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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Modified from detrex (https://github.com/IDEA-Research/detrex)
# Copyright 2022 The IDEA Authors. All rights reserved.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from ..bbox_utils import bbox_overlaps
__all__ = [
'_get_clones', 'bbox_overlaps', 'bbox_cxcywh_to_xyxy',
'bbox_xyxy_to_cxcywh', 'sigmoid_focal_loss', 'inverse_sigmoid',
'deformable_attention_core_func'
]
def _get_clones(module, N):
return nn.LayerList([copy.deepcopy(module) for _ in range(N)])
def bbox_cxcywh_to_xyxy(x):
cxcy, wh = paddle.split(x, 2, axis=-1)
return paddle.concat([cxcy - 0.5 * wh, cxcy + 0.5 * wh], axis=-1)
def bbox_xyxy_to_cxcywh(x):
x1, y1, x2, y2 = x.split(4, axis=-1)
return paddle.concat(
[(x1 + x2) / 2, (y1 + y2) / 2, (x2 - x1), (y2 - y1)], axis=-1)
def sigmoid_focal_loss(logit, label, normalizer=1.0, alpha=0.25, gamma=2.0):
prob = F.sigmoid(logit)
ce_loss = F.binary_cross_entropy_with_logits(logit, label, reduction="none")
p_t = prob * label + (1 - prob) * (1 - label)
loss = ce_loss * ((1 - p_t)**gamma)
if alpha >= 0:
alpha_t = alpha * label + (1 - alpha) * (1 - label)
loss = alpha_t * loss
return loss.mean(1).sum() / normalizer
def inverse_sigmoid(x, eps=1e-6):
x = x.clip(min=0., max=1.)
return paddle.log(x / (1 - x + eps) + eps)
def deformable_attention_core_func(value, value_spatial_shapes,
value_level_start_index, sampling_locations,
attention_weights):
"""
Args:
value (Tensor): [bs, value_length, n_head, c]
value_spatial_shapes (Tensor): [n_levels, 2]
value_level_start_index (Tensor): [n_levels]
sampling_locations (Tensor): [bs, query_length, n_head, n_levels, n_points, 2]
attention_weights (Tensor): [bs, query_length, n_head, n_levels, n_points]
Returns:
output (Tensor): [bs, Length_{query}, C]
"""
bs, _, n_head, c = value.shape
_, Len_q, _, n_levels, n_points, _ = sampling_locations.shape
value_list = value.split(
value_spatial_shapes.prod(1).split(n_levels), axis=1)
sampling_grids = 2 * sampling_locations - 1
sampling_value_list = []
for level, (h, w) in enumerate(value_spatial_shapes):
# N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
value_l_ = value_list[level].flatten(2).transpose(
[0, 2, 1]).reshape([bs * n_head, c, h, w])
# N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(
[0, 2, 1, 3, 4]).flatten(0, 1)
# N_*M_, D_, Lq_, P_
sampling_value_l_ = F.grid_sample(
value_l_,
sampling_grid_l_,
mode='bilinear',
padding_mode='zeros',
align_corners=False)
sampling_value_list.append(sampling_value_l_)
# (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_*M_, 1, Lq_, L_*P_)
attention_weights = attention_weights.transpose([0, 2, 1, 3, 4]).reshape(
[bs * n_head, 1, Len_q, n_levels * n_points])
output = (paddle.stack(
sampling_value_list, axis=-2).flatten(-2) *
attention_weights).sum(-1).reshape([bs, n_head * c, Len_q])
return output.transpose([0, 2, 1])
def get_valid_ratio(mask):
_, H, W = paddle.shape(mask)
valid_ratio_h = paddle.sum(mask[:, :, 0], 1) / H
valid_ratio_w = paddle.sum(mask[:, 0, :], 1) / W
# [b, 2]
return paddle.stack([valid_ratio_w, valid_ratio_h], -1)
def get_contrastive_denoising_training_group(targets,
num_classes,
num_queries,
class_embed,
num_denoising=100,
label_noise_ratio=0.5,
box_noise_scale=1.0):
if num_denoising <= 0:
return None, None, None, None
num_gts = [len(t) for t in targets["gt_class"]]
max_gt_num = max(num_gts)
if max_gt_num == 0:
return None, None, None, None
num_group = num_denoising // max_gt_num
num_group = 1 if num_group == 0 else num_group
# pad gt to max_num of a batch
bs = len(targets["gt_class"])
input_query_class = paddle.full(
[bs, max_gt_num], num_classes, dtype='int32')
input_query_bbox = paddle.zeros([bs, max_gt_num, 4])
pad_gt_mask = paddle.zeros([bs, max_gt_num])
for i in range(bs):
num_gt = num_gts[i]
if num_gt > 0:
input_query_class[i, :num_gt] = targets["gt_class"][i].squeeze(-1)
input_query_bbox[i, :num_gt] = targets["gt_bbox"][i]
