add .gitignore in the alldata directory

.gitignore

@@ -107,4 +107,4 @@ ENV/
 .vscode/
 .vs/
 .idea/
-trainer/all_data/**
+

trainer/all_data/.gitignore

@@ -0,0 +1 @@
+4digit*/**

trainer/all_data/folder.txt

@@ -0,0 +1 @@
+place dataset folder here

trainer/all_data/generate_digits_random_fs_bg_fg.py

@@ -0,0 +1,86 @@
+
+import cv2
+import numpy as np
+import os
+import random
+from PIL import Image, ImageDraw, ImageFont
+
+X_RAND_VALUE = 2
+Y_RAND_VALUE = 1
+ROTATE_ANGLE = 3
+
+
+BG_COLORS = [
+    (33, 40, 45), (36, 51, 62), (35, 37, 154),
+    (0, 38, 202), (239, 255, 255), (241, 255, 255)
+]
+
+DIGIT_COLORS = [(34, 199, 253), (25, 214, 253)]
+
+def generate_4digit_image():
+    bg_color = random.choice(BG_COLORS)
+    font_size = random.randint(24, 30)
+    font = ImageFont.truetype("arial.ttf", font_size)
+    
+    # 扩大画布尺寸(50x160)提供足够缓冲空间
+    canvas = np.zeros((50, 160, 3), dtype=np.uint8)
+    canvas[:,:] = bg_color
+    pil_img = Image.fromarray(canvas)
+    draw = ImageDraw.Draw(pil_img)
+    
+    digits = []
+    for i in range(4):
+        digit = str(random.randint(0, 9))
+        digits.append(digit)
+        x_offset = random.randint(-X_RAND_VALUE, X_RAND_VALUE)
+        y_offset = random.randint(-Y_RAND_VALUE, Y_RAND_VALUE)
+        digit_color = random.choice(DIGIT_COLORS)
+        # 调整数字绘制位置到画布中心区域
+        draw.text((20+i*32+x_offset, 12+y_offset), digit,
+                 font=font, fill=digit_color)
+    
+    angle = random.uniform(-ROTATE_ANGLE, ROTATE_ANGLE)
+    rotated = pil_img.rotate(angle, expand=True, fillcolor=bg_color)
+    # 安全裁剪区域(从扩大后的画布中心裁剪)
+    rotated = rotated.crop((20, 10, 148, 42))
+    
+    return np.array(rotated), ''.join(digits)
+
+def generate_train_dataset(num_samples=1000):
+    os.makedirs('4digit_train', exist_ok=True)
+    with open('4digit_train/labels.csv', 'w') as f:
+        f.write(f"filename,words\n")
+        for i in range(num_samples):
+            img, label = generate_4digit_image()
+            # print(f"type of label : {type(label)}")
+            label = str(label).zfill(4)
+            img_path = f'4digit_train/{i:04d}.jpg'
+            cv2.imwrite(img_path, img)
+            f.write(f"{i:04d}.jpg,{label}\n")
+
+def generate_valid_dataset(num_samples=200):
+    os.makedirs('4digit_valid', exist_ok=True)
+    with open('4digit_valid/labels.csv', 'w') as f:
+        f.write(f"filename,words\n")
+        for i in range(num_samples):
+            img, label = generate_4digit_image()
+            label = str(label).zfill(4)
+
+            img_path = f'4digit_valid/{i:04d}.jpg'
+            cv2.imwrite(img_path, img)
+            f.write(f"{i:04d}.jpg,{label}\n")            
+
+def generate_eval_dataset(num_samples=200):
+    os.makedirs('4digit_eval', exist_ok=True)
+    with open('4digit_eval/labels.csv', 'w') as f:
+        f.write(f"filename,words\n")
+        for i in range(num_samples):
+            img, label = generate_4digit_image()
+            label = str(label).zfill(4)
+            img_path = f'4digit_eval/{i:04d}.jpg'
+            cv2.imwrite(img_path, img)
+            f.write(f"{i:04d}.jpg,{label}\n")  
+if __name__ == "__main__":
+    generate_train_dataset()
+    generate_eval_dataset()
+    generate_valid_dataset()

