From fe0ddba2fdeb0ca6078ad1e13a4d32b4d91a4d5a Mon Sep 17 00:00:00 2001
From: yizt <csuyzt@163.com>
Date: Tue, 23 Jun 2020 10:12:11 +0800
Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E8=AF=84=E4=BC=B0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 eval.py | 76 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 eval.py

diff --git a/eval.py b/eval.py
new file mode 100644
index 0000000..8bf5f7c
--- /dev/null
+++ b/eval.py
@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""
+ @File    : eval.py
+ @Time    : 2020/6/22 下午5:05
+ @Author  : yizuotian
+ @Description    :  使用生成数据评估
+"""
+
+import argparse
+import itertools
+import sys
+
+import numpy as np
+import torch
+
+import crnn
+from config import cfg
+from train import Generator
+
+
+def inference_single_image(net, image, device=None):
+    image = np.expand_dims(image, axis=0)  # 扩展batch维
+    image = torch.FloatTensor(image)
+    if device:
+        image = image.to(device)
+    predict = net(image)[0].cpu().detach().numpy()  # [W,num_classes]
+    label = np.argmax(predict[:], axis=1)
+    label = [k for k, g in itertools.groupby(list(label))]
+    label = np.array(label)
+    return label[label > 0]  # 去除空格
+
+
+def main(args):
+    alpha = cfg.word.get_all_words()
+    if args.direction == 'horizontal':
+        net = crnn.CRNN(num_classes=len(alpha))
+    else:
+        net = crnn.CRNNV(num_classes=len(alpha))
+
+    device = torch.device(
+        'cuda:{}'.format(args.local_rank) if args.device == 'cuda' and torch.cuda.is_available() else 'cpu')
+
+    net.load_state_dict(torch.load(args.weight_path, map_location='cpu')['model'])
+    net.to(device)
+    net.eval()
+    # load image
+    data_set = Generator(cfg.word.get_all_words(), args.direction)
+
+    acc_list = []
+    for i in range(args.eval_num):
+        image, target, input_len, target_len = data_set[i]
+        predict_text = inference_single_image(net, image, device)
+        gt = target[:target_len]
+        # print("{} {}".format(gt, predict_text))
+        acc_list.append(len(gt) == len(predict_text) and np.allclose(gt, predict_text))
+
+    # 精度计算
+    acc = np.array(acc_list).mean()
+    print('acc:{:.3f}'.format(acc))
+
+
+if __name__ == '__main__':
+    """
+    Usage:
+    export KMP_DUPLICATE_LIB_OK=TRUE
+    python eval.py --weight-path /path/to/chk.pth --direction horizontal \
+    --eval-num 1000 --device cpu
+    """
+    parse = argparse.ArgumentParser()
+    parse.add_argument("--device", type=str, default='cpu', help="cpu or cuda")
+    parse.add_argument("--direction", type=str, choices=['horizontal', 'vertical'],
+                       default='horizontal', help="horizontal or vertical")
+    parse.add_argument("--weight-path", type=str, default=None, help="weight path")
+    parse.add_argument("--eval-num", type=int, default=1000, help="number of images to evaluate")
+    arguments = parse.parse_args(sys.argv[1:])
+    main(arguments)