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meta_archs.py
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meta_archs.py
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import math
import torch
from torch import nn
from torch.nn import functional as F
from .blocks import MaskedConv1D, Scale, LayerNorm
from .losses import ctr_diou_loss_1d, sigmoid_focal_loss, ctr_giou_loss_1d
from .models import register_meta_arch, make_backbone, make_neck, make_generator
from ..utils import batched_nms
class ClsHead(nn.Module):
"""
1D Conv heads for classification
"""
def __init__(
self,
input_dim,
feat_dim,
num_classes,
prior_prob=0.01,
num_layers=3,
kernel_size=3,
act_layer=nn.ReLU,
with_ln=False,
empty_cls=[],
detach_feat=False
):
super().__init__()
self.act = act_layer()
self.detach_feat = detach_feat
# build the head
self.head = nn.ModuleList()
self.norm = nn.ModuleList()
for idx in range(num_layers - 1):
if idx == 0:
in_dim = input_dim
out_dim = feat_dim
else:
in_dim = feat_dim
out_dim = feat_dim
self.head.append(
MaskedConv1D(
in_dim, out_dim, kernel_size,
stride=1,
padding=kernel_size // 2,
bias=(not with_ln)
)
)
if with_ln:
self.norm.append(
LayerNorm(out_dim)
)
else:
self.norm.append(nn.Identity())
# classifier
self.cls_head = MaskedConv1D(
feat_dim, num_classes, kernel_size,
stride=1, padding=kernel_size // 2
)
# use prior in model initialization to improve stability
# this will overwrite other weight init
bias_value = -(math.log((1 - prior_prob) / prior_prob))
torch.nn.init.constant_(self.cls_head.conv.bias, bias_value)
# a quick fix to empty categories:
# the weights assocaited with these categories will remain unchanged
# we set their bias to a large negative value to prevent their outputs
if len(empty_cls) > 0:
bias_value = -(math.log((1 - 1e-6) / 1e-6))
for idx in empty_cls:
torch.nn.init.constant_(self.cls_head.conv.bias[idx], bias_value)
def forward(self, fpn_feats, fpn_masks):
assert len(fpn_feats) == len(fpn_masks)
# apply the classifier for each pyramid level
out_logits = tuple()
for _, (cur_feat, cur_mask) in enumerate(zip(fpn_feats, fpn_masks)):
if self.detach_feat:
cur_out = cur_feat.detach()
else:
cur_out = cur_feat
for idx in range(len(self.head)):
cur_out, _ = self.head[idx](cur_out, cur_mask)
cur_out = self.act(self.norm[idx](cur_out))
cur_logits, _ = self.cls_head(cur_out, cur_mask)
out_logits += (cur_logits,)
# fpn_masks remains the same
return out_logits
class RegHead(nn.Module):
"""
Shared 1D Conv heads for regression
Simlar logic as PtTransformerClsHead with separated implementation for clarity
"""
def __init__(
self,
input_dim,
feat_dim,
fpn_levels,
num_layers=3,
kernel_size=3,
act_layer=nn.ReLU,
with_ln=False,
num_bins=16
):
super().__init__()
self.fpn_levels = fpn_levels
self.act = act_layer()
# build the conv head
self.head = nn.ModuleList()
self.norm = nn.ModuleList()
for idx in range(num_layers - 1):
if idx == 0:
in_dim = input_dim
out_dim = feat_dim
else:
in_dim = feat_dim
out_dim = feat_dim
self.head.append(
MaskedConv1D(
in_dim, out_dim, kernel_size,
stride=1,
padding=kernel_size // 2,
bias=(not with_ln)
)
)
if with_ln:
self.norm.append(
LayerNorm(out_dim)
)
else:
self.norm.append(nn.Identity())
self.scale = nn.ModuleList()
for idx in range(fpn_levels):
self.scale.append(Scale())
self.offset_head = MaskedConv1D(
feat_dim, 2 * (num_bins + 1), kernel_size,
stride=1, padding=kernel_size // 2
)
def forward(self, fpn_feats, fpn_masks):
assert len(fpn_feats) == len(fpn_masks)
assert len(fpn_feats) == self.fpn_levels
# apply the classifier for each pyramid level
out_offsets = tuple()
for l, (cur_feat, cur_mask) in enumerate(zip(fpn_feats, fpn_masks)):
cur_out = cur_feat
for idx in range(len(self.head)):
