]): Ground truth bboxes of each image, each
has shape (num_gt, 4).
gt_labels (list[Tensor]): Ground truth labels of each box, each has
shape (num_gt,).
feat_shape (list[int]): Shape of output feature,
[batch, channel, height, width].
img_shape (list[int]): Shape of input image,
[height, width, channel].
with_corner_emb (bool): Generate corner embedding target or not.
Default: False.
with_guiding_shift (bool): Generate guiding shift target or not.
Default: False.
with_centripetal_shift (bool): Generate centripetal shift target or
not. Default: False.
Returns:
dict: Ground truth of corner heatmap, corner offset, corner
embedding, guiding shift and centripetal shift. Containing the
following keys:
- topleft_heatmap (Tensor): Ground truth top-left corner
heatmap.
- bottomright_heatmap (Tensor): Ground truth bottom-right
corner heatmap.
- topleft_offset (Tensor): Ground truth top-left corner offset.
- bottomright_offset (Tensor): Ground truth bottom-right corner
offset.
- corner_embedding (list[list[list[int]]]): Ground truth corner
embedding. Not must have.
- topleft_guiding_shift (Tensor): Ground truth top-left corner
guiding shift. Not must have.
- bottomright_guiding_shift (Tensor): Ground truth bottom-right
corner guiding shift. Not must have.
- topleft_centripetal_shift (Tensor): Ground truth top-left
corner centripetal shift. Not must have.
- bottomright_centripetal_shift (Tensor): Ground truth
bottom-right corner centripetal shift. Not must have.
"""
batch_size, _, height, width = feat_shape
img_h, img_w = img_shape[:2]
width_ratio = float(width / img_w)
height_ratio = float(height / img_h)
gt_tl_heatmap = gt_bboxes[-1].new_zeros(
[batch_size, self.num_classes, height, width])
gt_br_heatmap = gt_bboxes[-1].new_zeros(
[batch_size, self.num_classes, height, width])
gt_tl_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width])
gt_br_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width])
if with_corner_emb:
match = []
# Guiding shift is a kind of offset, from center to corner
if with_guiding_shift:
gt_tl_guiding_shift = gt_bboxes[-1].new_zeros(
[batch_size, 2, height, width])
gt_br_guiding_shift = gt_bboxes[-1].new_zeros(
[batch_size, 2, height, width])
# Centripetal shift is also a kind of offset, from center to corner
# and normalized by log.
if with_centripetal_shift:
gt_tl_centripetal_shift = gt_bboxes[-1].new_zeros(
[batch_size, 2, height, width])
gt_br_centripetal_shift = gt_bboxes[-1].new_zeros(
[batch_size, 2, height, width])
for batch_id in range(batch_size):
# Ground truth of corner embedding per image is a list of coord set
corner_match = []
for box_id in range(len(gt_labels[batch_id])):
left, top, right, bottom = gt_bboxes[batch_id][box_id]
center_x = (left + right) / 2.0
center_y = (top + bottom) / 2.0
label = gt_labels[batch_id][box_id]
# Use coords in the feature level to generate ground truth
scale_left = left * width_ratio
scale_right = right * width_ratio
scale_top = top * height_ratio
scale_bottom = bottom * height_ratio
scale_center_x = center_x * width_ratio
scale_center_y = center_y * height_ratio
# Int coords on feature map/ground truth tensor
left_idx = int(min(scale_left, width - 1))
right_idx = int(min(scale_right, width - 1))
top_idx = int(min(scale_top, height - 1))
bottom_idx = int(min(scale_bottom, height - 1))
# Generate gaussian heatmap
scale_box_width = ceil(scale_right - scale_left)
scale_box_height = ceil(scale_bottom - scale_top)
radius = gaussian_radius((scale_box_height, scale_box_width),
min_overlap=0.3)
radius = max(0, int(radius))
gt_tl_heatmap[batch_id, label] = gen_gaussian_target(
gt_tl_heatmap[batch_id, label], [left_idx, top_idx],
radius)
gt_br_heatmap[batch_id, label] = gen_gaussian_target(
gt_br_heatmap[batch_id, label], [right_idx, bottom_idx],
radius)
# Generate corner offset
left_offset = scale_left - left_idx
top_offset = scale_top - top_idx
right_offset = scale_right - right_idx
bottom_offset = scale_bottom - bottom_idx
gt_tl_offset[batch_id, 0, top_idx, left_idx] = left_offset
gt_tl_offset[batch_id, 1, top_idx, left_idx] = top_offset
gt_br_offset[batch_id, 0, bottom_idx, right_idx] = right_offset
gt_br_offset[batch_id, 1, bottom_idx,
right_idx] = bottom_offset
# Generate corner embedding
if with_corner_emb:
corner_match.append([[top_idx, left_idx],
[bottom_idx, right_idx]])
# Generate guiding shift
if with_guiding_shift:
gt_tl_guiding_shift[batch_id, 0, top_idx,
left_idx] = scale_center_x - left_idx
gt_tl_guiding_shift[batch_id, 1, top_idx,
left_idx] = scale_center_y - top_idx
gt_br_guiding_shift[batch_id, 0, bottom_idx,
right_idx] = right_idx - scale_center_x
gt_br_guiding_shift[
batch_id, 1, bottom_idx,
right_idx] = bottom_idx - scale_center_y
# Generate centripetal shift
if with_centripetal_shift:
gt_tl_centripetal_shift[batch_id, 0, top_idx,
left_idx] = log(scale_center_x -
scale_left)
gt_tl_centripetal_shift[batch_id, 1, top_idx,
left_idx] = log(scale_center_y -
scale_top)
gt_br_centripetal_shift[batch_id, 0, bottom_idx,
right_idx] = log(scale_right -
scale_center_x)
gt_br_centripetal_shift[batch_id, 1, bottom_idx,
right_idx] = log(scale_bottom -
scale_center_y)
if with_corner_emb:
match.append(corner_match)
target_result = dict(
topleft_heatmap=gt_tl_heatmap,
topleft_offset=gt_tl_offset,
bottomright_heatmap=gt_br_heatmap,
bottomright_offset=gt_br_offset)
if with_corner_emb:
target_result.update(corner_embedding=match)
if with_guiding_shift:
target_result.update(
topleft_guiding_shift=gt_tl_guiding_shift,
bottomright_guiding_shift=gt_br_guiding_shift)
if with_centripetal_shift:
target_result.update(
topleft_centripetal_shift=gt_tl_centripetal_shift,
bottomright_centripetal_shift=gt_br_centripetal_shift)
return target_result
def loss(self,
tl_heats,
br_heats,
tl_embs,
br_embs,
tl_offs,
br_offs,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute losses of the head.
Args:
tl_heats (list[Tensor]): Top-left corner heatmaps for each level
with shape (N, num_classes, H, W).
br_heats (list[Tensor]): Bottom-right corner heatmaps for each
level with shape (N, num_classes, H, W).
tl_embs (list[Tensor]): Top-left corner embeddings for each level
with shape (N, corner_emb_channels, H, W).
br_embs (list[Tensor]): Bottom-right corner embeddings for each
level with shape (N, corner_emb_channels, H, W).
tl_offs (list[Tensor]): Top-left corner offsets for each level
with shape (N, corner_offset_channels, H, W).
br_offs (list[Tensor]): Bottom-right corner offsets for each level
with shape (N, corner_offset_channels, H, W).
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [left, top, right, bottom] format.
gt_labels (list[Tensor]): Class indices corresponding to each box.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components. Containing the
following losses:
- det_loss (list[Tensor]): Corner keypoint losses of all
feature levels.
- pull_loss (list[Tensor]): Part one of AssociativeEmbedding
losses of all feature levels.
- push_loss (list[Tensor]): Part two of AssociativeEmbedding
losses of all feature levels.
- off_loss (list[Tensor]): Corner offset losses of all feature
levels.
"""
targets = self.get_targets(
gt_bboxes,
gt_labels,
tl_heats[-1].shape,
img_metas[0]['pad_shape'],
with_corner_emb=self.with_corner_emb)
mlvl_targets = [targets for _ in range(self.num_feat_levels)]
det_losses, pull_losses, push_losses, off_losses = multi_apply(
self.loss_single, tl_heats, br_heats, tl_embs, br_embs, tl_offs,
br_offs, mlvl_targets)
loss_dict = dict(det_loss=det_losses, off_loss=off_losses)
if self.with_corner_emb:
loss_dict.update(pull_loss=pull_losses, push_loss=push_losses)
return loss_dict
def loss_single(self, tl_hmp, br_hmp, tl_emb, br_emb, tl_off, br_off,
targets):
"""Compute losses for single level.
Args:
tl_hmp (Tensor): Top-left corner heatmap for current level with
shape (N, num_classes, H, W).
br_hmp (Tensor): Bottom-right corner heatmap for current level with
shape (N, num_classes, H, W).
tl_emb (Tensor): Top-left corner embedding for current level with
shape (N, corner_emb_channels, H, W).
br_emb (Tensor): Bottom-right corner embedding for current level
with shape (N, corner_emb_channels, H, W).
tl_off (Tensor): Top-left corner offset for current level with
shape (N, corner_offset_channels, H, W).
br_off (Tensor): Bottom-right corner offset for current level with
shape (N, corner_offset_channels, H, W).
targets (dict): Corner target generated by `get_targets`.
Returns:
tuple[torch.Tensor]: Losses of the head's differnet branches
containing the following losses:
- det_loss (Tensor): Corner keypoint loss.
- pull_loss (Tensor): Part one of AssociativeEmbedding loss.
- push_loss (Tensor): Part two of AssociativeEmbedding loss.
- off_loss (Tensor): Corner offset loss.
"""
gt_tl_hmp = targets['topleft_heatmap']
gt_br_hmp = targets['bottomright_heatmap']
gt_tl_off = targets['topleft_offset']
gt_br_off = targets['bottomright_offset']
gt_embedding = targets['corner_embedding']
# Detection loss
tl_det_loss = self.loss_heatmap(
tl_hmp.sigmoid(),
gt_tl_hmp,
avg_factor=max(1,
gt_tl_hmp.eq(1).sum()))
br_det_loss = self.loss_heatmap(
br_hmp.sigmoid(),
gt_br_hmp,
avg_factor=max(1,
gt_br_hmp.eq(1).sum()))
det_loss = (tl_det_loss + br_det_loss) / 2.0
# AssociativeEmbedding loss
if self.with_corner_emb and self.loss_embedding is not None:
pull_loss, push_loss = self.loss_embedding(tl_emb, br_emb,
gt_embedding)
else:
pull_loss, push_loss = None, None
# Offset loss
# We only compute the offset loss at the real corner position.
# The value of real corner would be 1 in heatmap ground truth.
# The mask is computed in class agnostic mode and its shape is
# batch * 1 * width * height.
tl_off_mask = gt_tl_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
gt_tl_hmp)
br_off_mask = gt_br_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
gt_br_hmp)
tl_off_loss = self.loss_offset(
tl_off,
gt_tl_off,
tl_off_mask,
avg_factor=max(1, tl_off_mask.sum()))
br_off_loss = self.loss_offset(
br_off,
gt_br_off,
br_off_mask,
avg_factor=max(1, br_off_mask.sum()))
off_loss = (tl_off_loss + br_off_loss) / 2.0
return det_loss, pull_loss, push_loss, off_loss
def get_bboxes(self,
tl_heats,
br_heats,
tl_embs,
br_embs,
tl_offs,
br_offs,
img_metas,
rescale=False,
with_nms=True):
"""Transform network output for a batch into bbox predictions.
Args:
tl_heats (list[Tensor]): Top-left corner heatmaps for each level
with shape (N, num_classes, H, W).
br_heats (list[Tensor]): Bottom-right corner heatmaps for each
level with shape (N, num_classes, H, W).
tl_embs (list[Tensor]): Top-left corner embeddings for each level
with shape (N, corner_emb_channels, H, W).
br_embs (list[Tensor]): Bottom-right corner embeddings for each
level with shape (N, corner_emb_channels, H, W).
tl_offs (list[Tensor]): Top-left corner offsets for each level
with shape (N, corner_offset_channels, H, W).
br_offs (list[Tensor]): Bottom-right corner offsets for each level
with shape (N, corner_offset_channels, H, W).
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
"""
assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas)
result_list = []
for img_id in range(len(img_metas)):
result_list.append(
self._get_bboxes_single(
tl_heats[-1][img_id:img_id + 1, :],
br_heats[-1][img_id:img_id + 1, :],
tl_offs[-1][img_id:img_id + 1, :],
br_offs[-1][img_id:img_id + 1, :],
img_metas[img_id],
tl_emb=tl_embs[-1][img_id:img_id + 1, :],
br_emb=br_embs[-1][img_id:img_id + 1, :],
rescale=rescale,
with_nms=with_nms))
return result_list
def _get_bboxes_single(self,
tl_heat,
br_heat,
tl_off,
br_off,
img_meta,
tl_emb=None,
br_emb=None,
tl_centripetal_shift=None,
br_centripetal_shift=None,
rescale=False,
with_nms=True):
"""Transform outputs for a single batch item into bbox predictions.
Args:
tl_heat (Tensor): Top-left corner heatmap for current level with
shape (N, num_classes, H, W).
br_heat (Tensor): Bottom-right corner heatmap for current level
with shape (N, num_classes, H, W).
tl_off (Tensor): Top-left corner offset for current level with
shape (N, corner_offset_channels, H, W).
br_off (Tensor): Bottom-right corner offset for current level with
shape (N, corner_offset_channels, H, W).
img_meta (dict): Meta information of current image, e.g.,
image size, scaling factor, etc.
tl_emb (Tensor): Top-left corner embedding for current level with
shape (N, corner_emb_channels, H, W).
br_emb (Tensor): Bottom-right corner embedding for current level
with shape (N, corner_emb_channels, H, W).
tl_centripetal_shift: Top-left corner's centripetal shift for
current level with shape (N, 2, H, W).
br_centripetal_shift: Bottom-right corner's centripetal shift for
current level with shape (N, 2, H, W).
rescale (bool): If True, return boxes in original image space.
Default: False.
with_nms (bool): If True, do nms before return boxes.
Default: True.
"""
if isinstance(img_meta, (list, tuple)):
img_meta = img_meta[0]
batch_bboxes, batch_scores, batch_clses = self.decode_heatmap(
tl_heat=tl_heat.sigmoid(),
br_heat=br_heat.sigmoid(),
tl_off=tl_off,
br_off=br_off,
tl_emb=tl_emb,
br_emb=br_emb,
tl_centripetal_shift=tl_centripetal_shift,
br_centripetal_shift=br_centripetal_shift,
img_meta=img_meta,
k=self.test_cfg.corner_topk,
kernel=self.test_cfg.local_maximum_kernel,
distance_threshold=self.test_cfg.distance_threshold)
if rescale:
batch_bboxes /= img_meta['scale_factor']
bboxes = batch_bboxes.view([-1, 4])
scores = batch_scores.view([-1, 1])
clses = batch_clses.view([-1, 1])
idx = scores.argsort(dim=0, descending=True)
bboxes = bboxes[idx].view([-1, 4])
scores = scores[idx].view(-1)
clses = clses[idx].view(-1)
detections = torch.cat([bboxes, scores.unsqueeze(-1)], -1)
keepinds = (detections[:, -1] > -0.1)
detections = detections[keepinds]
labels = clses[keepinds]
if with_nms:
detections, labels = self._bboxes_nms(detections, labels,
self.test_cfg)
return detections, labels
def _bboxes_nms(self, bboxes, labels, cfg):
out_bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:, -1], labels,
cfg.nms_cfg)
out_labels = labels[keep]
if len(out_bboxes) > 0:
idx = torch.argsort(out_bboxes[:, -1], descending=True)
idx = idx[:cfg.max_per_img]
out_bboxes = out_bboxes[idx]
out_labels = out_labels[idx]
return out_bboxes, out_labels
def _gather_feat(self, feat, ind, mask=None):
"""Gather feature according to index.
Args:
feat (Tensor): Target feature map.
ind (Tensor): Target coord index.
mask (Tensor | None): Mask of featuremap. Default: None.
Returns:
feat (Tensor): Gathered feature.
"""
dim = feat.size(2)
ind = ind.unsqueeze(2).repeat(1, 1, dim)
feat = feat.gather(1, ind)
if mask is not None:
mask = mask.unsqueeze(2).expand_as(feat)
feat = feat[mask]
feat = feat.view(-1, dim)
return feat
def _local_maximum(self, heat, kernel=3):
"""Extract local maximum pixel with given kernal.
Args:
heat (Tensor): Target heatmap.
kernel (int): Kernel size of max pooling. Default: 3.
Returns:
heat (Tensor): A heatmap where local maximum pixels maintain its
own value and other positions are 0.
"""
pad = (kernel - 1) // 2
hmax = F.max_pool2d(heat, kernel, stride=1, padding=pad)
keep = (hmax == heat).float()
return heat * keep
def _transpose_and_gather_feat(self, feat, ind):
"""Transpose and gather feature according to index.
Args:
feat (Tensor): Target feature map.
ind (Tensor): Target coord index.
Returns:
feat (Tensor): Transposed and gathered feature.
"""
feat = feat.permute(0, 2, 3, 1).contiguous()
feat = feat.view(feat.size(0), -1, feat.size(3))
feat = self._gather_feat(feat, ind)
return feat
def _topk(self, scores, k=20):
"""Get top k positions from heatmap.
Args:
scores (Tensor): Target heatmap with shape
[batch, num_classes, height, width].
k (int): Target number. Default: 20.
Returns:
tuple[torch.Tensor]: Scores, indexes, categories and coords of
topk keypoint. Containing following Tensors:
- topk_scores (Tensor): Max scores of each topk keypoint.
- topk_inds (Tensor): Indexes of each topk keypoint.
- topk_clses (Tensor): Categories of each topk keypoint.
- topk_ys (Tensor): Y-coord of each topk keypoint.
- topk_xs (Tensor): X-coord of each topk keypoint.
"""
batch, _, height, width = scores.size()
topk_scores, topk_inds = torch.topk(scores.view(batch, -1), k)
topk_clses = topk_inds // (height * width)
topk_inds = topk_inds % (height * width)
topk_ys = topk_inds // width
topk_xs = (topk_inds % width).int().float()
return topk_scores, topk_inds, topk_clses, topk_ys, topk_xs
def decode_heatmap(self,
tl_heat,
br_heat,
tl_off,
br_off,
tl_emb=None,
br_emb=None,
tl_centripetal_shift=None,
br_centripetal_shift=None,
img_meta=None,
k=100,
kernel=3,
distance_threshold=0.5,
num_dets=1000):
"""Transform outputs for a single batch item into raw bbox predictions.
Args:
tl_heat (Tensor): Top-left corner heatmap for current level with
shape (N, num_classes, H, W).
br_heat (Tensor): Bottom-right corner heatmap for current level
with shape (N, num_classes, H, W).
tl_off (Tensor): Top-left corner offset for current level with
shape (N, corner_offset_channels, H, W).
br_off (Tensor): Bottom-right corner offset for current level with
shape (N, corner_offset_channels, H, W).
tl_emb (Tensor | None): Top-left corner embedding for current
level with shape (N, corner_emb_channels, H, W).
br_emb (Tensor | None): Bottom-right corner embedding for current
level with shape (N, corner_emb_channels, H, W).
tl_centripetal_shift (Tensor | None): Top-left centripetal shift
for current level with shape (N, 2, H, W).
br_centripetal_shift (Tensor | None): Bottom-right centripetal
shift for current level with shape (N, 2, H, W).
img_meta (dict): Meta information of current image, e.g.,
image size, scaling factor, etc.
k (int): Get top k corner keypoints from heatmap.
kernel (int): Max pooling kernel for extract local maximum pixels.
distance_threshold (float): Distance threshold. Top-left and
bottom-right corner keypoints with feature distance less than
the threshold will be regarded as keypoints from same object.
num_dets (int): Num of raw boxes before doing nms.
Returns:
tuple[torch.Tensor]: Decoded output of CornerHead, containing the
following Tensors:
- bboxes (Tensor): Coords of each box.
- scores (Tensor): Scores of each box.
- clses (Tensor): Categories of each box.
"""
with_embedding = tl_emb is not None and br_emb is not None
with_centripetal_shift = (
tl_centripetal_shift is not None
and br_centripetal_shift is not None)
assert with_embedding + with_centripetal_shift == 1
batch, _, height, width = tl_heat.size()
inp_h, inp_w, _ = img_meta['pad_shape']
# perform nms on heatmaps
tl_heat = self._local_maximum(tl_heat, kernel=kernel)
br_heat = self._local_maximum(br_heat, kernel=kernel)
tl_scores, tl_inds, tl_clses, tl_ys, tl_xs = self._topk(tl_heat, k=k)
br_scores, br_inds, br_clses, br_ys, br_xs = self._topk(br_heat, k=k)
# We use repeat instead of expand here because expand is a
# shallow-copy function. Thus it could cause unexpected testing result
# sometimes. Using expand will decrease about 10% mAP during testing
# compared to repeat.
tl_ys = tl_ys.view(batch, k, 1).repeat(1, 1, k)
tl_xs = tl_xs.view(batch, k, 1).repeat(1, 1, k)
br_ys = br_ys.view(batch, 1, k).repeat(1, k, 1)
br_xs = br_xs.view(batch, 1, k).repeat(1, k, 1)
tl_off = self._transpose_and_gather_feat(tl_off, tl_inds)
tl_off = tl_off.view(batch, k, 1, 2)
br_off = self._transpose_and_gather_feat(br_off, br_inds)
br_off = br_off.view(batch, 1, k, 2)
tl_xs = tl_xs + tl_off[..., 0]
tl_ys = tl_ys + tl_off[..., 1]
br_xs = br_xs + br_off[..., 0]
br_ys = br_ys + br_off[..., 1]
if with_centripetal_shift:
tl_centripetal_shift = self._transpose_and_gather_feat(
tl_centripetal_shift, tl_inds).view(batch, k, 1, 2).exp()
br_centripetal_shift = self._transpose_and_gather_feat(
br_centripetal_shift, br_inds).view(batch, 1, k, 2).exp()
tl_ctxs = tl_xs + tl_centripetal_shift[..., 0]
tl_ctys = tl_ys + tl_centripetal_shift[..., 1]
br_ctxs = br_xs - br_centripetal_shift[..., 0]
br_ctys = br_ys - br_centripetal_shift[..., 1]
# all possible boxes based on top k corners (ignoring class)
tl_xs *= (inp_w / width)
tl_ys *= (inp_h / height)
br_xs *= (inp_w / width)
br_ys *= (inp_h / height)
if with_centripetal_shift:
tl_ctxs *= (inp_w / width)
tl_ctys *= (inp_h / height)
br_ctxs *= (inp_w / width)
br_ctys *= (inp_h / height)
x_off = img_meta['border'][2]
y_off = img_meta['border'][0]
tl_xs -= x_off
tl_ys -= y_off
br_xs -= x_off
br_ys -= y_off
tl_xs *= tl_xs.gt(0.0).type_as(tl_xs)
tl_ys *= tl_ys.gt(0.0).type_as(tl_ys)
br_xs *= br_xs.gt(0.0).type_as(br_xs)
br_ys *= br_ys.gt(0.0).type_as(br_ys)
bboxes = torch.stack((tl_xs, tl_ys, br_xs, br_ys), dim=3)
area_bboxes = ((br_xs - tl_xs) * (br_ys - tl_ys)).abs()
if with_centripetal_shift:
tl_ctxs -= x_off
tl_ctys -= y_off
br_ctxs -= x_off
br_ctys -= y_off
tl_ctxs *= tl_ctxs.gt(0.0).type_as(tl_ctxs)
tl_ctys *= tl_ctys.gt(0.0).type_as(tl_ctys)
br_ctxs *= br_ctxs.gt(0.0).type_as(br_ctxs)
br_ctys *= br_ctys.gt(0.0).type_as(br_ctys)
ct_bboxes = torch.stack((tl_ctxs, tl_ctys, br_ctxs, br_ctys),
dim=3)
area_ct_bboxes = ((br_ctxs - tl_ctxs) * (br_ctys - tl_ctys)).abs()
rcentral = torch.zeros_like(ct_bboxes)
# magic nums from paper section 4.1
mu = torch.ones_like(area_bboxes) / 2.4
mu[area_bboxes > 3500] = 1 / 2.1 # large bbox have smaller mu
bboxes_center_x = (bboxes[..., 0] + bboxes[..., 2]) / 2
bboxes_center_y = (bboxes[..., 1] + bboxes[..., 3]) / 2
rcentral[..., 0] = bboxes_center_x - mu * (bboxes[..., 2] -
bboxes[..., 0]) / 2
rcentral[..., 1] = bboxes_center_y - mu * (bboxes[..., 3] -
bboxes[..., 1]) / 2
rcentral[..., 2] = bboxes_center_x + mu * (bboxes[..., 2] -
bboxes[..., 0]) / 2
rcentral[..., 3] = bboxes_center_y + mu * (bboxes[..., 3] -
bboxes[..., 1]) / 2
area_rcentral = ((rcentral[..., 2] - rcentral[..., 0]) *
(rcentral[..., 3] - rcentral[..., 1])).abs()
dists = area_ct_bboxes / area_rcentral
tl_ctx_inds = (ct_bboxes[..., 0] <= rcentral[..., 0]) | (
ct_bboxes[..., 0] >= rcentral[..., 2])
tl_cty_inds = (ct_bboxes[..., 1] <= rcentral[..., 1]) | (
ct_bboxes[..., 1] >= rcentral[..., 3])
br_ctx_inds = (ct_bboxes[..., 2] <= rcentral[..., 0]) | (
ct_bboxes[..., 2] >= rcentral[..., 2])
br_cty_inds = (ct_bboxes[..., 3] <= rcentral[..., 1]) | (
ct_bboxes[..., 3] >= rcentral[..., 3])
if with_embedding:
tl_emb = self._transpose_and_gather_feat(tl_emb, tl_inds)
tl_emb = tl_emb.view(batch, k, 1)
br_emb = self._transpose_and_gather_feat(br_emb, br_inds)
br_emb = br_emb.view(batch, 1, k)
dists = torch.abs(tl_emb - br_emb)
tl_scores = tl_scores.view(batch, k, 1).repeat(1, 1, k)
br_scores = br_scores.view(batch, 1, k).repeat(1, k, 1)
scores = (tl_scores + br_scores) / 2 # scores for all possible boxes
# tl and br should have same class
tl_clses = tl_clses.view(batch, k, 1).repeat(1, 1, k)
br_clses = br_clses.view(batch, 1, k).repeat(1, k, 1)
cls_inds = (tl_clses != br_clses)
# reject boxes based on distances
dist_inds = dists > distance_threshold
# reject boxes based on widths and heights
width_inds = (br_xs <= tl_xs)
height_inds = (br_ys <= tl_ys)
scores[cls_inds] = -1
scores[width_inds] = -1
scores[height_inds] = -1
scores[dist_inds] = -1
if with_centripetal_shift:
scores[tl_ctx_inds] = -1
scores[tl_cty_inds] = -1
scores[br_ctx_inds] = -1
scores[br_cty_inds] = -1
scores = scores.view(batch, -1)
scores, inds = torch.topk(scores, num_dets)
scores = scores.unsqueeze(2)
bboxes = bboxes.view(batch, -1, 4)
bboxes = self._gather_feat(bboxes, inds)
clses = tl_clses.contiguous().view(batch, -1, 1)
clses = self._gather_feat(clses, inds).float()
return bboxes, scores, clses
| 46,260 | 42.437559 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/yolact_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, xavier_init
from mmcv.runner import force_fp32
from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
from ..builder import HEADS, build_loss
from .anchor_head import AnchorHead
@HEADS.register_module()
class YOLACTHead(AnchorHead):
"""YOLACT box head used in https://arxiv.org/abs/1904.02689.
Note that YOLACT head is a light version of RetinaNet head.
Four differences are described as follows:
1. YOLACT box head has three-times fewer anchors.
2. YOLACT box head shares the convs for box and cls branches.
3. YOLACT box head uses OHEM instead of Focal loss.
4. YOLACT box head predicts a set of mask coefficients for each box.
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
anchor_generator (dict): Config dict for anchor generator
loss_cls (dict): Config of classification loss.
loss_bbox (dict): Config of localization loss.
num_head_convs (int): Number of the conv layers shared by
box and cls branches.
num_protos (int): Number of the mask coefficients.
use_ohem (bool): If true, ``loss_single_OHEM`` will be used for
cls loss calculation. If false, ``loss_single`` will be used.
conv_cfg (dict): Dictionary to construct and config conv layer.
norm_cfg (dict): Dictionary to construct and config norm layer.
"""
def __init__(self,
num_classes,
in_channels,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=3,
scales_per_octave=1,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
reduction='none',
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
num_head_convs=1,
num_protos=32,
use_ohem=True,
conv_cfg=None,
norm_cfg=None,
**kwargs):
self.num_head_convs = num_head_convs
self.num_protos = num_protos
self.use_ohem = use_ohem
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
super(YOLACTHead, self).__init__(
num_classes,
in_channels,
loss_cls=loss_cls,
loss_bbox=loss_bbox,
anchor_generator=anchor_generator,
**kwargs)
if self.use_ohem:
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.sampling = False
def _init_layers(self):
"""Initialize layers of the head."""
self.relu = nn.ReLU(inplace=True)
self.head_convs = nn.ModuleList()
for i in range(self.num_head_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.head_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.conv_cls = nn.Conv2d(
self.feat_channels,
self.num_anchors * self.cls_out_channels,
3,
padding=1)
self.conv_reg = nn.Conv2d(
self.feat_channels, self.num_anchors * 4, 3, padding=1)
self.conv_coeff = nn.Conv2d(
self.feat_channels,
self.num_anchors * self.num_protos,
3,
padding=1)
def init_weights(self):
"""Initialize weights of the head."""
for m in self.head_convs:
xavier_init(m.conv, distribution='uniform', bias=0)
xavier_init(self.conv_cls, distribution='uniform', bias=0)
xavier_init(self.conv_reg, distribution='uniform', bias=0)
xavier_init(self.conv_coeff, distribution='uniform', bias=0)
def forward_single(self, x):
"""Forward feature of a single scale level.
Args:
x (Tensor): Features of a single scale level.
Returns:
tuple:
cls_score (Tensor): Cls scores for a single scale level \
the channels number is num_anchors * num_classes.
bbox_pred (Tensor): Box energies / deltas for a single scale \
level, the channels number is num_anchors * 4.
coeff_pred (Tensor): Mask coefficients for a single scale \
level, the channels number is num_anchors * num_protos.
"""
for head_conv in self.head_convs:
x = head_conv(x)
cls_score = self.conv_cls(x)
bbox_pred = self.conv_reg(x)
coeff_pred = self.conv_coeff(x).tanh()
return cls_score, bbox_pred, coeff_pred
@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def loss(self,
cls_scores,
bbox_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""A combination of the func:``AnchorHead.loss`` and
func:``SSDHead.loss``.
When ``self.use_ohem == True``, it functions like ``SSDHead.loss``,
otherwise, it follows ``AnchorHead.loss``. Besides, it additionally
returns ``sampling_results``.
Args:
cls_scores (list[Tensor]): Box scores for each scale level
Has shape (N, num_anchors * num_classes, H, W)
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W)
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): Class indices corresponding to each box
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
boxes can be ignored when computing the loss. Default: None
Returns:
tuple:
dict[str, Tensor]: A dictionary of loss components.
List[:obj:``SamplingResult``]: Sampler results for each image.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.anchor_generator.num_levels
device = cls_scores[0].device
anchor_list, valid_flag_list = self.get_anchors(
featmap_sizes, img_metas, device=device)
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = self.get_targets(
anchor_list,
valid_flag_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
unmap_outputs=not self.use_ohem,
return_sampling_results=True)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg, sampling_results) = cls_reg_targets
if self.use_ohem:
num_images = len(img_metas)
all_cls_scores = torch.cat([
s.permute(0, 2, 3, 1).reshape(
num_images, -1, self.cls_out_channels) for s in cls_scores
], 1)
all_labels = torch.cat(labels_list, -1).view(num_images, -1)
all_label_weights = torch.cat(label_weights_list,
-1).view(num_images, -1)
all_bbox_preds = torch.cat([
b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
for b in bbox_preds
], -2)
all_bbox_targets = torch.cat(bbox_targets_list,
-2).view(num_images, -1, 4)
all_bbox_weights = torch.cat(bbox_weights_list,
-2).view(num_images, -1, 4)
# concat all level anchors to a single tensor
all_anchors = []
for i in range(num_images):
all_anchors.append(torch.cat(anchor_list[i]))
# check NaN and Inf
assert torch.isfinite(all_cls_scores).all().item(), \
'classification scores become infinite or NaN!'
assert torch.isfinite(all_bbox_preds).all().item(), \
'bbox predications become infinite or NaN!'
losses_cls, losses_bbox = multi_apply(
self.loss_single_OHEM,
all_cls_scores,
all_bbox_preds,
all_anchors,
all_labels,
all_label_weights,
all_bbox_targets,
all_bbox_weights,
num_total_samples=num_total_pos)
else:
num_total_samples = (
num_total_pos +
num_total_neg if self.sampling else num_total_pos)
# anchor number of multi levels
num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
# concat all level anchors and flags to a single tensor
concat_anchor_list = []
for i in range(len(anchor_list)):
concat_anchor_list.append(torch.cat(anchor_list[i]))
all_anchor_list = images_to_levels(concat_anchor_list,
num_level_anchors)
losses_cls, losses_bbox = multi_apply(
self.loss_single,
cls_scores,
bbox_preds,
all_anchor_list,
labels_list,
label_weights_list,
bbox_targets_list,
bbox_weights_list,
num_total_samples=num_total_samples)
return dict(
loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results
def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels,
label_weights, bbox_targets, bbox_weights,
num_total_samples):
""""See func:``SSDHead.loss``."""
loss_cls_all = self.loss_cls(cls_score, labels, label_weights)
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
pos_inds = ((labels >= 0) &
(labels < self.num_classes)).nonzero().reshape(-1)
neg_inds = (labels == self.num_classes).nonzero().view(-1)
num_pos_samples = pos_inds.size(0)
if num_pos_samples == 0:
num_neg_samples = neg_inds.size(0)
else:
num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
if num_neg_samples > neg_inds.size(0):
num_neg_samples = neg_inds.size(0)
topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
loss_cls_pos = loss_cls_all[pos_inds].sum()
loss_cls_neg = topk_loss_cls_neg.sum()
loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
if self.reg_decoded_bbox:
bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
loss_bbox = self.loss_bbox(
bbox_pred,
bbox_targets,
bbox_weights,
avg_factor=num_total_samples)
return loss_cls[None], loss_bbox
@force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds'))
def get_bboxes(self,
cls_scores,
bbox_preds,
coeff_preds,
img_metas,
cfg=None,
rescale=False):
""""Similiar to func:``AnchorHead.get_bboxes``, but additionally
processes coeff_preds.
Args:
cls_scores (list[Tensor]): Box scores for each scale level
with shape (N, num_anchors * num_classes, H, W)
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W)
coeff_preds (list[Tensor]): Mask coefficients for each scale
level with shape (N, num_anchors * num_protos, H, W)
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
cfg (mmcv.Config | None): Test / postprocessing configuration,
if None, test_cfg would be used
rescale (bool): If True, return boxes in original image space.
Default: False.
Returns:
list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
a 3-tuple. The first item is an (n, 5) tensor, where the
first 4 columns are bounding box positions
(tl_x, tl_y, br_x, br_y) and the 5-th column is a score
between 0 and 1. The second item is an (n,) tensor where each
item is the predicted class label of the corresponding box.
The third item is an (n, num_protos) tensor where each item
is the predicted mask coefficients of instance inside the
corresponding box.
"""
assert len(cls_scores) == len(bbox_preds)
num_levels = len(cls_scores)
device = cls_scores[0].device
featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
mlvl_anchors = self.anchor_generator.grid_anchors(
featmap_sizes, device=device)
det_bboxes = []
det_labels = []
det_coeffs = []
for img_id in range(len(img_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
bbox_pred_list = [
bbox_preds[i][img_id].detach() for i in range(num_levels)
]
coeff_pred_list = [
coeff_preds[i][img_id].detach() for i in range(num_levels)
]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list,
coeff_pred_list, mlvl_anchors,
img_shape, scale_factor, cfg,
rescale)
det_bboxes.append(bbox_res[0])
det_labels.append(bbox_res[1])
det_coeffs.append(bbox_res[2])
return det_bboxes, det_labels, det_coeffs
def _get_bboxes_single(self,
cls_score_list,
bbox_pred_list,
coeff_preds_list,
mlvl_anchors,
img_shape,
scale_factor,
cfg,
rescale=False):
""""Similiar to func:``AnchorHead._get_bboxes_single``, but
additionally processes coeff_preds_list and uses fast NMS instead of
traditional NMS.
Args:
cls_score_list (list[Tensor]): Box scores for a single scale level
Has shape (num_anchors * num_classes, H, W).
bbox_pred_list (list[Tensor]): Box energies / deltas for a single
scale level with shape (num_anchors * 4, H, W).
coeff_preds_list (list[Tensor]): Mask coefficients for a single
scale level with shape (num_anchors * num_protos, H, W).
mlvl_anchors (list[Tensor]): Box reference for a single scale level
with shape (num_total_anchors, 4).
img_shape (tuple[int]): Shape of the input image,
(height, width, 3).
scale_factor (ndarray): Scale factor of the image arange as
(w_scale, h_scale, w_scale, h_scale).
cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used.
rescale (bool): If True, return boxes in original image space.
Returns:
tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor,
where the first 4 columns are bounding box positions
(tl_x, tl_y, br_x, br_y) and the 5-th column is a score between
0 and 1. The second item is an (n,) tensor where each item is
the predicted class label of the corresponding box. The third
item is an (n, num_protos) tensor where each item is the
predicted mask coefficients of instance inside the
corresponding box.
"""
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors)
mlvl_bboxes = []
mlvl_scores = []
mlvl_coeffs = []
for cls_score, bbox_pred, coeff_pred, anchors in \
zip(cls_score_list, bbox_pred_list,
coeff_preds_list, mlvl_anchors):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
scores = cls_score.softmax(-1)
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
coeff_pred = coeff_pred.permute(1, 2,
0).reshape(-1, self.num_protos)
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
# Get maximum scores for foreground classes.
if self.use_sigmoid_cls:
max_scores, _ = scores.max(dim=1)
else:
# remind that we set FG labels to [0, num_class-1]
# since mmdet v2.0
# BG cat_id: num_class
max_scores, _ = scores[:, :-1].max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
anchors = anchors[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
coeff_pred = coeff_pred[topk_inds, :]
bboxes = self.bbox_coder.decode(
anchors, bbox_pred, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_coeffs.append(coeff_pred)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
mlvl_coeffs = torch.cat(mlvl_coeffs)
if self.use_sigmoid_cls:
# Add a dummy background class to the backend when using sigmoid
# remind that we set FG labels to [0, num_class-1] since mmdet v2.0
# BG cat_id: num_class
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores,
mlvl_coeffs,
cfg.score_thr,
cfg.iou_thr, cfg.top_k,
cfg.max_per_img)
return det_bboxes, det_labels, det_coeffs
@HEADS.register_module()
class YOLACTSegmHead(nn.Module):
"""YOLACT segmentation head used in https://arxiv.org/abs/1904.02689.
Apply a semantic segmentation loss on feature space using layers that are
only evaluated during training to increase performance with no speed
penalty.
Args:
in_channels (int): Number of channels in the input feature map.
num_classes (int): Number of categories excluding the background
category.
loss_segm (dict): Config of semantic segmentation loss.
"""
def __init__(self,
num_classes,
in_channels=256,
loss_segm=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0)):
super(YOLACTSegmHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.loss_segm = build_loss(loss_segm)
self._init_layers()
self.fp16_enabled = False
def _init_layers(self):
"""Initialize layers of the head."""
self.segm_conv = nn.Conv2d(
self.in_channels, self.num_classes, kernel_size=1)
def init_weights(self):
"""Initialize weights of the head."""
xavier_init(self.segm_conv, distribution='uniform')
def forward(self, x):
"""Forward feature from the upstream network.
Args:
x (Tensor): Feature from the upstream network, which is
a 4D-tensor.
Returns:
Tensor: Predicted semantic segmentation map with shape
(N, num_classes, H, W).
"""
return self.segm_conv(x)
@force_fp32(apply_to=('segm_pred', ))
def loss(self, segm_pred, gt_masks, gt_labels):
"""Compute loss of the head.
Args:
segm_pred (list[Tensor]): Predicted semantic segmentation map
with shape (N, num_classes, H, W).
gt_masks (list[Tensor]): Ground truth masks for each image with
the same shape of the input image.
gt_labels (list[Tensor]): Class indices corresponding to each box.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
loss_segm = []
num_imgs, num_classes, mask_h, mask_w = segm_pred.size()
for idx in range(num_imgs):
cur_segm_pred = segm_pred[idx]
cur_gt_masks = gt_masks[idx].float()
cur_gt_labels = gt_labels[idx]
segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks,
cur_gt_labels)
if segm_targets is None:
loss = self.loss_segm(cur_segm_pred,
torch.zeros_like(cur_segm_pred),
torch.zeros_like(cur_segm_pred))
else:
loss = self.loss_segm(
cur_segm_pred,
segm_targets,
avg_factor=num_imgs * mask_h * mask_w)
loss_segm.append(loss)
return dict(loss_segm=loss_segm)
def get_targets(self, segm_pred, gt_masks, gt_labels):
"""Compute semantic segmentation targets for each image.
Args:
segm_pred (Tensor): Predicted semantic segmentation map
with shape (num_classes, H, W).
gt_masks (Tensor): Ground truth masks for each image with
the same shape of the input image.
gt_labels (Tensor): Class indices corresponding to each box.
Returns:
Tensor: Semantic segmentation targets with shape
(num_classes, H, W).
"""
if gt_masks.size(0) == 0:
return None
num_classes, mask_h, mask_w = segm_pred.size()
with torch.no_grad():
downsampled_masks = F.interpolate(
gt_masks.unsqueeze(0), (mask_h, mask_w),
mode='bilinear',
align_corners=False).squeeze(0)
downsampled_masks = downsampled_masks.gt(0.5).float()
segm_targets = torch.zeros_like(segm_pred, requires_grad=False)
for obj_idx in range(downsampled_masks.size(0)):
segm_targets[gt_labels[obj_idx] - 1] = torch.max(
segm_targets[gt_labels[obj_idx] - 1],
downsampled_masks[obj_idx])
return segm_targets
@HEADS.register_module()
class YOLACTProtonet(nn.Module):
"""YOLACT mask head used in https://arxiv.org/abs/1904.02689.
This head outputs the mask prototypes for YOLACT.
Args:
in_channels (int): Number of channels in the input feature map.
proto_channels (tuple[int]): Output channels of protonet convs.
proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs.
include_last_relu (Bool): If keep the last relu of protonet.
num_protos (int): Number of prototypes.
num_classes (int): Number of categories excluding the background
category.
loss_mask_weight (float): Reweight the mask loss by this factor.
max_masks_to_train (int): Maximum number of masks to train for
each image.
"""
def __init__(self,
num_classes,
in_channels=256,
proto_channels=(256, 256, 256, None, 256, 32),
proto_kernel_sizes=(3, 3, 3, -2, 3, 1),
include_last_relu=True,
num_protos=32,
loss_mask_weight=1.0,
max_masks_to_train=100):
super(YOLACTProtonet, self).__init__()
self.in_channels = in_channels
self.proto_channels = proto_channels
self.proto_kernel_sizes = proto_kernel_sizes
self.include_last_relu = include_last_relu
self.protonet = self._init_layers()
self.loss_mask_weight = loss_mask_weight
self.num_protos = num_protos
self.num_classes = num_classes
self.max_masks_to_train = max_masks_to_train
self.fp16_enabled = False
def _init_layers(self):
"""A helper function to take a config setting and turn it into a
network."""
# Possible patterns:
# ( 256, 3) -> conv
# ( 256,-2) -> deconv
# (None,-2) -> bilinear interpolate
in_channels = self.in_channels
protonets = nn.ModuleList()
for num_channels, kernel_size in zip(self.proto_channels,
self.proto_kernel_sizes):
if kernel_size > 0:
layer = nn.Conv2d(
in_channels,
num_channels,
kernel_size,
padding=kernel_size // 2)
else:
if num_channels is None:
layer = InterpolateModule(
scale_factor=-kernel_size,
mode='bilinear',
align_corners=False)
else:
layer = nn.ConvTranspose2d(
in_channels,
num_channels,
-kernel_size,
padding=kernel_size // 2)
protonets.append(layer)
protonets.append(nn.ReLU(inplace=True))
in_channels = num_channels if num_channels is not None \
else in_channels
if not self.include_last_relu:
protonets = protonets[:-1]
return nn.Sequential(*protonets)
def init_weights(self):
"""Initialize weights of the head."""
for m in self.protonet:
if isinstance(m, nn.Conv2d):
xavier_init(m, distribution='uniform')
def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None):
"""Forward feature from the upstream network to get prototypes and
linearly combine the prototypes, using masks coefficients, into
instance masks. Finally, crop the instance masks with given bboxes.
Args:
x (Tensor): Feature from the upstream network, which is
a 4D-tensor.
coeff_pred (list[Tensor]): Mask coefficients for each scale
level with shape (N, num_anchors * num_protos, H, W).
bboxes (list[Tensor]): Box used for cropping with shape
(N, num_anchors * 4, H, W). During training, they are
ground truth boxes. During testing, they are predicted
boxes.
img_meta (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
sampling_results (List[:obj:``SamplingResult``]): Sampler results
for each image.
Returns:
list[Tensor]: Predicted instance segmentation masks.
"""
prototypes = self.protonet(x)
prototypes = prototypes.permute(0, 2, 3, 1).contiguous()
num_imgs = x.size(0)
# Training state
if self.training:
coeff_pred_list = []
for coeff_pred_per_level in coeff_pred:
coeff_pred_per_level = \
coeff_pred_per_level.permute(0, 2, 3, 1)\
.reshape(num_imgs, -1, self.num_protos)
coeff_pred_list.append(coeff_pred_per_level)
coeff_pred = torch.cat(coeff_pred_list, dim=1)
mask_pred_list = []
for idx in range(num_imgs):
cur_prototypes = prototypes[idx]
cur_coeff_pred = coeff_pred[idx]
cur_bboxes = bboxes[idx]
cur_img_meta = img_meta[idx]
# Testing state
if not self.training:
bboxes_for_cropping = cur_bboxes
else:
cur_sampling_results = sampling_results[idx]
pos_assigned_gt_inds = \
cur_sampling_results.pos_assigned_gt_inds
bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone()
pos_inds = cur_sampling_results.pos_inds
cur_coeff_pred = cur_coeff_pred[pos_inds]
# Linearly combine the prototypes with the mask coefficients
mask_pred = cur_prototypes @ cur_coeff_pred.t()
mask_pred = torch.sigmoid(mask_pred)
h, w = cur_img_meta['img_shape'][:2]
bboxes_for_cropping[:, 0] /= w
bboxes_for_cropping[:, 1] /= h
bboxes_for_cropping[:, 2] /= w
bboxes_for_cropping[:, 3] /= h
mask_pred = self.crop(mask_pred, bboxes_for_cropping)
mask_pred = mask_pred.permute(2, 0, 1).contiguous()
mask_pred_list.append(mask_pred)
return mask_pred_list
@force_fp32(apply_to=('mask_pred', ))
def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results):
"""Compute loss of the head.
Args:
mask_pred (list[Tensor]): Predicted prototypes with shape
(num_classes, H, W).
gt_masks (list[Tensor]): Ground truth masks for each image with
the same shape of the input image.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
img_meta (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
sampling_results (List[:obj:``SamplingResult``]): Sampler results
for each image.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
loss_mask = []
num_imgs = len(mask_pred)
total_pos = 0
for idx in range(num_imgs):
cur_mask_pred = mask_pred[idx]
cur_gt_masks = gt_masks[idx].float()
cur_gt_bboxes = gt_bboxes[idx]
cur_img_meta = img_meta[idx]
cur_sampling_results = sampling_results[idx]
pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds
num_pos = pos_assigned_gt_inds.size(0)
# Since we're producing (near) full image masks,
# it'd take too much vram to backprop on every single mask.
# Thus we select only a subset.
if num_pos > self.max_masks_to_train:
perm = torch.randperm(num_pos)
select = perm[:self.max_masks_to_train]
cur_mask_pred = cur_mask_pred[select]
pos_assigned_gt_inds = pos_assigned_gt_inds[select]
num_pos = self.max_masks_to_train
total_pos += num_pos
gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds]
mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks,
pos_assigned_gt_inds)
if num_pos == 0:
loss = cur_mask_pred.sum() * 0.
elif mask_targets is None:
loss = F.binary_cross_entropy(cur_mask_pred,
torch.zeros_like(cur_mask_pred),
torch.zeros_like(cur_mask_pred))
else:
cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1)
loss = F.binary_cross_entropy(
cur_mask_pred, mask_targets,
reduction='none') * self.loss_mask_weight
h, w = cur_img_meta['img_shape'][:2]
gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] -
gt_bboxes_for_reweight[:, 0]) / w
gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] -
gt_bboxes_for_reweight[:, 1]) / h
loss = loss.mean(dim=(1,
2)) / gt_bboxes_width / gt_bboxes_height
loss = torch.sum(loss)
loss_mask.append(loss)
if total_pos == 0:
total_pos += 1 # avoid nan
loss_mask = [x / total_pos for x in loss_mask]
return dict(loss_mask=loss_mask)
def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds):
"""Compute instance segmentation targets for each image.
Args:
mask_pred (Tensor): Predicted prototypes with shape
(num_classes, H, W).
gt_masks (Tensor): Ground truth masks for each image with
the same shape of the input image.
pos_assigned_gt_inds (Tensor): GT indices of the corresponding
positive samples.
Returns:
Tensor: Instance segmentation targets with shape
(num_instances, H, W).
"""
if gt_masks.size(0) == 0:
return None
mask_h, mask_w = mask_pred.shape[-2:]
gt_masks = F.interpolate(
gt_masks.unsqueeze(0), (mask_h, mask_w),
mode='bilinear',
align_corners=False).squeeze(0)
gt_masks = gt_masks.gt(0.5).float()
mask_targets = gt_masks[pos_assigned_gt_inds]
return mask_targets
def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale):
"""Resize, binarize, and format the instance mask predictions.
Args:
mask_pred (Tensor): shape (N, H, W).
label_pred (Tensor): shape (N, ).
img_meta (dict): Meta information of each image, e.g.,
image size, scaling factor, etc.
rescale (bool): If rescale is False, then returned masks will
fit the scale of imgs[0].
Returns:
list[ndarray]: Mask predictions grouped by their predicted classes.
"""
ori_shape = img_meta['ori_shape']
scale_factor = img_meta['scale_factor']
if rescale:
img_h, img_w = ori_shape[:2]
else:
img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32)
img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32)
cls_segms = [[] for _ in range(self.num_classes)]
if mask_pred.size(0) == 0:
return cls_segms
mask_pred = F.interpolate(
mask_pred.unsqueeze(0), (img_h, img_w),
mode='bilinear',
align_corners=False).squeeze(0) > 0.5
mask_pred = mask_pred.cpu().numpy().astype(np.uint8)
for m, l in zip(mask_pred, label_pred):
cls_segms[l].append(m)
return cls_segms
def crop(self, masks, boxes, padding=1):
"""Crop predicted masks by zeroing out everything not in the predicted
bbox.
Args:
masks (Tensor): shape [H, W, N].
boxes (Tensor): bbox coords in relative point form with
shape [N, 4].
Return:
Tensor: The cropped masks.
"""
h, w, n = masks.size()
x1, x2 = self.sanitize_coordinates(
boxes[:, 0], boxes[:, 2], w, padding, cast=False)
y1, y2 = self.sanitize_coordinates(
boxes[:, 1], boxes[:, 3], h, padding, cast=False)
rows = torch.arange(
w, device=masks.device, dtype=x1.dtype).view(1, -1,
1).expand(h, w, n)
cols = torch.arange(
h, device=masks.device, dtype=x1.dtype).view(-1, 1,
1).expand(h, w, n)
masks_left = rows >= x1.view(1, 1, -1)
masks_right = rows < x2.view(1, 1, -1)
masks_up = cols >= y1.view(1, 1, -1)
masks_down = cols < y2.view(1, 1, -1)
crop_mask = masks_left * masks_right * masks_up * masks_down
return masks * crop_mask.float()
def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True):
"""Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0,
and x2 <= image_size. Also converts from relative to absolute
coordinates and casts the results to long tensors.
Warning: this does things in-place behind the scenes so
copy if necessary.
Args:
_x1 (Tensor): shape (N, ).
_x2 (Tensor): shape (N, ).
img_size (int): Size of the input image.
padding (int): x1 >= padding, x2 <= image_size-padding.
cast (bool): If cast is false, the result won't be cast to longs.
Returns:
tuple:
x1 (Tensor): Sanitized _x1.
x2 (Tensor): Sanitized _x2.
"""
x1 = x1 * img_size
x2 = x2 * img_size
if cast:
x1 = x1.long()
x2 = x2.long()
x1 = torch.min(x1, x2)
x2 = torch.max(x1, x2)
x1 = torch.clamp(x1 - padding, min=0)
x2 = torch.clamp(x2 + padding, max=img_size)
return x1, x2
class InterpolateModule(nn.Module):
"""This is a module version of F.interpolate.
Any arguments you give it just get passed along for the ride.
"""
def __init__(self, *args, **kwargs):
super().__init__()
self.args = args
self.kwargs = kwargs
def forward(self, x):
"""Forward features from the upstream network."""
return F.interpolate(x, *self.args, **self.kwargs)
| 39,435 | 40.953191 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/base_dense_head.py | from abc import ABCMeta, abstractmethod
import torch.nn as nn
class BaseDenseHead(nn.Module, metaclass=ABCMeta):
"""Base class for DenseHeads."""
def __init__(self):
super(BaseDenseHead, self).__init__()
@abstractmethod
def loss(self, **kwargs):
"""Compute losses of the head."""
pass
@abstractmethod
def get_bboxes(self, **kwargs):
"""Transform network output for a batch into bbox predictions."""
pass
def forward_train(self,
x,
img_metas,
gt_bboxes,
gt_labels=None,
gt_bboxes_ignore=None,
proposal_cfg=None,
**kwargs):
"""
Args:
x (list[Tensor]): Features from FPN.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes (Tensor): Ground truth bboxes of the image,
shape (num_gts, 4).
gt_labels (Tensor): Ground truth labels of each box,
shape (num_gts,).
gt_bboxes_ignore (Tensor): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4).
proposal_cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used
Returns:
tuple:
losses: (dict[str, Tensor]): A dictionary of loss components.
proposal_list (list[Tensor]): Proposals of each image.
"""
outs = self(x)
if gt_labels is None:
loss_inputs = outs + (gt_bboxes, img_metas)
else:
loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
if proposal_cfg is None:
return losses
else:
proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg)
return losses, proposal_list
| 2,051 | 33.2 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/free_anchor_retina_head.py | import torch
import torch.nn.functional as F
from mmdet.core import bbox_overlaps
from ..builder import HEADS
from .retina_head import RetinaHead
EPS = 1e-12
@HEADS.register_module()
class FreeAnchorRetinaHead(RetinaHead):
"""FreeAnchor RetinaHead used in https://arxiv.org/abs/1909.02466.
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
stacked_convs (int): Number of conv layers in cls and reg tower.
Default: 4.
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None.
norm_cfg (dict): dictionary to construct and config norm layer.
Default: norm_cfg=dict(type='GN', num_groups=32,
requires_grad=True).
pre_anchor_topk (int): Number of boxes that be token in each bag.
bbox_thr (float): The threshold of the saturated linear function. It is
usually the same with the IoU threshold used in NMS.
gamma (float): Gamma parameter in focal loss.
alpha (float): Alpha parameter in focal loss.
""" # noqa: W605
def __init__(self,
num_classes,
in_channels,
stacked_convs=4,
conv_cfg=None,
norm_cfg=None,
pre_anchor_topk=50,
bbox_thr=0.6,
gamma=2.0,
alpha=0.5,
**kwargs):
super(FreeAnchorRetinaHead,
self).__init__(num_classes, in_channels, stacked_convs, conv_cfg,
norm_cfg, **kwargs)
self.pre_anchor_topk = pre_anchor_topk
self.bbox_thr = bbox_thr
self.gamma = gamma
self.alpha = alpha
def loss(self,
cls_scores,
bbox_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""Compute losses of the head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level
Has shape (N, num_anchors * num_classes, H, W)
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W)
gt_bboxes (list[Tensor]): each item are the truth boxes for each
image in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == len(self.anchor_generator.base_anchors)
anchor_list, _ = self.get_anchors(featmap_sizes, img_metas)
anchors = [torch.cat(anchor) for anchor in anchor_list]
# concatenate each level
cls_scores = [
cls.permute(0, 2, 3,
1).reshape(cls.size(0), -1, self.cls_out_channels)
for cls in cls_scores
]
bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(bbox_pred.size(0), -1, 4)
for bbox_pred in bbox_preds
]
cls_scores = torch.cat(cls_scores, dim=1)
bbox_preds = torch.cat(bbox_preds, dim=1)
cls_prob = torch.sigmoid(cls_scores)
box_prob = []
num_pos = 0
positive_losses = []
for _, (anchors_, gt_labels_, gt_bboxes_, cls_prob_,
bbox_preds_) in enumerate(
zip(anchors, gt_labels, gt_bboxes, cls_prob, bbox_preds)):
with torch.no_grad():
if len(gt_bboxes_) == 0:
image_box_prob = torch.zeros(
anchors_.size(0),
self.cls_out_channels).type_as(bbox_preds_)
else:
# box_localization: a_{j}^{loc}, shape: [j, 4]
pred_boxes = self.bbox_coder.decode(anchors_, bbox_preds_)
# object_box_iou: IoU_{ij}^{loc}, shape: [i, j]
object_box_iou = bbox_overlaps(gt_bboxes_, pred_boxes)
# object_box_prob: P{a_{j} -> b_{i}}, shape: [i, j]
t1 = self.bbox_thr
t2 = object_box_iou.max(
dim=1, keepdim=True).values.clamp(min=t1 + 1e-12)
object_box_prob = ((object_box_iou - t1) /
(t2 - t1)).clamp(
min=0, max=1)
# object_cls_box_prob: P{a_{j} -> b_{i}}, shape: [i, c, j]
num_obj = gt_labels_.size(0)
indices = torch.stack([
torch.arange(num_obj).type_as(gt_labels_), gt_labels_
],
dim=0)
object_cls_box_prob = torch.sparse_coo_tensor(
indices, object_box_prob)
# image_box_iou: P{a_{j} \in A_{+}}, shape: [c, j]
"""
from "start" to "end" implement:
image_box_iou = torch.sparse.max(object_cls_box_prob,
dim=0).t()
"""
# start
box_cls_prob = torch.sparse.sum(
object_cls_box_prob, dim=0).to_dense()
indices = torch.nonzero(box_cls_prob, as_tuple=False).t_()
if indices.numel() == 0:
image_box_prob = torch.zeros(
anchors_.size(0),
self.cls_out_channels).type_as(object_box_prob)
else:
nonzero_box_prob = torch.where(
(gt_labels_.unsqueeze(dim=-1) == indices[0]),
object_box_prob[:, indices[1]],
torch.tensor([
0
]).type_as(object_box_prob)).max(dim=0).values
# upmap to shape [j, c]
image_box_prob = torch.sparse_coo_tensor(
indices.flip([0]),
nonzero_box_prob,
size=(anchors_.size(0),
self.cls_out_channels)).to_dense()
# end
box_prob.append(image_box_prob)
# construct bags for objects
match_quality_matrix = bbox_overlaps(gt_bboxes_, anchors_)
_, matched = torch.topk(
match_quality_matrix,
self.pre_anchor_topk,
dim=1,
sorted=False)
del match_quality_matrix
# matched_cls_prob: P_{ij}^{cls}
matched_cls_prob = torch.gather(
cls_prob_[matched], 2,
gt_labels_.view(-1, 1, 1).repeat(1, self.pre_anchor_topk,
1)).squeeze(2)
# matched_box_prob: P_{ij}^{loc}
matched_anchors = anchors_[matched]
matched_object_targets = self.bbox_coder.encode(
matched_anchors,
gt_bboxes_.unsqueeze(dim=1).expand_as(matched_anchors))
loss_bbox = self.loss_bbox(
bbox_preds_[matched],
matched_object_targets,
reduction_override='none').sum(-1)
matched_box_prob = torch.exp(-loss_bbox)
# positive_losses: {-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )}
num_pos += len(gt_bboxes_)
positive_losses.append(
self.positive_bag_loss(matched_cls_prob, matched_box_prob))
positive_loss = torch.cat(positive_losses).sum() / max(1, num_pos)
# box_prob: P{a_{j} \in A_{+}}
box_prob = torch.stack(box_prob, dim=0)
# negative_loss:
# \sum_{j}{ FL((1 - P{a_{j} \in A_{+}}) * (1 - P_{j}^{bg})) } / n||B||
negative_loss = self.negative_bag_loss(cls_prob, box_prob).sum() / max(
1, num_pos * self.pre_anchor_topk)
# avoid the absence of gradients in regression subnet
# when no ground-truth in a batch
if num_pos == 0:
positive_loss = bbox_preds.sum() * 0
losses = {
'positive_bag_loss': positive_loss,
'negative_bag_loss': negative_loss
}
return losses
def positive_bag_loss(self, matched_cls_prob, matched_box_prob):
"""Compute positive bag loss.
:math:`-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )`.
:math:`P_{ij}^{cls}`: matched_cls_prob, classification probability of matched samples.
:math:`P_{ij}^{loc}`: matched_box_prob, box probability of matched samples.
Args:
matched_cls_prob (Tensor): Classification probabilty of matched
samples in shape (num_gt, pre_anchor_topk).
matched_box_prob (Tensor): BBox probability of matched samples,
in shape (num_gt, pre_anchor_topk).
Returns:
Tensor: Positive bag loss in shape (num_gt,).
""" # noqa: E501, W605
# bag_prob = Mean-max(matched_prob)
matched_prob = matched_cls_prob * matched_box_prob
weight = 1 / torch.clamp(1 - matched_prob, 1e-12, None)
weight /= weight.sum(dim=1).unsqueeze(dim=-1)
bag_prob = (weight * matched_prob).sum(dim=1)
# positive_bag_loss = -self.alpha * log(bag_prob)
return self.alpha * F.binary_cross_entropy(
bag_prob, torch.ones_like(bag_prob), reduction='none')
def negative_bag_loss(self, cls_prob, box_prob):
"""Compute negative bag loss.
:math:`FL((1 - P_{a_{j} \in A_{+}}) * (1 - P_{j}^{bg}))`.
:math:`P_{a_{j} \in A_{+}}`: Box_probability of matched samples.
:math:`P_{j}^{bg}`: Classification probability of negative samples.
Args:
cls_prob (Tensor): Classification probability, in shape
(num_img, num_anchors, num_classes).
box_prob (Tensor): Box probability, in shape
(num_img, num_anchors, num_classes).
Returns:
Tensor: Negative bag loss in shape (num_img, num_anchors, num_classes).
""" # noqa: E501, W605
prob = cls_prob * (1 - box_prob)
# There are some cases when neg_prob = 0.
# This will cause the neg_prob.log() to be inf without clamp.
prob = prob.clamp(min=EPS, max=1 - EPS)
negative_bag_loss = prob**self.gamma * F.binary_cross_entropy(
prob, torch.zeros_like(prob), reduction='none')
return (1 - self.alpha) * negative_bag_loss
| 11,148 | 40.140221 | 94 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/guided_anchor_head.py | import torch
import torch.nn as nn
from mmcv.cnn import bias_init_with_prob, normal_init
from mmcv.ops import DeformConv2d, MaskedConv2d
from mmcv.runner import force_fp32
from mmdet.core import (anchor_inside_flags, build_anchor_generator,
build_assigner, build_bbox_coder, build_sampler,
calc_region, images_to_levels, multi_apply,
multiclass_nms, unmap)
from ..builder import HEADS, build_loss
from .anchor_head import AnchorHead
class FeatureAdaption(nn.Module):
"""Feature Adaption Module.
Feature Adaption Module is implemented based on DCN v1.
It uses anchor shape prediction rather than feature map to
predict offsets of deform conv layer.
Args:
in_channels (int): Number of channels in the input feature map.
out_channels (int): Number of channels in the output feature map.
kernel_size (int): Deformable conv kernel size.
deform_groups (int): Deformable conv group size.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
deform_groups=4):
super(FeatureAdaption, self).__init__()
offset_channels = kernel_size * kernel_size * 2
self.conv_offset = nn.Conv2d(
2, deform_groups * offset_channels, 1, bias=False)
self.conv_adaption = DeformConv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
deform_groups=deform_groups)
self.relu = nn.ReLU(inplace=True)
def init_weights(self):
normal_init(self.conv_offset, std=0.1)
normal_init(self.conv_adaption, std=0.01)
def forward(self, x, shape):
offset = self.conv_offset(shape.detach())
x = self.relu(self.conv_adaption(x, offset))
return x
@HEADS.register_module()
class GuidedAnchorHead(AnchorHead):
"""Guided-Anchor-based head (GA-RPN, GA-RetinaNet, etc.).
This GuidedAnchorHead will predict high-quality feature guided
anchors and locations where anchors will be kept in inference.
There are mainly 3 categories of bounding-boxes.
- Sampled 9 pairs for target assignment. (approxes)
- The square boxes where the predicted anchors are based on. (squares)
- Guided anchors.
Please refer to https://arxiv.org/abs/1901.03278 for more details.
Args:
num_classes (int): Number of classes.
in_channels (int): Number of channels in the input feature map.
feat_channels (int): Number of hidden channels.
approx_anchor_generator (dict): Config dict for approx generator
square_anchor_generator (dict): Config dict for square generator
anchor_coder (dict): Config dict for anchor coder
bbox_coder (dict): Config dict for bbox coder
deform_groups: (int): Group number of DCN in
FeatureAdaption module.
loc_filter_thr (float): Threshold to filter out unconcerned regions.
loss_loc (dict): Config of location loss.
loss_shape (dict): Config of anchor shape loss.
loss_cls (dict): Config of classification loss.
loss_bbox (dict): Config of bbox regression loss.
"""
def __init__(
self,
num_classes,
in_channels,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[8],
strides=[4, 8, 16, 32, 64]),
anchor_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]
),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]
),
reg_decoded_bbox=False,
deform_groups=4,
loc_filter_thr=0.01,
train_cfg=None,
test_cfg=None,
loss_loc=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)): # yapf: disable
super(AnchorHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.feat_channels = feat_channels
self.deform_groups = deform_groups
self.loc_filter_thr = loc_filter_thr
# build approx_anchor_generator and square_anchor_generator
assert (approx_anchor_generator['octave_base_scale'] ==
square_anchor_generator['scales'][0])
assert (approx_anchor_generator['strides'] ==
square_anchor_generator['strides'])
self.approx_anchor_generator = build_anchor_generator(
approx_anchor_generator)
self.square_anchor_generator = build_anchor_generator(
square_anchor_generator)
self.approxs_per_octave = self.approx_anchor_generator \
.num_base_anchors[0]
self.reg_decoded_bbox = reg_decoded_bbox
# one anchor per location
self.num_anchors = 1
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
self.loc_focal_loss = loss_loc['type'] in ['FocalLoss']
self.sampling = loss_cls['type'] not in ['FocalLoss']
self.ga_sampling = train_cfg is not None and hasattr(
train_cfg, 'ga_sampler')
if self.use_sigmoid_cls:
self.cls_out_channels = self.num_classes
else:
self.cls_out_channels = self.num_classes + 1
# build bbox_coder
self.anchor_coder = build_bbox_coder(anchor_coder)
self.bbox_coder = build_bbox_coder(bbox_coder)
# build losses
self.loss_loc = build_loss(loss_loc)
self.loss_shape = build_loss(loss_shape)
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# use PseudoSampler when sampling is False
if self.sampling and hasattr(self.train_cfg, 'sampler'):
sampler_cfg = self.train_cfg.sampler
else:
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.ga_assigner = build_assigner(self.train_cfg.ga_assigner)
if self.ga_sampling:
ga_sampler_cfg = self.train_cfg.ga_sampler
else:
ga_sampler_cfg = dict(type='PseudoSampler')
self.ga_sampler = build_sampler(ga_sampler_cfg, context=self)
self.fp16_enabled = False
self._init_layers()
def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
self.conv_loc = nn.Conv2d(self.in_channels, 1, 1)
self.conv_shape = nn.Conv2d(self.in_channels, self.num_anchors * 2, 1)
self.feature_adaption = FeatureAdaption(
self.in_channels,
self.feat_channels,
kernel_size=3,
deform_groups=self.deform_groups)
self.conv_cls = MaskedConv2d(self.feat_channels,
self.num_anchors * self.cls_out_channels,
1)
self.conv_reg = MaskedConv2d(self.feat_channels, self.num_anchors * 4,
1)
def init_weights(self):
normal_init(self.conv_cls, std=0.01)
normal_init(self.conv_reg, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.conv_loc, std=0.01, bias=bias_cls)
normal_init(self.conv_shape, std=0.01)
self.feature_adaption.init_weights()
def forward_single(self, x):
loc_pred = self.conv_loc(x)
shape_pred = self.conv_shape(x)
x = self.feature_adaption(x, shape_pred)
# masked conv is only used during inference for speed-up
if not self.training:
mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr
else:
mask = None
cls_score = self.conv_cls(x, mask)
bbox_pred = self.conv_reg(x, mask)
return cls_score, bbox_pred, shape_pred, loc_pred
def forward(self, feats):
return multi_apply(self.forward_single, feats)
def get_sampled_approxs(self, featmap_sizes, img_metas, device='cuda'):
"""Get sampled approxs and inside flags according to feature map sizes.
