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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Dict, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from mmdet.models.utils import multi_apply
from torch import nn
from mmocr.registry import MODELS
from mmocr.structures import TextDetDataSample
from .seg_based_module_loss import SegBasedModuleLoss
@MODELS.register_module()
class PANModuleLoss(SegBasedModuleLoss):
"""The class for implementing PANet loss. This was partially adapted from
https://github.com/whai362/pan_pp.pytorch and
https://github.com/WenmuZhou/PAN.pytorch.
PANet: `Efficient and Accurate Arbitrary-
Shaped Text Detection with Pixel Aggregation Network
<https://arxiv.org/abs/1908.05900>`_.
Args:
loss_text (dict) The loss config for text map. Defaults to
dict(type='MaskedSquareDiceLoss').
loss_kernel (dict) The loss config for kernel map. Defaults to
dict(type='MaskedSquareDiceLoss').
loss_embedding (dict) The loss config for embedding map. Defaults to
dict(type='PANEmbLossV1').
weight_text (float): The weight of text loss. Defaults to 1.
weight_kernel (float): The weight of kernel loss. Defaults to 0.5.
weight_embedding (float): The weight of embedding loss.
Defaults to 0.25.
ohem_ratio (float): The negative/positive ratio in ohem. Defaults to 3.
shrink_ratio (tuple[float]) : The ratio of shrinking kernel. Defaults
to (1.0, 0.5).
max_shrink_dist (int or float): The maximum shrinking distance.
Defaults to 20.
reduction (str): The way to reduce the loss. Available options are
"mean" and "sum". Defaults to 'mean'.
"""
def __init__(
self,
loss_text: Dict = dict(type='MaskedSquareDiceLoss'),
loss_kernel: Dict = dict(type='MaskedSquareDiceLoss'),
loss_embedding: Dict = dict(type='PANEmbLossV1'),
weight_text: float = 1.0,
weight_kernel: float = 0.5,
weight_embedding: float = 0.25,
ohem_ratio: Union[int, float] = 3, # TODO Find a better name
shrink_ratio: Sequence[Union[int, float]] = (1.0, 0.5),
max_shrink_dist: Union[int, float] = 20,
reduction: str = 'mean') -> None:
super().__init__()
assert reduction in ['mean', 'sum'], "reduction must in ['mean','sum']"
self.weight_text = weight_text
self.weight_kernel = weight_kernel
self.weight_embedding = weight_embedding
self.shrink_ratio = shrink_ratio
self.ohem_ratio = ohem_ratio
self.reduction = reduction
self.max_shrink_dist = max_shrink_dist
self.loss_text = MODELS.build(loss_text)
self.loss_kernel = MODELS.build(loss_kernel)
self.loss_embedding = MODELS.build(loss_embedding)
def forward(self, preds: torch.Tensor,
data_samples: Sequence[TextDetDataSample]) -> Dict:
"""Compute PAN loss.
Args:
preds (dict): Raw predictions from model with
shape :math:`(N, C, H, W)`.
data_samples (list[TextDetDataSample]): The data samples.
Returns:
dict: The dict for pan losses with loss_text, loss_kernel,
loss_aggregation and loss_discrimination.
"""
gt_kernels, gt_masks = self.get_targets(data_samples)
target_size = gt_kernels.size()[2:]
preds = F.interpolate(preds, size=target_size, mode='bilinear')
pred_texts = preds[:, 0, :, :]
pred_kernels = preds[:, 1, :, :]
inst_embed = preds[:, 2:, :, :]
gt_kernels = gt_kernels.to(preds.device)
gt_masks = gt_masks.to(preds.device)
# compute embedding loss
loss_emb = self.loss_embedding(inst_embed, gt_kernels[0],
gt_kernels[1], gt_masks)
gt_kernels[gt_kernels <= 0.5] = 0
gt_kernels[gt_kernels > 0.5] = 1
# compute text loss
sampled_mask = self._ohem_batch(pred_texts.detach(), gt_kernels[0],
gt_masks)
pred_texts = torch.sigmoid(pred_texts)
loss_texts = self.loss_text(pred_texts, gt_kernels[0], sampled_mask)
# compute kernel loss
pred_kernels = torch.sigmoid(pred_kernels)
sampled_masks_kernel = (gt_kernels[0] > 0.5).float() * gt_masks
loss_kernels = self.loss_kernel(pred_kernels, gt_kernels[1],
sampled_masks_kernel)
losses = [loss_texts, loss_kernels, loss_emb]
if self.reduction == 'mean':
losses = [item.mean() for item in losses]
else:
losses = [item.sum() for item in losses]
results = dict()
results.update(
loss_text=self.weight_text * losses[0],
loss_kernel=self.weight_kernel * losses[1],
loss_embedding=self.weight_embedding * losses[2])
return results
def get_targets(
self,
data_samples: Sequence[TextDetDataSample],
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Generate the gt targets for PANet.
