你好，
我使用trainer.py进行模型训练，当我使用best_accuracy.pth作为读取函数时，结果不如正常使用easyocr好。我认为推理模型中存在问题，请问你能帮我解决吗？
我的example.py如下：

import torch
import torch.nn as nn
import torch.nn.init as init
import torchvision
from torchvision import models
from collections import namedtuple
from packaging import version

def init_weights(modules):
    for m in modules:
        if isinstance(m, nn.Conv2d):
            init.xavier_uniform_(m.weight.data)
        if m.bias is not None:
            m.bias.data.zero_()
    elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()
    elif isinstance(m, nn.Linear):
        m.weight.data.normal_(0, 0.01)
        m.bias.data.zero_()

class vgg16_bn(torch.nn.Module):
    def __init__(self, pretrained=True, freeze=True):
        super(vgg16_bn, self).__init__()
        if version.parse(torchvision.__version__) >= version.parse('0.13'):
            vgg_pretrained_features = models.vgg16_bn(
                weights=models.VGG16_BN_Weights.DEFAULT if pretrained else None
            ).features
        else: #torchvision.__version__ < 0.13
            models.vgg.model_urls['vgg16_bn'] = models.vgg.model_urls['vgg16_bn'].replace('https://', 'http://')
            vgg_pretrained_features = models.vgg16_bn(pretrained=pretrained).features

class BidirectionalLSTM(nn.Module):
    

self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
self.slice5 = torch.nn.Sequential()
for x in range(12): # conv2_2
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(12, 19): # conv3_3
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(19, 29): # conv4_3
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(29, 39): # conv5_3
self.slice4.add_module(str(x), vgg_pretrained_features[x])

# fc6, fc7 without atrous conv
self.slice5 = torch.nn.Sequential(
        nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
        nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6),
        nn.Conv2d(1024, 1024, kernel_size=1)
)

if not pretrained:
    init_weights(self.slice1.modules())
    init_weights(self.slice2.modules())
    init_weights(self.slice3.modules())
    init_weights(self.slice4.modules())

init_weights(self.slice5.modules())        # no pretrained model for fc6 and fc7

if freeze:
    for param in self.slice1.parameters():      # only first conv
        param.requires_grad= False

def forward(self, X):
h = self.slice1(X)
h_relu2_2 = h
h = self.slice2(h)
h_relu3_2 = h
h = self.slice3(h)
h_relu4_3 = h
h = self.slice4(h)
h_relu5_3 = h
h = self.slice5(h)
h_fc7 = h
vgg_outputs = namedtuple("VggOutputs", ['fc7', 'relu5_3', 'relu4_3', 'relu3_2', 'relu2_2'])
out = vgg_outputs(h_fc7, h_relu5_3, h_relu4_3, h_relu3_2, h_relu2_2)
return out

class VGG_FeatureExtractor(nn.Module):
    

def init(self, input_channel, output_channel=512):
super(VGG_FeatureExtractor, self).init()
self.output_channel = [int(output_channel / 8), int(output_channel / 4),
int(output_channel / 2), output_channel]
self.ConvNet = nn.Sequential(
nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True),
nn.MaxPool2d(2, 2),
nn.Conv2d(self.output_channel[0], self.output_channel[1], 3, 1, 1), nn.ReLU(True),
nn.MaxPool2d(2, 2),
nn.Conv2d(self.output_channel[1], self.output_channel[2], 3, 1, 1), nn.ReLU(True),
nn.Conv2d(self.output_channel[2], self.output_channel[2], 3, 1, 1), nn.ReLU(True),
nn.MaxPool2d((2, 1), (2, 1)),
nn.Conv2d(self.output_channel[2], self.output_channel[3], 3, 1, 1, bias=False),
nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True),
nn.Conv2d(self.output_channel[3], self.output_channel[3], 3, 1, 1, bias=False),
nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True),
nn.MaxPool2d((2, 1), (2, 1)),
nn.Conv2d(self.output_channel[3], self.output_channel[3], 2, 1, 0), nn.ReLU(True))