Args:
featmap_sizes (list[tuple]): Multi-level feature map sizes.
img_metas (list[dict]): Image meta info.
device (torch.device | str): device for returned tensors
Returns:
tuple: approxes of each image, inside flags of each image
"""
num_imgs = len(img_metas)
# since feature map sizes of all images are the same, we only compute
# approxes for one time
multi_level_approxs = self.approx_anchor_generator.grid_anchors(
featmap_sizes, device=device)
approxs_list = [multi_level_approxs for _ in range(num_imgs)]
# for each image, we compute inside flags of multi level approxes
inside_flag_list = []
for img_id, img_meta in enumerate(img_metas):
multi_level_flags = []
multi_level_approxs = approxs_list[img_id]
# obtain valid flags for each approx first
multi_level_approx_flags = self.approx_anchor_generator \
.valid_flags(featmap_sizes,
img_meta['pad_shape'],
device=device)
for i, flags in enumerate(multi_level_approx_flags):
approxs = multi_level_approxs[i]
inside_flags_list = []
for i in range(self.approxs_per_octave):
split_valid_flags = flags[i::self.approxs_per_octave]
split_approxs = approxs[i::self.approxs_per_octave, :]
inside_flags = anchor_inside_flags(
split_approxs, split_valid_flags,
img_meta['img_shape'][:2],
self.train_cfg.allowed_border)
inside_flags_list.append(inside_flags)
# inside_flag for a position is true if any anchor in this
# position is true
inside_flags = (
torch.stack(inside_flags_list, 0).sum(dim=0) > 0)
multi_level_flags.append(inside_flags)
inside_flag_list.append(multi_level_flags)
return approxs_list, inside_flag_list
def get_anchors(self,
featmap_sizes,
shape_preds,
loc_preds,
img_metas,
use_loc_filter=False,
device='cuda'):
"""Get squares according to feature map sizes and guided anchors.
Args:
featmap_sizes (list[tuple]): Multi-level feature map sizes.
shape_preds (list[tensor]): Multi-level shape predictions.
loc_preds (list[tensor]): Multi-level location predictions.
img_metas (list[dict]): Image meta info.
use_loc_filter (bool): Use loc filter or not.
device (torch.device | str): device for returned tensors
Returns:
tuple: square approxs of each image, guided anchors of each image,
loc masks of each image
"""
num_imgs = len(img_metas)
num_levels = len(featmap_sizes)
# since feature map sizes of all images are the same, we only compute
# squares for one time
multi_level_squares = self.square_anchor_generator.grid_anchors(
featmap_sizes, device=device)
squares_list = [multi_level_squares for _ in range(num_imgs)]
# for each image, we compute multi level guided anchors
guided_anchors_list = []
loc_mask_list = []
for img_id, img_meta in enumerate(img_metas):
multi_level_guided_anchors = []
multi_level_loc_mask = []
for i in range(num_levels):
squares = squares_list[img_id][i]
shape_pred = shape_preds[i][img_id]
loc_pred = loc_preds[i][img_id]
guided_anchors, loc_mask = self._get_guided_anchors_single(
squares,
shape_pred,
loc_pred,
use_loc_filter=use_loc_filter)
multi_level_guided_anchors.append(guided_anchors)
multi_level_loc_mask.append(loc_mask)
guided_anchors_list.append(multi_level_guided_anchors)
loc_mask_list.append(multi_level_loc_mask)
return squares_list, guided_anchors_list, loc_mask_list
def _get_guided_anchors_single(self,
squares,
shape_pred,
loc_pred,
use_loc_filter=False):
"""Get guided anchors and loc masks for a single level.
Args:
square (tensor): Squares of a single level.
shape_pred (tensor): Shape predections of a single level.
loc_pred (tensor): Loc predections of a single level.
use_loc_filter (list[tensor]): Use loc filter or not.
Returns:
tuple: guided anchors, location masks
"""
# calculate location filtering mask
loc_pred = loc_pred.sigmoid().detach()
if use_loc_filter:
loc_mask = loc_pred >= self.loc_filter_thr
else:
loc_mask = loc_pred >= 0.0
mask = loc_mask.permute(1, 2, 0).expand(-1, -1, self.num_anchors)
mask = mask.contiguous().view(-1)
# calculate guided anchors
squares = squares[mask]
anchor_deltas = shape_pred.permute(1, 2, 0).contiguous().view(
-1, 2).detach()[mask]
bbox_deltas = anchor_deltas.new_full(squares.size(), 0)
bbox_deltas[:, 2:] = anchor_deltas
guided_anchors = self.anchor_coder.decode(
squares, bbox_deltas, wh_ratio_clip=1e-6)
return guided_anchors, mask
def ga_loc_targets(self, gt_bboxes_list, featmap_sizes):
"""Compute location targets for guided anchoring.
Each feature map is divided into positive, negative and ignore regions.
- positive regions: target 1, weight 1
- ignore regions: target 0, weight 0
- negative regions: target 0, weight 0.1
Args:
gt_bboxes_list (list[Tensor]): Gt bboxes of each image.
featmap_sizes (list[tuple]): Multi level sizes of each feature
maps.
Returns:
tuple
"""
anchor_scale = self.approx_anchor_generator.octave_base_scale
anchor_strides = self.approx_anchor_generator.strides
# Currently only supports same stride in x and y direction.
for stride in anchor_strides:
assert (stride[0] == stride[1])
anchor_strides = [stride[0] for stride in anchor_strides]
center_ratio = self.train_cfg.center_ratio
ignore_ratio = self.train_cfg.ignore_ratio
img_per_gpu = len(gt_bboxes_list)
num_lvls = len(featmap_sizes)
r1 = (1 - center_ratio) / 2
r2 = (1 - ignore_ratio) / 2
all_loc_targets = []
all_loc_weights = []
all_ignore_map = []
for lvl_id in range(num_lvls):
h, w = featmap_sizes[lvl_id]
loc_targets = torch.zeros(
img_per_gpu,
1,
h,
w,
device=gt_bboxes_list[0].device,
dtype=torch.float32)
loc_weights = torch.full_like(loc_targets, -1)
ignore_map = torch.zeros_like(loc_targets)
all_loc_targets.append(loc_targets)
all_loc_weights.append(loc_weights)
all_ignore_map.append(ignore_map)
for img_id in range(img_per_gpu):
gt_bboxes = gt_bboxes_list[img_id]
scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) *
(gt_bboxes[:, 3] - gt_bboxes[:, 1]))
min_anchor_size = scale.new_full(
(1, ), float(anchor_scale * anchor_strides[0]))
# assign gt bboxes to different feature levels w.r.t. their scales
target_lvls = torch.floor(
torch.log2(scale) - torch.log2(min_anchor_size) + 0.5)
target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long()
for gt_id in range(gt_bboxes.size(0)):
lvl = target_lvls[gt_id].item()
# rescaled to corresponding feature map
gt_ = gt_bboxes[gt_id, :4] / anchor_strides[lvl]
# calculate ignore regions
ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region(
gt_, r2, featmap_sizes[lvl])
# calculate positive (center) regions
ctr_x1, ctr_y1, ctr_x2, ctr_y2 = calc_region(
gt_, r1, featmap_sizes[lvl])
all_loc_targets[lvl][img_id, 0, ctr_y1:ctr_y2 + 1,
ctr_x1:ctr_x2 + 1] = 1
all_loc_weights[lvl][img_id, 0, ignore_y1:ignore_y2 + 1,
ignore_x1:ignore_x2 + 1] = 0
all_loc_weights[lvl][img_id, 0, ctr_y1:ctr_y2 + 1,
ctr_x1:ctr_x2 + 1] = 1
# calculate ignore map on nearby low level feature
if lvl > 0:
d_lvl = lvl - 1
# rescaled to corresponding feature map
gt_ = gt_bboxes[gt_id, :4] / anchor_strides[d_lvl]
ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region(
gt_, r2, featmap_sizes[d_lvl])
all_ignore_map[d_lvl][img_id, 0, ignore_y1:ignore_y2 + 1,
ignore_x1:ignore_x2 + 1] = 1
# calculate ignore map on nearby high level feature
if lvl < num_lvls - 1:
u_lvl = lvl + 1
# rescaled to corresponding feature map
gt_ = gt_bboxes[gt_id, :4] / anchor_strides[u_lvl]
ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region(
gt_, r2, featmap_sizes[u_lvl])
all_ignore_map[u_lvl][img_id, 0, ignore_y1:ignore_y2 + 1,
ignore_x1:ignore_x2 + 1] = 1
for lvl_id in range(num_lvls):
# ignore negative regions w.r.t. ignore map
all_loc_weights[lvl_id][(all_loc_weights[lvl_id] < 0)
& (all_ignore_map[lvl_id] > 0)] = 0
# set negative regions with weight 0.1
all_loc_weights[lvl_id][all_loc_weights[lvl_id] < 0] = 0.1
# loc average factor to balance loss
loc_avg_factor = sum(
[t.size(0) * t.size(-1) * t.size(-2)
for t in all_loc_targets]) / 200
return all_loc_targets, all_loc_weights, loc_avg_factor
def _ga_shape_target_single(self,
flat_approxs,
inside_flags,
flat_squares,
gt_bboxes,
gt_bboxes_ignore,
img_meta,
unmap_outputs=True):
"""Compute guided anchoring targets.
This function returns sampled anchors and gt bboxes directly
rather than calculates regression targets.
Args:
flat_approxs (Tensor): flat approxs of a single image,
shape (n, 4)
inside_flags (Tensor): inside flags of a single image,
shape (n, ).
flat_squares (Tensor): flat squares of a single image,
shape (approxs_per_octave * n, 4)
gt_bboxes (Tensor): Ground truth bboxes of a single image.
img_meta (dict): Meta info of a single image.
approxs_per_octave (int): number of approxs per octave
cfg (dict): RPN train configs.
unmap_outputs (bool): unmap outputs or not.
Returns:
tuple
"""
if not inside_flags.any():
return (None, ) * 5
# assign gt and sample anchors
expand_inside_flags = inside_flags[:, None].expand(
-1, self.approxs_per_octave).reshape(-1)
approxs = flat_approxs[expand_inside_flags, :]
squares = flat_squares[inside_flags, :]
assign_result = self.ga_assigner.assign(approxs, squares,
self.approxs_per_octave,
gt_bboxes, gt_bboxes_ignore)
sampling_result = self.ga_sampler.sample(assign_result, squares,
gt_bboxes)
bbox_anchors = torch.zeros_like(squares)
bbox_gts = torch.zeros_like(squares)
bbox_weights = torch.zeros_like(squares)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
if len(pos_inds) > 0:
bbox_anchors[pos_inds, :] = sampling_result.pos_bboxes
bbox_gts[pos_inds, :] = sampling_result.pos_gt_bboxes
bbox_weights[pos_inds, :] = 1.0
# map up to original set of anchors
if unmap_outputs:
num_total_anchors = flat_squares.size(0)
bbox_anchors = unmap(bbox_anchors, num_total_anchors, inside_flags)
bbox_gts = unmap(bbox_gts, num_total_anchors, inside_flags)
bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
return (bbox_anchors, bbox_gts, bbox_weights, pos_inds, neg_inds)
def ga_shape_targets(self,
approx_list,
inside_flag_list,
square_list,
gt_bboxes_list,
img_metas,
gt_bboxes_ignore_list=None,
unmap_outputs=True):
"""Compute guided anchoring targets.
Args:
approx_list (list[list]): Multi level approxs of each image.
inside_flag_list (list[list]): Multi level inside flags of each
image.
square_list (list[list]): Multi level squares of each image.
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta info of each image.
gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
unmap_outputs (bool): unmap outputs or not.
Returns:
tuple
"""
num_imgs = len(img_metas)
assert len(approx_list) == len(inside_flag_list) == len(
square_list) == num_imgs
# anchor number of multi levels
num_level_squares = [squares.size(0) for squares in square_list[0]]
# concat all level anchors and flags to a single tensor
inside_flag_flat_list = []
approx_flat_list = []
square_flat_list = []
for i in range(num_imgs):
assert len(square_list[i]) == len(inside_flag_list[i])
inside_flag_flat_list.append(torch.cat(inside_flag_list[i]))
approx_flat_list.append(torch.cat(approx_list[i]))
square_flat_list.append(torch.cat(square_list[i]))
# compute targets for each image
if gt_bboxes_ignore_list is None:
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
(all_bbox_anchors, all_bbox_gts, all_bbox_weights, pos_inds_list,
neg_inds_list) = multi_apply(
self._ga_shape_target_single,
approx_flat_list,
inside_flag_flat_list,
square_flat_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
img_metas,
unmap_outputs=unmap_outputs)
# no valid anchors
if any([bbox_anchors is None for bbox_anchors in all_bbox_anchors]):
return None
# sampled anchors of all images
num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
# split targets to a list w.r.t. multiple levels
bbox_anchors_list = images_to_levels(all_bbox_anchors,
num_level_squares)
bbox_gts_list = images_to_levels(all_bbox_gts, num_level_squares)
bbox_weights_list = images_to_levels(all_bbox_weights,
num_level_squares)
return (bbox_anchors_list, bbox_gts_list, bbox_weights_list,
num_total_pos, num_total_neg)
def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts,
anchor_weights, anchor_total_num):
shape_pred = shape_pred.permute(0, 2, 3, 1).contiguous().view(-1, 2)
bbox_anchors = bbox_anchors.contiguous().view(-1, 4)
bbox_gts = bbox_gts.contiguous().view(-1, 4)
anchor_weights = anchor_weights.contiguous().view(-1, 4)
bbox_deltas = bbox_anchors.new_full(bbox_anchors.size(), 0)
bbox_deltas[:, 2:] += shape_pred
# filter out negative samples to speed-up weighted_bounded_iou_loss
inds = torch.nonzero(
anchor_weights[:, 0] > 0, as_tuple=False).squeeze(1)
bbox_deltas_ = bbox_deltas[inds]
bbox_anchors_ = bbox_anchors[inds]
bbox_gts_ = bbox_gts[inds]
anchor_weights_ = anchor_weights[inds]
pred_anchors_ = self.anchor_coder.decode(
bbox_anchors_, bbox_deltas_, wh_ratio_clip=1e-6)
loss_shape = self.loss_shape(
pred_anchors_,
bbox_gts_,
anchor_weights_,
avg_factor=anchor_total_num)
return loss_shape
def loss_loc_single(self, loc_pred, loc_target, loc_weight,
loc_avg_factor):
loss_loc = self.loss_loc(
loc_pred.reshape(-1, 1),
loc_target.reshape(-1).long(),
loc_weight.reshape(-1),
avg_factor=loc_avg_factor)
return loss_loc
@force_fp32(
apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds'))
def loss(self,
cls_scores,
bbox_preds,
shape_preds,
loc_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.approx_anchor_generator.num_levels
device = cls_scores[0].device
# get loc targets
loc_targets, loc_weights, loc_avg_factor = self.ga_loc_targets(
gt_bboxes, featmap_sizes)
# get sampled approxes
approxs_list, inside_flag_list = self.get_sampled_approxs(
featmap_sizes, img_metas, device=device)
# get squares and guided anchors
squares_list, guided_anchors_list, _ = self.get_anchors(
featmap_sizes, shape_preds, loc_preds, img_metas, device=device)
# get shape targets
shape_targets = self.ga_shape_targets(approxs_list, inside_flag_list,
squares_list, gt_bboxes,
img_metas)
if shape_targets is None:
return None
(bbox_anchors_list, bbox_gts_list, anchor_weights_list, anchor_fg_num,
anchor_bg_num) = shape_targets
anchor_total_num = (
anchor_fg_num if not self.ga_sampling else anchor_fg_num +
anchor_bg_num)
# get anchor targets
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = self.get_targets(
guided_anchors_list,
inside_flag_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg) = cls_reg_targets
num_total_samples = (
num_total_pos + num_total_neg if self.sampling else num_total_pos)
# anchor number of multi levels
num_level_anchors = [
anchors.size(0) for anchors in guided_anchors_list[0]
]
# concat all level anchors to a single tensor
concat_anchor_list = []
for i in range(len(guided_anchors_list)):
concat_anchor_list.append(torch.cat(guided_anchors_list[i]))
all_anchor_list = images_to_levels(concat_anchor_list,
num_level_anchors)
# get classification and bbox regression losses
losses_cls, losses_bbox = multi_apply(
self.loss_single,
cls_scores,
bbox_preds,
all_anchor_list,
labels_list,
label_weights_list,
bbox_targets_list,
bbox_weights_list,
num_total_samples=num_total_samples)
# get anchor location loss
losses_loc = []
for i in range(len(loc_preds)):
loss_loc = self.loss_loc_single(
loc_preds[i],
loc_targets[i],
loc_weights[i],
loc_avg_factor=loc_avg_factor)
losses_loc.append(loss_loc)
# get anchor shape loss
losses_shape = []
for i in range(len(shape_preds)):
loss_shape = self.loss_shape_single(
shape_preds[i],
bbox_anchors_list[i],
bbox_gts_list[i],
anchor_weights_list[i],
anchor_total_num=anchor_total_num)
losses_shape.append(loss_shape)
return dict(
loss_cls=losses_cls,
loss_bbox=losses_bbox,
loss_shape=losses_shape,
loss_loc=losses_loc)
@force_fp32(
apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds'))
def get_bboxes(self,
cls_scores,
bbox_preds,
shape_preds,
loc_preds,
img_metas,
cfg=None,
rescale=False):
assert len(cls_scores) == len(bbox_preds) == len(shape_preds) == len(
loc_preds)
num_levels = len(cls_scores)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
device = cls_scores[0].device
# get guided anchors
_, guided_anchors, loc_masks = self.get_anchors(
featmap_sizes,
shape_preds,
loc_preds,
img_metas,
use_loc_filter=not self.training,
device=device)
result_list = []
for img_id in range(len(img_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
bbox_pred_list = [
bbox_preds[i][img_id].detach() for i in range(num_levels)
]
guided_anchor_list = [
guided_anchors[img_id][i].detach() for i in range(num_levels)
]
loc_mask_list = [
loc_masks[img_id][i].detach() for i in range(num_levels)
]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
guided_anchor_list,
loc_mask_list, img_shape,
scale_factor, cfg, rescale)
result_list.append(proposals)
return result_list
def _get_bboxes_single(self,
cls_scores,
bbox_preds,
mlvl_anchors,
mlvl_masks,
img_shape,
scale_factor,
cfg,
rescale=False):
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
mlvl_bboxes = []
mlvl_scores = []
for cls_score, bbox_pred, anchors, mask in zip(cls_scores, bbox_preds,
mlvl_anchors,
mlvl_masks):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
# if no location is kept, end.
if mask.sum() == 0:
continue
# reshape scores and bbox_pred
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
scores = cls_score.softmax(-1)
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
# filter scores, bbox_pred w.r.t. mask.
# anchors are filtered in get_anchors() beforehand.
scores = scores[mask, :]
bbox_pred = bbox_pred[mask, :]
if scores.dim() == 0:
anchors = anchors.unsqueeze(0)
scores = scores.unsqueeze(0)
bbox_pred = bbox_pred.unsqueeze(0)
# filter anchors, bbox_pred, scores w.r.t. scores
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
if self.use_sigmoid_cls:
max_scores, _ = scores.max(dim=1)
else:
# remind that we set FG labels to [0, num_class-1]
# since mmdet v2.0
# BG cat_id: num_class
max_scores, _ = scores[:, :-1].max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
anchors = anchors[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
bboxes = self.bbox_coder.decode(
anchors, bbox_pred, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
if self.use_sigmoid_cls:
# Add a dummy background class to the backend when using sigmoid
# remind that we set FG labels to [0, num_class-1] since mmdet v2.0
# BG cat_id: num_class
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
# multi class NMS
det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
| 36,268 | 41.370327 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/sabl_retina_head.py | import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from mmcv.runner import force_fp32
from mmdet.core import (build_anchor_generator, build_assigner,
build_bbox_coder, build_sampler, images_to_levels,
multi_apply, multiclass_nms, unmap)
from ..builder import HEADS, build_loss
from .base_dense_head import BaseDenseHead
from .guided_anchor_head import GuidedAnchorHead
@HEADS.register_module()
class SABLRetinaHead(BaseDenseHead):
"""Side-Aware Boundary Localization (SABL) for RetinaNet.
The anchor generation, assigning and sampling in SABLRetinaHead
are the same as GuidedAnchorHead for guided anchoring.
Please refer to https://arxiv.org/abs/1912.04260 for more details.
Args:
num_classes (int): Number of classes.
in_channels (int): Number of channels in the input feature map.
stacked_convs (int): Number of Convs for classification \
and regression branches. Defaults to 4.
feat_channels (int): Number of hidden channels. \
Defaults to 256.
approx_anchor_generator (dict): Config dict for approx generator.
square_anchor_generator (dict): Config dict for square generator.
conv_cfg (dict): Config dict for ConvModule. Defaults to None.
norm_cfg (dict): Config dict for Norm Layer. Defaults to None.
bbox_coder (dict): Config dict for bbox coder.
reg_decoded_bbox (bool): Whether to regress decoded bbox. \
Defaults to False.
train_cfg (dict): Training config of SABLRetinaHead.
test_cfg (dict): Testing config of SABLRetinaHead.
loss_cls (dict): Config of classification loss.
loss_bbox_cls (dict): Config of classification loss for bbox branch.
loss_bbox_reg (dict): Config of regression loss for bbox branch.
"""
def __init__(self,
num_classes,
in_channels,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
conv_cfg=None,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder',
num_buckets=14,
scale_factor=3.0),
reg_decoded_bbox=False,
train_cfg=None,
test_cfg=None,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)):
super(SABLRetinaHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
self.feat_channels = feat_channels
self.num_buckets = bbox_coder['num_buckets']
self.side_num = int(np.ceil(self.num_buckets / 2))
assert (approx_anchor_generator['octave_base_scale'] ==
square_anchor_generator['scales'][0])
assert (approx_anchor_generator['strides'] ==
square_anchor_generator['strides'])
self.approx_anchor_generator = build_anchor_generator(
approx_anchor_generator)
self.square_anchor_generator = build_anchor_generator(
square_anchor_generator)
self.approxs_per_octave = (
self.approx_anchor_generator.num_base_anchors[0])
# one anchor per location
self.num_anchors = 1
self.stacked_convs = stacked_convs
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.reg_decoded_bbox = reg_decoded_bbox
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
self.sampling = loss_cls['type'] not in [
'FocalLoss', 'GHMC', 'QualityFocalLoss'
]
if self.use_sigmoid_cls:
self.cls_out_channels = num_classes
else:
self.cls_out_channels = num_classes + 1
self.bbox_coder = build_bbox_coder(bbox_coder)
self.loss_cls = build_loss(loss_cls)
self.loss_bbox_cls = build_loss(loss_bbox_cls)
self.loss_bbox_reg = build_loss(loss_bbox_reg)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if self.train_cfg:
self.assigner = build_assigner(self.train_cfg.assigner)
# use PseudoSampler when sampling is False
if self.sampling and hasattr(self.train_cfg, 'sampler'):
sampler_cfg = self.train_cfg.sampler
else:
sampler_cfg = dict(type='PseudoSampler')
self.sampler = build_sampler(sampler_cfg, context=self)
self.fp16_enabled = False
self._init_layers()
def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.reg_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.retina_cls = nn.Conv2d(
self.feat_channels, self.cls_out_channels, 3, padding=1)
self.retina_bbox_reg = nn.Conv2d(
self.feat_channels, self.side_num * 4, 3, padding=1)
self.retina_bbox_cls = nn.Conv2d(
self.feat_channels, self.side_num * 4, 3, padding=1)
def init_weights(self):
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.retina_cls, std=0.01, bias=bias_cls)
normal_init(self.retina_bbox_reg, std=0.01)
normal_init(self.retina_bbox_cls, std=0.01)
def forward_single(self, x):
cls_feat = x
reg_feat = x
for cls_conv in self.cls_convs:
cls_feat = cls_conv(cls_feat)
for reg_conv in self.reg_convs:
reg_feat = reg_conv(reg_feat)
cls_score = self.retina_cls(cls_feat)
bbox_cls_pred = self.retina_bbox_cls(reg_feat)
bbox_reg_pred = self.retina_bbox_reg(reg_feat)
bbox_pred = (bbox_cls_pred, bbox_reg_pred)
return cls_score, bbox_pred
def forward(self, feats):
return multi_apply(self.forward_single, feats)
def get_anchors(self, featmap_sizes, img_metas, device='cuda'):
"""Get squares according to feature map sizes and guided anchors.
Args:
featmap_sizes (list[tuple]): Multi-level feature map sizes.
img_metas (list[dict]): Image meta info.
device (torch.device | str): device for returned tensors
Returns:
tuple: square approxs of each image
"""
num_imgs = len(img_metas)
# since feature map sizes of all images are the same, we only compute
# squares for one time
multi_level_squares = self.square_anchor_generator.grid_anchors(
featmap_sizes, device=device)
squares_list = [multi_level_squares for _ in range(num_imgs)]
return squares_list
def get_target(self,
approx_list,
inside_flag_list,
square_list,
gt_bboxes_list,
img_metas,
gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=None,
sampling=True,
unmap_outputs=True):
"""Compute bucketing targets.
Args:
approx_list (list[list]): Multi level approxs of each image.
inside_flag_list (list[list]): Multi level inside flags of each
image.
square_list (list[list]): Multi level squares of each image.
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta info of each image.
gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
gt_bboxes_list (list[Tensor]): Gt bboxes of each image.
label_channels (int): Channel of label.
sampling (bool): Sample Anchors or not.
unmap_outputs (bool): unmap outputs or not.
Returns:
tuple: Returns a tuple containing learning targets.
- labels_list (list[Tensor]): Labels of each level.
- label_weights_list (list[Tensor]): Label weights of each \
level.
- bbox_cls_targets_list (list[Tensor]): BBox cls targets of \
each level.
- bbox_cls_weights_list (list[Tensor]): BBox cls weights of \
each level.
- bbox_reg_targets_list (list[Tensor]): BBox reg targets of \
each level.
- bbox_reg_weights_list (list[Tensor]): BBox reg weights of \
each level.
- num_total_pos (int): Number of positive samples in all \
images.
- num_total_neg (int): Number of negative samples in all \
images.
"""
num_imgs = len(img_metas)
assert len(approx_list) == len(inside_flag_list) == len(
square_list) == num_imgs
# anchor number of multi levels
num_level_squares = [squares.size(0) for squares in square_list[0]]
# concat all level anchors and flags to a single tensor
inside_flag_flat_list = []
approx_flat_list = []
square_flat_list = []
for i in range(num_imgs):
assert len(square_list[i]) == len(inside_flag_list[i])
inside_flag_flat_list.append(torch.cat(inside_flag_list[i]))
approx_flat_list.append(torch.cat(approx_list[i]))
square_flat_list.append(torch.cat(square_list[i]))
# compute targets for each image
if gt_bboxes_ignore_list is None:
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
if gt_labels_list is None:
gt_labels_list = [None for _ in range(num_imgs)]
(all_labels, all_label_weights, all_bbox_cls_targets,
all_bbox_cls_weights, all_bbox_reg_targets, all_bbox_reg_weights,
pos_inds_list, neg_inds_list) = multi_apply(
self._get_target_single,
approx_flat_list,
inside_flag_flat_list,
square_flat_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
gt_labels_list,
img_metas,
label_channels=label_channels,
sampling=sampling,
unmap_outputs=unmap_outputs)
# no valid anchors
if any([labels is None for labels in all_labels]):
return None
# sampled anchors of all images
num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
# split targets to a list w.r.t. multiple levels
labels_list = images_to_levels(all_labels, num_level_squares)
label_weights_list = images_to_levels(all_label_weights,
num_level_squares)
bbox_cls_targets_list = images_to_levels(all_bbox_cls_targets,
num_level_squares)
bbox_cls_weights_list = images_to_levels(all_bbox_cls_weights,
num_level_squares)
bbox_reg_targets_list = images_to_levels(all_bbox_reg_targets,
num_level_squares)
bbox_reg_weights_list = images_to_levels(all_bbox_reg_weights,
num_level_squares)
return (labels_list, label_weights_list, bbox_cls_targets_list,
bbox_cls_weights_list, bbox_reg_targets_list,
bbox_reg_weights_list, num_total_pos, num_total_neg)
def _get_target_single(self,
flat_approxs,
inside_flags,
flat_squares,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
img_meta,
label_channels=None,
sampling=True,
unmap_outputs=True):
"""Compute regression and classification targets for anchors in a
single image.
Args:
flat_approxs (Tensor): flat approxs of a single image,
shape (n, 4)
inside_flags (Tensor): inside flags of a single image,
shape (n, ).
flat_squares (Tensor): flat squares of a single image,
shape (approxs_per_octave * n, 4)
gt_bboxes (Tensor): Ground truth bboxes of a single image, \
shape (num_gts, 4).
gt_bboxes_ignore (Tensor): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4).
gt_labels (Tensor): Ground truth labels of each box,
shape (num_gts,).
img_meta (dict): Meta info of the image.
label_channels (int): Channel of label.
sampling (bool): Sample Anchors or not.
unmap_outputs (bool): unmap outputs or not.
Returns:
tuple:
- labels_list (Tensor): Labels in a single image
- label_weights (Tensor): Label weights in a single image
- bbox_cls_targets (Tensor): BBox cls targets in a single image
- bbox_cls_weights (Tensor): BBox cls weights in a single image
- bbox_reg_targets (Tensor): BBox reg targets in a single image
- bbox_reg_weights (Tensor): BBox reg weights in a single image
- num_total_pos (int): Number of positive samples \
in a single image
- num_total_neg (int): Number of negative samples \
in a single image
"""
if not inside_flags.any():
return (None, ) * 8
# assign gt and sample anchors
expand_inside_flags = inside_flags[:, None].expand(
-1, self.approxs_per_octave).reshape(-1)
approxs = flat_approxs[expand_inside_flags, :]
squares = flat_squares[inside_flags, :]
assign_result = self.assigner.assign(approxs, squares,
self.approxs_per_octave,
gt_bboxes, gt_bboxes_ignore)
sampling_result = self.sampler.sample(assign_result, squares,
gt_bboxes)
num_valid_squares = squares.shape[0]
bbox_cls_targets = squares.new_zeros(
(num_valid_squares, self.side_num * 4))
bbox_cls_weights = squares.new_zeros(
(num_valid_squares, self.side_num * 4))
bbox_reg_targets = squares.new_zeros(
(num_valid_squares, self.side_num * 4))
bbox_reg_weights = squares.new_zeros(
(num_valid_squares, self.side_num * 4))
labels = squares.new_full((num_valid_squares, ),
self.num_classes,
dtype=torch.long)
label_weights = squares.new_zeros(num_valid_squares, dtype=torch.float)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
if len(pos_inds) > 0:
(pos_bbox_reg_targets, pos_bbox_reg_weights, pos_bbox_cls_targets,
pos_bbox_cls_weights) = self.bbox_coder.encode(
sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
bbox_cls_targets[pos_inds, :] = pos_bbox_cls_targets
bbox_reg_targets[pos_inds, :] = pos_bbox_reg_targets
bbox_cls_weights[pos_inds, :] = pos_bbox_cls_weights
bbox_reg_weights[pos_inds, :] = pos_bbox_reg_weights
if gt_labels is None:
# Only rpn gives gt_labels as None
# Foreground is the first class
labels[pos_inds] = 0
else:
labels[pos_inds] = gt_labels[
sampling_result.pos_assigned_gt_inds]
if self.train_cfg.pos_weight <= 0:
label_weights[pos_inds] = 1.0
else:
label_weights[pos_inds] = self.train_cfg.pos_weight
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
# map up to original set of anchors
if unmap_outputs:
num_total_anchors = flat_squares.size(0)
labels = unmap(
labels, num_total_anchors, inside_flags, fill=self.num_classes)
label_weights = unmap(label_weights, num_total_anchors,
inside_flags)
bbox_cls_targets = unmap(bbox_cls_targets, num_total_anchors,
inside_flags)
bbox_cls_weights = unmap(bbox_cls_weights, num_total_anchors,
inside_flags)
bbox_reg_targets = unmap(bbox_reg_targets, num_total_anchors,
inside_flags)
bbox_reg_weights = unmap(bbox_reg_weights, num_total_anchors,
inside_flags)
return (labels, label_weights, bbox_cls_targets, bbox_cls_weights,
bbox_reg_targets, bbox_reg_weights, pos_inds, neg_inds)
def loss_single(self, cls_score, bbox_pred, labels, label_weights,
bbox_cls_targets, bbox_cls_weights, bbox_reg_targets,
bbox_reg_weights, num_total_samples):
# classification loss
labels = labels.reshape(-1)
label_weights = label_weights.reshape(-1)
cls_score = cls_score.permute(0, 2, 3,
1).reshape(-1, self.cls_out_channels)
loss_cls = self.loss_cls(
cls_score, labels, label_weights, avg_factor=num_total_samples)
# regression loss
bbox_cls_targets = bbox_cls_targets.reshape(-1, self.side_num * 4)
bbox_cls_weights = bbox_cls_weights.reshape(-1, self.side_num * 4)
bbox_reg_targets = bbox_reg_targets.reshape(-1, self.side_num * 4)
bbox_reg_weights = bbox_reg_weights.reshape(-1, self.side_num * 4)
(bbox_cls_pred, bbox_reg_pred) = bbox_pred
bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape(
-1, self.side_num * 4)
bbox_reg_pred = bbox_reg_pred.permute(0, 2, 3, 1).reshape(
-1, self.side_num * 4)
loss_bbox_cls = self.loss_bbox_cls(
bbox_cls_pred,
bbox_cls_targets.long(),
bbox_cls_weights,
avg_factor=num_total_samples * 4 * self.side_num)
loss_bbox_reg = self.loss_bbox_reg(
bbox_reg_pred,
bbox_reg_targets,
bbox_reg_weights,
avg_factor=num_total_samples * 4 * self.bbox_coder.offset_topk)
return loss_cls, loss_bbox_cls, loss_bbox_reg
@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def loss(self,
cls_scores,
bbox_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.approx_anchor_generator.num_levels
device = cls_scores[0].device
# get sampled approxes
approxs_list, inside_flag_list = GuidedAnchorHead.get_sampled_approxs(
self, featmap_sizes, img_metas, device=device)
square_list = self.get_anchors(featmap_sizes, img_metas, device=device)
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = self.get_target(
approxs_list,
inside_flag_list,
square_list,
gt_bboxes,
img_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
label_channels=label_channels,
sampling=self.sampling)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_cls_targets_list,
bbox_cls_weights_list, bbox_reg_targets_list, bbox_reg_weights_list,
num_total_pos, num_total_neg) = cls_reg_targets
num_total_samples = (
num_total_pos + num_total_neg if self.sampling else num_total_pos)
losses_cls, losses_bbox_cls, losses_bbox_reg = multi_apply(
self.loss_single,
cls_scores,
bbox_preds,
labels_list,
label_weights_list,
bbox_cls_targets_list,
bbox_cls_weights_list,
bbox_reg_targets_list,
bbox_reg_weights_list,
num_total_samples=num_total_samples)
return dict(
loss_cls=losses_cls,
loss_bbox_cls=losses_bbox_cls,
loss_bbox_reg=losses_bbox_reg)
@force_fp32(apply_to=('cls_scores', 'bbox_preds'))
def get_bboxes(self,
cls_scores,
bbox_preds,
img_metas,
cfg=None,
rescale=False):
assert len(cls_scores) == len(bbox_preds)
num_levels = len(cls_scores)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
device = cls_scores[0].device
mlvl_anchors = self.get_anchors(
featmap_sizes, img_metas, device=device)
result_list = []
for img_id in range(len(img_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
bbox_cls_pred_list = [
bbox_preds[i][0][img_id].detach() for i in range(num_levels)
]
bbox_reg_pred_list = [
bbox_preds[i][1][img_id].detach() for i in range(num_levels)
]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
proposals = self.get_bboxes_single(cls_score_list,
bbox_cls_pred_list,
bbox_reg_pred_list,
mlvl_anchors[img_id], img_shape,
scale_factor, cfg, rescale)
result_list.append(proposals)
return result_list
def get_bboxes_single(self,
cls_scores,
bbox_cls_preds,
bbox_reg_preds,
mlvl_anchors,
img_shape,
scale_factor,
cfg,
rescale=False):
cfg = self.test_cfg if cfg is None else cfg
mlvl_bboxes = []
mlvl_scores = []
mlvl_confids = []
assert len(cls_scores) == len(bbox_cls_preds) == len(
bbox_reg_preds) == len(mlvl_anchors)
for cls_score, bbox_cls_pred, bbox_reg_pred, anchors in zip(
cls_scores, bbox_cls_preds, bbox_reg_preds, mlvl_anchors):
assert cls_score.size()[-2:] == bbox_cls_pred.size(
)[-2:] == bbox_reg_pred.size()[-2::]
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
scores = cls_score.softmax(-1)
bbox_cls_pred = bbox_cls_pred.permute(1, 2, 0).reshape(
-1, self.side_num * 4)
bbox_reg_pred = bbox_reg_pred.permute(1, 2, 0).reshape(
-1, self.side_num * 4)
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
if self.use_sigmoid_cls:
max_scores, _ = scores.max(dim=1)
else:
max_scores, _ = scores[:, :-1].max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
anchors = anchors[topk_inds, :]
bbox_cls_pred = bbox_cls_pred[topk_inds, :]
bbox_reg_pred = bbox_reg_pred[topk_inds, :]
scores = scores[topk_inds, :]
bbox_preds = [
bbox_cls_pred.contiguous(),
bbox_reg_pred.contiguous()
]
bboxes, confids = self.bbox_coder.decode(
anchors.contiguous(), bbox_preds, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_confids.append(confids)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
mlvl_confids = torch.cat(mlvl_confids)
if self.use_sigmoid_cls:
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
det_bboxes, det_labels = multiclass_nms(
mlvl_bboxes,
mlvl_scores,
cfg.score_thr,
cfg.nms,
cfg.max_per_img,
score_factors=mlvl_confids)
return det_bboxes, det_labels
| 26,915 | 42.483037 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/fovea_head.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, normal_init
from mmcv.ops import DeformConv2d
from mmdet.core import multi_apply, multiclass_nms
from ..builder import HEADS
from .anchor_free_head import AnchorFreeHead
INF = 1e8
class FeatureAlign(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
deform_groups=4):
super(FeatureAlign, self).__init__()
offset_channels = kernel_size * kernel_size * 2
self.conv_offset = nn.Conv2d(
4, deform_groups * offset_channels, 1, bias=False)
self.conv_adaption = DeformConv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
deform_groups=deform_groups)
self.relu = nn.ReLU(inplace=True)
def init_weights(self):
normal_init(self.conv_offset, std=0.1)
normal_init(self.conv_adaption, std=0.01)
def forward(self, x, shape):
offset = self.conv_offset(shape)
x = self.relu(self.conv_adaption(x, offset))
return x
@HEADS.register_module()
class FoveaHead(AnchorFreeHead):
"""FoveaBox: Beyond Anchor-based Object Detector
https://arxiv.org/abs/1904.03797
"""
def __init__(self,
num_classes,
in_channels,
base_edge_list=(16, 32, 64, 128, 256),
scale_ranges=((8, 32), (16, 64), (32, 128), (64, 256), (128,
512)),
sigma=0.4,
with_deform=False,
deform_groups=4,
**kwargs):
self.base_edge_list = base_edge_list
self.scale_ranges = scale_ranges
self.sigma = sigma
self.with_deform = with_deform
self.deform_groups = deform_groups
super().__init__(num_classes, in_channels, **kwargs)
def _init_layers(self):
# box branch
super()._init_reg_convs()
self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
# cls branch
if not self.with_deform:
super()._init_cls_convs()
self.conv_cls = nn.Conv2d(
self.feat_channels, self.cls_out_channels, 3, padding=1)
else:
self.cls_convs = nn.ModuleList()
self.cls_convs.append(
ConvModule(
self.feat_channels, (self.feat_channels * 4),
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
bias=self.norm_cfg is None))
self.cls_convs.append(
ConvModule((self.feat_channels * 4), (self.feat_channels * 4),
1,
stride=1,
padding=0,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
bias=self.norm_cfg is None))
self.feature_adaption = FeatureAlign(
self.feat_channels,
self.feat_channels,
kernel_size=3,
deform_groups=self.deform_groups)
self.conv_cls = nn.Conv2d(
int(self.feat_channels * 4),
self.cls_out_channels,
3,
padding=1)
def init_weights(self):
super().init_weights()
if self.with_deform:
self.feature_adaption.init_weights()
def forward_single(self, x):
cls_feat = x
reg_feat = x
for reg_layer in self.reg_convs:
reg_feat = reg_layer(reg_feat)
bbox_pred = self.conv_reg(reg_feat)
if self.with_deform:
cls_feat = self.feature_adaption(cls_feat, bbox_pred.exp())
for cls_layer in self.cls_convs:
cls_feat = cls_layer(cls_feat)
cls_score = self.conv_cls(cls_feat)
return cls_score, bbox_pred
def _get_points_single(self, *args, **kwargs):
y, x = super()._get_points_single(*args, **kwargs)
return y + 0.5, x + 0.5
def loss(self,
cls_scores,
bbox_preds,
gt_bbox_list,
gt_label_list,
img_metas,
gt_bboxes_ignore=None):
assert len(cls_scores) == len(bbox_preds)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
bbox_preds[0].device)
num_imgs = cls_scores[0].size(0)
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
for cls_score in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
for bbox_pred in bbox_preds
]
flatten_cls_scores = torch.cat(flatten_cls_scores)
flatten_bbox_preds = torch.cat(flatten_bbox_preds)
flatten_labels, flatten_bbox_targets = self.get_targets(
gt_bbox_list, gt_label_list, featmap_sizes, points)
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
pos_inds = ((flatten_labels >= 0)
& (flatten_labels < self.num_classes)).nonzero().view(-1)
num_pos = len(pos_inds)
loss_cls = self.loss_cls(
flatten_cls_scores, flatten_labels, avg_factor=num_pos + num_imgs)
if num_pos > 0:
pos_bbox_preds = flatten_bbox_preds[pos_inds]
pos_bbox_targets = flatten_bbox_targets[pos_inds]
pos_weights = pos_bbox_targets.new_zeros(
pos_bbox_targets.size()) + 1.0
loss_bbox = self.loss_bbox(
pos_bbox_preds,
pos_bbox_targets,
pos_weights,
avg_factor=num_pos)
else:
loss_bbox = torch.tensor(
0,
dtype=flatten_bbox_preds.dtype,
device=flatten_bbox_preds.device)
return dict(loss_cls=loss_cls, loss_bbox=loss_bbox)
def get_targets(self, gt_bbox_list, gt_label_list, featmap_sizes, points):
label_list, bbox_target_list = multi_apply(
self._get_target_single,
gt_bbox_list,
gt_label_list,
featmap_size_list=featmap_sizes,
point_list=points)
flatten_labels = [
torch.cat([
labels_level_img.flatten() for labels_level_img in labels_level
]) for labels_level in zip(*label_list)
]
flatten_bbox_targets = [
torch.cat([
bbox_targets_level_img.reshape(-1, 4)
for bbox_targets_level_img in bbox_targets_level
]) for bbox_targets_level in zip(*bbox_target_list)
]
flatten_labels = torch.cat(flatten_labels)
flatten_bbox_targets = torch.cat(flatten_bbox_targets)
return flatten_labels, flatten_bbox_targets
def _get_target_single(self,
gt_bboxes_raw,
gt_labels_raw,
featmap_size_list=None,
point_list=None):
gt_areas = torch.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) *
(gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
label_list = []
bbox_target_list = []
# for each pyramid, find the cls and box target
for base_len, (lower_bound, upper_bound), stride, featmap_size, \
(y, x) in zip(self.base_edge_list, self.scale_ranges,
self.strides, featmap_size_list, point_list):
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
labels = gt_labels_raw.new_zeros(featmap_size) + self.num_classes
bbox_targets = gt_bboxes_raw.new(featmap_size[0], featmap_size[1],
4) + 1
# scale assignment
hit_indices = ((gt_areas >= lower_bound) &
(gt_areas <= upper_bound)).nonzero().flatten()
if len(hit_indices) == 0:
label_list.append(labels)
bbox_target_list.append(torch.log(bbox_targets))
continue
_, hit_index_order = torch.sort(-gt_areas[hit_indices])
hit_indices = hit_indices[hit_index_order]
gt_bboxes = gt_bboxes_raw[hit_indices, :] / stride
gt_labels = gt_labels_raw[hit_indices]
half_w = 0.5 * (gt_bboxes[:, 2] - gt_bboxes[:, 0])
half_h = 0.5 * (gt_bboxes[:, 3] - gt_bboxes[:, 1])
# valid fovea area: left, right, top, down
pos_left = torch.ceil(
gt_bboxes[:, 0] + (1 - self.sigma) * half_w - 0.5).long().\
clamp(0, featmap_size[1] - 1)
pos_right = torch.floor(
gt_bboxes[:, 0] + (1 + self.sigma) * half_w - 0.5).long().\
clamp(0, featmap_size[1] - 1)
pos_top = torch.ceil(
gt_bboxes[:, 1] + (1 - self.sigma) * half_h - 0.5).long().\
clamp(0, featmap_size[0] - 1)
pos_down = torch.floor(
gt_bboxes[:, 1] + (1 + self.sigma) * half_h - 0.5).long().\
clamp(0, featmap_size[0] - 1)
for px1, py1, px2, py2, label, (gt_x1, gt_y1, gt_x2, gt_y2) in \
zip(pos_left, pos_top, pos_right, pos_down, gt_labels,
gt_bboxes_raw[hit_indices, :]):
labels[py1:py2 + 1, px1:px2 + 1] = label
bbox_targets[py1:py2 + 1, px1:px2 + 1, 0] = \
(stride * x[py1:py2 + 1, px1:px2 + 1] - gt_x1) / base_len
bbox_targets[py1:py2 + 1, px1:px2 + 1, 1] = \
(stride * y[py1:py2 + 1, px1:px2 + 1] - gt_y1) / base_len
bbox_targets[py1:py2 + 1, px1:px2 + 1, 2] = \
(gt_x2 - stride * x[py1:py2 + 1, px1:px2 + 1]) / base_len
bbox_targets[py1:py2 + 1, px1:px2 + 1, 3] = \
(gt_y2 - stride * y[py1:py2 + 1, px1:px2 + 1]) / base_len
bbox_targets = bbox_targets.clamp(min=1. / 16, max=16.)
label_list.append(labels)
bbox_target_list.append(torch.log(bbox_targets))
return label_list, bbox_target_list
def get_bboxes(self,
cls_scores,
bbox_preds,
img_metas,
cfg=None,
rescale=None):
assert len(cls_scores) == len(bbox_preds)
num_levels = len(cls_scores)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
points = self.get_points(
featmap_sizes,
bbox_preds[0].dtype,
bbox_preds[0].device,
flatten=True)
result_list = []
for img_id in range(len(img_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
bbox_pred_list = [
bbox_preds[i][img_id].detach() for i in range(num_levels)
]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
det_bboxes = self._get_bboxes_single(cls_score_list,
bbox_pred_list, featmap_sizes,
points, img_shape,
scale_factor, cfg, rescale)
result_list.append(det_bboxes)
return result_list
def _get_bboxes_single(self,
cls_scores,
bbox_preds,
featmap_sizes,
point_list,
img_shape,
scale_factor,
cfg,
rescale=False):
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_scores) == len(bbox_preds) == len(point_list)
det_bboxes = []
det_scores = []
for cls_score, bbox_pred, featmap_size, stride, base_len, (y, x) \
in zip(cls_scores, bbox_preds, featmap_sizes, self.strides,
self.base_edge_list, point_list):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).sigmoid()
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).exp()
nms_pre = cfg.get('nms_pre', -1)
if (nms_pre > 0) and (scores.shape[0] > nms_pre):
max_scores, _ = scores.max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
y = y[topk_inds]
x = x[topk_inds]
x1 = (stride * x - base_len * bbox_pred[:, 0]).\
clamp(min=0, max=img_shape[1] - 1)
y1 = (stride * y - base_len * bbox_pred[:, 1]).\
clamp(min=0, max=img_shape[0] - 1)
x2 = (stride * x + base_len * bbox_pred[:, 2]).\
clamp(min=0, max=img_shape[1] - 1)
y2 = (stride * y + base_len * bbox_pred[:, 3]).\
clamp(min=0, max=img_shape[0] - 1)
bboxes = torch.stack([x1, y1, x2, y2], -1)
det_bboxes.append(bboxes)
det_scores.append(scores)
det_bboxes = torch.cat(det_bboxes)
if rescale:
det_bboxes /= det_bboxes.new_tensor(scale_factor)
det_scores = torch.cat(det_scores)
padding = det_scores.new_zeros(det_scores.shape[0], 1)
# remind that we set FG labels to [0, num_class-1] since mmdet v2.0
# BG cat_id: num_class
det_scores = torch.cat([det_scores, padding], dim=1)
det_bboxes, det_labels = multiclass_nms(det_bboxes, det_scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
| 14,405 | 41.122807 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/dense_heads/dense_test_mixins.py | from inspect import signature
import torch
from mmdet.core import bbox2result, bbox_mapping_back, multiclass_nms
class BBoxTestMixin(object):
"""Mixin class for test time augmentation of bboxes."""
def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
Returns:
tuple: (bboxes, scores)
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
img_shape = img_info[0]['img_shape']
scale_factor = img_info[0]['scale_factor']
flip = img_info[0]['flip']
flip_direction = img_info[0]['flip_direction']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
flip_direction)
recovered_bboxes.append(bboxes)
bboxes = torch.cat(recovered_bboxes, dim=0)
if aug_scores is None:
return bboxes
else:
scores = torch.cat(aug_scores, dim=0)
return bboxes, scores
def aug_test_bboxes(self, feats, img_metas, rescale=False):
"""Test det bboxes with test time augmentation.
Args:
feats (list[Tensor]): the outer list indicates test-time
augmentations and inner Tensor should have a shape NxCxHxW,
which contains features for all images in the batch.
img_metas (list[list[dict]]): the outer list indicates test-time
augs (multiscale, flip, etc.) and the inner list indicates
images in a batch. each dict has image information.
rescale (bool, optional): Whether to rescale the results.
Defaults to False.
Returns:
list[ndarray]: bbox results of each class
"""
# check with_nms argument
gb_sig = signature(self.get_bboxes)
gb_args = [p.name for p in gb_sig.parameters.values()]
gbs_sig = signature(self._get_bboxes_single)
gbs_args = [p.name for p in gbs_sig.parameters.values()]
assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
f'{self.__class__.__name__}' \
' does not support test-time augmentation'
aug_bboxes = []
aug_scores = []
aug_factors = [] # score_factors for NMS
for x, img_meta in zip(feats, img_metas):
# only one image in the batch
outs = self.forward(x)
bbox_inputs = outs + (img_meta, self.test_cfg, False, False)
bbox_outputs = self.get_bboxes(*bbox_inputs)[0]
aug_bboxes.append(bbox_outputs[0])
aug_scores.append(bbox_outputs[1])
# bbox_outputs of some detectors (e.g., ATSS, FCOS, YOLOv3)
# contains additional element to adjust scores before NMS
if len(bbox_outputs) >= 3:
aug_factors.append(bbox_outputs[2])
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = self.merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas)
merged_factors = torch.cat(aug_factors, dim=0) if aug_factors else None
det_bboxes, det_labels = multiclass_nms(
merged_bboxes,
merged_scores,
self.test_cfg.score_thr,
self.test_cfg.nms,
self.test_cfg.max_per_img,
score_factors=merged_factors)
if rescale:
_det_bboxes = det_bboxes
else:
_det_bboxes = det_bboxes.clone()
_det_bboxes[:, :4] *= det_bboxes.new_tensor(
img_metas[0][0]['scale_factor'])
bbox_results = bbox2result(_det_bboxes, det_labels, self.num_classes)
return bbox_results
| 3,983 | 39.653061 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/utils/gaussian_target.py | from math import sqrt
import torch
def gaussian2D(radius, sigma=1, dtype=torch.float32, device='cpu'):
"""Generate 2D gaussian kernel.
Args:
radius (int): Radius of gaussian kernel.
sigma (int): Sigma of gaussian function. Default: 1.
dtype (torch.dtype): Dtype of gaussian tensor. Default: torch.float32.
device (str): Device of gaussian tensor. Default: 'cpu'.
Returns:
h (Tensor): Gaussian kernel with a
``(2 * radius + 1) * (2 * radius + 1)`` shape.
"""
x = torch.arange(
-radius, radius + 1, dtype=dtype, device=device).view(1, -1)
y = torch.arange(
-radius, radius + 1, dtype=dtype, device=device).view(-1, 1)
h = (-(x * x + y * y) / (2 * sigma * sigma)).exp()
h[h < torch.finfo(h.dtype).eps * h.max()] = 0
return h
def gen_gaussian_target(heatmap, center, radius, k=1):
"""Generate 2D gaussian heatmap.
Args:
heatmap (Tensor): Input heatmap, the gaussian kernel will cover on
it and maintain the max value.
center (list[int]): Coord of gaussian kernel's center.
radius (int): Radius of gaussian kernel.
k (int): Coefficient of gaussian kernel. Default: 1.
Returns:
out_heatmap (Tensor): Updated heatmap covered by gaussian kernel.
"""
diameter = 2 * radius + 1
gaussian_kernel = gaussian2D(
radius, sigma=diameter / 6, dtype=heatmap.dtype, device=heatmap.device)
x, y = center
height, width = heatmap.shape[:2]
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = gaussian_kernel[radius - top:radius + bottom,
radius - left:radius + right]
out_heatmap = heatmap
torch.max(
masked_heatmap,
masked_gaussian * k,
out=out_heatmap[y - top:y + bottom, x - left:x + right])
return out_heatmap
def gaussian_radius(det_size, min_overlap):
r"""Generate 2D gaussian radius.
This function is modified from the `official github repo
<https://github.com/princeton-vl/CornerNet-Lite/blob/master/core/sample/
utils.py#L65>`_.
Given ``min_overlap``, radius could computed by a quadratic equation
according to Vieta's formulas.
There are 3 cases for computing gaussian radius, details are following:
- Explanation of figure: ``lt`` and ``br`` indicates the left-top and
bottom-right corner of ground truth box. ``x`` indicates the
generated corner at the limited position when ``radius=r``.
- Case1: one corner is inside the gt box and the other is outside.
.. code:: text
|< width >|
lt-+----------+ -
| | | ^
+--x----------+--+
| | | |
| | | | height
| | overlap | |
| | | |
| | | | v
+--+---------br--+ -
| | |
+----------+--x
To ensure IoU of generated box and gt box is larger than ``min_overlap``:
.. math::
\cfrac{(w-r)*(h-r)}{w*h+(w+h)r-r^2} \ge {iou} \quad\Rightarrow\quad
{r^2-(w+h)r+\cfrac{1-iou}{1+iou}*w*h} \ge 0 \\
{a} = 1,\quad{b} = {-(w+h)},\quad{c} = {\cfrac{1-iou}{1+iou}*w*h}
{r} \le \cfrac{-b-\sqrt{b^2-4*a*c}}{2*a}
- Case2: both two corners are inside the gt box.
.. code:: text
|< width >|
lt-+----------+ -
| | | ^
+--x-------+ |
| | | |
| |overlap| | height
| | | |
| +-------x--+
| | | v
+----------+-br -
To ensure IoU of generated box and gt box is larger than ``min_overlap``:
.. math::
\cfrac{(w-2*r)*(h-2*r)}{w*h} \ge {iou} \quad\Rightarrow\quad
{4r^2-2(w+h)r+(1-iou)*w*h} \ge 0 \\
{a} = 4,\quad {b} = {-2(w+h)},\quad {c} = {(1-iou)*w*h}
{r} \le \cfrac{-b-\sqrt{b^2-4*a*c}}{2*a}
- Case3: both two corners are outside the gt box.
.. code:: text
|< width >|
x--+----------------+
| | |
+-lt-------------+ | -
| | | | ^
| | | |
| | overlap | | height
| | | |
| | | | v
| +------------br--+ -
| | |
+----------------+--x
To ensure IoU of generated box and gt box is larger than ``min_overlap``:
.. math::
\cfrac{w*h}{(w+2*r)*(h+2*r)} \ge {iou} \quad\Rightarrow\quad
{4*iou*r^2+2*iou*(w+h)r+(iou-1)*w*h} \le 0 \\
{a} = {4*iou},\quad {b} = {2*iou*(w+h)},\quad {c} = {(iou-1)*w*h} \\
{r} \le \cfrac{-b+\sqrt{b^2-4*a*c}}{2*a}
Args:
det_size (list[int]): Shape of object.
min_overlap (float): Min IoU with ground truth for boxes generated by
keypoints inside the gaussian kernel.
Returns:
radius (int): Radius of gaussian kernel.
"""
height, width = det_size
a1 = 1
b1 = (height + width)
c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
sq1 = sqrt(b1**2 - 4 * a1 * c1)
r1 = (b1 - sq1) / (2 * a1)
a2 = 4
b2 = 2 * (height + width)
c2 = (1 - min_overlap) * width * height
sq2 = sqrt(b2**2 - 4 * a2 * c2)
r2 = (b2 - sq2) / (2 * a2)
a3 = 4 * min_overlap
b3 = -2 * min_overlap * (height + width)
c3 = (min_overlap - 1) * width * height
sq3 = sqrt(b3**2 - 4 * a3 * c3)
r3 = (b3 + sq3) / (2 * a3)
return min(r1, r2, r3)
| 5,784 | 30.102151 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/utils/res_layer.py | from mmcv.cnn import build_conv_layer, build_norm_layer
from torch import nn as nn
class ResLayer(nn.Sequential):
"""ResLayer to build ResNet style backbone.
Args:
block (nn.Module): block used to build ResLayer.
inplanes (int): inplanes of block.
planes (int): planes of block.
num_blocks (int): number of blocks.
stride (int): stride of the first block. Default: 1
avg_down (bool): Use AvgPool instead of stride conv when
downsampling in the bottleneck. Default: False
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
downsample_first (bool): Downsample at the first block or last block.
False for Hourglass, True for ResNet. Default: True
"""
def __init__(self,
block,
inplanes,
planes,
num_blocks,
stride=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
downsample_first=True,
**kwargs):
self.block = block
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = []
conv_stride = stride
if avg_down and stride != 1:
conv_stride = 1
downsample.append(
nn.AvgPool2d(
kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False))
downsample.extend([
build_conv_layer(
conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=conv_stride,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1]
])
downsample = nn.Sequential(*downsample)
layers = []
if downsample_first:
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
inplanes = planes * block.expansion
for _ in range(1, num_blocks):
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
else: # downsample_first=False is for HourglassModule
for _ in range(num_blocks - 1):
layers.append(
block(
inplanes=inplanes,
planes=inplanes,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
super(ResLayer, self).__init__(*layers)
| 3,655 | 34.495146 | 77 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/standard_roi_head.py | import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
@HEADS.register_module()
class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin):
"""Simplest base roi head including one bbox head and one mask head."""
def init_assigner_sampler(self):
"""Initialize assigner and sampler."""
self.bbox_assigner = None
self.bbox_sampler = None
if self.train_cfg:
self.bbox_assigner = build_assigner(self.train_cfg.assigner)
self.bbox_sampler = build_sampler(
self.train_cfg.sampler, context=self)
def init_bbox_head(self, bbox_roi_extractor, bbox_head):
"""Initialize ``bbox_head``"""
self.bbox_roi_extractor = build_roi_extractor(bbox_roi_extractor)
self.bbox_head = build_head(bbox_head)
def init_mask_head(self, mask_roi_extractor, mask_head):
"""Initialize ``mask_head``"""
if mask_roi_extractor is not None:
self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor)
self.share_roi_extractor = False
else:
self.share_roi_extractor = True
self.mask_roi_extractor = self.bbox_roi_extractor
self.mask_head = build_head(mask_head)
def init_weights(self, pretrained):
"""Initialize the weights in head.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if self.with_shared_head:
self.shared_head.init_weights(pretrained=pretrained)
if self.with_bbox:
self.bbox_roi_extractor.init_weights()
self.bbox_head.init_weights()
if self.with_mask:
self.mask_head.init_weights()
if not self.share_roi_extractor:
self.mask_roi_extractor.init_weights()
def forward_dummy(self, x, proposals):
"""Dummy forward function."""
# bbox head
outs = ()
rois = bbox2roi([proposals])
if self.with_bbox:
bbox_results = self._bbox_forward(x, rois)
outs = outs + (bbox_results['cls_score'],
bbox_results['bbox_pred'])
# mask head
if self.with_mask:
mask_rois = rois[:100]
mask_results = self._mask_forward(x, mask_rois)
outs = outs + (mask_results['mask_pred'], )
return outs
def forward_train(self,
x,
img_metas,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None):
"""
Args:
x (list[Tensor]): list of multi-level img features.
img_metas (list[dict]): list of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmdet/datasets/pipelines/formatting.py:Collect`.
proposals (list[Tensors]): list of region proposals.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
gt_masks (None | Tensor) : true segmentation masks for each box
used if the architecture supports a segmentation task.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
# assign gts and sample proposals
if self.with_bbox or self.with_mask:
num_imgs = len(img_metas)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
for i in range(num_imgs):
assign_result = self.bbox_assigner.assign(
proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
gt_labels[i])
sampling_result = self.bbox_sampler.sample(
assign_result,
proposal_list[i],
gt_bboxes[i],
gt_labels[i],
feats=[lvl_feat[i][None] for lvl_feat in x])
sampling_results.append(sampling_result)
losses = dict()
# bbox head forward and loss
if self.with_bbox:
bbox_results = self._bbox_forward_train(x, sampling_results,
gt_bboxes, gt_labels,
img_metas)
losses.update(bbox_results['loss_bbox'])
# mask head forward and loss
if self.with_mask:
mask_results = self._mask_forward_train(x, sampling_results,
bbox_results['bbox_feats'],
gt_masks, img_metas)
losses.update(mask_results['loss_mask'])
return losses
def _bbox_forward(self, x, rois):
"""Box head forward function used in both training and testing."""
# TODO: a more flexible way to decide which feature maps to use
bbox_feats = self.bbox_roi_extractor(
x[:self.bbox_roi_extractor.num_inputs], rois)
if self.with_shared_head:
bbox_feats = self.shared_head(bbox_feats)
cls_score, bbox_pred = self.bbox_head(bbox_feats)
bbox_results = dict(
cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
return bbox_results
def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
img_metas):
"""Run forward function and calculate loss for box head in training."""
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_results = self._bbox_forward(x, rois)
bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
gt_labels, self.train_cfg)
loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
bbox_results['bbox_pred'], rois,
*bbox_targets)
bbox_results.update(loss_bbox=loss_bbox)
return bbox_results
def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
img_metas):
"""Run forward function and calculate loss for mask head in
training."""
if not self.share_roi_extractor:
pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
mask_results = self._mask_forward(x, pos_rois)
else:
pos_inds = []
device = bbox_feats.device
for res in sampling_results:
pos_inds.append(
torch.ones(
res.pos_bboxes.shape[0],
device=device,
dtype=torch.uint8))
pos_inds.append(
torch.zeros(
res.neg_bboxes.shape[0],
device=device,
dtype=torch.uint8))
pos_inds = torch.cat(pos_inds)
mask_results = self._mask_forward(
x, pos_inds=pos_inds, bbox_feats=bbox_feats)
mask_targets = self.mask_head.get_targets(sampling_results, gt_masks,
self.train_cfg)
pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
loss_mask = self.mask_head.loss(mask_results['mask_pred'],
mask_targets, pos_labels)
mask_results.update(loss_mask=loss_mask, mask_targets=mask_targets)
return mask_results
def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None):
"""Mask head forward function used in both training and testing."""
assert ((rois is not None) ^
(pos_inds is not None and bbox_feats is not None))
if rois is not None:
mask_feats = self.mask_roi_extractor(
x[:self.mask_roi_extractor.num_inputs], rois)
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
else:
assert bbox_feats is not None
mask_feats = bbox_feats[pos_inds]
mask_pred = self.mask_head(mask_feats)
mask_results = dict(mask_pred=mask_pred, mask_feats=mask_feats)
return mask_results
async def async_simple_test(self,
x,
proposal_list,
img_metas,
proposals=None,
rescale=False):
"""Async test without augmentation."""
assert self.with_bbox, 'Bbox head must be implemented.'
det_bboxes, det_labels = await self.async_test_bboxes(
x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
bbox_results = bbox2result(det_bboxes, det_labels,
self.bbox_head.num_classes)
if not self.with_mask:
return bbox_results
else:
segm_results = await self.async_test_mask(
x,
img_metas,
det_bboxes,
det_labels,
rescale=rescale,
mask_test_cfg=self.test_cfg.get('mask'))
return bbox_results, segm_results
def simple_test(self,
x,
proposal_list,
img_metas,
proposals=None,
rescale=False):
"""Test without augmentation."""
assert self.with_bbox, 'Bbox head must be implemented.'
det_bboxes, det_labels = self.simple_test_bboxes(
x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
bbox_results = [
bbox2result(det_bboxes[i], det_labels[i],
self.bbox_head.num_classes)
for i in range(len(det_bboxes))
]
if not self.with_mask:
return bbox_results
else:
segm_results = self.simple_test_mask(
x, img_metas, det_bboxes, det_labels, rescale=rescale)
return list(zip(bbox_results, segm_results))
def aug_test(self, x, proposal_list, img_metas, rescale=False):
"""Test with augmentations.
If rescale is False, then returned bboxes and masks will fit the scale
of imgs[0].
