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更换文档检测模型

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liuyebo
2024-08-27 14:42:45 +08:00
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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.
"""
This code is based on https://github.com/PeizeSun/SparseR-CNN/blob/main/projects/SparseRCNN/sparsercnn/loss.py
Ths copyright of PeizeSun/SparseR-CNN is as follows:
MIT License [see LICENSE for details]
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from scipy.optimize import linear_sum_assignment
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.metric import accuracy
from ppdet.core.workspace import register
from ppdet.modeling.losses.iou_loss import GIoULoss
__all__ = ["SparseRCNNLoss"]
@register
class SparseRCNNLoss(nn.Layer):
""" This class computes the loss for SparseRCNN.
The process happens in two steps:
1) we compute hungarian assignment between ground truth boxes and the outputs of the model
2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
"""
__shared__ = ['num_classes']
def __init__(self,
losses,
focal_loss_alpha,
focal_loss_gamma,
num_classes=80,
class_weight=2.,
l1_weight=5.,
giou_weight=2.):
""" Create the criterion.
Parameters:
num_classes: number of object categories, omitting the special no-object category
weight_dict: dict containing as key the names of the losses and as values their relative weight.
losses: list of all the losses to be applied. See get_loss for list of available losses.
matcher: module able to compute a matching between targets and proposals
"""
super().__init__()
self.num_classes = num_classes
weight_dict = {
"loss_ce": class_weight,
"loss_bbox": l1_weight,
"loss_giou": giou_weight
}
self.weight_dict = weight_dict
self.losses = losses
self.giou_loss = GIoULoss(reduction="sum")
self.focal_loss_alpha = focal_loss_alpha
self.focal_loss_gamma = focal_loss_gamma
self.matcher = HungarianMatcher(focal_loss_alpha, focal_loss_gamma,
class_weight, l1_weight, giou_weight)
def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
"""Classification loss (NLL)
targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
"""
assert 'pred_logits' in outputs
src_logits = outputs['pred_logits']
idx = self._get_src_permutation_idx(indices)
target_classes_o = paddle.concat([
paddle.gather(
t["labels"], J, axis=0) for t, (_, J) in zip(targets, indices)
])
target_classes = paddle.full(
src_logits.shape[:2], self.num_classes, dtype="int32")
for i, ind in enumerate(zip(idx[0], idx[1])):
target_classes[int(ind[0]), int(ind[1])] = target_classes_o[i]
target_classes.stop_gradient = True
src_logits = src_logits.flatten(start_axis=0, stop_axis=1)
# prepare one_hot target.
target_classes = target_classes.flatten(start_axis=0, stop_axis=1)
class_ids = paddle.arange(0, self.num_classes)
labels = (target_classes.unsqueeze(-1) == class_ids).astype("float32")
labels.stop_gradient = True
# comp focal loss.
class_loss = sigmoid_focal_loss(
src_logits,
labels,
alpha=self.focal_loss_alpha,
gamma=self.focal_loss_gamma,
reduction="sum", ) / num_boxes
losses = {'loss_ce': class_loss}
if log:
label_acc = target_classes_o.unsqueeze(-1)
src_idx = [src for (src, _) in indices]
pred_list = []
for i in range(outputs["pred_logits"].shape[0]):
pred_list.append(
paddle.gather(
outputs["pred_logits"][i], src_idx[i], axis=0))
pred = F.sigmoid(paddle.concat(pred_list, axis=0))
acc = accuracy(pred, label_acc.astype("int64"))
losses["acc"] = acc
return losses
def loss_boxes(self, outputs, targets, indices, num_boxes):
"""Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss
targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]
The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size.
