164 lines
6.5 KiB
Markdown
164 lines
6.5 KiB
Markdown
# mmocr 模型支持列表
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mmocr 是一个基于 PyTorch 和 mmdetection 的开源工具箱，用于文本检测，文本识别以及相应的下游任务，例如关键信息提取，是 [OpenMMLab](https://openmmlab.com/)项目的一部分。
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## 安装
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参照 [install.md](https://mmocr.readthedocs.io/en/latest/install.html)。
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## 支持列表
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| Model | Task | TorchScript | OnnxRuntime | TensorRT | ncnn | PPLNN | OpenVINO | Model config |
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| :----- | :--------------- | :---------: | :---------: | :------: | :--: | :---: | :------: | :-----------------------------------------------------------------------------: |
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| DBNet | text-detection | Y | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/dbnet) |
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| PSENet | text-detection | Y | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/psenet) |
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| PANet | text-detection | Y | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmocr/tree/main/configs/textdet/panet) |
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| CRNN | text-recognition | Y | Y | Y | Y | Y | N | [config](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/crnn) |
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| SAR | text-recognition | N | Y | N | N | N | N | [config](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/sar) |
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| SATRN | text-recognition | Y | Y | Y | N | N | N | [config](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/satrn) |
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## 注意事项
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请注意，ncnn、pplnn 和 OpenVINO 仅支持 DBNet 的 DBNet18 配置。
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对于在 ICDAR 数据集上预训 [checkpoint](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_icdar2015_20210219-42dbe46a.pth) 的 PANet，如果要将模型转为具有 fp16 TensorRT，请尝试以下脚本。
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```python
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# Copyright (c) OpenMMLab. All rights reserved.
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from typing import Sequence
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import torch
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import torch.nn.functional as F
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from mmdeploy.core import FUNCTION_REWRITER
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from mmdeploy.utils.constants import Backend
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FACTOR = 32
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ENABLE = False
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CHANNEL_THRESH = 400
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@FUNCTION_REWRITER.register_rewriter(
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func_name='mmocr.models.textdet.necks.FPEM_FFM.forward',
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backend=Backend.TENSORRT.value)
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def fpem_ffm__forward__trt(ctx, self, x: Sequence[torch.Tensor], *args,
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**kwargs) -> Sequence[torch.Tensor]:
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"""Rewrite `forward` of FPEM_FFM for tensorrt backend.
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Rewrite this function avoid overflow for tensorrt-fp16 with the checkpoint
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`https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm
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_sbn_600e_icdar2015_20210219-42dbe46a.pth`
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Args:
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ctx (ContextCaller): The context with additional information.
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self: The instance of the class FPEM_FFM.
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x (List[Tensor]): A list of feature maps of shape (N, C, H, W).
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Returns:
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outs (List[Tensor]): A list of feature maps of shape (N, C, H, W).
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"""
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c2, c3, c4, c5 = x
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# reduce channel
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c2 = self.reduce_conv_c2(c2)
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c3 = self.reduce_conv_c3(c3)
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c4 = self.reduce_conv_c4(c4)
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if ENABLE:
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bn_w = self.reduce_conv_c5[1].weight / torch.sqrt(
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self.reduce_conv_c5[1].running_var + self.reduce_conv_c5[1].eps)
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bn_b = self.reduce_conv_c5[
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1].bias - self.reduce_conv_c5[1].running_mean * bn_w
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bn_w = bn_w.reshape(1, -1, 1, 1).repeat(1, 1, c5.size(2), c5.size(3))
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bn_b = bn_b.reshape(1, -1, 1, 1).repeat(1, 1, c5.size(2), c5.size(3))
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conv_b = self.reduce_conv_c5[0].bias.reshape(1, -1, 1, 1).repeat(
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1, 1, c5.size(2), c5.size(3))
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c5 = FACTOR * (self.reduce_conv_c5[:-1](c5)) - (FACTOR - 1) * (
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bn_w * conv_b + bn_b)
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c5 = self.reduce_conv_c5[-1](c5)
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else:
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c5 = self.reduce_conv_c5(c5)
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# FPEM
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for i, fpem in enumerate(self.fpems):
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c2, c3, c4, c5 = fpem(c2, c3, c4, c5)
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if i == 0:
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c2_ffm = c2
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c3_ffm = c3
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c4_ffm = c4
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c5_ffm = c5
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else:
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c2_ffm += c2
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c3_ffm += c3
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c4_ffm += c4
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c5_ffm += c5
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# FFM
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c5 = F.interpolate(
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c5_ffm,
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c2_ffm.size()[-2:],
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mode='bilinear',
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align_corners=self.align_corners)
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c4 = F.interpolate(
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c4_ffm,
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c2_ffm.size()[-2:],
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mode='bilinear',
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align_corners=self.align_corners)
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c3 = F.interpolate(
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c3_ffm,
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c2_ffm.size()[-2:],
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mode='bilinear',
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align_corners=self.align_corners)
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outs = [c2_ffm, c3, c4, c5]
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return tuple(outs)
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@FUNCTION_REWRITER.register_rewriter(
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func_name='mmdet.models.backbones.resnet.BasicBlock.forward',
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backend=Backend.TENSORRT.value)
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def basic_block__forward__trt(ctx, self, x: torch.Tensor) -> torch.Tensor:
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"""Rewrite `forward` of BasicBlock for tensorrt backend.
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Rewrite this function avoid overflow for tensorrt-fp16 with the checkpoint
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`https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm
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_sbn_600e_icdar2015_20210219-42dbe46a.pth`
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Args:
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ctx (ContextCaller): The context with additional information.
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self: The instance of the class FPEM_FFM.
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x (Tensor): The input tensor of shape (N, C, H, W).
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Returns:
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outs (Tensor): The output tensor of shape (N, C, H, W).
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"""
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if self.conv1.in_channels < CHANNEL_THRESH:
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return ctx.origin_func(self, x)
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identity = x
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out = self.conv1(x)
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out = self.norm1(out)
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out = self.relu(out)
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out = self.conv2(out)
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if torch.abs(self.norm2(out)).max() < 65504:
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out = self.norm2(out)
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out += identity
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out = self.relu(out)
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return out
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else:
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global ENABLE
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ENABLE = True
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# the output of the last bn layer exceeds the range of fp16
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w1 = self.norm2.weight / torch.sqrt(self.norm2.running_var +
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self.norm2.eps)
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bias = self.norm2.bias - self.norm2.running_mean * w1
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w1 = w1.reshape(1, -1, 1, 1).repeat(1, 1, out.size(2), out.size(3))
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bias = bias.reshape(1, -1, 1, 1).repeat(1, 1, out.size(2),
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out.size(3)) + identity
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out = self.relu(w1 * (out / FACTOR) + bias / FACTOR)
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return out
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```
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