PaddleOCR MCP Server

# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import, division, print_function import os import paddle import paddle.nn as nn from paddle import ParamAttr from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear from paddle.regularizer import L2Decay from paddle.nn.initializer import KaimingNormal from paddle.utils.download import get_path_from_url MODEL_URLS = { "PPLCNet_x0.25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams", "PPLCNet_x0.35": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams", "PPLCNet_x0.5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams", "PPLCNet_x0.75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams", "PPLCNet_x1.0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams", "PPLCNet_x1.5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams", "PPLCNet_x2.0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams", "PPLCNet_x2.5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams", } MODEL_STAGES_PATTERN = { "PPLCNet": ["blocks2", "blocks3", "blocks4", "blocks5", "blocks6"] } __all__ = list(MODEL_URLS.keys()) # Each element(list) represents a depthwise block, which is composed of k, in_c, out_c, s, use_se. # k: kernel_size # in_c: input channel number in depthwise block # out_c: output channel number in depthwise block # s: stride in depthwise block # use_se: whether to use SE block NET_CONFIG = { "blocks2": # k, in_c, out_c, s, use_se [[3, 16, 32, 1, False]], "blocks3": [[3, 32, 64, 2, False], [3, 64, 64, 1, False]], "blocks4": [[3, 64, 128, 2, False], [3, 128, 128, 1, False]], "blocks5": [ [3, 128, 256, 2, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False], ], "blocks6": [[5, 256, 512, 2, True], [5, 512, 512, 1, True]], } def make_divisible(v, divisor=8, min_value=None): if min_value is None: min_value = divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) if new_v < 0.9 * v: new_v += divisor return new_v class ConvBNLayer(nn.Layer): def __init__(self, num_channels, filter_size, num_filters, stride, num_groups=1): super().__init__() self.conv = Conv2D( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=num_groups, weight_attr=ParamAttr(initializer=KaimingNormal()), bias_attr=False, ) self.bn = BatchNorm( num_filters, param_attr=ParamAttr(regularizer=L2Decay(0.0)), bias_attr=ParamAttr(regularizer=L2Decay(0.0)), ) self.hardswish = nn.Hardswish() def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.hardswish(x) return x class DepthwiseSeparable(nn.Layer): def __init__(self, num_channels, num_filters, stride, dw_size=3, use_se=False): super().__init__() self.use_se = use_se self.dw_conv = ConvBNLayer( num_channels=num_channels, num_filters=num_channels, filter_size=dw_size, stride=stride, num_groups=num_channels, ) if use_se: self.se = SEModule(num_channels) self.pw_conv = ConvBNLayer( num_channels=num_channels, filter_size=1, num_filters=num_filters, stride=1 ) def forward(self, x): x = self.dw_conv(x) if self.use_se: x = self.se(x) x = self.pw_conv(x) return x class SEModule(nn.Layer): def __init__(self, channel, reduction=4): super().__init__() self.avg_pool = AdaptiveAvgPool2D(1) self.conv1 = Conv2D( in_channels=channel, out_channels=channel // reduction, kernel_size=1, stride=1, padding=0, ) self.relu = nn.ReLU() self.conv2 = Conv2D( in_channels=channel // reduction, out_channels=channel, kernel_size=1, stride=1, padding=0, ) self.hardsigmoid = nn.Hardsigmoid() def forward(self, x): identity = x x = self.avg_pool(x) x = self.conv1(x) x = self.relu(x) x = self.conv2(x) x = self.hardsigmoid(x) x = paddle.multiply(x=identity, y=x) return x class PPLCNet(nn.Layer): def __init__(self, in_channels=3, scale=1.0, pretrained=False, use_ssld=False): super().__init__() self.out_channels = [ int(NET_CONFIG["blocks3"][-1][2] * scale), int(NET_CONFIG["blocks4"][-1][2] * scale), int(NET_CONFIG["blocks5"][-1][2] * scale), int(NET_CONFIG["blocks6"][-1][2] * scale), ] self.scale = scale self.conv1 = ConvBNLayer( num_channels=in_channels, filter_size=3, num_filters=make_divisible(16 * scale), stride=2, ) self.blocks2 = nn.Sequential( *[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se, ) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"]) ] ) self.blocks3 = nn.Sequential( *[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se, ) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"]) ] ) self.blocks4 = nn.Sequential( *[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se, ) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"]) ] ) self.blocks5 = nn.Sequential( *[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se, ) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"]) ] ) self.blocks6 = nn.Sequential( *[ DepthwiseSeparable( num_channels=make_divisible(in_c * scale), num_filters=make_divisible(out_c * scale), dw_size=k, stride=s, use_se=se, ) for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks6"]) ] ) if pretrained: self._load_pretrained( MODEL_URLS["PPLCNet_x{}".format(scale)], use_ssld=use_ssld ) def forward(self, x): outs = [] x = self.conv1(x) x = self.blocks2(x) x = self.blocks3(x) outs.append(x) x = self.blocks4(x) outs.append(x) x = self.blocks5(x) outs.append(x) x = self.blocks6(x) outs.append(x) return outs def _load_pretrained(self, pretrained_url, use_ssld=False): if use_ssld: pretrained_url = pretrained_url.replace("_pretrained", "_ssld_pretrained") print(pretrained_url) local_weight_path = get_path_from_url( pretrained_url, os.path.expanduser("~/.paddleclas/weights") ) param_state_dict = paddle.load(local_weight_path) self.set_dict(param_state_dict) return