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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple
import numpy as np
import torch
from mmengine.structures import InstanceData
from torch import Tensor, nn
from mmocr.registry import MODELS
from mmocr.structures import KIEDataSample
@MODELS.register_module()
class SDMGRPostProcessor:
"""Postprocessor for SDMGR. It converts the node and edge scores into
labels and edge labels. If the link_type is not "none", it reconstructs the
edge labels with different strategies specified by ``link_type``, which is
generally known as the "openset" mode. In "openset" mode, only the edges
connecting from "key" to "value" nodes will be constructed.
Args:
link_type (str): The type of link to be constructed.
Defaults to 'none'. Options are:
- 'none': The simplest link type involving no edge
postprocessing. The edge prediction will be returned as-is.
- 'one-to-one': One key node can be connected to one value node.
- 'one-to-many': One key node can be connected to multiple value
nodes.
- 'many-to-one': Multiple key nodes can be connected to one value
node.
- 'many-to-many': No restrictions on the number of edges that a
key/value node can have.
key_node_idx (int, optional): The label index of the key node. It must
be specified if ``link_type`` is not "none". Defaults to None.
value_node_idx (int, optional): The index of the value node. It must be
specified if ``link_type`` is not "none". Defaults to None.
"""
def __init__(self,
link_type: str = 'none',
key_node_idx: Optional[int] = None,
value_node_idx: Optional[int] = None):
assert link_type in [
'one-to-one', 'one-to-many', 'many-to-one', 'many-to-many', 'none'
]
self.link_type = link_type
if link_type != 'none':
assert key_node_idx is not None and value_node_idx is not None
self.key_node_idx = key_node_idx
self.value_node_idx = value_node_idx
self.softmax = nn.Softmax(dim=-1)
def __call__(self, preds: Tuple[Tensor, Tensor],
data_samples: List[KIEDataSample]) -> List[KIEDataSample]:
"""Postprocess raw outputs from SDMGR heads and pack the results into a
list of KIEDataSample.
Args:
preds (tuple[Tensor]): A tuple of raw outputs from SDMGR heads.
data_samples (list[KIEDataSample]): A list of N datasamples,
containing meta information and gold annotations for each of
the images.
Returns:
List[KIEDataSample]: A list of datasamples of prediction results.
Results are stored in ``pred_instances.labels``,
``pred_instances.scores``, ``pred_instances.edge_labels`` and
``pred_instances.edge_scores``.
- labels (Tensor): An integer tensor of shape (N, ) indicating bbox
labels for each image.
- scores (Tensor): A float tensor of shape (N, ), indicating the
confidence scores for node label predictions.
- edge_labels (Tensor): An integer tensor of shape (N, N)
indicating the connection between nodes. Options are 0, 1.
- edge_scores (Tensor): A float tensor of shape (N, ), indicating
the confidence scores for edge predictions.
"""
node_preds, edge_preds = preds
all_node_scores = self.softmax(node_preds)
all_edge_scores = self.softmax(edge_preds)
chunk_size = [
data_sample.gt_instances.bboxes.shape[0]
for data_sample in data_samples
]
node_scores, node_preds = torch.max(all_node_scores, dim=-1)
edge_scores, edge_preds = torch.max(all_edge_scores, dim=-1)
node_preds = node_preds.split(chunk_size, dim=0)
node_scores = node_scores.split(chunk_size, dim=0)
sq_chunks = [chunk**2 for chunk in chunk_size]
edge_preds = list(edge_preds.split(sq_chunks, dim=0))
edge_scores = list(edge_scores.split(sq_chunks, dim=0))
for i, chunk in enumerate(chunk_size):
edge_preds[i] = edge_preds[i].reshape((chunk, chunk))
edge_scores[i] = edge_scores[i].reshape((chunk, chunk))
for i in range(len(data_samples)):
data_samples[i].pred_instances = InstanceData()
data_samples[i].pred_instances.labels = node_preds[i].cpu()
data_samples[i].pred_instances.scores = node_scores[i].cpu()
if self.link_type != 'none':
edge_scores[i], edge_preds[i] = self.decode_edges(
node_preds[i], edge_scores[i], edge_preds[i])
data_samples[i].pred_instances.edge_labels = edge_preds[i].cpu()
data_samples[i].pred_instances.edge_scores = edge_scores[i].cpu()
return data_samples
def decode_edges(self, node_labels: Tensor, edge_scores: Tensor,
edge_labels: Tensor) -> Tuple[Tensor, Tensor]:
"""Reconstruct the edges and update edge scores according to
``link_type``.
Args:
data_sample (KIEDataSample): A datasample containing prediction
results.
Returns:
tuple(Tensor, Tensor):
- edge_scores (Tensor): A float tensor of shape (N, N)
indicating the confidence scores for edge predictions.
- edge_labels (Tensor): An integer tensor of shape (N, N)
indicating the connection between nodes. Options are 0, 1.
"""
# Obtain the scores of the existence of edges.
pos_edges_scores = edge_scores.clone()
edge_labels_mask = edge_labels.bool()
pos_edges_scores[
~edge_labels_mask] = 1 - pos_edges_scores[~edge_labels_mask]
# Temporarily convert the directed graph to undirected by adding
# reversed edges to every pair of nodes if they were already connected
# by an directed edge before.
edge_labels = torch.max(edge_labels, edge_labels.T)
# Maximize edge scores
edge_labels_mask = edge_labels.bool()
edge_scores[~edge_labels_mask] = pos_edges_scores[~edge_labels_mask]
new_edge_scores = torch.max(edge_scores, edge_scores.T)
# Only reconstruct the edges from key nodes to value nodes.
key_nodes_mask = node_labels == self.key_node_idx
value_nodes_mask = node_labels == self.value_node_idx
key2value_mask = key_nodes_mask[:, None] * value_nodes_mask[None, :]
if self.link_type == 'many-to-many':
new_edge_labels = (key2value_mask * edge_labels).int()
else:
new_edge_labels = torch.zeros_like(edge_labels).int()
tmp_edge_scores = new_edge_scores.clone().cpu()
tmp_edge_scores[~edge_labels_mask] = -1
tmp_edge_scores[~key2value_mask] = -1
# Greedily extract valid edges
while (tmp_edge_scores > -1).any():
i, j = np.unravel_index(
torch.argmax(tmp_edge_scores), tmp_edge_scores.shape)
new_edge_labels[i, j] = 1
if self.link_type == 'one-to-one':
tmp_edge_scores[i, :] = -1
tmp_edge_scores[:, j] = -1
elif self.link_type == 'one-to-many':
tmp_edge_scores[:, j] = -1
elif self.link_type == 'many-to-one':
tmp_edge_scores[i, :] = -1
return new_edge_scores.cpu(), new_edge_labels.cpu()
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