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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from mmocr.models.builder import HEADS
from mmocr.utils import check_argument
from .head_mixin import HeadMixin
@HEADS.register_module()
class PANHead(HeadMixin, BaseModule):
"""The class for PANet head.
Args:
in_channels (list[int]): A list of 4 numbers of input channels.
out_channels (int): Number of output channels.
downsample_ratio (float): Downsample ratio.
loss (dict): Configuration dictionary for loss type. Supported loss
types are "PANLoss" and "PSELoss".
postprocessor (dict): Config of postprocessor for PANet.
train_cfg, test_cfg (dict): Depreciated.
init_cfg (dict or list[dict], optional): Initialization configs.
"""
def __init__(self,
in_channels,
out_channels,
downsample_ratio=0.25,
loss=dict(type='PANLoss'),
postprocessor=dict(
type='PANPostprocessor', text_repr_type='poly'),
train_cfg=None,
test_cfg=None,
init_cfg=dict(
type='Normal',
mean=0,
std=0.01,
override=dict(name='out_conv')),
**kwargs):
old_keys = ['text_repr_type', 'decoding_type']
for key in old_keys:
if kwargs.get(key, None):
postprocessor[key] = kwargs.get(key)
warnings.warn(
f'{key} is deprecated, please specify '
'it in postprocessor config dict. See '
'https://github.com/open-mmlab/mmocr/pull/640'
' for details.', UserWarning)
BaseModule.__init__(self, init_cfg=init_cfg)
HeadMixin.__init__(self, loss, postprocessor)
assert check_argument.is_type_list(in_channels, int)
assert isinstance(out_channels, int)
assert 0 <= downsample_ratio <= 1
self.in_channels = in_channels
self.out_channels = out_channels
self.downsample_ratio = downsample_ratio
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.out_conv = nn.Conv2d(
in_channels=np.sum(np.array(in_channels)),
out_channels=out_channels,
kernel_size=1)
def forward(self, inputs):
r"""
Args:
inputs (list[Tensor] | Tensor): Each tensor has the shape of
:math:`(N, C_i, W, H)`, where :math:`\sum_iC_i=C_{in}` and
:math:`C_{in}` is ``input_channels``.
Returns:
Tensor: A tensor of shape :math:`(N, C_{out}, W, H)` where
:math:`C_{out}` is ``output_channels``.
"""
if isinstance(inputs, tuple):
outputs = torch.cat(inputs, dim=1)
else:
outputs = inputs
outputs = self.out_conv(outputs)
return outputs
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