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111 lines
4.0 KiB
111 lines
4.0 KiB
3 years ago
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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3 years ago
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Modified from DETR (https://github.com/facebookresearch/detr)
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from ..bbox_utils import bbox_overlaps
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__all__ = [
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'_get_clones', 'bbox_overlaps', 'bbox_cxcywh_to_xyxy',
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'bbox_xyxy_to_cxcywh', 'sigmoid_focal_loss', 'inverse_sigmoid',
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'deformable_attention_core_func'
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]
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def _get_clones(module, N):
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return nn.LayerList([copy.deepcopy(module) for _ in range(N)])
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def bbox_cxcywh_to_xyxy(x):
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x_c, y_c, w, h = x.unbind(-1)
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b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
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return paddle.stack(b, axis=-1)
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def bbox_xyxy_to_cxcywh(x):
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x0, y0, x1, y1 = x.unbind(-1)
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b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
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return paddle.stack(b, axis=-1)
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def sigmoid_focal_loss(logit, label, normalizer=1.0, alpha=0.25, gamma=2.0):
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prob = F.sigmoid(logit)
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ce_loss = F.binary_cross_entropy_with_logits(
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logit, label, reduction="none")
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p_t = prob * label + (1 - prob) * (1 - label)
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loss = ce_loss * ((1 - p_t)**gamma)
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if alpha >= 0:
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alpha_t = alpha * label + (1 - alpha) * (1 - label)
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loss = alpha_t * loss
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return loss.mean(1).sum() / normalizer
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def inverse_sigmoid(x, eps=1e-6):
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x = x.clip(min=0., max=1.)
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return paddle.log(x / (1 - x + eps) + eps)
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def deformable_attention_core_func(value, value_spatial_shapes,
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sampling_locations, attention_weights):
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"""
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Args:
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value (Tensor): [bs, value_length, n_head, c]
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value_spatial_shapes (Tensor): [n_levels, 2]
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sampling_locations (Tensor): [bs, query_length, n_head, n_levels, n_points, 2]
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attention_weights (Tensor): [bs, query_length, n_head, n_levels, n_points]
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Returns:
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output (Tensor): [bs, Length_{query}, C]
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"""
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bs, Len_v, n_head, c = value.shape
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_, Len_q, n_head, n_levels, n_points, _ = sampling_locations.shape
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value_list = value.split(value_spatial_shapes.prod(1).tolist(), axis=1)
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sampling_grids = 2 * sampling_locations - 1
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sampling_value_list = []
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for level, (h, w) in enumerate(value_spatial_shapes.tolist()):
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# N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
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value_l_ = value_list[level].flatten(2).transpose(
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[0, 2, 1]).reshape([bs * n_head, c, h, w])
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# N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
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sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(
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[0, 2, 1, 3, 4]).flatten(0, 1)
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# N_*M_, D_, Lq_, P_
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sampling_value_l_ = F.grid_sample(
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value_l_,
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sampling_grid_l_,
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mode='bilinear',
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padding_mode='zeros',
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align_corners=False)
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sampling_value_list.append(sampling_value_l_)
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# (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_*M_, 1, Lq_, L_*P_)
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attention_weights = attention_weights.transpose([0, 2, 1, 3, 4]).reshape(
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[bs * n_head, 1, Len_q, n_levels * n_points])
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output = (paddle.stack(
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sampling_value_list, axis=-2).flatten(-2) *
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attention_weights).sum(-1).reshape([bs, n_head * c, Len_q])
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return output.transpose([0, 2, 1])
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