trainer/all_data/split_dataset.py

@@ -0,0 +1,47 @@
+
+import os
+import shutil
+import csv
+
+def split_dataset(labels_path, img_source_dir, train_dir='train', valid_dir='valid'):
+    # 创建目标文件夹
+    os.makedirs(train_dir, exist_ok=True)
+    os.makedirs(valid_dir, exist_ok=True)
+    
+    # 初始化CSV写入器
+    train_csv = open(os.path.join(train_dir, 'labels.csv'), 'w', newline='')
+    valid_csv = open(os.path.join(valid_dir, 'labels.csv'), 'w', newline='')
+    train_writer = csv.writer(train_csv)
+    valid_writer = csv.writer(valid_csv)
+    
+    with open(labels_path, 'r') as f:
+        lines = f.readlines()
+    
+    for i, line in enumerate(lines):
+        parts = line.strip().split(',')
+        img_name = parts[0].strip()
+
+
+        label = parts[1] if len(parts) > 1 else ''
+        src_path = os.path.join(img_source_dir, img_name)
+        print(f"处理图片: {img_name}, 标签: {label}")
+        if i < 700:  # 训练集
+            dst_path = os.path.join(train_dir, img_name)
+            train_writer.writerow([img_name, label])
+        else:        # 验证集
+            dst_path = os.path.join(valid_dir, img_name)
+            valid_writer.writerow([img_name, label])
+        
+        if os.path.exists(src_path):
+            shutil.copy2(src_path, dst_path)
+        else:
+            print(f"警告：源图片不存在 {src_path}")
+    
+    train_csv.close()
+    valid_csv.close()
+
+# 使用示例
+split_dataset(
+    labels_path='en_sample/labels.csv',
+    img_source_dir='en_sample'
+)

trainer/config_files/4digit_config.yaml

@@ -8,8 +8,8 @@ manualSeed: 1111
 workers: 6
 batch_size: 32 #32
 num_iter: 3000
-valInterval: 5
+valInterval: 10
-saved_model: '' #'saved_models/en_filtered/iter_300000.pth'
+saved_model: 'saved_models/4digit/best_accuracy.pth'
 FT: False
 optim: False # default is Adadelta
 lr: 1.

trainer/export_onnx.py

@@ -0,0 +1,87 @@
+
+import torch
+import argparse
+from model import Model
+import os
+import torch.backends.cudnn as cudnn
+import yaml
+from utils import AttrDict
+import pandas as pd
+from utils import CTCLabelConverter, AttnLabelConverter, Averager
+
+
+cudnn.benchmark = True
+cudnn.deterministic = False
+
+def get_config(file_path):
+    with open(file_path, 'r', encoding="utf8") as stream:
+        opt = yaml.safe_load(stream)
+    opt = AttrDict(opt)
+
+    if opt.lang_char == 'None' and opt.symbol=='None':
+        opt.character = opt.number
+    elif opt.lang_char == 'None':
+        characters = ''
+        for data in opt['select_data'].split('-'):
+            csv_path = os.path.join(opt['train_data'], data, 'labels.csv')
+            df = pd.read_csv(csv_path, sep='^([^,]+),', engine='python',dtype={'words': str}, usecols=['filename', 'words'], keep_default_na=False)
+            all_char = ''.join(df['words'])
+            characters += ''.join(set(all_char))
+        characters = sorted(set(characters))
+        opt.character= ''.join(characters)
+    else:
+        opt.character = opt.number + opt.symbol + opt.lang_char
+    os.makedirs(f'./saved_models/{opt.experiment_name}', exist_ok=True)
+    if 'CTC' in opt.Prediction:
+        converter = CTCLabelConverter(opt.character)
+    else:
+        converter = AttnLabelConverter(opt.character)
+    opt.num_class = len(converter.character)
+    print(f"converter.character : {converter.character}")
+    print(f"字符集: {opt.character}")
+    print(f"字符集长度: {opt.num_class}")
+    os.makedirs(f'./saved_models/{opt.experiment_name}', exist_ok=True)
+    return opt
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='PyTorch模型转ONNX格式工具')
+    parser.add_argument('--input', type=str, default='digit_cnn.pth',
+                      help='输入PyTorch模型路径 (默认: digit_cnn.pth)')
+    parser.add_argument('--output', type=str, default='digit_cnn.onnx',
+                      help='输出ONNX模型路径 (默认: digit_cnn.onnx)')
+    parser.add_argument('--opset', type=int, default=11,
+                      help='ONNX算子集版本 (默认: 11)')
+    return parser.parse_args()
+
+def convert_to_onnx(input_path, output_path, opset_version,opt):
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    if opt.rgb:
+        opt.input_channel = 3
+    model = Model(opt).to(device)
+    torch.load(input_path)
+    model.eval()
+
+    dummy_input = torch.randn(1, 3, 32, 128).to(device)
+    
+    torch.onnx.export(
+        model,
+        dummy_input,
+        output_path,
+        export_params=True,
+        opset_version=opset_version,
+        do_constant_folding=True,
+        input_names=['input'],
+        output_names=['output'],
+        dynamic_axes={
+            'input': {0: 'batch_size'},
+            'output': {0: 'batch_size'}
+        }
+    )
+    print(f"模型已成功转换为 {output_path} (opset {opset_version})")
+
+if __name__ == '__main__':
+    args = parse_args()
+    opt = get_config("config_files/4digit_config.yaml")
+
+    convert_to_onnx(args.input, args.output, args.opset,opt)