"""
det_bboxes, det_labels = self.aug_test_bboxes(x, img_metas,
proposal_list,
self.test_cfg)
if rescale:
_det_bboxes = det_bboxes
else:
_det_bboxes = det_bboxes.clone()
_det_bboxes[:, :4] *= det_bboxes.new_tensor(
img_metas[0][0]['scale_factor'])
bbox_results = bbox2result(_det_bboxes, det_labels,
self.bbox_head.num_classes)
# det_bboxes always keep the original scale
if self.with_mask:
segm_results = self.aug_test_mask(x, img_metas, det_bboxes,
det_labels)
return [(bbox_results, segm_results)]
else:
return [bbox_results]
| 12,006 | 40.690972 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/grid_roi_head.py | import torch
from mmdet.core import bbox2result, bbox2roi
from ..builder import HEADS, build_head, build_roi_extractor
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class GridRoIHead(StandardRoIHead):
"""Grid roi head for Grid R-CNN.
https://arxiv.org/abs/1811.12030
"""
def __init__(self, grid_roi_extractor, grid_head, **kwargs):
assert grid_head is not None
super(GridRoIHead, self).__init__(**kwargs)
if grid_roi_extractor is not None:
self.grid_roi_extractor = build_roi_extractor(grid_roi_extractor)
self.share_roi_extractor = False
else:
self.share_roi_extractor = True
self.grid_roi_extractor = self.bbox_roi_extractor
self.grid_head = build_head(grid_head)
def init_weights(self, pretrained):
"""Initialize the weights in head.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
super(GridRoIHead, self).init_weights(pretrained)
self.grid_head.init_weights()
if not self.share_roi_extractor:
self.grid_roi_extractor.init_weights()
def _random_jitter(self, sampling_results, img_metas, amplitude=0.15):
"""Ramdom jitter positive proposals for training."""
for sampling_result, img_meta in zip(sampling_results, img_metas):
bboxes = sampling_result.pos_bboxes
random_offsets = bboxes.new_empty(bboxes.shape[0], 4).uniform_(
-amplitude, amplitude)
# before jittering
cxcy = (bboxes[:, 2:4] + bboxes[:, :2]) / 2
wh = (bboxes[:, 2:4] - bboxes[:, :2]).abs()
# after jittering
new_cxcy = cxcy + wh * random_offsets[:, :2]
new_wh = wh * (1 + random_offsets[:, 2:])
# xywh to xyxy
new_x1y1 = (new_cxcy - new_wh / 2)
new_x2y2 = (new_cxcy + new_wh / 2)
new_bboxes = torch.cat([new_x1y1, new_x2y2], dim=1)
# clip bboxes
max_shape = img_meta['img_shape']
if max_shape is not None:
new_bboxes[:, 0::2].clamp_(min=0, max=max_shape[1] - 1)
new_bboxes[:, 1::2].clamp_(min=0, max=max_shape[0] - 1)
sampling_result.pos_bboxes = new_bboxes
return sampling_results
def forward_dummy(self, x, proposals):
"""Dummy forward function."""
# bbox head
outs = ()
rois = bbox2roi([proposals])
if self.with_bbox:
bbox_results = self._bbox_forward(x, rois)
outs = outs + (bbox_results['cls_score'],
bbox_results['bbox_pred'])
# grid head
grid_rois = rois[:100]
grid_feats = self.grid_roi_extractor(
x[:self.grid_roi_extractor.num_inputs], grid_rois)
if self.with_shared_head:
grid_feats = self.shared_head(grid_feats)
grid_pred = self.grid_head(grid_feats)
outs = outs + (grid_pred, )
# mask head
if self.with_mask:
mask_rois = rois[:100]
mask_results = self._mask_forward(x, mask_rois)
outs = outs + (mask_results['mask_pred'], )
return outs
def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
img_metas):
"""Run forward function and calculate loss for box head in training."""
bbox_results = super(GridRoIHead,
self)._bbox_forward_train(x, sampling_results,
gt_bboxes, gt_labels,
img_metas)
# Grid head forward and loss
sampling_results = self._random_jitter(sampling_results, img_metas)
pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
# GN in head does not support zero shape input
if pos_rois.shape[0] == 0:
return bbox_results
grid_feats = self.grid_roi_extractor(
x[:self.grid_roi_extractor.num_inputs], pos_rois)
if self.with_shared_head:
grid_feats = self.shared_head(grid_feats)
# Accelerate training
max_sample_num_grid = self.train_cfg.get('max_num_grid', 192)
sample_idx = torch.randperm(
grid_feats.shape[0])[:min(grid_feats.shape[0], max_sample_num_grid
)]
grid_feats = grid_feats[sample_idx]
grid_pred = self.grid_head(grid_feats)
grid_targets = self.grid_head.get_targets(sampling_results,
self.train_cfg)
grid_targets = grid_targets[sample_idx]
loss_grid = self.grid_head.loss(grid_pred, grid_targets)
bbox_results['loss_bbox'].update(loss_grid)
return bbox_results
def simple_test(self,
x,
proposal_list,
img_metas,
proposals=None,
rescale=False):
"""Test without augmentation."""
assert self.with_bbox, 'Bbox head must be implemented.'
det_bboxes, det_labels = self.simple_test_bboxes(
x, img_metas, proposal_list, self.test_cfg, rescale=False)
# pack rois into bboxes
grid_rois = bbox2roi([det_bbox[:, :4] for det_bbox in det_bboxes])
if grid_rois.shape[0] != 0:
grid_feats = self.grid_roi_extractor(
x[:len(self.grid_roi_extractor.featmap_strides)], grid_rois)
self.grid_head.test_mode = True
grid_pred = self.grid_head(grid_feats)
# split batch grid head prediction back to each image
num_roi_per_img = tuple(len(det_bbox) for det_bbox in det_bboxes)
grid_pred = {
k: v.split(num_roi_per_img, 0)
for k, v in grid_pred.items()
}
# apply bbox post-processing to each image individually
bbox_results = []
num_imgs = len(det_bboxes)
for i in range(num_imgs):
if det_bboxes[i].shape[0] == 0:
bbox_results.append(grid_rois.new_tensor([]))
else:
det_bbox = self.grid_head.get_bboxes(
det_bboxes[i], grid_pred['fused'][i], [img_metas[i]])
if rescale:
det_bbox[:, :4] /= img_metas[i]['scale_factor']
bbox_results.append(
bbox2result(det_bbox, det_labels[i],
self.bbox_head.num_classes))
else:
bbox_results = [
grid_rois.new_tensor([]) for _ in range(len(det_bboxes))
]
if not self.with_mask:
return bbox_results
else:
segm_results = self.simple_test_mask(
x, img_metas, det_bboxes, det_labels, rescale=rescale)
return list(zip(bbox_results, segm_results))
| 7,100 | 39.118644 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/cascade_roi_head.py | import torch
import torch.nn as nn
from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner,
build_sampler, merge_aug_bboxes, merge_aug_masks,
multiclass_nms)
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
@HEADS.register_module()
class CascadeRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin):
"""Cascade roi head including one bbox head and one mask head.
https://arxiv.org/abs/1712.00726
"""
def __init__(self,
num_stages,
stage_loss_weights,
bbox_roi_extractor=None,
bbox_head=None,
mask_roi_extractor=None,
mask_head=None,
shared_head=None,
train_cfg=None,
test_cfg=None):
assert bbox_roi_extractor is not None
assert bbox_head is not None
assert shared_head is None, \
'Shared head is not supported in Cascade RCNN anymore'
self.num_stages = num_stages
self.stage_loss_weights = stage_loss_weights
super(CascadeRoIHead, self).__init__(
bbox_roi_extractor=bbox_roi_extractor,
bbox_head=bbox_head,
mask_roi_extractor=mask_roi_extractor,
mask_head=mask_head,
shared_head=shared_head,
train_cfg=train_cfg,
test_cfg=test_cfg)
def init_bbox_head(self, bbox_roi_extractor, bbox_head):
"""Initialize box head and box roi extractor.
Args:
bbox_roi_extractor (dict): Config of box roi extractor.
bbox_head (dict): Config of box in box head.
"""
self.bbox_roi_extractor = nn.ModuleList()
self.bbox_head = nn.ModuleList()
if not isinstance(bbox_roi_extractor, list):
bbox_roi_extractor = [
bbox_roi_extractor for _ in range(self.num_stages)
]
if not isinstance(bbox_head, list):
bbox_head = [bbox_head for _ in range(self.num_stages)]
assert len(bbox_roi_extractor) == len(bbox_head) == self.num_stages
for roi_extractor, head in zip(bbox_roi_extractor, bbox_head):
self.bbox_roi_extractor.append(build_roi_extractor(roi_extractor))
self.bbox_head.append(build_head(head))
def init_mask_head(self, mask_roi_extractor, mask_head):
"""Initialize mask head and mask roi extractor.
Args:
mask_roi_extractor (dict): Config of mask roi extractor.
mask_head (dict): Config of mask in mask head.
"""
self.mask_head = nn.ModuleList()
if not isinstance(mask_head, list):
mask_head = [mask_head for _ in range(self.num_stages)]
assert len(mask_head) == self.num_stages
for head in mask_head:
self.mask_head.append(build_head(head))
if mask_roi_extractor is not None:
self.share_roi_extractor = False
self.mask_roi_extractor = nn.ModuleList()
if not isinstance(mask_roi_extractor, list):
mask_roi_extractor = [
mask_roi_extractor for _ in range(self.num_stages)
]
assert len(mask_roi_extractor) == self.num_stages
for roi_extractor in mask_roi_extractor:
self.mask_roi_extractor.append(
build_roi_extractor(roi_extractor))
else:
self.share_roi_extractor = True
self.mask_roi_extractor = self.bbox_roi_extractor
def init_assigner_sampler(self):
"""Initialize assigner and sampler for each stage."""
self.bbox_assigner = []
self.bbox_sampler = []
if self.train_cfg is not None:
for idx, rcnn_train_cfg in enumerate(self.train_cfg):
self.bbox_assigner.append(
build_assigner(rcnn_train_cfg.assigner))
self.current_stage = idx
self.bbox_sampler.append(
build_sampler(rcnn_train_cfg.sampler, context=self))
def init_weights(self, pretrained):
"""Initialize the weights in head.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if self.with_shared_head:
self.shared_head.init_weights(pretrained=pretrained)
for i in range(self.num_stages):
if self.with_bbox:
self.bbox_roi_extractor[i].init_weights()
self.bbox_head[i].init_weights()
if self.with_mask:
if not self.share_roi_extractor:
self.mask_roi_extractor[i].init_weights()
self.mask_head[i].init_weights()
def forward_dummy(self, x, proposals):
"""Dummy forward function."""
# bbox head
outs = ()
rois = bbox2roi([proposals])
if self.with_bbox:
for i in range(self.num_stages):
bbox_results = self._bbox_forward(i, x, rois)
outs = outs + (bbox_results['cls_score'],
bbox_results['bbox_pred'])
# mask heads
if self.with_mask:
mask_rois = rois[:100]
for i in range(self.num_stages):
mask_results = self._mask_forward(i, x, mask_rois)
outs = outs + (mask_results['mask_pred'], )
return outs
def _bbox_forward(self, stage, x, rois):
"""Box head forward function used in both training and testing."""
bbox_roi_extractor = self.bbox_roi_extractor[stage]
bbox_head = self.bbox_head[stage]
bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs],
rois)
# do not support caffe_c4 model anymore
cls_score, bbox_pred = bbox_head(bbox_feats)
bbox_results = dict(
cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
return bbox_results
def _bbox_forward_train(self, stage, x, sampling_results, gt_bboxes,
gt_labels, rcnn_train_cfg):
"""Run forward function and calculate loss for box head in training."""
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_results = self._bbox_forward(stage, x, rois)
bbox_targets = self.bbox_head[stage].get_targets(
sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg)
loss_bbox = self.bbox_head[stage].loss(bbox_results['cls_score'],
bbox_results['bbox_pred'], rois,
*bbox_targets)
bbox_results.update(
loss_bbox=loss_bbox, rois=rois, bbox_targets=bbox_targets)
return bbox_results
def _mask_forward(self, stage, x, rois):
"""Mask head forward function used in both training and testing."""
mask_roi_extractor = self.mask_roi_extractor[stage]
mask_head = self.mask_head[stage]
mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs],
rois)
# do not support caffe_c4 model anymore
mask_pred = mask_head(mask_feats)
mask_results = dict(mask_pred=mask_pred)
return mask_results
def _mask_forward_train(self,
stage,
x,
sampling_results,
gt_masks,
rcnn_train_cfg,
bbox_feats=None):
"""Run forward function and calculate loss for mask head in
training."""
pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
mask_results = self._mask_forward(stage, x, pos_rois)
mask_targets = self.mask_head[stage].get_targets(
sampling_results, gt_masks, rcnn_train_cfg)
pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
loss_mask = self.mask_head[stage].loss(mask_results['mask_pred'],
mask_targets, pos_labels)
mask_results.update(loss_mask=loss_mask)
return mask_results
def forward_train(self,
x,
img_metas,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None):
"""
Args:
x (list[Tensor]): list of multi-level img features.
img_metas (list[dict]): list of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmdet/datasets/pipelines/formatting.py:Collect`.
proposals (list[Tensors]): list of region proposals.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
gt_masks (None | Tensor) : true segmentation masks for each box
used if the architecture supports a segmentation task.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
losses = dict()
for i in range(self.num_stages):
self.current_stage = i
rcnn_train_cfg = self.train_cfg[i]
lw = self.stage_loss_weights[i]
# assign gts and sample proposals
sampling_results = []
if self.with_bbox or self.with_mask:
bbox_assigner = self.bbox_assigner[i]
bbox_sampler = self.bbox_sampler[i]
num_imgs = len(img_metas)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
for j in range(num_imgs):
assign_result = bbox_assigner.assign(
proposal_list[j], gt_bboxes[j], gt_bboxes_ignore[j],
gt_labels[j])
sampling_result = bbox_sampler.sample(
assign_result,
proposal_list[j],
gt_bboxes[j],
gt_labels[j],
feats=[lvl_feat[j][None] for lvl_feat in x])
sampling_results.append(sampling_result)
# bbox head forward and loss
bbox_results = self._bbox_forward_train(i, x, sampling_results,
gt_bboxes, gt_labels,
rcnn_train_cfg)
for name, value in bbox_results['loss_bbox'].items():
losses[f's{i}.{name}'] = (
value * lw if 'loss' in name else value)
# mask head forward and loss
if self.with_mask:
mask_results = self._mask_forward_train(
i, x, sampling_results, gt_masks, rcnn_train_cfg,
bbox_results['bbox_feats'])
for name, value in mask_results['loss_mask'].items():
losses[f's{i}.{name}'] = (
value * lw if 'loss' in name else value)
# refine bboxes
if i < self.num_stages - 1:
pos_is_gts = [res.pos_is_gt for res in sampling_results]
# bbox_targets is a tuple
roi_labels = bbox_results['bbox_targets'][0]
with torch.no_grad():
roi_labels = torch.where(
roi_labels == self.bbox_head[i].num_classes,
bbox_results['cls_score'][:, :-1].argmax(1),
roi_labels)
proposal_list = self.bbox_head[i].refine_bboxes(
bbox_results['rois'], roi_labels,
bbox_results['bbox_pred'], pos_is_gts, img_metas)
return losses
def simple_test(self, x, proposal_list, img_metas, rescale=False):
"""Test without augmentation."""
assert self.with_bbox, 'Bbox head must be implemented.'
num_imgs = len(proposal_list)
img_shapes = tuple(meta['img_shape'] for meta in img_metas)
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
# "ms" in variable names means multi-stage
ms_bbox_result = {}
ms_segm_result = {}
ms_scores = []
rcnn_test_cfg = self.test_cfg
rois = bbox2roi(proposal_list)
for i in range(self.num_stages):
bbox_results = self._bbox_forward(i, x, rois)
# split batch bbox prediction back to each image
cls_score = bbox_results['cls_score']
bbox_pred = bbox_results['bbox_pred']
num_proposals_per_img = tuple(
len(proposals) for proposals in proposal_list)
rois = rois.split(num_proposals_per_img, 0)
cls_score = cls_score.split(num_proposals_per_img, 0)
if isinstance(bbox_pred, torch.Tensor):
bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
else:
bbox_pred = self.bbox_head[i].bbox_pred_split(
bbox_pred, num_proposals_per_img)
ms_scores.append(cls_score)
if i < self.num_stages - 1:
bbox_label = [s[:, :-1].argmax(dim=1) for s in cls_score]
rois = torch.cat([
self.bbox_head[i].regress_by_class(rois[j], bbox_label[j],
bbox_pred[j],
img_metas[j])
for j in range(num_imgs)
])
# average scores of each image by stages
cls_score = [
sum([score[i] for score in ms_scores]) / float(len(ms_scores))
for i in range(num_imgs)
]
# apply bbox post-processing to each image individually
det_bboxes = []
det_labels = []
for i in range(num_imgs):
det_bbox, det_label = self.bbox_head[-1].get_bboxes(
rois[i],
cls_score[i],
bbox_pred[i],
img_shapes[i],
scale_factors[i],
rescale=rescale,
cfg=rcnn_test_cfg)
det_bboxes.append(det_bbox)
det_labels.append(det_label)
bbox_results = [
bbox2result(det_bboxes[i], det_labels[i],
self.bbox_head[-1].num_classes)
for i in range(num_imgs)
]
ms_bbox_result['ensemble'] = bbox_results
if self.with_mask:
if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
mask_classes = self.mask_head[-1].num_classes
segm_results = [[[] for _ in range(mask_classes)]
for _ in range(num_imgs)]
else:
if rescale and not isinstance(scale_factors[0], float):
scale_factors = [
torch.from_numpy(scale_factor).to(det_bboxes[0].device)
for scale_factor in scale_factors
]
_bboxes = [
det_bboxes[i][:, :4] *
scale_factors[i] if rescale else det_bboxes[i][:, :4]
for i in range(len(det_bboxes))
]
mask_rois = bbox2roi(_bboxes)
num_mask_rois_per_img = tuple(
_bbox.size(0) for _bbox in _bboxes)
aug_masks = []
for i in range(self.num_stages):
mask_results = self._mask_forward(i, x, mask_rois)
mask_pred = mask_results['mask_pred']
# split batch mask prediction back to each image
mask_pred = mask_pred.split(num_mask_rois_per_img, 0)
aug_masks.append(
[m.sigmoid().cpu().numpy() for m in mask_pred])
# apply mask post-processing to each image individually
segm_results = []
for i in range(num_imgs):
if det_bboxes[i].shape[0] == 0:
segm_results.append(
[[]
for _ in range(self.mask_head[-1].num_classes)])
else:
aug_mask = [mask[i] for mask in aug_masks]
merged_masks = merge_aug_masks(
aug_mask, [[img_metas[i]]] * self.num_stages,
rcnn_test_cfg)
segm_result = self.mask_head[-1].get_seg_masks(
merged_masks, _bboxes[i], det_labels[i],
rcnn_test_cfg, ori_shapes[i], scale_factors[i],
rescale)
segm_results.append(segm_result)
ms_segm_result['ensemble'] = segm_results
if self.with_mask:
results = list(
zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble']))
else:
results = ms_bbox_result['ensemble']
return results
def aug_test(self, features, proposal_list, img_metas, rescale=False):
"""Test with augmentations.
If rescale is False, then returned bboxes and masks will fit the scale
of imgs[0].
"""
rcnn_test_cfg = self.test_cfg
aug_bboxes = []
aug_scores = []
for x, img_meta in zip(features, img_metas):
# only one image in the batch
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
scale_factor, flip, flip_direction)
# "ms" in variable names means multi-stage
ms_scores = []
rois = bbox2roi([proposals])
for i in range(self.num_stages):
bbox_results = self._bbox_forward(i, x, rois)
ms_scores.append(bbox_results['cls_score'])
if i < self.num_stages - 1:
bbox_label = bbox_results['cls_score'][:, :-1].argmax(
dim=1)
rois = self.bbox_head[i].regress_by_class(
rois, bbox_label, bbox_results['bbox_pred'],
img_meta[0])
cls_score = sum(ms_scores) / float(len(ms_scores))
bboxes, scores = self.bbox_head[-1].get_bboxes(
rois,
cls_score,
bbox_results['bbox_pred'],
img_shape,
scale_factor,
rescale=False,
cfg=None)
aug_bboxes.append(bboxes)
aug_scores.append(scores)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms,
rcnn_test_cfg.max_per_img)
bbox_result = bbox2result(det_bboxes, det_labels,
self.bbox_head[-1].num_classes)
if self.with_mask:
if det_bboxes.shape[0] == 0:
segm_result = [[[]
for _ in range(self.mask_head[-1].num_classes)]
]
else:
aug_masks = []
aug_img_metas = []
for x, img_meta in zip(features, img_metas):
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
_bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
scale_factor, flip, flip_direction)
mask_rois = bbox2roi([_bboxes])
for i in range(self.num_stages):
mask_results = self._mask_forward(i, x, mask_rois)
aug_masks.append(
mask_results['mask_pred'].sigmoid().cpu().numpy())
aug_img_metas.append(img_meta)
merged_masks = merge_aug_masks(aug_masks, aug_img_metas,
self.test_cfg)
ori_shape = img_metas[0][0]['ori_shape']
segm_result = self.mask_head[-1].get_seg_masks(
merged_masks,
det_bboxes,
det_labels,
rcnn_test_cfg,
ori_shape,
scale_factor=1.0,
rescale=False)
return [(bbox_result, segm_result)]
else:
return [bbox_result]
| 21,967 | 42.50099 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/dynamic_roi_head.py | import numpy as np
import torch
from mmdet.core import bbox2roi
from mmdet.models.losses import SmoothL1Loss
from ..builder import HEADS
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class DynamicRoIHead(StandardRoIHead):
"""RoI head for `Dynamic R-CNN <https://arxiv.org/abs/2004.06002>`_."""
def __init__(self, **kwargs):
super(DynamicRoIHead, self).__init__(**kwargs)
assert isinstance(self.bbox_head.loss_bbox, SmoothL1Loss)
# the IoU history of the past `update_iter_interval` iterations
self.iou_history = []
# the beta history of the past `update_iter_interval` iterations
self.beta_history = []
def forward_train(self,
x,
img_metas,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None):
"""Forward function for training.
Args:
x (list[Tensor]): list of multi-level img features.
img_metas (list[dict]): list of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmdet/datasets/pipelines/formatting.py:Collect`.
proposals (list[Tensors]): list of region proposals.
gt_bboxes (list[Tensor]): each item are the truth boxes for each
image in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
gt_masks (None | Tensor) : true segmentation masks for each box
used if the architecture supports a segmentation task.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
# assign gts and sample proposals
if self.with_bbox or self.with_mask:
num_imgs = len(img_metas)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
cur_iou = []
for i in range(num_imgs):
assign_result = self.bbox_assigner.assign(
proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
gt_labels[i])
sampling_result = self.bbox_sampler.sample(
assign_result,
proposal_list[i],
gt_bboxes[i],
gt_labels[i],
feats=[lvl_feat[i][None] for lvl_feat in x])
# record the `iou_topk`-th largest IoU in an image
iou_topk = min(self.train_cfg.dynamic_rcnn.iou_topk,
len(assign_result.max_overlaps))
ious, _ = torch.topk(assign_result.max_overlaps, iou_topk)
cur_iou.append(ious[-1].item())
sampling_results.append(sampling_result)
# average the current IoUs over images
cur_iou = np.mean(cur_iou)
self.iou_history.append(cur_iou)
losses = dict()
# bbox head forward and loss
if self.with_bbox:
bbox_results = self._bbox_forward_train(x, sampling_results,
gt_bboxes, gt_labels,
img_metas)
losses.update(bbox_results['loss_bbox'])
# mask head forward and loss
if self.with_mask:
mask_results = self._mask_forward_train(x, sampling_results,
bbox_results['bbox_feats'],
gt_masks, img_metas)
losses.update(mask_results['loss_mask'])
# update IoU threshold and SmoothL1 beta
update_iter_interval = self.train_cfg.dynamic_rcnn.update_iter_interval
if len(self.iou_history) % update_iter_interval == 0:
new_iou_thr, new_beta = self.update_hyperparameters()
return losses
def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
img_metas):
num_imgs = len(img_metas)
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_results = self._bbox_forward(x, rois)
bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
gt_labels, self.train_cfg)
# record the `beta_topk`-th smallest target
# `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets
# and bbox_weights, respectively
pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1)
num_pos = len(pos_inds)
cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1)
beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs,
num_pos)
cur_target = torch.kthvalue(cur_target, beta_topk)[0].item()
self.beta_history.append(cur_target)
loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
bbox_results['bbox_pred'], rois,
*bbox_targets)
bbox_results.update(loss_bbox=loss_bbox)
return bbox_results
def update_hyperparameters(self):
"""Update hyperparameters like IoU thresholds for assigner and beta for
SmoothL1 loss based on the training statistics.
Returns:
tuple[float]: the updated ``iou_thr`` and ``beta``.
"""
new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou,
np.mean(self.iou_history))
self.iou_history = []
self.bbox_assigner.pos_iou_thr = new_iou_thr
self.bbox_assigner.neg_iou_thr = new_iou_thr
self.bbox_assigner.min_pos_iou = new_iou_thr
new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta,
np.median(self.beta_history))
self.beta_history = []
self.bbox_head.loss_bbox.beta = new_beta
return new_iou_thr, new_beta
| 6,415 | 42.060403 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/point_rend_roi_head.py | # Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa
import torch
import torch.nn.functional as F
from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
from mmdet.core import bbox2roi, bbox_mapping, merge_aug_masks
from .. import builder
from ..builder import HEADS
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class PointRendRoIHead(StandardRoIHead):
"""`PointRend <https://arxiv.org/abs/1912.08193>`_."""
def __init__(self, point_head, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.with_bbox and self.with_mask
self.init_point_head(point_head)
def init_point_head(self, point_head):
"""Initialize ``point_head``"""
self.point_head = builder.build_head(point_head)
def init_weights(self, pretrained):
"""Initialize the weights in head.
Args:
pretrained (str, optional): Path to pre-trained weights.
"""
super().init_weights(pretrained)
self.point_head.init_weights()
def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
img_metas):
"""Run forward function and calculate loss for mask head and point head
in training."""
mask_results = super()._mask_forward_train(x, sampling_results,
bbox_feats, gt_masks,
img_metas)
if mask_results['loss_mask'] is not None:
loss_point = self._mask_point_forward_train(
x, sampling_results, mask_results['mask_pred'], gt_masks,
img_metas)
mask_results['loss_mask'].update(loss_point)
return mask_results
def _mask_point_forward_train(self, x, sampling_results, mask_pred,
gt_masks, img_metas):
"""Run forward function and calculate loss for point head in
training."""
pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
rel_roi_points = self.point_head.get_roi_rel_points_train(
mask_pred, pos_labels, cfg=self.train_cfg)
rois = bbox2roi([res.pos_bboxes for res in sampling_results])
fine_grained_point_feats = self._get_fine_grained_point_feats(
x, rois, rel_roi_points, img_metas)
coarse_point_feats = point_sample(mask_pred, rel_roi_points)
mask_point_pred = self.point_head(fine_grained_point_feats,
coarse_point_feats)
mask_point_target = self.point_head.get_targets(
rois, rel_roi_points, sampling_results, gt_masks, self.train_cfg)
loss_mask_point = self.point_head.loss(mask_point_pred,
mask_point_target, pos_labels)
return loss_mask_point
def _get_fine_grained_point_feats(self, x, rois, rel_roi_points,
img_metas):
"""Sample fine grained feats from each level feature map and
concatenate them together."""
num_imgs = len(img_metas)
fine_grained_feats = []
for idx in range(self.mask_roi_extractor.num_inputs):
feats = x[idx]
spatial_scale = 1. / float(
self.mask_roi_extractor.featmap_strides[idx])
point_feats = []
for batch_ind in range(num_imgs):
# unravel batch dim
feat = feats[batch_ind].unsqueeze(0)
inds = (rois[:, 0].long() == batch_ind)
if inds.any():
rel_img_points = rel_roi_point_to_rel_img_point(
rois[inds], rel_roi_points[inds], feat.shape[2:],
spatial_scale).unsqueeze(0)
point_feat = point_sample(feat, rel_img_points)
point_feat = point_feat.squeeze(0).transpose(0, 1)
point_feats.append(point_feat)
fine_grained_feats.append(torch.cat(point_feats, dim=0))
return torch.cat(fine_grained_feats, dim=1)
def _mask_point_forward_test(self, x, rois, label_pred, mask_pred,
img_metas):
"""Mask refining process with point head in testing."""
refined_mask_pred = mask_pred.clone()
for subdivision_step in range(self.test_cfg.subdivision_steps):
refined_mask_pred = F.interpolate(
refined_mask_pred,
scale_factor=self.test_cfg.scale_factor,
mode='bilinear',
align_corners=False)
# If `subdivision_num_points` is larger or equal to the
# resolution of the next step, then we can skip this step
num_rois, channels, mask_height, mask_width = \
refined_mask_pred.shape
if (self.test_cfg.subdivision_num_points >=
self.test_cfg.scale_factor**2 * mask_height * mask_width
and
subdivision_step < self.test_cfg.subdivision_steps - 1):
continue
point_indices, rel_roi_points = \
self.point_head.get_roi_rel_points_test(
refined_mask_pred, label_pred, cfg=self.test_cfg)
fine_grained_point_feats = self._get_fine_grained_point_feats(
x, rois, rel_roi_points, img_metas)
coarse_point_feats = point_sample(mask_pred, rel_roi_points)
mask_point_pred = self.point_head(fine_grained_point_feats,
coarse_point_feats)
point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1)
refined_mask_pred = refined_mask_pred.reshape(
num_rois, channels, mask_height * mask_width)
refined_mask_pred = refined_mask_pred.scatter_(
2, point_indices, mask_point_pred)
refined_mask_pred = refined_mask_pred.view(num_rois, channels,
mask_height, mask_width)
return refined_mask_pred
def simple_test_mask(self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False):
"""Obtain mask prediction without augmentation."""
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
num_imgs = len(det_bboxes)
if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
segm_results = [[[] for _ in range(self.mask_head.num_classes)]
for _ in range(num_imgs)]
else:
# if det_bboxes is rescaled to the original image size, we need to
# rescale it back to the testing scale to obtain RoIs.
if rescale and not isinstance(scale_factors[0], float):
scale_factors = [
torch.from_numpy(scale_factor).to(det_bboxes[0].device)
for scale_factor in scale_factors
]
_bboxes = [
det_bboxes[i][:, :4] *
scale_factors[i] if rescale else det_bboxes[i][:, :4]
for i in range(len(det_bboxes))
]
mask_rois = bbox2roi(_bboxes)
mask_results = self._mask_forward(x, mask_rois)
# split batch mask prediction back to each image
mask_pred = mask_results['mask_pred']
num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes]
mask_preds = mask_pred.split(num_mask_roi_per_img, 0)
mask_rois = mask_rois.split(num_mask_roi_per_img, 0)
# apply mask post-processing to each image individually
segm_results = []
for i in range(num_imgs):
if det_bboxes[i].shape[0] == 0:
segm_results.append(
[[] for _ in range(self.mask_head.num_classes)])
else:
x_i = [xx[[i]] for xx in x]
mask_rois_i = mask_rois[i]
mask_rois_i[:, 0] = 0 # TODO: remove this hack
mask_pred_i = self._mask_point_forward_test(
x_i, mask_rois_i, det_labels[i], mask_preds[i],
[img_metas])
segm_result = self.mask_head.get_seg_masks(
mask_pred_i, _bboxes[i], det_labels[i], self.test_cfg,
ori_shapes[i], scale_factors[i], rescale)
segm_results.append(segm_result)
return segm_results
def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
"""Test for mask head with test time augmentation."""
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes)]
else:
aug_masks = []
for x, img_meta in zip(feats, img_metas):
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
_bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
scale_factor, flip)
mask_rois = bbox2roi([_bboxes])
mask_results = self._mask_forward(x, mask_rois)
mask_results['mask_pred'] = self._mask_point_forward_test(
x, mask_rois, det_labels, mask_results['mask_pred'],
img_metas)
# convert to numpy array to save memory
aug_masks.append(
mask_results['mask_pred'].sigmoid().cpu().numpy())
merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
ori_shape = img_metas[0][0]['ori_shape']
segm_result = self.mask_head.get_seg_masks(
merged_masks,
det_bboxes,
det_labels,
self.test_cfg,
ori_shape,
scale_factor=1.0,
rescale=False)
return segm_result
| 10,311 | 46.086758 | 101 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/base_roi_head.py | from abc import ABCMeta, abstractmethod
import torch.nn as nn
from ..builder import build_shared_head
class BaseRoIHead(nn.Module, metaclass=ABCMeta):
"""Base class for RoIHeads."""
def __init__(self,
bbox_roi_extractor=None,
bbox_head=None,
mask_roi_extractor=None,
mask_head=None,
shared_head=None,
train_cfg=None,
test_cfg=None):
super(BaseRoIHead, self).__init__()
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if shared_head is not None:
self.shared_head = build_shared_head(shared_head)
if bbox_head is not None:
self.init_bbox_head(bbox_roi_extractor, bbox_head)
if mask_head is not None:
self.init_mask_head(mask_roi_extractor, mask_head)
self.init_assigner_sampler()
@property
def with_bbox(self):
"""bool: whether the RoI head contains a `bbox_head`"""
return hasattr(self, 'bbox_head') and self.bbox_head is not None
@property
def with_mask(self):
"""bool: whether the RoI head contains a `mask_head`"""
return hasattr(self, 'mask_head') and self.mask_head is not None
@property
def with_shared_head(self):
"""bool: whether the RoI head contains a `shared_head`"""
return hasattr(self, 'shared_head') and self.shared_head is not None
@abstractmethod
def init_weights(self, pretrained):
"""Initialize the weights in head.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
pass
@abstractmethod
def init_bbox_head(self):
"""Initialize ``bbox_head``"""
pass
@abstractmethod
def init_mask_head(self):
"""Initialize ``mask_head``"""
pass
@abstractmethod
def init_assigner_sampler(self):
"""Initialize assigner and sampler."""
pass
@abstractmethod
def forward_train(self,
x,
img_meta,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None,
**kwargs):
"""Forward function during training."""
pass
async def async_simple_test(self, x, img_meta, **kwargs):
"""Asynchronized test function."""
raise NotImplementedError
def simple_test(self,
x,
proposal_list,
img_meta,
proposals=None,
rescale=False,
**kwargs):
"""Test without augmentation."""
pass
def aug_test(self, x, proposal_list, img_metas, rescale=False, **kwargs):
"""Test with augmentations.
If rescale is False, then returned bboxes and masks will fit the scale
of imgs[0].
"""
pass
| 3,060 | 27.607477 | 78 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/mask_scoring_roi_head.py | import torch
from mmdet.core import bbox2roi
from ..builder import HEADS, build_head
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class MaskScoringRoIHead(StandardRoIHead):
"""Mask Scoring RoIHead for Mask Scoring RCNN.
https://arxiv.org/abs/1903.00241
"""
def __init__(self, mask_iou_head, **kwargs):
assert mask_iou_head is not None
super(MaskScoringRoIHead, self).__init__(**kwargs)
self.mask_iou_head = build_head(mask_iou_head)
def init_weights(self, pretrained):
"""Initialize the weights in head.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
super(MaskScoringRoIHead, self).init_weights(pretrained)
self.mask_iou_head.init_weights()
def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
img_metas):
"""Run forward function and calculate loss for Mask head in
training."""
pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
mask_results = super(MaskScoringRoIHead,
self)._mask_forward_train(x, sampling_results,
bbox_feats, gt_masks,
img_metas)
if mask_results['loss_mask'] is None:
return mask_results
# mask iou head forward and loss
pos_mask_pred = mask_results['mask_pred'][
range(mask_results['mask_pred'].size(0)), pos_labels]
mask_iou_pred = self.mask_iou_head(mask_results['mask_feats'],
pos_mask_pred)
pos_mask_iou_pred = mask_iou_pred[range(mask_iou_pred.size(0)),
pos_labels]
mask_iou_targets = self.mask_iou_head.get_targets(
sampling_results, gt_masks, pos_mask_pred,
mask_results['mask_targets'], self.train_cfg)
loss_mask_iou = self.mask_iou_head.loss(pos_mask_iou_pred,
mask_iou_targets)
mask_results['loss_mask'].update(loss_mask_iou)
return mask_results
def simple_test_mask(self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False):
"""Obtain mask prediction without augmentation."""
# image shapes of images in the batch
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
num_imgs = len(det_bboxes)
if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
num_classes = self.mask_head.num_classes
segm_results = [[[] for _ in range(num_classes)]
for _ in range(num_imgs)]
mask_scores = [[[] for _ in range(num_classes)]
for _ in range(num_imgs)]
else:
# if det_bboxes is rescaled to the original image size, we need to
# rescale it back to the testing scale to obtain RoIs.
if rescale and not isinstance(scale_factors[0], float):
scale_factors = [
torch.from_numpy(scale_factor).to(det_bboxes[0].device)
for scale_factor in scale_factors
]
_bboxes = [
det_bboxes[i][:, :4] *
scale_factors[i] if rescale else det_bboxes[i]
for i in range(num_imgs)
]
mask_rois = bbox2roi(_bboxes)
mask_results = self._mask_forward(x, mask_rois)
concat_det_labels = torch.cat(det_labels)
# get mask scores with mask iou head
mask_feats = mask_results['mask_feats']
mask_pred = mask_results['mask_pred']
mask_iou_pred = self.mask_iou_head(
mask_feats, mask_pred[range(concat_det_labels.size(0)),
concat_det_labels])
# split batch mask prediction back to each image
num_bboxes_per_img = tuple(len(_bbox) for _bbox in _bboxes)
mask_preds = mask_pred.split(num_bboxes_per_img, 0)
mask_iou_preds = mask_iou_pred.split(num_bboxes_per_img, 0)
# apply mask post-processing to each image individually
segm_results = []
mask_scores = []
for i in range(num_imgs):
if det_bboxes[i].shape[0] == 0:
segm_results.append(
[[] for _ in range(self.mask_head.num_classes)])
mask_scores.append(
[[] for _ in range(self.mask_head.num_classes)])
else:
segm_result = self.mask_head.get_seg_masks(
mask_preds[i], _bboxes[i], det_labels[i],
self.test_cfg, ori_shapes[i], scale_factors[i],
rescale)
# get mask scores with mask iou head
mask_score = self.mask_iou_head.get_mask_scores(
mask_iou_preds[i], det_bboxes[i], det_labels[i])
segm_results.append(segm_result)
mask_scores.append(mask_score)
return list(zip(segm_results, mask_scores))
| 5,503 | 43.747967 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/htc_roi_head.py | import torch
import torch.nn.functional as F
from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
merge_aug_masks, multiclass_nms)
from ..builder import HEADS, build_head, build_roi_extractor
from .cascade_roi_head import CascadeRoIHead
@HEADS.register_module()
class HybridTaskCascadeRoIHead(CascadeRoIHead):
"""Hybrid task cascade roi head including one bbox head and one mask head.
https://arxiv.org/abs/1901.07518
"""
def __init__(self,
num_stages,
stage_loss_weights,
semantic_roi_extractor=None,
semantic_head=None,
semantic_fusion=('bbox', 'mask'),
interleaved=True,
mask_info_flow=True,
**kwargs):
super(HybridTaskCascadeRoIHead,
self).__init__(num_stages, stage_loss_weights, **kwargs)
assert self.with_bbox and self.with_mask
assert not self.with_shared_head # shared head is not supported
if semantic_head is not None:
self.semantic_roi_extractor = build_roi_extractor(
semantic_roi_extractor)
self.semantic_head = build_head(semantic_head)
self.semantic_fusion = semantic_fusion
self.interleaved = interleaved
self.mask_info_flow = mask_info_flow
def init_weights(self, pretrained):
"""Initialize the weights in head.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
super(HybridTaskCascadeRoIHead, self).init_weights(pretrained)
if self.with_semantic:
self.semantic_head.init_weights()
@property
def with_semantic(self):
"""bool: whether the head has semantic head"""
if hasattr(self, 'semantic_head') and self.semantic_head is not None:
return True
else:
return False
def forward_dummy(self, x, proposals):
"""Dummy forward function."""
outs = ()
# semantic head
if self.with_semantic:
_, semantic_feat = self.semantic_head(x)
else:
semantic_feat = None
# bbox heads
rois = bbox2roi([proposals])
for i in range(self.num_stages):
bbox_results = self._bbox_forward(
i, x, rois, semantic_feat=semantic_feat)
outs = outs + (bbox_results['cls_score'],
bbox_results['bbox_pred'])
# mask heads
if self.with_mask:
mask_rois = rois[:100]
mask_roi_extractor = self.mask_roi_extractor[-1]
mask_feats = mask_roi_extractor(
x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
if self.with_semantic and 'mask' in self.semantic_fusion:
mask_semantic_feat = self.semantic_roi_extractor(
[semantic_feat], mask_rois)
mask_feats += mask_semantic_feat
last_feat = None
for i in range(self.num_stages):
mask_head = self.mask_head[i]
if self.mask_info_flow:
mask_pred, last_feat = mask_head(mask_feats, last_feat)
else:
mask_pred = mask_head(mask_feats)
outs = outs + (mask_pred, )
return outs
def _bbox_forward_train(self,
stage,
x,
sampling_results,
gt_bboxes,
gt_labels,
rcnn_train_cfg,
semantic_feat=None):
"""Run forward function and calculate loss for box head in training."""
bbox_head = self.bbox_head[stage]
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_results = self._bbox_forward(
stage, x, rois, semantic_feat=semantic_feat)
bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes,
gt_labels, rcnn_train_cfg)
loss_bbox = bbox_head.loss(bbox_results['cls_score'],
bbox_results['bbox_pred'], rois,
*bbox_targets)
bbox_results.update(
loss_bbox=loss_bbox,
rois=rois,
bbox_targets=bbox_targets,
)
return bbox_results
def _mask_forward_train(self,
stage,
x,
sampling_results,
gt_masks,
rcnn_train_cfg,
semantic_feat=None):
"""Run forward function and calculate loss for mask head in
training."""
mask_roi_extractor = self.mask_roi_extractor[stage]
mask_head = self.mask_head[stage]
pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs],
pos_rois)
# semantic feature fusion
# element-wise sum for original features and pooled semantic features
if self.with_semantic and 'mask' in self.semantic_fusion:
mask_semantic_feat = self.semantic_roi_extractor([semantic_feat],
pos_rois)
if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]:
mask_semantic_feat = F.adaptive_avg_pool2d(
mask_semantic_feat, mask_feats.shape[-2:])
mask_feats += mask_semantic_feat
# mask information flow
# forward all previous mask heads to obtain last_feat, and fuse it
# with the normal mask feature
if self.mask_info_flow:
last_feat = None
for i in range(stage):
last_feat = self.mask_head[i](
mask_feats, last_feat, return_logits=False)
mask_pred = mask_head(mask_feats, last_feat, return_feat=False)
else:
mask_pred = mask_head(mask_feats, return_feat=False)
mask_targets = mask_head.get_targets(sampling_results, gt_masks,
rcnn_train_cfg)
pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
loss_mask = mask_head.loss(mask_pred, mask_targets, pos_labels)
mask_results = dict(loss_mask=loss_mask)
return mask_results
def _bbox_forward(self, stage, x, rois, semantic_feat=None):
"""Box head forward function used in both training and testing."""
bbox_roi_extractor = self.bbox_roi_extractor[stage]
bbox_head = self.bbox_head[stage]
bbox_feats = bbox_roi_extractor(
x[:len(bbox_roi_extractor.featmap_strides)], rois)
if self.with_semantic and 'bbox' in self.semantic_fusion:
bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat],
rois)
if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]:
bbox_semantic_feat = F.adaptive_avg_pool2d(
bbox_semantic_feat, bbox_feats.shape[-2:])
bbox_feats += bbox_semantic_feat
cls_score, bbox_pred = bbox_head(bbox_feats)
bbox_results = dict(cls_score=cls_score, bbox_pred=bbox_pred)
return bbox_results
def _mask_forward_test(self, stage, x, bboxes, semantic_feat=None):
"""Mask head forward function for testing."""
mask_roi_extractor = self.mask_roi_extractor[stage]
mask_head = self.mask_head[stage]
mask_rois = bbox2roi([bboxes])
mask_feats = mask_roi_extractor(
x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
if self.with_semantic and 'mask' in self.semantic_fusion:
mask_semantic_feat = self.semantic_roi_extractor([semantic_feat],
mask_rois)
if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]:
mask_semantic_feat = F.adaptive_avg_pool2d(
mask_semantic_feat, mask_feats.shape[-2:])
mask_feats += mask_semantic_feat
if self.mask_info_flow:
last_feat = None
last_pred = None
for i in range(stage):
mask_pred, last_feat = self.mask_head[i](mask_feats, last_feat)
if last_pred is not None:
mask_pred = mask_pred + last_pred
last_pred = mask_pred
mask_pred = mask_head(mask_feats, last_feat, return_feat=False)
if last_pred is not None:
mask_pred = mask_pred + last_pred
else:
mask_pred = mask_head(mask_feats)
return mask_pred
def forward_train(self,
x,
img_metas,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None,
gt_semantic_seg=None):
"""
Args:
x (list[Tensor]): list of multi-level img features.
img_metas (list[dict]): list of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmdet/datasets/pipelines/formatting.py:Collect`.
proposal_list (list[Tensors]): list of region proposals.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
gt_bboxes_ignore (None, list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
gt_masks (None, Tensor) : true segmentation masks for each box
used if the architecture supports a segmentation task.
gt_semantic_seg (None, list[Tensor]): semantic segmentation masks
used if the architecture supports semantic segmentation task.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
# semantic segmentation part
# 2 outputs: segmentation prediction and embedded features
losses = dict()
if self.with_semantic:
semantic_pred, semantic_feat = self.semantic_head(x)
loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg)
losses['loss_semantic_seg'] = loss_seg
else:
semantic_feat = None
for i in range(self.num_stages):
self.current_stage = i
rcnn_train_cfg = self.train_cfg[i]
lw = self.stage_loss_weights[i]
# assign gts and sample proposals
sampling_results = []
bbox_assigner = self.bbox_assigner[i]
bbox_sampler = self.bbox_sampler[i]
num_imgs = len(img_metas)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
for j in range(num_imgs):
assign_result = bbox_assigner.assign(proposal_list[j],
gt_bboxes[j],
gt_bboxes_ignore[j],
gt_labels[j])
sampling_result = bbox_sampler.sample(
assign_result,
proposal_list[j],
gt_bboxes[j],
gt_labels[j],
feats=[lvl_feat[j][None] for lvl_feat in x])
sampling_results.append(sampling_result)
# bbox head forward and loss
bbox_results = \
self._bbox_forward_train(
i, x, sampling_results, gt_bboxes, gt_labels,
rcnn_train_cfg, semantic_feat)
roi_labels = bbox_results['bbox_targets'][0]
for name, value in bbox_results['loss_bbox'].items():
losses[f's{i}.{name}'] = (
value * lw if 'loss' in name else value)
# mask head forward and loss
if self.with_mask:
# interleaved execution: use regressed bboxes by the box branch
# to train the mask branch
if self.interleaved:
pos_is_gts = [res.pos_is_gt for res in sampling_results]
with torch.no_grad():
proposal_list = self.bbox_head[i].refine_bboxes(
bbox_results['rois'], roi_labels,
bbox_results['bbox_pred'], pos_is_gts, img_metas)
# re-assign and sample 512 RoIs from 512 RoIs
sampling_results = []
for j in range(num_imgs):
assign_result = bbox_assigner.assign(
proposal_list[j], gt_bboxes[j],
gt_bboxes_ignore[j], gt_labels[j])
sampling_result = bbox_sampler.sample(
assign_result,
proposal_list[j],
gt_bboxes[j],
gt_labels[j],
feats=[lvl_feat[j][None] for lvl_feat in x])
sampling_results.append(sampling_result)
mask_results = self._mask_forward_train(
i, x, sampling_results, gt_masks, rcnn_train_cfg,
semantic_feat)
for name, value in mask_results['loss_mask'].items():
losses[f's{i}.{name}'] = (
value * lw if 'loss' in name else value)
# refine bboxes (same as Cascade R-CNN)
if i < self.num_stages - 1 and not self.interleaved:
pos_is_gts = [res.pos_is_gt for res in sampling_results]
with torch.no_grad():
proposal_list = self.bbox_head[i].refine_bboxes(
bbox_results['rois'], roi_labels,
bbox_results['bbox_pred'], pos_is_gts, img_metas)
return losses
def simple_test(self, x, proposal_list, img_metas, rescale=False):
"""Test without augmentation."""
if self.with_semantic:
_, semantic_feat = self.semantic_head(x)
else:
semantic_feat = None
num_imgs = len(proposal_list)
img_shapes = tuple(meta['img_shape'] for meta in img_metas)
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
# "ms" in variable names means multi-stage
ms_bbox_result = {}
ms_segm_result = {}
ms_scores = []
rcnn_test_cfg = self.test_cfg
rois = bbox2roi(proposal_list)
for i in range(self.num_stages):
bbox_head = self.bbox_head[i]
bbox_results = self._bbox_forward(
i, x, rois, semantic_feat=semantic_feat)
# split batch bbox prediction back to each image
cls_score = bbox_results['cls_score']
bbox_pred = bbox_results['bbox_pred']
num_proposals_per_img = tuple(len(p) for p in proposal_list)
rois = rois.split(num_proposals_per_img, 0)
cls_score = cls_score.split(num_proposals_per_img, 0)
bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
ms_scores.append(cls_score)
if i < self.num_stages - 1:
bbox_label = [s[:, :-1].argmax(dim=1) for s in cls_score]
rois = torch.cat([
bbox_head.regress_by_class(rois[i], bbox_label[i],
bbox_pred[i], img_metas[i])
for i in range(num_imgs)
])
# average scores of each image by stages
cls_score = [
sum([score[i] for score in ms_scores]) / float(len(ms_scores))
for i in range(num_imgs)
]
# apply bbox post-processing to each image individually
det_bboxes = []
det_labels = []
for i in range(num_imgs):
det_bbox, det_label = self.bbox_head[-1].get_bboxes(
rois[i],
cls_score[i],
bbox_pred[i],
img_shapes[i],
scale_factors[i],
rescale=rescale,
cfg=rcnn_test_cfg)
det_bboxes.append(det_bbox)
det_labels.append(det_label)
bbox_result = [
bbox2result(det_bboxes[i], det_labels[i],
self.bbox_head[-1].num_classes)
for i in range(num_imgs)
]
ms_bbox_result['ensemble'] = bbox_result
if self.with_mask:
if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
mask_classes = self.mask_head[-1].num_classes
segm_results = [[[] for _ in range(mask_classes)]
for _ in range(num_imgs)]
else:
if rescale and not isinstance(scale_factors[0], float):
scale_factors = [
torch.from_numpy(scale_factor).to(det_bboxes[0].device)
for scale_factor in scale_factors
]
_bboxes = [
det_bboxes[i][:, :4] *
scale_factors[i] if rescale else det_bboxes[i]
for i in range(num_imgs)
]
mask_rois = bbox2roi(_bboxes)
aug_masks = []
mask_roi_extractor = self.mask_roi_extractor[-1]
mask_feats = mask_roi_extractor(
x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
if self.with_semantic and 'mask' in self.semantic_fusion:
mask_semantic_feat = self.semantic_roi_extractor(
[semantic_feat], mask_rois)
mask_feats += mask_semantic_feat
last_feat = None
num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes)
for i in range(self.num_stages):
mask_head = self.mask_head[i]
if self.mask_info_flow:
mask_pred, last_feat = mask_head(mask_feats, last_feat)
else:
mask_pred = mask_head(mask_feats)
# split batch mask prediction back to each image
mask_pred = mask_pred.split(num_bbox_per_img, 0)
aug_masks.append(
[mask.sigmoid().cpu().numpy() for mask in mask_pred])
# apply mask post-processing to each image individually
segm_results = []
for i in range(num_imgs):
if det_bboxes[i].shape[0] == 0:
segm_results.append(
[[]
for _ in range(self.mask_head[-1].num_classes)])
else:
aug_mask = [mask[i] for mask in aug_masks]
merged_mask = merge_aug_masks(
aug_mask, [[img_metas[i]]] * self.num_stages,
rcnn_test_cfg)
segm_result = self.mask_head[-1].get_seg_masks(
merged_mask, _bboxes[i], det_labels[i],
rcnn_test_cfg, ori_shapes[i], scale_factors[i],
rescale)
segm_results.append(segm_result)
ms_segm_result['ensemble'] = segm_results
if self.with_mask:
results = list(
zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble']))
else:
results = ms_bbox_result['ensemble']
return results
def aug_test(self, img_feats, proposal_list, img_metas, rescale=False):
"""Test with augmentations.
If rescale is False, then returned bboxes and masks will fit the scale
of imgs[0].
"""
if self.with_semantic:
semantic_feats = [
self.semantic_head(feat)[1] for feat in img_feats
]
else:
semantic_feats = [None] * len(img_metas)
rcnn_test_cfg = self.test_cfg
aug_bboxes = []
aug_scores = []
for x, img_meta, semantic in zip(img_feats, img_metas, semantic_feats):
# only one image in the batch
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
scale_factor, flip, flip_direction)
# "ms" in variable names means multi-stage
ms_scores = []
rois = bbox2roi([proposals])
for i in range(self.num_stages):
bbox_head = self.bbox_head[i]
bbox_results = self._bbox_forward(
i, x, rois, semantic_feat=semantic)
ms_scores.append(bbox_results['cls_score'])
if i < self.num_stages - 1:
bbox_label = bbox_results['cls_score'].argmax(dim=1)
rois = bbox_head.regress_by_class(
rois, bbox_label, bbox_results['bbox_pred'],
img_meta[0])
cls_score = sum(ms_scores) / float(len(ms_scores))
bboxes, scores = self.bbox_head[-1].get_bboxes(
rois,
cls_score,
bbox_results['bbox_pred'],
img_shape,
scale_factor,
rescale=False,
cfg=None)
aug_bboxes.append(bboxes)
aug_scores.append(scores)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms,
rcnn_test_cfg.max_per_img)
bbox_result = bbox2result(det_bboxes, det_labels,
self.bbox_head[-1].num_classes)
if self.with_mask:
if det_bboxes.shape[0] == 0:
segm_result = [[[]
for _ in range(self.mask_head[-1].num_classes -
1)]]
else:
aug_masks = []
aug_img_metas = []
for x, img_meta, semantic in zip(img_feats, img_metas,
semantic_feats):
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
_bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
scale_factor, flip, flip_direction)
mask_rois = bbox2roi([_bboxes])
mask_feats = self.mask_roi_extractor[-1](
x[:len(self.mask_roi_extractor[-1].featmap_strides)],
mask_rois)
if self.with_semantic:
semantic_feat = semantic
mask_semantic_feat = self.semantic_roi_extractor(
[semantic_feat], mask_rois)
if mask_semantic_feat.shape[-2:] != mask_feats.shape[
-2:]:
mask_semantic_feat = F.adaptive_avg_pool2d(
mask_semantic_feat, mask_feats.shape[-2:])
mask_feats += mask_semantic_feat
last_feat = None
for i in range(self.num_stages):
mask_head = self.mask_head[i]
if self.mask_info_flow:
mask_pred, last_feat = mask_head(
mask_feats, last_feat)
else:
mask_pred = mask_head(mask_feats)
aug_masks.append(mask_pred.sigmoid().cpu().numpy())
aug_img_metas.append(img_meta)
merged_masks = merge_aug_masks(aug_masks, aug_img_metas,
self.test_cfg)
ori_shape = img_metas[0][0]['ori_shape']
segm_result = self.mask_head[-1].get_seg_masks(
merged_masks,
det_bboxes,
det_labels,
rcnn_test_cfg,
ori_shape,
scale_factor=1.0,
rescale=False)
return [(bbox_result, segm_result)]
else:
return [bbox_result]
| 25,919 | 42.932203 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/test_mixins.py | import logging
import sys
import torch
from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes,
merge_aug_masks, multiclass_nms)
logger = logging.getLogger(__name__)
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import completed
class BBoxTestMixin(object):
if sys.version_info >= (3, 7):
async def async_test_bboxes(self,
x,
img_metas,
proposals,
rcnn_test_cfg,
rescale=False,
bbox_semaphore=None,
global_lock=None):
"""Asynchronized test for box head without augmentation."""
rois = bbox2roi(proposals)
roi_feats = self.bbox_roi_extractor(
x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
if self.with_shared_head:
roi_feats = self.shared_head(roi_feats)
sleep_interval = rcnn_test_cfg.get('async_sleep_interval', 0.017)
async with completed(
__name__, 'bbox_head_forward',
sleep_interval=sleep_interval):
cls_score, bbox_pred = self.bbox_head(roi_feats)
img_shape = img_metas[0]['img_shape']
scale_factor = img_metas[0]['scale_factor']
det_bboxes, det_labels = self.bbox_head.get_bboxes(
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=rescale,
cfg=rcnn_test_cfg)
return det_bboxes, det_labels
def simple_test_bboxes(self,
x,
img_metas,
proposals,
rcnn_test_cfg,
rescale=False):
"""Test only det bboxes without augmentation."""
rois = bbox2roi(proposals)
bbox_results = self._bbox_forward(x, rois)
img_shapes = tuple(meta['img_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
# split batch bbox prediction back to each image
cls_score = bbox_results['cls_score']
bbox_pred = bbox_results['bbox_pred']
num_proposals_per_img = tuple(len(p) for p in proposals)
rois = rois.split(num_proposals_per_img, 0)
cls_score = cls_score.split(num_proposals_per_img, 0)
# some detector with_reg is False, bbox_pred will be None
if bbox_pred is not None:
# the bbox prediction of some detectors like SABL is not Tensor
if isinstance(bbox_pred, torch.Tensor):
bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
else:
bbox_pred = self.bbox_head.bbox_pred_split(
bbox_pred, num_proposals_per_img)
else:
bbox_pred = (None, ) * len(proposals)
# apply bbox post-processing to each image individually
det_bboxes = []
det_labels = []
for i in range(len(proposals)):
det_bbox, det_label = self.bbox_head.get_bboxes(
rois[i],
cls_score[i],
bbox_pred[i],
img_shapes[i],
scale_factors[i],
rescale=rescale,
cfg=rcnn_test_cfg)
det_bboxes.append(det_bbox)
det_labels.append(det_label)
return det_bboxes, det_labels
def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
"""Test det bboxes with test time augmentation."""
aug_bboxes = []
aug_scores = []
for x, img_meta in zip(feats, img_metas):
# only one image in the batch
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
# TODO more flexible
proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
scale_factor, flip, flip_direction)
rois = bbox2roi([proposals])
bbox_results = self._bbox_forward(x, rois)
bboxes, scores = self.bbox_head.get_bboxes(
rois,
bbox_results['cls_score'],
bbox_results['bbox_pred'],
img_shape,
scale_factor,
rescale=False,
cfg=None)
aug_bboxes.append(bboxes)
aug_scores.append(scores)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms,
rcnn_test_cfg.max_per_img)
return det_bboxes, det_labels
class MaskTestMixin(object):
if sys.version_info >= (3, 7):
async def async_test_mask(self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False,
mask_test_cfg=None):
"""Asynchronized test for mask head without augmentation."""
# image shape of the first image in the batch (only one)
ori_shape = img_metas[0]['ori_shape']
scale_factor = img_metas[0]['scale_factor']
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes)]
else:
_bboxes = (
det_bboxes[:, :4] *
scale_factor if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
mask_feats = self.mask_roi_extractor(
x[:len(self.mask_roi_extractor.featmap_strides)],
mask_rois)
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
if mask_test_cfg and mask_test_cfg.get('async_sleep_interval'):
sleep_interval = mask_test_cfg['async_sleep_interval']
else:
sleep_interval = 0.035
async with completed(
__name__,
'mask_head_forward',
sleep_interval=sleep_interval):
mask_pred = self.mask_head(mask_feats)
segm_result = self.mask_head.get_seg_masks(
mask_pred, _bboxes, det_labels, self.test_cfg, ori_shape,
scale_factor, rescale)
return segm_result
def simple_test_mask(self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False):
"""Simple test for mask head without augmentation."""
# image shapes of images in the batch
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
num_imgs = len(det_bboxes)
if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
segm_results = [[[] for _ in range(self.mask_head.num_classes)]
for _ in range(num_imgs)]
else:
# if det_bboxes is rescaled to the original image size, we need to
# rescale it back to the testing scale to obtain RoIs.
if rescale and not isinstance(scale_factors[0], float):
scale_factors = [
torch.from_numpy(scale_factor).to(det_bboxes[0].device)
for scale_factor in scale_factors
]
_bboxes = [
det_bboxes[i][:, :4] *
scale_factors[i] if rescale else det_bboxes[i][:, :4]
for i in range(len(det_bboxes))
]
mask_rois = bbox2roi(_bboxes)
mask_results = self._mask_forward(x, mask_rois)
mask_pred = mask_results['mask_pred']
# split batch mask prediction back to each image
num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes]
mask_preds = mask_pred.split(num_mask_roi_per_img, 0)
# apply mask post-processing to each image individually
segm_results = []
for i in range(num_imgs):
if det_bboxes[i].shape[0] == 0:
segm_results.append(
[[] for _ in range(self.mask_head.num_classes)])
else:
segm_result = self.mask_head.get_seg_masks(
mask_preds[i], _bboxes[i], det_labels[i],
self.test_cfg, ori_shapes[i], scale_factors[i],
rescale)
segm_results.append(segm_result)
return segm_results
def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
"""Test for mask head with test time augmentation."""
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes)]
else:
aug_masks = []
for x, img_meta in zip(feats, img_metas):
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
_bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
scale_factor, flip, flip_direction)
mask_rois = bbox2roi([_bboxes])
mask_results = self._mask_forward(x, mask_rois)
# convert to numpy array to save memory
aug_masks.append(
mask_results['mask_pred'].sigmoid().cpu().numpy())
merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
ori_shape = img_metas[0][0]['ori_shape']
segm_result = self.mask_head.get_seg_masks(
merged_masks,
det_bboxes,
det_labels,
self.test_cfg,
ori_shape,
scale_factor=1.0,
rescale=False)
return segm_result
| 10,852 | 41.897233 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py | from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from mmcv import ops
class BaseRoIExtractor(nn.Module, metaclass=ABCMeta):
"""Base class for RoI extractor.
Args:
roi_layer (dict): Specify RoI layer type and arguments.
out_channels (int): Output channels of RoI layers.
featmap_strides (int): Strides of input feature maps.
"""
def __init__(self, roi_layer, out_channels, featmap_strides):
super(BaseRoIExtractor, self).__init__()
self.roi_layers = self.build_roi_layers(roi_layer, featmap_strides)
self.out_channels = out_channels
self.featmap_strides = featmap_strides
self.fp16_enabled = False
@property
def num_inputs(self):
"""int: Number of input feature maps."""
return len(self.featmap_strides)
def init_weights(self):
pass
def build_roi_layers(self, layer_cfg, featmap_strides):
"""Build RoI operator to extract feature from each level feature map.
Args:
layer_cfg (dict): Dictionary to construct and config RoI layer
operation. Options are modules under ``mmcv/ops`` such as
``RoIAlign``.
featmap_strides (int): The stride of input feature map w.r.t to the
original image size, which would be used to scale RoI
coordinate (original image coordinate system) to feature
coordinate system.
Returns:
nn.ModuleList: The RoI extractor modules for each level feature
map.
"""
cfg = layer_cfg.copy()
layer_type = cfg.pop('type')
assert hasattr(ops, layer_type)
layer_cls = getattr(ops, layer_type)
roi_layers = nn.ModuleList(
[layer_cls(spatial_scale=1 / s, **cfg) for s in featmap_strides])
return roi_layers
def roi_rescale(self, rois, scale_factor):
"""Scale RoI coordinates by scale factor.
Args:
rois (torch.Tensor): RoI (Region of Interest), shape (n, 5)
scale_factor (float): Scale factor that RoI will be multiplied by.
Returns:
torch.Tensor: Scaled RoI.
"""
cx = (rois[:, 1] + rois[:, 3]) * 0.5
cy = (rois[:, 2] + rois[:, 4]) * 0.5
w = rois[:, 3] - rois[:, 1]
h = rois[:, 4] - rois[:, 2]
new_w = w * scale_factor
new_h = h * scale_factor
x1 = cx - new_w * 0.5
x2 = cx + new_w * 0.5
y1 = cy - new_h * 0.5
y2 = cy + new_h * 0.5
new_rois = torch.stack((rois[:, 0], x1, y1, x2, y2), dim=-1)
return new_rois
@abstractmethod
def forward(self, feats, rois, roi_scale_factor=None):
pass
| 2,760 | 31.869048 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py | import torch
from mmcv.runner import force_fp32
from mmdet.models.builder import ROI_EXTRACTORS
from .base_roi_extractor import BaseRoIExtractor
@ROI_EXTRACTORS.register_module()
class SingleRoIExtractor(BaseRoIExtractor):
"""Extract RoI features from a single level feature map.
If there are multiple input feature levels, each RoI is mapped to a level
according to its scale. The mapping rule is proposed in
`FPN <https://arxiv.org/abs/1612.03144>`_.
Args:
roi_layer (dict): Specify RoI layer type and arguments.
out_channels (int): Output channels of RoI layers.
featmap_strides (int): Strides of input feature maps.
finest_scale (int): Scale threshold of mapping to level 0. Default: 56.
"""
def __init__(self,
roi_layer,
out_channels,
featmap_strides,
finest_scale=56):
super(SingleRoIExtractor, self).__init__(roi_layer, out_channels,
featmap_strides)
self.finest_scale = finest_scale
def map_roi_levels(self, rois, num_levels):
"""Map rois to corresponding feature levels by scales.
- scale < finest_scale * 2: level 0
- finest_scale * 2 <= scale < finest_scale * 4: level 1
- finest_scale * 4 <= scale < finest_scale * 8: level 2
- scale >= finest_scale * 8: level 3
Args:
rois (Tensor): Input RoIs, shape (k, 5).
num_levels (int): Total level number.