cur_out, _ = self.head[idx](cur_out, cur_mask)
cur_out = self.act(self.norm[idx](cur_out))
cur_offsets, _ = self.offset_head(cur_out, cur_mask)
out_offsets += (F.relu(self.scale[l](cur_offsets)),)
# fpn_masks remains the same
return out_offsets
@register_meta_arch("TriDet")
class TriDet(nn.Module):
"""
Transformer based model for single stage action localization
"""
def __init__(
self,
backbone_type, # a string defines which backbone we use
fpn_type, # a string defines which fpn we use
backbone_arch, # a tuple defines # layers in embed / stem / branch
scale_factor, # scale factor between branch layers
input_dim, # input feat dim
max_seq_len, # max sequence length (used for training)
max_buffer_len_factor, # max buffer size (defined a factor of max_seq_len)
n_sgp_win_size, # window size w for sgp
embd_kernel_size, # kernel size of the embedding network
embd_dim, # output feat channel of the embedding network
embd_with_ln, # attach layernorm to embedding network
fpn_dim, # feature dim on FPN,
sgp_mlp_dim, # the numnber of dim in SGP
fpn_with_ln, # if to apply layer norm at the end of fpn
head_dim, # feature dim for head
regression_range, # regression range on each level of FPN
head_num_layers, # number of layers in the head (including the classifier)
head_kernel_size, # kernel size for reg/cls heads
boudary_kernel_size, # kernel size for boundary heads
head_with_ln, # attache layernorm to reg/cls heads
use_abs_pe, # if to use abs position encoding
num_bins, # the bin number in Trident-head (exclude 0)
iou_weight_power, # the power of iou weight in loss
downsample_type, # how to downsample feature in FPN
input_noise, # add gaussian noise with the variance, play a similar role to position embedding
k, # the K in SGP
init_conv_vars, # initialization of gaussian variance for the weight in SGP
use_trident_head, # if use the Trident-head
num_classes, # number of action classes
train_cfg, # other cfg for training
test_cfg # other cfg for testing
):
super().__init__()
# re-distribute params to backbone / neck / head
self.fpn_strides = [scale_factor ** i for i in range(backbone_arch[-1] + 1)]
self.input_noise = input_noise
self.reg_range = regression_range
assert len(self.fpn_strides) == len(self.reg_range)
self.scale_factor = scale_factor
self.iou_weight_power = iou_weight_power
# #classes = num_classes + 1 (background) with last category as background
# e.g., num_classes = 10 -> 0, 1, ..., 9 as actions, 10 as background
self.num_classes = num_classes
# check the feature pyramid and local attention window size
self.max_seq_len = max_seq_len
if isinstance(n_sgp_win_size, int):
self.sgp_win_size = [n_sgp_win_size] * len(self.fpn_strides)
else:
assert len(n_sgp_win_size) == len(self.fpn_strides)
self.sgp_win_size = n_sgp_win_size
max_div_factor = 1
for l, (s, w) in enumerate(zip(self.fpn_strides, self.sgp_win_size)):
stride = s * w if w > 1 else s
if max_div_factor < stride:
max_div_factor = stride
self.max_div_factor = max_div_factor
# training time config
self.train_center_sample = train_cfg['center_sample']
assert self.train_center_sample in ['radius', 'none']
self.train_center_sample_radius = train_cfg['center_sample_radius']
self.train_loss_weight = train_cfg['loss_weight']
self.train_cls_prior_prob = train_cfg['cls_prior_prob']
self.train_dropout = train_cfg['dropout']
self.train_droppath = train_cfg['droppath']
self.train_label_smoothing = train_cfg['label_smoothing']
# test time config
self.test_pre_nms_thresh = test_cfg['pre_nms_thresh']
self.test_pre_nms_topk = test_cfg['pre_nms_topk']
self.test_iou_threshold = test_cfg['iou_threshold']
self.test_min_score = test_cfg['min_score']
self.test_max_seg_num = test_cfg['max_seg_num']
