read image_size from property file

src/common/face2rec2.py

@@ -31,6 +31,7 @@ import traceback
 #from builtins import range
 from easydict import EasyDict as edict
 import face_preprocess
+import face_image
 
 try:
     import multiprocessing
@@ -51,6 +52,9 @@ def read_list(path_in):
             item = edict()
             item.flag = 0
             item.image_path, item.label, item.bbox, item.landmark, item.aligned = face_preprocess.parse_lst_line(line)
+            if not item.aligned and item.landmark is None:
+              print('ignore line', line)
+              continue
             item.id = _id
             yield item
             if item.label!=last[0]:
@@ -89,7 +93,7 @@ def image_encode(args, i, item, q_out):
       else:
         img = cv2.imread(fullpath, args.color)
         assert item.landmark is not None
-        img = face_preprocess.preprocess(img, bbox = item.bbox, landmark=item.landmark, image_size='112,112')
+        img = face_preprocess.preprocess(img, bbox = item.bbox, landmark=item.landmark, image_size='%d,%d'%(args.image_h, args.image_w))
         s = mx.recordio.pack_img(header, img, quality=args.quality, img_fmt=args.encoding)
         q_out.put((i, s, oitem))
     else: #flag==1 or 2
@@ -179,7 +183,6 @@ def parse_args():
                         help='specify the encoding of the images.')
     rgroup.add_argument('--pack-label', type=bool, default=False,
         help='Whether to also pack multi dimensional label in the record file')
-    rgroup.add_argument('--image-size', type=str, default='112,96', choices=['112,96', '112,112'], help='image size, set to 112,96 or 112,112')
     args = parser.parse_args()
     args.prefix = os.path.abspath(args.prefix)
     #args.root = os.path.abspath(args.root)
@@ -194,6 +197,11 @@ if __name__ == '__main__':
             working_dir = args.prefix
         else:
             working_dir = os.path.dirname(args.prefix)
+        prop = face_image.load_property(working_dir)
+        image_size = prop.image_size
+        print('image_size', image_size)
+        args.image_h = image_size[0]
+        args.image_w = image_size[1]
         files = [os.path.join(working_dir, fname) for fname in os.listdir(working_dir)
                     if os.path.isfile(os.path.join(working_dir, fname))]
         count = 0

src/common/face_image.py

@@ -2,6 +2,18 @@
 from easydict import EasyDict as edict
 import os
 
+
+def load_property(data_dir):
+  prop = edict()
+  for line in open(os.path.join(data_dir, 'property')):
+    vec = line.strip().split(',')
+    assert len(vec)==3
+    prop.num_classes = int(vec[0])
+    prop.image_size = [int(vec[1]), int(vec[2])]
+  return prop
+
+
+
 def get_dataset_webface(input_dir):
   clean_list_file = input_dir+"_clean_list.txt"
   ret = []

src/eval/do_ver.sh

@@ -1,2 +1,3 @@
 
-python -u verification.py --data-dir /opt/jiaguo/faces_vgg_112x112 --image-size 112,112 --model '../../model/softmax1010d3-r101-p0_0_96_112_0,21|22|32'
+#python -u verification.py --gpu 0 --data-dir /opt/jiaguo/faces_vgg_112x112 --image-size 112,112 --model '../../model/softmax1010d3-r101-p0_0_96_112_0,21|22|32' --target agedb_30
+python -u verification.py --gpu 0 --data-dir /opt/jiaguo/faces_normed --image-size 112,96 --model '../../model31/sphere-m51-p0_0_96_112_0,90' --target agedb_30 --batch-size 128

src/eval/verification.py

@@ -38,6 +38,8 @@ import pickle
 from sklearn.decomposition import PCA
 import mxnet as mx
 from mxnet import ndarray as nd
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))
+import face_image
 
 
 
@@ -250,13 +252,14 @@ if __name__ == '__main__':
   # general
   parser.add_argument('--data-dir', default='', help='')
   parser.add_argument('--model', default='../model/softmax,50', help='path to load model.')
-  parser.add_argument('--image-size', default='112,96', help='image size.')
-  parser.add_argument('--target', default='lfw,cfp_ff,cfp_fp', help='test targets.')
+  parser.add_argument('--target', default='lfw,cfp_ff,cfp_fp,agedb_30', help='test targets.')
   parser.add_argument('--gpu', default=0, type=int, help='gpu id')
-  parser.add_argument('--batch-size', default=128, type=int, help='')
+  parser.add_argument('--batch-size', default=32, type=int, help='')
   args = parser.parse_args()
 
-  image_size = [int(x) for x in args.image_size.split(',')]
+  prop = face_image.load_property(args.data_dir)
+  image_size = prop.image_size
+  print('image_size', image_size)
   ctx = mx.gpu(args.gpu)
   nets = []
   prefix = args.model.split(',')[0]

src/symbols/symbol_utils.py

@@ -27,7 +27,7 @@ def get_fc1(last_conv, num_classes, fc_type):
     pool1 = mx.sym.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
     flat = mx.sym.Flatten(data=pool1)
     if len(fc_type)>1:
-      if fc_type[1]=='D':
+      if fc_type[1]=='X':
         print('dropout mode')
         flat = mx.symbol.Dropout(data=flat, p=0.2)
       fc_type = fc_type[0]

src/train_softmax.py

@@ -18,7 +18,8 @@ import mxnet as mx
 from mxnet import ndarray as nd
 import argparse
 import mxnet.optimizer as optimizer
-#sys.path.append(os.path.join(os.path.dirname(__file__), 'common'))
+sys.path.append(os.path.join(os.path.dirname(__file__), 'common'))
+import face_image
 sys.path.append(os.path.join(os.path.dirname(__file__), 'eval'))
 sys.path.append(os.path.join(os.path.dirname(__file__), 'symbols'))
 import fresnet
@@ -118,8 +119,6 @@ def parse_args():
       help='feature incay')
   parser.add_argument('--use-deformable', type=int, default=0,
       help='')
-  parser.add_argument('--image-size', type=str, default='112,96',
-      help='')
   parser.add_argument('--patch', type=str, default='0_0_96_112_0',
       help='')
   parser.add_argument('--lr-steps', type=str, default='',
@@ -284,9 +283,6 @@ def train_net(args):
     args.rescale_threshold = 0
     args.image_channel = 3
     ppatch = [int(x) for x in args.patch.split('_')]
-    image_size = [int(x) for x in args.image_size.split(',')]
-    args.image_h = image_size[0]
-    args.image_w = image_size[1]
     assert len(ppatch)==5
 
 
@@ -295,9 +291,13 @@ def train_net(args):
     path_imgrec = None
     path_imglist = None
     val_rec = None
+    prop = face_image.load_property(args.data_dir)
+    args.num_classes = prop.num_classes
+    image_size = prop.image_size
+    args.image_h = image_size[0]
+    args.image_w = image_size[1]
+    print('image_size', image_size)
 
-    for line in open(os.path.join(args.data_dir, 'property')):
-      args.num_classes = int(line.strip())
     assert(args.num_classes>0)
     print('num_classes', args.num_classes)