Returns:
Tensor: Level index (0-based) of each RoI, shape (k, )
"""
scale = torch.sqrt(
(rois[:, 3] - rois[:, 1]) * (rois[:, 4] - rois[:, 2]))
target_lvls = torch.floor(torch.log2(scale / self.finest_scale + 1e-6))
target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long()
return target_lvls
@force_fp32(apply_to=('feats', ), out_fp16=True)
def forward(self, feats, rois, roi_scale_factor=None):
"""Forward function."""
out_size = self.roi_layers[0].output_size
num_levels = len(feats)
roi_feats = feats[0].new_zeros(
rois.size(0), self.out_channels, *out_size)
# TODO: remove this when parrots supports
if torch.__version__ == 'parrots':
roi_feats.requires_grad = True
if num_levels == 1:
if len(rois) == 0:
return roi_feats
return self.roi_layers[0](feats[0], rois)
target_lvls = self.map_roi_levels(rois, num_levels)
if roi_scale_factor is not None:
rois = self.roi_rescale(rois, roi_scale_factor)
for i in range(num_levels):
inds = target_lvls == i
if inds.any():
rois_ = rois[inds, :]
roi_feats_t = self.roi_layers[i](feats[i], rois_)
roi_feats[inds] = roi_feats_t
else:
roi_feats += sum(
x.view(-1)[0]
for x in self.parameters()) * 0. + feats[i].sum() * 0.
return roi_feats
| 3,114 | 36.53012 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/bbox_heads/bbox_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import auto_fp16, force_fp32
from torch.nn.modules.utils import _pair
from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
from mmdet.models.builder import HEADS, build_loss
from mmdet.models.losses import accuracy
@HEADS.register_module()
class BBoxHead(nn.Module):
"""Simplest RoI head, with only two fc layers for classification and
regression respectively."""
def __init__(self,
with_avg_pool=False,
with_cls=True,
with_reg=True,
roi_feat_size=7,
in_channels=256,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
reg_decoded_bbox=False,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=1.0)):
super(BBoxHead, self).__init__()
assert with_cls or with_reg
self.with_avg_pool = with_avg_pool
self.with_cls = with_cls
self.with_reg = with_reg
self.roi_feat_size = _pair(roi_feat_size)
self.roi_feat_area = self.roi_feat_size[0] * self.roi_feat_size[1]
self.in_channels = in_channels
self.num_classes = num_classes
self.reg_class_agnostic = reg_class_agnostic
self.reg_decoded_bbox = reg_decoded_bbox
self.fp16_enabled = False
self.bbox_coder = build_bbox_coder(bbox_coder)
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
in_channels = self.in_channels
if self.with_avg_pool:
self.avg_pool = nn.AvgPool2d(self.roi_feat_size)
else:
in_channels *= self.roi_feat_area
if self.with_cls:
# need to add background class
self.fc_cls = nn.Linear(in_channels, num_classes + 1)
if self.with_reg:
out_dim_reg = 4 if reg_class_agnostic else 4 * num_classes
self.fc_reg = nn.Linear(in_channels, out_dim_reg)
self.debug_imgs = None
def init_weights(self):
# conv layers are already initialized by ConvModule
if self.with_cls:
nn.init.normal_(self.fc_cls.weight, 0, 0.01)
nn.init.constant_(self.fc_cls.bias, 0)
if self.with_reg:
nn.init.normal_(self.fc_reg.weight, 0, 0.001)
nn.init.constant_(self.fc_reg.bias, 0)
@auto_fp16()
def forward(self, x):
if self.with_avg_pool:
x = self.avg_pool(x)
x = x.view(x.size(0), -1)
cls_score = self.fc_cls(x) if self.with_cls else None
bbox_pred = self.fc_reg(x) if self.with_reg else None
return cls_score, bbox_pred
def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes,
pos_gt_labels, cfg):
num_pos = pos_bboxes.size(0)
num_neg = neg_bboxes.size(0)
num_samples = num_pos + num_neg
# original implementation uses new_zeros since BG are set to be 0
# now use empty & fill because BG cat_id = num_classes,
# FG cat_id = [0, num_classes-1]
labels = pos_bboxes.new_full((num_samples, ),
self.num_classes,
dtype=torch.long)
label_weights = pos_bboxes.new_zeros(num_samples)
bbox_targets = pos_bboxes.new_zeros(num_samples, 4)
bbox_weights = pos_bboxes.new_zeros(num_samples, 4)
if num_pos > 0:
labels[:num_pos] = pos_gt_labels
pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight
label_weights[:num_pos] = pos_weight
if not self.reg_decoded_bbox:
pos_bbox_targets = self.bbox_coder.encode(
pos_bboxes, pos_gt_bboxes)
else:
pos_bbox_targets = pos_gt_bboxes
bbox_targets[:num_pos, :] = pos_bbox_targets
bbox_weights[:num_pos, :] = 1
if num_neg > 0:
label_weights[-num_neg:] = 1.0
return labels, label_weights, bbox_targets, bbox_weights
def get_targets(self,
sampling_results,
gt_bboxes,
gt_labels,
rcnn_train_cfg,
concat=True):
pos_bboxes_list = [res.pos_bboxes for res in sampling_results]
neg_bboxes_list = [res.neg_bboxes for res in sampling_results]
pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]
pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results]
labels, label_weights, bbox_targets, bbox_weights = multi_apply(
self._get_target_single,
pos_bboxes_list,
neg_bboxes_list,
pos_gt_bboxes_list,
pos_gt_labels_list,
cfg=rcnn_train_cfg)
if concat:
labels = torch.cat(labels, 0)
label_weights = torch.cat(label_weights, 0)
bbox_targets = torch.cat(bbox_targets, 0)
bbox_weights = torch.cat(bbox_weights, 0)
return labels, label_weights, bbox_targets, bbox_weights
@force_fp32(apply_to=('cls_score', 'bbox_pred'))
def loss(self,
cls_score,
bbox_pred,
rois,
labels,
label_weights,
bbox_targets,
bbox_weights,
reduction_override=None):
losses = dict()
if cls_score is not None:
avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
if cls_score.numel() > 0:
losses['loss_cls'] = self.loss_cls(
cls_score,
labels,
label_weights,
avg_factor=avg_factor,
reduction_override=reduction_override)
losses['acc'] = accuracy(cls_score, labels)
if bbox_pred is not None:
bg_class_ind = self.num_classes
# 0~self.num_classes-1 are FG, self.num_classes is BG
pos_inds = (labels >= 0) & (labels < bg_class_ind)
# do not perform bounding box regression for BG anymore.
if pos_inds.any():
if self.reg_decoded_bbox:
bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred)
if self.reg_class_agnostic:
pos_bbox_pred = bbox_pred.view(
bbox_pred.size(0), 4)[pos_inds.type(torch.bool)]
else:
pos_bbox_pred = bbox_pred.view(
bbox_pred.size(0), -1,
4)[pos_inds.type(torch.bool),
labels[pos_inds.type(torch.bool)]]
losses['loss_bbox'] = self.loss_bbox(
pos_bbox_pred,
bbox_targets[pos_inds.type(torch.bool)],
bbox_weights[pos_inds.type(torch.bool)],
avg_factor=bbox_targets.size(0),
reduction_override=reduction_override)
else:
losses['loss_bbox'] = bbox_pred[pos_inds].sum()
return losses
@force_fp32(apply_to=('cls_score', 'bbox_pred'))
def get_bboxes(self,
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=False,
cfg=None):
if isinstance(cls_score, list):
cls_score = sum(cls_score) / float(len(cls_score))
scores = F.softmax(cls_score, dim=1) if cls_score is not None else None
if bbox_pred is not None:
bboxes = self.bbox_coder.decode(
rois[:, 1:], bbox_pred, max_shape=img_shape)
else:
bboxes = rois[:, 1:].clone()
if img_shape is not None:
bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1])
bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0])
if rescale and bboxes.size(0) > 0:
if isinstance(scale_factor, float):
bboxes /= scale_factor
else:
scale_factor = bboxes.new_tensor(scale_factor)
bboxes = (bboxes.view(bboxes.size(0), -1, 4) /
scale_factor).view(bboxes.size()[0], -1)
if cfg is None:
return bboxes, scores
else:
det_bboxes, det_labels = multiclass_nms(bboxes, scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
@force_fp32(apply_to=('bbox_preds', ))
def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
"""Refine bboxes during training.
Args:
rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
and bs is the sampled RoIs per image. The first column is
the image id and the next 4 columns are x1, y1, x2, y2.
labels (Tensor): Shape (n*bs, ).
bbox_preds (Tensor): Shape (n*bs, 4) or (n*bs, 4*#class).
pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
is a gt bbox.
img_metas (list[dict]): Meta info of each image.
Returns:
list[Tensor]: Refined bboxes of each image in a mini-batch.
Example:
>>> # xdoctest: +REQUIRES(module:kwarray)
>>> import kwarray
>>> import numpy as np
>>> from mmdet.core.bbox.demodata import random_boxes
>>> self = BBoxHead(reg_class_agnostic=True)
>>> n_roi = 2
>>> n_img = 4
>>> scale = 512
>>> rng = np.random.RandomState(0)
>>> img_metas = [{'img_shape': (scale, scale)}
... for _ in range(n_img)]
>>> # Create rois in the expected format
>>> roi_boxes = random_boxes(n_roi, scale=scale, rng=rng)
>>> img_ids = torch.randint(0, n_img, (n_roi,))
>>> img_ids = img_ids.float()
>>> rois = torch.cat([img_ids[:, None], roi_boxes], dim=1)
>>> # Create other args
>>> labels = torch.randint(0, 2, (n_roi,)).long()
>>> bbox_preds = random_boxes(n_roi, scale=scale, rng=rng)
>>> # For each image, pretend random positive boxes are gts
>>> is_label_pos = (labels.numpy() > 0).astype(np.int)
>>> lbl_per_img = kwarray.group_items(is_label_pos,
... img_ids.numpy())
>>> pos_per_img = [sum(lbl_per_img.get(gid, []))
... for gid in range(n_img)]
>>> pos_is_gts = [
>>> torch.randint(0, 2, (npos,)).byte().sort(
>>> descending=True)[0]
>>> for npos in pos_per_img
>>> ]
>>> bboxes_list = self.refine_bboxes(rois, labels, bbox_preds,
>>> pos_is_gts, img_metas)
>>> print(bboxes_list)
"""
img_ids = rois[:, 0].long().unique(sorted=True)
assert img_ids.numel() <= len(img_metas)
bboxes_list = []
for i in range(len(img_metas)):
inds = torch.nonzero(
rois[:, 0] == i, as_tuple=False).squeeze(dim=1)
num_rois = inds.numel()
bboxes_ = rois[inds, 1:]
label_ = labels[inds]
bbox_pred_ = bbox_preds[inds]
img_meta_ = img_metas[i]
pos_is_gts_ = pos_is_gts[i]
bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
img_meta_)
# filter gt bboxes
pos_keep = 1 - pos_is_gts_
keep_inds = pos_is_gts_.new_ones(num_rois)
keep_inds[:len(pos_is_gts_)] = pos_keep
bboxes_list.append(bboxes[keep_inds.type(torch.bool)])
return bboxes_list
@force_fp32(apply_to=('bbox_pred', ))
def regress_by_class(self, rois, label, bbox_pred, img_meta):
"""Regress the bbox for the predicted class. Used in Cascade R-CNN.
Args:
rois (Tensor): shape (n, 4) or (n, 5)
label (Tensor): shape (n, )
bbox_pred (Tensor): shape (n, 4*(#class)) or (n, 4)
img_meta (dict): Image meta info.
Returns:
Tensor: Regressed bboxes, the same shape as input rois.
"""
assert rois.size(1) == 4 or rois.size(1) == 5, repr(rois.shape)
if not self.reg_class_agnostic:
label = label * 4
inds = torch.stack((label, label + 1, label + 2, label + 3), 1)
bbox_pred = torch.gather(bbox_pred, 1, inds)
assert bbox_pred.size(1) == 4
if rois.size(1) == 4:
new_rois = self.bbox_coder.decode(
rois, bbox_pred, max_shape=img_meta['img_shape'])
else:
bboxes = self.bbox_coder.decode(
rois[:, 1:], bbox_pred, max_shape=img_meta['img_shape'])
new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
return new_rois
| 13,645 | 39.734328 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/bbox_heads/sabl_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, kaiming_init, normal_init, xavier_init
from mmcv.runner import force_fp32
from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
from mmdet.models.builder import HEADS, build_loss
from mmdet.models.losses import accuracy
@HEADS.register_module()
class SABLHead(nn.Module):
"""Side-Aware Boundary Localization (SABL) for RoI-Head.
Side-Aware features are extracted by conv layers
with an attention mechanism.
Boundary Localization with Bucketing and Bucketing Guided Rescoring
are implemented in BucketingBBoxCoder.
Please refer to https://arxiv.org/abs/1912.04260 for more details.
Args:
cls_in_channels (int): Input channels of cls RoI feature. \
Defaults to 256.
reg_in_channels (int): Input channels of reg RoI feature. \
Defaults to 256.
roi_feat_size (int): Size of RoI features. Defaults to 7.
reg_feat_up_ratio (int): Upsample ratio of reg features. \
Defaults to 2.
reg_pre_kernel (int): Kernel of 2D conv layers before \
attention pooling. Defaults to 3.
reg_post_kernel (int): Kernel of 1D conv layers after \
attention pooling. Defaults to 3.
reg_pre_num (int): Number of pre convs. Defaults to 2.
reg_post_num (int): Number of post convs. Defaults to 1.
num_classes (int): Number of classes in dataset. Defaults to 80.
cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024.
reg_offset_out_channels (int): Hidden and output channel \
of reg offset branch. Defaults to 256.
reg_cls_out_channels (int): Hidden and output channel \
of reg cls branch. Defaults to 256.
num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1.
num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0.
reg_class_agnostic (bool): Class agnostic regresion or not. \
Defaults to True.
norm_cfg (dict): Config of norm layers. Defaults to None.
bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'.
loss_cls (dict): Config of classification loss.
loss_bbox_cls (dict): Config of classification loss for bbox branch.
loss_bbox_reg (dict): Config of regression loss for bbox branch.
"""
def __init__(self,
num_classes,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder',
num_buckets=14,
scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=0.1, loss_weight=1.0)):
super(SABLHead, self).__init__()
self.cls_in_channels = cls_in_channels
self.reg_in_channels = reg_in_channels
self.roi_feat_size = roi_feat_size
self.reg_feat_up_ratio = int(reg_feat_up_ratio)
self.num_buckets = bbox_coder['num_buckets']
assert self.reg_feat_up_ratio // 2 >= 1
self.up_reg_feat_size = roi_feat_size * self.reg_feat_up_ratio
assert self.up_reg_feat_size == bbox_coder['num_buckets']
self.reg_pre_kernel = reg_pre_kernel
self.reg_post_kernel = reg_post_kernel
self.reg_pre_num = reg_pre_num
self.reg_post_num = reg_post_num
self.num_classes = num_classes
self.cls_out_channels = cls_out_channels
self.reg_offset_out_channels = reg_offset_out_channels
self.reg_cls_out_channels = reg_cls_out_channels
self.num_cls_fcs = num_cls_fcs
self.num_reg_fcs = num_reg_fcs
self.reg_class_agnostic = reg_class_agnostic
assert self.reg_class_agnostic
self.norm_cfg = norm_cfg
self.bbox_coder = build_bbox_coder(bbox_coder)
self.loss_cls = build_loss(loss_cls)
self.loss_bbox_cls = build_loss(loss_bbox_cls)
self.loss_bbox_reg = build_loss(loss_bbox_reg)
self.cls_fcs = self._add_fc_branch(self.num_cls_fcs,
self.cls_in_channels,
self.roi_feat_size,
self.cls_out_channels)
self.side_num = int(np.ceil(self.num_buckets / 2))
if self.reg_feat_up_ratio > 1:
self.upsample_x = nn.ConvTranspose1d(
reg_in_channels,
reg_in_channels,
self.reg_feat_up_ratio,
stride=self.reg_feat_up_ratio)
self.upsample_y = nn.ConvTranspose1d(
reg_in_channels,
reg_in_channels,
self.reg_feat_up_ratio,
stride=self.reg_feat_up_ratio)
self.reg_pre_convs = nn.ModuleList()
for i in range(self.reg_pre_num):
reg_pre_conv = ConvModule(
reg_in_channels,
reg_in_channels,
kernel_size=reg_pre_kernel,
padding=reg_pre_kernel // 2,
norm_cfg=norm_cfg,
act_cfg=dict(type='ReLU'))
self.reg_pre_convs.append(reg_pre_conv)
self.reg_post_conv_xs = nn.ModuleList()
for i in range(self.reg_post_num):
reg_post_conv_x = ConvModule(
reg_in_channels,
reg_in_channels,
kernel_size=(1, reg_post_kernel),
padding=(0, reg_post_kernel // 2),
norm_cfg=norm_cfg,
act_cfg=dict(type='ReLU'))
self.reg_post_conv_xs.append(reg_post_conv_x)
self.reg_post_conv_ys = nn.ModuleList()
for i in range(self.reg_post_num):
reg_post_conv_y = ConvModule(
reg_in_channels,
reg_in_channels,
kernel_size=(reg_post_kernel, 1),
padding=(reg_post_kernel // 2, 0),
norm_cfg=norm_cfg,
act_cfg=dict(type='ReLU'))
self.reg_post_conv_ys.append(reg_post_conv_y)
self.reg_conv_att_x = nn.Conv2d(reg_in_channels, 1, 1)
self.reg_conv_att_y = nn.Conv2d(reg_in_channels, 1, 1)
self.fc_cls = nn.Linear(self.cls_out_channels, self.num_classes + 1)
self.relu = nn.ReLU(inplace=True)
self.reg_cls_fcs = self._add_fc_branch(self.num_reg_fcs,
self.reg_in_channels, 1,
self.reg_cls_out_channels)
self.reg_offset_fcs = self._add_fc_branch(self.num_reg_fcs,
self.reg_in_channels, 1,
self.reg_offset_out_channels)
self.fc_reg_cls = nn.Linear(self.reg_cls_out_channels, 1)
self.fc_reg_offset = nn.Linear(self.reg_offset_out_channels, 1)
def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size,
fc_out_channels):
in_channels = in_channels * roi_feat_size * roi_feat_size
branch_fcs = nn.ModuleList()
for i in range(num_branch_fcs):
fc_in_channels = (in_channels if i == 0 else fc_out_channels)
branch_fcs.append(nn.Linear(fc_in_channels, fc_out_channels))
return branch_fcs
def init_weights(self):
for module_list in [
self.reg_cls_fcs, self.reg_offset_fcs, self.cls_fcs
]:
for m in module_list.modules():
if isinstance(m, nn.Linear):
xavier_init(m, distribution='uniform')
if self.reg_feat_up_ratio > 1:
kaiming_init(self.upsample_x, distribution='normal')
kaiming_init(self.upsample_y, distribution='normal')
normal_init(self.reg_conv_att_x, 0, 0.01)
normal_init(self.reg_conv_att_y, 0, 0.01)
normal_init(self.fc_reg_offset, 0, 0.001)
normal_init(self.fc_reg_cls, 0, 0.01)
normal_init(self.fc_cls, 0, 0.01)
def cls_forward(self, cls_x):
cls_x = cls_x.view(cls_x.size(0), -1)
for fc in self.cls_fcs:
cls_x = self.relu(fc(cls_x))
cls_score = self.fc_cls(cls_x)
return cls_score
def attention_pool(self, reg_x):
"""Extract direction-specific features fx and fy with attention
methanism."""
reg_fx = reg_x
reg_fy = reg_x
reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid()
reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid()
reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2)
reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3)
reg_fx = (reg_fx * reg_fx_att).sum(dim=2)
reg_fy = (reg_fy * reg_fy_att).sum(dim=3)
return reg_fx, reg_fy
def side_aware_feature_extractor(self, reg_x):
"""Refine and extract side-aware features without split them."""
for reg_pre_conv in self.reg_pre_convs:
reg_x = reg_pre_conv(reg_x)
reg_fx, reg_fy = self.attention_pool(reg_x)
if self.reg_post_num > 0:
reg_fx = reg_fx.unsqueeze(2)
reg_fy = reg_fy.unsqueeze(3)
for i in range(self.reg_post_num):
reg_fx = self.reg_post_conv_xs[i](reg_fx)
reg_fy = self.reg_post_conv_ys[i](reg_fy)
reg_fx = reg_fx.squeeze(2)
reg_fy = reg_fy.squeeze(3)
if self.reg_feat_up_ratio > 1:
reg_fx = self.relu(self.upsample_x(reg_fx))
reg_fy = self.relu(self.upsample_y(reg_fy))
reg_fx = torch.transpose(reg_fx, 1, 2)
reg_fy = torch.transpose(reg_fy, 1, 2)
return reg_fx.contiguous(), reg_fy.contiguous()
def reg_pred(self, x, offfset_fcs, cls_fcs):
"""Predict bucketing esimation (cls_pred) and fine regression (offset
pred) with side-aware features."""
x_offset = x.view(-1, self.reg_in_channels)
x_cls = x.view(-1, self.reg_in_channels)
for fc in offfset_fcs:
x_offset = self.relu(fc(x_offset))
for fc in cls_fcs:
x_cls = self.relu(fc(x_cls))
offset_pred = self.fc_reg_offset(x_offset)
cls_pred = self.fc_reg_cls(x_cls)
offset_pred = offset_pred.view(x.size(0), -1)
cls_pred = cls_pred.view(x.size(0), -1)
return offset_pred, cls_pred
def side_aware_split(self, feat):
"""Split side-aware features aligned with orders of bucketing
targets."""
l_end = int(np.ceil(self.up_reg_feat_size / 2))
r_start = int(np.floor(self.up_reg_feat_size / 2))
feat_fl = feat[:, :l_end]
feat_fr = feat[:, r_start:].flip(dims=(1, ))
feat_fl = feat_fl.contiguous()
feat_fr = feat_fr.contiguous()
feat = torch.cat([feat_fl, feat_fr], dim=-1)
return feat
def bbox_pred_split(self, bbox_pred, num_proposals_per_img):
"""Split batch bbox prediction back to each image."""
bucket_cls_preds, bucket_offset_preds = bbox_pred
bucket_cls_preds = bucket_cls_preds.split(num_proposals_per_img, 0)
bucket_offset_preds = bucket_offset_preds.split(
num_proposals_per_img, 0)
bbox_pred = tuple(zip(bucket_cls_preds, bucket_offset_preds))
return bbox_pred
def reg_forward(self, reg_x):
outs = self.side_aware_feature_extractor(reg_x)
edge_offset_preds = []
edge_cls_preds = []
reg_fx = outs[0]
reg_fy = outs[1]
offset_pred_x, cls_pred_x = self.reg_pred(reg_fx, self.reg_offset_fcs,
self.reg_cls_fcs)
offset_pred_y, cls_pred_y = self.reg_pred(reg_fy, self.reg_offset_fcs,
self.reg_cls_fcs)
offset_pred_x = self.side_aware_split(offset_pred_x)
offset_pred_y = self.side_aware_split(offset_pred_y)
cls_pred_x = self.side_aware_split(cls_pred_x)
cls_pred_y = self.side_aware_split(cls_pred_y)
edge_offset_preds = torch.cat([offset_pred_x, offset_pred_y], dim=-1)
edge_cls_preds = torch.cat([cls_pred_x, cls_pred_y], dim=-1)
return (edge_cls_preds, edge_offset_preds)
def forward(self, x):
bbox_pred = self.reg_forward(x)
cls_score = self.cls_forward(x)
return cls_score, bbox_pred
def get_targets(self, sampling_results, gt_bboxes, gt_labels,
rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_results]
neg_proposals = [res.neg_bboxes for res in sampling_results]
pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results]
pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
cls_reg_targets = self.bucket_target(pos_proposals, neg_proposals,
pos_gt_bboxes, pos_gt_labels,
rcnn_train_cfg)
(labels, label_weights, bucket_cls_targets, bucket_cls_weights,
bucket_offset_targets, bucket_offset_weights) = cls_reg_targets
return (labels, label_weights, (bucket_cls_targets,
bucket_offset_targets),
(bucket_cls_weights, bucket_offset_weights))
def bucket_target(self,
pos_proposals_list,
neg_proposals_list,
pos_gt_bboxes_list,
pos_gt_labels_list,
rcnn_train_cfg,
concat=True):
(labels, label_weights, bucket_cls_targets, bucket_cls_weights,
bucket_offset_targets, bucket_offset_weights) = multi_apply(
self._bucket_target_single,
pos_proposals_list,
neg_proposals_list,
pos_gt_bboxes_list,
pos_gt_labels_list,
cfg=rcnn_train_cfg)
if concat:
labels = torch.cat(labels, 0)
label_weights = torch.cat(label_weights, 0)
bucket_cls_targets = torch.cat(bucket_cls_targets, 0)
bucket_cls_weights = torch.cat(bucket_cls_weights, 0)
bucket_offset_targets = torch.cat(bucket_offset_targets, 0)
bucket_offset_weights = torch.cat(bucket_offset_weights, 0)
return (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
bucket_offset_targets, bucket_offset_weights)
def _bucket_target_single(self, pos_proposals, neg_proposals,
pos_gt_bboxes, pos_gt_labels, cfg):
"""Compute bucketing estimation targets and fine regression targets for
a single image.
Args:
pos_proposals (Tensor): positive proposals of a single image,
Shape (n_pos, 4)
neg_proposals (Tensor): negative proposals of a single image,
Shape (n_neg, 4).
pos_gt_bboxes (Tensor): gt bboxes assigned to positive proposals
of a single image, Shape (n_pos, 4).
pos_gt_labels (Tensor): gt labels assigned to positive proposals
of a single image, Shape (n_pos, ).
cfg (dict): Config of calculating targets
Returns:
tuple:
- labels (Tensor): Labels in a single image. \
Shape (n,).
- label_weights (Tensor): Label weights in a single image.\
Shape (n,)
- bucket_cls_targets (Tensor): Bucket cls targets in \
a single image. Shape (n, num_buckets*2).
- bucket_cls_weights (Tensor): Bucket cls weights in \
a single image. Shape (n, num_buckets*2).
- bucket_offset_targets (Tensor): Bucket offset targets \
in a single image. Shape (n, num_buckets*2).
- bucket_offset_targets (Tensor): Bucket offset weights \
in a single image. Shape (n, num_buckets*2).
"""
num_pos = pos_proposals.size(0)
num_neg = neg_proposals.size(0)
num_samples = num_pos + num_neg
labels = pos_gt_bboxes.new_full((num_samples, ),
self.num_classes,
dtype=torch.long)
label_weights = pos_proposals.new_zeros(num_samples)
bucket_cls_targets = pos_proposals.new_zeros(num_samples,
4 * self.side_num)
bucket_cls_weights = pos_proposals.new_zeros(num_samples,
4 * self.side_num)
bucket_offset_targets = pos_proposals.new_zeros(
num_samples, 4 * self.side_num)
bucket_offset_weights = pos_proposals.new_zeros(
num_samples, 4 * self.side_num)
if num_pos > 0:
labels[:num_pos] = pos_gt_labels
label_weights[:num_pos] = 1.0
(pos_bucket_offset_targets, pos_bucket_offset_weights,
pos_bucket_cls_targets,
pos_bucket_cls_weights) = self.bbox_coder.encode(
pos_proposals, pos_gt_bboxes)
bucket_cls_targets[:num_pos, :] = pos_bucket_cls_targets
bucket_cls_weights[:num_pos, :] = pos_bucket_cls_weights
bucket_offset_targets[:num_pos, :] = pos_bucket_offset_targets
bucket_offset_weights[:num_pos, :] = pos_bucket_offset_weights
if num_neg > 0:
label_weights[-num_neg:] = 1.0
return (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
bucket_offset_targets, bucket_offset_weights)
def loss(self,
cls_score,
bbox_pred,
rois,
labels,
label_weights,
bbox_targets,
bbox_weights,
reduction_override=None):
losses = dict()
if cls_score is not None:
avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
losses['loss_cls'] = self.loss_cls(
cls_score,
labels,
label_weights,
avg_factor=avg_factor,
reduction_override=reduction_override)
losses['acc'] = accuracy(cls_score, labels)
if bbox_pred is not None:
bucket_cls_preds, bucket_offset_preds = bbox_pred
bucket_cls_targets, bucket_offset_targets = bbox_targets
bucket_cls_weights, bucket_offset_weights = bbox_weights
# edge cls
bucket_cls_preds = bucket_cls_preds.view(-1, self.side_num)
bucket_cls_targets = bucket_cls_targets.view(-1, self.side_num)
bucket_cls_weights = bucket_cls_weights.view(-1, self.side_num)
losses['loss_bbox_cls'] = self.loss_bbox_cls(
bucket_cls_preds,
bucket_cls_targets,
bucket_cls_weights,
avg_factor=bucket_cls_targets.size(0),
reduction_override=reduction_override)
losses['loss_bbox_reg'] = self.loss_bbox_reg(
bucket_offset_preds,
bucket_offset_targets,
bucket_offset_weights,
avg_factor=bucket_offset_targets.size(0),
reduction_override=reduction_override)
return losses
@force_fp32(apply_to=('cls_score', 'bbox_pred'))
def get_bboxes(self,
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=False,
cfg=None):
if isinstance(cls_score, list):
cls_score = sum(cls_score) / float(len(cls_score))
scores = F.softmax(cls_score, dim=1) if cls_score is not None else None
if bbox_pred is not None:
bboxes, confids = self.bbox_coder.decode(rois[:, 1:], bbox_pred,
img_shape)
else:
bboxes = rois[:, 1:].clone()
confids = None
if img_shape is not None:
bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1)
bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1)
if rescale and bboxes.size(0) > 0:
if isinstance(scale_factor, float):
bboxes /= scale_factor
else:
bboxes /= torch.from_numpy(scale_factor).to(bboxes.device)
if cfg is None:
return bboxes, scores
else:
det_bboxes, det_labels = multiclass_nms(
bboxes,
scores,
cfg.score_thr,
cfg.nms,
cfg.max_per_img,
score_factors=confids)
return det_bboxes, det_labels
@force_fp32(apply_to=('bbox_preds', ))
def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
"""Refine bboxes during training.
Args:
rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
and bs is the sampled RoIs per image.
labels (Tensor): Shape (n*bs, ).
bbox_preds (list[Tensor]): Shape [(n*bs, num_buckets*2), \
(n*bs, num_buckets*2)].
pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
is a gt bbox.
img_metas (list[dict]): Meta info of each image.
Returns:
list[Tensor]: Refined bboxes of each image in a mini-batch.
"""
img_ids = rois[:, 0].long().unique(sorted=True)
assert img_ids.numel() == len(img_metas)
bboxes_list = []
for i in range(len(img_metas)):
inds = torch.nonzero(
rois[:, 0] == i, as_tuple=False).squeeze(dim=1)
num_rois = inds.numel()
bboxes_ = rois[inds, 1:]
label_ = labels[inds]
edge_cls_preds, edge_offset_preds = bbox_preds
edge_cls_preds_ = edge_cls_preds[inds]
edge_offset_preds_ = edge_offset_preds[inds]
bbox_pred_ = [edge_cls_preds_, edge_offset_preds_]
img_meta_ = img_metas[i]
pos_is_gts_ = pos_is_gts[i]
bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
img_meta_)
# filter gt bboxes
pos_keep = 1 - pos_is_gts_
keep_inds = pos_is_gts_.new_ones(num_rois)
keep_inds[:len(pos_is_gts_)] = pos_keep
bboxes_list.append(bboxes[keep_inds.type(torch.bool)])
return bboxes_list
@force_fp32(apply_to=('bbox_pred', ))
def regress_by_class(self, rois, label, bbox_pred, img_meta):
"""Regress the bbox for the predicted class. Used in Cascade R-CNN.
Args:
rois (Tensor): shape (n, 4) or (n, 5)
label (Tensor): shape (n, )
bbox_pred (list[Tensor]): shape [(n, num_buckets *2), \
(n, num_buckets *2)]
img_meta (dict): Image meta info.
Returns:
Tensor: Regressed bboxes, the same shape as input rois.
"""
assert rois.size(1) == 4 or rois.size(1) == 5
if rois.size(1) == 4:
new_rois, _ = self.bbox_coder.decode(rois, bbox_pred,
img_meta['img_shape'])
else:
bboxes, _ = self.bbox_coder.decode(rois[:, 1:], bbox_pred,
img_meta['img_shape'])
new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
return new_rois
| 24,585 | 41.907504 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.models.builder import HEADS
from .bbox_head import BBoxHead
@HEADS.register_module()
class ConvFCBBoxHead(BBoxHead):
r"""More general bbox head, with shared conv and fc layers and two optional
separated branches.
.. code-block:: none
/-> cls convs -> cls fcs -> cls
shared convs -> shared fcs
\-> reg convs -> reg fcs -> reg
""" # noqa: W605
def __init__(self,
num_shared_convs=0,
num_shared_fcs=0,
num_cls_convs=0,
num_cls_fcs=0,
num_reg_convs=0,
num_reg_fcs=0,
conv_out_channels=256,
fc_out_channels=1024,
conv_cfg=None,
norm_cfg=None,
*args,
**kwargs):
super(ConvFCBBoxHead, self).__init__(*args, **kwargs)
assert (num_shared_convs + num_shared_fcs + num_cls_convs +
num_cls_fcs + num_reg_convs + num_reg_fcs > 0)
if num_cls_convs > 0 or num_reg_convs > 0:
assert num_shared_fcs == 0
if not self.with_cls:
assert num_cls_convs == 0 and num_cls_fcs == 0
if not self.with_reg:
assert num_reg_convs == 0 and num_reg_fcs == 0
self.num_shared_convs = num_shared_convs
self.num_shared_fcs = num_shared_fcs
self.num_cls_convs = num_cls_convs
self.num_cls_fcs = num_cls_fcs
self.num_reg_convs = num_reg_convs
self.num_reg_fcs = num_reg_fcs
self.conv_out_channels = conv_out_channels
self.fc_out_channels = fc_out_channels
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
# add shared convs and fcs
self.shared_convs, self.shared_fcs, last_layer_dim = \
self._add_conv_fc_branch(
self.num_shared_convs, self.num_shared_fcs, self.in_channels,
True)
self.shared_out_channels = last_layer_dim
# add cls specific branch
self.cls_convs, self.cls_fcs, self.cls_last_dim = \
self._add_conv_fc_branch(
self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels)
# add reg specific branch
self.reg_convs, self.reg_fcs, self.reg_last_dim = \
self._add_conv_fc_branch(
self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels)
if self.num_shared_fcs == 0 and not self.with_avg_pool:
if self.num_cls_fcs == 0:
self.cls_last_dim *= self.roi_feat_area
if self.num_reg_fcs == 0:
self.reg_last_dim *= self.roi_feat_area
self.relu = nn.ReLU(inplace=True)
# reconstruct fc_cls and fc_reg since input channels are changed
if self.with_cls:
self.fc_cls = nn.Linear(self.cls_last_dim, self.num_classes + 1)
if self.with_reg:
out_dim_reg = (4 if self.reg_class_agnostic else 4 *
self.num_classes)
self.fc_reg = nn.Linear(self.reg_last_dim, out_dim_reg)
def _add_conv_fc_branch(self,
num_branch_convs,
num_branch_fcs,
in_channels,
is_shared=False):
"""Add shared or separable branch.
convs -> avg pool (optional) -> fcs
"""
last_layer_dim = in_channels
# add branch specific conv layers
branch_convs = nn.ModuleList()
if num_branch_convs > 0:
for i in range(num_branch_convs):
conv_in_channels = (
last_layer_dim if i == 0 else self.conv_out_channels)
branch_convs.append(
ConvModule(
conv_in_channels,
self.conv_out_channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
last_layer_dim = self.conv_out_channels
# add branch specific fc layers
branch_fcs = nn.ModuleList()
if num_branch_fcs > 0:
# for shared branch, only consider self.with_avg_pool
# for separated branches, also consider self.num_shared_fcs
if (is_shared
or self.num_shared_fcs == 0) and not self.with_avg_pool:
last_layer_dim *= self.roi_feat_area
for i in range(num_branch_fcs):
fc_in_channels = (
last_layer_dim if i == 0 else self.fc_out_channels)
branch_fcs.append(
nn.Linear(fc_in_channels, self.fc_out_channels))
last_layer_dim = self.fc_out_channels
return branch_convs, branch_fcs, last_layer_dim
def init_weights(self):
super(ConvFCBBoxHead, self).init_weights()
# conv layers are already initialized by ConvModule
for module_list in [self.shared_fcs, self.cls_fcs, self.reg_fcs]:
for m in module_list.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
nn.init.constant_(m.bias, 0)
def forward(self, x):
# shared part
if self.num_shared_convs > 0:
for conv in self.shared_convs:
x = conv(x)
if self.num_shared_fcs > 0:
if self.with_avg_pool:
x = self.avg_pool(x)
x = x.flatten(1)
for fc in self.shared_fcs:
x = self.relu(fc(x))
# separate branches
x_cls = x
x_reg = x
for conv in self.cls_convs:
x_cls = conv(x_cls)
if x_cls.dim() > 2:
if self.with_avg_pool:
x_cls = self.avg_pool(x_cls)
x_cls = x_cls.flatten(1)
for fc in self.cls_fcs:
x_cls = self.relu(fc(x_cls))
for conv in self.reg_convs:
x_reg = conv(x_reg)
if x_reg.dim() > 2:
if self.with_avg_pool:
x_reg = self.avg_pool(x_reg)
x_reg = x_reg.flatten(1)
for fc in self.reg_fcs:
x_reg = self.relu(fc(x_reg))
cls_score = self.fc_cls(x_cls) if self.with_cls else None
bbox_pred = self.fc_reg(x_reg) if self.with_reg else None
return cls_score, bbox_pred
@HEADS.register_module()
class Shared2FCBBoxHead(ConvFCBBoxHead):
def __init__(self, fc_out_channels=1024, *args, **kwargs):
super(Shared2FCBBoxHead, self).__init__(
num_shared_convs=0,
num_shared_fcs=2,
num_cls_convs=0,
num_cls_fcs=0,
num_reg_convs=0,
num_reg_fcs=0,
fc_out_channels=fc_out_channels,
*args,
**kwargs)
@HEADS.register_module()
class Shared4Conv1FCBBoxHead(ConvFCBBoxHead):
def __init__(self, fc_out_channels=1024, *args, **kwargs):
super(Shared4Conv1FCBBoxHead, self).__init__(
num_shared_convs=4,
num_shared_fcs=1,
num_cls_convs=0,
num_cls_fcs=0,
num_reg_convs=0,
num_reg_fcs=0,
fc_out_channels=fc_out_channels,
*args,
**kwargs)
| 7,436 | 35.101942 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule, normal_init, xavier_init
from mmdet.models.backbones.resnet import Bottleneck
from mmdet.models.builder import HEADS
from .bbox_head import BBoxHead
class BasicResBlock(nn.Module):
"""Basic residual block.
This block is a little different from the block in the ResNet backbone.
The kernel size of conv1 is 1 in this block while 3 in ResNet BasicBlock.
Args:
in_channels (int): Channels of the input feature map.
out_channels (int): Channels of the output feature map.
conv_cfg (dict): The config dict for convolution layers.
norm_cfg (dict): The config dict for normalization layers.
"""
def __init__(self,
in_channels,
out_channels,
conv_cfg=None,
norm_cfg=dict(type='BN')):
super(BasicResBlock, self).__init__()
# main path
self.conv1 = ConvModule(
in_channels,
in_channels,
kernel_size=3,
padding=1,
bias=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg)
self.conv2 = ConvModule(
in_channels,
out_channels,
kernel_size=1,
bias=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
# identity path
self.conv_identity = ConvModule(
in_channels,
out_channels,
kernel_size=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
identity = x
x = self.conv1(x)
x = self.conv2(x)
identity = self.conv_identity(identity)
out = x + identity
out = self.relu(out)
return out
@HEADS.register_module()
class DoubleConvFCBBoxHead(BBoxHead):
r"""Bbox head used in Double-Head R-CNN
.. code-block:: none
/-> cls
/-> shared convs ->
\-> reg
roi features
/-> cls
\-> shared fc ->
\-> reg
""" # noqa: W605
def __init__(self,
num_convs=0,
num_fcs=0,
conv_out_channels=1024,
fc_out_channels=1024,
conv_cfg=None,
norm_cfg=dict(type='BN'),
**kwargs):
kwargs.setdefault('with_avg_pool', True)
super(DoubleConvFCBBoxHead, self).__init__(**kwargs)
assert self.with_avg_pool
assert num_convs > 0
assert num_fcs > 0
self.num_convs = num_convs
self.num_fcs = num_fcs
self.conv_out_channels = conv_out_channels
self.fc_out_channels = fc_out_channels
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
# increase the channel of input features
self.res_block = BasicResBlock(self.in_channels,
self.conv_out_channels)
# add conv heads
self.conv_branch = self._add_conv_branch()
# add fc heads
self.fc_branch = self._add_fc_branch()
out_dim_reg = 4 if self.reg_class_agnostic else 4 * self.num_classes
self.fc_reg = nn.Linear(self.conv_out_channels, out_dim_reg)
self.fc_cls = nn.Linear(self.fc_out_channels, self.num_classes + 1)
self.relu = nn.ReLU(inplace=True)
def _add_conv_branch(self):
"""Add the fc branch which consists of a sequential of conv layers."""
branch_convs = nn.ModuleList()
for i in range(self.num_convs):
branch_convs.append(
Bottleneck(
inplanes=self.conv_out_channels,
planes=self.conv_out_channels // 4,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
return branch_convs
def _add_fc_branch(self):
"""Add the fc branch which consists of a sequential of fc layers."""
branch_fcs = nn.ModuleList()
for i in range(self.num_fcs):
fc_in_channels = (
self.in_channels *
self.roi_feat_area if i == 0 else self.fc_out_channels)
branch_fcs.append(nn.Linear(fc_in_channels, self.fc_out_channels))
return branch_fcs
def init_weights(self):
# conv layers are already initialized by ConvModule
normal_init(self.fc_cls, std=0.01)
normal_init(self.fc_reg, std=0.001)
for m in self.fc_branch.modules():
if isinstance(m, nn.Linear):
xavier_init(m, distribution='uniform')
def forward(self, x_cls, x_reg):
# conv head
x_conv = self.res_block(x_reg)
for conv in self.conv_branch:
x_conv = conv(x_conv)
if self.with_avg_pool:
x_conv = self.avg_pool(x_conv)
x_conv = x_conv.view(x_conv.size(0), -1)
bbox_pred = self.fc_reg(x_conv)
# fc head
x_fc = x_cls.view(x_cls.size(0), -1)
for fc in self.fc_branch:
x_fc = self.relu(fc(x_fc))
cls_score = self.fc_cls(x_fc)
return cls_score, bbox_pred
| 5,380 | 30.104046 | 78 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/shared_heads/res_layer.py | import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import auto_fp16, load_checkpoint
from mmdet.models.backbones import ResNet
from mmdet.models.builder import SHARED_HEADS
from mmdet.models.utils import ResLayer as _ResLayer
from mmdet.utils import get_root_logger
@SHARED_HEADS.register_module()
class ResLayer(nn.Module):
def __init__(self,
depth,
stage=3,
stride=2,
dilation=1,
style='pytorch',
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
with_cp=False,
dcn=None):
super(ResLayer, self).__init__()
self.norm_eval = norm_eval
self.norm_cfg = norm_cfg
self.stage = stage
self.fp16_enabled = False
block, stage_blocks = ResNet.arch_settings[depth]
stage_block = stage_blocks[stage]
planes = 64 * 2**stage
inplanes = 64 * 2**(stage - 1) * block.expansion
res_layer = _ResLayer(
block,
inplanes,
planes,
stage_block,
stride=stride,
dilation=dilation,
style=style,
with_cp=with_cp,
norm_cfg=self.norm_cfg,
dcn=dcn)
self.add_module(f'layer{stage + 1}', res_layer)
def init_weights(self, pretrained=None):
"""Initialize the weights in the module.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
else:
raise TypeError('pretrained must be a str or None')
@auto_fp16()
def forward(self, x):
res_layer = getattr(self, f'layer{self.stage + 1}')
out = res_layer(x)
return out
def train(self, mode=True):
super(ResLayer, self).train(mode)
if self.norm_eval:
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
| 2,454 | 30.474359 | 74 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/mask_heads/grid_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, kaiming_init, normal_init
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class GridHead(nn.Module):
def __init__(self,
grid_points=9,
num_convs=8,
roi_feat_size=14,
in_channels=256,
conv_kernel_size=3,
point_feat_channels=64,
deconv_kernel_size=4,
class_agnostic=False,
loss_grid=dict(
type='CrossEntropyLoss', use_sigmoid=True,
loss_weight=15),
conv_cfg=None,
norm_cfg=dict(type='GN', num_groups=36)):
super(GridHead, self).__init__()
self.grid_points = grid_points
self.num_convs = num_convs
self.roi_feat_size = roi_feat_size
self.in_channels = in_channels
self.conv_kernel_size = conv_kernel_size
self.point_feat_channels = point_feat_channels
self.conv_out_channels = self.point_feat_channels * self.grid_points
self.class_agnostic = class_agnostic
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
if isinstance(norm_cfg, dict) and norm_cfg['type'] == 'GN':
assert self.conv_out_channels % norm_cfg['num_groups'] == 0
assert self.grid_points >= 4
self.grid_size = int(np.sqrt(self.grid_points))
if self.grid_size * self.grid_size != self.grid_points:
raise ValueError('grid_points must be a square number')
# the predicted heatmap is half of whole_map_size
if not isinstance(self.roi_feat_size, int):
raise ValueError('Only square RoIs are supporeted in Grid R-CNN')
self.whole_map_size = self.roi_feat_size * 4
# compute point-wise sub-regions
self.sub_regions = self.calc_sub_regions()
self.convs = []
for i in range(self.num_convs):
in_channels = (
self.in_channels if i == 0 else self.conv_out_channels)
stride = 2 if i == 0 else 1
padding = (self.conv_kernel_size - 1) // 2
self.convs.append(
ConvModule(
in_channels,
self.conv_out_channels,
self.conv_kernel_size,
stride=stride,
padding=padding,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
bias=True))
self.convs = nn.Sequential(*self.convs)
self.deconv1 = nn.ConvTranspose2d(
self.conv_out_channels,
self.conv_out_channels,
kernel_size=deconv_kernel_size,
stride=2,
padding=(deconv_kernel_size - 2) // 2,
groups=grid_points)
self.norm1 = nn.GroupNorm(grid_points, self.conv_out_channels)
self.deconv2 = nn.ConvTranspose2d(
self.conv_out_channels,
grid_points,
kernel_size=deconv_kernel_size,
stride=2,
padding=(deconv_kernel_size - 2) // 2,
groups=grid_points)
# find the 4-neighbor of each grid point
self.neighbor_points = []
grid_size = self.grid_size
for i in range(grid_size): # i-th column
for j in range(grid_size): # j-th row
neighbors = []
if i > 0: # left: (i - 1, j)
neighbors.append((i - 1) * grid_size + j)
if j > 0: # up: (i, j - 1)
neighbors.append(i * grid_size + j - 1)
if j < grid_size - 1: # down: (i, j + 1)
neighbors.append(i * grid_size + j + 1)
if i < grid_size - 1: # right: (i + 1, j)
neighbors.append((i + 1) * grid_size + j)
self.neighbor_points.append(tuple(neighbors))
# total edges in the grid
self.num_edges = sum([len(p) for p in self.neighbor_points])
self.forder_trans = nn.ModuleList() # first-order feature transition
self.sorder_trans = nn.ModuleList() # second-order feature transition
for neighbors in self.neighbor_points:
fo_trans = nn.ModuleList()
so_trans = nn.ModuleList()
for _ in range(len(neighbors)):
# each transition module consists of a 5x5 depth-wise conv and
# 1x1 conv.
fo_trans.append(
nn.Sequential(
nn.Conv2d(
self.point_feat_channels,
self.point_feat_channels,
5,
stride=1,
padding=2,
groups=self.point_feat_channels),
nn.Conv2d(self.point_feat_channels,
self.point_feat_channels, 1)))
so_trans.append(
nn.Sequential(
nn.Conv2d(
self.point_feat_channels,
self.point_feat_channels,
5,
1,
2,
groups=self.point_feat_channels),
nn.Conv2d(self.point_feat_channels,
self.point_feat_channels, 1)))
self.forder_trans.append(fo_trans)
self.sorder_trans.append(so_trans)
self.loss_grid = build_loss(loss_grid)
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
# TODO: compare mode = "fan_in" or "fan_out"
kaiming_init(m)
for m in self.modules():
if isinstance(m, nn.ConvTranspose2d):
normal_init(m, std=0.001)
nn.init.constant_(self.deconv2.bias, -np.log(0.99 / 0.01))
def forward(self, x):
assert x.shape[-1] == x.shape[-2] == self.roi_feat_size
# RoI feature transformation, downsample 2x
x = self.convs(x)
c = self.point_feat_channels
# first-order fusion
x_fo = [None for _ in range(self.grid_points)]
for i, points in enumerate(self.neighbor_points):
x_fo[i] = x[:, i * c:(i + 1) * c]
for j, point_idx in enumerate(points):
x_fo[i] = x_fo[i] + self.forder_trans[i][j](
x[:, point_idx * c:(point_idx + 1) * c])
# second-order fusion
x_so = [None for _ in range(self.grid_points)]
for i, points in enumerate(self.neighbor_points):
x_so[i] = x[:, i * c:(i + 1) * c]
for j, point_idx in enumerate(points):
x_so[i] = x_so[i] + self.sorder_trans[i][j](x_fo[point_idx])
# predicted heatmap with fused features
x2 = torch.cat(x_so, dim=1)
x2 = self.deconv1(x2)
x2 = F.relu(self.norm1(x2), inplace=True)
heatmap = self.deconv2(x2)
# predicted heatmap with original features (applicable during training)
if self.training:
x1 = x
x1 = self.deconv1(x1)
x1 = F.relu(self.norm1(x1), inplace=True)
heatmap_unfused = self.deconv2(x1)
else:
heatmap_unfused = heatmap
return dict(fused=heatmap, unfused=heatmap_unfused)
def calc_sub_regions(self):
"""Compute point specific representation regions.
See Grid R-CNN Plus (https://arxiv.org/abs/1906.05688) for details.
"""
# to make it consistent with the original implementation, half_size
# is computed as 2 * quarter_size, which is smaller
half_size = self.whole_map_size // 4 * 2
sub_regions = []
for i in range(self.grid_points):
x_idx = i // self.grid_size
y_idx = i % self.grid_size
if x_idx == 0:
sub_x1 = 0
elif x_idx == self.grid_size - 1:
sub_x1 = half_size
else:
ratio = x_idx / (self.grid_size - 1) - 0.25
sub_x1 = max(int(ratio * self.whole_map_size), 0)
if y_idx == 0:
sub_y1 = 0
elif y_idx == self.grid_size - 1:
sub_y1 = half_size
else:
ratio = y_idx / (self.grid_size - 1) - 0.25
sub_y1 = max(int(ratio * self.whole_map_size), 0)
sub_regions.append(
(sub_x1, sub_y1, sub_x1 + half_size, sub_y1 + half_size))
return sub_regions
def get_targets(self, sampling_results, rcnn_train_cfg):
# mix all samples (across images) together.
pos_bboxes = torch.cat([res.pos_bboxes for res in sampling_results],
dim=0).cpu()
pos_gt_bboxes = torch.cat(
[res.pos_gt_bboxes for res in sampling_results], dim=0).cpu()
assert pos_bboxes.shape == pos_gt_bboxes.shape
# expand pos_bboxes to 2x of original size
x1 = pos_bboxes[:, 0] - (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
y1 = pos_bboxes[:, 1] - (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
x2 = pos_bboxes[:, 2] + (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
y2 = pos_bboxes[:, 3] + (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
pos_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
pos_bbox_ws = (pos_bboxes[:, 2] - pos_bboxes[:, 0]).unsqueeze(-1)
pos_bbox_hs = (pos_bboxes[:, 3] - pos_bboxes[:, 1]).unsqueeze(-1)
num_rois = pos_bboxes.shape[0]
map_size = self.whole_map_size
# this is not the final target shape
targets = torch.zeros((num_rois, self.grid_points, map_size, map_size),
dtype=torch.float)
# pre-compute interpolation factors for all grid points.
# the first item is the factor of x-dim, and the second is y-dim.
# for a 9-point grid, factors are like (1, 0), (0.5, 0.5), (0, 1)
factors = []
for j in range(self.grid_points):
x_idx = j // self.grid_size
y_idx = j % self.grid_size
factors.append((1 - x_idx / (self.grid_size - 1),
1 - y_idx / (self.grid_size - 1)))
radius = rcnn_train_cfg.pos_radius
radius2 = radius**2
for i in range(num_rois):
# ignore small bboxes
if (pos_bbox_ws[i] <= self.grid_size
or pos_bbox_hs[i] <= self.grid_size):
continue
# for each grid point, mark a small circle as positive
for j in range(self.grid_points):
factor_x, factor_y = factors[j]
gridpoint_x = factor_x * pos_gt_bboxes[i, 0] + (
1 - factor_x) * pos_gt_bboxes[i, 2]
gridpoint_y = factor_y * pos_gt_bboxes[i, 1] + (
1 - factor_y) * pos_gt_bboxes[i, 3]
cx = int((gridpoint_x - pos_bboxes[i, 0]) / pos_bbox_ws[i] *
map_size)
cy = int((gridpoint_y - pos_bboxes[i, 1]) / pos_bbox_hs[i] *
map_size)
for x in range(cx - radius, cx + radius + 1):
for y in range(cy - radius, cy + radius + 1):
if x >= 0 and x < map_size and y >= 0 and y < map_size:
if (x - cx)**2 + (y - cy)**2 <= radius2:
targets[i, j, y, x] = 1
# reduce the target heatmap size by a half
# proposed in Grid R-CNN Plus (https://arxiv.org/abs/1906.05688).
sub_targets = []
for i in range(self.grid_points):
sub_x1, sub_y1, sub_x2, sub_y2 = self.sub_regions[i]
sub_targets.append(targets[:, [i], sub_y1:sub_y2, sub_x1:sub_x2])
sub_targets = torch.cat(sub_targets, dim=1)
sub_targets = sub_targets.to(sampling_results[0].pos_bboxes.device)
return sub_targets
def loss(self, grid_pred, grid_targets):
loss_fused = self.loss_grid(grid_pred['fused'], grid_targets)
loss_unfused = self.loss_grid(grid_pred['unfused'], grid_targets)
loss_grid = loss_fused + loss_unfused
return dict(loss_grid=loss_grid)
def get_bboxes(self, det_bboxes, grid_pred, img_metas):
# TODO: refactoring
assert det_bboxes.shape[0] == grid_pred.shape[0]
det_bboxes = det_bboxes.cpu()
cls_scores = det_bboxes[:, [4]]
det_bboxes = det_bboxes[:, :4]
grid_pred = grid_pred.sigmoid().cpu()
R, c, h, w = grid_pred.shape
half_size = self.whole_map_size // 4 * 2
assert h == w == half_size
assert c == self.grid_points
# find the point with max scores in the half-sized heatmap
grid_pred = grid_pred.view(R * c, h * w)
pred_scores, pred_position = grid_pred.max(dim=1)
xs = pred_position % w
ys = pred_position // w
# get the position in the whole heatmap instead of half-sized heatmap
for i in range(self.grid_points):
xs[i::self.grid_points] += self.sub_regions[i][0]
ys[i::self.grid_points] += self.sub_regions[i][1]
# reshape to (num_rois, grid_points)
pred_scores, xs, ys = tuple(
map(lambda x: x.view(R, c), [pred_scores, xs, ys]))
# get expanded pos_bboxes
widths = (det_bboxes[:, 2] - det_bboxes[:, 0]).unsqueeze(-1)
heights = (det_bboxes[:, 3] - det_bboxes[:, 1]).unsqueeze(-1)
x1 = (det_bboxes[:, 0, None] - widths / 2)
y1 = (det_bboxes[:, 1, None] - heights / 2)
# map the grid point to the absolute coordinates
abs_xs = (xs.float() + 0.5) / w * widths + x1
abs_ys = (ys.float() + 0.5) / h * heights + y1
# get the grid points indices that fall on the bbox boundaries
x1_inds = [i for i in range(self.grid_size)]
y1_inds = [i * self.grid_size for i in range(self.grid_size)]
x2_inds = [
self.grid_points - self.grid_size + i
for i in range(self.grid_size)
]
y2_inds = [(i + 1) * self.grid_size - 1 for i in range(self.grid_size)]
# voting of all grid points on some boundary
bboxes_x1 = (abs_xs[:, x1_inds] * pred_scores[:, x1_inds]).sum(
dim=1, keepdim=True) / (
pred_scores[:, x1_inds].sum(dim=1, keepdim=True))
bboxes_y1 = (abs_ys[:, y1_inds] * pred_scores[:, y1_inds]).sum(
dim=1, keepdim=True) / (
pred_scores[:, y1_inds].sum(dim=1, keepdim=True))
bboxes_x2 = (abs_xs[:, x2_inds] * pred_scores[:, x2_inds]).sum(
dim=1, keepdim=True) / (
pred_scores[:, x2_inds].sum(dim=1, keepdim=True))
bboxes_y2 = (abs_ys[:, y2_inds] * pred_scores[:, y2_inds]).sum(
dim=1, keepdim=True) / (
pred_scores[:, y2_inds].sum(dim=1, keepdim=True))
bbox_res = torch.cat(
[bboxes_x1, bboxes_y1, bboxes_x2, bboxes_y2, cls_scores], dim=1)
bbox_res[:, [0, 2]].clamp_(min=0, max=img_metas[0]['img_shape'][1])
bbox_res[:, [1, 3]].clamp_(min=0, max=img_metas[0]['img_shape'][0])
return bbox_res
| 15,432 | 41.869444 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule, Linear, constant_init, xavier_init
from mmcv.runner import auto_fp16
from mmdet.models.builder import HEADS
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class CoarseMaskHead(FCNMaskHead):
"""Coarse mask head used in PointRend.
Compared with standard ``FCNMaskHead``, ``CoarseMaskHead`` will downsample
the input feature map instead of upsample it.
Args:
num_convs (int): Number of conv layers in the head. Default: 0.
num_fcs (int): Number of fc layers in the head. Default: 2.
fc_out_channels (int): Number of output channels of fc layer.
Default: 1024.
downsample_factor (int): The factor that feature map is downsampled by.
Default: 2.
"""
def __init__(self,
num_convs=0,
num_fcs=2,
fc_out_channels=1024,
downsample_factor=2,
*arg,
**kwarg):
super(CoarseMaskHead, self).__init__(
*arg, num_convs=num_convs, upsample_cfg=dict(type=None), **kwarg)
self.num_fcs = num_fcs
assert self.num_fcs > 0
self.fc_out_channels = fc_out_channels
self.downsample_factor = downsample_factor
assert self.downsample_factor >= 1
# remove conv_logit
delattr(self, 'conv_logits')
if downsample_factor > 1:
downsample_in_channels = (
self.conv_out_channels
if self.num_convs > 0 else self.in_channels)
self.downsample_conv = ConvModule(
downsample_in_channels,
self.conv_out_channels,
kernel_size=downsample_factor,
stride=downsample_factor,
padding=0,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg)
else:
self.downsample_conv = None
self.output_size = (self.roi_feat_size[0] // downsample_factor,
self.roi_feat_size[1] // downsample_factor)
self.output_area = self.output_size[0] * self.output_size[1]
last_layer_dim = self.conv_out_channels * self.output_area
self.fcs = nn.ModuleList()
for i in range(num_fcs):
fc_in_channels = (
last_layer_dim if i == 0 else self.fc_out_channels)
self.fcs.append(Linear(fc_in_channels, self.fc_out_channels))
last_layer_dim = self.fc_out_channels
output_channels = self.num_classes * self.output_area
self.fc_logits = Linear(last_layer_dim, output_channels)
def init_weights(self):
for m in self.fcs.modules():
if isinstance(m, nn.Linear):
xavier_init(m)
constant_init(self.fc_logits, 0.001)
@auto_fp16()
def forward(self, x):
for conv in self.convs:
x = conv(x)
if self.downsample_conv is not None:
x = self.downsample_conv(x)
x = x.flatten(1)
for fc in self.fcs:
x = self.relu(fc(x))
mask_pred = self.fc_logits(x).view(
x.size(0), self.num_classes, *self.output_size)
return mask_pred
| 3,233 | 34.152174 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/mask_heads/maskiou_head.py | import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import Conv2d, Linear, MaxPool2d, kaiming_init, normal_init
from mmcv.runner import force_fp32
from torch.nn.modules.utils import _pair
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class MaskIoUHead(nn.Module):
"""Mask IoU Head.
This head predicts the IoU of predicted masks and corresponding gt masks.
"""
def __init__(self,
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
num_classes=80,
loss_iou=dict(type='MSELoss', loss_weight=0.5)):
super(MaskIoUHead, self).__init__()
self.in_channels = in_channels
self.conv_out_channels = conv_out_channels
self.fc_out_channels = fc_out_channels
self.num_classes = num_classes
self.fp16_enabled = False
self.convs = nn.ModuleList()
for i in range(num_convs):
if i == 0:
# concatenation of mask feature and mask prediction
in_channels = self.in_channels + 1
else:
in_channels = self.conv_out_channels
stride = 2 if i == num_convs - 1 else 1
self.convs.append(
Conv2d(
in_channels,
self.conv_out_channels,
3,
stride=stride,
padding=1))
roi_feat_size = _pair(roi_feat_size)
pooled_area = (roi_feat_size[0] // 2) * (roi_feat_size[1] // 2)
self.fcs = nn.ModuleList()
for i in range(num_fcs):
in_channels = (
self.conv_out_channels *
pooled_area if i == 0 else self.fc_out_channels)
self.fcs.append(Linear(in_channels, self.fc_out_channels))
self.fc_mask_iou = Linear(self.fc_out_channels, self.num_classes)
self.relu = nn.ReLU()
self.max_pool = MaxPool2d(2, 2)
self.loss_iou = build_loss(loss_iou)
def init_weights(self):
for conv in self.convs:
kaiming_init(conv)
for fc in self.fcs:
kaiming_init(
fc,
a=1,
mode='fan_in',
nonlinearity='leaky_relu',
distribution='uniform')
normal_init(self.fc_mask_iou, std=0.01)
def forward(self, mask_feat, mask_pred):
mask_pred = mask_pred.sigmoid()
mask_pred_pooled = self.max_pool(mask_pred.unsqueeze(1))
x = torch.cat((mask_feat, mask_pred_pooled), 1)
for conv in self.convs:
x = self.relu(conv(x))
x = x.flatten(1)
for fc in self.fcs:
x = self.relu(fc(x))
mask_iou = self.fc_mask_iou(x)
return mask_iou
@force_fp32(apply_to=('mask_iou_pred', ))
def loss(self, mask_iou_pred, mask_iou_targets):
pos_inds = mask_iou_targets > 0
if pos_inds.sum() > 0:
loss_mask_iou = self.loss_iou(mask_iou_pred[pos_inds],
mask_iou_targets[pos_inds])
else:
loss_mask_iou = mask_iou_pred.sum() * 0
return dict(loss_mask_iou=loss_mask_iou)
@force_fp32(apply_to=('mask_pred', ))
def get_targets(self, sampling_results, gt_masks, mask_pred, mask_targets,
rcnn_train_cfg):
"""Compute target of mask IoU.
Mask IoU target is the IoU of the predicted mask (inside a bbox) and
the gt mask of corresponding gt mask (the whole instance).
The intersection area is computed inside the bbox, and the gt mask area
is computed with two steps, firstly we compute the gt area inside the
bbox, then divide it by the area ratio of gt area inside the bbox and
the gt area of the whole instance.
Args:
sampling_results (list[:obj:`SamplingResult`]): sampling results.
gt_masks (BitmapMask | PolygonMask): Gt masks (the whole instance)
of each image, with the same shape of the input image.
mask_pred (Tensor): Predicted masks of each positive proposal,
shape (num_pos, h, w).
mask_targets (Tensor): Gt mask of each positive proposal,
binary map of the shape (num_pos, h, w).
rcnn_train_cfg (dict): Training config for R-CNN part.
Returns:
Tensor: mask iou target (length == num positive).
"""
pos_proposals = [res.pos_bboxes for res in sampling_results]
pos_assigned_gt_inds = [
res.pos_assigned_gt_inds for res in sampling_results
]
# compute the area ratio of gt areas inside the proposals and
# the whole instance
area_ratios = map(self._get_area_ratio, pos_proposals,
pos_assigned_gt_inds, gt_masks)
area_ratios = torch.cat(list(area_ratios))
assert mask_targets.size(0) == area_ratios.size(0)
mask_pred = (mask_pred > rcnn_train_cfg.mask_thr_binary).float()
mask_pred_areas = mask_pred.sum((-1, -2))
# mask_pred and mask_targets are binary maps
overlap_areas = (mask_pred * mask_targets).sum((-1, -2))
# compute the mask area of the whole instance
gt_full_areas = mask_targets.sum((-1, -2)) / (area_ratios + 1e-7)
mask_iou_targets = overlap_areas / (
mask_pred_areas + gt_full_areas - overlap_areas)
return mask_iou_targets
def _get_area_ratio(self, pos_proposals, pos_assigned_gt_inds, gt_masks):
"""Compute area ratio of the gt mask inside the proposal and the gt
mask of the corresponding instance."""
num_pos = pos_proposals.size(0)
if num_pos > 0:
area_ratios = []
proposals_np = pos_proposals.cpu().numpy()
pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
# compute mask areas of gt instances (batch processing for speedup)
gt_instance_mask_area = gt_masks.areas
for i in range(num_pos):
gt_mask = gt_masks[pos_assigned_gt_inds[i]]
# crop the gt mask inside the proposal
bbox = proposals_np[i, :].astype(np.int32)
gt_mask_in_proposal = gt_mask.crop(bbox)
ratio = gt_mask_in_proposal.areas[0] / (
gt_instance_mask_area[pos_assigned_gt_inds[i]] + 1e-7)
area_ratios.append(ratio)
area_ratios = torch.from_numpy(np.stack(area_ratios)).float().to(
pos_proposals.device)
else:
area_ratios = pos_proposals.new_zeros((0, ))
return area_ratios
@force_fp32(apply_to=('mask_iou_pred', ))
def get_mask_scores(self, mask_iou_pred, det_bboxes, det_labels):
"""Get the mask scores.
mask_score = bbox_score * mask_iou
"""
inds = range(det_labels.size(0))
mask_scores = mask_iou_pred[inds, det_labels] * det_bboxes[inds, -1]
mask_scores = mask_scores.cpu().numpy()
det_labels = det_labels.cpu().numpy()
return [mask_scores[det_labels == i] for i in range(self.num_classes)]
| 7,332 | 38.213904 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, ConvModule, build_upsample_layer
from mmcv.ops.carafe import CARAFEPack
from mmcv.runner import auto_fp16, force_fp32
from torch.nn.modules.utils import _pair
from mmdet.core import mask_target
from mmdet.models.builder import HEADS, build_loss
BYTES_PER_FLOAT = 4
# TODO: This memory limit may be too much or too little. It would be better to
# determine it based on available resources.
GPU_MEM_LIMIT = 1024**3 # 1 GB memory limit
@HEADS.register_module()
class FCNMaskHead(nn.Module):
def __init__(self,
num_convs=4,
roi_feat_size=14,
in_channels=256,
conv_kernel_size=3,
conv_out_channels=256,
num_classes=80,
class_agnostic=False,
upsample_cfg=dict(type='deconv', scale_factor=2),
conv_cfg=None,
norm_cfg=None,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)):
super(FCNMaskHead, self).__init__()
self.upsample_cfg = upsample_cfg.copy()
if self.upsample_cfg['type'] not in [
None, 'deconv', 'nearest', 'bilinear', 'carafe'
]:
raise ValueError(
f'Invalid upsample method {self.upsample_cfg["type"]}, '
'accepted methods are "deconv", "nearest", "bilinear", '
'"carafe"')
self.num_convs = num_convs
# WARN: roi_feat_size is reserved and not used
self.roi_feat_size = _pair(roi_feat_size)
self.in_channels = in_channels
self.conv_kernel_size = conv_kernel_size
self.conv_out_channels = conv_out_channels
self.upsample_method = self.upsample_cfg.get('type')
self.scale_factor = self.upsample_cfg.pop('scale_factor', None)
self.num_classes = num_classes
self.class_agnostic = class_agnostic
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.fp16_enabled = False
self.loss_mask = build_loss(loss_mask)
self.convs = nn.ModuleList()
for i in range(self.num_convs):
in_channels = (
self.in_channels if i == 0 else self.conv_out_channels)
padding = (self.conv_kernel_size - 1) // 2
self.convs.append(
ConvModule(
in_channels,
self.conv_out_channels,
self.conv_kernel_size,
padding=padding,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg))
upsample_in_channels = (
self.conv_out_channels if self.num_convs > 0 else in_channels)
upsample_cfg_ = self.upsample_cfg.copy()
if self.upsample_method is None:
self.upsample = None
elif self.upsample_method == 'deconv':
upsample_cfg_.update(
in_channels=upsample_in_channels,
out_channels=self.conv_out_channels,
kernel_size=self.scale_factor,
stride=self.scale_factor)
self.upsample = build_upsample_layer(upsample_cfg_)
elif self.upsample_method == 'carafe':
upsample_cfg_.update(
channels=upsample_in_channels, scale_factor=self.scale_factor)
self.upsample = build_upsample_layer(upsample_cfg_)
else:
# suppress warnings
align_corners = (None
if self.upsample_method == 'nearest' else False)
upsample_cfg_.update(
scale_factor=self.scale_factor,
mode=self.upsample_method,
align_corners=align_corners)
self.upsample = build_upsample_layer(upsample_cfg_)
out_channels = 1 if self.class_agnostic else self.num_classes
logits_in_channel = (
self.conv_out_channels
if self.upsample_method == 'deconv' else upsample_in_channels)
self.conv_logits = Conv2d(logits_in_channel, out_channels, 1)
self.relu = nn.ReLU(inplace=True)
self.debug_imgs = None
def init_weights(self):
for m in [self.upsample, self.conv_logits]:
if m is None:
continue
elif isinstance(m, CARAFEPack):
m.init_weights()
else:
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu')
nn.init.constant_(m.bias, 0)
@auto_fp16()
def forward(self, x):
for conv in self.convs:
x = conv(x)
if self.upsample is not None:
x = self.upsample(x)
if self.upsample_method == 'deconv':
x = self.relu(x)
mask_pred = self.conv_logits(x)
return mask_pred
def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_results]
pos_assigned_gt_inds = [
res.pos_assigned_gt_inds for res in sampling_results
]
mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
gt_masks, rcnn_train_cfg)
return mask_targets
@force_fp32(apply_to=('mask_pred', ))
def loss(self, mask_pred, mask_targets, labels):
loss = dict()
if mask_pred.size(0) == 0:
loss_mask = mask_pred.sum()
else:
if self.class_agnostic:
loss_mask = self.loss_mask(mask_pred, mask_targets,
torch.zeros_like(labels))
else:
loss_mask = self.loss_mask(mask_pred, mask_targets, labels)
loss['loss_mask'] = loss_mask
return loss
def get_seg_masks(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg,
ori_shape, scale_factor, rescale):
"""Get segmentation masks from mask_pred and bboxes.