self.test_nms_method = test_cfg['nms_method']
assert self.test_nms_method in ['soft', 'hard', 'none']
self.test_duration_thresh = test_cfg['duration_thresh']
self.test_multiclass_nms = test_cfg['multiclass_nms']
self.test_nms_sigma = test_cfg['nms_sigma']
self.test_voting_thresh = test_cfg['voting_thresh']
self.num_bins = num_bins
self.use_trident_head = use_trident_head
# we will need a better way to dispatch the params to backbones / necks
# backbone network: conv + transformer
assert backbone_type in ['SGP', 'conv']
if backbone_type == 'SGP':
self.backbone = make_backbone(
'SGP',
**{
'n_in': input_dim,
'n_embd': embd_dim,
'sgp_mlp_dim': sgp_mlp_dim,
'n_embd_ks': embd_kernel_size,
'max_len': max_seq_len,
'arch': backbone_arch,
'scale_factor': scale_factor,
'with_ln': embd_with_ln,
'path_pdrop': self.train_droppath,
'downsample_type': downsample_type,
'sgp_win_size': self.sgp_win_size,
'use_abs_pe': use_abs_pe,
'k': k,
'init_conv_vars': init_conv_vars
}
)
else:
self.backbone = make_backbone(
'conv',
**{
'n_in': input_dim,
'n_embd': embd_dim,
'n_embd_ks': embd_kernel_size,
'arch': backbone_arch,
'scale_factor': scale_factor,
'with_ln': embd_with_ln
}
)
# fpn network: convs
assert fpn_type in ['fpn', 'identity']
self.neck = make_neck(
fpn_type,
**{
'in_channels': [embd_dim] * (backbone_arch[-1] + 1),
'out_channel': fpn_dim,
'scale_factor': scale_factor,
'with_ln': fpn_with_ln
}
)
# location generator: points
self.point_generator = make_generator(
'point',
**{
'max_seq_len': max_seq_len * max_buffer_len_factor,
'fpn_levels': len(self.fpn_strides),
'scale_factor': scale_factor,
'regression_range': self.reg_range,
'strides': self.fpn_strides
}
)
# classfication and regerssion heads
self.cls_head = ClsHead(
fpn_dim, head_dim, self.num_classes,
kernel_size=head_kernel_size,
prior_prob=self.train_cls_prior_prob,
with_ln=head_with_ln,
num_layers=head_num_layers,
empty_cls=train_cfg['head_empty_cls']
)
if use_trident_head:
self.start_head = ClsHead(
fpn_dim, head_dim, self.num_classes,
kernel_size=boudary_kernel_size,
prior_prob=self.train_cls_prior_prob,
with_ln=head_with_ln,
num_layers=head_num_layers,
empty_cls=train_cfg['head_empty_cls'],
detach_feat=True
)
self.end_head = ClsHead(
fpn_dim, head_dim, self.num_classes,
kernel_size=boudary_kernel_size,
prior_prob=self.train_cls_prior_prob,
with_ln=head_with_ln,
num_layers=head_num_layers,
empty_cls=train_cfg['head_empty_cls'],
detach_feat=True
)
self.reg_head = RegHead(
fpn_dim, head_dim, len(self.fpn_strides),
kernel_size=head_kernel_size,
num_layers=head_num_layers,
with_ln=head_with_ln,
num_bins=num_bins
)
else:
self.reg_head = RegHead(
fpn_dim, head_dim, len(self.fpn_strides),
kernel_size=head_kernel_size,
num_layers=head_num_layers,
with_ln=head_with_ln,
num_bins=0
)
# maintain an EMA of #foreground to stabilize the loss normalizer
# useful for small mini-batch training
self.loss_normalizer = train_cfg['init_loss_norm']
self.loss_normalizer_momentum = 0.9
@property
def device(self):
# a hacky way to get the device type
# will throw an error if parameters are on different devices
return list(set(p.device for p in self.parameters()))[0]
def decode_offset(self, out_offsets, pred_start_neighbours, pred_end_neighbours):
# decode the offset value from the network output
# If a normal regression head is used, the offsets is predicted directly in the out_offsets.
# If the Trident-head is used, the predicted offset is calculated using the value from
# center offset head (out_offsets), start boundary head (pred_left) and end boundary head (pred_right)
if not self.use_trident_head:
if self.training:
out_offsets = torch.cat(out_offsets, dim=1)
return out_offsets
else:
# Make an adaption for train and validation, when training, the out_offsets is a list with feature outputs
# from each FPN level. Each feature with shape [batchsize, T_level, (Num_bin+1)x2].
# For validation, the out_offsets is a feature with shape [T_level, (Num_bin+1)x2]
if self.training:
out_offsets = torch.cat(out_offsets, dim=1)
out_offsets = out_offsets.view(out_offsets.shape[:2] + (2, -1))
pred_start_neighbours = torch.cat(pred_start_neighbours, dim=1)
pred_end_neighbours = torch.cat(pred_end_neighbours, dim=1)
pred_left_dis = torch.softmax(pred_start_neighbours + out_offsets[:, :, :1, :], dim=-1)
pred_right_dis = torch.softmax(pred_end_neighbours + out_offsets[:, :, 1:, :], dim=-1)
else:
out_offsets = out_offsets.view(out_offsets.shape[0], 2, -1)
pred_left_dis = torch.softmax(pred_start_neighbours + out_offsets[None, :, 0, :], dim=-1)
pred_right_dis = torch.softmax(pred_end_neighbours + out_offsets[None, :, 1, :], dim=-1)
max_range_num = pred_left_dis.shape[-1]
left_range_idx = torch.arange(max_range_num - 1, -1, -1, device=pred_start_neighbours.device,
dtype=torch.float).unsqueeze(-1)
right_range_idx = torch.arange(max_range_num, device=pred_end_neighbours.device,
dtype=torch.float).unsqueeze(-1)
pred_left_dis = pred_left_dis.masked_fill(torch.isnan(pred_right_dis), 0)
pred_right_dis = pred_right_dis.masked_fill(torch.isnan(pred_right_dis), 0)
# calculate the value of expectation for the offset:
decoded_offset_left = torch.matmul(pred_left_dis, left_range_idx)
decoded_offset_right = torch.matmul(pred_right_dis, right_range_idx)
return torch.cat([decoded_offset_left, decoded_offset_right], dim=-1)
def forward(self, video_list):
# batch the video list into feats (B, C, T) and masks (B, 1, T)
batched_inputs, batched_masks = self.preprocessing(video_list)
# forward the network (backbone -> neck -> heads)
feats, masks = self.backbone(batched_inputs, batched_masks)
fpn_feats, fpn_masks = self.neck(feats, masks)
# compute the point coordinate along the FPN
# this is used for computing the GT or decode the final results
# points: List[T x 4] with length = # fpn levels
# (shared across all samples in the mini-batch)
points = self.point_generator(fpn_feats)
# out_cls: List[B, #cls + 1, T_i]
out_cls_logits = self.cls_head(fpn_feats, fpn_masks)
if self.use_trident_head:
out_lb_logits = self.start_head(fpn_feats, fpn_masks)
out_rb_logits = self.end_head(fpn_feats, fpn_masks)
else:
out_lb_logits = None
out_rb_logits = None
# out_offset: List[B, 2, T_i]
out_offsets = self.reg_head(fpn_feats, fpn_masks)
# permute the outputs
# out_cls: F List[B, #cls, T_i] -> F List[B, T_i, #cls]
out_cls_logits = [x.permute(0, 2, 1) for x in out_cls_logits]
# out_offset: F List[B, 2 (xC), T_i] -> F List[B, T_i, 2 (xC)]
out_offsets = [x.permute(0, 2, 1) for x in out_offsets]
# fpn_masks: F list[B, 1, T_i] -> F List[B, T_i]
fpn_masks = [x.squeeze(1) for x in fpn_masks]
# return loss during training
if self.training:
# generate segment/lable List[N x 2] / List[N] with length = B
assert video_list[0]['segments'] is not None, "GT action labels does not exist"
assert video_list[0]['labels'] is not None, "GT action labels does not exist"
gt_segments = [x['segments'].to(self.device) for x in video_list]
gt_labels = [x['labels'].to(self.device) for x in video_list]
# compute the gt labels for cls & reg
# list of prediction targets
gt_cls_labels, gt_offsets = self.label_points(
points, gt_segments, gt_labels)
# compute the loss and return
losses = self.losses(
fpn_masks,
out_cls_logits, out_offsets,
gt_cls_labels, gt_offsets,
out_lb_logits, out_rb_logits,
)
return losses
else:
# decode the actions (sigmoid / stride, etc)
results = self.inference(
video_list, points, fpn_masks,
out_cls_logits, out_offsets,
out_lb_logits, out_rb_logits,
)
return results
@torch.no_grad()
def preprocessing(self, video_list, padding_val=0.0):
"""
Generate batched features and masks from a list of dict items
"""
feats = [x['feats'] for x in video_list]
feats_lens = torch.as_tensor([feat.shape[-1] for feat in feats])
max_len = feats_lens.max(0).values.item()
if self.training:
assert max_len <= self.max_seq_len, "Input length must be smaller than max_seq_len during training"
# set max_len to self.max_seq_len
max_len = self.max_seq_len
# batch input shape B, C, T
batch_shape = [len(feats), feats[0].shape[0], max_len]
batched_inputs = feats[0].new_full(batch_shape, padding_val)
for feat, pad_feat in zip(feats, batched_inputs):
pad_feat[..., :feat.shape[-1]].copy_(feat)
if self.input_noise > 0:
# trick, adding noise slightly increases the variability between input features.
noise = torch.randn_like(batched_inputs) * self.input_noise
batched_inputs += noise
else:
assert len(video_list) == 1, "Only support batch_size = 1 during inference"
# input length < self.max_seq_len, pad to max_seq_len
if max_len <= self.max_seq_len:
max_len = self.max_seq_len
else:
# pad the input to the next divisible size
stride = self.max_div_factor
max_len = (max_len + (stride - 1)) // stride * stride
padding_size = [0, max_len - feats_lens[0]]
batched_inputs = F.pad(feats[0], padding_size, value=padding_val).unsqueeze(0)
# generate the mask
batched_masks = torch.arange(max_len)[None, :] < feats_lens[:, None]
# push to device
batched_inputs = batched_inputs.to(self.device)
batched_masks = batched_masks.unsqueeze(1).to(self.device)
return batched_inputs, batched_masks
@torch.no_grad()
def label_points(self, points, gt_segments, gt_labels):
# concat points on all fpn levels List[T x 4] -> F T x 4
# This is shared for all samples in the mini-batch
num_levels = len(points)
concat_points = torch.cat(points, dim=0)
gt_cls, gt_offset = [], []
# loop over each video sample
for gt_segment, gt_label in zip(gt_segments, gt_labels):
cls_targets, reg_targets = self.label_points_single_video(
concat_points, gt_segment, gt_label
)
# append to list (len = # images, each of size FT x C)
gt_cls.append(cls_targets)
gt_offset.append(reg_targets)
return gt_cls, gt_offset
@torch.no_grad()
def label_points_single_video(self, concat_points, gt_segment, gt_label):
# concat_points : F T x 4 (t, regressoin range, stride)
# gt_segment : N (#Events) x 2
# gt_label : N (#Events) x 1
num_pts = concat_points.shape[0]
num_gts = gt_segment.shape[0]
# corner case where current sample does not have actions
if num_gts == 0:
cls_targets = gt_segment.new_full((num_pts, self.num_classes), 0)
reg_targets = gt_segment.new_zeros((num_pts, 2))
return cls_targets, reg_targets
# compute the lengths of all segments -> F T x N
lens = gt_segment[:, 1] - gt_segment[:, 0]
lens = lens[None, :].repeat(num_pts, 1)
# compute the distance of every point to each segment boundary
# auto broadcasting for all reg target-> F T x N x2
gt_segs = gt_segment[None].expand(num_pts, num_gts, 2)
left = concat_points[:, 0, None] - gt_segs[:, :, 0]
right = gt_segs[:, :, 1] - concat_points[:, 0, None]
reg_targets = torch.stack((left, right), dim=-1)
if self.train_center_sample == 'radius':
# center of all segments F T x N
center_pts = 0.5 * (gt_segs[:, :, 0] + gt_segs[:, :, 1])
# center sampling based on stride radius
# compute the new boundaries:
# concat_points[:, 3] stores the stride
t_mins = \
center_pts - concat_points[:, 3, None] * self.train_center_sample_radius
t_maxs = \
center_pts + concat_points[:, 3, None] * self.train_center_sample_radius
# prevent t_mins / maxs from over-running the action boundary
# left: torch.maximum(t_mins, gt_segs[:, :, 0])
# right: torch.minimum(t_maxs, gt_segs[:, :, 1])
# F T x N (distance to the new boundary)
cb_dist_left = concat_points[:, 0, None] - torch.maximum(t_mins, gt_segs[:, :, 0])
