move more to config

recognition/sample_config.py

@@ -13,6 +13,15 @@ config.net_input = 1
 config.net_output = 'E'
 config.net_multiplier = 1.0
 config.val_targets = ['lfw', 'cfp_fp', 'agedb_30']
+config.ce_loss = False
+config.fc7_lr_mult = 1.0
+config.fc7_wd_mult = 1.0
+config.fc7_no_bias = False
+config.max_steps = 0
+config.data_rand_mirror = True
+config.data_cutoff = False
+config.data_color = 0
+config.data_images_filter = 0
 
 
 # network settings
@@ -51,7 +60,7 @@ dataset = edict()
 
 dataset.emore = edict()
 dataset.emore.dataset = 'emore'
-dataset.emore.dataset_path = './faces_emore'
+dataset.emore.dataset_path = '../datasets/faces_emore'
 dataset.emore.num_classes = 85742
 dataset.emore.image_shape = (112,112,3)
 dataset.emore.val_targets = ['lfw', 'cfp_fp', 'agedb_30']
@@ -59,10 +68,6 @@ dataset.emore.val_targets = ['lfw', 'cfp_fp', 'agedb_30']
 loss = edict()
 loss.softmax = edict()
 loss.softmax.loss_name = 'softmax'
-loss.softmax.loss_s = -1.0
-loss.softmax.loss_m1 = 0.0
-loss.softmax.loss_m2 = 0.0
-loss.softmax.loss_m3 = 0.0
 
 loss.nsoftmax = edict()
 loss.nsoftmax.loss_name = 'margin_softmax'
@@ -116,7 +121,7 @@ default = edict()
 # default network
 default.network = 'r100'
 default.pretrained = ''
-default.pretrained_epoch = 0
+default.pretrained_epoch = 1
 # default dataset
 default.dataset = 'emore'
 default.loss = 'arcface'

recognition/train.py

@@ -78,25 +78,16 @@ def parse_args():
   args, rest = parser.parse_known_args()
   generate_config(args.network, args.dataset, args.loss)
   parser.add_argument('--models-root', default=default.models_root, help='root directory to save model.')
-  parser.add_argument('--pretrained', default='', help='pretrained model to load')
+  parser.add_argument('--pretrained', default=default.pretrained, help='pretrained model to load')
+  parser.add_argument('--pretrained-epoch', default=default.pretrained_epoch, help='pretrained epoch to load')
   parser.add_argument('--ckpt', type=int, default=default.ckpt, help='checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save')
   parser.add_argument('--verbose', type=int, default=default.verbose, help='do verification testing and model saving every verbose batches')
-  parser.add_argument('--max-steps', type=int, default=0, help='max training batches')
-  parser.add_argument('--end-epoch', type=int, default=100000, help='training epoch size.')
   parser.add_argument('--lr', type=float, default=default.lr, help='start learning rate')
   parser.add_argument('--lr-steps', type=str, default=default.lr_steps, help='steps of lr changing')
   parser.add_argument('--wd', type=float, default=default.wd, help='weight decay')
   parser.add_argument('--mom', type=float, default=default.mom, help='momentum')
   parser.add_argument('--frequent', type=int, default=default.frequent, help='')
-  parser.add_argument('--fc7-wd-mult', type=float, default=1.0, help='weight decay mult for fc7')
-  parser.add_argument('--fc7-lr-mult', type=float, default=1.0, help='lr mult for fc7')
-  parser.add_argument("--fc7-no-bias", default=False, action="store_true" , help="fc7 no bias flag")
   parser.add_argument('--per-batch-size', type=int, default=default.per_batch_size, help='batch size in each context')
-  parser.add_argument('--rand-mirror', type=int, default=1, help='if do random mirror in training')
-  parser.add_argument('--cutoff', type=int, default=0, help='cut off aug')
-  parser.add_argument('--color', type=int, default=0, help='color jittering aug')
-  parser.add_argument('--images-filter', type=int, default=0, help='minimum images per identity filter')
-  parser.add_argument('--ce-loss', default=False, action='store_true', help='if output ce loss')
   args = parser.parse_args()
   return args
 