Args:
mask_pred (Tensor or ndarray): shape (n, #class, h, w).
For single-scale testing, mask_pred is the direct output of
model, whose type is Tensor, while for multi-scale testing,
it will be converted to numpy array outside of this method.
det_bboxes (Tensor): shape (n, 4/5)
det_labels (Tensor): shape (n, )
img_shape (Tensor): shape (3, )
rcnn_test_cfg (dict): rcnn testing config
ori_shape: original image size
Returns:
list[list]: encoded masks
"""
if isinstance(mask_pred, torch.Tensor):
mask_pred = mask_pred.sigmoid()
else:
mask_pred = det_bboxes.new_tensor(mask_pred)
device = mask_pred.device
cls_segms = [[] for _ in range(self.num_classes)
] # BG is not included in num_classes
bboxes = det_bboxes[:, :4]
labels = det_labels
if rescale:
img_h, img_w = ori_shape[:2]
else:
if isinstance(scale_factor, float):
img_h = np.round(ori_shape[0] * scale_factor).astype(np.int32)
img_w = np.round(ori_shape[1] * scale_factor).astype(np.int32)
else:
w_scale, h_scale = scale_factor[0], scale_factor[1]
img_h = np.round(ori_shape[0] * h_scale.item()).astype(
np.int32)
img_w = np.round(ori_shape[1] * w_scale.item()).astype(
np.int32)
scale_factor = 1.0
if not isinstance(scale_factor, (float, torch.Tensor)):
scale_factor = bboxes.new_tensor(scale_factor)
bboxes = bboxes / scale_factor
N = len(mask_pred)
# The actual implementation split the input into chunks,
# and paste them chunk by chunk.
if device.type == 'cpu':
# CPU is most efficient when they are pasted one by one with
# skip_empty=True, so that it performs minimal number of
# operations.
num_chunks = N
else:
# GPU benefits from parallelism for larger chunks,
# but may have memory issue
num_chunks = int(
np.ceil(N * img_h * img_w * BYTES_PER_FLOAT / GPU_MEM_LIMIT))
assert (num_chunks <=
N), 'Default GPU_MEM_LIMIT is too small; try increasing it'
chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
threshold = rcnn_test_cfg.mask_thr_binary
im_mask = torch.zeros(
N,
img_h,
img_w,
device=device,
dtype=torch.bool if threshold >= 0 else torch.uint8)
if not self.class_agnostic:
mask_pred = mask_pred[range(N), labels][:, None]
for inds in chunks:
masks_chunk, spatial_inds = _do_paste_mask(
mask_pred[inds],
bboxes[inds],
img_h,
img_w,
skip_empty=device.type == 'cpu')
if threshold >= 0:
masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
else:
# for visualization and debugging
masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
im_mask[(inds, ) + spatial_inds] = masks_chunk
for i in range(N):
cls_segms[labels[i]].append(im_mask[i].cpu().numpy())
return cls_segms
def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True):
"""Paste instance masks acoording to boxes.
This implementation is modified from
https://github.com/facebookresearch/detectron2/
Args:
masks (Tensor): N, 1, H, W
boxes (Tensor): N, 4
img_h (int): Height of the image to be pasted.
img_w (int): Width of the image to be pasted.
skip_empty (bool): Only paste masks within the region that
tightly bound all boxes, and returns the results this region only.
An important optimization for CPU.
Returns:
tuple: (Tensor, tuple). The first item is mask tensor, the second one
is the slice object.
If skip_empty == False, the whole image will be pasted. It will
return a mask of shape (N, img_h, img_w) and an empty tuple.
If skip_empty == True, only area around the mask will be pasted.
A mask of shape (N, h', w') and its start and end coordinates
in the original image will be returned.
"""
# On GPU, paste all masks together (up to chunk size)
# by using the entire image to sample the masks
# Compared to pasting them one by one,
# this has more operations but is faster on COCO-scale dataset.
device = masks.device
if skip_empty:
x0_int, y0_int = torch.clamp(
boxes.min(dim=0).values.floor()[:2] - 1,
min=0).to(dtype=torch.int32)
x1_int = torch.clamp(
boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32)
y1_int = torch.clamp(
boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32)
else:
x0_int, y0_int = 0, 0
x1_int, y1_int = img_w, img_h
x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1
N = masks.shape[0]
img_y = torch.arange(
y0_int, y1_int, device=device, dtype=torch.float32) + 0.5
img_x = torch.arange(
x0_int, x1_int, device=device, dtype=torch.float32) + 0.5
img_y = (img_y - y0) / (y1 - y0) * 2 - 1
img_x = (img_x - x0) / (x1 - x0) * 2 - 1
# img_x, img_y have shapes (N, w), (N, h)
if torch.isinf(img_x).any():
inds = torch.where(torch.isinf(img_x))
img_x[inds] = 0
if torch.isinf(img_y).any():
inds = torch.where(torch.isinf(img_y))
img_y[inds] = 0
gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1))
gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
grid = torch.stack([gx, gy], dim=3)
img_masks = F.grid_sample(
masks.to(dtype=torch.float32), grid, align_corners=False)
if skip_empty:
return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int))
else:
return img_masks[:, 0], ()
| 12,399 | 38.240506 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, kaiming_init
from mmcv.runner import auto_fp16, force_fp32
from mmdet.models.builder import HEADS
@HEADS.register_module()
class FusedSemanticHead(nn.Module):
r"""Multi-level fused semantic segmentation head.
.. code-block:: none
in_1 -> 1x1 conv ---
|
in_2 -> 1x1 conv -- |
||
in_3 -> 1x1 conv - ||
||| /-> 1x1 conv (mask prediction)
in_4 -> 1x1 conv -----> 3x3 convs (*4)
| \-> 1x1 conv (feature)
in_5 -> 1x1 conv ---
""" # noqa: W605
def __init__(self,
num_ins,
fusion_level,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=183,
ignore_label=255,
loss_weight=0.2,
conv_cfg=None,
norm_cfg=None):
super(FusedSemanticHead, self).__init__()
self.num_ins = num_ins
self.fusion_level = fusion_level
self.num_convs = num_convs
self.in_channels = in_channels
self.conv_out_channels = conv_out_channels
self.num_classes = num_classes
self.ignore_label = ignore_label
self.loss_weight = loss_weight
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.fp16_enabled = False
self.lateral_convs = nn.ModuleList()
for i in range(self.num_ins):
self.lateral_convs.append(
ConvModule(
self.in_channels,
self.in_channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
inplace=False))
self.convs = nn.ModuleList()
for i in range(self.num_convs):
in_channels = self.in_channels if i == 0 else conv_out_channels
self.convs.append(
ConvModule(
in_channels,
conv_out_channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.conv_embedding = ConvModule(
conv_out_channels,
conv_out_channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg)
self.conv_logits = nn.Conv2d(conv_out_channels, self.num_classes, 1)
self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_label)
def init_weights(self):
kaiming_init(self.conv_logits)
@auto_fp16()
def forward(self, feats):
x = self.lateral_convs[self.fusion_level](feats[self.fusion_level])
fused_size = tuple(x.shape[-2:])
for i, feat in enumerate(feats):
if i != self.fusion_level:
feat = F.interpolate(
feat, size=fused_size, mode='bilinear', align_corners=True)
x += self.lateral_convs[i](feat)
for i in range(self.num_convs):
x = self.convs[i](x)
mask_pred = self.conv_logits(x)
x = self.conv_embedding(x)
return mask_pred, x
@force_fp32(apply_to=('mask_pred', ))
def loss(self, mask_pred, labels):
labels = labels.squeeze(1).long()
loss_semantic_seg = self.criterion(mask_pred, labels)
loss_semantic_seg *= self.loss_weight
return loss_semantic_seg
| 3,610 | 32.435185 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/roi_heads/mask_heads/mask_point_head.py | # Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, normal_init
from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class MaskPointHead(nn.Module):
"""A mask point head use in PointRend.
``MaskPointHead`` use shared multi-layer perceptron (equivalent to
nn.Conv1d) to predict the logit of input points. The fine-grained feature
and coarse feature will be concatenate together for predication.
Args:
num_fcs (int): Number of fc layers in the head. Default: 3.
in_channels (int): Number of input channels. Default: 256.
fc_channels (int): Number of fc channels. Default: 256.
num_classes (int): Number of classes for logits. Default: 80.
class_agnostic (bool): Whether use class agnostic classification.
If so, the output channels of logits will be 1. Default: False.
coarse_pred_each_layer (bool): Whether concatenate coarse feature with
the output of each fc layer. Default: True.
conv_cfg (dict | None): Dictionary to construct and config conv layer.
Default: dict(type='Conv1d'))
norm_cfg (dict | None): Dictionary to construct and config norm layer.
Default: None.
loss_point (dict): Dictionary to construct and config loss layer of
point head. Default: dict(type='CrossEntropyLoss', use_mask=True,
loss_weight=1.0).
"""
def __init__(self,
num_classes,
num_fcs=3,
in_channels=256,
fc_channels=256,
class_agnostic=False,
coarse_pred_each_layer=True,
conv_cfg=dict(type='Conv1d'),
norm_cfg=None,
act_cfg=dict(type='ReLU'),
loss_point=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)):
super().__init__()
self.num_fcs = num_fcs
self.in_channels = in_channels
self.fc_channles = fc_channels
self.num_classes = num_classes
self.class_agnostic = class_agnostic
self.coarse_pred_each_layer = coarse_pred_each_layer
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.loss_point = build_loss(loss_point)
fc_in_channels = in_channels + num_classes
self.fcs = nn.ModuleList()
for _ in range(num_fcs):
fc = ConvModule(
fc_in_channels,
fc_channels,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.fcs.append(fc)
fc_in_channels = fc_channels
fc_in_channels += num_classes if self.coarse_pred_each_layer else 0
out_channels = 1 if self.class_agnostic else self.num_classes
self.fc_logits = nn.Conv1d(
fc_in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def init_weights(self):
"""Initialize last classification layer of MaskPointHead, conv layers
are already initialized by ConvModule."""
normal_init(self.fc_logits, std=0.001)
def forward(self, fine_grained_feats, coarse_feats):
"""Classify each point base on fine grained and coarse feats.
Args:
fine_grained_feats (Tensor): Fine grained feature sampled from FPN,
shape (num_rois, in_channels, num_points).
coarse_feats (Tensor): Coarse feature sampled from CoarseMaskHead,
shape (num_rois, num_classes, num_points).
Returns:
Tensor: Point classification results,
shape (num_rois, num_class, num_points).
"""
x = torch.cat([fine_grained_feats, coarse_feats], dim=1)
for fc in self.fcs:
x = fc(x)
if self.coarse_pred_each_layer:
x = torch.cat((x, coarse_feats), dim=1)
return self.fc_logits(x)
def get_targets(self, rois, rel_roi_points, sampling_results, gt_masks,
cfg):
"""Get training targets of MaskPointHead for all images.
Args:
rois (Tensor): Region of Interest, shape (num_rois, 5).
rel_roi_points: Points coordinates relative to RoI, shape
(num_rois, num_points, 2).
sampling_results (:obj:`SamplingResult`): Sampling result after
sampling and assignment.
gt_masks (Tensor) : Ground truth segmentation masks of
corresponding boxes, shape (num_rois, height, width).
cfg (dict): Training cfg.
Returns:
Tensor: Point target, shape (num_rois, num_points).
"""
num_imgs = len(sampling_results)
rois_list = []
rel_roi_points_list = []
for batch_ind in range(num_imgs):
inds = (rois[:, 0] == batch_ind)
rois_list.append(rois[inds])
rel_roi_points_list.append(rel_roi_points[inds])
pos_assigned_gt_inds_list = [
res.pos_assigned_gt_inds for res in sampling_results
]
cfg_list = [cfg for _ in range(num_imgs)]
point_targets = map(self._get_target_single, rois_list,
rel_roi_points_list, pos_assigned_gt_inds_list,
gt_masks, cfg_list)
point_targets = list(point_targets)
if len(point_targets) > 0:
point_targets = torch.cat(point_targets)
return point_targets
def _get_target_single(self, rois, rel_roi_points, pos_assigned_gt_inds,
gt_masks, cfg):
"""Get training target of MaskPointHead for each image."""
num_pos = rois.size(0)
num_points = cfg.num_points
if num_pos > 0:
gt_masks_th = (
gt_masks.to_tensor(rois.dtype, rois.device).index_select(
0, pos_assigned_gt_inds))
gt_masks_th = gt_masks_th.unsqueeze(1)
rel_img_points = rel_roi_point_to_rel_img_point(
rois, rel_roi_points, gt_masks_th.shape[2:])
point_targets = point_sample(gt_masks_th,
rel_img_points).squeeze(1)
else:
point_targets = rois.new_zeros((0, num_points))
return point_targets
def loss(self, point_pred, point_targets, labels):
"""Calculate loss for MaskPointHead.
Args:
point_pred (Tensor): Point predication result, shape
(num_rois, num_classes, num_points).
point_targets (Tensor): Point targets, shape (num_roi, num_points).
labels (Tensor): Class label of corresponding boxes,
shape (num_rois, )
Returns:
dict[str, Tensor]: a dictionary of point loss components
"""
loss = dict()
if self.class_agnostic:
loss_point = self.loss_point(point_pred, point_targets,
torch.zeros_like(labels))
else:
loss_point = self.loss_point(point_pred, point_targets, labels)
loss['loss_point'] = loss_point
return loss
def _get_uncertainty(self, mask_pred, labels):
"""Estimate uncertainty based on pred logits.
We estimate uncertainty as L1 distance between 0.0 and the logits
prediction in 'mask_pred' for the foreground class in `classes`.
Args:
mask_pred (Tensor): mask predication logits, shape (num_rois,
num_classes, mask_height, mask_width).
labels (list[Tensor]): Either predicted or ground truth label for
each predicted mask, of length num_rois.
Returns:
scores (Tensor): Uncertainty scores with the most uncertain
locations having the highest uncertainty score,
shape (num_rois, 1, mask_height, mask_width)
"""
if mask_pred.shape[1] == 1:
gt_class_logits = mask_pred.clone()
else:
inds = torch.arange(mask_pred.shape[0], device=mask_pred.device)
gt_class_logits = mask_pred[inds, labels].unsqueeze(1)
return -torch.abs(gt_class_logits)
def get_roi_rel_points_train(self, mask_pred, labels, cfg):
"""Get ``num_points`` most uncertain points with random points during
train.
Sample points in [0, 1] x [0, 1] coordinate space based on their
uncertainty. The uncertainties are calculated for each point using
'_get_uncertainty()' function that takes point's logit prediction as
input.
Args:
mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
mask_height, mask_width) for class-specific or class-agnostic
prediction.
labels (list): The ground truth class for each instance.
cfg (dict): Training config of point head.
Returns:
point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
that contains the coordinates sampled points.
"""
num_points = cfg.num_points
oversample_ratio = cfg.oversample_ratio
importance_sample_ratio = cfg.importance_sample_ratio
assert oversample_ratio >= 1
assert 0 <= importance_sample_ratio <= 1
batch_size = mask_pred.shape[0]
num_sampled = int(num_points * oversample_ratio)
point_coords = torch.rand(
batch_size, num_sampled, 2, device=mask_pred.device)
point_logits = point_sample(mask_pred, point_coords)
# It is crucial to calculate uncertainty based on the sampled
# prediction value for the points. Calculating uncertainties of the
# coarse predictions first and sampling them for points leads to
# incorrect results. To illustrate this: assume uncertainty func(
# logits)=-abs(logits), a sampled point between two coarse
# predictions with -1 and 1 logits has 0 logits, and therefore 0
# uncertainty value. However, if we calculate uncertainties for the
# coarse predictions first, both will have -1 uncertainty,
# and sampled point will get -1 uncertainty.
point_uncertainties = self._get_uncertainty(point_logits, labels)
num_uncertain_points = int(importance_sample_ratio * num_points)
num_random_points = num_points - num_uncertain_points
idx = torch.topk(
point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
shift = num_sampled * torch.arange(
batch_size, dtype=torch.long, device=mask_pred.device)
idx += shift[:, None]
point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(
batch_size, num_uncertain_points, 2)
if num_random_points > 0:
rand_roi_coords = torch.rand(
batch_size, num_random_points, 2, device=mask_pred.device)
point_coords = torch.cat((point_coords, rand_roi_coords), dim=1)
return point_coords
def get_roi_rel_points_test(self, mask_pred, pred_label, cfg):
"""Get ``num_points`` most uncertain points during test.
Args:
mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
mask_height, mask_width) for class-specific or class-agnostic
prediction.
pred_label (list): The predication class for each instance.
cfg (dict): Testing config of point head.
Returns:
point_indices (Tensor): A tensor of shape (num_rois, num_points)
that contains indices from [0, mask_height x mask_width) of the
most uncertain points.
point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
that contains [0, 1] x [0, 1] normalized coordinates of the
most uncertain points from the [mask_height, mask_width] grid .
"""
num_points = cfg.subdivision_num_points
uncertainty_map = self._get_uncertainty(mask_pred, pred_label)
num_rois, _, mask_height, mask_width = uncertainty_map.shape
h_step = 1.0 / mask_height
w_step = 1.0 / mask_width
uncertainty_map = uncertainty_map.view(num_rois,
mask_height * mask_width)
num_points = min(mask_height * mask_width, num_points)
point_indices = uncertainty_map.topk(num_points, dim=1)[1]
point_coords = uncertainty_map.new_zeros(num_rois, num_points, 2)
point_coords[:, :, 0] = w_step / 2.0 + (point_indices %
mask_width).float() * w_step
point_coords[:, :, 1] = h_step / 2.0 + (point_indices //
mask_width).float() * h_step
return point_indices, point_coords
| 13,190 | 42.82392 | 126 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/ghm_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(
(labels >= 0) & (labels < label_channels), as_tuple=False).squeeze()
if inds.numel() > 0:
bin_labels[inds, labels[inds]] = 1
bin_label_weights = label_weights.view(-1, 1).expand(
label_weights.size(0), label_channels)
return bin_labels, bin_label_weights
# TODO: code refactoring to make it consistent with other losses
@LOSSES.register_module()
class GHMC(nn.Module):
"""GHM Classification Loss.
Details of the theorem can be viewed in the paper
`Gradient Harmonized Single-stage Detector
<https://arxiv.org/abs/1811.05181>`_.
Args:
bins (int): Number of the unit regions for distribution calculation.
momentum (float): The parameter for moving average.
use_sigmoid (bool): Can only be true for BCE based loss now.
loss_weight (float): The weight of the total GHM-C loss.
"""
def __init__(self, bins=10, momentum=0, use_sigmoid=True, loss_weight=1.0):
super(GHMC, self).__init__()
self.bins = bins
self.momentum = momentum
edges = torch.arange(bins + 1).float() / bins
self.register_buffer('edges', edges)
self.edges[-1] += 1e-6
if momentum > 0:
acc_sum = torch.zeros(bins)
self.register_buffer('acc_sum', acc_sum)
self.use_sigmoid = use_sigmoid
if not self.use_sigmoid:
raise NotImplementedError
self.loss_weight = loss_weight
def forward(self, pred, target, label_weight, *args, **kwargs):
"""Calculate the GHM-C loss.
Args:
pred (float tensor of size [batch_num, class_num]):
The direct prediction of classification fc layer.
target (float tensor of size [batch_num, class_num]):
Binary class target for each sample.
label_weight (float tensor of size [batch_num, class_num]):
the value is 1 if the sample is valid and 0 if ignored.
Returns:
The gradient harmonized loss.
"""
# the target should be binary class label
if pred.dim() != target.dim():
target, label_weight = _expand_onehot_labels(
target, label_weight, pred.size(-1))
target, label_weight = target.float(), label_weight.float()
edges = self.edges
mmt = self.momentum
weights = torch.zeros_like(pred)
# gradient length
g = torch.abs(pred.sigmoid().detach() - target)
valid = label_weight > 0
tot = max(valid.float().sum().item(), 1.0)
n = 0 # n valid bins
for i in range(self.bins):
inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
num_in_bin = inds.sum().item()
if num_in_bin > 0:
if mmt > 0:
self.acc_sum[i] = mmt * self.acc_sum[i] \
+ (1 - mmt) * num_in_bin
weights[inds] = tot / self.acc_sum[i]
else:
weights[inds] = tot / num_in_bin
n += 1
if n > 0:
weights = weights / n
loss = F.binary_cross_entropy_with_logits(
pred, target, weights, reduction='sum') / tot
return loss * self.loss_weight
# TODO: code refactoring to make it consistent with other losses
@LOSSES.register_module()
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
`Gradient Harmonized Single-stage Detector
<https://arxiv.org/abs/1811.05181>`_.
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
bins (int): Number of the unit regions for distribution calculation.
momentum (float): The parameter for moving average.
loss_weight (float): The weight of the total GHM-R loss.
"""
def __init__(self, mu=0.02, bins=10, momentum=0, loss_weight=1.0):
super(GHMR, self).__init__()
self.mu = mu
self.bins = bins
edges = torch.arange(bins + 1).float() / bins
self.register_buffer('edges', edges)
self.edges[-1] = 1e3
self.momentum = momentum
if momentum > 0:
acc_sum = torch.zeros(bins)
self.register_buffer('acc_sum', acc_sum)
self.loss_weight = loss_weight
# TODO: support reduction parameter
def forward(self, pred, target, label_weight, avg_factor=None):
"""Calculate the GHM-R loss.
Args:
pred (float tensor of size [batch_num, 4 (* class_num)]):
The prediction of box regression layer. Channel number can be 4
or 4 * class_num depending on whether it is class-agnostic.
target (float tensor of size [batch_num, 4 (* class_num)]):
The target regression values with the same size of pred.
label_weight (float tensor of size [batch_num, 4 (* class_num)]):
The weight of each sample, 0 if ignored.
Returns:
The gradient harmonized loss.
"""
mu = self.mu
edges = self.edges
mmt = self.momentum
# ASL1 loss
diff = pred - target
loss = torch.sqrt(diff * diff + mu * mu) - mu
# gradient length
g = torch.abs(diff / torch.sqrt(mu * mu + diff * diff)).detach()
weights = torch.zeros_like(g)
valid = label_weight > 0
tot = max(label_weight.float().sum().item(), 1.0)
n = 0 # n: valid bins
for i in range(self.bins):
inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
num_in_bin = inds.sum().item()
if num_in_bin > 0:
n += 1
if mmt > 0:
self.acc_sum[i] = mmt * self.acc_sum[i] \
+ (1 - mmt) * num_in_bin
weights[inds] = tot / self.acc_sum[i]
else:
weights[inds] = tot / num_in_bin
if n > 0:
weights /= n
loss = loss * weights
loss = loss.sum() / tot
return loss * self.loss_weight
| 6,365 | 35.797688 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/mse_loss.py | import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def mse_loss(pred, target):
"""Warpper of mse loss."""
return F.mse_loss(pred, target, reduction='none')
@LOSSES.register_module()
class MSELoss(nn.Module):
"""MSELoss.
Args:
reduction (str, optional): The method that reduces the loss to a
scalar. Options are "none", "mean" and "sum".
loss_weight (float, optional): The weight of the loss. Defaults to 1.0
"""
def __init__(self, reduction='mean', loss_weight=1.0):
super().__init__()
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self, pred, target, weight=None, avg_factor=None):
"""Forward function of loss.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
weight (torch.Tensor, optional): Weight of the loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
Returns:
torch.Tensor: The calculated loss
"""
loss = self.loss_weight * mse_loss(
pred,
target,
weight,
reduction=self.reduction,
avg_factor=avg_factor)
return loss
| 1,463 | 28.28 | 78 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/pisa_loss.py | import torch
from mmdet.core import bbox_overlaps
def isr_p(cls_score,
bbox_pred,
bbox_targets,
rois,
sampling_results,
loss_cls,
bbox_coder,
k=2,
bias=0,
num_class=80):
"""Importance-based Sample Reweighting (ISR_P), positive part.
Args:
cls_score (Tensor): Predicted classification scores.
bbox_pred (Tensor): Predicted bbox deltas.
bbox_targets (tuple[Tensor]): A tuple of bbox targets, the are
labels, label_weights, bbox_targets, bbox_weights, respectively.
rois (Tensor): Anchors (single_stage) in shape (n, 4) or RoIs
(two_stage) in shape (n, 5).
sampling_results (obj): Sampling results.
loss_cls (func): Classification loss func of the head.
bbox_coder (obj): BBox coder of the head.
k (float): Power of the non-linear mapping.
bias (float): Shift of the non-linear mapping.
num_class (int): Number of classes, default: 80.
Return:
tuple([Tensor]): labels, imp_based_label_weights, bbox_targets,
bbox_target_weights
"""
labels, label_weights, bbox_targets, bbox_weights = bbox_targets
pos_label_inds = ((labels >= 0) &
(labels < num_class)).nonzero().reshape(-1)
pos_labels = labels[pos_label_inds]
# if no positive samples, return the original targets
num_pos = float(pos_label_inds.size(0))
if num_pos == 0:
return labels, label_weights, bbox_targets, bbox_weights
# merge pos_assigned_gt_inds of per image to a single tensor
gts = list()
last_max_gt = 0
for i in range(len(sampling_results)):
gt_i = sampling_results[i].pos_assigned_gt_inds
gts.append(gt_i + last_max_gt)
if len(gt_i) != 0:
last_max_gt = gt_i.max() + 1
gts = torch.cat(gts)
assert len(gts) == num_pos
cls_score = cls_score.detach()
bbox_pred = bbox_pred.detach()
# For single stage detectors, rois here indicate anchors, in shape (N, 4)
# For two stage detectors, rois are in shape (N, 5)
if rois.size(-1) == 5:
pos_rois = rois[pos_label_inds][:, 1:]
else:
pos_rois = rois[pos_label_inds]
if bbox_pred.size(-1) > 4:
bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
pos_delta_pred = bbox_pred[pos_label_inds, pos_labels].view(-1, 4)
else:
pos_delta_pred = bbox_pred[pos_label_inds].view(-1, 4)
# compute iou of the predicted bbox and the corresponding GT
pos_delta_target = bbox_targets[pos_label_inds].view(-1, 4)
pos_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_pred)
target_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_target)
ious = bbox_overlaps(pos_bbox_pred, target_bbox_pred, is_aligned=True)
pos_imp_weights = label_weights[pos_label_inds]
# Two steps to compute IoU-HLR. Samples are first sorted by IoU locally,
# then sorted again within the same-rank group
max_l_num = pos_labels.bincount().max()
for label in pos_labels.unique():
l_inds = (pos_labels == label).nonzero().view(-1)
l_gts = gts[l_inds]
for t in l_gts.unique():
t_inds = l_inds[l_gts == t]
t_ious = ious[t_inds]
_, t_iou_rank_idx = t_ious.sort(descending=True)
_, t_iou_rank = t_iou_rank_idx.sort()
ious[t_inds] += max_l_num - t_iou_rank.float()
l_ious = ious[l_inds]
_, l_iou_rank_idx = l_ious.sort(descending=True)
_, l_iou_rank = l_iou_rank_idx.sort() # IoU-HLR
# linearly map HLR to label weights
pos_imp_weights[l_inds] *= (max_l_num - l_iou_rank.float()) / max_l_num
pos_imp_weights = (bias + pos_imp_weights * (1 - bias)).pow(k)
# normalize to make the new weighted loss value equal to the original loss
pos_loss_cls = loss_cls(
cls_score[pos_label_inds], pos_labels, reduction_override='none')
if pos_loss_cls.dim() > 1:
ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds][:,
None]
new_pos_loss_cls = pos_loss_cls * pos_imp_weights[:, None]
else:
ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds]
new_pos_loss_cls = pos_loss_cls * pos_imp_weights
pos_loss_cls_ratio = ori_pos_loss_cls.sum() / new_pos_loss_cls.sum()
pos_imp_weights = pos_imp_weights * pos_loss_cls_ratio
label_weights[pos_label_inds] = pos_imp_weights
bbox_targets = labels, label_weights, bbox_targets, bbox_weights
return bbox_targets
def carl_loss(cls_score,
labels,
bbox_pred,
bbox_targets,
loss_bbox,
k=1,
bias=0.2,
avg_factor=None,
sigmoid=False,
num_class=80):
"""Classification-Aware Regression Loss (CARL).
Args:
cls_score (Tensor): Predicted classification scores.
labels (Tensor): Targets of classification.
bbox_pred (Tensor): Predicted bbox deltas.
bbox_targets (Tensor): Target of bbox regression.
loss_bbox (func): Regression loss func of the head.
bbox_coder (obj): BBox coder of the head.
k (float): Power of the non-linear mapping.
bias (float): Shift of the non-linear mapping.
avg_factor (int): Average factor used in regression loss.
sigmoid (bool): Activation of the classification score.
num_class (int): Number of classes, default: 80.
Return:
dict: CARL loss dict.
"""
pos_label_inds = ((labels >= 0) &
(labels < num_class)).nonzero().reshape(-1)
if pos_label_inds.numel() == 0:
return dict(loss_carl=cls_score.sum()[None] * 0.)
pos_labels = labels[pos_label_inds]
# multiply pos_cls_score with the corresponding bbox weight
# and remain gradient
if sigmoid:
pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels]
else:
pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels]
carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k)
# normalize carl_loss_weight to make its sum equal to num positive
num_pos = float(pos_cls_score.size(0))
weight_ratio = num_pos / carl_loss_weights.sum()
carl_loss_weights *= weight_ratio
if avg_factor is None:
avg_factor = bbox_targets.size(0)
# if is class agnostic, bbox pred is in shape (N, 4)
# otherwise, bbox pred is in shape (N, #classes, 4)
if bbox_pred.size(-1) > 4:
bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
pos_bbox_preds = bbox_pred[pos_label_inds, pos_labels]
else:
pos_bbox_preds = bbox_pred[pos_label_inds]
ori_loss_reg = loss_bbox(
pos_bbox_preds,
bbox_targets[pos_label_inds],
reduction_override='none') / avg_factor
loss_carl = (ori_loss_reg * carl_loss_weights[:, None]).sum()
return dict(loss_carl=loss_carl[None])
| 7,076 | 38.099448 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/balanced_l1_loss.py | import numpy as np
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,
alpha=0.5,
gamma=1.5,
reduction='mean'):
"""Calculate balanced L1 loss.
Please see the `Libra R-CNN <https://arxiv.org/pdf/1904.02701.pdf>`_
Args:
pred (torch.Tensor): The prediction with shape (N, 4).
target (torch.Tensor): The learning target of the prediction with
shape (N, 4).
beta (float): The loss is a piecewise function of prediction and target
and ``beta`` serves as a threshold for the difference between the
prediction and target. Defaults to 1.0.
alpha (float): The denominator ``alpha`` in the balanced L1 loss.
Defaults to 0.5.
gamma (float): The ``gamma`` in the balanced L1 loss.
Defaults to 1.5.
reduction (str, optional): The method that reduces the loss to a
scalar. Options are "none", "mean" and "sum".
Returns:
torch.Tensor: The calculated loss
"""
assert beta > 0
assert pred.size() == target.size() and target.numel() > 0
diff = torch.abs(pred - target)
b = np.e**(gamma / alpha) - 1
loss = torch.where(
diff < beta, alpha / b *
(b * diff + 1) * torch.log(b * diff / beta + 1) - alpha * diff,
gamma * diff + gamma / b - alpha * beta)
return loss
@LOSSES.register_module()
class BalancedL1Loss(nn.Module):
"""Balanced L1 Loss.
arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)
Args:
alpha (float): The denominator ``alpha`` in the balanced L1 loss.
Defaults to 0.5.
gamma (float): The ``gamma`` in the balanced L1 loss. Defaults to 1.5.
beta (float, optional): The loss is a piecewise function of prediction
and target. ``beta`` serves as a threshold for the difference
between the prediction and target. Defaults to 1.0.
reduction (str, optional): The method that reduces the loss to a
scalar. Options are "none", "mean" and "sum".
loss_weight (float, optional): The weight of the loss. Defaults to 1.0
"""
def __init__(self,
alpha=0.5,
gamma=1.5,
beta=1.0,
reduction='mean',
loss_weight=1.0):
super(BalancedL1Loss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.beta = beta
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
"""Forward function of loss.
Args:
pred (torch.Tensor): The prediction with shape (N, 4).
target (torch.Tensor): The learning target of the prediction with
shape (N, 4).
weight (torch.Tensor, optional): Sample-wise loss weight with
shape (N, ).
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Options are "none", "mean" and "sum".
Returns:
torch.Tensor: The calculated loss
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
loss_bbox = self.loss_weight * balanced_l1_loss(
pred,
target,
weight,
alpha=self.alpha,
gamma=self.gamma,
beta=self.beta,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss_bbox
| 4,116 | 33.596639 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/iou_loss.py | import math
import torch
import torch.nn as nn
from mmdet.core import bbox_overlaps
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def iou_loss(pred, target, eps=1e-6):
"""IoU loss.
Computing the IoU loss between a set of predicted bboxes and target bboxes.
The loss is calculated as negative log of IoU.
Args:
pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
shape (n, 4).
target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
eps (float): Eps to avoid log(0).
Return:
torch.Tensor: Loss tensor.
"""
ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
loss = -ious.log()
return loss
@weighted_loss
def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
"""BIoULoss.
This is an implementation of paper
`Improving Object Localization with Fitness NMS and Bounded IoU Loss.
<https://arxiv.org/abs/1711.00164>`_.
Args:
pred (torch.Tensor): Predicted bboxes.
target (torch.Tensor): Target bboxes.
beta (float): beta parameter in smoothl1.
eps (float): eps to avoid NaN.
"""
pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5
pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5
pred_w = pred[:, 2] - pred[:, 0]
pred_h = pred[:, 3] - pred[:, 1]
with torch.no_grad():
target_ctrx = (target[:, 0] + target[:, 2]) * 0.5
target_ctry = (target[:, 1] + target[:, 3]) * 0.5
target_w = target[:, 2] - target[:, 0]
target_h = target[:, 3] - target[:, 1]
dx = target_ctrx - pred_ctrx
dy = target_ctry - pred_ctry
loss_dx = 1 - torch.max(
(target_w - 2 * dx.abs()) /
(target_w + 2 * dx.abs() + eps), torch.zeros_like(dx))
loss_dy = 1 - torch.max(
(target_h - 2 * dy.abs()) /
(target_h + 2 * dy.abs() + eps), torch.zeros_like(dy))
loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w /
(target_w + eps))
loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h /
(target_h + eps))
loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh],
dim=-1).view(loss_dx.size(0), -1)
loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta,
loss_comb - 0.5 * beta)
return loss
@weighted_loss
def giou_loss(pred, target, eps=1e-7):
r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding
Box Regression <https://arxiv.org/abs/1902.09630>`_.
Args:
pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
shape (n, 4).
target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
eps (float): Eps to avoid log(0).
Return:
Tensor: Loss tensor.
"""
gious = bbox_overlaps(pred, target, mode='giou', is_aligned=True, eps=eps)
loss = 1 - gious
return loss
@weighted_loss
def diou_loss(pred, target, eps=1e-7):
r"""`Implementation of Distance-IoU Loss: Faster and Better
Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.
Code is modified from https://github.com/Zzh-tju/DIoU.
Args:
pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
shape (n, 4).
target (Tensor): Corresponding gt bboxes, shape (n, 4).
eps (float): Eps to avoid log(0).
Return:
Tensor: Loss tensor.
"""
# overlap
lt = torch.max(pred[:, :2], target[:, :2])
rb = torch.min(pred[:, 2:], target[:, 2:])
wh = (rb - lt).clamp(min=0)
overlap = wh[:, 0] * wh[:, 1]
# union
ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
union = ap + ag - overlap + eps
# IoU
ious = overlap / union
# enclose area
enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
cw = enclose_wh[:, 0]
ch = enclose_wh[:, 1]
c2 = cw**2 + ch**2 + eps
b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
b2_x1, b2_y1 = target[:, 0], target[:, 1]
b2_x2, b2_y2 = target[:, 2], target[:, 3]
left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
rho2 = left + right
# DIoU
dious = ious - rho2 / c2
loss = 1 - dious
return loss
@weighted_loss
def ciou_loss(pred, target, eps=1e-7):
r"""`Implementation of paper `Enhancing Geometric Factors into
Model Learning and Inference for Object Detection and Instance
Segmentation <https://arxiv.org/abs/2005.03572>`_.
Code is modified from https://github.com/Zzh-tju/CIoU.
Args:
pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
shape (n, 4).
target (Tensor): Corresponding gt bboxes, shape (n, 4).
eps (float): Eps to avoid log(0).
Return:
Tensor: Loss tensor.
"""
# overlap
lt = torch.max(pred[:, :2], target[:, :2])
rb = torch.min(pred[:, 2:], target[:, 2:])
wh = (rb - lt).clamp(min=0)
overlap = wh[:, 0] * wh[:, 1]
# union
ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
union = ap + ag - overlap + eps
# IoU
ious = overlap / union
# enclose area
enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
cw = enclose_wh[:, 0]
ch = enclose_wh[:, 1]
c2 = cw**2 + ch**2 + eps
b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
b2_x1, b2_y1 = target[:, 0], target[:, 1]
b2_x2, b2_y2 = target[:, 2], target[:, 3]
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
rho2 = left + right
factor = 4 / math.pi**2
v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
# CIoU
cious = ious - (rho2 / c2 + v**2 / (1 - ious + v))
loss = 1 - cious
return loss
@LOSSES.register_module()
class IoULoss(nn.Module):
"""IoULoss.
Computing the IoU loss between a set of predicted bboxes and target bboxes.
Args:
eps (float): Eps to avoid log(0).
reduction (str): Options are "none", "mean" and "sum".
loss_weight (float): Weight of loss.
"""
def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
super(IoULoss, self).__init__()
self.eps = eps
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None. Options are "none", "mean" and "sum".
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if (weight is not None) and (not torch.any(weight > 0)) and (
reduction != 'none'):
return (pred * weight).sum() # 0
if weight is not None and weight.dim() > 1:
# TODO: remove this in the future
# reduce the weight of shape (n, 4) to (n,) to match the
# iou_loss of shape (n,)
assert weight.shape == pred.shape
weight = weight.mean(-1)
loss = self.loss_weight * iou_loss(
pred,
target,
weight,
eps=self.eps,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss
@LOSSES.register_module()
class BoundedIoULoss(nn.Module):
def __init__(self, beta=0.2, eps=1e-3, reduction='mean', loss_weight=1.0):
super(BoundedIoULoss, self).__init__()
self.beta = beta
self.eps = eps
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
if weight is not None and not torch.any(weight > 0):
return (pred * weight).sum() # 0
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
loss = self.loss_weight * bounded_iou_loss(
pred,
target,
weight,
beta=self.beta,
eps=self.eps,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss
@LOSSES.register_module()
class GIoULoss(nn.Module):
def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
super(GIoULoss, self).__init__()
self.eps = eps
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
if weight is not None and not torch.any(weight > 0):
return (pred * weight).sum() # 0
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if weight is not None and weight.dim() > 1:
# TODO: remove this in the future
# reduce the weight of shape (n, 4) to (n,) to match the
# giou_loss of shape (n,)
assert weight.shape == pred.shape
weight = weight.mean(-1)
loss = self.loss_weight * giou_loss(
pred,
target,
weight,
eps=self.eps,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss
@LOSSES.register_module()
class DIoULoss(nn.Module):
def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
super(DIoULoss, self).__init__()
self.eps = eps
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
if weight is not None and not torch.any(weight > 0):
return (pred * weight).sum() # 0
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if weight is not None and weight.dim() > 1:
# TODO: remove this in the future
# reduce the weight of shape (n, 4) to (n,) to match the
# giou_loss of shape (n,)
assert weight.shape == pred.shape
weight = weight.mean(-1)
loss = self.loss_weight * diou_loss(
pred,
target,
weight,
eps=self.eps,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss
@LOSSES.register_module()
class CIoULoss(nn.Module):
def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
super(CIoULoss, self).__init__()
self.eps = eps
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
if weight is not None and not torch.any(weight > 0):
return (pred * weight).sum() # 0
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if weight is not None and weight.dim() > 1:
# TODO: remove this in the future
# reduce the weight of shape (n, 4) to (n,) to match the
# giou_loss of shape (n,)
assert weight.shape == pred.shape
weight = weight.mean(-1)
loss = self.loss_weight * ciou_loss(
pred,
target,
weight,
eps=self.eps,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss
| 13,454 | 31.188995 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/smooth_l1_loss.py | import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
beta (float, optional): The threshold in the piecewise function.
Defaults to 1.0.
Returns:
torch.Tensor: Calculated loss
"""
assert beta > 0
assert pred.size() == target.size() and target.numel() > 0
diff = torch.abs(pred - target)
loss = torch.where(diff < beta, 0.5 * diff * diff / beta,
diff - 0.5 * beta)
return loss
@weighted_loss
def l1_loss(pred, target):
"""L1 loss.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
Returns:
torch.Tensor: Calculated loss
"""
assert pred.size() == target.size() and target.numel() > 0
loss = torch.abs(pred - target)
return loss
@LOSSES.register_module()
class SmoothL1Loss(nn.Module):
"""Smooth L1 loss.
Args:
beta (float, optional): The threshold in the piecewise function.
Defaults to 1.0.
reduction (str, optional): The method to reduce the loss.
Options are "none", "mean" and "sum". Defaults to "mean".
loss_weight (float, optional): The weight of loss.
"""
def __init__(self, beta=1.0, reduction='mean', loss_weight=1.0):
super(SmoothL1Loss, self).__init__()
self.beta = beta
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
loss_bbox = self.loss_weight * smooth_l1_loss(
pred,
target,
weight,
beta=self.beta,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss_bbox
@LOSSES.register_module()
class L1Loss(nn.Module):
"""L1 loss.
Args:
reduction (str, optional): The method to reduce the loss.
Options are "none", "mean" and "sum".
loss_weight (float, optional): The weight of loss.
"""
def __init__(self, reduction='mean', loss_weight=1.0):
super(L1Loss, self).__init__()
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
loss_bbox = self.loss_weight * l1_loss(
pred, target, weight, reduction=reduction, avg_factor=avg_factor)
return loss_bbox
| 4,423 | 31.291971 | 78 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/gfocal_loss.py | import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def quality_focal_loss(pred, target, beta=2.0, use_sigmoid=True):
r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning
Qualified and Distributed Bounding Boxes for Dense Object Detection
<https://arxiv.org/abs/2006.04388>`_.
Args:
pred (torch.Tensor): Predicted joint representation of classification
and quality (IoU) estimation with shape (N, C), C is the number of
classes.
target (tuple([torch.Tensor])): Target category label with shape (N,)
and target quality label with shape (N,).
beta (float): The beta parameter for calculating the modulating factor.
Defaults to 2.0.
Returns:
torch.Tensor: Loss tensor with shape (N,).
"""
assert len(target) == 2, """target for QFL must be a tuple of two elements,
including category label and quality label, respectively"""
# label denotes the category id, score denotes the quality score
label, score = target
if use_sigmoid:
func = F.binary_cross_entropy_with_logits
else:
func = F.binary_cross_entropy
# negatives are supervised by 0 quality score
pred_sigmoid = pred.sigmoid() if use_sigmoid else pred
scale_factor = pred_sigmoid
zerolabel = scale_factor.new_zeros(pred.shape)
loss = func(pred, zerolabel, reduction='none') * scale_factor.pow(beta)
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
bg_class_ind = pred.size(1)
pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1)
pos_label = label[pos].long()
# positives are supervised by bbox quality (IoU) score
scale_factor = score[pos] - pred_sigmoid[pos, pos_label]
loss[pos, pos_label] = func(pred[pos, pos_label], score[pos],
reduction='none') * scale_factor.abs().pow(beta)
loss = loss.sum(dim=1, keepdim=False)
return loss
@weighted_loss
def distribution_focal_loss(pred, label):
r"""Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning
Qualified and Distributed Bounding Boxes for Dense Object Detection
<https://arxiv.org/abs/2006.04388>`_.
Args:
pred (torch.Tensor): Predicted general distribution of bounding boxes
(before softmax) with shape (N, n+1), n is the max value of the
integral set `{0, ..., n}` in paper.
label (torch.Tensor): Target distance label for bounding boxes with
shape (N,).
Returns:
torch.Tensor: Loss tensor with shape (N,).
"""
dis_left = label.long()
dis_right = dis_left + 1
weight_left = dis_right.float() - label
weight_right = label - dis_left.float()
loss = F.cross_entropy(pred, dis_left, reduction='none') * weight_left \
+ F.cross_entropy(pred, dis_right, reduction='none') * weight_right
return loss
@LOSSES.register_module()
class QualityFocalLoss(nn.Module):
r"""Quality Focal Loss (QFL) is a variant of `Generalized Focal Loss:
Learning Qualified and Distributed Bounding Boxes for Dense Object
Detection <https://arxiv.org/abs/2006.04388>`_.
Args:
use_sigmoid (bool): Whether sigmoid operation is conducted in QFL.
Defaults to True.
beta (float): The beta parameter for calculating the modulating factor.
Defaults to 2.0.
reduction (str): Options are "none", "mean" and "sum".
loss_weight (float): Loss weight of current loss.
"""
def __init__(self,
use_sigmoid=True,
beta=2.0,
reduction='mean',
loss_weight=1.0):
super(QualityFocalLoss, self).__init__()
# assert use_sigmoid is True, 'Only sigmoid in QFL supported now.'
self.use_sigmoid = use_sigmoid
self.beta = beta
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
"""Forward function.
Args:
pred (torch.Tensor): Predicted joint representation of
classification and quality (IoU) estimation with shape (N, C),
C is the number of classes.
target (tuple([torch.Tensor])): Target category label with shape
(N,) and target quality label with shape (N,).
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
loss_cls = self.loss_weight * quality_focal_loss(
pred,
target,
weight,
beta=self.beta,
use_sigmoid=self.use_sigmoid,
reduction=reduction,
avg_factor=avg_factor)
return loss_cls
@LOSSES.register_module()
class DistributionFocalLoss(nn.Module):
r"""Distribution Focal Loss (DFL) is a variant of `Generalized Focal Loss:
Learning Qualified and Distributed Bounding Boxes for Dense Object
Detection <https://arxiv.org/abs/2006.04388>`_.
Args:
reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
loss_weight (float): Loss weight of current loss.
"""
def __init__(self, reduction='mean', loss_weight=1.0):
super(DistributionFocalLoss, self).__init__()
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
"""Forward function.
Args:
pred (torch.Tensor): Predicted general distribution of bounding
boxes (before softmax) with shape (N, n+1), n is the max value
of the integral set `{0, ..., n}` in paper.
target (torch.Tensor): Target distance label for bounding boxes
with shape (N,).
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
loss_cls = self.loss_weight * distribution_focal_loss(
pred, target, weight, reduction=reduction, avg_factor=avg_factor)
return loss_cls
| 7,336 | 38.446237 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/varifocal_loss.py | import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def varifocal_loss(pred,
target,
weight=None,
alpha=0.75,
gamma=2.0,
iou_weighted=True,
reduction='mean',
avg_factor=None):
"""`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the
number of classes
target (torch.Tensor): The learning target of the iou-aware
classification score with shape (N, C), C is the number of classes.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
alpha (float, optional): A balance factor for the negative part of
Varifocal Loss, which is different from the alpha of Focal Loss.
Defaults to 0.75.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
iou_weighted (bool, optional): Whether to weight the loss of the
positive example with the iou target. Defaults to True.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'. Options are "none", "mean" and
"sum".
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
"""
# pred and target should be of the same size
assert pred.size() == target.size()
pred_sigmoid = pred.sigmoid()
target = target.type_as(pred)
if iou_weighted:
focal_weight = target * (target > 0.0).float() + \
alpha * (pred_sigmoid - target).abs().pow(gamma) * \
(target <= 0.0).float()
else:
focal_weight = (target > 0.0).float() + \
alpha * (pred_sigmoid - target).abs().pow(gamma) * \
(target <= 0.0).float()
loss = F.binary_cross_entropy_with_logits(
pred, target, reduction='none') * focal_weight
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
@LOSSES.register_module()
class VarifocalLoss(nn.Module):
def __init__(self,
use_sigmoid=True,
alpha=0.75,
gamma=2.0,
iou_weighted=True,
reduction='mean',
loss_weight=1.0):
"""`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_
Args:
use_sigmoid (bool, optional): Whether the prediction is
used for sigmoid or softmax. Defaults to True.
alpha (float, optional): A balance factor for the negative part of
Varifocal Loss, which is different from the alpha of Focal
Loss. Defaults to 0.75.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
iou_weighted (bool, optional): Whether to weight the loss of the
positive examples with the iou target. Defaults to True.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'. Options are "none", "mean" and
"sum".
loss_weight (float, optional): Weight of loss. Defaults to 1.0.
"""
super(VarifocalLoss, self).__init__()
assert use_sigmoid is True, \
'Only sigmoid varifocal loss supported now.'
assert alpha >= 0.0
self.use_sigmoid = use_sigmoid
self.alpha = alpha
self.gamma = gamma
self.iou_weighted = iou_weighted
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Options are "none", "mean" and "sum".
Returns:
torch.Tensor: The calculated loss
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if self.use_sigmoid:
loss_cls = self.loss_weight * varifocal_loss(
pred,
target,
weight,
alpha=self.alpha,
gamma=self.gamma,
iou_weighted=self.iou_weighted,
reduction=reduction,
avg_factor=avg_factor)
else:
raise NotImplementedError
return loss_cls
| 5,265 | 38.893939 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/utils.py | import functools
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
reduction_enum = F._Reduction.get_enum(reduction)
# none: 0, elementwise_mean:1, sum: 2
if reduction_enum == 0:
return loss
elif reduction_enum == 1:
return loss.mean()
elif reduction_enum == 2:
return loss.sum()
def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
"""Apply element-wise weight and reduce loss.
Args:
loss (Tensor): Element-wise loss.
weight (Tensor): Element-wise weights.
reduction (str): Same as built-in losses of PyTorch.
avg_factor (float): Avarage factor when computing the mean of losses.
Returns:
Tensor: Processed loss values.
"""
# if weight is specified, apply element-wise weight
if weight is not None:
loss = loss * weight
# if avg_factor is not specified, just reduce the loss
if avg_factor is None:
loss = reduce_loss(loss, reduction)
else:
# if reduction is mean, then average the loss by avg_factor
if reduction == 'mean':
loss = loss.sum() / avg_factor
# if reduction is 'none', then do nothing, otherwise raise an error
elif reduction != 'none':
raise ValueError('avg_factor can not be used with reduction="sum"')
return loss
def weighted_loss(loss_func):
"""Create a weighted version of a given loss function.
To use this decorator, the loss function must have the signature like
`loss_func(pred, target, **kwargs)`. The function only needs to compute
element-wise loss without any reduction. This decorator will add weight
and reduction arguments to the function. The decorated function will have
the signature like `loss_func(pred, target, weight=None, reduction='mean',
avg_factor=None, **kwargs)`.
:Example:
>>> import torch
>>> @weighted_loss
>>> def l1_loss(pred, target):
>>> return (pred - target).abs()
>>> pred = torch.Tensor([0, 2, 3])
>>> target = torch.Tensor([1, 1, 1])
>>> weight = torch.Tensor([1, 0, 1])
>>> l1_loss(pred, target)
tensor(1.3333)
>>> l1_loss(pred, target, weight)
tensor(1.)
>>> l1_loss(pred, target, reduction='none')
tensor([1., 1., 2.])
>>> l1_loss(pred, target, weight, avg_factor=2)
tensor(1.5000)
"""
@functools.wraps(loss_func)
def wrapper(pred,
target,
weight=None,
reduction='mean',
avg_factor=None,
**kwargs):
# get element-wise loss
loss = loss_func(pred, target, **kwargs)
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
return wrapper
| 3,003 | 29.343434 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/ae_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
def ae_loss_per_image(tl_preds, br_preds, match):
"""Associative Embedding Loss in one image.
Associative Embedding Loss including two parts: pull loss and push loss.
Pull loss makes embedding vectors from same object closer to each other.
Push loss distinguish embedding vector from different objects, and makes
the gap between them is large enough.
During computing, usually there are 3 cases:
- no object in image: both pull loss and push loss will be 0.
- one object in image: push loss will be 0 and pull loss is computed
by the two corner of the only object.
- more than one objects in image: pull loss is computed by corner pairs
from each object, push loss is computed by each object with all
other objects. We use confusion matrix with 0 in diagonal to
compute the push loss.
Args:
tl_preds (tensor): Embedding feature map of left-top corner.
br_preds (tensor): Embedding feature map of bottim-right corner.
match (list): Downsampled coordinates pair of each ground truth box.
"""
tl_list, br_list, me_list = [], [], []
if len(match) == 0: # no object in image
pull_loss = tl_preds.sum() * 0.
push_loss = tl_preds.sum() * 0.
else:
for m in match:
[tl_y, tl_x], [br_y, br_x] = m
tl_e = tl_preds[:, tl_y, tl_x].view(-1, 1)
br_e = br_preds[:, br_y, br_x].view(-1, 1)
tl_list.append(tl_e)
br_list.append(br_e)
me_list.append((tl_e + br_e) / 2.0)
tl_list = torch.cat(tl_list)
br_list = torch.cat(br_list)
me_list = torch.cat(me_list)
assert tl_list.size() == br_list.size()
# N is object number in image, M is dimension of embedding vector
N, M = tl_list.size()
pull_loss = (tl_list - me_list).pow(2) + (br_list - me_list).pow(2)
pull_loss = pull_loss.sum() / N
margin = 1 # exp setting of CornerNet, details in section 3.3 of paper
# confusion matrix of push loss
conf_mat = me_list.expand((N, N, M)).permute(1, 0, 2) - me_list
conf_weight = 1 - torch.eye(N).type_as(me_list)
conf_mat = conf_weight * (margin - conf_mat.sum(-1).abs())
if N > 1: # more than one object in current image
push_loss = F.relu(conf_mat).sum() / (N * (N - 1))
else:
push_loss = tl_preds.sum() * 0.
return pull_loss, push_loss
@LOSSES.register_module()
class AssociativeEmbeddingLoss(nn.Module):
"""Associative Embedding Loss.
More details can be found in
`Associative Embedding <https://arxiv.org/abs/1611.05424>`_ and
`CornerNet <https://arxiv.org/abs/1808.01244>`_ .
Code is modified from `kp_utils.py <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L180>`_ # noqa: E501
Args:
pull_weight (float): Loss weight for corners from same object.
push_weight (float): Loss weight for corners from different object.
"""
def __init__(self, pull_weight=0.25, push_weight=0.25):
super(AssociativeEmbeddingLoss, self).__init__()
self.pull_weight = pull_weight
self.push_weight = push_weight
def forward(self, pred, target, match):
"""Forward function."""
batch = pred.size(0)
pull_all, push_all = 0.0, 0.0
for i in range(batch):
pull, push = ae_loss_per_image(pred[i], target[i], match[i])
pull_all += self.pull_weight * pull
push_all += self.push_weight * push
return pull_all, push_all
| 3,757 | 36.207921 | 143 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/accuracy.py | import torch.nn as nn
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class)
target (torch.Tensor): The target of each prediction, shape (N, )
topk (int | tuple[int], optional): If the predictions in ``topk``
matches the target, the predictions will be regarded as
correct ones. Defaults to 1.
thresh (float, optional): If not None, predictions with scores under
this threshold are considered incorrect. Default to None.
Returns:
float | tuple[float]: If the input ``topk`` is a single integer,
the function will return a single float as accuracy. If
``topk`` is a tuple containing multiple integers, the
function will return a tuple containing accuracies of
each ``topk`` number.
"""
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
if pred.size(0) == 0:
accu = [pred.new_tensor(0.) for i in range(len(topk))]
return accu[0] if return_single else accu
assert pred.ndim == 2 and target.ndim == 1
assert pred.size(0) == target.size(0)
assert maxk <= pred.size(1), \
f'maxk {maxk} exceeds pred dimension {pred.size(1)}'
pred_value, pred_label = pred.topk(maxk, dim=1)
pred_label = pred_label.t() # transpose to shape (maxk, N)
correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
if thresh is not None:
# Only prediction values larger than thresh are counted as correct
correct = correct & (pred_value > thresh).t()
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / pred.size(0)))
return res[0] if return_single else res
class Accuracy(nn.Module):
def __init__(self, topk=(1, ), thresh=None):
"""Module to calculate the accuracy.
Args:
topk (tuple, optional): The criterion used to calculate the
accuracy. Defaults to (1,).
thresh (float, optional): If not None, predictions with scores
under this threshold are considered incorrect. Default to None.
"""
super().__init__()
self.topk = topk
self.thresh = thresh
def forward(self, pred, target):
"""Forward function to calculate accuracy.
Args:
pred (torch.Tensor): Prediction of models.
target (torch.Tensor): Target for each prediction.
Returns:
tuple[float]: The accuracies under different topk criterions.
"""
return accuracy(pred, target, self.topk, self.thresh)
| 2,905 | 36.74026 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/focal_loss.py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from ..builder import LOSSES
from .utils import weight_reduce_loss
# This method is only for debugging
def py_sigmoid_focal_loss(pred,
target,
weight=None,
gamma=2.0,
alpha=0.25,
reduction='mean',
avg_factor=None):
"""PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_.
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the
number of classes
target (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): Sample-wise loss weight.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
"""
pred_sigmoid = pred.sigmoid()
target = target.type_as(pred)
pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
focal_weight = (alpha * target + (1 - alpha) *
(1 - target)) * pt.pow(gamma)
loss = F.binary_cross_entropy_with_logits(
pred, target, reduction='none') * focal_weight
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
def sigmoid_focal_loss(pred,
target,
weight=None,
gamma=2.0,
alpha=0.25,
reduction='mean',
avg_factor=None):
r"""A warpper of cuda version `Focal Loss
<https://arxiv.org/abs/1708.02002>`_.
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the number
of classes.
target (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): Sample-wise loss weight.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum".
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
"""
# Function.apply does not accept keyword arguments, so the decorator
# "weighted_loss" is not applicable
loss = _sigmoid_focal_loss(pred.contiguous(), target, gamma, alpha, None,
'none')
if weight is not None:
if weight.shape != loss.shape:
if weight.size(0) == loss.size(0):
# For most cases, weight is of shape (num_priors, ),
# which means it does not have the second axis num_class
weight = weight.view(-1, 1)
else:
# Sometimes, weight per anchor per class is also needed. e.g.
# in FSAF. But it may be flattened of shape
# (num_priors x num_class, ), while loss is still of shape
# (num_priors, num_class).
assert weight.numel() == loss.numel()
weight = weight.view(loss.size(0), -1)
assert weight.ndim == loss.ndim
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
@LOSSES.register_module()
class FocalLoss(nn.Module):
def __init__(self,
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
reduction='mean',
loss_weight=1.0):
"""`Focal Loss <https://arxiv.org/abs/1708.02002>`_
Args:
use_sigmoid (bool, optional): Whether to the prediction is
used for sigmoid or softmax. Defaults to True.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'. Options are "none", "mean" and
"sum".
loss_weight (float, optional): Weight of loss. Defaults to 1.0.
"""
super(FocalLoss, self).__init__()
assert use_sigmoid is True, 'Only sigmoid focal loss supported now.'
self.use_sigmoid = use_sigmoid
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Options are "none", "mean" and "sum".
Returns:
torch.Tensor: The calculated loss
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if self.use_sigmoid:
loss_cls = self.loss_weight * sigmoid_focal_loss(
pred,
target,
weight,
gamma=self.gamma,
alpha=self.alpha,
reduction=reduction,
avg_factor=avg_factor)
else:
raise NotImplementedError
return loss_cls
| 6,417 | 39.620253 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/cross_entropy_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def cross_entropy(pred,
label,
weight=None,
reduction='mean',
avg_factor=None,
class_weight=None):
"""Calculate the CrossEntropy loss.
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the number
of classes.
label (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): Sample-wise loss weight.
reduction (str, optional): The method used to reduce the loss.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
class_weight (list[float], optional): The weight for each class.
Returns:
torch.Tensor: The calculated loss
"""
# element-wise losses
loss = F.cross_entropy(pred, label, weight=class_weight, reduction='none')
# apply weights and do the reduction
if weight is not None:
weight = weight.float()
loss = weight_reduce_loss(
loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
return loss
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(
(labels >= 0) & (labels < label_channels), as_tuple=False).squeeze()
if inds.numel() > 0:
bin_labels[inds, labels[inds]] = 1
if label_weights is None:
bin_label_weights = None
else:
bin_label_weights = label_weights.view(-1, 1).expand(
label_weights.size(0), label_channels)
return bin_labels, bin_label_weights
def binary_cross_entropy(pred,
label,
weight=None,
reduction='mean',
avg_factor=None,
class_weight=None):
"""Calculate the binary CrossEntropy loss.
Args:
pred (torch.Tensor): The prediction with shape (N, 1).
label (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): Sample-wise loss weight.
reduction (str, optional): The method used to reduce the loss.
Options are "none", "mean" and "sum".
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
class_weight (list[float], optional): The weight for each class.
Returns:
torch.Tensor: The calculated loss
"""
if pred.dim() != label.dim():
label, weight = _expand_onehot_labels(label, weight, pred.size(-1))
# weighted element-wise losses
if weight is not None:
weight = weight.float()
loss = F.binary_cross_entropy_with_logits(
pred, label.float(), pos_weight=class_weight, reduction='none')
# do the reduction for the weighted loss
loss = weight_reduce_loss(
loss, weight, reduction=reduction, avg_factor=avg_factor)
return loss
def mask_cross_entropy(pred,
target,
label,
reduction='mean',
avg_factor=None,
class_weight=None):
"""Calculate the CrossEntropy loss for masks.
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the number
of classes.
target (torch.Tensor): The learning label of the prediction.
label (torch.Tensor): ``label`` indicates the class label of the mask'
corresponding object. This will be used to select the mask in the
of the class which the object belongs to when the mask prediction
if not class-agnostic.
reduction (str, optional): The method used to reduce the loss.
Options are "none", "mean" and "sum".
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
class_weight (list[float], optional): The weight for each class.
Returns:
torch.Tensor: The calculated loss
"""
# TODO: handle these two reserved arguments
assert reduction == 'mean' and avg_factor is None
num_rois = pred.size()[0]
inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
pred_slice = pred[inds, label].squeeze(1)
return F.binary_cross_entropy_with_logits(
pred_slice, target, weight=class_weight, reduction='mean')[None]
@LOSSES.register_module()
class CrossEntropyLoss(nn.Module):
def __init__(self,
use_sigmoid=False,
use_mask=False,
reduction='mean',
class_weight=None,
loss_weight=1.0):
"""CrossEntropyLoss.
Args:
use_sigmoid (bool, optional): Whether the prediction uses sigmoid
of softmax. Defaults to False.
use_mask (bool, optional): Whether to use mask cross entropy loss.
Defaults to False.
reduction (str, optional): . Defaults to 'mean'.
Options are "none", "mean" and "sum".
class_weight (list[float], optional): Weight of each class.
Defaults to None.
loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
"""
super(CrossEntropyLoss, self).__init__()
assert (use_sigmoid is False) or (use_mask is False)
self.use_sigmoid = use_sigmoid
self.use_mask = use_mask
self.reduction = reduction
self.loss_weight = loss_weight
self.class_weight = class_weight
if self.use_sigmoid:
self.cls_criterion = binary_cross_entropy
elif self.use_mask:
self.cls_criterion = mask_cross_entropy
else:
self.cls_criterion = cross_entropy
def forward(self,
cls_score,
label,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
"""Forward function.
Args:
cls_score (torch.Tensor): The prediction.
label (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): Sample-wise loss weight.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction (str, optional): The method used to reduce the loss.
Options are "none", "mean" and "sum".
Returns:
torch.Tensor: The calculated loss
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if self.class_weight is not None:
class_weight = cls_score.new_tensor(self.class_weight)
else:
class_weight = None
loss_cls = self.loss_weight * self.cls_criterion(
cls_score,
label,
weight,
class_weight=class_weight,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss_cls
| 7,352 | 35.58209 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/losses/gaussian_focal_loss.py | import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
"""`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian
distribution.
Args:
pred (torch.Tensor): The prediction.
gaussian_target (torch.Tensor): The learning target of the prediction
in gaussian distribution.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 2.0.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 4.0.
"""
eps = 1e-12
pos_weights = gaussian_target.eq(1)
neg_weights = (1 - gaussian_target).pow(gamma)
pos_loss = -(pred + eps).log() * (1 - pred).pow(alpha) * pos_weights
neg_loss = -(1 - pred + eps).log() * pred.pow(alpha) * neg_weights
return pos_loss + neg_loss
@LOSSES.register_module()
class GaussianFocalLoss(nn.Module):
"""GaussianFocalLoss is a variant of focal loss.
More details can be found in the `paper
<https://arxiv.org/abs/1808.01244>`_
Code is modified from `kp_utils.py
<https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L152>`_ # noqa: E501
Please notice that the target in GaussianFocalLoss is a gaussian heatmap,
not 0/1 binary target.
Args:
alpha (float): Power of prediction.
gamma (float): Power of target for negtive samples.
reduction (str): Options are "none", "mean" and "sum".
loss_weight (float): Loss weight of current loss.
"""
def __init__(self,
alpha=2.0,
gamma=4.0,
reduction='mean',
loss_weight=1.0):
super(GaussianFocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction
in gaussian distribution.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
loss_reg = self.loss_weight * gaussian_focal_loss(
pred,
target,
weight,
alpha=self.alpha,
gamma=self.gamma,
reduction=reduction,
avg_factor=avg_factor)
return loss_reg
| 3,211 | 34.688889 | 108 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/hrnet.py | import torch.nn as nn
from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.utils import get_root_logger
from ..builder import BACKBONES
from .resnet import BasicBlock, Bottleneck
class HRModule(nn.Module):
"""High-Resolution Module for HRNet.
In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange
is in this module.
"""
def __init__(self,
num_branches,
blocks,
num_blocks,
in_channels,
num_channels,
multiscale_output=True,
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN')):
super(HRModule, self).__init__()
self._check_branches(num_branches, num_blocks, in_channels,
num_channels)
self.in_channels = in_channels
self.num_branches = num_branches
self.multiscale_output = multiscale_output
self.norm_cfg = norm_cfg
self.conv_cfg = conv_cfg
self.with_cp = with_cp
self.branches = self._make_branches(num_branches, blocks, num_blocks,
num_channels)
self.fuse_layers = self._make_fuse_layers()
self.relu = nn.ReLU(inplace=False)
def _check_branches(self, num_branches, num_blocks, in_channels,
num_channels):
if num_branches != len(num_blocks):
error_msg = f'NUM_BRANCHES({num_branches}) ' \
f'!= NUM_BLOCKS({len(num_blocks)})'
raise ValueError(error_msg)
if num_branches != len(num_channels):
error_msg = f'NUM_BRANCHES({num_branches}) ' \
f'!= NUM_CHANNELS({len(num_channels)})'
raise ValueError(error_msg)
if num_branches != len(in_channels):
error_msg = f'NUM_BRANCHES({num_branches}) ' \
f'!= NUM_INCHANNELS({len(in_channels)})'
raise ValueError(error_msg)
def _make_one_branch(self,
branch_index,
block,
num_blocks,
num_channels,
stride=1):
downsample = None
if stride != 1 or \
self.in_channels[branch_index] != \
num_channels[branch_index] * block.expansion:
downsample = nn.Sequential(
build_conv_layer(
self.conv_cfg,
self.in_channels[branch_index],
num_channels[branch_index] * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
build_norm_layer(self.norm_cfg, num_channels[branch_index] *
block.expansion)[1])
layers = []
layers.append(
block(
self.in_channels[branch_index],
num_channels[branch_index],
stride,
downsample=downsample,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
self.in_channels[branch_index] = \
num_channels[branch_index] * block.expansion
for i in range(1, num_blocks[branch_index]):
layers.append(
block(
self.in_channels[branch_index],
num_channels[branch_index],
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
return nn.Sequential(*layers)
def _make_branches(self, num_branches, block, num_blocks, num_channels):
branches = []
for i in range(num_branches):
branches.append(
self._make_one_branch(i, block, num_blocks, num_channels))
return nn.ModuleList(branches)
def _make_fuse_layers(self):
if self.num_branches == 1:
return None
num_branches = self.num_branches
in_channels = self.in_channels
fuse_layers = []
num_out_branches = num_branches if self.multiscale_output else 1
for i in range(num_out_branches):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[i],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg, in_channels[i])[1],
nn.Upsample(
scale_factor=2**(j - i), mode='nearest')))
elif j == i:
fuse_layer.append(None)
else:
conv_downsamples = []
for k in range(i - j):
if k == i - j - 1:
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[i],
kernel_size=3,
stride=2,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[i])[1]))
else:
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[j],
kernel_size=3,
stride=2,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[j])[1],
nn.ReLU(inplace=False)))
fuse_layer.append(nn.Sequential(*conv_downsamples))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
def forward(self, x):
"""Forward function."""
if self.num_branches == 1:
return [self.branches[0](x[0])]
for i in range(self.num_branches):
x[i] = self.branches[i](x[i])
x_fuse = []
for i in range(len(self.fuse_layers)):
y = 0
for j in range(self.num_branches):
if i == j:
y += x[j]
else:
y += self.fuse_layers[i][j](x[j])
x_fuse.append(self.relu(y))
return x_fuse
@BACKBONES.register_module()
class HRNet(nn.Module):
"""HRNet backbone.