trainer/saved_models/4digit/log_dataset.txt

@@ -128,3 +128,138 @@ Total_batch_size: 32 = 32
 dataset_root:    all_data/4digit_valid	 dataset: /
 sub-directory:	/.	 num samples: 200
 --------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+dataset_root: all_data
+opt.select_data: ['4digit_train']
+opt.batch_ratio: ['1']
+--------------------------------------------------------------------------------
+dataset_root:    all_data	 dataset: 4digit_train
+sub-directory:	/4digit_train	 num samples: 1000
+num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
+num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
+--------------------------------------------------------------------------------
+Total_batch_size: 32 = 32
+--------------------------------------------------------------------------------
+dataset_root:    all_data/4digit_valid	 dataset: /
+sub-directory:	/.	 num samples: 200
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+dataset_root: all_data
+opt.select_data: ['4digit_train']
+opt.batch_ratio: ['1']
+--------------------------------------------------------------------------------
+dataset_root:    all_data	 dataset: 4digit_train
+sub-directory:	/4digit_train	 num samples: 1000
+num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
+num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
+--------------------------------------------------------------------------------
+Total_batch_size: 32 = 32
+--------------------------------------------------------------------------------
+dataset_root:    all_data/4digit_valid	 dataset: /
+sub-directory:	/.	 num samples: 200
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+dataset_root: all_data
+opt.select_data: ['4digit_train']
+opt.batch_ratio: ['1']
+--------------------------------------------------------------------------------
+dataset_root:    all_data	 dataset: 4digit_train
+sub-directory:	/4digit_train	 num samples: 1000
+num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
+num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
+--------------------------------------------------------------------------------
+Total_batch_size: 32 = 32
+--------------------------------------------------------------------------------
+dataset_root:    all_data/4digit_valid	 dataset: /
+sub-directory:	/.	 num samples: 200
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+dataset_root: all_data
+opt.select_data: ['4digit_train']
+opt.batch_ratio: ['1']
+--------------------------------------------------------------------------------
+dataset_root:    all_data	 dataset: 4digit_train
+sub-directory:	/4digit_train	 num samples: 1000
+num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
+num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
+--------------------------------------------------------------------------------
+Total_batch_size: 32 = 32
+--------------------------------------------------------------------------------
+dataset_root:    all_data/4digit_valid	 dataset: /
+sub-directory:	/.	 num samples: 200
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+dataset_root: all_data
+opt.select_data: ['4digit_train']
+opt.batch_ratio: ['1']
+--------------------------------------------------------------------------------
+dataset_root:    all_data	 dataset: 4digit_train
+sub-directory:	/4digit_train	 num samples: 1000
+num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
+num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
+--------------------------------------------------------------------------------
+Total_batch_size: 32 = 32
+--------------------------------------------------------------------------------
+dataset_root:    all_data/4digit_valid	 dataset: /
+sub-directory:	/.	 num samples: 200
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+dataset_root: all_data
+opt.select_data: ['4digit_train']
+opt.batch_ratio: ['1']
+--------------------------------------------------------------------------------
+dataset_root:    all_data	 dataset: 4digit_train
+sub-directory:	/4digit_train	 num samples: 1000
+num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
+num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
+--------------------------------------------------------------------------------
+Total_batch_size: 32 = 32
+--------------------------------------------------------------------------------
+dataset_root:    all_data/4digit_valid	 dataset: /
+sub-directory:	/.	 num samples: 200
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+dataset_root: all_data
+opt.select_data: ['4digit_train']
+opt.batch_ratio: ['1']
+--------------------------------------------------------------------------------
+dataset_root:    all_data	 dataset: 4digit_train
+sub-directory:	/4digit_train	 num samples: 1000
+num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
+num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
+--------------------------------------------------------------------------------
+Total_batch_size: 32 = 32
+--------------------------------------------------------------------------------
+dataset_root:    all_data/4digit_valid	 dataset: /
+sub-directory:	/.	 num samples: 200
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+dataset_root: all_data
+opt.select_data: ['4digit_train']
+opt.batch_ratio: ['1']
+--------------------------------------------------------------------------------
+dataset_root:    all_data	 dataset: 4digit_train
+sub-directory:	/4digit_train	 num samples: 1000
+num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
+num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
+--------------------------------------------------------------------------------
+Total_batch_size: 32 = 32
+--------------------------------------------------------------------------------
+dataset_root:    all_data/4digit_valid	 dataset: /
+sub-directory:	/.	 num samples: 200
+--------------------------------------------------------------------------------
+--------------------------------------------------------------------------------
+dataset_root: all_data
+opt.select_data: ['4digit_train']
+opt.batch_ratio: ['1']
+--------------------------------------------------------------------------------
+dataset_root:    all_data	 dataset: 4digit_train
+sub-directory:	/4digit_train	 num samples: 1000
+num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
+num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
+--------------------------------------------------------------------------------
+Total_batch_size: 32 = 32
+--------------------------------------------------------------------------------
+dataset_root:    all_data/4digit_valid	 dataset: /
+sub-directory:	/.	 num samples: 200
+--------------------------------------------------------------------------------