@@ -107,14 +98,16 @@ def get_symbol(args):
   gt_label = all_label
   is_softmax = True
   if config.loss_name=='softmax': #softmax
-    _weight = mx.symbol.Variable("fc7_weight", shape=(config.num_classes, config.emb_size), lr_mult=args.fc7_lr_mult, wd_mult=args.fc7_wd_mult)
-    if args.fc7_no_bias:
+    _weight = mx.symbol.Variable("fc7_weight", shape=(config.num_classes, config.emb_size), 
+        lr_mult=config.fc7_lr_mult, wd_mult=config.fc7_wd_mult, init=mx.init.Normal(0.01))
+    if config.fc7_no_bias:
       fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, no_bias = True, num_hidden=config.num_classes, name='fc7')
     else:
       _bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
       fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=config.num_classes, name='fc7')
   elif config.loss_name=='margin_softmax':
-    _weight = mx.symbol.Variable("fc7_weight", shape=(config.num_classes, config.emb_size), lr_mult=args.fc7_lr_mult, wd_mult=args.fc7_wd_mult)
+    _weight = mx.symbol.Variable("fc7_weight", shape=(config.num_classes, config.emb_size), 
+        lr_mult=config.fc7_lr_mult, wd_mult=config.fc7_wd_mult, init=mx.init.Normal(0.01))
     s = config.loss_s
     _weight = mx.symbol.L2Normalization(_weight, mode='instance')
     nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
@@ -170,7 +163,7 @@ def get_symbol(args):
   if is_softmax:
     softmax = mx.symbol.SoftmaxOutput(data=fc7, label = gt_label, name='softmax', normalization='valid')
     out_list.append(softmax)
-    if args.ce_loss:
+    if config.ce_loss:
       #ce_loss = mx.symbol.softmax_cross_entropy(data=fc7, label = gt_label, name='ce_loss')/args.per_batch_size
       body = mx.symbol.SoftmaxActivation(data=fc7)
       body = mx.symbol.log(body)
@@ -200,7 +193,6 @@ def train_net(args):
     print('prefix', prefix)
     if not os.path.exists(prefix_dir):
       os.makedirs(prefix_dir)
-    end_epoch = args.end_epoch
     args.ctx_num = len(ctx)
     args.batch_size = args.per_batch_size*args.ctx_num
     args.rescale_threshold = 0
@@ -229,9 +221,8 @@ def train_net(args):
         data_shape_dict = {'data' : (args.per_batch_size,)+data_shape}
         spherenet.init_weights(sym, data_shape_dict, args.num_layers)
     else:
-      vec = args.pretrained.split(',')
-      print('loading', vec)
-      _, arg_params, aux_params = mx.model.load_checkpoint(vec[0], int(vec[1]))
+      print('loading', args.pretrained, args.pretrained_epoch)
+      _, arg_params, aux_params = mx.model.load_checkpoint(args.pretrained, args.pretrained_epoch)
       sym = get_symbol(args)
 
     #label_name = 'softmax_label'
@@ -250,9 +241,9 @@ def train_net(args):
           data_shape           = data_shape,
           path_imgrec          = path_imgrec,
           shuffle              = True,
-          rand_mirror          = args.rand_mirror,
+          rand_mirror          = config.data_rand_mirror,
           mean                 = mean,
-          cutoff               = args.cutoff,
+          cutoff               = config.data_cutoff,
           ctx_num              = args.ctx_num,
           images_per_identity  = config.images_per_identity,
           triplet_params       = triplet_params,
@@ -267,15 +258,15 @@ def train_net(args):
           data_shape           = data_shape,
           path_imgrec          = path_imgrec,
           shuffle              = True,
-          rand_mirror          = args.rand_mirror,
+          rand_mirror          = config.data_rand_mirror,
           mean                 = mean,
-          cutoff               = args.cutoff,
-          color_jittering      = args.color,
-          images_filter        = args.images_filter,
+          cutoff               = config.data_cutoff,
+          color_jittering      = config.data_color,
+          images_filter        = config.data_images_filter,
       )
       metric1 = AccMetric()
       eval_metrics = [mx.metric.create(metric1)]
-      if args.ce_loss:
+      if config.ce_loss:
         metric2 = LossValueMetric()
         eval_metrics.append( mx.metric.create(metric2) )
 
@@ -370,7 +361,7 @@ def train_net(args):
           arg, aux = model.get_params()
           mx.model.save_checkpoint(prefix, msave, model.symbol, arg, aux)
         print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1]))
-      if args.max_steps>0 and mbatch>args.max_steps:
+      if config.max_steps>0 and mbatch>config.max_steps:
         sys.exit(0)
 
     epoch_cb = None
@@ -378,7 +369,7 @@ def train_net(args):
 
     model.fit(train_dataiter,
         begin_epoch        = begin_epoch,
-        num_epoch          = end_epoch,
+        num_epoch          = 999999,
         eval_data          = val_dataiter,
         eval_metric        = eval_metrics,
         kvstore            = 'device',