High-Resolution Representations for Labeling Pixels and Regions
arXiv: https://arxiv.org/abs/1904.04514
Args:
extra (dict): detailed configuration for each stage of HRNet.
in_channels (int): Number of input image channels. Default: 3.
conv_cfg (dict): dictionary to construct and config conv layer.
norm_cfg (dict): dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
zero_init_residual (bool): whether to use zero init for last norm layer
in resblocks to let them behave as identity.
Example:
>>> from mmdet.models import HRNet
>>> import torch
>>> extra = dict(
>>> stage1=dict(
>>> num_modules=1,
>>> num_branches=1,
>>> block='BOTTLENECK',
>>> num_blocks=(4, ),
>>> num_channels=(64, )),
>>> stage2=dict(
>>> num_modules=1,
>>> num_branches=2,
>>> block='BASIC',
>>> num_blocks=(4, 4),
>>> num_channels=(32, 64)),
>>> stage3=dict(
>>> num_modules=4,
>>> num_branches=3,
>>> block='BASIC',
>>> num_blocks=(4, 4, 4),
>>> num_channels=(32, 64, 128)),
>>> stage4=dict(
>>> num_modules=3,
>>> num_branches=4,
>>> block='BASIC',
>>> num_blocks=(4, 4, 4, 4),
>>> num_channels=(32, 64, 128, 256)))
>>> self = HRNet(extra, in_channels=1)
>>> self.eval()
>>> inputs = torch.rand(1, 1, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 32, 8, 8)
(1, 64, 4, 4)
(1, 128, 2, 2)
(1, 256, 1, 1)
"""
blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck}
def __init__(self,
extra,
in_channels=3,
conv_cfg=None,
norm_cfg=dict(type='BN'),
norm_eval=True,
with_cp=False,
zero_init_residual=False):
super(HRNet, self).__init__()
self.extra = extra
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.zero_init_residual = zero_init_residual
# stem net
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
self.conv1 = build_conv_layer(
self.conv_cfg,
in_channels,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
self.conv_cfg,
64,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
# stage 1
self.stage1_cfg = self.extra['stage1']
num_channels = self.stage1_cfg['num_channels'][0]
block_type = self.stage1_cfg['block']
num_blocks = self.stage1_cfg['num_blocks'][0]
block = self.blocks_dict[block_type]
stage1_out_channels = num_channels * block.expansion
self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
# stage 2
self.stage2_cfg = self.extra['stage2']
num_channels = self.stage2_cfg['num_channels']
block_type = self.stage2_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition1 = self._make_transition_layer([stage1_out_channels],
num_channels)
self.stage2, pre_stage_channels = self._make_stage(
self.stage2_cfg, num_channels)
# stage 3
self.stage3_cfg = self.extra['stage3']
num_channels = self.stage3_cfg['num_channels']
block_type = self.stage3_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition2 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage3, pre_stage_channels = self._make_stage(
self.stage3_cfg, num_channels)
# stage 4
self.stage4_cfg = self.extra['stage4']
num_channels = self.stage4_cfg['num_channels']
block_type = self.stage4_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition3 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels)
@property
def norm1(self):
"""nn.Module: the normalization layer named "norm1" """
return getattr(self, self.norm1_name)
@property
def norm2(self):
"""nn.Module: the normalization layer named "norm2" """
return getattr(self, self.norm2_name)
def _make_transition_layer(self, num_channels_pre_layer,
num_channels_cur_layer):
num_branches_cur = len(num_channels_cur_layer)
num_branches_pre = len(num_channels_pre_layer)
transition_layers = []
for i in range(num_branches_cur):
if i < num_branches_pre:
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
transition_layers.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
num_channels_pre_layer[i],
num_channels_cur_layer[i],
kernel_size=3,
stride=1,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg,
num_channels_cur_layer[i])[1],
nn.ReLU(inplace=True)))
else:
transition_layers.append(None)
else:
conv_downsamples = []
for j in range(i + 1 - num_branches_pre):
in_channels = num_channels_pre_layer[-1]
out_channels = num_channels_cur_layer[i] \
if j == i - num_branches_pre else in_channels
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels,
out_channels,
kernel_size=3,
stride=2,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg, out_channels)[1],
nn.ReLU(inplace=True)))
transition_layers.append(nn.Sequential(*conv_downsamples))
return nn.ModuleList(transition_layers)
def _make_layer(self, block, inplanes, planes, blocks, stride=1):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
build_conv_layer(
self.conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
build_norm_layer(self.norm_cfg, planes * block.expansion)[1])
layers = []
layers.append(
block(
inplanes,
planes,
stride,
downsample=downsample,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(
inplanes,
planes,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
return nn.Sequential(*layers)
def _make_stage(self, layer_config, in_channels, multiscale_output=True):
num_modules = layer_config['num_modules']
num_branches = layer_config['num_branches']
num_blocks = layer_config['num_blocks']
num_channels = layer_config['num_channels']
block = self.blocks_dict[layer_config['block']]
hr_modules = []
for i in range(num_modules):
# multi_scale_output is only used for the last module
if not multiscale_output and i == num_modules - 1:
reset_multiscale_output = False
else:
reset_multiscale_output = True
hr_modules.append(
HRModule(
num_branches,
block,
num_blocks,
in_channels,
num_channels,
reset_multiscale_output,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
return nn.Sequential(*hr_modules), in_channels
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
constant_init(m.norm3, 0)
elif isinstance(m, BasicBlock):
constant_init(m.norm2, 0)
else:
raise TypeError('pretrained must be a str or None')
def forward(self, x):
"""Forward function."""
x = self.conv1(x)
x = self.norm1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.norm2(x)
x = self.relu(x)
x = self.layer1(x)
x_list = []
for i in range(self.stage2_cfg['num_branches']):
if self.transition1[i] is not None:
x_list.append(self.transition1[i](x))
else:
x_list.append(x)
y_list = self.stage2(x_list)
x_list = []
for i in range(self.stage3_cfg['num_branches']):
if self.transition2[i] is not None:
x_list.append(self.transition2[i](y_list[-1]))
else:
x_list.append(y_list[i])
y_list = self.stage3(x_list)
x_list = []
for i in range(self.stage4_cfg['num_branches']):
if self.transition3[i] is not None:
x_list.append(self.transition3[i](y_list[-1]))
else:
x_list.append(y_list[i])
y_list = self.stage4(x_list)
return y_list
def train(self, mode=True):
"""Convert the model into training mode whill keeping the normalization
layer freezed."""
super(HRNet, self).train(mode)
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only
if isinstance(m, _BatchNorm):
m.eval()
| 20,359 | 36.843866 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/regnet.py | import numpy as np
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import ResNet
from .resnext import Bottleneck
@BACKBONES.register_module()
class RegNet(ResNet):
"""RegNet backbone.
More details can be found in `paper <https://arxiv.org/abs/2003.13678>`_ .
Args:
arch (dict): The parameter of RegNets.
- w0 (int): initial width
- wa (float): slope of width
- wm (float): quantization parameter to quantize the width
- depth (int): depth of the backbone
- group_w (int): width of group
- bot_mul (float): bottleneck ratio, i.e. expansion of bottlneck.
strides (Sequence[int]): Strides of the first block of each stage.
base_channels (int): Base channels after stem layer.
in_channels (int): Number of input image channels. Default: 3.
dilations (Sequence[int]): Dilation of each stage.
out_indices (Sequence[int]): Output from which stages.
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer.
frozen_stages (int): Stages to be frozen (all param fixed). -1 means
not freezing any parameters.
norm_cfg (dict): dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
zero_init_residual (bool): whether to use zero init for last norm layer
in resblocks to let them behave as identity.
Example:
>>> from mmdet.models import RegNet
>>> import torch
>>> self = RegNet(
arch=dict(
w0=88,
wa=26.31,
wm=2.25,
group_w=48,
depth=25,
bot_mul=1.0))
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 96, 8, 8)
(1, 192, 4, 4)
(1, 432, 2, 2)
(1, 1008, 1, 1)
"""
arch_settings = {
'regnetx_400mf':
dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, bot_mul=1.0),
'regnetx_800mf':
dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, bot_mul=1.0),
'regnetx_1.6gf':
dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, bot_mul=1.0),
'regnetx_3.2gf':
dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, bot_mul=1.0),
'regnetx_4.0gf':
dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, bot_mul=1.0),
'regnetx_6.4gf':
dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, bot_mul=1.0),
'regnetx_8.0gf':
dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, bot_mul=1.0),
'regnetx_12gf':
dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, bot_mul=1.0),
}
def __init__(self,
arch,
in_channels=3,
stem_channels=32,
base_channels=32,
strides=(2, 2, 2, 2),
dilations=(1, 1, 1, 1),
out_indices=(0, 1, 2, 3),
style='pytorch',
deep_stem=False,
avg_down=False,
frozen_stages=-1,
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
dcn=None,
stage_with_dcn=(False, False, False, False),
plugins=None,
with_cp=False,
zero_init_residual=True):
super(ResNet, self).__init__()
# Generate RegNet parameters first
if isinstance(arch, str):
assert arch in self.arch_settings, \
f'"arch": "{arch}" is not one of the' \
' arch_settings'
arch = self.arch_settings[arch]
elif not isinstance(arch, dict):
raise ValueError('Expect "arch" to be either a string '
f'or a dict, got {type(arch)}')
widths, num_stages = self.generate_regnet(
arch['w0'],
arch['wa'],
arch['wm'],
arch['depth'],
)
# Convert to per stage format
stage_widths, stage_blocks = self.get_stages_from_blocks(widths)
# Generate group widths and bot muls
group_widths = [arch['group_w'] for _ in range(num_stages)]
self.bottleneck_ratio = [arch['bot_mul'] for _ in range(num_stages)]
# Adjust the compatibility of stage_widths and group_widths
stage_widths, group_widths = self.adjust_width_group(
stage_widths, self.bottleneck_ratio, group_widths)
# Group params by stage
self.stage_widths = stage_widths
self.group_widths = group_widths
self.depth = sum(stage_blocks)
self.stem_channels = stem_channels
self.base_channels = base_channels
self.num_stages = num_stages
assert num_stages >= 1 and num_stages <= 4
self.strides = strides
self.dilations = dilations
assert len(strides) == len(dilations) == num_stages
self.out_indices = out_indices
assert max(out_indices) < num_stages
self.style = style
self.deep_stem = deep_stem
self.avg_down = avg_down
self.frozen_stages = frozen_stages
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.with_cp = with_cp
self.norm_eval = norm_eval
self.dcn = dcn
self.stage_with_dcn = stage_with_dcn
if dcn is not None:
assert len(stage_with_dcn) == num_stages
self.plugins = plugins
self.zero_init_residual = zero_init_residual
self.block = Bottleneck
expansion_bak = self.block.expansion
self.block.expansion = 1
self.stage_blocks = stage_blocks[:num_stages]
self._make_stem_layer(in_channels, stem_channels)
self.inplanes = stem_channels
self.res_layers = []
for i, num_blocks in enumerate(self.stage_blocks):
stride = self.strides[i]
dilation = self.dilations[i]
group_width = self.group_widths[i]
width = int(round(self.stage_widths[i] * self.bottleneck_ratio[i]))
stage_groups = width // group_width
dcn = self.dcn if self.stage_with_dcn[i] else None
if self.plugins is not None:
stage_plugins = self.make_stage_plugins(self.plugins, i)
else:
stage_plugins = None
res_layer = self.make_res_layer(
block=self.block,
inplanes=self.inplanes,
planes=self.stage_widths[i],
num_blocks=num_blocks,
stride=stride,
dilation=dilation,
style=self.style,
avg_down=self.avg_down,
with_cp=self.with_cp,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
dcn=dcn,
plugins=stage_plugins,
groups=stage_groups,
base_width=group_width,
base_channels=self.stage_widths[i])
self.inplanes = self.stage_widths[i]
layer_name = f'layer{i + 1}'
self.add_module(layer_name, res_layer)
self.res_layers.append(layer_name)
self._freeze_stages()
self.feat_dim = stage_widths[-1]
self.block.expansion = expansion_bak
def _make_stem_layer(self, in_channels, base_channels):
self.conv1 = build_conv_layer(
self.conv_cfg,
in_channels,
base_channels,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, base_channels, postfix=1)
self.add_module(self.norm1_name, norm1)
self.relu = nn.ReLU(inplace=True)
def generate_regnet(self,
initial_width,
width_slope,
width_parameter,
depth,
divisor=8):
"""Generates per block width from RegNet parameters.
Args:
initial_width ([int]): Initial width of the backbone
width_slope ([float]): Slope of the quantized linear function
width_parameter ([int]): Parameter used to quantize the width.
depth ([int]): Depth of the backbone.
divisor (int, optional): The divisor of channels. Defaults to 8.
Returns:
list, int: return a list of widths of each stage and the number \
of stages
"""
assert width_slope >= 0
assert initial_width > 0
assert width_parameter > 1
assert initial_width % divisor == 0
widths_cont = np.arange(depth) * width_slope + initial_width
ks = np.round(
np.log(widths_cont / initial_width) / np.log(width_parameter))
widths = initial_width * np.power(width_parameter, ks)
widths = np.round(np.divide(widths, divisor)) * divisor
num_stages = len(np.unique(widths))
widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist()
return widths, num_stages
@staticmethod
def quantize_float(number, divisor):
"""Converts a float to closest non-zero int divisible by divior.
Args:
number (int): Original number to be quantized.
divisor (int): Divisor used to quantize the number.
Returns:
int: quantized number that is divisible by devisor.
"""
return int(round(number / divisor) * divisor)
def adjust_width_group(self, widths, bottleneck_ratio, groups):
"""Adjusts the compatibility of widths and groups.
Args:
widths (list[int]): Width of each stage.
bottleneck_ratio (float): Bottleneck ratio.
groups (int): number of groups in each stage
Returns:
tuple(list): The adjusted widths and groups of each stage.
"""
bottleneck_width = [
int(w * b) for w, b in zip(widths, bottleneck_ratio)
]
groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_width)]
bottleneck_width = [
self.quantize_float(w_bot, g)
for w_bot, g in zip(bottleneck_width, groups)
]
widths = [
int(w_bot / b)
for w_bot, b in zip(bottleneck_width, bottleneck_ratio)
]
return widths, groups
def get_stages_from_blocks(self, widths):
"""Gets widths/stage_blocks of network at each stage.
Args:
widths (list[int]): Width in each stage.
Returns:
tuple(list): width and depth of each stage
"""
width_diff = [
width != width_prev
for width, width_prev in zip(widths + [0], [0] + widths)
]
stage_widths = [
width for width, diff in zip(widths, width_diff[:-1]) if diff
]
stage_blocks = np.diff([
depth for depth, diff in zip(range(len(width_diff)), width_diff)
if diff
]).tolist()
return stage_widths, stage_blocks
def forward(self, x):
"""Forward function."""
x = self.conv1(x)
x = self.norm1(x)
x = self.relu(x)
outs = []
for i, layer_name in enumerate(self.res_layers):
res_layer = getattr(self, layer_name)
x = res_layer(x)
if i in self.out_indices:
outs.append(x)
return tuple(outs)
| 12,269 | 36.638037 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/detectors_resnext.py | import math
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .detectors_resnet import Bottleneck as _Bottleneck
from .detectors_resnet import DetectoRS_ResNet
class Bottleneck(_Bottleneck):
expansion = 4
def __init__(self,
inplanes,
planes,
groups=1,
base_width=4,
base_channels=64,
**kwargs):
"""Bottleneck block for ResNeXt.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
if groups == 1:
width = self.planes
else:
width = math.floor(self.planes *
(base_width / base_channels)) * groups
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, width, postfix=1)
self.norm2_name, norm2 = build_norm_layer(
self.norm_cfg, width, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
self.norm_cfg, self.planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
self.conv_cfg,
self.inplanes,
width,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
self.with_modulated_dcn = False
if self.with_dcn:
fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if self.with_sac:
self.conv2 = build_conv_layer(
self.sac,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
elif not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
self.conv_cfg,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
self.conv2 = build_conv_layer(
self.dcn,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
self.conv_cfg,
width,
self.planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
@BACKBONES.register_module()
class DetectoRS_ResNeXt(DetectoRS_ResNet):
"""ResNeXt backbone for DetectoRS.
Args:
groups (int): The number of groups in ResNeXt.
base_width (int): The base width of ResNeXt.
"""
arch_settings = {
50: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
152: (Bottleneck, (3, 8, 36, 3))
}
def __init__(self, groups=1, base_width=4, **kwargs):
self.groups = groups
self.base_width = base_width
super(DetectoRS_ResNeXt, self).__init__(**kwargs)
def make_res_layer(self, **kwargs):
return super().make_res_layer(
groups=self.groups,
base_width=self.base_width,
base_channels=self.base_channels,
**kwargs)
| 3,872 | 30.487805 | 77 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/resnet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer,
constant_init, kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.utils import get_root_logger
from ..builder import BACKBONES
from ..utils import ResLayer
class BasicBlock(nn.Module):
expansion = 1
def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
plugins=None):
super(BasicBlock, self).__init__()
assert dcn is None, 'Not implemented yet.'
assert plugins is None, 'Not implemented yet.'
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
3,
stride=stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
conv_cfg, planes, planes, 3, padding=1, bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
self.with_cp = with_cp
@property
def norm1(self):
"""nn.Module: normalization layer after the first convolution layer"""
return getattr(self, self.norm1_name)
@property
def norm2(self):
"""nn.Module: normalization layer after the second convolution layer"""
return getattr(self, self.norm2_name)
def forward(self, x):
"""Forward function."""
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.norm2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
plugins=None):
"""Bottleneck block for ResNet.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottleneck, self).__init__()
assert style in ['pytorch', 'caffe']
assert dcn is None or isinstance(dcn, dict)
assert plugins is None or isinstance(plugins, list)
if plugins is not None:
allowed_position = ['after_conv1', 'after_conv2', 'after_conv3']
assert all(p['position'] in allowed_position for p in plugins)
self.inplanes = inplanes
self.planes = planes
self.stride = stride
self.dilation = dilation
self.style = style
self.with_cp = with_cp
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.dcn = dcn
self.with_dcn = dcn is not None
self.plugins = plugins
self.with_plugins = plugins is not None
if self.with_plugins:
# collect plugins for conv1/conv2/conv3
self.after_conv1_plugins = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv1'
]
self.after_conv2_plugins = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv2'
]
self.after_conv3_plugins = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv3'
]
if self.style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = stride
else:
self.conv1_stride = stride
self.conv2_stride = 1
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
norm_cfg, planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
if self.with_dcn:
fallback_on_stride = dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
conv_cfg,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
self.conv2 = build_conv_layer(
dcn,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
conv_cfg,
planes,
planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
if self.with_plugins:
self.after_conv1_plugin_names = self.make_block_plugins(
planes, self.after_conv1_plugins)
self.after_conv2_plugin_names = self.make_block_plugins(
planes, self.after_conv2_plugins)
self.after_conv3_plugin_names = self.make_block_plugins(
planes * self.expansion, self.after_conv3_plugins)
def make_block_plugins(self, in_channels, plugins):
"""make plugins for block.
Args:
in_channels (int): Input channels of plugin.
plugins (list[dict]): List of plugins cfg to build.
Returns:
list[str]: List of the names of plugin.
"""
assert isinstance(plugins, list)
plugin_names = []
for plugin in plugins:
plugin = plugin.copy()
name, layer = build_plugin_layer(
plugin,
in_channels=in_channels,
postfix=plugin.pop('postfix', ''))
assert not hasattr(self, name), f'duplicate plugin {name}'
self.add_module(name, layer)
plugin_names.append(name)
return plugin_names
def forward_plugin(self, x, plugin_names):
out = x
for name in plugin_names:
out = getattr(self, name)(x)
return out
@property
def norm1(self):
"""nn.Module: normalization layer after the first convolution layer"""
return getattr(self, self.norm1_name)
@property
def norm2(self):
"""nn.Module: normalization layer after the second convolution layer"""
return getattr(self, self.norm2_name)
@property
def norm3(self):
"""nn.Module: normalization layer after the third convolution layer"""
return getattr(self, self.norm3_name)
def forward(self, x):
"""Forward function."""
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv1_plugin_names)
out = self.conv2(out)
out = self.norm2(out)
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv2_plugin_names)
out = self.conv3(out)
out = self.norm3(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv3_plugin_names)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out
@BACKBONES.register_module()
class ResNet(nn.Module):
"""ResNet backbone.
Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
stem_channels (int | None): Number of stem channels. If not specified,
it will be the same as `base_channels`. Default: None.
base_channels (int): Number of base channels of res layer. Default: 64.
in_channels (int): Number of input image channels. Default: 3.
num_stages (int): Resnet stages. Default: 4.
strides (Sequence[int]): Strides of the first block of each stage.
dilations (Sequence[int]): Dilation of each stage.
out_indices (Sequence[int]): Output from which stages.
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer.
deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
avg_down (bool): Use AvgPool instead of stride conv when
downsampling in the bottleneck.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters.
norm_cfg (dict): Dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
plugins (list[dict]): List of plugins for stages, each dict contains:
- cfg (dict, required): Cfg dict to build plugin.
- position (str, required): Position inside block to insert
plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
- stages (tuple[bool], optional): Stages to apply plugin, length
should be same as 'num_stages'.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
zero_init_residual (bool): Whether to use zero init for last norm layer
in resblocks to let them behave as identity.
Example:
>>> from mmdet.models import ResNet
>>> import torch
>>> self = ResNet(depth=18)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 64, 8, 8)
(1, 128, 4, 4)
(1, 256, 2, 2)
(1, 512, 1, 1)
"""
arch_settings = {
18: (BasicBlock, (2, 2, 2, 2)),
34: (BasicBlock, (3, 4, 6, 3)),
50: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
152: (Bottleneck, (3, 8, 36, 3))
}
def __init__(self,
depth,
in_channels=3,
stem_channels=None,
base_channels=64,
num_stages=4,
strides=(1, 2, 2, 2),
dilations=(1, 1, 1, 1),
out_indices=(0, 1, 2, 3),
style='pytorch',
deep_stem=False,
avg_down=False,
frozen_stages=-1,
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
dcn=None,
stage_with_dcn=(False, False, False, False),
plugins=None,
with_cp=False,
zero_init_residual=True):
super(ResNet, self).__init__()
if depth not in self.arch_settings:
raise KeyError(f'invalid depth {depth} for resnet')
self.depth = depth
if stem_channels is None:
stem_channels = base_channels
self.stem_channels = stem_channels
self.base_channels = base_channels
self.num_stages = num_stages
assert num_stages >= 1 and num_stages <= 4
self.strides = strides
self.dilations = dilations
assert len(strides) == len(dilations) == num_stages
self.out_indices = out_indices
assert max(out_indices) < num_stages
self.style = style
self.deep_stem = deep_stem
self.avg_down = avg_down
self.frozen_stages = frozen_stages
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.with_cp = with_cp
self.norm_eval = norm_eval
self.dcn = dcn
self.stage_with_dcn = stage_with_dcn
if dcn is not None:
assert len(stage_with_dcn) == num_stages
self.plugins = plugins
self.zero_init_residual = zero_init_residual
self.block, stage_blocks = self.arch_settings[depth]
self.stage_blocks = stage_blocks[:num_stages]
self.inplanes = stem_channels
self._make_stem_layer(in_channels, stem_channels)
self.res_layers = []
for i, num_blocks in enumerate(self.stage_blocks):
stride = strides[i]
dilation = dilations[i]
dcn = self.dcn if self.stage_with_dcn[i] else None
if plugins is not None:
stage_plugins = self.make_stage_plugins(plugins, i)
else:
stage_plugins = None
planes = base_channels * 2**i
res_layer = self.make_res_layer(
block=self.block,
inplanes=self.inplanes,
planes=planes,
num_blocks=num_blocks,
stride=stride,
dilation=dilation,
style=self.style,
avg_down=self.avg_down,
with_cp=with_cp,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
dcn=dcn,
plugins=stage_plugins)
self.inplanes = planes * self.block.expansion
layer_name = f'layer{i + 1}'
self.add_module(layer_name, res_layer)
self.res_layers.append(layer_name)
self._freeze_stages()
self.feat_dim = self.block.expansion * base_channels * 2**(
len(self.stage_blocks) - 1)
def make_stage_plugins(self, plugins, stage_idx):
"""Make plugins for ResNet ``stage_idx`` th stage.
Currently we support to insert ``context_block``,
``empirical_attention_block``, ``nonlocal_block`` into the backbone
like ResNet/ResNeXt. They could be inserted after conv1/conv2/conv3 of
Bottleneck.
An example of plugins format could be:
Examples:
>>> plugins=[
... dict(cfg=dict(type='xxx', arg1='xxx'),
... stages=(False, True, True, True),
... position='after_conv2'),
... dict(cfg=dict(type='yyy'),
... stages=(True, True, True, True),
... position='after_conv3'),
... dict(cfg=dict(type='zzz', postfix='1'),
... stages=(True, True, True, True),
... position='after_conv3'),
... dict(cfg=dict(type='zzz', postfix='2'),
... stages=(True, True, True, True),
... position='after_conv3')
... ]
>>> self = ResNet(depth=18)
>>> stage_plugins = self.make_stage_plugins(plugins, 0)
>>> assert len(stage_plugins) == 3
Suppose ``stage_idx=0``, the structure of blocks in the stage would be:
.. code-block:: none
conv1-> conv2->conv3->yyy->zzz1->zzz2
Suppose 'stage_idx=1', the structure of blocks in the stage would be:
.. code-block:: none
conv1-> conv2->xxx->conv3->yyy->zzz1->zzz2
If stages is missing, the plugin would be applied to all stages.
Args:
plugins (list[dict]): List of plugins cfg to build. The postfix is
required if multiple same type plugins are inserted.
stage_idx (int): Index of stage to build
Returns:
list[dict]: Plugins for current stage
"""
stage_plugins = []
for plugin in plugins:
plugin = plugin.copy()
stages = plugin.pop('stages', None)
assert stages is None or len(stages) == self.num_stages
# whether to insert plugin into current stage
if stages is None or stages[stage_idx]:
stage_plugins.append(plugin)
return stage_plugins
def make_res_layer(self, **kwargs):
"""Pack all blocks in a stage into a ``ResLayer``."""
return ResLayer(**kwargs)
@property
def norm1(self):
"""nn.Module: the normalization layer named "norm1" """
return getattr(self, self.norm1_name)
def _make_stem_layer(self, in_channels, stem_channels):
if self.deep_stem:
self.stem = nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels,
stem_channels // 2,
kernel_size=3,
stride=2,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
nn.ReLU(inplace=True),
build_conv_layer(
self.conv_cfg,
stem_channels // 2,
stem_channels // 2,
kernel_size=3,
stride=1,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
nn.ReLU(inplace=True),
build_conv_layer(
self.conv_cfg,
stem_channels // 2,
stem_channels,
kernel_size=3,
stride=1,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg, stem_channels)[1],
nn.ReLU(inplace=True))
else:
self.conv1 = build_conv_layer(
self.conv_cfg,
in_channels,
stem_channels,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, stem_channels, postfix=1)
self.add_module(self.norm1_name, norm1)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
def _freeze_stages(self):
if self.frozen_stages >= 0:
if self.deep_stem:
self.stem.eval()
for param in self.stem.parameters():
param.requires_grad = False
else:
self.norm1.eval()
for m in [self.conv1, self.norm1]:
for param in m.parameters():
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
m = getattr(self, f'layer{i}')
m.eval()
for param in m.parameters():
param.requires_grad = False
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.dcn is not None:
for m in self.modules():
if isinstance(m, Bottleneck) and hasattr(
m.conv2, 'conv_offset'):
constant_init(m.conv2.conv_offset, 0)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
constant_init(m.norm3, 0)
elif isinstance(m, BasicBlock):
constant_init(m.norm2, 0)
else:
raise TypeError('pretrained must be a str or None')
def forward(self, x):
"""Forward function."""
if self.deep_stem:
x = self.stem(x)
else:
x = self.conv1(x)
x = self.norm1(x)
x = self.relu(x)
x = self.maxpool(x)
outs = []
for i, layer_name in enumerate(self.res_layers):
res_layer = getattr(self, layer_name)
x = res_layer(x)
if i in self.out_indices:
outs.append(x)
return tuple(outs)
def train(self, mode=True):
"""Convert the model into training mode while keep normalization layer
freezed."""
super(ResNet, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only
if isinstance(m, _BatchNorm):
m.eval()
@BACKBONES.register_module()
class ResNetV1d(ResNet):
r"""ResNetV1d variant described in `Bag of Tricks
<https://arxiv.org/pdf/1812.01187.pdf>`_.
Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in
the input stem with three 3x3 convs. And in the downsampling block, a 2x2
avg_pool with stride 2 is added before conv, whose stride is changed to 1.
"""
def __init__(self, **kwargs):
super(ResNetV1d, self).__init__(
deep_stem=True, avg_down=True, **kwargs)
| 23,378 | 34.156391 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/detectors_resnet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer, constant_init
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
r"""Bottleneck for the ResNet backbone in `DetectoRS
<https://arxiv.org/pdf/2006.02334.pdf>`_.
This bottleneck allows the users to specify whether to use
SAC (Switchable Atrous Convolution) and RFP (Recursive Feature Pyramid).
Args:
inplanes (int): The number of input channels.
planes (int): The number of output channels before expansion.
rfp_inplanes (int, optional): The number of channels from RFP.
Default: None. If specified, an additional conv layer will be
added for ``rfp_feat``. Otherwise, the structure is the same as
base class.
sac (dict, optional): Dictionary to construct SAC. Default: None.
"""
expansion = 4
def __init__(self,
inplanes,
planes,
rfp_inplanes=None,
sac=None,
**kwargs):
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
assert sac is None or isinstance(sac, dict)
self.sac = sac
self.with_sac = sac is not None
if self.with_sac:
self.conv2 = build_conv_layer(
self.sac,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
bias=False)
self.rfp_inplanes = rfp_inplanes
if self.rfp_inplanes:
self.rfp_conv = build_conv_layer(
None,
self.rfp_inplanes,
planes * self.expansion,
1,
stride=1,
bias=True)
self.init_weights()
def init_weights(self):
"""Initialize the weights."""
if self.rfp_inplanes:
constant_init(self.rfp_conv, 0)
def rfp_forward(self, x, rfp_feat):
"""The forward function that also takes the RFP features as input."""
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv1_plugin_names)
out = self.conv2(out)
out = self.norm2(out)
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv2_plugin_names)
out = self.conv3(out)
out = self.norm3(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv3_plugin_names)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
if self.rfp_inplanes:
rfp_feat = self.rfp_conv(rfp_feat)
out = out + rfp_feat
out = self.relu(out)
return out
class ResLayer(nn.Sequential):
"""ResLayer to build ResNet style backbone for RPF in detectoRS.
The difference between this module and base class is that we pass
``rfp_inplanes`` to the first block.
Args:
block (nn.Module): block used to build ResLayer.
inplanes (int): inplanes of block.
planes (int): planes of block.
num_blocks (int): number of blocks.
stride (int): stride of the first block. Default: 1
avg_down (bool): Use AvgPool instead of stride conv when
downsampling in the bottleneck. Default: False
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
downsample_first (bool): Downsample at the first block or last block.
False for Hourglass, True for ResNet. Default: True
rfp_inplanes (int, optional): The number of channels from RFP.
Default: None. If specified, an additional conv layer will be
added for ``rfp_feat``. Otherwise, the structure is the same as
base class.
"""
def __init__(self,
block,
inplanes,
planes,
num_blocks,
stride=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
downsample_first=True,
rfp_inplanes=None,
**kwargs):
self.block = block
assert downsample_first, f'downsampel_first={downsample_first} is ' \
'not supported in DetectoRS'
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = []
conv_stride = stride
if avg_down and stride != 1:
conv_stride = 1
downsample.append(
nn.AvgPool2d(
kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False))
downsample.extend([
build_conv_layer(
conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=conv_stride,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1]
])
downsample = nn.Sequential(*downsample)
layers = []
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
rfp_inplanes=rfp_inplanes,
**kwargs))
inplanes = planes * block.expansion
for _ in range(1, num_blocks):
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
super(ResLayer, self).__init__(*layers)
@BACKBONES.register_module()
class DetectoRS_ResNet(ResNet):
"""ResNet backbone for DetectoRS.
Args:
sac (dict, optional): Dictionary to construct SAC (Switchable Atrous
Convolution). Default: None.
stage_with_sac (list): Which stage to use sac. Default: (False, False,
False, False).
rfp_inplanes (int, optional): The number of channels from RFP.
Default: None. If specified, an additional conv layer will be
added for ``rfp_feat``. Otherwise, the structure is the same as
base class.
output_img (bool): If ``True``, the input image will be inserted into
the starting position of output. Default: False.
pretrained (str, optional): The pretrained model to load.
"""
arch_settings = {
50: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
152: (Bottleneck, (3, 8, 36, 3))
}
def __init__(self,
sac=None,
stage_with_sac=(False, False, False, False),
rfp_inplanes=None,
output_img=False,
pretrained=None,
**kwargs):
self.sac = sac
self.stage_with_sac = stage_with_sac
self.rfp_inplanes = rfp_inplanes
self.output_img = output_img
self.pretrained = pretrained
super(DetectoRS_ResNet, self).__init__(**kwargs)
self.inplanes = self.stem_channels
self.res_layers = []
for i, num_blocks in enumerate(self.stage_blocks):
stride = self.strides[i]
dilation = self.dilations[i]
dcn = self.dcn if self.stage_with_dcn[i] else None
sac = self.sac if self.stage_with_sac[i] else None
if self.plugins is not None:
stage_plugins = self.make_stage_plugins(self.plugins, i)
else:
stage_plugins = None
planes = self.base_channels * 2**i
res_layer = self.make_res_layer(
block=self.block,
inplanes=self.inplanes,
planes=planes,
num_blocks=num_blocks,
stride=stride,
dilation=dilation,
style=self.style,
avg_down=self.avg_down,
with_cp=self.with_cp,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
dcn=dcn,
sac=sac,
rfp_inplanes=rfp_inplanes if i > 0 else None,
plugins=stage_plugins)
self.inplanes = planes * self.block.expansion
layer_name = f'layer{i + 1}'
self.add_module(layer_name, res_layer)
self.res_layers.append(layer_name)
self._freeze_stages()
def make_res_layer(self, **kwargs):
"""Pack all blocks in a stage into a ``ResLayer`` for DetectoRS."""
return ResLayer(**kwargs)
def forward(self, x):
"""Forward function."""
outs = list(super(DetectoRS_ResNet, self).forward(x))
if self.output_img:
outs.insert(0, x)
return tuple(outs)
def rfp_forward(self, x, rfp_feats):
"""Forward function for RFP."""
if self.deep_stem:
x = self.stem(x)
else:
x = self.conv1(x)
x = self.norm1(x)
x = self.relu(x)
x = self.maxpool(x)
outs = []
for i, layer_name in enumerate(self.res_layers):
res_layer = getattr(self, layer_name)
rfp_feat = rfp_feats[i] if i > 0 else None
for layer in res_layer:
x = layer.rfp_forward(x, rfp_feat)
if i in self.out_indices:
outs.append(x)
return tuple(outs)
| 10,517 | 33.372549 | 78 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/ssd_vgg.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import VGG, constant_init, kaiming_init, normal_init, xavier_init
from mmcv.runner import load_checkpoint
from mmdet.utils import get_root_logger
from ..builder import BACKBONES
@BACKBONES.register_module()
class SSDVGG(VGG):
"""VGG Backbone network for single-shot-detection.
Args:
input_size (int): width and height of input, from {300, 512}.
depth (int): Depth of vgg, from {11, 13, 16, 19}.
out_indices (Sequence[int]): Output from which stages.
Example:
>>> self = SSDVGG(input_size=300, depth=11)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 300, 300)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 1024, 19, 19)
(1, 512, 10, 10)
(1, 256, 5, 5)
(1, 256, 3, 3)
(1, 256, 1, 1)
"""
extra_setting = {
300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256),
512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128),
}
def __init__(self,
input_size,
depth,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indices=(22, 34),
l2_norm_scale=20.):
# TODO: in_channels for mmcv.VGG
super(SSDVGG, self).__init__(
depth,
with_last_pool=with_last_pool,
ceil_mode=ceil_mode,
out_indices=out_indices)
assert input_size in (300, 512)
self.input_size = input_size
self.features.add_module(
str(len(self.features)),
nn.MaxPool2d(kernel_size=3, stride=1, padding=1))
self.features.add_module(
str(len(self.features)),
nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6))
self.features.add_module(
str(len(self.features)), nn.ReLU(inplace=True))
self.features.add_module(
str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1))
self.features.add_module(
str(len(self.features)), nn.ReLU(inplace=True))
self.out_feature_indices = out_feature_indices
self.inplanes = 1024
self.extra = self._make_extra_layers(self.extra_setting[input_size])
self.l2_norm = L2Norm(
self.features[out_feature_indices[0] - 1].out_channels,
l2_norm_scale)
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.features.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
elif isinstance(m, nn.Linear):
normal_init(m, std=0.01)
else:
raise TypeError('pretrained must be a str or None')
for m in self.extra.modules():
if isinstance(m, nn.Conv2d):
xavier_init(m, distribution='uniform')
constant_init(self.l2_norm, self.l2_norm.scale)
def forward(self, x):
"""Forward function."""
outs = []
for i, layer in enumerate(self.features):
x = layer(x)
if i in self.out_feature_indices:
outs.append(x)
for i, layer in enumerate(self.extra):
x = F.relu(layer(x), inplace=True)
if i % 2 == 1:
outs.append(x)
outs[0] = self.l2_norm(outs[0])
if len(outs) == 1:
return outs[0]
else:
return tuple(outs)
def _make_extra_layers(self, outplanes):
layers = []
kernel_sizes = (1, 3)
num_layers = 0
outplane = None
for i in range(len(outplanes)):
if self.inplanes == 'S':
self.inplanes = outplane
continue
k = kernel_sizes[num_layers % 2]
if outplanes[i] == 'S':
outplane = outplanes[i + 1]
conv = nn.Conv2d(
self.inplanes, outplane, k, stride=2, padding=1)
else:
outplane = outplanes[i]
conv = nn.Conv2d(
self.inplanes, outplane, k, stride=1, padding=0)
layers.append(conv)
self.inplanes = outplanes[i]
num_layers += 1
if self.input_size == 512:
layers.append(nn.Conv2d(self.inplanes, 256, 4, padding=1))
return nn.Sequential(*layers)
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20., eps=1e-10):
"""L2 normalization layer.
Args:
n_dims (int): Number of dimensions to be normalized
scale (float, optional): Defaults to 20..
eps (float, optional): Used to avoid division by zero.
Defaults to 1e-10.
"""
super(L2Norm, self).__init__()
self.n_dims = n_dims
self.weight = nn.Parameter(torch.Tensor(self.n_dims))
self.eps = eps
self.scale = scale
def forward(self, x):
"""Forward function."""
# normalization layer convert to FP32 in FP16 training
x_float = x.float()
norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps
return (self.weight[None, :, None, None].float().expand_as(x_float) *
x_float / norm).type_as(x)
| 5,882 | 33.605882 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/resnext.py | import math
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
expansion = 4
def __init__(self,
inplanes,
planes,
groups=1,
base_width=4,
base_channels=64,
**kwargs):
"""Bottleneck block for ResNeXt.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
if groups == 1:
width = self.planes
else:
width = math.floor(self.planes *
(base_width / base_channels)) * groups
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, width, postfix=1)
self.norm2_name, norm2 = build_norm_layer(
self.norm_cfg, width, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
self.norm_cfg, self.planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
self.conv_cfg,
self.inplanes,
width,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
self.with_modulated_dcn = False
if self.with_dcn:
fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
self.conv_cfg,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
self.conv2 = build_conv_layer(
self.dcn,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
self.conv_cfg,
width,
self.planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
@BACKBONES.register_module()
class ResNeXt(ResNet):
"""ResNeXt backbone.
Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
in_channels (int): Number of input image channels. Default: 3.
num_stages (int): Resnet stages. Default: 4.
groups (int): Group of resnext.
base_width (int): Base width of resnext.
strides (Sequence[int]): Strides of the first block of each stage.
dilations (Sequence[int]): Dilation of each stage.
out_indices (Sequence[int]): Output from which stages.
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer.
frozen_stages (int): Stages to be frozen (all param fixed). -1 means
not freezing any parameters.
norm_cfg (dict): dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
zero_init_residual (bool): whether to use zero init for last norm layer
in resblocks to let them behave as identity.
"""
arch_settings = {
50: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
152: (Bottleneck, (3, 8, 36, 3))
}
def __init__(self, groups=1, base_width=4, **kwargs):
self.groups = groups
self.base_width = base_width
super(ResNeXt, self).__init__(**kwargs)
def make_res_layer(self, **kwargs):
"""Pack all blocks in a stage into a ``ResLayer``"""
return ResLayer(
groups=self.groups,
base_width=self.base_width,
base_channels=self.base_channels,
**kwargs)
| 4,731 | 34.578947 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/hourglass.py | import torch.nn as nn
from mmcv.cnn import ConvModule
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import BasicBlock
class HourglassModule(nn.Module):
"""Hourglass Module for HourglassNet backbone.
Generate module recursively and use BasicBlock as the base unit.
Args:
depth (int): Depth of current HourglassModule.
stage_channels (list[int]): Feature channels of sub-modules in current
and follow-up HourglassModule.
stage_blocks (list[int]): Number of sub-modules stacked in current and
follow-up HourglassModule.
norm_cfg (dict): Dictionary to construct and config norm layer.
"""
def __init__(self,
depth,
stage_channels,
stage_blocks,
norm_cfg=dict(type='BN', requires_grad=True)):
super(HourglassModule, self).__init__()
self.depth = depth
cur_block = stage_blocks[0]
next_block = stage_blocks[1]
cur_channel = stage_channels[0]
next_channel = stage_channels[1]
self.up1 = ResLayer(
BasicBlock, cur_channel, cur_channel, cur_block, norm_cfg=norm_cfg)
self.low1 = ResLayer(
BasicBlock,
cur_channel,
next_channel,
cur_block,
stride=2,
norm_cfg=norm_cfg)
if self.depth > 1:
self.low2 = HourglassModule(depth - 1, stage_channels[1:],
stage_blocks[1:])
else:
self.low2 = ResLayer(
BasicBlock,
next_channel,
next_channel,
next_block,
norm_cfg=norm_cfg)
self.low3 = ResLayer(
BasicBlock,
next_channel,
cur_channel,
cur_block,
norm_cfg=norm_cfg,
downsample_first=False)
self.up2 = nn.Upsample(scale_factor=2)
def forward(self, x):
"""Forward function."""
up1 = self.up1(x)
low1 = self.low1(x)
low2 = self.low2(low1)
low3 = self.low3(low2)
up2 = self.up2(low3)
return up1 + up2
@BACKBONES.register_module()
class HourglassNet(nn.Module):
"""HourglassNet backbone.
Stacked Hourglass Networks for Human Pose Estimation.
More details can be found in the `paper
<https://arxiv.org/abs/1603.06937>`_ .
Args:
downsample_times (int): Downsample times in a HourglassModule.
num_stacks (int): Number of HourglassModule modules stacked,
1 for Hourglass-52, 2 for Hourglass-104.
stage_channels (list[int]): Feature channel of each sub-module in a
HourglassModule.
stage_blocks (list[int]): Number of sub-modules stacked in a
HourglassModule.
feat_channel (int): Feature channel of conv after a HourglassModule.
norm_cfg (dict): Dictionary to construct and config norm layer.
Example:
>>> from mmdet.models import HourglassNet
>>> import torch
>>> self = HourglassNet()
>>> self.eval()
>>> inputs = torch.rand(1, 3, 511, 511)
>>> level_outputs = self.forward(inputs)
>>> for level_output in level_outputs:
... print(tuple(level_output.shape))
(1, 256, 128, 128)
(1, 256, 128, 128)
"""
def __init__(self,
downsample_times=5,
num_stacks=2,
stage_channels=(256, 256, 384, 384, 384, 512),
stage_blocks=(2, 2, 2, 2, 2, 4),
feat_channel=256,
norm_cfg=dict(type='BN', requires_grad=True)):
super(HourglassNet, self).__init__()
self.num_stacks = num_stacks
assert self.num_stacks >= 1
assert len(stage_channels) == len(stage_blocks)
assert len(stage_channels) > downsample_times
cur_channel = stage_channels[0]
self.stem = nn.Sequential(
ConvModule(3, 128, 7, padding=3, stride=2, norm_cfg=norm_cfg),
ResLayer(BasicBlock, 128, 256, 1, stride=2, norm_cfg=norm_cfg))
self.hourglass_modules = nn.ModuleList([
HourglassModule(downsample_times, stage_channels, stage_blocks)
for _ in range(num_stacks)
])
self.inters = ResLayer(
BasicBlock,
cur_channel,
cur_channel,
num_stacks - 1,
norm_cfg=norm_cfg)
self.conv1x1s = nn.ModuleList([
ConvModule(
cur_channel, cur_channel, 1, norm_cfg=norm_cfg, act_cfg=None)
for _ in range(num_stacks - 1)
])
self.out_convs = nn.ModuleList([
ConvModule(
cur_channel, feat_channel, 3, padding=1, norm_cfg=norm_cfg)
for _ in range(num_stacks)
])
self.remap_convs = nn.ModuleList([
ConvModule(
feat_channel, cur_channel, 1, norm_cfg=norm_cfg, act_cfg=None)
for _ in range(num_stacks - 1)
])
self.relu = nn.ReLU(inplace=True)
def init_weights(self, pretrained=None):
"""Init module weights.
We do nothing in this function because all modules we used
(ConvModule, BasicBlock and etc.) have default initialization, and
currently we don't provide pretrained model of HourglassNet.
Detector's __init__() will call backbone's init_weights() with
pretrained as input, so we keep this function.
"""
# Training Centripetal Model needs to reset parameters for Conv2d
for m in self.modules():
if isinstance(m, nn.Conv2d):
m.reset_parameters()
def forward(self, x):
"""Forward function."""
inter_feat = self.stem(x)
out_feats = []
for ind in range(self.num_stacks):
single_hourglass = self.hourglass_modules[ind]
out_conv = self.out_convs[ind]
hourglass_feat = single_hourglass(inter_feat)
out_feat = out_conv(hourglass_feat)
out_feats.append(out_feat)
if ind < self.num_stacks - 1:
inter_feat = self.conv1x1s[ind](
inter_feat) + self.remap_convs[ind](
out_feat)
inter_feat = self.inters[ind](self.relu(inter_feat))
return out_feats
| 6,452 | 31.427136 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/res2net.py | import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.utils import get_root_logger
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottle2neck(_Bottleneck):
expansion = 4
def __init__(self,
inplanes,
planes,
scales=4,
base_width=26,
base_channels=64,
stage_type='normal',
**kwargs):
"""Bottle2neck block for Res2Net.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottle2neck, self).__init__(inplanes, planes, **kwargs)
assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.'
width = int(math.floor(self.planes * (base_width / base_channels)))
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, width * scales, postfix=1)
self.norm3_name, norm3 = build_norm_layer(
self.norm_cfg, self.planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
self.conv_cfg,
self.inplanes,
width * scales,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
if stage_type == 'stage' and self.conv2_stride != 1:
self.pool = nn.AvgPool2d(
kernel_size=3, stride=self.conv2_stride, padding=1)
convs = []
bns = []
fallback_on_stride = False
if self.with_dcn:
fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
for i in range(scales - 1):
convs.append(
build_conv_layer(
self.conv_cfg,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
bias=False))
bns.append(
build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
for i in range(scales - 1):
convs.append(
build_conv_layer(
self.dcn,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
bias=False))
bns.append(
build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = build_conv_layer(
self.conv_cfg,
width * scales,
self.planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.stage_type = stage_type
self.scales = scales
self.width = width
delattr(self, 'conv2')
delattr(self, self.norm2_name)
def forward(self, x):
"""Forward function."""
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv1_plugin_names)
spx = torch.split(out, self.width, 1)
sp = self.convs[0](spx[0].contiguous())
sp = self.relu(self.bns[0](sp))
out = sp
for i in range(1, self.scales - 1):
if self.stage_type == 'stage':
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp.contiguous())
sp = self.relu(self.bns[i](sp))
out = torch.cat((out, sp), 1)
if self.stage_type == 'normal' or self.conv2_stride == 1:
out = torch.cat((out, spx[self.scales - 1]), 1)
elif self.stage_type == 'stage':
out = torch.cat((out, self.pool(spx[self.scales - 1])), 1)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv2_plugin_names)
out = self.conv3(out)
out = self.norm3(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv3_plugin_names)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out
class Res2Layer(nn.Sequential):
"""Res2Layer to build Res2Net style backbone.
Args:
block (nn.Module): block used to build ResLayer.
inplanes (int): inplanes of block.
planes (int): planes of block.
num_blocks (int): number of blocks.
stride (int): stride of the first block. Default: 1
avg_down (bool): Use AvgPool instead of stride conv when
downsampling in the bottle2neck. Default: False
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
scales (int): Scales used in Res2Net. Default: 4
base_width (int): Basic width of each scale. Default: 26
"""
def __init__(self,
block,
inplanes,
planes,
num_blocks,
stride=1,
avg_down=True,
conv_cfg=None,
norm_cfg=dict(type='BN'),
scales=4,
base_width=26,
**kwargs):
self.block = block
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.AvgPool2d(
kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False),
build_conv_layer(
conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=1,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1],
)
layers = []
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
scales=scales,
base_width=base_width,
stage_type='stage',
**kwargs))
inplanes = planes * block.expansion
for i in range(1, num_blocks):
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
scales=scales,
base_width=base_width,
**kwargs))
super(Res2Layer, self).__init__(*layers)
@BACKBONES.register_module()
class Res2Net(ResNet):
"""Res2Net backbone.
Args:
scales (int): Scales used in Res2Net. Default: 4
base_width (int): Basic width of each scale. Default: 26
depth (int): Depth of res2net, from {50, 101, 152}.
in_channels (int): Number of input image channels. Default: 3.
num_stages (int): Res2net stages. Default: 4.
strides (Sequence[int]): Strides of the first block of each stage.
dilations (Sequence[int]): Dilation of each stage.
out_indices (Sequence[int]): Output from which stages.
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer.
deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
avg_down (bool): Use AvgPool instead of stride conv when
downsampling in the bottle2neck.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters.
norm_cfg (dict): Dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
plugins (list[dict]): List of plugins for stages, each dict contains:
- cfg (dict, required): Cfg dict to build plugin.
- position (str, required): Position inside block to insert
plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
- stages (tuple[bool], optional): Stages to apply plugin, length
should be same as 'num_stages'.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
zero_init_residual (bool): Whether to use zero init for last norm layer
in resblocks to let them behave as identity.
Example:
>>> from mmdet.models import Res2Net
>>> import torch
>>> self = Res2Net(depth=50, scales=4, base_width=26)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 256, 8, 8)
(1, 512, 4, 4)
(1, 1024, 2, 2)
(1, 2048, 1, 1)
"""
arch_settings = {
50: (Bottle2neck, (3, 4, 6, 3)),
101: (Bottle2neck, (3, 4, 23, 3)),
152: (Bottle2neck, (3, 8, 36, 3))
}
def __init__(self,
scales=4,
base_width=26,
style='pytorch',
deep_stem=True,
avg_down=True,
**kwargs):
self.scales = scales
self.base_width = base_width
super(Res2Net, self).__init__(
style='pytorch', deep_stem=True, avg_down=True, **kwargs)
def make_res_layer(self, **kwargs):
return Res2Layer(
scales=self.scales,
base_width=self.base_width,
base_channels=self.base_channels,
**kwargs)
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.dcn is not None:
for m in self.modules():
if isinstance(m, Bottle2neck):
# dcn in Res2Net bottle2neck is in ModuleList
for n in m.convs:
if hasattr(n, 'conv_offset'):
constant_init(n.conv_offset, 0)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottle2neck):
constant_init(m.norm3, 0)
else:
raise TypeError('pretrained must be a str or None')
| 12,675 | 35.011364 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/models/backbones/darknet.py | # Copyright (c) 2019 Western Digital Corporation or its affiliates.
import logging
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
class ResBlock(nn.Module):
"""The basic residual block used in Darknet. Each ResBlock consists of two
ConvModules and the input is added to the final output. Each ConvModule is
composed of Conv, BN, and LeakyReLU. In YoloV3 paper, the first convLayer
has half of the number of the filters as much as the second convLayer. The
first convLayer has filter size of 1x1 and the second one has the filter
size of 3x3.
Args:
in_channels (int): The input channels. Must be even.
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Dictionary to construct and config norm layer.
Default: dict(type='BN', requires_grad=True)
act_cfg (dict): Config dict for activation layer.
Default: dict(type='LeakyReLU', negative_slope=0.1).
"""
def __init__(self,
in_channels,
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1)):
super(ResBlock, self).__init__()
assert in_channels % 2 == 0 # ensure the in_channels is even
half_in_channels = in_channels // 2
# shortcut
cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
self.conv1 = ConvModule(in_channels, half_in_channels, 1, **cfg)
self.conv2 = ConvModule(
half_in_channels, in_channels, 3, padding=1, **cfg)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.conv2(out)
out = out + residual
return out
@BACKBONES.register_module()
class Darknet(nn.Module):
"""Darknet backbone.
Args:
depth (int): Depth of Darknet. Currently only support 53.
out_indices (Sequence[int]): Output from which stages.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. Default: -1.
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Dictionary to construct and config norm layer.
Default: dict(type='BN', requires_grad=True)
act_cfg (dict): Config dict for activation layer.
Default: dict(type='LeakyReLU', negative_slope=0.1).
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
Example:
>>> from mmdet.models import Darknet
>>> import torch
>>> self = Darknet(depth=53)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 416, 416)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
...
(1, 256, 52, 52)
(1, 512, 26, 26)
(1, 1024, 13, 13)
"""
# Dict(depth: (layers, channels))
arch_settings = {
53: ((1, 2, 8, 8, 4), ((32, 64), (64, 128), (128, 256), (256, 512),
(512, 1024)))
}
def __init__(self,
depth=53,
out_indices=(3, 4, 5),
frozen_stages=-1,
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
norm_eval=True):
super(Darknet, self).__init__()
if depth not in self.arch_settings:
raise KeyError(f'invalid depth {depth} for darknet')
self.depth = depth
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.layers, self.channels = self.arch_settings[depth]
cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
self.conv1 = ConvModule(3, 32, 3, padding=1, **cfg)
self.cr_blocks = ['conv1']
for i, n_layers in enumerate(self.layers):
layer_name = f'conv_res_block{i + 1}'
in_c, out_c = self.channels[i]
self.add_module(
layer_name,
self.make_conv_res_block(in_c, out_c, n_layers, **cfg))
self.cr_blocks.append(layer_name)
self.norm_eval = norm_eval
def forward(self, x):
outs = []
for i, layer_name in enumerate(self.cr_blocks):
cr_block = getattr(self, layer_name)
x = cr_block(x)
if i in self.out_indices:
outs.append(x)
return tuple(outs)
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = logging.getLogger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
else:
raise TypeError('pretrained must be a str or None')
def _freeze_stages(self):
if self.frozen_stages >= 0:
for i in range(self.frozen_stages):
m = getattr(self, self.cr_blocks[i])
m.eval()
for param in m.parameters():
param.requires_grad = False
def train(self, mode=True):
super(Darknet, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()
@staticmethod
def make_conv_res_block(in_channels,
out_channels,
res_repeat,
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
act_cfg=dict(type='LeakyReLU',
negative_slope=0.1)):
"""In Darknet backbone, ConvLayer is usually followed by ResBlock. This
function will make that. The Conv layers always have 3x3 filters with
stride=2. The number of the filters in Conv layer is the same as the
out channels of the ResBlock.
Args:
in_channels (int): The number of input channels.
out_channels (int): The number of output channels.
res_repeat (int): The number of ResBlocks.
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Dictionary to construct and config norm layer.
Default: dict(type='BN', requires_grad=True)
act_cfg (dict): Config dict for activation layer.
Default: dict(type='LeakyReLU', negative_slope=0.1).
"""
cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
model = nn.Sequential()
model.add_module(
'conv',
ConvModule(
in_channels, out_channels, 3, stride=2, padding=1, **cfg))
for idx in range(res_repeat):
model.add_module('res{}'.format(idx),
ResBlock(out_channels, **cfg))
return model
| 7,574 | 36.875 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/datasets/custom.py | import os.path as osp
import warnings
import mmcv
import numpy as np
from torch.utils.data import Dataset
from mmdet.core import eval_map, eval_recalls
from .builder import DATASETS
from .pipelines import Compose
@DATASETS.register_module()
class CustomDataset(Dataset):
"""Custom dataset for detection.
The annotation format is shown as follows. The `ann` field is optional for
testing.
.. code-block:: none
[
{
'filename': 'a.jpg',
'width': 1280,
'height': 720,
'ann': {
'bboxes': <np.ndarray> (n, 4) in (x1, y1, x2, y2) order.
'labels': <np.ndarray> (n, ),
'bboxes_ignore': <np.ndarray> (k, 4), (optional field)
'labels_ignore': <np.ndarray> (k, 4) (optional field)
}
},
...
]
Args:
ann_file (str): Annotation file path.
pipeline (list[dict]): Processing pipeline.
classes (str | Sequence[str], optional): Specify classes to load.
If is None, ``cls.CLASSES`` will be used. Default: None.
data_root (str, optional): Data root for ``ann_file``,
``img_prefix``, ``seg_prefix``, ``proposal_file`` if specified.
test_mode (bool, optional): If set True, annotation will not be loaded.
filter_empty_gt (bool, optional): If set true, images without bounding
boxes of the dataset's classes will be filtered out. This option
only works when `test_mode=False`, i.e., we never filter images
during tests.
"""
CLASSES = None
def __init__(self,
ann_file,
pipeline,
classes=None,
data_root=None,
img_prefix='',
seg_prefix=None,
proposal_file=None,
test_mode=False,
filter_empty_gt=True):
self.ann_file = ann_file
self.data_root = data_root
self.img_prefix = img_prefix
self.seg_prefix = seg_prefix
self.proposal_file = proposal_file
self.test_mode = test_mode
self.filter_empty_gt = filter_empty_gt
self.CLASSES = self.get_classes(classes)
# join paths if data_root is specified
if self.data_root is not None:
if not osp.isabs(self.ann_file):
self.ann_file = osp.join(self.data_root, self.ann_file)
if not (self.img_prefix is None or osp.isabs(self.img_prefix)):
self.img_prefix = osp.join(self.data_root, self.img_prefix)
if not (self.seg_prefix is None or osp.isabs(self.seg_prefix)):
self.seg_prefix = osp.join(self.data_root, self.seg_prefix)
if not (self.proposal_file is None
or osp.isabs(self.proposal_file)):
self.proposal_file = osp.join(self.data_root,
self.proposal_file)
# load annotations (and proposals)
self.data_infos = self.load_annotations(self.ann_file)
if self.proposal_file is not None:
self.proposals = self.load_proposals(self.proposal_file)
else:
self.proposals = None
# filter images too small and containing no annotations
if not test_mode:
valid_inds = self._filter_imgs()
self.data_infos = [self.data_infos[i] for i in valid_inds]
if self.proposals is not None:
self.proposals = [self.proposals[i] for i in valid_inds]
# set group flag for the sampler
self._set_group_flag()
# processing pipeline
self.pipeline = Compose(pipeline)
def __len__(self):
"""Total number of samples of data."""
return len(self.data_infos)
def load_annotations(self, ann_file):
"""Load annotation from annotation file."""
return mmcv.load(ann_file)
def load_proposals(self, proposal_file):
"""Load proposal from proposal file."""
return mmcv.load(proposal_file)
def get_ann_info(self, idx):
"""Get annotation by index.
Args:
idx (int): Index of data.
Returns:
dict: Annotation info of specified index.
"""
return self.data_infos[idx]['ann']
def get_cat_ids(self, idx):
"""Get category ids by index.
Args:
idx (int): Index of data.
Returns:
list[int]: All categories in the image of specified index.
"""
return self.data_infos[idx]['ann']['labels'].astype(np.int).tolist()
def pre_pipeline(self, results):
"""Prepare results dict for pipeline."""
results['img_prefix'] = self.img_prefix
results['seg_prefix'] = self.seg_prefix
results['proposal_file'] = self.proposal_file
results['bbox_fields'] = []
results['mask_fields'] = []
results['seg_fields'] = []
def _filter_imgs(self, min_size=32):
"""Filter images too small."""
if self.filter_empty_gt:
warnings.warn(
'CustomDataset does not support filtering empty gt images.')
valid_inds = []
for i, img_info in enumerate(self.data_infos):
if min(img_info['width'], img_info['height']) >= min_size:
valid_inds.append(i)
return valid_inds
def _set_group_flag(self):
"""Set flag according to image aspect ratio.
Images with aspect ratio greater than 1 will be set as group 1,
otherwise group 0.
"""
self.flag = np.zeros(len(self), dtype=np.uint8)
for i in range(len(self)):
img_info = self.data_infos[i]
if img_info['width'] / img_info['height'] > 1:
self.flag[i] = 1
def _rand_another(self, idx):
"""Get another random index from the same group as the given index."""
pool = np.where(self.flag == self.flag[idx])[0]
return np.random.choice(pool)
def __getitem__(self, idx):
"""Get training/test data after pipeline.
Args:
idx (int): Index of data.
Returns:
dict: Training/test data (with annotation if `test_mode` is set \
True).
"""
if self.test_mode:
return self.prepare_test_img(idx)
while True:
data = self.prepare_train_img(idx)
if data is None:
idx = self._rand_another(idx)
continue
return data
def prepare_train_img(self, idx):
"""Get training data and annotations after pipeline.
Args:
idx (int): Index of data.
Returns:
dict: Training data and annotation after pipeline with new keys \
introduced by pipeline.
"""
img_info = self.data_infos[idx]
ann_info = self.get_ann_info(idx)
results = dict(img_info=img_info, ann_info=ann_info)
if self.proposals is not None:
results['proposals'] = self.proposals[idx]
self.pre_pipeline(results)
return self.pipeline(results)
def prepare_test_img(self, idx):
"""Get testing data after pipeline.
Args:
idx (int): Index of data.
Returns:
dict: Testing data after pipeline with new keys intorduced by \
piepline.
"""
img_info = self.data_infos[idx]
results = dict(img_info=img_info)
if self.proposals is not None:
results['proposals'] = self.proposals[idx]
self.pre_pipeline(results)
return self.pipeline(results)
@classmethod
def get_classes(cls, classes=None):
"""Get class names of current dataset.
Args:
classes (Sequence[str] | str | None): If classes is None, use
default CLASSES defined by builtin dataset. If classes is a
string, take it as a file name. The file contains the name of
classes where each line contains one class name. If classes is
a tuple or list, override the CLASSES defined by the dataset.
Returns:
tuple[str] or list[str]: Names of categories of the dataset.
"""
if classes is None:
return cls.CLASSES
if isinstance(classes, str):
# take it as a file path
class_names = mmcv.list_from_file(classes)
elif isinstance(classes, (tuple, list)):
class_names = classes
else:
raise ValueError(f'Unsupported type {type(classes)} of classes.')
return class_names
def format_results(self, results, **kwargs):
"""Place holder to format result to dataset specific output."""
pass
def evaluate(self,
results,
metric='mAP',
logger=None,
proposal_nums=(100, 300, 1000),
iou_thr=0.5,
scale_ranges=None):
"""Evaluate the dataset.
Args:
results (list): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | None | str): Logger used for printing
related information during evaluation. Default: None.
proposal_nums (Sequence[int]): Proposal number used for evaluating
recalls, such as recall@100, recall@1000.
Default: (100, 300, 1000).
iou_thr (float | list[float]): IoU threshold. It must be a float
when evaluating mAP, and can be a list when evaluating recall.
Default: 0.5.
scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP.
Default: None.
"""
if not isinstance(metric, str):
assert len(metric) == 1
metric = metric[0]
allowed_metrics = ['mAP', 'recall']
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported')
annotations = [self.get_ann_info(i) for i in range(len(self))]
eval_results = {}
if metric == 'mAP':
assert isinstance(iou_thr, float)
mean_ap, _ = eval_map(
results,
annotations,
scale_ranges=scale_ranges,
iou_thr=iou_thr,
dataset=self.CLASSES,
logger=logger)
eval_results['mAP'] = mean_ap
elif metric == 'recall':
gt_bboxes = [ann['bboxes'] for ann in annotations]
if isinstance(iou_thr, float):
iou_thr = [iou_thr]
recalls = eval_recalls(
gt_bboxes, results, proposal_nums, iou_thr, logger=logger)
for i, num in enumerate(proposal_nums):
for j, iou in enumerate(iou_thr):
eval_results[f'recall@{num}@{iou}'] = recalls[i, j]
if recalls.shape[1] > 1:
ar = recalls.mean(axis=1)
for i, num in enumerate(proposal_nums):
eval_results[f'AR@{num}'] = ar[i]
return eval_results
| 11,329 | 34.29595 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/datasets/dataset_wrappers.py | import bisect
import math
from collections import defaultdict
import numpy as np
from mmcv.utils import print_log
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class ConcatDataset(_ConcatDataset):
"""A wrapper of concatenated dataset.
Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
concat the group flag for image aspect ratio.
Args:
datasets (list[:obj:`Dataset`]): A list of datasets.
separate_eval (bool): Whether to evaluate the results
separately if it is used as validation dataset.
Defaults to True.
"""
def __init__(self, datasets, separate_eval=True):
super(ConcatDataset, self).__init__(datasets)
self.CLASSES = datasets[0].CLASSES
self.separate_eval = separate_eval
if not separate_eval:
if any([isinstance(ds, CocoDataset) for ds in datasets]):
raise NotImplementedError(
'Evaluating concatenated CocoDataset as a whole is not'
' supported! Please set "separate_eval=True"')
elif len(set([type(ds) for ds in datasets])) != 1:
raise NotImplementedError(
'All the datasets should have same types')
if hasattr(datasets[0], 'flag'):
flags = []
for i in range(0, len(datasets)):
flags.append(datasets[i].flag)
self.flag = np.concatenate(flags)
def get_cat_ids(self, idx):
"""Get category ids of concatenated dataset by index.
Args:
idx (int): Index of data.
Returns:
list[int]: All categories in the image of specified index.
"""
if idx < 0:
if -idx > len(self):
raise ValueError(
'absolute value of index should not exceed dataset length')
idx = len(self) + idx
dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
if dataset_idx == 0:
sample_idx = idx
else:
sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
return self.datasets[dataset_idx].get_cat_ids(sample_idx)
def evaluate(self, results, logger=None, **kwargs):
"""Evaluate the results.
Args:
results (list[list | tuple]): Testing results of the dataset.
logger (logging.Logger | str | None): Logger used for printing
related information during evaluation. Default: None.
Returns:
dict[str: float]: AP results of the total dataset or each separate
dataset if `self.separate_eval=True`.