trainer/saved_models/4digit/opt.txt

@@ -90,3 +90,417 @@ freeze_SequenceModeling: False
 character: 0123456789
 num_class: 11
 ---------------------------------------
+------------ Options -------------
+number: 0123456789
+experiment_name: 4digit
+symbol: None
+lang_char: None
+train_data: all_data
+valid_data: all_data/4digit_valid
+manualSeed: 1111
+workers: 6
+batch_size: 32
+num_iter: 3000
+valInterval: 5
+saved_model: 
+FT: False
+optim: False
+lr: 1.0
+beta1: 0.9
+rho: 0.95
+eps: 1e-08
+grad_clip: 5
+select_data: ['4digit_train']
+batch_ratio: ['1']
+total_data_usage_ratio: 1.0
+batch_max_length: 34
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: 0.0
+sensitive: True
+PAD: True
+data_filtering_off: False
+Transformation: TPS
+FeatureExtraction: ResNet
+SequenceModeling: BiLSTM
+Prediction: CTC
+num_fiducial: 20
+input_channel: 3
+output_channel: 256
+hidden_size: 256
+decode: greedy
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False
+character: 0123456789
+num_class: 11
+---------------------------------------
+------------ Options -------------
+number: 0123456789
+experiment_name: 4digit
+symbol: None
+lang_char: None
+train_data: all_data
+valid_data: all_data/4digit_valid
+manualSeed: 1111
+workers: 6
+batch_size: 32
+num_iter: 3000
+valInterval: 5
+saved_model: 
+FT: False
+optim: False
+lr: 1.0
+beta1: 0.9
+rho: 0.95
+eps: 1e-08
+grad_clip: 5
+select_data: ['4digit_train']
+batch_ratio: ['1']
+total_data_usage_ratio: 1.0
+batch_max_length: 34
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: 0.0
+sensitive: True
+PAD: True
+data_filtering_off: False
+Transformation: TPS
+FeatureExtraction: ResNet
+SequenceModeling: BiLSTM
+Prediction: CTC
+num_fiducial: 20
+input_channel: 3
+output_channel: 256
+hidden_size: 256
+decode: greedy
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False
+character: 0123456789
+num_class: 11
+---------------------------------------
+------------ Options -------------
+number: 0123456789
+experiment_name: 4digit
+symbol: None
+lang_char: None
+train_data: all_data
+valid_data: all_data/4digit_valid
+manualSeed: 1111
+workers: 6
+batch_size: 32
+num_iter: 3000
+valInterval: 5
+saved_model: 
+FT: False
+optim: False
+lr: 1.0
+beta1: 0.9
+rho: 0.95
+eps: 1e-08
+grad_clip: 5
+select_data: ['4digit_train']
+batch_ratio: ['1']
+total_data_usage_ratio: 1.0
+batch_max_length: 34
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: 0.0
+sensitive: True
+PAD: True
+data_filtering_off: False
+Transformation: TPS
+FeatureExtraction: ResNet
+SequenceModeling: BiLSTM
+Prediction: CTC
+num_fiducial: 20
+input_channel: 3
+output_channel: 256
+hidden_size: 256
+decode: greedy
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False
+character: 0123456789
+num_class: 11
+---------------------------------------
+------------ Options -------------
+number: 0123456789
+experiment_name: 4digit
+symbol: None
+lang_char: None
+train_data: all_data
+valid_data: all_data/4digit_valid
+manualSeed: 1111
+workers: 6
+batch_size: 32
+num_iter: 3000
+valInterval: 5
+saved_model: 
+FT: False
+optim: False
+lr: 1.0
+beta1: 0.9
+rho: 0.95
+eps: 1e-08
+grad_clip: 5
+select_data: ['4digit_train']
+batch_ratio: ['1']
+total_data_usage_ratio: 1.0
+batch_max_length: 34
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: 0.0
+sensitive: True
+PAD: True
+data_filtering_off: False
+Transformation: TPS
+FeatureExtraction: ResNet
+SequenceModeling: BiLSTM
+Prediction: CTC
+num_fiducial: 20
+input_channel: 3
+output_channel: 256
+hidden_size: 256
+decode: greedy
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False
+character: 0123456789
+num_class: 11
+---------------------------------------
+------------ Options -------------
+number: 0123456789
+experiment_name: 4digit
+symbol: None
+lang_char: None
+train_data: all_data
+valid_data: all_data/4digit_valid
+manualSeed: 1111
+workers: 6
+batch_size: 32
+num_iter: 3000
+valInterval: 5
+saved_model: 
+FT: False
+optim: False
+lr: 1.0
+beta1: 0.9
+rho: 0.95
+eps: 1e-08
+grad_clip: 5
+select_data: ['4digit_train']
+batch_ratio: ['1']
+total_data_usage_ratio: 1.0
+batch_max_length: 34
