add .gitignore to saved_models

trainer/all_data/generate_digits_random_fs_bg_fg.py

@@ -1,86 +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()
+
+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/export_onnx.py

@@ -1,87 +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)
+
+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/.gitignore

@@ -0,0 +1 @@
+./**