"""
assert len(results) == self.cumulative_sizes[-1], \
('Dataset and results have different sizes: '
f'{self.cumulative_sizes[-1]} v.s. {len(results)}')
# Check whether all the datasets support evaluation
for dataset in self.datasets:
assert hasattr(dataset, 'evaluate'), \
f'{type(dataset)} does not implement evaluate function'
if self.separate_eval:
dataset_idx = -1
total_eval_results = dict()
for size, dataset in zip(self.cumulative_sizes, self.datasets):
start_idx = 0 if dataset_idx == -1 else \
self.cumulative_sizes[dataset_idx]
end_idx = self.cumulative_sizes[dataset_idx + 1]
results_per_dataset = results[start_idx:end_idx]
print_log(
f'\nEvaluateing {dataset.ann_file} with '
f'{len(results_per_dataset)} images now',
logger=logger)
eval_results_per_dataset = dataset.evaluate(
results_per_dataset, logger=logger, **kwargs)
dataset_idx += 1
for k, v in eval_results_per_dataset.items():
total_eval_results.update({f'{dataset_idx}_{k}': v})
return total_eval_results
elif any([isinstance(ds, CocoDataset) for ds in self.datasets]):
raise NotImplementedError(
'Evaluating concatenated CocoDataset as a whole is not'
' supported! Please set "separate_eval=True"')
elif len(set([type(ds) for ds in self.datasets])) != 1:
raise NotImplementedError(
'All the datasets should have same types')
else:
original_data_infos = self.datasets[0].data_infos
self.datasets[0].data_infos = sum(
[dataset.data_infos for dataset in self.datasets], [])
eval_results = self.datasets[0].evaluate(
results, logger=logger, **kwargs)
self.datasets[0].data_infos = original_data_infos
return eval_results
@DATASETS.register_module()
class RepeatDataset(object):
"""A wrapper of repeated dataset.
The length of repeated dataset will be `times` larger than the original
dataset. This is useful when the data loading time is long but the dataset
is small. Using RepeatDataset can reduce the data loading time between
epochs.
Args:
dataset (:obj:`Dataset`): The dataset to be repeated.
times (int): Repeat times.
"""
def __init__(self, dataset, times):
self.dataset = dataset
self.times = times
self.CLASSES = dataset.CLASSES
if hasattr(self.dataset, 'flag'):
self.flag = np.tile(self.dataset.flag, times)
self._ori_len = len(self.dataset)
def __getitem__(self, idx):
return self.dataset[idx % self._ori_len]
def get_cat_ids(self, idx):
"""Get category ids of repeat dataset by index.
Args:
idx (int): Index of data.
Returns:
list[int]: All categories in the image of specified index.
"""
return self.dataset.get_cat_ids(idx % self._ori_len)
def __len__(self):
"""Length after repetition."""
return self.times * self._ori_len
# Modified from https://github.com/facebookresearch/detectron2/blob/41d475b75a230221e21d9cac5d69655e3415e3a4/detectron2/data/samplers/distributed_sampler.py#L57 # noqa
@DATASETS.register_module()
class ClassBalancedDataset(object):
"""A wrapper of repeated dataset with repeat factor.
Suitable for training on class imbalanced datasets like LVIS. Following
the sampling strategy in the `paper <https://arxiv.org/abs/1908.03195>`_,
in each epoch, an image may appear multiple times based on its
"repeat factor".
The repeat factor for an image is a function of the frequency the rarest
category labeled in that image. The "frequency of category c" in [0, 1]
is defined by the fraction of images in the training set (without repeats)
in which category c appears.
The dataset needs to instantiate :func:`self.get_cat_ids` to support
ClassBalancedDataset.
The repeat factor is computed as followed.
1. For each category c, compute the fraction # of images
that contain it: :math:`f(c)`
2. For each category c, compute the category-level repeat factor:
:math:`r(c) = max(1, sqrt(t/f(c)))`
3. For each image I, compute the image-level repeat factor:
:math:`r(I) = max_{c in I} r(c)`
Args:
dataset (:obj:`CustomDataset`): The dataset to be repeated.
oversample_thr (float): frequency threshold below which data is
repeated. For categories with ``f_c >= oversample_thr``, there is
no oversampling. For categories with ``f_c < oversample_thr``, the
degree of oversampling following the square-root inverse frequency
heuristic above.
filter_empty_gt (bool, optional): If set true, images without bounding
boxes will not be oversampled. Otherwise, they will be categorized
as the pure background class and involved into the oversampling.
Default: True.
"""
def __init__(self, dataset, oversample_thr, filter_empty_gt=True):
self.dataset = dataset
self.oversample_thr = oversample_thr
self.filter_empty_gt = filter_empty_gt
self.CLASSES = dataset.CLASSES
repeat_factors = self._get_repeat_factors(dataset, oversample_thr)
repeat_indices = []
for dataset_idx, repeat_factor in enumerate(repeat_factors):
repeat_indices.extend([dataset_idx] * math.ceil(repeat_factor))
self.repeat_indices = repeat_indices
flags = []
if hasattr(self.dataset, 'flag'):
for flag, repeat_factor in zip(self.dataset.flag, repeat_factors):
flags.extend([flag] * int(math.ceil(repeat_factor)))
assert len(flags) == len(repeat_indices)
self.flag = np.asarray(flags, dtype=np.uint8)
def _get_repeat_factors(self, dataset, repeat_thr):
"""Get repeat factor for each images in the dataset.
Args:
dataset (:obj:`CustomDataset`): The dataset
repeat_thr (float): The threshold of frequency. If an image
contains the categories whose frequency below the threshold,
it would be repeated.
Returns:
list[float]: The repeat factors for each images in the dataset.
"""
# 1. For each category c, compute the fraction # of images
# that contain it: f(c)
category_freq = defaultdict(int)
num_images = len(dataset)
for idx in range(num_images):
cat_ids = set(self.dataset.get_cat_ids(idx))
if len(cat_ids) == 0 and not self.filter_empty_gt:
cat_ids = set([len(self.CLASSES)])
for cat_id in cat_ids:
category_freq[cat_id] += 1
for k, v in category_freq.items():
category_freq[k] = v / num_images
# 2. For each category c, compute the category-level repeat factor:
# r(c) = max(1, sqrt(t/f(c)))
category_repeat = {
cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
for cat_id, cat_freq in category_freq.items()
}
# 3. For each image I, compute the image-level repeat factor:
# r(I) = max_{c in I} r(c)
repeat_factors = []
for idx in range(num_images):
cat_ids = set(self.dataset.get_cat_ids(idx))
if len(cat_ids) == 0 and not self.filter_empty_gt:
cat_ids = set([len(self.CLASSES)])
repeat_factor = 1
if len(cat_ids) > 0:
repeat_factor = max(
{category_repeat[cat_id]
for cat_id in cat_ids})
repeat_factors.append(repeat_factor)
return repeat_factors
def __getitem__(self, idx):
ori_index = self.repeat_indices[idx]
return self.dataset[ori_index]
def __len__(self):
"""Length after repetition."""
return len(self.repeat_indices)
| 11,088 | 38.183746 | 167 | py |
GFocalV2 | GFocalV2-master/mmdet/datasets/builder.py | import copy
import platform
import random
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg
from torch.utils.data import DataLoader
from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
if platform.system() != 'Windows':
# https://github.com/pytorch/pytorch/issues/973
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
hard_limit = rlimit[1]
soft_limit = min(4096, hard_limit)
resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
DATASETS = Registry('dataset')
PIPELINES = Registry('pipeline')
def _concat_dataset(cfg, default_args=None):
from .dataset_wrappers import ConcatDataset
ann_files = cfg['ann_file']
img_prefixes = cfg.get('img_prefix', None)
seg_prefixes = cfg.get('seg_prefix', None)
proposal_files = cfg.get('proposal_file', None)
separate_eval = cfg.get('separate_eval', True)
datasets = []
num_dset = len(ann_files)
for i in range(num_dset):
data_cfg = copy.deepcopy(cfg)
# pop 'separate_eval' since it is not a valid key for common datasets.
if 'separate_eval' in data_cfg:
data_cfg.pop('separate_eval')
data_cfg['ann_file'] = ann_files[i]
if isinstance(img_prefixes, (list, tuple)):
data_cfg['img_prefix'] = img_prefixes[i]
if isinstance(seg_prefixes, (list, tuple)):
data_cfg['seg_prefix'] = seg_prefixes[i]
if isinstance(proposal_files, (list, tuple)):
data_cfg['proposal_file'] = proposal_files[i]
datasets.append(build_dataset(data_cfg, default_args))
return ConcatDataset(datasets, separate_eval)
def build_dataset(cfg, default_args=None):
from .dataset_wrappers import (ConcatDataset, RepeatDataset,
ClassBalancedDataset)
if isinstance(cfg, (list, tuple)):
dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
elif cfg['type'] == 'ConcatDataset':
dataset = ConcatDataset(
[build_dataset(c, default_args) for c in cfg['datasets']],
cfg.get('separate_eval', True))
elif cfg['type'] == 'RepeatDataset':
dataset = RepeatDataset(
build_dataset(cfg['dataset'], default_args), cfg['times'])
elif cfg['type'] == 'ClassBalancedDataset':
dataset = ClassBalancedDataset(
build_dataset(cfg['dataset'], default_args), cfg['oversample_thr'])
elif isinstance(cfg.get('ann_file'), (list, tuple)):
dataset = _concat_dataset(cfg, default_args)
else:
dataset = build_from_cfg(cfg, DATASETS, default_args)
return dataset
def build_dataloader(dataset,
samples_per_gpu,
workers_per_gpu,
num_gpus=1,
dist=True,
shuffle=True,
seed=None,
**kwargs):
"""Build PyTorch DataLoader.
In distributed training, each GPU/process has a dataloader.
In non-distributed training, there is only one dataloader for all GPUs.
Args:
dataset (Dataset): A PyTorch dataset.
samples_per_gpu (int): Number of training samples on each GPU, i.e.,
batch size of each GPU.
workers_per_gpu (int): How many subprocesses to use for data loading
for each GPU.
num_gpus (int): Number of GPUs. Only used in non-distributed training.
dist (bool): Distributed training/test or not. Default: True.
shuffle (bool): Whether to shuffle the data at every epoch.
Default: True.
kwargs: any keyword argument to be used to initialize DataLoader
Returns:
DataLoader: A PyTorch dataloader.
"""
rank, world_size = get_dist_info()
if dist:
# DistributedGroupSampler will definitely shuffle the data to satisfy
# that images on each GPU are in the same group
if shuffle:
sampler = DistributedGroupSampler(dataset, samples_per_gpu,
world_size, rank)
else:
sampler = DistributedSampler(
dataset, world_size, rank, shuffle=False)
batch_size = samples_per_gpu
num_workers = workers_per_gpu
else:
sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else None
batch_size = num_gpus * samples_per_gpu
num_workers = num_gpus * workers_per_gpu
init_fn = partial(
worker_init_fn, num_workers=num_workers, rank=rank,
seed=seed) if seed is not None else None
data_loader = DataLoader(
dataset,
batch_size=batch_size,
sampler=sampler,
num_workers=num_workers,
collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
pin_memory=False,
worker_init_fn=init_fn,
**kwargs)
return data_loader
def worker_init_fn(worker_id, num_workers, rank, seed):
# The seed of each worker equals to
# num_worker * rank + worker_id + user_seed
worker_seed = num_workers * rank + worker_id + seed
np.random.seed(worker_seed)
random.seed(worker_seed)
| 5,291 | 35.75 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/datasets/samplers/group_sampler.py | from __future__ import division
import math
import numpy as np
import torch
from mmcv.runner import get_dist_info
from torch.utils.data import Sampler
class GroupSampler(Sampler):
def __init__(self, dataset, samples_per_gpu=1):
assert hasattr(dataset, 'flag')
self.dataset = dataset
self.samples_per_gpu = samples_per_gpu
self.flag = dataset.flag.astype(np.int64)
self.group_sizes = np.bincount(self.flag)
self.num_samples = 0
for i, size in enumerate(self.group_sizes):
self.num_samples += int(np.ceil(
size / self.samples_per_gpu)) * self.samples_per_gpu
def __iter__(self):
indices = []
for i, size in enumerate(self.group_sizes):
if size == 0:
continue
indice = np.where(self.flag == i)[0]
assert len(indice) == size
np.random.shuffle(indice)
num_extra = int(np.ceil(size / self.samples_per_gpu)
) * self.samples_per_gpu - len(indice)
indice = np.concatenate(
[indice, np.random.choice(indice, num_extra)])
indices.append(indice)
indices = np.concatenate(indices)
indices = [
indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu]
for i in np.random.permutation(
range(len(indices) // self.samples_per_gpu))
]
indices = np.concatenate(indices)
indices = indices.astype(np.int64).tolist()
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return self.num_samples
class DistributedGroupSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
process can pass a DistributedSampler instance as a DataLoader sampler,
and load a subset of the original dataset that is exclusive to it.
.. note::
Dataset is assumed to be of constant size.
Arguments:
dataset: Dataset used for sampling.
num_replicas (optional): Number of processes participating in
distributed training.
rank (optional): Rank of the current process within num_replicas.
"""
def __init__(self,
dataset,
samples_per_gpu=1,
num_replicas=None,
rank=None):
_rank, _num_replicas = get_dist_info()
if num_replicas is None:
num_replicas = _num_replicas
if rank is None:
rank = _rank
self.dataset = dataset
self.samples_per_gpu = samples_per_gpu
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
assert hasattr(self.dataset, 'flag')
self.flag = self.dataset.flag
self.group_sizes = np.bincount(self.flag)
self.num_samples = 0
for i, j in enumerate(self.group_sizes):
self.num_samples += int(
math.ceil(self.group_sizes[i] * 1.0 / self.samples_per_gpu /
self.num_replicas)) * self.samples_per_gpu
self.total_size = self.num_samples * self.num_replicas
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = []
for i, size in enumerate(self.group_sizes):
if size > 0:
indice = np.where(self.flag == i)[0]
assert len(indice) == size
indice = indice[list(torch.randperm(int(size),
generator=g))].tolist()
extra = int(
math.ceil(
size * 1.0 / self.samples_per_gpu / self.num_replicas)
) * self.samples_per_gpu * self.num_replicas - len(indice)
# pad indice
tmp = indice.copy()
for _ in range(extra // size):
indice.extend(tmp)
indice.extend(tmp[:extra % size])
indices.extend(indice)
assert len(indices) == self.total_size
indices = [
indices[j] for i in list(
torch.randperm(
len(indices) // self.samples_per_gpu, generator=g))
for j in range(i * self.samples_per_gpu, (i + 1) *
self.samples_per_gpu)
]
# subsample
offset = self.num_samples * self.rank
indices = indices[offset:offset + self.num_samples]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
| 4,898 | 33.744681 | 78 | py |
GFocalV2 | GFocalV2-master/mmdet/datasets/samplers/distributed_sampler.py | import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
class DistributedSampler(_DistributedSampler):
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
super().__init__(dataset, num_replicas=num_replicas, rank=rank)
self.shuffle = shuffle
def __iter__(self):
# deterministically shuffle based on epoch
if self.shuffle:
g = torch.Generator()
g.manual_seed(self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = torch.arange(len(self.dataset)).tolist()
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)
| 978 | 32.758621 | 77 | py |
GFocalV2 | GFocalV2-master/mmdet/datasets/pipelines/formating.py | from collections.abc import Sequence
import mmcv
import numpy as np
import torch
from mmcv.parallel import DataContainer as DC
from ..builder import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
:class:`Sequence`, :class:`int` and :class:`float`.
Args:
data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
be converted.
"""
if isinstance(data, torch.Tensor):
return data
elif isinstance(data, np.ndarray):
return torch.from_numpy(data)
elif isinstance(data, Sequence) and not mmcv.is_str(data):
return torch.tensor(data)
elif isinstance(data, int):
return torch.LongTensor([data])
elif isinstance(data, float):
return torch.FloatTensor([data])
else:
raise TypeError(f'type {type(data)} cannot be converted to tensor.')
@PIPELINES.register_module()
class ToTensor(object):
"""Convert some results to :obj:`torch.Tensor` by given keys.
Args:
keys (Sequence[str]): Keys that need to be converted to Tensor.
"""
def __init__(self, keys):
self.keys = keys
def __call__(self, results):
"""Call function to convert data in results to :obj:`torch.Tensor`.
Args:
results (dict): Result dict contains the data to convert.
Returns:
dict: The result dict contains the data converted
to :obj:`torch.Tensor`.
"""
for key in self.keys:
results[key] = to_tensor(results[key])
return results
def __repr__(self):
return self.__class__.__name__ + f'(keys={self.keys})'
@PIPELINES.register_module()
class ImageToTensor(object):
"""Convert image to :obj:`torch.Tensor` by given keys.
The dimension order of input image is (H, W, C). The pipeline will convert
it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
(1, H, W).
Args:
keys (Sequence[str]): Key of images to be converted to Tensor.
"""
def __init__(self, keys):
self.keys = keys
def __call__(self, results):
"""Call function to convert image in results to :obj:`torch.Tensor` and
transpose the channel order.
Args:
results (dict): Result dict contains the image data to convert.
Returns:
dict: The result dict contains the image converted
to :obj:`torch.Tensor` and transposed to (C, H, W) order.
"""
for key in self.keys:
img = results[key]
if len(img.shape) < 3:
img = np.expand_dims(img, -1)
results[key] = to_tensor(img.transpose(2, 0, 1))
return results
def __repr__(self):
return self.__class__.__name__ + f'(keys={self.keys})'
@PIPELINES.register_module()
class Transpose(object):
"""Transpose some results by given keys.
Args:
keys (Sequence[str]): Keys of results to be transposed.
order (Sequence[int]): Order of transpose.
"""
def __init__(self, keys, order):
self.keys = keys
self.order = order
def __call__(self, results):
"""Call function to transpose the channel order of data in results.
Args:
results (dict): Result dict contains the data to transpose.
Returns:
dict: The result dict contains the data transposed to \
``self.order``.
"""
for key in self.keys:
results[key] = results[key].transpose(self.order)
return results
def __repr__(self):
return self.__class__.__name__ + \
f'(keys={self.keys}, order={self.order})'
@PIPELINES.register_module()
class ToDataContainer(object):
"""Convert results to :obj:`mmcv.DataContainer` by given fields.
Args:
fields (Sequence[dict]): Each field is a dict like
``dict(key='xxx', **kwargs)``. The ``key`` in result will
be converted to :obj:`mmcv.DataContainer` with ``**kwargs``.
Default: ``(dict(key='img', stack=True), dict(key='gt_bboxes'),
dict(key='gt_labels'))``.
"""
def __init__(self,
fields=(dict(key='img', stack=True), dict(key='gt_bboxes'),
dict(key='gt_labels'))):
self.fields = fields
def __call__(self, results):
"""Call function to convert data in results to
:obj:`mmcv.DataContainer`.
Args:
results (dict): Result dict contains the data to convert.
Returns:
dict: The result dict contains the data converted to \
:obj:`mmcv.DataContainer`.
"""
for field in self.fields:
field = field.copy()
key = field.pop('key')
results[key] = DC(results[key], **field)
return results
def __repr__(self):
return self.__class__.__name__ + f'(fields={self.fields})'
@PIPELINES.register_module()
class DefaultFormatBundle(object):
"""Default formatting bundle.
It simplifies the pipeline of formatting common fields, including "img",
"proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg".
These fields are formatted as follows.
- img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True)
- proposals: (1)to tensor, (2)to DataContainer
- gt_bboxes: (1)to tensor, (2)to DataContainer
- gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
- gt_labels: (1)to tensor, (2)to DataContainer
- gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
- gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \
(3)to DataContainer (stack=True)
"""
def __call__(self, results):
"""Call function to transform and format common fields in results.
Args:
results (dict): Result dict contains the data to convert.
Returns:
dict: The result dict contains the data that is formatted with \
default bundle.
"""
if 'img' in results:
img = results['img']
# add default meta keys
results = self._add_default_meta_keys(results)
if len(img.shape) < 3:
img = np.expand_dims(img, -1)
img = np.ascontiguousarray(img.transpose(2, 0, 1))
results['img'] = DC(to_tensor(img), stack=True)
for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']:
if key not in results:
continue
results[key] = DC(to_tensor(results[key]))
if 'gt_masks' in results:
results['gt_masks'] = DC(results['gt_masks'], cpu_only=True)
if 'gt_semantic_seg' in results:
results['gt_semantic_seg'] = DC(
to_tensor(results['gt_semantic_seg'][None, ...]), stack=True)
return results
def _add_default_meta_keys(self, results):
"""Add default meta keys.
We set default meta keys including `pad_shape`, `scale_factor` and
`img_norm_cfg` to avoid the case where no `Resize`, `Normalize` and
`Pad` are implemented during the whole pipeline.
Args:
results (dict): Result dict contains the data to convert.
Returns:
results (dict): Updated result dict contains the data to convert.
"""
img = results['img']
results.setdefault('pad_shape', img.shape)
results.setdefault('scale_factor', 1.0)
num_channels = 1 if len(img.shape) < 3 else img.shape[2]
results.setdefault(
'img_norm_cfg',
dict(
mean=np.zeros(num_channels, dtype=np.float32),
std=np.ones(num_channels, dtype=np.float32),
to_rgb=False))
return results
def __repr__(self):
return self.__class__.__name__
@PIPELINES.register_module()
class Collect(object):
"""Collect data from the loader relevant to the specific task.
This is usually the last stage of the data loader pipeline. Typically keys
is set to some subset of "img", "proposals", "gt_bboxes",
"gt_bboxes_ignore", "gt_labels", and/or "gt_masks".
The "img_meta" item is always populated. The contents of the "img_meta"
dictionary depends on "meta_keys". By default this includes:
- "img_shape": shape of the image input to the network as a tuple \
(h, w, c). Note that images may be zero padded on the \
bottom/right if the batch tensor is larger than this shape.
- "scale_factor": a float indicating the preprocessing scale
- "flip": a boolean indicating if image flip transform was used
- "filename": path to the image file
- "ori_shape": original shape of the image as a tuple (h, w, c)
- "pad_shape": image shape after padding
- "img_norm_cfg": a dict of normalization information:
- mean - per channel mean subtraction
- std - per channel std divisor
- to_rgb - bool indicating if bgr was converted to rgb
Args:
keys (Sequence[str]): Keys of results to be collected in ``data``.
meta_keys (Sequence[str], optional): Meta keys to be converted to
``mmcv.DataContainer`` and collected in ``data[img_metas]``.
Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'flip', 'flip_direction',
'img_norm_cfg')``
"""
def __init__(self,
keys,
meta_keys=('filename', 'ori_filename', 'ori_shape',
'img_shape', 'pad_shape', 'scale_factor', 'flip',
'flip_direction', 'img_norm_cfg')):
self.keys = keys
self.meta_keys = meta_keys
def __call__(self, results):
"""Call function to collect keys in results. The keys in ``meta_keys``
will be converted to :obj:mmcv.DataContainer.
Args:
results (dict): Result dict contains the data to collect.
Returns:
dict: The result dict contains the following keys
- keys in``self.keys``
- ``img_metas``
"""
data = {}
img_meta = {}
for key in self.meta_keys:
img_meta[key] = results[key]
data['img_metas'] = DC(img_meta, cpu_only=True)
for key in self.keys:
data[key] = results[key]
return data
def __repr__(self):
return self.__class__.__name__ + \
f'(keys={self.keys}, meta_keys={self.meta_keys})'
@PIPELINES.register_module()
class WrapFieldsToLists(object):
"""Wrap fields of the data dictionary into lists for evaluation.
This class can be used as a last step of a test or validation
pipeline for single image evaluation or inference.
Example:
>>> test_pipeline = [
>>> dict(type='LoadImageFromFile'),
>>> dict(type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
>>> dict(type='Pad', size_divisor=32),
>>> dict(type='ImageToTensor', keys=['img']),
>>> dict(type='Collect', keys=['img']),
>>> dict(type='WrapIntoLists')
>>> ]
"""
def __call__(self, results):
"""Call function to wrap fields into lists.
Args:
results (dict): Result dict contains the data to wrap.
Returns:
dict: The result dict where value of ``self.keys`` are wrapped \
into list.
"""
# Wrap dict fields into lists
for key, val in results.items():
results[key] = [val]
return results
def __repr__(self):
return f'{self.__class__.__name__}()'
| 12,033 | 31.969863 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/utils/contextmanagers.py | import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def completed(trace_name='',
name='',
sleep_interval=0.05,
streams: List[torch.cuda.Stream] = None):
"""Async context manager that waits for work to complete on given CUDA
streams."""
if not torch.cuda.is_available():
yield
return
stream_before_context_switch = torch.cuda.current_stream()
if not streams:
streams = [stream_before_context_switch]
else:
streams = [s if s else stream_before_context_switch for s in streams]
end_events = [
torch.cuda.Event(enable_timing=DEBUG_COMPLETED_TIME) for _ in streams
]
if DEBUG_COMPLETED_TIME:
start = torch.cuda.Event(enable_timing=True)
stream_before_context_switch.record_event(start)
cpu_start = time.monotonic()
logger.debug('%s %s starting, streams: %s', trace_name, name, streams)
grad_enabled_before = torch.is_grad_enabled()
try:
yield
finally:
current_stream = torch.cuda.current_stream()
assert current_stream == stream_before_context_switch
if DEBUG_COMPLETED_TIME:
cpu_end = time.monotonic()
for i, stream in enumerate(streams):
event = end_events[i]
stream.record_event(event)
grad_enabled_after = torch.is_grad_enabled()
# observed change of torch.is_grad_enabled() during concurrent run of
# async_test_bboxes code
assert (grad_enabled_before == grad_enabled_after
), 'Unexpected is_grad_enabled() value change'
are_done = [e.query() for e in end_events]
logger.debug('%s %s completed: %s streams: %s', trace_name, name,
are_done, streams)
with torch.cuda.stream(stream_before_context_switch):
while not all(are_done):
await asyncio.sleep(sleep_interval)
are_done = [e.query() for e in end_events]
logger.debug(
'%s %s completed: %s streams: %s',
trace_name,
name,
are_done,
streams,
)
current_stream = torch.cuda.current_stream()
assert current_stream == stream_before_context_switch
if DEBUG_COMPLETED_TIME:
cpu_time = (cpu_end - cpu_start) * 1000
stream_times_ms = ''
for i, stream in enumerate(streams):
elapsed_time = start.elapsed_time(end_events[i])
stream_times_ms += f' {stream} {elapsed_time:.2f} ms'
logger.info('%s %s %.2f ms %s', trace_name, name, cpu_time,
stream_times_ms)
@contextlib.asynccontextmanager
async def concurrent(streamqueue: asyncio.Queue,
trace_name='concurrent',
name='stream'):
"""Run code concurrently in different streams.
:param streamqueue: asyncio.Queue instance.
Queue tasks define the pool of streams used for concurrent execution.
"""
if not torch.cuda.is_available():
yield
return
initial_stream = torch.cuda.current_stream()
with torch.cuda.stream(initial_stream):
stream = await streamqueue.get()
assert isinstance(stream, torch.cuda.Stream)
try:
with torch.cuda.stream(stream):
logger.debug('%s %s is starting, stream: %s', trace_name, name,
stream)
yield
current = torch.cuda.current_stream()
assert current == stream
logger.debug('%s %s has finished, stream: %s', trace_name,
name, stream)
finally:
streamqueue.task_done()
streamqueue.put_nowait(stream)
| 4,077 | 32.42623 | 79 | py |
GFocalV2 | GFocalV2-master/mmdet/utils/profiling.py | import contextlib
import sys
import time
import torch
if sys.version_info >= (3, 7):
@contextlib.contextmanager
def profile_time(trace_name,
name,
enabled=True,
stream=None,
end_stream=None):
"""Print time spent by CPU and GPU.
Useful as a temporary context manager to find sweet spots of code
suitable for async implementation.
"""
if (not enabled) or not torch.cuda.is_available():
yield
return
stream = stream if stream else torch.cuda.current_stream()
end_stream = end_stream if end_stream else stream
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
stream.record_event(start)
try:
cpu_start = time.monotonic()
yield
finally:
cpu_end = time.monotonic()
end_stream.record_event(end)
end.synchronize()
cpu_time = (cpu_end - cpu_start) * 1000
gpu_time = start.elapsed_time(end)
msg = f'{trace_name} {name} cpu_time {cpu_time:.2f} ms '
msg += f'gpu_time {gpu_time:.2f} ms stream {stream}'
print(msg, end_stream)
| 1,288 | 31.225 | 73 | py |
qml | qml-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# QML documentation build configuration file, created by
# sphinx-quickstart on Sun Jun 4 14:41:04 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
print os.getcwd()
sys.path.insert(0, os.path.abspath('../../build/lib.linux-x86_64-2.7/'))
import sphinx_rtd_theme
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.5.4'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['sphinx.ext.autodoc','sphinx.ext.mathjax']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'QML'
copyright = u'2017, Anders S. Christensen'
author = u'Anders S. Christensen'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
iversion = u'0.4.0'
# The full version, including alpha/beta/rc tags.
release = u'0.4.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = []
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
# html_theme = 'alabaster'
html_theme = "sphinx_rtd_theme"
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
# html_static_path = ['_static']
# -- Options for HTMLHelp output ------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = 'QMLdoc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'QML.tex', u'QML Documentation',
u'Anders S. Christensen', 'manual'),
]
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'qml', u'QML Documentation',
[author], 1)
]
# -- Options for Texinfo output -------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'QML', u'QML Documentation',
author, 'QML', 'One line description of project.',
'Miscellaneous'),
]
html_sidebars = { '**': ['globaltoc.html', 'relations.html', 'sourcelink.html', 'searchbox.html'], }
| 4,965 | 30.0375 | 100 | py |
GBM-perf | GBM-perf-master/v1-keep-old-before_docker/run/2a-xgboost-gpu.py |
import xgboost as xgb
import pandas as pd
from sklearn import metrics
import time
dtrain = xgb.DMatrix('train-10m.libsvm')
dtest = xgb.DMatrix('test-10m.libsvm')
#params = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic'}
#params = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'updater':'grow_colmaker'}
#params = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'updater':'grow_fast_histmaker'}
#params = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'updater':'grow_gpu'}
#params = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'updater':'grow_gpu_hist'}
params = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'gpu_hist'}
num_round = 100
start = time.time()
md = xgb.train(params, dtrain, num_round)
end = time.time()
print(end - start)
phat = md.predict(dtest)
ytest = pd.read_csv("test.csv")['dep_delayed_15min']=='Y'
metrics.roc_auc_score(ytest, phat)
| 955 | 27.969697 | 101 | py |
GBM-perf | GBM-perf-master/analysis/dask/xgb-dask.py | import pandas as pd
from sklearn import metrics
from dask.distributed import Client, LocalCluster
import dask.dataframe as dd
import xgboost as xgb
cluster = LocalCluster(n_workers=16, threads_per_worker=1)
client = Client(cluster)
d_train = pd.read_csv("https://raw.githubusercontent.com/szilard/benchm-ml--data/master/int_enc/train-1m-intenc.csv")
d_test = pd.read_csv("https://raw.githubusercontent.com/szilard/benchm-ml--data/master/int_enc/test-1m-intenc.csv")
dx_train = dd.from_pandas(d_train, npartitions=16)
dx_test = dd.from_pandas(d_test, npartitions=1)
X_train = dx_train.iloc[:, :-1].to_dask_array(lengths=True)
y_train = dx_train.iloc[:,-1:].to_dask_array(lengths=True)
X_test = dx_test.iloc[:, :-1].to_dask_array(lengths=True)
y_test = dx_test.iloc[:,-1:].to_dask_array(lengths=True)
X_train.persist()
y_train.persist()
client.has_what()
dxgb_train = xgb.dask.DaskDMatrix(client, X_train, y_train)
dxgb_test = xgb.dask.DaskDMatrix(client, X_test)
param = {'objective':'binary:logistic', 'tree_method':'hist', 'max_depth':10, 'eta':0.1}
%time md = xgb.dask.train(client, param, dxgb_train, num_boost_round = 100)
y_pred = xgb.dask.predict(client, md, dxgb_test)
y_pred_loc = y_pred.compute()
y_test_loc = y_test.compute()
print(metrics.roc_auc_score(y_test_loc, y_pred_loc))
| 1,319 | 29 | 117 | py |
GBM-perf | GBM-perf-master/analysis/dask/xgb.py | import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
import xgboost as xgb
d_train = pd.read_csv("https://raw.githubusercontent.com/szilard/benchm-ml--data/master/int_enc/train-1m-intenc.csv")
d_test = pd.read_csv("https://raw.githubusercontent.com/szilard/benchm-ml--data/master/int_enc/test-1m-intenc.csv")
X_train = d_train.iloc[:, :-1].to_numpy()
y_train = d_train.iloc[:,-1:].to_numpy()
X_test = d_test.iloc[:, :-1].to_numpy()
y_test = d_test.iloc[:,-1:].to_numpy()
dxgb_train = xgb.DMatrix(X_train, label = y_train)
dxgb_test = xgb.DMatrix(X_test)
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'hist'}
%time md = xgb.train(param, dxgb_train, num_boost_round = 100)
y_pred = md.predict(dxgb_test)
print(metrics.roc_auc_score(y_test, y_pred))
| 858 | 28.62069 | 117 | py |
GBM-perf | GBM-perf-master/analysis/dask/old_encoding/xgb-intenc.py | import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
import xgboost as xgb
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_cat+vars_num].to_numpy()
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
X_train = X_all[0:d_train.shape[0],]
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0]),]
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
dxgb_train = xgb.DMatrix(X_train, label = y_train)
dxgb_test = xgb.DMatrix(X_test)
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'hist'}
%time md = xgb.train(param, dxgb_train, num_boost_round = 100)
y_pred = md.predict(dxgb_test)
print(metrics.roc_auc_score(y_test, y_pred))
## m5.4xlarge 16c (8+8HT)
## Wall time: 3.29 s
## 0.7524071455769888
| 1,248 | 28.738095 | 101 | py |
GBM-perf | GBM-perf-master/analysis/dask/old_encoding/xgb-dask.py | import pandas as pd
from sklearn import metrics
from dask.distributed import Client, LocalCluster
import dask.dataframe as dd
import dask.array as da
from dask_ml import preprocessing
import xgboost as xgb
cluster = LocalCluster(n_workers=16, threads_per_worker=1)
client = Client(cluster)
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
dx_all = dd.from_pandas(d_all, npartitions=16)
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
dx_all[col] = preprocessing.LabelEncoder().fit_transform(dx_all[col])
X_all = dx_all[vars_cat+vars_num].to_dask_array(lengths=True)
y_all = da.where((dx_all["dep_delayed_15min"]=="Y").to_dask_array(lengths=True),1,0)
X_train = X_all[0:d_train.shape[0],]
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0]),]
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
X_train.persist()
y_train.persist()
client.has_what()
dxgb_train = xgb.dask.DaskDMatrix(client, X_train, y_train)
dxgb_test = xgb.dask.DaskDMatrix(client, X_test)
param = {'objective':'binary:logistic', 'tree_method':'hist', 'max_depth':10, 'eta':0.1}
%time md = xgb.dask.train(client, param, dxgb_train, num_boost_round = 100)
y_pred = xgb.dask.predict(client, md, dxgb_test)
y_pred_loc = y_pred.compute()
y_test_loc = y_test.compute()
print(metrics.roc_auc_score(y_test_loc, y_pred_loc))
## m5.4xlarge 16c (8+8HT)
## Wall time: 20.5 s
## 0.7958538649110775
| 1,692 | 28.189655 | 101 | py |
GBM-perf | GBM-perf-master/analysis/dask/old_encoding/xgb-dask-deprecated.py | import pandas as pd
from sklearn import metrics
from dask.distributed import Client, LocalCluster
import dask.dataframe as dd
import dask.array as da
from dask_ml import preprocessing
import dask_xgboost as dxgb
cluster = LocalCluster(n_workers=16, threads_per_worker=1)
client = Client(cluster)
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
dx_all = dd.from_pandas(d_all, npartitions=16)
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
dx_all[col] = preprocessing.LabelEncoder().fit_transform(dx_all[col])
X_all = dx_all[vars_cat+vars_num].to_dask_array(lengths=True)
y_all = da.where((dx_all["dep_delayed_15min"]=="Y").to_dask_array(lengths=True),1,0)
X_train = X_all[0:d_train.shape[0],]
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0]),]
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
X_train.persist()
y_train.persist()
client.has_what()
param = {'objective':'binary:logistic', 'tree_method':'hist', 'max_depth':10, 'eta':0.1}
%time md = dxgb.train(client, param, X_train, y_train, num_boost_round = 100)
y_pred = dxgb.predict(client, md, X_test)
y_pred_loc = y_pred.compute()
y_test_loc = y_test.compute()
print(metrics.roc_auc_score(y_test_loc, y_pred_loc))
## m5.4xlarge 16c (8+8HT)
## Wall time: 34.3 s
## 0.7928378346764724
| 1,581 | 28.849057 | 101 | py |
GBM-perf | GBM-perf-master/analysis/intenc/xgboost.py | import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
import xgboost as xgb
d_train = pd.read_csv("https://raw.githubusercontent.com/szilard/benchm-ml--data/master/int_enc/train-1m-intenc.csv")
d_test = pd.read_csv("https://raw.githubusercontent.com/szilard/benchm-ml--data/master/int_enc/test-1m-intenc.csv")
X_train = d_train.iloc[:, :-1].to_numpy()
y_train = d_train.iloc[:,-1:].to_numpy()
X_test = d_test.iloc[:, :-1].to_numpy()
y_test = d_test.iloc[:,-1:].to_numpy()
dxgb_train = xgb.DMatrix(X_train, label = y_train)
dxgb_test = xgb.DMatrix(X_test)
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'hist'}
%time md = xgb.train(param, dxgb_train, num_boost_round = 100)
y_pred = md.predict(dxgb_test)
print(metrics.roc_auc_score(y_test, y_pred))
## m5.4xlarge 16c (8+8HT)
## Wall time: 3.3 s
## 0.7527781837199401
| 926 | 27.96875 | 117 | py |
GBM-perf | GBM-perf-master/analysis/random_forest/xgboost.py |
import pandas as pd
import numpy as np
from sklearn import preprocessing
from scipy import sparse
from sklearn import metrics
import xgboost as xgb
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all_cat = preprocessing.OneHotEncoder(categories="auto").fit_transform(d_all[vars_cat])
X_all = sparse.hstack((X_all_cat, d_all[vars_num])).tocsr()
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
X_train = X_all[0:d_train.shape[0],]
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0]),]
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
md = xgb.XGBClassifier(max_depth=10, n_estimators=100, learning_rate=0.1,
tree_method="hist", n_jobs=-1)
%time md.fit(X_train, y_train)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.confusion_matrix(y_test, y_pred>0.7))
print(metrics.roc_auc_score(y_test, y_pred))
dxgb_train = xgb.DMatrix(X_train, label = y_train)
dxgb_test = xgb.DMatrix(X_test)
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'hist'}
%time md = xgb.train(param, dxgb_train, num_boost_round = 100)
y_pred = md.predict(dxgb_test)
print(metrics.roc_auc_score(y_test, y_pred))
| 1,634 | 29.277778 | 101 | py |
GBM-perf | GBM-perf-master/analysis/rapids-ai/xgb-ohe.py | import cudf
import xgboost as xgb
import pandas as pd
import numpy as np
from scipy import sparse
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all_cat = preprocessing.OneHotEncoder(categories="auto").fit_transform(d_all[vars_cat])
X_all = sparse.hstack((X_all_cat, d_all[vars_num])).tocsr()
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
X_train = X_all[0:d_train.shape[0]]
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
#--- RAM 1.3G (0.7GB before ipython kernel)
## ******* CPU
dxgb_train = xgb.DMatrix(X_train, label = y_train)
dxgb_test = xgb.DMatrix(X_test)
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'hist'}
%time md = xgb.train(param, dxgb_train, num_boost_round = 100)
y_pred = md.predict(dxgb_test)
print(metrics.roc_auc_score(y_test, y_pred))
#Wall time: 8.39 s
#0.7490139621617369
#--- 1.4GB RAM
## ******* data RAM (CPU), train GPU
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'gpu_hist'}
%time md = xgb.train(param, dxgb_train, num_boost_round = 100)
y_pred = md.predict(dxgb_test)
print(metrics.roc_auc_score(y_test, y_pred))
#Wall time: 7.89 s
#0.748896157113143
#--- 0.9GB GPU mem / 2.3GB RAM
#--- reset GPU (restart kernel, reload data)
## ******* all GPU (data+train)
#X_train_GPU = cudf.DataFrame.from_pandas(X_train) ## doesn't work from sparse matrix
#y_train_GPU = cudf.Series(y_train)
#X_test_GPU = cudf.DataFrame.from_pandas(X_test)
#y_test_GPU = cudf.Series(y_test)
#X_train_GPU = cudf.DataFrame.from_pandas(d_train[vars_cat+vars_num]) ## cannot create DMatrix later with cat.vars
#y_train_GPU = cudf.Series(y_train)
#X_test_GPU = cudf.DataFrame.from_pandas(d_test[vars_cat+vars_num])
#y_test_GPU = cudf.Series(y_test)
X_train_GPU = cudf.DataFrame.from_pandas(pd.DataFrame(data=X_train.todense()))
y_train_GPU = cudf.Series(y_train)
X_test_GPU = cudf.DataFrame.from_pandas(pd.DataFrame(data=X_test.todense()))
y_test_GPU = cudf.Series(y_test)
# TODO: try CuPy sparse mx
#--- 7GB GPU mem / 2GB RAM (max during transfer 7GB RAM)
dxgb_train_GPU = xgb.DMatrix(X_train_GPU, label = y_train_GPU)
dxgb_test_GPU = xgb.DMatrix(X_test_GPU)
#--- 12GB GPU mem
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'gpu_hist'}
%time md = xgb.train(param, dxgb_train_GPU, num_boost_round = 100)
#!!!! crashes kernel (too much GPU mem?)
y_pred = md.predict(dxgb_test_GPU)
print(metrics.roc_auc_score(y_test, y_pred))
| 3,112 | 28.932692 | 121 | py |
GBM-perf | GBM-perf-master/analysis/rapids-ai/xgb-intenc.py | import cudf
import xgboost as xgb
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
X_train = X_all[0:d_train.shape[0]]
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
#--- RAM 1.1GB (0.7GB before python kernel)
## ******* CPU
dxgb_train = xgb.DMatrix(X_train, label = y_train)
dxgb_test = xgb.DMatrix(X_test)
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'hist'}
%time md = xgb.train(param, dxgb_train, num_boost_round = 100)
y_pred = md.predict(dxgb_test)
print(metrics.roc_auc_score(y_test, y_pred))
#Wall time: 5.83 s
#0.7523232418031532
#--- 1.2GB RAM
## ******* data RAM (CPU), train GPU
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'gpu_hist'}
%time md = xgb.train(param, dxgb_train, num_boost_round = 100)
y_pred = md.predict(dxgb_test)
print(metrics.roc_auc_score(y_test, y_pred))
#Wall time: 4.57 s (1st time more because of copy to GPU)
#Wall time: 3.06 s
#0.7527066683633643
#--- 0.85GB GPU mem
#--- reset GPU (restart kernel, reload data)
## ******* all GPU (data+train)
X_train_GPU = cudf.DataFrame.from_pandas(X_train)
y_train_GPU = cudf.Series(y_train)
X_test_GPU = cudf.DataFrame.from_pandas(X_test)
y_test_GPU = cudf.Series(y_test)
#--- 0.8 GPU mem / 2GB RAM
dxgb_train_GPU = xgb.DMatrix(X_train_GPU, label = y_train_GPU)
dxgb_test_GPU = xgb.DMatrix(X_test_GPU)
#--- 0.9GB GPU mem
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'gpu_hist'}
%time md = xgb.train(param, dxgb_train_GPU, num_boost_round = 100)
y_pred = md.predict(dxgb_test_GPU)
print(metrics.roc_auc_score(y_test, y_pred))
#Wall time: 3.16 s
#0.7527066683633643
#--- 1GB GPU mem
#type(X_train) # pd DF
#type(y_train) # np array
#type(X_train_GPU) # cuDF
#type(y_train_GPU) # cuSer
| 2,532 | 24.585859 | 105 | py |
GBM-perf | GBM-perf-master/analysis/tensorflow-df/tfdf.py | import tensorflow_decision_forests as tfdf
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_train["dep_delayed_15min"] = np.where(d_train["dep_delayed_15min"]=="Y",1,0)
d_test["dep_delayed_15min"] = np.where(d_test["dep_delayed_15min"]=="Y",1,0)
dtf_train = tfdf.keras.pd_dataframe_to_tf_dataset(d_train, label="dep_delayed_15min")
dtf_test = tfdf.keras.pd_dataframe_to_tf_dataset(d_test, label="dep_delayed_15min")
md = tfdf.keras.GradientBoostedTreesModel(max_depth=10, num_trees=100, shrinkage=0.1)
%time md.fit(x=dtf_train)
y_pred = md.predict(dtf_test)
print(metrics.roc_auc_score(d_test["dep_delayed_15min"], y_pred))
| 836 | 28.892857 | 85 | py |
GBM-perf | GBM-perf-master/analysis/tabnet/tabnet-base.py | from pytorch_tabnet.tab_model import TabNetClassifier
import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-0.1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
X_train = X_all[0:d_train.shape[0]].to_numpy()
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
cat_emb_dim = np.floor(np.log(cat_dims)).astype(int)
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dim,
## optimizer_fn=torch.optim.Adam,
## optimizer_params=dict(lr=2e-2),
## mask_type='sparsemax',
n_steps=1,
)
%%time
md.fit( X_train=X_train, y_train=y_train,
max_epochs=10, patience=0,
## batch_size=1024, virtual_batch_size=128,
## weights=0,
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
| 1,659 | 28.642857 | 80 | py |
GBM-perf | GBM-perf-master/analysis/tabnet/tabnet.py | from pytorch_tabnet.tab_model import TabNetClassifier
import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-0.1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
X_train = X_all[0:d_train.shape[0]].to_numpy()
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
cat_emb_dim = np.floor(np.log(cat_dims)).astype(int)
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dim,
## optimizer_fn=torch.optim.Adam,
## optimizer_params=dict(lr=2e-2),
## mask_type='sparsemax',
n_steps=1,
)
%%time
md.fit( X_train=X_train, y_train=y_train,
max_epochs=10, patience=0,
## batch_size=1024, virtual_batch_size=128,
## weights=0,
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
| 1,658 | 29.163636 | 80 | py |
GBM-perf | GBM-perf-master/analysis/tabnet/tabnet-20best.py | from pytorch_tabnet.tab_model import TabNetClassifier
import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-0.1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
idx_trvl = np.random.choice(["train", "valid"], p =[.8, .2], size=(d_train.shape[0],))
X_train = X_all[0:d_train.shape[0]][idx_trvl=="train"].to_numpy()
y_train = y_all[0:d_train.shape[0]][idx_trvl=="train"]
X_valid = X_all[0:d_train.shape[0]][idx_trvl=="valid"].to_numpy()
y_valid = y_all[0:d_train.shape[0]][idx_trvl=="valid"]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
cat_emb_dim = np.floor(np.log(cat_dims)).astype(int)
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dim,
## optimizer_fn=torch.optim.Adam,
## optimizer_params=dict(lr=2e-2),
## mask_type='sparsemax',
n_steps=1,
)
%%time
md.fit( X_train=X_train, y_train=y_train,
eval_set=[(X_train, y_train), (X_valid, y_valid)],
eval_name=['train', 'valid'],
eval_metric=['auc'],
max_epochs=20, patience=20,
## batch_size=1024, virtual_batch_size=128,
## weights=0,
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
| 2,019 | 32.114754 | 86 | py |
GBM-perf | GBM-perf-master/analysis/tabnet/tabnet-best-evil.py | from pytorch_tabnet.tab_model import TabNetClassifier
import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-0.1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
X_train = X_all[0:d_train.shape[0]].to_numpy()
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
cat_emb_dim = np.floor(np.log(cat_dims)).astype(int)
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dim,
## optimizer_fn=torch.optim.Adam,
## optimizer_params=dict(lr=2e-2),
## mask_type='sparsemax',
n_steps=1,
)
%%time
md.fit( X_train=X_train, y_train=y_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
eval_name=['train', 'test_EVIL'],
eval_metric=['auc'],
max_epochs=100, patience=100,
## batch_size=1024, virtual_batch_size=128,
## weights=0,
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
| 1,776 | 30.175439 | 80 | py |
GBM-perf | GBM-perf-master/analysis/tabnet/old-keep/tabnet-estop.py | from pytorch_tabnet.tab_model import TabNetClassifier
import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-0.1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
idx_trvl = np.random.choice(["train", "valid"], p =[.8, .2], size=(d_train.shape[0],))
X_train = X_all[0:d_train.shape[0]][idx_trvl=="train"].to_numpy()
y_train = y_all[0:d_train.shape[0]][idx_trvl=="train"]
X_valid = X_all[0:d_train.shape[0]][idx_trvl=="valid"].to_numpy()
y_valid = y_all[0:d_train.shape[0]][idx_trvl=="valid"]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=1,
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-2),
scheduler_params={"step_size":50, # how to use learning rate scheduler
"gamma":0.9},
scheduler_fn=torch.optim.lr_scheduler.StepLR,
mask_type='entmax' # "sparsemax"
)
%%time
md.fit( X_train=X_train, y_train=y_train,
eval_set=[(X_train, y_train), (X_valid, y_valid)],
eval_name=['train', 'valid'],
eval_metric=['auc'],
max_epochs=1000, patience=3,
batch_size=1024, virtual_batch_size=128,
num_workers=0,
weights=1,
drop_last=False
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
| 2,176 | 32.492308 | 93 | py |
GBM-perf | GBM-perf-master/analysis/tabnet/old-keep/tabnet-min.py | from pytorch_tabnet.tab_model import TabNetClassifier
import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-0.1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
X_train = X_all[0:d_train.shape[0]].to_numpy()
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=1
)
%%time
md.fit( X_train=X_train, y_train=y_train,
max_epochs=10, patience=0
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
| 1,330 | 26.163265 | 80 | py |
GBM-perf | GBM-perf-master/analysis/tabnet/old-keep/tabnet-1cLR.py | from pytorch_tabnet.tab_model import TabNetClassifier
import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-0.1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
X_train = X_all[0:d_train.shape[0]].to_numpy()
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
MAX_EPOCH = 10
BS = 1024
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=1,
## optimizer_fn=torch.optim.Adam,
## optimizer_params=dict(lr=2e-2),
scheduler_fn=torch.optim.lr_scheduler.OneCycleLR,
scheduler_params=dict(max_lr=0.05,
steps_per_epoch=int(X_train.shape[0] / BS),
epochs=MAX_EPOCH,
is_batch_level=True),
mask_type='entmax' # "sparsemax"
)
%%time
md.fit( X_train=X_train, y_train=y_train,
max_epochs=MAX_EPOCH, patience=0,
## batch_size=1024, virtual_batch_size=128,
## weights=0,
drop_last = True
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
| 1,973 | 31.360656 | 88 | py |
GBM-perf | GBM-perf-master/analysis/tabnet/old-keep/tabnet-epoch10.py | from pytorch_tabnet.tab_model import TabNetClassifier
import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-0.1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
X_train = X_all[0:d_train.shape[0]].to_numpy()
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=1,
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-2),
scheduler_params={"step_size":50, # how to use learning rate scheduler
"gamma":0.9},
scheduler_fn=torch.optim.lr_scheduler.StepLR,
mask_type='entmax' # "sparsemax"
)
%%time
md.fit( X_train=X_train, y_train=y_train,
eval_set=[(X_train, y_train)],
eval_name=['train'],
eval_metric=['auc'],
max_epochs=10, patience=0,
batch_size=1024, virtual_batch_size=128,
num_workers=0,
weights=1,
drop_last=False
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
| 1,897 | 30.633333 | 93 | py |
GBM-perf | GBM-perf-master/analysis/tabnet/xgb/xgb.py | import xgboost as xgb
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import metrics
d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-0.1m.csv")
d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
d_all = pd.concat([d_train,d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
X_train = X_all[0:d_train.shape[0]]
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
dxgb_train = xgb.DMatrix(X_train, label = y_train)
dxgb_test = xgb.DMatrix(X_test)
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic', 'tree_method':'hist'}
%time md = xgb.train(param, dxgb_train, num_boost_round = 100)
y_pred = md.predict(dxgb_test)
print(metrics.roc_auc_score(y_test, y_pred))
| 1,156 | 27.925 | 101 | py |
GBM-perf | GBM-perf-master/analysis/h2o4gpu/xgboost.py |
import pandas as pd
import numpy as np
from sklearn import preprocessing
from scipy import sparse
from sklearn import metrics
import xgboost as xgb
import time
d_train = pd.read_csv("train.csv")
d_test = pd.read_csv("test.csv")
d = pd.concat([d_train, d_test])
vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
vars_num = ["DepTime","Distance"]
for col in vars_cat:
d[col] = preprocessing.LabelEncoder().fit_transform(d[col])
X_cat = preprocessing.OneHotEncoder().fit_transform(d[vars_cat])
X = sparse.hstack((X_cat, d[vars_num]), format = "csr")
y = np.where(d["dep_delayed_15min"]=="Y",1,0)
X_train = X[0:d_train.shape[0],:]
X_test = X[d_train.shape[0]:,:]
y_train = y[0:d_train.shape[0]]
y_test = y[d_train.shape[0]:]
dxgb_train = xgb.DMatrix(X_train, label = y_train)
dxgb_test = xgb.DMatrix(X_test)
param = {'max_depth':10, 'eta':0.1, 'objective':'binary:logistic',
'tree_method':'gpu_hist',
'silent':1}
start = time.time()
md = xgb.train(param, dxgb_train, num_boost_round = 100)
end = time.time()
print(end - start)
y_pred = md.predict(dxgb_test)
metrics.roc_auc_score(y_test, y_pred)
| 1,234 | 23.215686 | 83 | py |
TempoQR | TempoQR-main/qa_datasets.py | from pathlib import Path
import pkg_resources
import pickle
from collections import defaultdict
from typing import Dict, Tuple, List
import json
import numpy as np
import torch
# from qa_models import QA_model
import utils
from tqdm import tqdm
from transformers import RobertaTokenizer
from transformers import DistilBertTokenizer
from transformers import BertTokenizer
import random
from torch.utils.data import Dataset, DataLoader
# from nltk import word_tokenize
# warning: padding id 0 is being used, can have issue like in Tucker
# however since so many entities (and timestamps?), it may not pose problem
import pdb
from copy import deepcopy
from collections import defaultdict
import random
from hard_supervision_functions import retrieve_times
class QA_Dataset(Dataset):
def __init__(self,
split,
dataset_name,
tokenization_needed=True):
filename = 'data/{dataset_name}/questions/{split}.pickle'.format(
dataset_name=dataset_name,
split=split
)
questions = pickle.load(open(filename, 'rb'))
#probably change for bert/roberta?
self.tokenizer_class = DistilBertTokenizer
self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
self.all_dicts = utils.getAllDicts(dataset_name)
print('Total questions = ', len(questions))
self.data = questions
self.tokenization_needed = tokenization_needed
def getEntitiesLocations(self, question):
question_text = question['question']
entities = question['entities']
ent2id = self.all_dicts['ent2id']
loc_ent = []
for e in entities:
e_id = ent2id[e]
location = question_text.find(e)
loc_ent.append((location, e_id))
return loc_ent
def getTimesLocations(self, question):
question_text = question['question']
times = question['times']
ts2id = self.all_dicts['ts2id']
loc_time = []
for t in times:
t_id = ts2id[(t,0,0)] + len(self.all_dicts['ent2id']) # add num entities
location = question_text.find(str(t))
loc_time.append((location, t_id))
return loc_time
def isTimeString(self, s):
# todo: cant do len == 4 since 3 digit times also there
if 'Q' not in s:
return True
else:
return False
def textToEntTimeId(self, text):
if self.isTimeString(text):
t = int(text)
ts2id = self.all_dicts['ts2id']
t_id = ts2id[(t,0,0)] + len(self.all_dicts['ent2id'])
return t_id
else:
ent2id = self.all_dicts['ent2id']
e_id = ent2id[text]
return e_id
def getOrderedEntityTimeIds(self, question):
loc_ent = self.getEntitiesLocations(question)
loc_time = self.getTimesLocations(question)
loc_all = loc_ent + loc_time
loc_all.sort()
ordered_ent_time = [x[1] for x in loc_all]
return ordered_ent_time
def entitiesToIds(self, entities):
output = []
ent2id = self.all_dicts['ent2id']
for e in entities:
output.append(ent2id[e])
return output
def getIdType(self, id):
if id < len(self.all_dicts['ent2id']):
return 'entity'
else:
return 'time'
def getEntityToText(self, entity_wd_id):
return self.all_dicts['wd_id_to_text'][entity_wd_id]
def getEntityIdToText(self, id):
ent = self.all_dicts['id2ent'][id]
return self.getEntityToText(ent)
def getEntityIdToWdId(self, id):
return self.all_dicts['id2ent'][id]
def timesToIds(self, times):
output = []
ts2id = self.all_dicts['ts2id']
for t in times:
output.append(ts2id[(t, 0, 0)])
return output
def getAnswersFromScores(self, scores, largest=True, k=10):
_, ind = torch.topk(scores, k, largest=largest)
predict = ind
answers = []
for a_id in predict:
a_id = a_id.item()
type = self.getIdType(a_id)
if type == 'entity':
# answers.append(self.getEntityIdToText(a_id))
answers.append(self.getEntityIdToWdId(a_id))
else:
time_id = a_id - len(self.all_dicts['ent2id'])
time = self.all_dicts['id2ts'][time_id]
answers.append(time[0])
return answers
def getAnswersFromScoresWithScores(self, scores, largest=True, k=10):
s, ind = torch.topk(scores, k, largest=largest)
predict = ind
answers = []
for a_id in predict:
a_id = a_id.item()
type = self.getIdType(a_id)
if type == 'entity':
# answers.append(self.getEntityIdToText(a_id))
answers.append(self.getEntityIdToWdId(a_id))
else:
time_id = a_id - len(self.all_dicts['ent2id'])
time = self.all_dicts['id2ts'][time_id]
answers.append(time[0])
return s, answers
# from pytorch Transformer:
# If a BoolTensor is provided, the positions with the value of True will be ignored
# while the position with the value of False will be unchanged.
#
# so we want to pad with True
def padding_tensor(self, sequences, max_len = -1):
"""
:param sequences: list of tensors
:return:
"""
num = len(sequences)
if max_len == -1:
max_len = max([s.size(0) for s in sequences])
out_dims = (num, max_len)
out_tensor = sequences[0].data.new(*out_dims).fill_(0)
# mask = sequences[0].data.new(*out_dims).fill_(0)
mask = torch.ones((num, max_len), dtype=torch.bool) # fills with True
for i, tensor in enumerate(sequences):
length = tensor.size(0)
out_tensor[i, :length] = tensor
mask[i, :length] = False # fills good area with False
return out_tensor, mask
def toOneHot(self, indices, vec_len):
indices = torch.LongTensor(indices)
one_hot = torch.FloatTensor(vec_len)
one_hot.zero_()
one_hot.scatter_(0, indices, 1)
return one_hot
def prepare_data(self, data):
# we want to prepare answers lists for each question
# then at batch prep time, we just stack these
# and use scatter
question_text = []
entity_time_ids = []
num_total_entities = len(self.all_dicts['ent2id'])
answers_arr = []
for question in data:
# first pp is question text
# needs to be changed after making PD dataset
# to randomly sample from list
q_text = question['paraphrases'][0]
question_text.append(q_text)
et_id = self.getOrderedEntityTimeIds(question)
entity_time_ids.append(torch.tensor(et_id, dtype=torch.long))
if question['answer_type'] == 'entity':
answers = self.entitiesToIds(question['answers'])
else:
# adding num_total_entities to each time id
answers = [x + num_total_entities for x in self.timesToIds(question['answers'])]
answers_arr.append(answers)
# answers_arr = self.get_stacked_answers_long(answers_arr)
return {'question_text': question_text,
'entity_time_ids': entity_time_ids,
'answers_arr': answers_arr}
def is_template_keyword(self, word):
if '{' in word and '}' in word:
return True
else:
return False
def get_keyword_dict(self, template, nl_question):
template_tokenized = self.tokenize_template(template)
keywords = []
for word in template_tokenized:
if not self.is_template_keyword(word):
# replace only first occurence
nl_question = nl_question.replace(word, '*', 1)
else:
keywords.append(word[1:-1]) # no brackets
text_for_keywords = []
for word in nl_question.split('*'):
if word != '':
text_for_keywords.append(word)
keyword_dict = {}
for keyword, text in zip(keywords, text_for_keywords):
keyword_dict[keyword] = text
return keyword_dict
def addEntityAnnotation(self, data):
for i in range(len(data)):
question = data[i]
keyword_dicts = [] # we want for each paraphrase
template = question['template']
#for nl_question in question['paraphrases']:
nl_question = question['paraphrases'][0]
keyword_dict = self.get_keyword_dict(template, nl_question)
keyword_dicts.append(keyword_dict)
data[i]['keyword_dicts'] = keyword_dicts
#print(keyword_dicts)
#print(template, nl_question)
return data
def tokenize_template(self, template):
output = []
buffer = ''
i = 0
while i < len(template):
c = template[i]
if c == '{':
if buffer != '':
output.append(buffer)
buffer = ''
while template[i] != '}':
buffer += template[i]
i += 1
buffer += template[i]
output.append(buffer)
buffer = ''
else:
buffer += c
i += 1
if buffer != '':
output.append(buffer)
return output
class QA_Dataset_Baseline(QA_Dataset):
def __init__(self, split, dataset_name, tokenization_needed=True):
super().__init__(split, dataset_name, tokenization_needed)
print('Preparing data for split %s' % split)
# self.data = self.data[:30000]
# new_data = []
# # qn_type = 'simple_time'
# qn_type = 'simple_entity'
# print('Only {} questions'.format(qn_type))
# for qn in self.data:
# if qn['type'] == qn_type:
# new_data.append(qn)
# self.data = new_data
ents = self.all_dicts['ent2id'].keys()
self.all_dicts['tsstr2id'] = {str(k[0]):v for k,v in self.all_dicts['ts2id'].items()}
times = self.all_dicts['tsstr2id'].keys()
rels = self.all_dicts['rel2id'].keys()
self.prepared_data = self.prepare_data_(self.data)
self.num_total_entities = len(self.all_dicts['ent2id'])
self.num_total_times = len(self.all_dicts['ts2id'])
self.answer_vec_size = self.num_total_entities + self.num_total_times
def prepare_data_(self, data):
# we want to prepare answers lists for each question
# then at batch prep time, we just stack these
# and use scatter
question_text = []
heads = []
tails = []
times = []
num_total_entities = len(self.all_dicts['ent2id'])
answers_arr = []
ent2id = self.all_dicts['ent2id']
self.data_ids_filtered=[]
# self.data=[]
for i,question in enumerate(data):
self.data_ids_filtered.append(i)
# first pp is question text
# needs to be changed after making PD dataset
# to randomly sample from list
q_text = question['paraphrases'][0]
entities_list_with_locations = self.getEntitiesLocations(question)
entities_list_with_locations.sort()
entities = [id for location, id in entities_list_with_locations] # ordering necessary otherwise set->list conversion causes randomness
head = entities[0] # take an entity
if len(entities) > 1:
tail = entities[1]
else:
tail = entities[0]
times_in_question = question['times']
if len(times_in_question) > 0:
time = self.timesToIds(times_in_question)[0] # take a time. if no time then 0
# exit(0)
else:
time = 0
time += num_total_entities
heads.append(head)
times.append(time)
tails.append(tail)
question_text.append(q_text)
if question['answer_type'] == 'entity':
answers = self.entitiesToIds(question['answers'])
else:
# adding num_total_entities to each time id
answers = [x + num_total_entities for x in self.timesToIds(question['answers'])]
answers_arr.append(answers)
# answers_arr = self.get_stacked_answers_long(answers_arr)
self.data=[self.data[idx] for idx in self.data_ids_filtered]
return {'question_text': question_text,
'head': heads,
'tail': tails,
'time': times,
'answers_arr': answers_arr}
def print_prepared_data(self):
for k, v in self.prepared_data.items():
print(k, v)
def __len__(self):
return len(self.data)
# return len(self.prepared_data['question_text'])
def __getitem__(self, index):
data = self.prepared_data
question_text = data['question_text'][index]
head = data['head'][index]
tail = data['tail'][index]
time = data['time'][index]
answers_arr = data['answers_arr'][index]
answers_single = random.choice(answers_arr)
return question_text, head, tail, time, answers_single #,answers_khot
def _collate_fn(self, items):
batch_sentences = [item[0] for item in items]
b = self.tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
heads = torch.from_numpy(np.array([item[1] for item in items]))
tails = torch.from_numpy(np.array([item[2] for item in items]))
times = torch.from_numpy(np.array([item[3] for item in items]))
answers_single = torch.from_numpy(np.array([item[4] for item in items]))
return b['input_ids'], b['attention_mask'], heads, tails, times, answers_single
def get_dataset_ques_info(self):
type2num={}
for question in self.data:
if question["type"] not in type2num: type2num[question["type"]]=0
type2num[question["type"]]+=1
return {"type2num":type2num, "total_num":len(self.data_ids_filtered)}.__str__()
# replace entity mention tokens
# rather than add + layernorm
class QA_Dataset_TempoQR(QA_Dataset):
def __init__(self, split, dataset_name, args, tokenization_needed=True):
super().__init__(split, dataset_name, tokenization_needed)
print('Preparing data for split %s' % split)
ents = self.all_dicts['ent2id'].keys()
self.all_dicts['tsstr2id'] = {str(k[0]):v for k,v in self.all_dicts['ts2id'].items()}
times = self.all_dicts['tsstr2id'].keys()
rels = self.all_dicts['rel2id'].keys()
self.split = split
#This is hard supervision
#args: given TKG, whether to corrupt hard, and how to use the reitrieved timestmaps
self.data = retrieve_times(args.tkg_file, args.dataset_name, self.data, args.corrupt_hard, args.fuse)
self.data = self.addEntityAnnotation(self.data)
self.num_total_entities = len(self.all_dicts['ent2id'])
self.num_total_times = len(self.all_dicts['ts2id'])
self.padding_idx = self.num_total_entities + self.num_total_times # padding id for embedding of ent/time
self.answer_vec_size = self.num_total_entities + self.num_total_times
self.prepared_data = self.prepare_data2(self.data)
def __len__(self):
return len(self.data)
def getEntityTimeTextIds(self, question, pp_id=0):
keyword_dict = question['keyword_dicts'][pp_id]
keyword_id_dict = question['annotation'] # this does not depend on paraphrase
output_text = []
output_ids = []
entity_time_keywords = set(['head', 'tail', 'time', 'event_head', 'time1', 'time2'])
#print(keyword_dict, keyword_id_dict)
for keyword, value in keyword_dict.items():
if keyword in entity_time_keywords:
wd_id_or_time = keyword_id_dict[keyword]
output_text.append(value)
output_ids.append(wd_id_or_time)
#print(output_text, output_ids)
return output_text, output_ids
def get_entity_aware_tokenization(self, nl_question, ent_times, ent_times_ids):
# what we want finally is that after proper tokenization
# of nl question, we know which indices are beginning tokens
# of entities and times in the question
index_et_pairs = []
index_et_text_pairs = []
for e_text, e_id in zip(ent_times, ent_times_ids):
location = nl_question.find(e_text)
pair = (location, e_id)
index_et_pairs.append(pair)
pair = (location, e_text)
index_et_text_pairs.append(pair)
index_et_pairs.sort()
index_et_text_pairs.sort()
my_tokenized_question = []
start_index = 0
arr = []
for pair, pair_id in zip(index_et_text_pairs, index_et_pairs):
end_index = pair[0]
if nl_question[start_index: end_index] != '':
my_tokenized_question.append(nl_question[start_index: end_index])
arr.append(self.padding_idx)
start_index = end_index
end_index = start_index + len(pair[1])
# todo: assuming entity name can't be blank
# my_tokenized_question.append(nl_question[start_index: end_index])
my_tokenized_question.append(self.tokenizer.mask_token)
matrix_id = self.textToEntTimeId(pair_id[1]) # get id in embedding matrix
arr.append(matrix_id)
start_index = end_index
if nl_question[start_index:] != '':
my_tokenized_question.append(nl_question[start_index:])
arr.append(self.padding_idx)
tokenized, valid_ids = self.tokenize(my_tokenized_question)
entity_time_final = []
index = 0
for vid in valid_ids:
if vid == 0:
entity_time_final.append(self.padding_idx)
else:
entity_time_final.append(arr[index])
index += 1
entity_mask = [] # want 0 if entity, 1 if not, since will multiply this later with word embedding
for x in entity_time_final:
if x == self.padding_idx:
entity_mask.append(1.)
else:
entity_mask.append(0.)