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: 0.0
+sensitive: True
+PAD: True
+data_filtering_off: False
+Transformation: TPS
+FeatureExtraction: ResNet
+SequenceModeling: BiLSTM
+Prediction: CTC
+num_fiducial: 20
+input_channel: 3
+output_channel: 256
+hidden_size: 256
+decode: greedy
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False
+character: 0123456789
+num_class: 11
+---------------------------------------
+------------ Options -------------
+number: 0123456789
+experiment_name: 4digit
+symbol: None
+lang_char: None
+train_data: all_data
+valid_data: all_data/4digit_valid
+manualSeed: 1111
+workers: 6
+batch_size: 32
+num_iter: 3000
+valInterval: 5
+saved_model: saved_models/4digit/best_accuracy.pth
+FT: False
+optim: False
+lr: 1.0
+beta1: 0.9
+rho: 0.95
+eps: 1e-08
+grad_clip: 5
+select_data: ['4digit_train']
+batch_ratio: ['1']
+total_data_usage_ratio: 1.0
+batch_max_length: 34
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: 0.0
+sensitive: True
+PAD: True
+data_filtering_off: False
+Transformation: TPS
+FeatureExtraction: ResNet
+SequenceModeling: BiLSTM
+Prediction: CTC
+num_fiducial: 20
+input_channel: 3
+output_channel: 256
+hidden_size: 256
+decode: greedy
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False
+character: 0123456789
+num_class: 11
+---------------------------------------
+------------ Options -------------
+number: 0123456789
+experiment_name: 4digit
+symbol: None
+lang_char: None
+train_data: all_data
+valid_data: all_data/4digit_valid
+manualSeed: 1111
+workers: 6
+batch_size: 32
+num_iter: 3000
+valInterval: 5
+saved_model: saved_models/4digit/best_accuracy.pth
+FT: False
+optim: False
+lr: 1.0
+beta1: 0.9
+rho: 0.95
+eps: 1e-08
+grad_clip: 5
+select_data: ['4digit_train']
+batch_ratio: ['1']
+total_data_usage_ratio: 1.0
+batch_max_length: 34
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: 0.0
+sensitive: True
+PAD: True
+data_filtering_off: False
+Transformation: TPS
+FeatureExtraction: ResNet
+SequenceModeling: BiLSTM
+Prediction: CTC
+num_fiducial: 20
+input_channel: 3
+output_channel: 256
+hidden_size: 256
+decode: greedy
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False
+character: 0123456789
+num_class: 11
+---------------------------------------
+------------ Options -------------
+number: 0123456789
+experiment_name: 4digit
+symbol: None
+lang_char: None
+train_data: all_data
+valid_data: all_data/4digit_valid
+manualSeed: 1111
+workers: 6
+batch_size: 32
+num_iter: 3000
+valInterval: 5
+saved_model: saved_models/4digit/best_accuracy.pth
+FT: False
+optim: False
+lr: 1.0
+beta1: 0.9
+rho: 0.95
+eps: 1e-08
+grad_clip: 5
+select_data: ['4digit_train']
+batch_ratio: ['1']
+total_data_usage_ratio: 1.0
+batch_max_length: 34
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: 0.0
+sensitive: True
+PAD: True
+data_filtering_off: False
+Transformation: TPS
+FeatureExtraction: ResNet
+SequenceModeling: BiLSTM
+Prediction: CTC
+num_fiducial: 20
+input_channel: 3
+output_channel: 256
+hidden_size: 256
+decode: greedy
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False
+character: 0123456789
+num_class: 11
+---------------------------------------
+------------ Options -------------
+number: 0123456789
+experiment_name: 4digit
+symbol: None
+lang_char: None
+train_data: all_data
+valid_data: all_data/4digit_valid
+manualSeed: 1111
+workers: 6
+batch_size: 32
+num_iter: 3000
+valInterval: 10
+saved_model: saved_models/4digit/best_accuracy.pth
+FT: False
+optim: False
+lr: 1.0
+beta1: 0.9
+rho: 0.95
+eps: 1e-08
+grad_clip: 5
+select_data: ['4digit_train']
+batch_ratio: ['1']
+total_data_usage_ratio: 1.0
+batch_max_length: 34
+imgH: 32
+imgW: 128
+rgb: True
+contrast_adjust: 0.0
+sensitive: True
+PAD: True
+data_filtering_off: False
+Transformation: TPS
+FeatureExtraction: ResNet
+SequenceModeling: BiLSTM
+Prediction: CTC
+num_fiducial: 20
+input_channel: 3
+output_channel: 256
+hidden_size: 256
+decode: greedy
+new_prediction: False
+freeze_FeatureFxtraction: False
+freeze_SequenceModeling: False
+character: 0123456789
+num_class: 11
+---------------------------------------