cb_dist_right = torch.minimum(t_maxs, gt_segs[:, :, 1]) - concat_points[:, 0, None]
# F T x N x 2
center_seg = torch.stack((cb_dist_left, cb_dist_right), -1)
# F T x N
inside_gt_seg_mask = center_seg.min(-1)[0] > 0
else:
# inside an gt action
inside_gt_seg_mask = reg_targets.min(-1)[0] > 0
# limit the regression range for each location
max_regress_distance = reg_targets.max(-1)[0]
# F T x N
inside_regress_range = torch.logical_and(
(max_regress_distance >= concat_points[:, 1, None]),
(max_regress_distance <= concat_points[:, 2, None])
)
# if there are still more than one actions for one moment
# pick the one with the shortest duration (easiest to regress)
lens.masked_fill_(inside_gt_seg_mask == 0, float('inf'))
lens.masked_fill_(inside_regress_range == 0, float('inf'))
# F T x N -> F T
min_len, min_len_inds = lens.min(dim=1)
# corner case: multiple actions with very similar durations (e.g., THUMOS14)
min_len_mask = torch.logical_and(
(lens <= (min_len[:, None] + 1e-3)), (lens < float('inf'))
).to(reg_targets.dtype)
# cls_targets: F T x C; reg_targets F T x 2
gt_label_one_hot = F.one_hot(
gt_label, self.num_classes
).to(reg_targets.dtype)
cls_targets = min_len_mask @ gt_label_one_hot
# to prevent multiple GT actions with the same label and boundaries
cls_targets.clamp_(min=0.0, max=1.0)
# OK to use min_len_inds
reg_targets = reg_targets[range(num_pts), min_len_inds]
# normalization based on stride
reg_targets /= concat_points[:, 3, None]
return cls_targets, reg_targets
def losses(
self, fpn_masks,
out_cls_logits, out_offsets,
gt_cls_labels, gt_offsets,
out_start, out_end,
):
# fpn_masks, out_*: F (List) [B, T_i, C]
# gt_* : B (list) [F T, C]
# fpn_masks -> (B, FT)
valid_mask = torch.cat(fpn_masks, dim=1)
if self.use_trident_head:
out_start_logits = []
out_end_logits = []
for i in range(len(out_start)):
x = (F.pad(out_start[i], (self.num_bins, 0), mode='constant', value=0)).unsqueeze(-1) # pad left
x_size = list(x.size()) # bz, cls_num, T+num_bins, 1
x_size[-1] = self.num_bins + 1 # bz, cls_num, T+num_bins, num_bins + 1
x_size[-2] = x_size[-2] - self.num_bins # bz, cls_num, T+num_bins, num_bins + 1
x_stride = list(x.stride())
x_stride[-2] = x_stride[-1]
x = x.as_strided(size=x_size, stride=x_stride)
out_start_logits.append(x.permute(0, 2, 1, 3))
x = (F.pad(out_end[i], (0, self.num_bins), mode='constant', value=0)).unsqueeze(-1) # pad right
x = x.as_strided(size=x_size, stride=x_stride)
out_end_logits.append(x.permute(0, 2, 1, 3))
else:
out_start_logits = None
out_end_logits = None
# 1. classification loss
# stack the list -> (B, FT) -> (# Valid, )
gt_cls = torch.stack(gt_cls_labels)
pos_mask = torch.logical_and((gt_cls.sum(-1) > 0), valid_mask)
decoded_offsets = self.decode_offset(out_offsets, out_start_logits, out_end_logits) # bz, stack_T, num_class, 2
decoded_offsets = decoded_offsets[pos_mask]
if self.use_trident_head:
