运行demo报错 about text_recognition_toolbox HOT 3 OPEN

RuntimeError: Error(s) in loading state_dict for DAN:
Missing key(s) in state_dict: "feature_extractor.conv1.weight", "feature_extractor.bn1.weight", "

DAN设置如下：
TrainReader:
dataloader: dataset,BatchBalancedDataset
select_data: '/'
batch_ratio: '1.0'
total_data_usage_ratio: 1.0
padding: True
augment: False
batch_size: 64
shuffle: True
num_workers: 0
lmdb_sets_dir: train_set###百度网盘下载的训练文件夹

EvalReader:
dataloader: dataset,evaldataloader
select_data: '/'
batch_size: 2
padding: True
shuffle: True
num_workers: 0
lmdb_sets_dir: test_set ###百度网盘下载的测试文件夹

TestReader:
dataloader: dataset,evaldataloader
select_data: '/'
batch_size: 64
padding: True
shuffle: True
num_workers: 0
lmdb_sets_dir:

Global:
algorithm: DAN
use_gpu: True
gpu_num: '0'
device: cuda:0
num_iters: 800000
highest_acc_save_type: False
data_filtering_off: False
resumed_optimizer: False
batch_max_length: 50
print_batch_step: 10
save_model_dir: output/DAN
eval_batch_step: 2000
image_shape: [1, 32, 256]
character_type: ch
loss_type: attn
use_space_char: false
character_dict_path: keys.txt
seed: 1234
pretrain_weights: models/DAN.pth ####百度下载的模型文件
save_inference_dir: results
infer_img: test_pic ###存放测试图片的文件夹

Architecture:
function: networks.DAN,DAN
compress_layer: False
layers: [3, 4, 6, 6, 3]

CAM:
depth: 8
num_channel: 512

Loss:
function: loss,AttnLoss
blank_idx: 0

Optimizer:
function: adam
base_lr: 0.001
momentum: 0.9
weight_decay: 1.0e-4
lr_decay_epoch: 10
max_epoch: 1000

pytorch版本1.3
python3.7

抱歉才看到，等我定位到问题回复你。

from text_recognition_toolbox.

DAN目前还没实现inference的部分，我会尽快更新的，感谢关注。

from text_recognition_toolbox.

RuntimeError: Error(s) in loading state_dict for DAN:
Missing key(s) in state_dict: "feature_extractor.conv1.weight", "feature_extractor.bn1.weight", "

DAN设置如下：
TrainReader:
dataloader: dataset,BatchBalancedDataset
select_data: '/'
batch_ratio: '1.0'
total_data_usage_ratio: 1.0
padding: True
augment: False
batch_size: 64
shuffle: True
num_workers: 0
lmdb_sets_dir: train_set###百度网盘下载的训练文件夹

EvalReader:
dataloader: dataset,evaldataloader
select_data: '/'
batch_size: 2
padding: True
shuffle: True
num_workers: 0
lmdb_sets_dir: test_set ###百度网盘下载的测试文件夹

TestReader:
dataloader: dataset,evaldataloader
select_data: '/'
batch_size: 64
padding: True
shuffle: True
num_workers: 0
lmdb_sets_dir:

Global:
algorithm: DAN
use_gpu: True
gpu_num: '0'
device: cuda:0
num_iters: 800000
highest_acc_save_type: False
data_filtering_off: False
resumed_optimizer: False
batch_max_length: 50
print_batch_step: 10
save_model_dir: output/DAN
eval_batch_step: 2000
image_shape: [1, 32, 256]
character_type: ch
loss_type: attn
use_space_char: false
character_dict_path: keys.txt
seed: 1234
pretrain_weights: models/DAN.pth ####百度下载的模型文件
save_inference_dir: results
infer_img: test_pic ###存放测试图片的文件夹

Architecture:
function: networks.DAN,DAN
compress_layer: False
layers: [3, 4, 6, 6, 3]

CAM:
depth: 8
num_channel: 512

Loss:
function: loss,AttnLoss
blank_idx: 0

Optimizer:
function: adam
base_lr: 0.001
momentum: 0.9
weight_decay: 1.0e-4
lr_decay_epoch: 10
max_epoch: 1000

pytorch版本1.3
python3.7

DAN的预测脚本更新了，可以再试试。

from text_recognition_toolbox.