"""
assert 'pred_boxes' in outputs # [batch_size, num_proposals, 4]
src_idx = [src for (src, _) in indices]
src_boxes_list = []
for i in range(outputs["pred_boxes"].shape[0]):
src_boxes_list.append(
paddle.gather(
outputs["pred_boxes"][i], src_idx[i], axis=0))
src_boxes = paddle.concat(src_boxes_list, axis=0)
target_boxes = paddle.concat(
[
paddle.gather(
t['boxes'], I, axis=0)
for t, (_, I) in zip(targets, indices)
],
axis=0)
target_boxes.stop_gradient = True
losses = {}
losses['loss_giou'] = self.giou_loss(src_boxes,
target_boxes) / num_boxes
image_size = paddle.concat([v["img_whwh_tgt"] for v in targets])
src_boxes_ = src_boxes / image_size
target_boxes_ = target_boxes / image_size
loss_bbox = F.l1_loss(src_boxes_, target_boxes_, reduction='sum')
losses['loss_bbox'] = loss_bbox / num_boxes
return losses
def _get_src_permutation_idx(self, indices):
# permute predictions following indices
batch_idx = paddle.concat(
[paddle.full_like(src, i) for i, (src, _) in enumerate(indices)])
src_idx = paddle.concat([src for (src, _) in indices])
return batch_idx, src_idx
def _get_tgt_permutation_idx(self, indices):
# permute targets following indices
batch_idx = paddle.concat(
[paddle.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
tgt_idx = paddle.concat([tgt for (_, tgt) in indices])
return batch_idx, tgt_idx
def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
loss_map = {
'labels': self.loss_labels,
'boxes': self.loss_boxes,
}
assert loss in loss_map, f'do you really want to compute {loss} loss?'
return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs)
def forward(self, outputs, targets):
""" This performs the loss computation.
Parameters:
outputs: dict of tensors, see the output specification of the model for the format
targets: list of dicts, such that len(targets) == batch_size.
The expected keys in each dict depends on the losses applied, see each loss' doc
"""
outputs_without_aux = {
k: v
for k, v in outputs.items() if k != 'aux_outputs'
}
# Retrieve the matching between the outputs of the last layer and the targets
indices = self.matcher(outputs_without_aux, targets)
# Compute the average number of target boxes across all nodes, for normalization purposes
num_boxes = sum(len(t["labels"]) for t in targets)
num_boxes = paddle.to_tensor(
[num_boxes],
dtype="float32",
place=next(iter(outputs.values())).place)
# Compute all the requested losses
losses = {}
for loss in self.losses:
losses.update(
self.get_loss(loss, outputs, targets, indices, num_boxes))
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if 'aux_outputs' in outputs:
for i, aux_outputs in enumerate(outputs['aux_outputs']):
indices = self.matcher(aux_outputs, targets)
for loss in self.losses:
kwargs = {}
if loss == 'labels':
# Logging is enabled only for the last layer
kwargs = {'log': False}
l_dict = self.get_loss(loss, aux_outputs, targets, indices,
num_boxes, **kwargs)
w_dict = {}
for k in l_dict.keys():
if k in self.weight_dict:
w_dict[k + f'_{i}'] = l_dict[k] * self.weight_dict[
k]
else:
w_dict[k + f'_{i}'] = l_dict[k]
losses.update(w_dict)
return losses
class HungarianMatcher(nn.Layer):
"""This class computes an assignment between the targets and the predictions of the network
For efficiency reasons, the targets don't include the no_object. Because of this, in general,
there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
while the others are un-matched (and thus treated as non-objects).
"""
def __init__(self,
focal_loss_alpha,
focal_loss_gamma,
cost_class: float=1,
cost_bbox: float=1,
cost_giou: float=1):
"""Creates the matcher
Params:
cost_class: This is the relative weight of the classification error in the matching cost
cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost
cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost
"""
super().__init__()
self.cost_class = cost_class
self.cost_bbox = cost_bbox
self.cost_giou = cost_giou
self.focal_loss_alpha = focal_loss_alpha
self.focal_loss_gamma = focal_loss_gamma
assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0"
@paddle.no_grad()
def forward(self, outputs, targets):
""" Performs the matching
Args:
outputs: This is a dict that contains at least these entries:
"pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
"pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates
eg. outputs = {"pred_logits": pred_logits, "pred_boxes": pred_boxes}
targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
"labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
objects in the target) containing the class labels
"boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates
eg. targets = [{"labels":labels, "boxes": boxes}, ...,{"labels":labels, "boxes": boxes}]
Returns:
A list of size batch_size, containing tuples of (index_i, index_j) where:
- index_i is the indices of the selected predictions (in order)
- index_j is the indices of the corresponding selected targets (in order)
For each batch element, it holds:
len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
"""
bs, num_queries = outputs["pred_logits"].shape[:2]
if sum(len(v["labels"]) for v in targets) == 0:
return [(paddle.to_tensor(
[], dtype=paddle.int64), paddle.to_tensor(
[], dtype=paddle.int64)) for _ in range(bs)]
# We flatten to compute the cost matrices in a batch
out_prob = F.sigmoid(outputs["pred_logits"].flatten(
start_axis=0, stop_axis=1))
out_bbox = outputs["pred_boxes"].flatten(start_axis=0, stop_axis=1)
# Also concat the target labels and boxes
tgt_ids = paddle.concat([v["labels"] for v in targets])
assert (tgt_ids > -1).all()
tgt_bbox = paddle.concat([v["boxes"] for v in targets])
