Shortcuts

MMOCR Support

MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. It is a part of the OpenMMLab project.

MMOCR installation tutorial

Please refer to install.md for installation.

List of MMOCR models supported by MMDeploy

Model Task TorchScript OnnxRuntime TensorRT ncnn PPLNN OpenVINO Model config
DBNet text-detection Y Y Y Y Y Y config
PSENet text-detection Y Y Y Y N Y config
PANet text-detection Y Y Y Y N Y config
CRNN text-recognition Y Y Y Y Y N config
SAR text-recognition N Y N N N N config
SATRN text-recognition Y Y Y N N N config

Reminder

Note that ncnn, pplnn, and OpenVINO only support the configs of DBNet18 for DBNet.

For CRNN models with TensorRT-int8 backend, we recommend TensorRT 7.2.3.4 and CUDA 10.2.

For the PANet with the checkpoint pretrained on ICDAR dataset, if you want to convert the model to TensorRT with 16 bits float point, please try the following script.

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence

import torch
import torch.nn.functional as F

from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils.constants import Backend

FACTOR = 32
ENABLE = False
CHANNEL_THRESH = 400


@FUNCTION_REWRITER.register_rewriter(
    func_name='mmocr.models.textdet.necks.FPEM_FFM.forward',
    backend=Backend.TENSORRT.value)
def fpem_ffm__forward__trt(ctx, self, x: Sequence[torch.Tensor], *args,
                           **kwargs) -> Sequence[torch.Tensor]:
    """Rewrite `forward` of FPEM_FFM for tensorrt backend.

    Rewrite this function avoid overflow for tensorrt-fp16 with the checkpoint
    `https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm
    _sbn_600e_icdar2015_20210219-42dbe46a.pth`

    Args:
        ctx (ContextCaller): The context with additional information.
        self: The instance of the class FPEM_FFM.
        x (List[Tensor]): A list of feature maps of shape (N, C, H, W).

    Returns:
        outs (List[Tensor]): A list of feature maps of shape (N, C, H, W).
    """
    c2, c3, c4, c5 = x
    # reduce channel
    c2 = self.reduce_conv_c2(c2)
    c3 = self.reduce_conv_c3(c3)
    c4 = self.reduce_conv_c4(c4)

    if ENABLE:
        bn_w = self.reduce_conv_c5[1].weight / torch.sqrt(
            self.reduce_conv_c5[1].running_var + self.reduce_conv_c5[1].eps)
        bn_b = self.reduce_conv_c5[
            1].bias - self.reduce_conv_c5[1].running_mean * bn_w
        bn_w = bn_w.reshape(1, -1, 1, 1).repeat(1, 1, c5.size(2), c5.size(3))
        bn_b = bn_b.reshape(1, -1, 1, 1).repeat(1, 1, c5.size(2), c5.size(3))
        conv_b = self.reduce_conv_c5[0].bias.reshape(1, -1, 1, 1).repeat(
            1, 1, c5.size(2), c5.size(3))
        c5 = FACTOR * (self.reduce_conv_c5[:-1](c5)) - (FACTOR - 1) * (
            bn_w * conv_b + bn_b)
        c5 = self.reduce_conv_c5[-1](c5)
    else:
        c5 = self.reduce_conv_c5(c5)

    # FPEM
    for i, fpem in enumerate(self.fpems):
        c2, c3, c4, c5 = fpem(c2, c3, c4, c5)
        if i == 0:
            c2_ffm = c2
            c3_ffm = c3
            c4_ffm = c4
            c5_ffm = c5
        else:
            c2_ffm += c2
            c3_ffm += c3
            c4_ffm += c4
            c5_ffm += c5

    # FFM
    c5 = F.interpolate(
        c5_ffm,
        c2_ffm.size()[-2:],
        mode='bilinear',
        align_corners=self.align_corners)
    c4 = F.interpolate(
        c4_ffm,
        c2_ffm.size()[-2:],
        mode='bilinear',
        align_corners=self.align_corners)
    c3 = F.interpolate(
        c3_ffm,
        c2_ffm.size()[-2:],
        mode='bilinear',
        align_corners=self.align_corners)
    outs = [c2_ffm, c3, c4, c5]
    return tuple(outs)


@FUNCTION_REWRITER.register_rewriter(
    func_name='mmdet.models.backbones.resnet.BasicBlock.forward',
    backend=Backend.TENSORRT.value)
def basic_block__forward__trt(ctx, self, x: torch.Tensor) -> torch.Tensor:
    """Rewrite `forward` of BasicBlock for tensorrt backend.

    Rewrite this function avoid overflow for tensorrt-fp16 with the checkpoint
    `https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm
    _sbn_600e_icdar2015_20210219-42dbe46a.pth`

    Args:
        ctx (ContextCaller): The context with additional information.
        self: The instance of the class FPEM_FFM.
        x (Tensor): The input tensor of shape (N, C, H, W).

    Returns:
        outs (Tensor): The output tensor of shape (N, C, H, W).
    """
    if self.conv1.in_channels < CHANNEL_THRESH:
        return ctx.origin_func(self, x)

    identity = x

    out = self.conv1(x)
    out = self.norm1(out)
    out = self.relu(out)

    out = self.conv2(out)

    if torch.abs(self.norm2(out)).max() < 65504:
        out = self.norm2(out)
        out += identity
        out = self.relu(out)
        return out
    else:
        global ENABLE
        ENABLE = True
        # the output of the last bn layer exceeds the range of fp16
        w1 = self.norm2.weight / torch.sqrt(self.norm2.running_var +
                                            self.norm2.eps)
        bias = self.norm2.bias - self.norm2.running_mean * w1
        w1 = w1.reshape(1, -1, 1, 1).repeat(1, 1, out.size(2), out.size(3))
        bias = bias.reshape(1, -1, 1, 1).repeat(1, 1, out.size(2),
                                                out.size(3)) + identity
        out = self.relu(w1 * (out / FACTOR) + bias / FACTOR)

        return out

Read the Docs v: latest
Versions
latest
stable
dev-1.x
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.