pad_gt_mask[i, :num_gt] = 1
# each group has positive and negative queries.
input_query_class = input_query_class.tile([1, 2 * num_group])
input_query_bbox = input_query_bbox.tile([1, 2 * num_group, 1])
pad_gt_mask = pad_gt_mask.tile([1, 2 * num_group])
# positive and negative mask
negative_gt_mask = paddle.zeros([bs, max_gt_num * 2, 1])
negative_gt_mask[:, max_gt_num:] = 1
negative_gt_mask = negative_gt_mask.tile([1, num_group, 1])
positive_gt_mask = 1 - negative_gt_mask
# contrastive denoising training positive index
positive_gt_mask = positive_gt_mask.squeeze(-1) * pad_gt_mask
dn_positive_idx = paddle.nonzero(positive_gt_mask)[:, 1]
dn_positive_idx = paddle.split(dn_positive_idx,
[n * num_group for n in num_gts])
# total denoising queries
num_denoising = int(max_gt_num * 2 * num_group)
if label_noise_ratio > 0:
input_query_class = input_query_class.flatten()
pad_gt_mask = pad_gt_mask.flatten()
# half of bbox prob
mask = paddle.rand(input_query_class.shape) < (label_noise_ratio * 0.5)
chosen_idx = paddle.nonzero(mask * pad_gt_mask).squeeze(-1)
# randomly put a new one here
new_label = paddle.randint_like(
chosen_idx, 0, num_classes, dtype=input_query_class.dtype)
input_query_class.scatter_(chosen_idx, new_label)
input_query_class.reshape_([bs, num_denoising])
pad_gt_mask.reshape_([bs, num_denoising])
if box_noise_scale > 0:
known_bbox = bbox_cxcywh_to_xyxy(input_query_bbox)
diff = paddle.tile(input_query_bbox[..., 2:] * 0.5,
[1, 1, 2]) * box_noise_scale
rand_sign = paddle.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0
rand_part = paddle.rand(input_query_bbox.shape)
rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * (
1 - negative_gt_mask)
rand_part *= rand_sign
known_bbox += rand_part * diff
known_bbox.clip_(min=0.0, max=1.0)
input_query_bbox = bbox_xyxy_to_cxcywh(known_bbox)
input_query_bbox = inverse_sigmoid(input_query_bbox)
class_embed = paddle.concat(
[class_embed, paddle.zeros([1, class_embed.shape[-1]])])
input_query_class = paddle.gather(
class_embed, input_query_class.flatten(),
axis=0).reshape([bs, num_denoising, -1])
tgt_size = num_denoising + num_queries
attn_mask = paddle.ones([tgt_size, tgt_size]) < 0
# match query cannot see the reconstruct
attn_mask[num_denoising:, :num_denoising] = True
# reconstruct cannot see each other
for i in range(num_group):
if i == 0:
attn_mask[max_gt_num * 2 * i:max_gt_num * 2 * (i + 1), max_gt_num *
2 * (i + 1):num_denoising] = True
if i == num_group - 1:
attn_mask[max_gt_num * 2 * i:max_gt_num * 2 * (i + 1), :max_gt_num *
i * 2] = True
else:
attn_mask[max_gt_num * 2 * i:max_gt_num * 2 * (i + 1), max_gt_num *
2 * (i + 1):num_denoising] = True
attn_mask[max_gt_num * 2 * i:max_gt_num * 2 * (i + 1), :max_gt_num *
2 * i] = True
attn_mask = ~attn_mask
dn_meta = {
"dn_positive_idx": dn_positive_idx,
"dn_num_group": num_group,
"dn_num_split": [num_denoising, num_queries]
}
return input_query_class, input_query_bbox, attn_mask, dn_meta
def get_sine_pos_embed(pos_tensor,
num_pos_feats=128,
temperature=10000,
exchange_xy=True):
"""generate sine position embedding from a position tensor
Args:
pos_tensor (torch.Tensor): Shape as `(None, n)`.
num_pos_feats (int): projected shape for each float in the tensor. Default: 128
temperature (int): The temperature used for scaling
the position embedding. Default: 10000.
exchange_xy (bool, optional): exchange pos x and pos y. \
For example, input tensor is `[x, y]`, the results will # noqa
be `[pos(y), pos(x)]`. Defaults: True.
Returns:
torch.Tensor: Returned position embedding # noqa
with shape `(None, n * num_pos_feats)`.
"""
scale = 2. * math.pi
dim_t = 2. * paddle.floor_divide(
paddle.arange(num_pos_feats), paddle.to_tensor(2))
dim_t = scale / temperature**(dim_t / num_pos_feats)
def sine_func(x):
x *= dim_t
return paddle.stack(
(x[:, :, 0::2].sin(), x[:, :, 1::2].cos()), axis=3).flatten(2)
pos_res = [sine_func(x) for x in pos_tensor.split(pos_tensor.shape[-1], -1)]
if exchange_xy:
pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
pos_res = paddle.concat(pos_res, axis=2)
return pos_res
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