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
# Compute the classification cost.
alpha = self.focal_loss_alpha
gamma = self.focal_loss_gamma
neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(
1 - out_prob + 1e-8).log())
pos_cost_class = alpha * ((1 - out_prob)
**gamma) * (-(out_prob + 1e-8).log())
cost_class = paddle.gather(
pos_cost_class, tgt_ids, axis=1) - paddle.gather(
neg_cost_class, tgt_ids, axis=1)
# Compute the L1 cost between boxes
image_size_out = paddle.concat(
[v["img_whwh"].unsqueeze(0) for v in targets])
image_size_out = image_size_out.unsqueeze(1).tile(
[1, num_queries, 1]).flatten(
start_axis=0, stop_axis=1)
image_size_tgt = paddle.concat([v["img_whwh_tgt"] for v in targets])
out_bbox_ = out_bbox / image_size_out
tgt_bbox_ = tgt_bbox / image_size_tgt
cost_bbox = F.l1_loss(
out_bbox_.unsqueeze(-2), tgt_bbox_,
reduction='none').sum(-1) # [batch_size * num_queries, num_tgts]
# Compute the giou cost betwen boxes
cost_giou = -get_bboxes_giou(out_bbox, tgt_bbox)
# Final cost matrix
C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
C = C.reshape([bs, num_queries, -1])
sizes = [len(v["boxes"]) for v in targets]
indices = [
linear_sum_assignment(c[i].numpy())
for i, c in enumerate(C.split(sizes, -1))
]
return [(paddle.to_tensor(
i, dtype="int32"), paddle.to_tensor(
j, dtype="int32")) for i, j in indices]
def box_area(boxes):
assert (boxes[:, 2:] >= boxes[:, :2]).all()
wh = boxes[:, 2:] - boxes[:, :2]
return wh[:, 0] * wh[:, 1]
def boxes_iou(boxes1, boxes2):
'''
Compute iou
Args:
boxes1 (paddle.tensor) shape (N, 4)
boxes2 (paddle.tensor) shape (M, 4)
Return:
(paddle.tensor) shape (N, M)
'''
area1 = box_area(boxes1)
area2 = box_area(boxes2)
lt = paddle.maximum(boxes1.unsqueeze(-2)[:, :, :2], boxes2[:, :2])
rb = paddle.minimum(boxes1.unsqueeze(-2)[:, :, 2:], boxes2[:, 2:])
wh = (rb - lt).astype("float32").clip(min=1e-9)
inter = wh[:, :, 0] * wh[:, :, 1]
union = area1.unsqueeze(-1) + area2 - inter + 1e-9
iou = inter / union
return iou, union
def get_bboxes_giou(boxes1, boxes2, eps=1e-9):
"""calculate the ious of boxes1 and boxes2
Args:
boxes1 (Tensor): shape [N, 4]
boxes2 (Tensor): shape [M, 4]
eps (float): epsilon to avoid divide by zero
Return:
ious (Tensor): ious of boxes1 and boxes2, with the shape [N, M]
"""
assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
iou, union = boxes_iou(boxes1, boxes2)
lt = paddle.minimum(boxes1.unsqueeze(-2)[:, :, :2], boxes2[:, :2])
rb = paddle.maximum(boxes1.unsqueeze(-2)[:, :, 2:], boxes2[:, 2:])
wh = (rb - lt).astype("float32").clip(min=eps)
enclose_area = wh[:, :, 0] * wh[:, :, 1]
giou = iou - (enclose_area - union) / enclose_area
return giou
def sigmoid_focal_loss(inputs, targets, alpha, gamma, reduction="sum"):
assert reduction in ["sum", "mean"
], f'do not support this {reduction} reduction?'
p = F.sigmoid(inputs)
ce_loss = F.binary_cross_entropy_with_logits(
inputs, targets, reduction="none")
p_t = p * targets + (1 - p) * (1 - targets)
loss = ce_loss * ((1 - p_t)**gamma)
if alpha >= 0:
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
loss = alpha_t * loss
if reduction == "mean":
loss = loss.mean()
elif reduction == "sum":
loss = loss.sum()
return loss