update README

.gitignore

 *.mdb
-*.pth
 *.tar
 *.ipynb
 *.zip
@@ -198,7 +197,6 @@ $RECYCLE.BIN/
 
 # Windows shortcuts
 *.lnk
-
 .idea/
 .vscode/
 output/

README.md

@@ -7,9 +7,11 @@ CenterFace是一种人脸检测算法，采用了轻量级网络mobileNetV2作
 <div align=center>
     <img src="./Architecture of the CenterFace.png"/>
 </div>
+
 ## 算法原理
 CenterFace模型是一种基于单阶段人脸检测算法，作者借鉴了CenterNet的思想，将人脸检测转换为标准点问题，根据人脸中心点来回归人脸框的大小和五个标志点。
 
+
 ## 环境配置
 ### Docker（方法一）
 
@@ -140,6 +142,8 @@ python gen_data.py
 
 
 ## 训练
+默认训练模型保存在./exp/下，如需修改为自己的路径，可以对centerface_pytorch/src/lib/opts_pose.py的284行进行修改
+
 ### 单机单卡
 
 ```
@@ -158,7 +162,9 @@ bash train_multi.sh
 #### 单卡推理
 
 ```
-cd ./src
+cd lib/external/
+bash make.sh
+cd ../../
 python test_wider_face.py
 ```
 

src/lib/models/utils.py

@@ -5,13 +5,15 @@ from __future__ import print_function
 import torch
 import torch.nn as nn
 
+
 def _sigmoid(x):
-  y = torch.clamp(x.sigmoid_(), min=1e-4, max=1-1e-4)
-  return y
+    y = torch.clamp(x.sigmoid_(), min=1e-4, max=1-1e-4)
+    return y
+
 
 def _gather_feat(feat, ind, mask=None):
-    dim  = feat.size(2)
-    ind  = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
+    dim = feat.size(2)
+    ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
     feat = feat.gather(1, ind)
     if mask is not None:
         mask = mask.unsqueeze(2).expand_as(feat)
@@ -19,32 +21,36 @@ def _gather_feat(feat, ind, mask=None):
         feat = feat.view(-1, dim)
     return feat
 
+
 def _tranpose_and_gather_feat(feat, ind):
     feat = feat.permute(0, 2, 3, 1).contiguous()
     feat = feat.view(feat.size(0), -1, feat.size(3))
     feat = _gather_feat(feat, ind)
     return feat
 
+
 def flip_tensor(x):
     return torch.flip(x, [3])
     # tmp = x.detach().cpu().numpy()[..., ::-1].copy()
     # return torch.from_numpy(tmp).to(x.device)
 
+
 def flip_lr(x, flip_idx):
-  tmp = x.detach().cpu().numpy()[..., ::-1].copy()
-  shape = tmp.shape
-  for e in flip_idx:
-    tmp[:, e[0], ...], tmp[:, e[1], ...] = \
-      tmp[:, e[1], ...].copy(), tmp[:, e[0], ...].copy()
-  return torch.from_numpy(tmp.reshape(shape)).to(x.device)
+    tmp = x.detach().cpu().numpy()[..., ::-1].copy()
+    shape = tmp.shape
+    for e in flip_idx:
+        tmp[:, e[0], ...], tmp[:, e[1], ...] = \
+            tmp[:, e[1], ...].copy(), tmp[:, e[0], ...].copy()
+    return torch.from_numpy(tmp.reshape(shape)).to(x.device)
+
 
 def flip_lr_off(x, flip_idx):
-  tmp = x.detach().cpu().numpy()[..., ::-1].copy()
-  shape = tmp.shape
-  tmp = tmp.reshape(tmp.shape[0], 17, 2, 
-                    tmp.shape[2], tmp.shape[3])
-  tmp[:, :, 0, :, :] *= -1
-  for e in flip_idx:
-    tmp[:, e[0], ...], tmp[:, e[1], ...] = \
-      tmp[:, e[1], ...].copy(), tmp[:, e[0], ...].copy()
-  return torch.from_numpy(tmp.reshape(shape)).to(x.device)
+    tmp = x.detach().cpu().numpy()[..., ::-1].copy()
+    shape = tmp.shape
+    tmp = tmp.reshape(tmp.shape[0], 17, 2,
+                      tmp.shape[2], tmp.shape[3])
+    tmp[:, :, 0, :, :] *= -1
+    for e in flip_idx:
+        tmp[:, e[0], ...], tmp[:, e[1], ...] = \
+            tmp[:, e[1], ...].copy(), tmp[:, e[0], ...].copy()
+    return torch.from_numpy(tmp.reshape(shape)).to(x.device)
\ No newline at end of file

src/lib/opts_pose.py

@@ -98,7 +98,7 @@ class opts(object):
                                  help='total training epochs.')
         self.parser.add_argument('--batch_size', type=int, default=32,
                                  help='batch size')
-        self.parser.add_argument('--master_batch_size', type=int, default=15,
+        self.parser.add_argument('--master_batch_size', type=int, default=-1,
                                  help='batch size on the master gpu.')
         self.parser.add_argument('--num_iters', type=int, default=-1,
                                  help='default: #samples / batch_size.')

src/test_wider_face.py

@@ -2,15 +2,15 @@ import os
 import sys
 import cv2
 
+path = os.path.dirname(__file__)
+CENTERNET_PATH = os.path.join(path, '../src/lib')
+sys.path.insert(0, CENTERNET_PATH)
+
 from opts_pose import opts
 from detectors.detector_factory import detector_factory
 
 import scipy.io as sio
 
-path = os.path.dirname(__file__)
-CENTERNET_PATH = os.path.join(path, '../src/lib')
-sys.path.insert(0, CENTERNET_PATH)
-
 
 def test_img(model_path, debug, threshold=0.4):
     TASK = 'multi_pose'
@@ -61,7 +61,7 @@ def test_vedio(model_path, debug, vedio_path=None):
 
 def test_wider_Face(model_path, debug, threshold=0.05):
     from progress.bar import Bar
-    Path = '/your/path/WIDER_val/images' # WIDER_val/images path
+    Path = '../datasets/images/val' # WIDER_val/images path
     wider_face_mat = sio.loadmat('../evaluate/ground_truth/wider_face_val.mat')
     event_list = wider_face_mat['event_list']
     file_list = wider_face_mat['file_list']

src/train.sh

 #!/usr/bin/env bash
 
-python main.py --input_h 512 --input_w 512 --batch_size 8 --lr 5e-4 --data_dir ../datasets/
+python main.py --input_h 640 --input_w 640 --batch_size 8 --lr 5e-4 --data_dir ../datasets/
\ No newline at end of file

src/train_multi.sh

 #!/usr/bin/env bash
 
-python main.py --input_h 512 --input_w 512 --batch_size 8 --lr 5e-4 --data_dir ../datasets/ --gpus 0,1
+python main.py --input_h 640 --input_w 640 --batch_size 8 --lr 5e-4 --data_dir ../datasets/ --gpus 0,1
\ No newline at end of file