Args:
results (dict): The input result dictionary.
Returns:
results (dict): The output result dictionary.
"""
gt_kernels, gt_masks = multi_apply(self._get_target_single,
data_samples)
# gt_kernels: (N, kernel_number, H, W)->(kernel_number, N, H, W)
gt_kernels = torch.stack(gt_kernels, dim=0).permute(1, 0, 2, 3)
gt_masks = torch.stack(gt_masks, dim=0)
return gt_kernels, gt_masks
def _get_target_single(self, data_sample: TextDetDataSample
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Generate loss target from a data sample.
Args:
data_sample (TextDetDataSample): The data sample.
Returns:
tuple: A tuple of four tensors as the targets of one prediction.
"""
gt_polygons = data_sample.gt_instances.polygons
gt_ignored = data_sample.gt_instances.ignored
gt_kernels = []
for ratio in self.shrink_ratio:
# TODO pass `gt_ignored` to `_generate_kernels`
gt_kernel, _ = self._generate_kernels(
data_sample.img_shape,
gt_polygons,
ratio,
ignore_flags=None,
max_shrink_dist=self.max_shrink_dist)
gt_kernels.append(gt_kernel)
gt_polygons_ignored = data_sample.gt_instances[gt_ignored].polygons
gt_mask = self._generate_effective_mask(data_sample.img_shape,
gt_polygons_ignored)
gt_kernels = np.stack(gt_kernels, axis=0)
gt_kernels = torch.from_numpy(gt_kernels).float()
gt_mask = torch.from_numpy(gt_mask).float()
return gt_kernels, gt_mask
def _ohem_batch(self, text_scores: torch.Tensor, gt_texts: torch.Tensor,
gt_mask: torch.Tensor) -> torch.Tensor:
"""OHEM sampling for a batch of imgs.
Args:
text_scores (Tensor): The text scores of size :math:`(H, W)`.
gt_texts (Tensor): The gt text masks of size :math:`(H, W)`.
gt_mask (Tensor): The gt effective mask of size :math:`(H, W)`.
Returns:
Tensor: The sampled mask of size :math:`(H, W)`.
"""
assert isinstance(text_scores, torch.Tensor)
assert isinstance(gt_texts, torch.Tensor)
assert isinstance(gt_mask, torch.Tensor)
assert len(text_scores.shape) == 3
assert text_scores.shape == gt_texts.shape
assert gt_texts.shape == gt_mask.shape
sampled_masks = []
for i in range(text_scores.shape[0]):
sampled_masks.append(
self._ohem_single(text_scores[i], gt_texts[i], gt_mask[i]))
sampled_masks = torch.stack(sampled_masks)
return sampled_masks
def _ohem_single(self, text_score: torch.Tensor, gt_text: torch.Tensor,
gt_mask: torch.Tensor) -> torch.Tensor:
"""Sample the top-k maximal negative samples and all positive samples.
Args:
text_score (Tensor): The text score of size :math:`(H, W)`.
gt_text (Tensor): The ground truth text mask of size
:math:`(H, W)`.
gt_mask (Tensor): The effective region mask of size :math:`(H, W)`.
Returns:
Tensor: The sampled pixel mask of size :math:`(H, W)`.
"""
assert isinstance(text_score, torch.Tensor)
assert isinstance(gt_text, torch.Tensor)
assert isinstance(gt_mask, torch.Tensor)
assert len(text_score.shape) == 2
assert text_score.shape == gt_text.shape
assert gt_text.shape == gt_mask.shape
pos_num = (int)(torch.sum(gt_text > 0.5).item()) - (int)(
torch.sum((gt_text > 0.5) * (gt_mask <= 0.5)).item())
neg_num = (int)(torch.sum(gt_text <= 0.5).item())
neg_num = (int)(min(pos_num * self.ohem_ratio, neg_num))
if pos_num == 0 or neg_num == 0:
warnings.warn('pos_num = 0 or neg_num = 0')
return gt_mask.bool()
neg_score = text_score[gt_text <= 0.5]
neg_score_sorted, _ = torch.sort(neg_score, descending=True)
threshold = neg_score_sorted[neg_num - 1]
sampled_mask = (((text_score >= threshold) + (gt_text > 0.5)) > 0) * (
gt_mask > 0.5)
return sampled_mask
@MODELS.register_module()
class PANEmbLossV1(nn.Module):
"""The class for implementing EmbLossV1. This was partially adapted from
https://github.com/whai362/pan_pp.pytorch.