# the boundary head predicts the classification score for each categories.
pred_offsets = decoded_offsets[gt_cls[pos_mask].bool()]
# cat the predicted offsets -> (B, FT, 2 (xC)) -> # (#Pos, 2 (xC))
vid = torch.where(gt_cls[pos_mask])[0]
gt_offsets = torch.stack(gt_offsets)[pos_mask][vid]
else:
pred_offsets = decoded_offsets
gt_offsets = torch.stack(gt_offsets)[pos_mask]
# update the loss normalizer
num_pos = pos_mask.sum().item()
self.loss_normalizer = self.loss_normalizer_momentum * self.loss_normalizer + (
1 - self.loss_normalizer_momentum
) * max(num_pos, 1)
# gt_cls is already one hot encoded now, simply masking out
gt_target = gt_cls[valid_mask]
# optinal label smoothing
gt_target *= 1 - self.train_label_smoothing
gt_target += self.train_label_smoothing / (self.num_classes + 1)
# focal loss
cls_loss = sigmoid_focal_loss(
torch.cat(out_cls_logits, dim=1)[valid_mask],
gt_target,
reduction='none'
)
if self.use_trident_head:
# couple the classification loss with iou score
iou_rate = ctr_giou_loss_1d(
pred_offsets,
gt_offsets,
reduction='none'
)
rated_mask = gt_target > self.train_label_smoothing / (self.num_classes + 1)
cls_loss[rated_mask] *= (1 - iou_rate) ** self.iou_weight_power
cls_loss = cls_loss.sum()
cls_loss /= self.loss_normalizer
# 2. regression using IoU/GIoU loss (defined on positive samples)
if num_pos == 0:
reg_loss = 0 * pred_offsets.sum()
else:
# giou loss defined on positive samples
reg_loss = ctr_diou_loss_1d(
pred_offsets,
gt_offsets,
reduction='sum'
)
reg_loss /= self.loss_normalizer
if self.train_loss_weight > 0:
loss_weight = self.train_loss_weight
else:
loss_weight = cls_loss.detach() / max(reg_loss.item(), 0.01)
# return a dict of losses
final_loss = cls_loss + reg_loss * loss_weight
return {'cls_loss': cls_loss,
'reg_loss': reg_loss,
'final_loss': final_loss}
@torch.no_grad()
def inference(
self,
video_list,
points, fpn_masks,
out_cls_logits, out_offsets,
out_lb_logits, out_rb_logits,
):
# video_list B (list) [dict]
# points F (list) [T_i, 4]
# fpn_masks, out_*: F (List) [B, T_i, C]
results = []
# 1: gather video meta information
vid_idxs = [x['video_id'] for x in video_list]
vid_fps = [x['fps'] for x in video_list]
vid_lens = [x['duration'] for x in video_list]
vid_ft_stride = [x['feat_stride'] for x in video_list]
vid_ft_nframes = [x['feat_num_frames'] for x in video_list]
# 2: inference on each single video and gather the results
# upto this point, all results use timestamps defined on feature grids
for idx, (vidx, fps, vlen, stride, nframes) in enumerate(
zip(vid_idxs, vid_fps, vid_lens, vid_ft_stride, vid_ft_nframes)
):
# gather per-video outputs
cls_logits_per_vid = [x[idx] for x in out_cls_logits]
offsets_per_vid = [x[idx] for x in out_offsets]
fpn_masks_per_vid = [x[idx] for x in fpn_masks]
if self.use_trident_head:
lb_logits_per_vid = [x[idx] for x in out_lb_logits]
rb_logits_per_vid = [x[idx] for x in out_rb_logits]
else:
lb_logits_per_vid = [None for x in range(len(out_cls_logits))]
rb_logits_per_vid = [None for x in range(len(out_cls_logits))]
# inference on a single video (should always be the case)
results_per_vid = self.inference_single_video(
points, fpn_masks_per_vid,
cls_logits_per_vid, offsets_per_vid,
lb_logits_per_vid, rb_logits_per_vid
)
# pass through video meta info
results_per_vid['video_id'] = vidx
results_per_vid['fps'] = fps
results_per_vid['duration'] = vlen
results_per_vid['feat_stride'] = stride
results_per_vid['feat_num_frames'] = nframes
results.append(results_per_vid)
# step 3: postprocssing
results = self.postprocessing(results)
return results
@torch.no_grad()
def inference_single_video(
self,
points,
fpn_masks,
out_cls_logits,
out_offsets,
lb_logits_per_vid, rb_logits_per_vid
):
# points F (list) [T_i, 4]
# fpn_masks, out_*: F (List) [T_i, C]
segs_all = []
scores_all = []
cls_idxs_all = []
# loop over fpn levels
for cls_i, offsets_i, pts_i, mask_i, sb_cls_i, eb_cls_i in zip(
out_cls_logits, out_offsets, points, fpn_masks, lb_logits_per_vid, rb_logits_per_vid
):
pred_prob = (cls_i.sigmoid() * mask_i.unsqueeze(-1)).flatten()
# Apply filtering to make NMS faster following detectron2
# 1. Keep seg with confidence score > a threshold
keep_idxs1 = (pred_prob > self.test_pre_nms_thresh)
pred_prob = pred_prob[keep_idxs1]
topk_idxs = keep_idxs1.nonzero(as_tuple=True)[0]
# 2. Keep top k top scoring boxes only
num_topk = min(self.test_pre_nms_topk, topk_idxs.size(0))
pred_prob, idxs = pred_prob.sort(descending=True)
pred_prob = pred_prob[:num_topk].clone()
topk_idxs = topk_idxs[idxs[:num_topk]].clone()
# fix a warning in pytorch 1.9
pt_idxs = torch.div(
topk_idxs, self.num_classes, rounding_mode='floor'
)
cls_idxs = torch.fmod(topk_idxs, self.num_classes)