trainer/test.py

@@ -29,12 +29,12 @@ def validation(model, criterion, evaluation_loader, converter, opt, device):
         # For max length prediction
         length_for_pred = torch.IntTensor([opt.batch_max_length] * batch_size).to(device)
         text_for_pred = torch.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0).to(device)
-
         text_for_loss, length_for_loss = converter.encode(labels, batch_max_length=opt.batch_max_length)
         
         start_time = time.time()
         if 'CTC' in opt.Prediction:
             preds = model(image, text_for_pred)
+            # print(f"preds shape : {preds.shape}")
             forward_time = time.time() - start_time
 
             # Calculate evaluation loss for CTC decoder.

trainer/train.py

@@ -213,6 +213,7 @@ def train(opt, show_number = 2, amp=False):
                 preds = model(image, text).log_softmax(2)
                 preds_size = torch.IntTensor([preds.size(1)] * batch_size)
                 preds = preds.permute(1, 0, 2)
+                print(f"preds shape : {preds.shape}")
                 torch.backends.cudnn.enabled = False
                 cost = criterion(preds, text.to(device), preds_size.to(device), length.to(device))
                 torch.backends.cudnn.enabled = True
@@ -265,6 +266,9 @@ def train(opt, show_number = 2, amp=False):
                 #show_number = min(show_number, len(labels))
                 
                 start = random.randint(0,len(labels) - show_number )    
+                print(f"start index for showing results: {start}")
+                print(f"labels length: {len(labels)}")
+                print(f"labels : {labels}")
                 for gt, pred, confidence in zip(labels[start:start+show_number], preds[start:start+show_number], confidence_score[start:start+show_number]):
                     if 'Attn' in opt.Prediction:
                         gt = gt[:gt.find('[s]')]