def forward(self, input):
return self.ConvNet(input)

class ResNet_FeatureExtractor(nn.Module):
    """ FeatureExtractor of FAN ( [http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf](http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf) ) """
    

def init(self, input_channel, output_channel=512):
super(ResNet_FeatureExtractor, self).init()
self.ConvNet = ResNet(input_channel, output_channel, BasicBlock, [1, 2, 5, 3])

def forward(self, input):
return self.ConvNet(input)

class BasicBlock(nn.Module):
    expansion = 1
    

def init(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).init()
self.conv1 = self._conv3x3(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = self._conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride

def _conv3x3(self, in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)

def forward(self, x):
residual = x

out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)

out = self.conv2(out)
out = self.bn2(out)

if self.downsample is not None:
    residual = self.downsample(x)
out += residual
out = self.relu(out)

return out

class ResNet(nn.Module):
    

def init(self, input_channel, output_channel, block, layers):
super(ResNet, self).init()

self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel]

self.inplanes = int(output_channel / 8)
self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 16),
                         kernel_size=3, stride=1, padding=1, bias=False)
self.bn0_1 = nn.BatchNorm2d(int(output_channel / 16))
self.conv0_2 = nn.Conv2d(int(output_channel / 16), self.inplanes,
                         kernel_size=3, stride=1, padding=1, bias=False)
self.bn0_2 = nn.BatchNorm2d(self.inplanes)
self.relu = nn.ReLU(inplace=True)

self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0])
self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[
                       0], kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.output_channel_block[0])

self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1)
self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[
                       1], kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(self.output_channel_block[1])

self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1))
self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1)
self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[
                       2], kernel_size=3, stride=1, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.output_channel_block[2])

self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1)
self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
                         3], kernel_size=2, stride=(2, 1), padding=(0, 1), bias=False)
self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3])
self.conv4_2 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
                         3], kernel_size=2, stride=1, padding=0, bias=False)
self.bn4_2 = nn.BatchNorm2d(self.output_channel_block[3])

def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)

layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
    layers.append(block(self.inplanes, planes))

return nn.Sequential(*layers)

def forward(self, x):
x = self.conv0_1(x)
x = self.bn0_1(x)
x = self.relu(x)
x = self.conv0_2(x)
x = self.bn0_2(x)
x = self.relu(x)

x = self.maxpool1(x)
x = self.layer1(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)

x = self.maxpool2(x)
x = self.layer2(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)

x = self.maxpool3(x)
x = self.layer3(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu(x)

x = self.layer4(x)
x = self.conv4_1(x)
x = self.bn4_1(x)
x = self.relu(x)
x = self.conv4_2(x)
x = self.bn4_2(x)
x = self.relu(x)

return x

class Model(nn.Module):
    

def init(self, input_channel, output_channel, hidden_size, num_class):
super(Model, self).init()
""" FeatureExtraction """
self.FeatureExtraction = ResNet_FeatureExtractor(input_channel, output_channel)
self.FeatureExtraction_output = output_channel # int(imgH/16-1) * 512
self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None, 1)) # Transform final (imgH/16-1) -> 1

""" Sequence modeling"""
self.SequenceModeling = nn.Sequential(
    BidirectionalLSTM(self.FeatureExtraction_output, hidden_size, hidden_size),
    BidirectionalLSTM(hidden_size, hidden_size, hidden_size))
self.SequenceModeling_output = hidden_size

""" Prediction """
self.Prediction = nn.Linear(self.SequenceModeling_output, num_class)

def forward(self, input, text):
""" Feature extraction stage """
visual_feature = self.FeatureExtraction(input)
visual_feature = self.AdaptiveAvgPool(visual_feature.permute(0, 3, 1, 2)) # [b, c, h, w] -> [b, w, c, h]
visual_feature = visual_feature.squeeze(3)

""" Sequence modeling stage """
contextual_feature = self.SequenceModeling(visual_feature)

""" Prediction stage """
prediction = self.Prediction(contextual_feature.contiguous())

return prediction