Args:
feature_dim (int): The dimension of the feature. Defaults to 4.
delta_aggregation (float): The delta for aggregation. Defaults to 0.5.
delta_discrimination (float): The delta for discrimination.
Defaults to 1.5.
"""
def __init__(self,
feature_dim: int = 4,
delta_aggregation: float = 0.5,
delta_discrimination: float = 1.5) -> None:
super().__init__()
self.feature_dim = feature_dim
self.delta_aggregation = delta_aggregation
self.delta_discrimination = delta_discrimination
self.weights = (1.0, 1.0)
def _forward_single(self, emb: torch.Tensor, instance: torch.Tensor,
kernel: torch.Tensor,
training_mask: torch.Tensor) -> torch.Tensor:
"""Compute the loss for a single image.
Args:
emb (torch.Tensor): The embedding feature.
instance (torch.Tensor): The instance feature.
kernel (torch.Tensor): The kernel feature.
training_mask (torch.Tensor): The effective mask.
"""
training_mask = (training_mask > 0.5).float()
kernel = (kernel > 0.5).float()
instance = instance * training_mask
instance_kernel = (instance * kernel).view(-1)
instance = instance.view(-1)
emb = emb.view(self.feature_dim, -1)
unique_labels, unique_ids = torch.unique(
instance_kernel, sorted=True, return_inverse=True)
num_instance = unique_labels.size(0)
if num_instance <= 1:
return 0
emb_mean = emb.new_zeros((self.feature_dim, num_instance),
dtype=torch.float32)
for i, lb in enumerate(unique_labels):
if lb == 0:
continue
ind_k = instance_kernel == lb
emb_mean[:, i] = torch.mean(emb[:, ind_k], dim=1)
l_agg = emb.new_zeros(num_instance, dtype=torch.float32)
for i, lb in enumerate(unique_labels):
if lb == 0:
continue
ind = instance == lb
emb_ = emb[:, ind]
dist = (emb_ - emb_mean[:, i:i + 1]).norm(p=2, dim=0)
dist = F.relu(dist - self.delta_aggregation)**2
l_agg[i] = torch.mean(torch.log(dist + 1.0))
l_agg = torch.mean(l_agg[1:])
if num_instance > 2:
emb_interleave = emb_mean.permute(1, 0).repeat(num_instance, 1)
emb_band = emb_mean.permute(1, 0).repeat(1, num_instance).view(
-1, self.feature_dim)
mask = (1 - torch.eye(num_instance, dtype=torch.int8)).view(
-1, 1).repeat(1, self.feature_dim)
mask = mask.view(num_instance, num_instance, -1)
mask[0, :, :] = 0
mask[:, 0, :] = 0
mask = mask.view(num_instance * num_instance, -1)
dist = emb_interleave - emb_band
dist = dist[mask > 0].view(-1, self.feature_dim).norm(p=2, dim=1)
dist = F.relu(2 * self.delta_discrimination - dist)**2
l_dis = torch.mean(torch.log(dist + 1.0))
else:
l_dis = 0
l_agg = self.weights[0] * l_agg
l_dis = self.weights[1] * l_dis
l_reg = torch.mean(torch.log(torch.norm(emb_mean, 2, 0) + 1.0)) * 0.001
loss = l_agg + l_dis + l_reg
return loss
def forward(self, emb: torch.Tensor, instance: torch.Tensor,
kernel: torch.Tensor,
training_mask: torch.Tensor) -> torch.Tensor:
"""Compute the loss for a batch image.
Args:
emb (torch.Tensor): The embedding feature.
instance (torch.Tensor): The instance feature.
kernel (torch.Tensor): The kernel feature.
training_mask (torch.Tensor): The effective mask.
"""
loss_batch = emb.new_zeros((emb.size(0)), dtype=torch.float32)
for i in range(loss_batch.size(0)):
loss_batch[i] = self._forward_single(emb[i], instance[i],
kernel[i], training_mask[i])
return loss_batch
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