#print(entity_time_final)
return tokenized, entity_time_final, entity_mask
def prepare_data2(self, data):
# we want to prepare answers lists for each question
# then at batch prep time, we just stack these
# and use scatter
heads = []
times = []
start_times = []
end_times = []
tails = []
tails2 = []
question_text = []
tokenized_question = []
entity_time_ids_tokenized_question = []
entity_mask_tokenized_question = []
pp_id = 0
num_total_entities = len(self.all_dicts['ent2id'])
answers_arr = []
for question in tqdm(data):
# randomly sample pp
# in test there is only 1 pp, so always pp_id=0
# TODO: this random is causing assertion bug later on
# pp_id = random.randint(0, len(question['paraphrases']) - 1)
pp_id = 0
nl_question = question['paraphrases'][pp_id]
q_text = nl_question
et_text, et_ids = self.getEntityTimeTextIds(question, pp_id)
entities_list_with_locations = self.getEntitiesLocations(question)
entities_list_with_locations.sort()
entities = [id for location, id in
entities_list_with_locations] # ordering necessary otherwise set->list conversion causes randomness
head = entities[0] # take an entity
if len(entities) > 1:
tail = entities[1]
if len(entities) > 2:
tail2 = entities[2]
else:
tail2 = tail
else:
tail = entities[0]
tail2 = tail
times_in_question = question['times']
if len(times_in_question) > 0:
time = self.timesToIds(times_in_question)[0] # take a time. if no time then 0
start_time = time
end_time = time
# exit(0)
else:
time = 0
#check for retrieved timestmaps
if len(question['fact']) > 0:
ts = []
#add all timestmaps and sort
for f in question['fact']:
for t in range(int(f[0]), int(f[1])+1):
ts.append(t)
ts = sorted(ts)
sorted_times = self.timesToIds(ts)
try:
start_time = sorted_times[0] # for random random.choice(sorted_times)
except:
start_time = 0
try:
end_time = sorted_times[-1]
except:
end_time = 0
else:
start_time = 0
end_time = 0
# print('No time in qn!')
time += num_total_entities
heads.append(head)
times.append(time)
start_times.append(start_time)
end_times.append(end_time)
tails.append(tail)
tails2.append(tail2)
tokenized, entity_time_final, entity_mask = self.get_entity_aware_tokenization(nl_question, et_text, et_ids)
assert len(tokenized) == len(entity_time_final)
question_text.append(nl_question)
tokenized_question.append(self.tokenizer.convert_tokens_to_ids(tokenized))
entity_mask_tokenized_question.append(entity_mask)
entity_time_ids_tokenized_question.append(entity_time_final)
if question['answer_type'] == 'entity':
answers = self.entitiesToIds(question['answers'])
else:
# adding num_total_entities to each time id
answers = [x + num_total_entities for x in self.timesToIds(question['answers'])]
answers_arr.append(answers)
return {'question_text': question_text,
'tokenized_question': tokenized_question,
'entity_time_ids': entity_time_ids_tokenized_question,
'entity_mask': entity_mask_tokenized_question,
'head': heads,
'tail': tails,
'time': times,
'start_time': start_times,
'end_time': end_times,
'tail2': tails2,
'answers_arr': answers_arr}
# tokenization function taken from NER code
def tokenize(self, words):
""" tokenize input"""
tokens = []
valid_positions = []
tokens.append(self.tokenizer.cls_token)
valid_positions.append(0)
for i, word in enumerate(words):
token = self.tokenizer.tokenize(word)
tokens.extend(token)
for i in range(len(token)):
if i == 0:
valid_positions.append(1)
else:
valid_positions.append(0)
tokens.append(self.tokenizer.sep_token)
valid_positions.append(0)
return tokens, valid_positions
def __getitem__(self, index):
data = self.prepared_data
question_text = data['question_text'][index]
entity_time_ids = np.array(data['entity_time_ids'][index], dtype=np.long)
answers_arr = data['answers_arr'][index]
answers_single = random.choice(answers_arr)
# answers_khot = self.toOneHot(answers_arr, self.answer_vec_size)
tokenized_question = data['tokenized_question'][index]
entity_mask = data['entity_mask'][index]
head = data['head'][index]
tail = data['tail'][index]
tail2 = data['tail2'][index]
time = data['time'][index]
start_time = data['start_time'][index]
end_time = data['end_time'][index]
return question_text, tokenized_question, entity_time_ids, entity_mask, head, tail, time, start_time, end_time, tail2, answers_single
def pad_for_batch(self, to_pad, padding_val, dtype=np.long):
padded = np.ones([len(to_pad), len(max(to_pad, key=lambda x: len(x)))], dtype=dtype) * padding_val
for i, j in enumerate(to_pad):
padded[i][0:len(j)] = j
return padded
# do this before padding for batch
def get_attention_mask(self, tokenized):
# first make zeros array of appropriate size
mask = np.zeros([len(tokenized), len(max(tokenized, key=lambda x: len(x)))], dtype=np.long)
# now set ones everywhere needed
for i, j in enumerate(tokenized):
mask[i][0:len(j)] = np.ones(len(j), dtype=np.long)
return mask
def _collate_fn(self, items):
# please don't tokenize again
# b = self.tokenizer(batch_sentences, padding=True, truncation=False, return_tensors="pt")
tokenized_questions = [item[1] for item in items]
attention_mask = torch.from_numpy(self.get_attention_mask(tokenized_questions))
input_ids = torch.from_numpy(self.pad_for_batch(tokenized_questions, self.tokenizer.pad_token_id, np.long))
entity_time_ids_list = [item[2] for item in items]
entity_time_ids_padded = self.pad_for_batch(entity_time_ids_list, self.padding_idx, np.long)
entity_time_ids_padded = torch.from_numpy(entity_time_ids_padded)
entity_mask = [item[3] for item in items] # 0 if entity, 1 if not
entity_mask_padded = self.pad_for_batch(entity_mask, 1.0,
np.float32) # doesnt matter probably cuz attention mask will be used. maybe pad with 1?
entity_mask_padded = torch.from_numpy(entity_mask_padded)
# can make foll mask in forward function using attention mask
# entity_time_ids_padded_mask = ~(attention_mask.bool())
heads = torch.from_numpy(np.array([item[4] for item in items]))
tails = torch.from_numpy(np.array([item[5] for item in items]))
times = torch.from_numpy(np.array([item[6] for item in items]))
start_times = torch.from_numpy(np.array([item[7] for item in items]))
end_times = torch.from_numpy(np.array([item[8] for item in items]))
tails2 = torch.from_numpy(np.array([item[9] for item in items]))
# answers_khot = torch.stack([item[4] for item in items])
answers_single = torch.from_numpy(np.array([item[10] for item in items]))
return input_ids, attention_mask, entity_time_ids_padded, entity_mask_padded, heads, tails, times, start_times, end_times, tails2, answers_single
| 27,598 | 38.825397 | 153 | py |
TempoQR | TempoQR-main/utils.py | import pickle
import random
import torch
import numpy as np
from tcomplex import TComplEx
from tqdm import tqdm
def loadTkbcModel(tkbc_model_file):
print('Loading tkbc model from', tkbc_model_file)
x = torch.load(tkbc_model_file,map_location=torch.device("cpu"))
num_ent = x['embeddings.0.weight'].shape[0]
num_rel = x['embeddings.1.weight'].shape[0]
num_ts = x['embeddings.2.weight'].shape[0]
print('Number ent,rel,ts from loaded model:', num_ent, num_rel, num_ts)
sizes = [num_ent, num_rel, num_ent, num_ts]
rank = x['embeddings.0.weight'].shape[1] // 2 # complex has 2*rank embedding size
tkbc_model = TComplEx(sizes, rank, no_time_emb=False)
tkbc_model.load_state_dict(x)
tkbc_model.cuda()
print('Loaded tkbc model')
return tkbc_model
def loadTkbcModel_complex(tkbc_model_file):
print('Loading complex tkbc model from', tkbc_model_file)
tcomplex_file = 'models/wikidata_big/kg_embeddings/tcomplex_17dec.ckpt' #TODO: hack
tcomplex_params = torch.load(tcomplex_file)
complex_params = torch.load(tkbc_model_file)
num_ent = tcomplex_params['embeddings.0.weight'].shape[0]
num_rel = tcomplex_params['embeddings.1.weight'].shape[0]
num_ts = tcomplex_params['embeddings.2.weight'].shape[0]
print('Number ent,rel,ts from loaded model:', num_ent, num_rel, num_ts)
sizes = [num_ent, num_rel, num_ent, num_ts]
rank = tcomplex_params['embeddings.0.weight'].shape[1] // 2 # complex has 2*rank embedding size
# now put complex params in tcomplex model
tcomplex_params['embeddings.0.weight'] = complex_params['embeddings.0.weight']
tcomplex_params['embeddings.1.weight'] = complex_params['embeddings.1.weight']
torch.nn.init.xavier_uniform_(tcomplex_params['embeddings.2.weight']) # randomize time embeddings
tkbc_model = TComplEx(sizes, rank, no_time_emb=False)
tkbc_model.load_state_dict(tcomplex_params)
tkbc_model.cuda()
print('Loaded complex tkbc model')
return tkbc_model
def dataIdsToLiterals(d, all_dicts):
new_datapoint = []
id2rel = all_dicts['id2rel']
id2ent = all_dicts['id2ent']
id2ts = all_dicts['id2ts']
wd_id_to_text = all_dicts['wd_id_to_text']
new_datapoint.append(wd_id_to_text[id2ent[d[0]]])
new_datapoint.append(wd_id_to_text[id2rel[d[1]]])
new_datapoint.append(wd_id_to_text[id2ent[d[2]]])
new_datapoint.append(id2ts[d[3]])
new_datapoint.append(id2ts[d[4]])
return new_datapoint
def getAllDicts(dataset_name):
# base_path = '/scratche/home/apoorv/tkbc/tkbc_env/lib/python3.7/site-packages/tkbc-0.0.0-py3.7.egg/tkbc/data/wikidata_small/'
base_path = 'data/{dataset_name}/kg/tkbc_processed_data/{dataset_name}/'.format(
dataset_name=dataset_name
)
dicts = {}
for f in ['ent_id', 'rel_id', 'ts_id']:
in_file = open(str(base_path + f), 'rb')
dicts[f] = pickle.load(in_file)
rel2id = dicts['rel_id']
ent2id = dicts['ent_id']
ts2id = dicts['ts_id']
file_ent = 'data/{dataset_name}/kg/wd_id2entity_text.txt'.format(
dataset_name=dataset_name
)
file_rel = 'data/{dataset_name}/kg/wd_id2relation_text.txt'.format(
dataset_name=dataset_name
)
def readDict(filename):
f = open(filename, 'r')
d = {}
for line in f:
line = line.strip().split('\t')
if len(line) == 1:
line.append('') # in case literal was blank or whitespace
d[line[0]] = line[1]
f.close()
return d
e = readDict(file_ent)
r = readDict(file_rel)
wd_id_to_text = dict(list(e.items()) + list(r.items()))
def getReverseDict(d):
return {value: key for key, value in d.items()}
id2rel = getReverseDict(rel2id)
id2ent = getReverseDict(ent2id)
id2ts = getReverseDict(ts2id)
all_dicts = {'rel2id': rel2id,
'id2rel': id2rel,
'ent2id': ent2id,
'id2ent': id2ent,
'ts2id': ts2id,
'id2ts': id2ts,
'wd_id_to_text': wd_id_to_text
}
return all_dicts
def checkQuestion(question, target_type):
question_type = question['type']
if target_type != question_type:
return False
return True
# def getDataPoint(question, all_dicts):
def predictTime(question, model, all_dicts, k=1):
entities = list(question['entities'])
times = question['times']
target_type = 'simple_time'
if checkQuestion(question, target_type) == False:
print('Not Entity question')
return set()
ent2id = all_dicts['ent2id']
rel2id = all_dicts['rel2id']
id2ts = all_dicts['id2ts']
annotation = question['annotation']
head = ent2id[annotation['head']]
tail = ent2id[annotation['tail']]
# relation = rel2id[list(question['relations'])[0]]
relation = list(question['relations'])[0]
if 'P' not in relation:
relation = 'P' + relation
relation = rel2id[relation] #+ model.embeddings[1].weight.shape[0]//2 #+ 90
data_point = [head, relation, tail, 1, 1]
data_batch = torch.from_numpy(np.array([data_point])).cuda()
time_scores = model.forward_over_time(data_batch)
val, ind = torch.topk(time_scores, k, dim=1)
topk_set = set()
for row in ind:
for x in row:
topk_set.add(id2ts[x.item()][0])
return topk_set
def predictTail(question, model, all_dicts, k=1):
entities = list(question['entities'])
times = list(question['times'])
target_type = 'simple_entity'
if checkQuestion(question, target_type) == False:
print('Not Entity question')
return set()
ent2id = all_dicts['ent2id']
rel2id = all_dicts['rel2id']
ts2id = all_dicts['ts2id']
id2ent = all_dicts['id2ent']
head = ent2id[entities[0]]
try:
time = ts2id[(times[0],0,0)]
except:
return set()
relation = list(question['relations'])[0]
if 'P' not in relation:
relation = 'P' + relation
relation = rel2id[relation] #+ model.embeddings[1].weight.shape[0]//2 #+ 90
data_point = [head, relation, 1, time, time]
data_batch = torch.from_numpy(np.array([data_point])).cuda()
predictions, factors, time = model.forward(data_batch)
val, ind = torch.topk(predictions, k, dim=1)
topk_set = set()
for row in ind:
for x in row:
topk_set.add(id2ent[x.item()])
return topk_set
def checkIfTkbcEmbeddingsTrained(tkbc_model, dataset_name, split='test'):
filename = 'data/{dataset_name}/questions/{split}.pickle'.format(
dataset_name=dataset_name,
split=split
)
questions = pickle.load(open(filename, 'rb'))
all_dicts = getAllDicts(dataset_name)
for question_type in ['simple_entity', 'simple_time']:
correct_count = 0
total_count = 0
k = 1 # hit at k
for i in tqdm(range(len(questions))):
this_question_type = questions[i]['type']
if question_type == this_question_type and question_type == 'simple_entity':
which_question_function = predictTail
elif question_type == this_question_type and question_type == 'simple_time':
which_question_function = predictTime
else:
continue
total_count += 1
id = i
predicted = which_question_function(questions[id], tkbc_model, all_dicts, k)
intersection_set = set(questions[id]['answers']).intersection(predicted)
if len(intersection_set) > 0:
correct_count += 1
print(question_type, correct_count, total_count, correct_count/total_count)
def print_info(args):
print('#######################')
print('Model: '+ args.model)
print('Supervision (if applicable): '+args.supervision)
print('TKG Embeddings: '+args.tkbc_model_file)
print('TKG for QA (if applicable): '+args.tkg_file)
print('#######################')
| 7,990 | 35.824885 | 130 | py |
TempoQR | TempoQR-main/qa_baselines.py | import math
import torch
from torch import nn
import numpy as np
from tcomplex import TComplEx
from transformers import RobertaModel
from transformers import BertModel
from transformers import DistilBertModel
import pdb
# training data: questions
# model:
# 1. tkbc model embeddings (may or may not be frozen)
# 2. question sentence embeddings (may or may not be frozen)
# 3. linear layer to project question embeddings (unfrozen)
# 4. transformer that takes these embeddings (unfrozen) (cats them along a dimension, also takes a mask)
# 5. average output embeddings of transformer or take last token embedding?
# 6. linear projection of this embedding to tkbc embedding dimension
# 7. score with all possible entities/times and sigmoid
# 8. BCE loss (multiple correct possible)
class QA_baseline(nn.Module):
def __init__(self, tkbc_model, args):
super().__init__()
self.tkbc_embedding_dim = tkbc_model.embeddings[0].weight.shape[1]
self.sentence_embedding_dim = 768 # hardwired from roberta
if args.model =='bert':
self.pretrained_weights = 'bert-base-uncased'
self.lm_model = BertModel.from_pretrained(self.pretrained_weights)
elif args.model =='roberta':
self.pretrained_weights = 'roberta-base'
self.lm_model = RobertaModel.from_pretrained(self.pretrained_weights)
else:
self.pretrained_weights = 'distilbert-base-uncased'
self.lm_model = DistilBertModel.from_pretrained(self.pretrained_weights)
if args.lm_frozen == 1:
print('Freezing LM params')
for param in self.lm_model.parameters():
param.requires_grad = False
else:
print('Unfrozen LM params')
# creating combined embedding of time and entities (entities come first)
self.tkbc_model = tkbc_model
num_entities = tkbc_model.embeddings[0].weight.shape[0]
num_times = tkbc_model.embeddings[2].weight.shape[0]
ent_emb_matrix = tkbc_model.embeddings[0].weight.data
time_emb_matrix = tkbc_model.embeddings[2].weight.data
full_embed_matrix = torch.cat([ent_emb_matrix, time_emb_matrix], dim=0)
self.entity_time_embedding = nn.Embedding(num_entities + num_times, self.tkbc_embedding_dim)
self.entity_time_embedding.weight.data.copy_(full_embed_matrix)
self.num_entities = num_entities
self.num_times = num_times
if args.frozen == 1:
print('Freezing entity/time embeddings')
self.entity_time_embedding.weight.requires_grad = False
for param in self.tkbc_model.parameters():
param.requires_grad = False
else:
print('Unfrozen entity/time embeddings')
# print('Random starting embedding')
self.linear = nn.Linear(768, self.tkbc_embedding_dim) # to project question embedding
self.linear1 = nn.Linear(self.tkbc_embedding_dim, self.tkbc_embedding_dim)
self.linear2 = nn.Linear(self.tkbc_embedding_dim, self.tkbc_embedding_dim)
self.loss = nn.CrossEntropyLoss(reduction='mean')
self.dropout = torch.nn.Dropout(0.3)
self.bn1 = torch.nn.BatchNorm1d(self.tkbc_embedding_dim)
self.bn2 = torch.nn.BatchNorm1d(self.tkbc_embedding_dim)
return
def getQuestionEmbedding(self, question_tokenized, attention_mask):
lm_last_hidden_states = self.lm_model(question_tokenized, attention_mask=attention_mask)[0]
states = lm_last_hidden_states.transpose(1,0)
cls_embedding = states[0]
question_embedding = cls_embedding
# question_embedding = torch.mean(roberta_last_hidden_states, dim=1)
return question_embedding
class QA_lm(QA_baseline):
def __init__(self, tkbc_model, args):
super().__init__(tkbc_model, args)
self.final_linear = nn.Linear(4*self.tkbc_embedding_dim, self.tkbc_embedding_dim)
return
def forward(self, a):
question_tokenized = a[0].cuda()
question_attention_mask = a[1].cuda()
heads = a[2].cuda()
tails = a[3].cuda()
times = a[4].cuda()
head_embedding = self.entity_time_embedding(heads)
tail_embedding = self.entity_time_embedding(tails)
time_embedding = self.entity_time_embedding(times)
question_embedding = self.getQuestionEmbedding(question_tokenized, question_attention_mask)
relation_embedding = self.linear(question_embedding)
output = self.final_linear(torch.cat((head_embedding, relation_embedding, tail_embedding, time_embedding), dim=-1))
scores = torch.matmul(output, self.entity_time_embedding.weight.data.T)
return scores
class QA_embedkgqa(QA_baseline):
def __init__(self, tkbc_model, args):
super().__init__(tkbc_model, args)
return
def score(self, head_embedding, relation_embedding):
lhs = head_embedding
rel = relation_embedding
right = torch.cat((self.entity_embedding.weight, self.time_embedding.weight), dim=0)
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
right = right[:, :self.rank], right[:, self.rank:]
return (lhs[0] * rel[0] - lhs[1] * rel[1]) @ right[0].transpose(0, 1) + (lhs[0] * rel[1] + lhs[1] * rel[0]) @ right[1].transpose(0, 1)
def forward(self, a):
question_tokenized = a[0].cuda()
question_attention_mask = a[1].cuda()
heads = a[2].cuda()
head_embedding = self.entity_embedding(heads)
question_embedding = self.getQuestionEmbedding(question_tokenized, question_attention_mask)
relation_embedding = self.linear(question_embedding)
relation_embedding1 = self.dropout(self.bn1(self.linear1(relation_embedding)))
scores = self.score(head_embedding, relation_embedding1)
# exit(0)
# scores = torch.cat((scores_entity, scores_time), dim=1)
return scores
class QA_cronkgqa(QA_baseline):
def __init__(self, tkbc_model, args):
super().__init__(tkbc_model, args)
self.supervision = args.supervision
return
def infer_time(self, head_embedding, tail_embedding, relation_embedding):
lhs = head_embedding
rhs = tail_embedding
rel = relation_embedding
time = self.tkbc_model.embeddings[2].weight # + self.tkbc_model.lin2(self.tkbc_model.time_embedding.weight)
# time = self.entity_time_embedding.weight
lhs = lhs[:, :self.tkbc_model.rank], lhs[:, self.tkbc_model.rank:]
rel = rel[:, :self.tkbc_model.rank], rel[:, self.tkbc_model.rank:]
rhs = rhs[:, :self.tkbc_model.rank], rhs[:, self.tkbc_model.rank:]
time = time[:, :self.tkbc_model.rank], time[:, self.tkbc_model.rank:]
return torch.cat([
(lhs[0] * rel[0] * rhs[0] - lhs[1] * rel[1] * rhs[0] -
lhs[1] * rel[0] * rhs[1] + lhs[0] * rel[1] * rhs[1]),
(lhs[1] * rel[0] * rhs[0] - lhs[0] * rel[1] * rhs[0] +
lhs[0] * rel[0] * rhs[1] - lhs[1] * rel[1] * rhs[1])], dim=-1
)
# scoring function from TComplEx
def score_time(self, head_embedding, tail_embedding, relation_embedding):
lhs = head_embedding
rhs = tail_embedding
rel = relation_embedding
time = self.tkbc_model.embeddings[2].weight
# time = self.entity_time_embedding.weight
lhs = lhs[:, :self.tkbc_model.rank], lhs[:, self.tkbc_model.rank:]
rel = rel[:, :self.tkbc_model.rank], rel[:, self.tkbc_model.rank:]
rhs = rhs[:, :self.tkbc_model.rank], rhs[:, self.tkbc_model.rank:]
time = time[:, :self.tkbc_model.rank], time[:, self.tkbc_model.rank:]
return (
(lhs[0] * rel[0] * rhs[0] - lhs[1] * rel[1] * rhs[0] -
lhs[1] * rel[0] * rhs[1] + lhs[0] * rel[1] * rhs[1]) @ time[0].t() +
(lhs[1] * rel[0] * rhs[0] - lhs[0] * rel[1] * rhs[0] +
lhs[0] * rel[0] * rhs[1] - lhs[1] * rel[1] * rhs[1]) @ time[1].t()
)
def score_entity(self, head_embedding, tail_embedding,relation_embedding, time_embedding):
lhs = head_embedding[:, :self.tkbc_model.rank], head_embedding[:, self.tkbc_model.rank:]
rel = relation_embedding
time = time_embedding
rel = rel[:, :self.tkbc_model.rank], rel[:, self.tkbc_model.rank:]
time = time[:, :self.tkbc_model.rank], time[:, self.tkbc_model.rank:]
right = self.tkbc_model.embeddings[0].weight
# right = self.entity_time_embedding.weight
right = right[:, :self.tkbc_model.rank], right[:, self.tkbc_model.rank:]
rt = rel[0] * time[0], rel[1] * time[0], rel[0] * time[1], rel[1] * time[1]
full_rel = rt[0] - rt[3], rt[1] + rt[2]
return (
(lhs[0] * full_rel[0] - lhs[1] * full_rel[1]) @ right[0].t() +
(lhs[1] * full_rel[0] + lhs[0] * full_rel[1]) @ right[1].t()
)
def forward(self, a):
question_tokenized = a[0].cuda()
question_attention_mask = a[1].cuda()
heads = a[2].cuda()
tails = a[3].cuda()
times = a[4].cuda()
head_embedding = self.entity_time_embedding(heads)
tail_embedding = self.entity_time_embedding(tails)
time_embedding = self.entity_time_embedding(times)
question_embedding = self.getQuestionEmbedding(question_tokenized, question_attention_mask)
relation_embedding = self.linear(question_embedding)
if self.supervision == 'soft':
t1_emb = self.infer_time(head_embedding, tail_embedding, relation_embedding)
t2_emb = self.infer_time(tail_embedding, head_embedding, relation_embedding)
time_embedding = (time_embedding + t1_emb + t2_emb)/3 #just the mean
relation_embedding1 = self.dropout(self.bn1(self.linear1(relation_embedding)))
relation_embedding2 = self.dropout(self.bn2(self.linear2(relation_embedding)))
scores_time = self.score_time(head_embedding, tail_embedding, relation_embedding1)
scores_entity = self.score_entity(head_embedding, tail_embedding,relation_embedding2, time_embedding)
scores = torch.cat((scores_entity, scores_time), dim=1)
return scores
| 9,331 | 38.542373 | 136 | py |
TempoQR | TempoQR-main/train_qa_model.py | import argparse
from typing import Dict
import logging
import torch
from torch import optim
import pickle
import numpy as np
from qa_baselines import QA_baseline, QA_lm, QA_embedkgqa, QA_cronkgqa
from qa_tempoqr import QA_TempoQR
from qa_datasets import QA_Dataset, QA_Dataset_TempoQR, QA_Dataset_Baseline
from torch.utils.data import Dataset, DataLoader
import utils
from tqdm import tqdm
from utils import loadTkbcModel, loadTkbcModel_complex, print_info
from collections import defaultdict
from datetime import datetime
from collections import OrderedDict
parser = argparse.ArgumentParser(
description="Temporal KGQA"
)
parser.add_argument(
'--tkbc_model_file', default='tcomplex.ckpt', type=str,
help="Pretrained tkbc model checkpoint"
)
parser.add_argument(
'--tkg_file', default='full.txt', type=str,
help="TKG to use for hard-supervision"
)
parser.add_argument(
'--model', default='tempoqr', type=str,
help="Which model to use."
)
parser.add_argument(
'--supervision', default='soft', type=str,
help="Which supervision to use."
)
parser.add_argument(
'--load_from', default='', type=str,
help="Pretrained qa model checkpoint"
)
parser.add_argument(
'--save_to', default='', type=str,
help="Where to save checkpoint."
)
parser.add_argument(
'--max_epochs', default=20, type=int,
help="Number of epochs."
)
parser.add_argument(
'--eval_k', default=1, type=int,
help="Hits@k used for eval. Default 10."
)
parser.add_argument(
'--valid_freq', default=1, type=int,
help="Number of epochs between each valid."
)
parser.add_argument(
'--batch_size', default=150, type=int,
help="Batch size."
)
parser.add_argument(
'--valid_batch_size', default=50, type=int,
help="Valid batch size."
)
parser.add_argument(
'--frozen', default=1, type=int,
help="Whether entity/time embeddings are frozen or not. Default frozen."
)
parser.add_argument(
'--lm_frozen', default=1, type=int,
help="Whether language model params are frozen or not. Default frozen."
)
parser.add_argument(
'--lr', default=2e-4, type=float,
help="Learning rate"
)
parser.add_argument(
'--mode', default='train', type=str,
help="Whether train or eval."
)
parser.add_argument(
'--eval_split', default='valid', type=str,
help="Which split to validate on"
)
parser.add_argument(
'--dataset_name', default='wikidata_big', type=str,
help="Which dataset."
)
parser.add_argument(
'--lm', default='distill_bert', type=str,
help="Lm to use."
)
parser.add_argument(
'--fuse', default='add', type=str,
help="For fusing time embeddings."
)
parser.add_argument(
'--extra_entities', default=False, type=bool,
help="For some question types."
)
parser.add_argument(
'--corrupt_hard', default=0., type=float,
help="For some question types."
)
parser.add_argument(
'--test', default="test", type=str,
help="Test data."
)
args = parser.parse_args()
print_info(args)
def eval(qa_model, dataset, batch_size = 128, split='valid', k=10):
num_workers = 4
qa_model.eval()
eval_log = []
print_numbers_only = False
k_for_reporting = k # not change name in fn signature since named param used in places
k_list = [1,10]
#k_list = [1,2,5, 10]
max_k = max(k_list)
eval_log.append("Split %s" % (split))
print('Evaluating split', split)
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False,
num_workers=num_workers, collate_fn=dataset._collate_fn)
topk_answers = []
total_loss = 0
loader = tqdm(data_loader, total=len(data_loader), unit="batches")
for i_batch, a in enumerate(loader):
# if size of split is multiple of batch size, we need this
# todo: is there a more elegant way?
if i_batch * batch_size == len(dataset.data):
break
answers_khot = a[-1] # last one assumed to be target
scores = qa_model.forward(a)
for s in scores:
pred = dataset.getAnswersFromScores(s, k=max_k)
topk_answers.append(pred)
loss = qa_model.loss(scores, answers_khot.cuda())
total_loss += loss.item()
eval_log.append('Loss %f' % total_loss)
eval_log.append('Eval batch size %d' % batch_size)
# do eval for each k in k_list
# want multiple hit@k
eval_accuracy_for_reporting = 0
for k in k_list:
hits_at_k = 0
total = 0
question_types_count = defaultdict(list)
simple_complex_count = defaultdict(list)
entity_time_count = defaultdict(list)
for i, question in enumerate(dataset.data):
actual_answers = question['answers']
question_type = question['type']
if 'simple' in question_type:
simple_complex_type = 'simple'
else:
simple_complex_type = 'complex'
entity_time_type = question['answer_type']
# question_type = question['template']
predicted = topk_answers[i][:k]
if len(set(actual_answers).intersection(set(predicted))) > 0:
val_to_append = 1
hits_at_k += 1
else:
val_to_append = 0
question_types_count[question_type].append(val_to_append)
simple_complex_count[simple_complex_type].append(val_to_append)
entity_time_count[entity_time_type].append(val_to_append)
total += 1
eval_accuracy = hits_at_k/total
if k == k_for_reporting:
eval_accuracy_for_reporting = eval_accuracy
if not print_numbers_only:
eval_log.append('Hits at %d: %f' % (k, round(eval_accuracy, 3)))
else:
eval_log.append(str(round(eval_accuracy, 3)))
question_types_count = dict(sorted(question_types_count.items(), key=lambda x: x[0].lower()))
simple_complex_count = dict(sorted(simple_complex_count.items(), key=lambda x: x[0].lower()))
entity_time_count = dict(sorted(entity_time_count.items(), key=lambda x: x[0].lower()))
# for dictionary in [question_types_count]:
for dictionary in [question_types_count, simple_complex_count, entity_time_count]:
# for dictionary in [simple_complex_count, entity_time_count]:
for key, value in dictionary.items():
hits_at_k = sum(value)/len(value)
s = '{q_type} \t {hits_at_k} \t total questions: {num_questions}'.format(
q_type = key,
hits_at_k = round(hits_at_k, 3),
num_questions = len(value)
)
if print_numbers_only:
s = str(round(hits_at_k, 3))
eval_log.append(s)
eval_log.append('')
# print eval log as well as return it
for s in eval_log:
print(s)
return eval_accuracy_for_reporting, eval_log
def append_log_to_file(eval_log, epoch, filename):
f = open(filename, 'a+')
now = datetime.now()
dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
f.write('Log time: %s\n' % dt_string)
f.write('Epoch %d\n' % epoch)
for line in eval_log:
f.write('%s\n' % line)
f.write('\n')
f.close()
def train(qa_model, dataset, valid_dataset, args,result_filename=None):
num_workers = 5
optimizer = torch.optim.Adam(qa_model.parameters(), lr=args.lr)
optimizer.zero_grad()
batch_size = args.batch_size
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers,
collate_fn=dataset._collate_fn)
max_eval_score = 0
if args.save_to == '':
args.save_to = 'temp'
if result_filename is None:
result_filename = 'results/{dataset_name}/{model_file}.log'.format(
dataset_name = args.dataset_name,
model_file = args.save_to
)
checkpoint_file_name = 'models/{dataset_name}/qa_models/{model_file}.ckpt'.format(
dataset_name = args.dataset_name,
model_file = args.save_to
)
# if not loading from any previous file
# we want to make new log file
# also log the config ie. args to the file
if args.load_from == '':
print('Creating new log file')
f = open(result_filename, 'a+')
now = datetime.now()
dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
f.write('Log time: %s\n' % dt_string)
f.write('Config: \n')
for key, value in vars(args).items():
key = str(key)
value = str(value)
f.write('%s:\t%s\n' % (key, value))
f.write('\n')
f.close()
max_eval_score= 0.
print('Starting training')
for epoch in range(args.max_epochs):
qa_model.train()
epoch_loss = 0
loader = tqdm(data_loader, total=len(data_loader), unit="batches")
running_loss = 0
for i_batch, a in enumerate(loader):
qa_model.zero_grad()
# so that don't need 'if condition' here
# scores = qa_model.forward(question_tokenized.cuda(),
# question_attention_mask.cuda(), entities_times_padded.cuda(),
# entities_times_padded_mask.cuda(), question_text)
answers_khot = a[-1] # last one assumed to be target
scores = qa_model.forward(a)
loss = qa_model.loss(scores, answers_khot.cuda())
loss.backward()
optimizer.step()
epoch_loss += loss.item()
running_loss += loss.item()
loader.set_postfix(Loss=running_loss/((i_batch+1)*batch_size), Epoch=epoch)
loader.set_description('{}/{}'.format(epoch, args.max_epochs))
loader.update()
print('Epoch loss = ', epoch_loss)
if (epoch + 1) % args.valid_freq == 0:
print('Starting eval')
eval_score, eval_log = eval(qa_model, valid_dataset, batch_size=args.valid_batch_size, split=args.eval_split, k = args.eval_k)
if eval_score > max_eval_score:
print('Valid score increased')
save_model(qa_model, checkpoint_file_name)
max_eval_score = eval_score
# log each time, not max
# can interpret max score from logs later
append_log_to_file(eval_log, epoch, result_filename)
def save_model(qa_model, filename):
print('Saving model to', filename)
torch.save(qa_model.state_dict(), filename)
print('Saved model to ', filename)
return
if args.model != 'embedkgqa': #TODO this is a hack
tkbc_model = loadTkbcModel('models/{dataset_name}/kg_embeddings/{tkbc_model_file}'.format(
dataset_name = args.dataset_name, tkbc_model_file=args.tkbc_model_file
))
else:
tkbc_model = loadTkbcModel_complex('models/{dataset_name}/kg_embeddings/{tkbc_model_file}'.format(
dataset_name = args.dataset_name, tkbc_model_file=args.tkbc_model_file
))
if args.mode == 'test_kge':
utils.checkIfTkbcEmbeddingsTrained(tkbc_model, args.dataset_name, args.eval_split)
exit(0)
train_split = 'train'
test = args.test
if args.model == 'bert' or args.model == 'roberta':
qa_model = QA_lm(tkbc_model, args)
dataset = QA_Dataset_Baseline(split=train_split, dataset_name=args.dataset_name)
#valid_dataset = QA_Dataset_baseline(split=args.eval_split, dataset_name=args.dataset_name)
test_dataset = QA_Dataset_Baseline(split=test, dataset_name=args.dataset_name)
elif args.model == 'embedkgqa':
qa_model = QA_embedkgqa(tkbc_model, args)
dataset = QA_Dataset_Baseline(split=train_split, dataset_name=args.dataset_name)
#valid_dataset = QA_Dataset_baseline(split=args.eval_split, dataset_name=args.dataset_name)
test_dataset = QA_Dataset_Baseline(split=test, dataset_name=args.dataset_name)
elif args.model == 'cronkgqa' and args.supervision != 'hard':
qa_model = QA_cronkgqa(tkbc_model, args)
dataset = QA_Dataset_Baseline(split=train_split, dataset_name=args.dataset_name)
#valid_dataset = QA_Dataset_baseline(split=args.eval_split, dataset_name=args.dataset_name)
test_dataset = QA_Dataset_Baseline(split=test, dataset_name=args.dataset_name)
elif args.model in ['tempoqr', 'entityqr', 'cronkgqa']: #supervised models
qa_model = QA_TempoQR(tkbc_model, args)
if args.mode == 'train':
dataset = QA_Dataset_TempoQR(split=train_split, dataset_name=args.dataset_name, args=args)
#valid_dataset = QA_Dataset_TempoQR(split=args.eval_split, dataset_name=args.dataset_name, args=args)
test_dataset = QA_Dataset_TempoQR(split=test, dataset_name=args.dataset_name, args=args)
else:
print('Model %s not implemented!' % args.model)
exit(0)
print('Model is', args.model)
if args.load_from != '':
filename = 'models/{dataset_name}/qa_models/{model_file}.ckpt'.format(
dataset_name=args.dataset_name,
model_file=args.load_from
)
print('Loading model from', filename)
qa_model.load_state_dict(torch.load(filename))
print('Loaded qa model from ', filename)
else:
print('Not loading from checkpoint. Starting fresh!')
qa_model = qa_model.cuda()
if args.mode == 'eval':
score, log = eval(qa_model, test_dataset, batch_size=args.valid_batch_size, split=args.eval_split, k = args.eval_k)
exit(0)
result_filename = 'results/{dataset_name}/{model_file}.log'.format(
dataset_name=args.dataset_name,
model_file=args.save_to
)
train(qa_model, dataset, test_dataset, args,result_filename=result_filename)
# score, log = eval(qa_model, test_dataset, batch_size=args.valid_batch_size, split="test", k=args.eval_k)
# log=["######## TEST EVALUATION FINAL (BEST) #########"]+log
# append_log_to_file(log,0,result_filename)
print('Training finished')
| 13,849 | 33.798995 | 138 | py |
TempoQR | TempoQR-main/qa_tempoqr.py | import math
import torch
from torch import nn
import numpy as np
from tcomplex import TComplEx
from transformers import DistilBertModel
from torch.nn import LayerNorm
import pdb
# training data: questions
# model:
# 1. tkbc model embeddings (may or may not be frozen)
# 2. question sentence embeddings (may or may not be frozen)
# 3. linear layer to project question embeddings (unfrozen)
# 4. transformer that takes these embeddings (unfrozen) (cats them along a dimension, also takes a mask)
# 5. average output embeddings of transformer or take last token embedding?
# 6. linear projection of this embedding to tkbc embedding dimension
# 7. score with all possible entities/times and sigmoid
# 8. BCE loss (multiple correct possible)
class QA_TempoQR(nn.Module):
def __init__(self, tkbc_model, args):
super().__init__()
self.model = args.model
self.supervision = args.supervision
self.extra_entities = args.extra_entities
self.fuse = args.fuse
self.tkbc_embedding_dim = tkbc_model.embeddings[0].weight.shape[1]
self.sentence_embedding_dim = 768 # hardwired from
self.pretrained_weights = 'distilbert-base-uncased'
self.lm_model = DistilBertModel.from_pretrained(self.pretrained_weights)
if args.lm_frozen == 1:
print('Freezing LM params')
for param in self.lm_model.parameters():
param.requires_grad = False
else:
print('Unfrozen LM params')
# transformer
self.transformer_dim = self.tkbc_embedding_dim # keeping same so no need to project embeddings
self.nhead = 8
self.num_layers = 6
self.transformer_dropout = 0.1
self.encoder_layer = nn.TransformerEncoderLayer(d_model=self.transformer_dim, nhead=self.nhead,
dropout=self.transformer_dropout)
encoder_norm = LayerNorm(self.transformer_dim)
self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=self.num_layers,
norm=encoder_norm)
self.project_sentence_to_transformer_dim = nn.Linear(self.sentence_embedding_dim, self.transformer_dim)
self.project_entity = nn.Linear(self.tkbc_embedding_dim, self.tkbc_embedding_dim)
#TKG embeddings
self.tkbc_model = tkbc_model
num_entities = tkbc_model.embeddings[0].weight.shape[0]
num_times = tkbc_model.embeddings[2].weight.shape[0]
ent_emb_matrix = tkbc_model.embeddings[0].weight.data
time_emb_matrix = tkbc_model.embeddings[2].weight.data
full_embed_matrix = torch.cat([ent_emb_matrix, time_emb_matrix], dim=0)
# +1 is for padding idx
self.entity_time_embedding = nn.Embedding(num_entities + num_times + 1,
self.tkbc_embedding_dim,
padding_idx=num_entities + num_times)
self.entity_time_embedding.weight.data[:-1, :].copy_(full_embed_matrix)
if args.frozen == 1:
print('Freezing entity/time embeddings')
self.entity_time_embedding.weight.requires_grad = False
for param in self.tkbc_model.parameters():
param.requires_grad = False
else:
print('Unfrozen entity/time embeddings')
# position embedding for transformer
self.max_seq_length = 100 # randomly defining max length of tokens for question
self.position_embedding = nn.Embedding(self.max_seq_length, self.tkbc_embedding_dim)
# print('Random starting embedding')
self.loss = nn.CrossEntropyLoss(reduction='mean')
self.layer_norm = nn.LayerNorm(self.transformer_dim)
self.linear = nn.Linear(768, self.tkbc_embedding_dim) # to project question embedding
self.linearT = nn.Linear(768, self.tkbc_embedding_dim) # to project question embedding
self.lin_cat = nn.Linear(3*self.transformer_dim, self.transformer_dim)
self.linear1 = nn.Linear(self.tkbc_embedding_dim, self.tkbc_embedding_dim)
self.linear2 = nn.Linear(self.tkbc_embedding_dim, self.tkbc_embedding_dim)
self.dropout = torch.nn.Dropout(0.3)
self.bn1 = torch.nn.BatchNorm1d(self.tkbc_embedding_dim)
self.bn2 = torch.nn.BatchNorm1d(self.tkbc_embedding_dim)
return
def invert_binary_tensor(self, tensor):
ones_tensor = torch.ones(tensor.shape, dtype=torch.float32).cuda()
inverted = ones_tensor - tensor
return inverted
def infer_time(self, head_embedding, tail_embedding, relation_embedding):
lhs = head_embedding
rhs = tail_embedding
rel = relation_embedding
time = self.tkbc_model.embeddings[2].weight # + self.tkbc_model.lin2(self.tkbc_model.time_embedding.weight)
# time = self.entity_time_embedding.weight
lhs = lhs[:, :self.tkbc_model.rank], lhs[:, self.tkbc_model.rank:]
rel = rel[:, :self.tkbc_model.rank], rel[:, self.tkbc_model.rank:]
rhs = rhs[:, :self.tkbc_model.rank], rhs[:, self.tkbc_model.rank:]
time = time[:, :self.tkbc_model.rank], time[:, self.tkbc_model.rank:]
return torch.cat([
(lhs[0] * rel[0] * rhs[0] - lhs[1] * rel[1] * rhs[0] -
lhs[1] * rel[0] * rhs[1] + lhs[0] * rel[1] * rhs[1]),
(lhs[1] * rel[0] * rhs[0] - lhs[0] * rel[1] * rhs[0] +
lhs[0] * rel[0] * rhs[1] - lhs[1] * rel[1] * rhs[1])], dim=-1
)
# scoring function from TComplEx
def score_time(self, head_embedding, tail_embedding, relation_embedding):
lhs = head_embedding
rhs = tail_embedding
rel = relation_embedding
time = self.tkbc_model.embeddings[2].weight
lhs = lhs[:, :self.tkbc_model.rank], lhs[:, self.tkbc_model.rank:]
rel = rel[:, :self.tkbc_model.rank], rel[:, self.tkbc_model.rank:]
rhs = rhs[:, :self.tkbc_model.rank], rhs[:, self.tkbc_model.rank:]
time = time[:, :self.tkbc_model.rank], time[:, self.tkbc_model.rank:]
return (
(lhs[0] * rel[0] * rhs[0] - lhs[1] * rel[1] * rhs[0] -
lhs[1] * rel[0] * rhs[1] + lhs[0] * rel[1] * rhs[1]) @ time[0].t() +
(lhs[1] * rel[0] * rhs[0] - lhs[0] * rel[1] * rhs[0] +
lhs[0] * rel[0] * rhs[1] - lhs[1] * rel[1] * rhs[1]) @ time[1].t()
)
def score_entity(self, head_embedding, tail_embedding, relation_embedding, time_embedding):
lhs = head_embedding[:, :self.tkbc_model.rank], head_embedding[:, self.tkbc_model.rank:]
rel = relation_embedding
time = time_embedding
rel = rel[:, :self.tkbc_model.rank], rel[:, self.tkbc_model.rank:]
time = time[:, :self.tkbc_model.rank], time[:, self.tkbc_model.rank:]
right = self.tkbc_model.embeddings[0].weight
# right = self.entity_time_embedding.weight
right = right[:, :self.tkbc_model.rank], right[:, self.tkbc_model.rank:]
rt = rel[0] * time[0], rel[1] * time[0], rel[0] * time[1], rel[1] * time[1]
full_rel = rt[0] - rt[3], rt[1] + rt[2]
return (
(lhs[0] * full_rel[0] - lhs[1] * full_rel[1]) @ right[0].t() +
(lhs[1] * full_rel[0] + lhs[0] * full_rel[1]) @ right[1].t()
)
def forward(self, a):
#Tokenized questions, where entities are masked from the sentence to have TKG embeddings
question_tokenized = a[0].cuda()
question_attention_mask = a[1].cuda()
entities_times_padded = a[2].cuda()
entity_mask_padded = a[3].cuda()
#Annotated entities/timestamps
heads = a[4].cuda()
tails = a[5].cuda()
times = a[6].cuda()
#t1 and t2 by Hard Supervision
t1 = a[7].cuda()
t2 = a[8].cuda()
#One extra entity for new before & after question type
tails2 = a[9].cuda()
#TKG embeddings
head_embedding = self.entity_time_embedding(heads)
tail_embedding = self.entity_time_embedding(tails)
tail_embedding2 = self.entity_time_embedding(tails2)
time_embedding = self.entity_time_embedding(times)
#Hard Supervision
t1_emb = self.tkbc_model.embeddings[2](t1)
t2_emb = self.tkbc_model.embeddings[2](t2)
#entity embeddings to replace in sentence
entity_time_embedding = self.entity_time_embedding(entities_times_padded)
#context-aware step
outputs = self.lm_model(question_tokenized, attention_mask=question_attention_mask)
last_hidden_states = outputs.last_hidden_state
if self.supervision == 'soft':
#infer time embeddings for t1 and t2 with Soft Supervision
lm_last_hidden_states = outputs[0]
states = lm_last_hidden_states.transpose(1,0)
cls_embedding = states[0]
cls_embedding = self.linearT(cls_embedding)
t1_emb = self.infer_time(head_embedding, tail_embedding, cls_embedding)
t2_emb = self.infer_time(tail_embedding, head_embedding, cls_embedding)
if self.extra_entities == True and not self.training: #this is for the created before & after questions - we expect one more entity
t3_emb = self.infer_time(tail_embedding2, head_embedding, cls_embedding)
t2_emb = t2_emb+t3_emb #for simplicity, can be omitted and treated as t3_emb
#entity-aware step
question_embedding = self.project_sentence_to_transformer_dim(last_hidden_states)
entity_mask = entity_mask_padded.unsqueeze(-1).expand(question_embedding.shape)
masked_question_embedding = question_embedding * entity_mask # set entity positions 0
entity_time_embedding_projected = self.project_entity(entity_time_embedding)
#time-aware step
if self.model == 'tempoqr':
time_pos_embeddings1 = t1_emb.unsqueeze(0).transpose(0,1)
time_pos_embeddings1 = time_pos_embeddings1.expand(entity_time_embedding_projected.shape)
time_pos_embeddings2 = t2_emb.unsqueeze(0).transpose(0,1)
time_pos_embeddings2 = time_pos_embeddings2.expand(entity_time_embedding_projected.shape)
if self.fuse == 'cat':
entity_time_embedding_projected = self.lin_cat(torch.cat((entity_time_embedding_projected, time_pos_embeddings1,time_pos_embeddings2), dim=-1))
else:
entity_time_embedding_projected = entity_time_embedding_projected + time_pos_embeddings1 + time_pos_embeddings2
# Transformer information fusion layer
masked_entity_time_embedding = entity_time_embedding_projected * self.invert_binary_tensor(entity_mask)
combined_embed = masked_question_embedding + masked_entity_time_embedding
# also need to add position embedding
sequence_length = combined_embed.shape[1]
v = np.arange(0, sequence_length, dtype=np.long)
indices_for_position_embedding = torch.from_numpy(v).cuda()
position_embedding = self.position_embedding(indices_for_position_embedding)
position_embedding = position_embedding.unsqueeze(0).expand(combined_embed.shape)
combined_embed = combined_embed + position_embedding
combined_embed = self.layer_norm(combined_embed)
combined_embed = torch.transpose(combined_embed, 0, 1)
mask2 = ~(question_attention_mask.bool()).cuda()
output = self.transformer_encoder(combined_embed, src_key_padding_mask=mask2)
# Answer Predictions
if self.model == 'cronkgqa': #no transformer, go back and get cls_embedding
relation_embedding = cls_embedding
else:
relation_embedding = output[0]# self.linear(output[0]) #cls token embedding
relation_embedding1 = self.dropout(self.bn1(self.linear1(relation_embedding)))
relation_embedding2 = self.dropout(self.bn1(self.linear2(relation_embedding)))
scores_time = self.score_time(head_embedding, tail_embedding, relation_embedding1)
if self.model == 'cronkgqa' or (self.model == 'entityqr' and self.supervision != 'none'): #supervision for cronkgqa and entityqr
time_embedding = (time_embedding + t1_emb + t2_emb)/3 #just take the mean
scores_entity1 = self.score_entity(head_embedding, tail_embedding, relation_embedding2, time_embedding)
scores_entity2 = self.score_entity(tail_embedding, head_embedding, relation_embedding2, time_embedding)
scores_entity = torch.maximum(scores_entity1, scores_entity2)
scores = torch.cat((scores_entity, scores_time), dim=1)
return scores
| 11,619 | 39.347222 | 147 | py |
TempoQR | TempoQR-main/tcomplex.py | # Copyright (c) Facebook, Inc. and its affiliates.
from abc import ABC, abstractmethod
from typing import Tuple, List, Dict
import math
import torch
from torch import nn
import numpy as np
class TKBCModel(nn.Module, ABC):
@abstractmethod
def get_rhs(self, chunk_begin: int, chunk_size: int):
pass
@abstractmethod
def get_queries(self, queries: torch.Tensor):
pass
@abstractmethod
def score(self, x: torch.Tensor):
pass
@abstractmethod
def forward_over_time(self, x: torch.Tensor):
pass
def get_ranking(
self, queries: torch.Tensor,
filters: Dict[Tuple[int, int, int], List[int]],
batch_size: int = 1000, chunk_size: int = -1
):
"""
Returns filtered ranking for each queries.
:param queries: a torch.LongTensor of quadruples (lhs, rel, rhs, timestamp)
:param filters: filters[(lhs, rel, ts)] gives the elements to filter from ranking
:param batch_size: maximum number of queries processed at once
:param chunk_size: maximum number of candidates processed at once
:return:
"""
if chunk_size < 0:
chunk_size = self.sizes[2]
ranks = torch.ones(len(queries))
with torch.no_grad():
c_begin = 0
while c_begin < self.sizes[2]:
b_begin = 0
rhs = self.get_rhs(c_begin, chunk_size)
while b_begin < len(queries):
these_queries = queries[b_begin:b_begin + batch_size]
q = self.get_queries(these_queries)
scores = q @ rhs
targets = self.score(these_queries)
assert not torch.any(torch.isinf(scores)), "inf scores"
assert not torch.any(torch.isnan(scores)), "nan scores"
assert not torch.any(torch.isinf(targets)), "inf targets"
assert not torch.any(torch.isnan(targets)), "nan targets"
# set filtered and true scores to -1e6 to be ignored
# take care that scores are chunked
for i, query in enumerate(these_queries):
filter_out = filters[(query[0].item(), query[1].item(), query[3].item())]
filter_out += [queries[b_begin + i, 2].item()]
if chunk_size < self.sizes[2]:
filter_in_chunk = [
int(x - c_begin) for x in filter_out
if c_begin <= x < c_begin + chunk_size
]
scores[i, torch.LongTensor(filter_in_chunk)] = -1e6
else:
scores[i, torch.LongTensor(filter_out)] = -1e6
ranks[b_begin:b_begin + batch_size] += torch.sum(
(scores >= targets).float(), dim=1
).cpu()
b_begin += batch_size
c_begin += chunk_size
return ranks
def get_auc(
self, queries: torch.Tensor, batch_size: int = 1000
):
"""
Returns filtered ranking for each queries.
:param queries: a torch.LongTensor of quadruples (lhs, rel, rhs, begin, end)
:param batch_size: maximum number of queries processed at once
:return:
"""
all_scores, all_truth = [], []
all_ts_ids = None
with torch.no_grad():
b_begin = 0
while b_begin < len(queries):
these_queries = queries[b_begin:b_begin + batch_size]
scores = self.forward_over_time(these_queries)
all_scores.append(scores.cpu().numpy())
if all_ts_ids is None:
all_ts_ids = torch.arange(0, scores.shape[1]).cuda()[None, :]
assert not torch.any(torch.isinf(scores) + torch.isnan(scores)), "inf or nan scores"
truth = (all_ts_ids <= these_queries[:, 4][:, None]) * (all_ts_ids >= these_queries[:, 3][:, None])
all_truth.append(truth.cpu().numpy())
b_begin += batch_size
return np.concatenate(all_truth), np.concatenate(all_scores)
def get_time_ranking(
self, queries: torch.Tensor, filters: List[List[int]], chunk_size: int = -1
):
"""
Returns filtered ranking for a batch of queries ordered by timestamp.
:param queries: a torch.LongTensor of quadruples (lhs, rel, rhs, timestamp)
:param filters: ordered filters
:param chunk_size: maximum number of candidates processed at once
:return:
"""
if chunk_size < 0:
chunk_size = self.sizes[2]
ranks = torch.ones(len(queries))
with torch.no_grad():
c_begin = 0
q = self.get_queries(queries)
targets = self.score(queries)
while c_begin < self.sizes[2]:
rhs = self.get_rhs(c_begin, chunk_size)
scores = q @ rhs
# set filtered and true scores to -1e6 to be ignored
# take care that scores are chunked
for i, (query, filter) in enumerate(zip(queries, filters)):
filter_out = filter + [query[2].item()]
if chunk_size < self.sizes[2]:
filter_in_chunk = [
int(x - c_begin) for x in filter_out
if c_begin <= x < c_begin + chunk_size
]
max_to_filter = max(filter_in_chunk + [-1])
assert max_to_filter < scores.shape[1], f"fuck {scores.shape[1]} {max_to_filter}"
scores[i, filter_in_chunk] = -1e6
else:
scores[i, filter_out] = -1e6
ranks += torch.sum(
(scores >= targets).float(), dim=1
).cpu()
c_begin += chunk_size
return ranks
class ComplEx(TKBCModel):
def __init__(
self, sizes: Tuple[int, int, int, int], rank: int,
init_size: float = 1e-3
):
super(ComplEx, self).__init__()
self.sizes = sizes
self.rank = rank
self.embeddings = nn.ModuleList([
nn.Embedding(s, 2 * rank, sparse=True)
for s in [sizes[0], sizes[1]]
])
self.embeddings[0].weight.data *= init_size
self.embeddings[1].weight.data *= init_size
@staticmethod
def has_time():
return False
def forward_over_time(self, x):
raise NotImplementedError("no.")
def score(self, x):
lhs = self.embeddings[0](x[:, 0])
rel = self.embeddings[1](x[:, 1])
rhs = self.embeddings[0](x[:, 2])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
return torch.sum(
(lhs[0] * rel[0] - lhs[1] * rel[1]) * rhs[0] +
(lhs[0] * rel[1] + lhs[1] * rel[0]) * rhs[1],
1, keepdim=True
)
def forward(self, x):
lhs = self.embeddings[0](x[:, 0])
rel = self.embeddings[1](x[:, 1])
rhs = self.embeddings[0](x[:, 2])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
right = self.embeddings[0].weight
right = right[:, :self.rank], right[:, self.rank:]
return (
(lhs[0] * rel[0] - lhs[1] * rel[1]) @ right[0].transpose(0, 1) +
(lhs[0] * rel[1] + lhs[1] * rel[0]) @ right[1].transpose(0, 1)
), (
torch.sqrt(lhs[0] ** 2 + lhs[1] ** 2),
torch.sqrt(rel[0] ** 2 + rel[1] ** 2),
torch.sqrt(rhs[0] ** 2 + rhs[1] ** 2)
), None
def get_rhs(self, chunk_begin: int, chunk_size: int):
return self.embeddings[0].weight.data[
chunk_begin:chunk_begin + chunk_size
].transpose(0, 1)
def get_queries(self, queries: torch.Tensor):
lhs = self.embeddings[0](queries[:, 0])
rel = self.embeddings[1](queries[:, 1])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
return torch.cat([
lhs[0] * rel[0] - lhs[1] * rel[1],
lhs[0] * rel[1] + lhs[1] * rel[0]
], 1)
class TComplEx(TKBCModel):
def __init__(
self, sizes: Tuple[int, int, int, int], rank: int,
no_time_emb=False, init_size: float = 1e-2
):
super(TComplEx, self).__init__()
self.sizes = sizes
self.rank = rank
self.embeddings = nn.ModuleList([
nn.Embedding(s, 2 * rank, sparse=True)
for s in [sizes[0], sizes[1], sizes[3]]
])
self.embeddings[0].weight.data *= init_size
self.embeddings[1].weight.data *= init_size
self.embeddings[2].weight.data *= init_size
self.no_time_emb = no_time_emb
@staticmethod
def has_time():
return True
def score(self, x):
lhs = self.embeddings[0](x[:, 0])
rel = self.embeddings[1](x[:, 1])
rhs = self.embeddings[0](x[:, 2])
time = self.embeddings[2](x[:, 3])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
time = time[:, :self.rank], time[:, self.rank:]
return torch.sum(
(lhs[0] * rel[0] * time[0] - lhs[1] * rel[1] * time[0] -
lhs[1] * rel[0] * time[1] - lhs[0] * rel[1] * time[1]) * rhs[0] +
(lhs[1] * rel[0] * time[0] + lhs[0] * rel[1] * time[0] +
lhs[0] * rel[0] * time[1] - lhs[1] * rel[1] * time[1]) * rhs[1],
1, keepdim=True
)
def forward(self, x):
lhs = self.embeddings[0](x[:, 0])
rel = self.embeddings[1](x[:, 1])
rhs = self.embeddings[0](x[:, 2])
time = self.embeddings[2](x[:, 3])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
time = time[:, :self.rank], time[:, self.rank:]
right = self.embeddings[0].weight
right = right[:, :self.rank], right[:, self.rank:]
rt = rel[0] * time[0], rel[1] * time[0], rel[0] * time[1], rel[1] * time[1]
full_rel = rt[0] - rt[3], rt[1] + rt[2]
return (
(lhs[0] * full_rel[0] - lhs[1] * full_rel[1]) @ right[0].t() +
(lhs[1] * full_rel[0] + lhs[0] * full_rel[1]) @ right[1].t()
), (
torch.sqrt(lhs[0] ** 2 + lhs[1] ** 2),
torch.sqrt(full_rel[0] ** 2 + full_rel[1] ** 2),
torch.sqrt(rhs[0] ** 2 + rhs[1] ** 2)
), self.embeddings[2].weight[:-1] if self.no_time_emb else self.embeddings[2].weight
def forward_over_time(self, x):
lhs = self.embeddings[0](x[:, 0])
rel = self.embeddings[1](x[:, 1])
rhs = self.embeddings[0](x[:, 2])
time = self.embeddings[2].weight
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
time = time[:, :self.rank], time[:, self.rank:]
return (
(lhs[0] * rel[0] * rhs[0] - lhs[1] * rel[1] * rhs[0] -
lhs[1] * rel[0] * rhs[1] + lhs[0] * rel[1] * rhs[1]) @ time[0].t() +
(lhs[1] * rel[0] * rhs[0] - lhs[0] * rel[1] * rhs[0] +
lhs[0] * rel[0] * rhs[1] - lhs[1] * rel[1] * rhs[1]) @ time[1].t()
)
def get_rhs(self, chunk_begin: int, chunk_size: int):
return self.embeddings[0].weight.data[
chunk_begin:chunk_begin + chunk_size
].transpose(0, 1)
def get_queries(self, queries: torch.Tensor):
lhs = self.embeddings[0](queries[:, 0])
rel = self.embeddings[1](queries[:, 1])
time = self.embeddings[2](queries[:, 3])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
time = time[:, :self.rank], time[:, self.rank:]
return torch.cat([
lhs[0] * rel[0] * time[0] - lhs[1] * rel[1] * time[0] -
lhs[1] * rel[0] * time[1] - lhs[0] * rel[1] * time[1],
lhs[1] * rel[0] * time[0] + lhs[0] * rel[1] * time[0] +
lhs[0] * rel[0] * time[1] - lhs[1] * rel[1] * time[1]
], 1)
class TNTComplEx(TKBCModel):
def __init__(
self, sizes: Tuple[int, int, int, int], rank: int,
no_time_emb=False, init_size: float = 1e-2
):
super(TNTComplEx, self).__init__()
self.sizes = sizes
self.rank = rank
self.embeddings = nn.ModuleList([
nn.Embedding(s, 2 * rank, sparse=True)
for s in [sizes[0], sizes[1], sizes[3], sizes[1]] # last embedding modules contains no_time embeddings
])
self.embeddings[0].weight.data *= init_size
self.embeddings[1].weight.data *= init_size
self.embeddings[2].weight.data *= init_size
self.embeddings[3].weight.data *= init_size
self.no_time_emb = no_time_emb
@staticmethod
def has_time():
return True
def score(self, x):
lhs = self.embeddings[0](x[:, 0])
rel = self.embeddings[1](x[:, 1])
rel_no_time = self.embeddings[3](x[:, 1])
rhs = self.embeddings[0](x[:, 2])
time = self.embeddings[2](x[:, 3])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
time = time[:, :self.rank], time[:, self.rank:]
rnt = rel_no_time[:, :self.rank], rel_no_time[:, self.rank:]
rt = rel[0] * time[0], rel[1] * time[0], rel[0] * time[1], rel[1] * time[1]
full_rel = (rt[0] - rt[3]) + rnt[0], (rt[1] + rt[2]) + rnt[1]
return torch.sum(
(lhs[0] * full_rel[0] - lhs[1] * full_rel[1]) * rhs[0] +
(lhs[1] * full_rel[0] + lhs[0] * full_rel[1]) * rhs[1],
1, keepdim=True
)
def forward(self, x):
lhs = self.embeddings[0](x[:, 0])
rel = self.embeddings[1](x[:, 1])
rel_no_time = self.embeddings[3](x[:, 1])
rhs = self.embeddings[0](x[:, 2])
time = self.embeddings[2](x[:, 3])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
time = time[:, :self.rank], time[:, self.rank:]
rnt = rel_no_time[:, :self.rank], rel_no_time[:, self.rank:]
right = self.embeddings[0].weight
right = right[:, :self.rank], right[:, self.rank:]
rt = rel[0] * time[0], rel[1] * time[0], rel[0] * time[1], rel[1] * time[1]
rrt = rt[0] - rt[3], rt[1] + rt[2]
full_rel = rrt[0] + rnt[0], rrt[1] + rnt[1]
regularizer = (
math.pow(2, 1 / 3) * torch.sqrt(lhs[0] ** 2 + lhs[1] ** 2),
torch.sqrt(rrt[0] ** 2 + rrt[1] ** 2),
torch.sqrt(rnt[0] ** 2 + rnt[1] ** 2),
math.pow(2, 1 / 3) * torch.sqrt(rhs[0] ** 2 + rhs[1] ** 2)
)
return ((
(lhs[0] * full_rel[0] - lhs[1] * full_rel[1]) @ right[0].t() +
(lhs[1] * full_rel[0] + lhs[0] * full_rel[1]) @ right[1].t()
), regularizer,
self.embeddings[2].weight[:-1] if self.no_time_emb else self.embeddings[2].weight
)
def forward_over_time(self, x):
lhs = self.embeddings[0](x[:, 0])
rel = self.embeddings[1](x[:, 1])
rhs = self.embeddings[0](x[:, 2])
time = self.embeddings[2].weight
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
rhs = rhs[:, :self.rank], rhs[:, self.rank:]
time = time[:, :self.rank], time[:, self.rank:]
rel_no_time = self.embeddings[3](x[:, 1])
rnt = rel_no_time[:, :self.rank], rel_no_time[:, self.rank:]
score_time = (
(lhs[0] * rel[0] * rhs[0] - lhs[1] * rel[1] * rhs[0] -
lhs[1] * rel[0] * rhs[1] + lhs[0] * rel[1] * rhs[1]) @ time[0].t() +
(lhs[1] * rel[0] * rhs[0] - lhs[0] * rel[1] * rhs[0] +
lhs[0] * rel[0] * rhs[1] - lhs[1] * rel[1] * rhs[1]) @ time[1].t()
)
base = torch.sum(
(lhs[0] * rnt[0] * rhs[0] - lhs[1] * rnt[1] * rhs[0] -
lhs[1] * rnt[0] * rhs[1] + lhs[0] * rnt[1] * rhs[1]) +
(lhs[1] * rnt[1] * rhs[0] - lhs[0] * rnt[0] * rhs[0] +
lhs[0] * rnt[1] * rhs[1] - lhs[1] * rnt[0] * rhs[1]),
dim=1, keepdim=True
)
return score_time + base
def get_rhs(self, chunk_begin: int, chunk_size: int):
return self.embeddings[0].weight.data[
chunk_begin:chunk_begin + chunk_size
].transpose(0, 1)
def get_queries(self, queries: torch.Tensor):
lhs = self.embeddings[0](queries[:, 0])
rel = self.embeddings[1](queries[:, 1])
rel_no_time = self.embeddings[3](queries[:, 1])
time = self.embeddings[2](queries[:, 3])
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
time = time[:, :self.rank], time[:, self.rank:]
rnt = rel_no_time[:, :self.rank], rel_no_time[:, self.rank:]
rt = rel[0] * time[0], rel[1] * time[0], rel[0] * time[1], rel[1] * time[1]
full_rel = (rt[0] - rt[3]) + rnt[0], (rt[1] + rt[2]) + rnt[1]
return torch.cat([
lhs[0] * full_rel[0] - lhs[1] * full_rel[1],
lhs[1] * full_rel[0] + lhs[0] * full_rel[1]
], 1)
| 18,136 | 38.004301 | 115 | py |
bonito | bonito-master/setup.py | import os
import re
from setuptools import setup, find_packages
from setuptools.command.install import install
__pkg_name__ = 'bonito'
require_file = 'requirements.txt'
package_name = "ont-%s" % __pkg_name__
verstrline = open(os.path.join(__pkg_name__, '__init__.py'), 'r').read()
vsre = r"^__version__ = ['\"]([^'\"]*)['\"]"
mo = re.search(vsre, verstrline, re.M)
if mo:
__version__ = mo.group(1)
else:
raise RuntimeError('Unable to find version string in "{}/__init__.py".'.format(__pkg_name__))
with open(require_file) as f:
requirements = [r.split()[0] for r in f.read().splitlines()]
with open('README.md', encoding='utf-8') as f:
long_description = f.read()
setup(
name=package_name,
version=__version__,
packages=find_packages(),
include_package_data=True,
install_requires=requirements,
long_description=long_description,
long_description_content_type='text/markdown',
author='Oxford Nanopore Technologies, Ltd',
author_email='support@nanoporetech.com',
url='https://github.com/nanoporetech/bonito',
entry_points = {
'console_scripts': [
'{0} = {0}:main'.format(__pkg_name__)
]
},
dependency_links=[
'https://download.pytorch.org/whl/cu113',
]
)
| 1,270 | 26.042553 | 97 | py |
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