# 3. For efficiency, pad the boarder head with num_bins zeros (Pad left for start branch and Pad right
# for end branch). Then we re-arrange the output of boundary branch to [class_num, T, num_bins + 1 (the
# neighbour bin for each instant)]. In this way, the output can be directly added to the center offset
# later.
if self.use_trident_head:
# pad the boarder
x = (F.pad(sb_cls_i, (self.num_bins, 0), mode='constant', value=0)).unsqueeze(-1) # pad left
x_size = list(x.size()) # cls_num, T+num_bins, 1
x_size[-1] = self.num_bins + 1
x_size[-2] = x_size[-2] - self.num_bins # cls_num, T, num_bins + 1
x_stride = list(x.stride())
x_stride[-2] = x_stride[-1]
pred_start_neighbours = x.as_strided(size=x_size, stride=x_stride)
x = (F.pad(eb_cls_i, (0, self.num_bins), mode='constant', value=0)).unsqueeze(-1) # pad right
pred_end_neighbours = x.as_strided(size=x_size, stride=x_stride)
else:
pred_start_neighbours = None
pred_end_neighbours = None
decoded_offsets = self.decode_offset(offsets_i, pred_start_neighbours, pred_end_neighbours)
# pick topk output from the prediction
if self.use_trident_head:
offsets = decoded_offsets[cls_idxs, pt_idxs]
else:
offsets = decoded_offsets[pt_idxs]
pts = pts_i[pt_idxs]
# 4. compute predicted segments (denorm by stride for output offsets)
seg_left = pts[:, 0] - offsets[:, 0] * pts[:, 3]
seg_right = pts[:, 0] + offsets[:, 1] * pts[:, 3]
pred_segs = torch.stack((seg_left, seg_right), -1)
# 5. Keep seg with duration > a threshold (relative to feature grids)
seg_areas = seg_right - seg_left
keep_idxs2 = seg_areas > self.test_duration_thresh
# *_all : N (filtered # of segments) x 2 / 1
segs_all.append(pred_segs[keep_idxs2])
scores_all.append(pred_prob[keep_idxs2])
cls_idxs_all.append(cls_idxs[keep_idxs2])
# cat along the FPN levels (F N_i, C)
segs_all, scores_all, cls_idxs_all = [
torch.cat(x) for x in [segs_all, scores_all, cls_idxs_all]
]
results = {'segments': segs_all,
'scores': scores_all,
'labels': cls_idxs_all}
return results
@torch.no_grad()
def postprocessing(self, results):
# input : list of dictionary items
# (1) push to CPU; (2) NMS; (3) convert to actual time stamps
processed_results = []
for results_per_vid in results:
# unpack the meta info
vidx = results_per_vid['video_id']
fps = results_per_vid['fps']
vlen = results_per_vid['duration']
stride = results_per_vid['feat_stride']
nframes = results_per_vid['feat_num_frames']
# 1: unpack the results and move to CPU
segs = results_per_vid['segments'].detach().cpu()
scores = results_per_vid['scores'].detach().cpu()
labels = results_per_vid['labels'].detach().cpu()
if self.test_nms_method != 'none':
# 2: batched nms (only implemented on CPU)
segs, scores, labels = batched_nms(
segs, scores, labels,
self.test_iou_threshold,
self.test_min_score,
self.test_max_seg_num,
use_soft_nms=(self.test_nms_method == 'soft'),
multiclass=self.test_multiclass_nms,
sigma=self.test_nms_sigma,
voting_thresh=self.test_voting_thresh
)
# 3: convert from feature grids to seconds
if segs.shape[0] > 0:
segs = (segs * stride + 0.5 * nframes) / fps
# truncate all boundaries within [0, duration]
segs[segs <= 0.0] *= 0.0
segs[segs >= vlen] = segs[segs >= vlen] * 0.0 + vlen
# 4: repack the results
processed_results.append(
{'video_id': vidx,
'segments': segs,
'scores': scores,
'labels': labels}
)
return processed_results