add c2c label

src/train_softmax.py

@@ -137,6 +137,10 @@ def parse_args():
       help='')
   parser.add_argument('--margin-verbose', type=int, default=0,
       help='')
+  parser.add_argument('--c2c-threshold', type=float, default=0.0,
+      help='')
+  parser.add_argument('--output-c2c', type=int, default=0,
+      help='')
   parser.add_argument('--margin', type=int, default=4,
       help='')
   parser.add_argument('--beta', type=float, default=1000.,
@@ -214,7 +218,12 @@ def get_symbol(args, arg_params, aux_params):
     embedding = fresnet.get_symbol(args.emb_size, args.num_layers, 
         version_se=args.version_se, version_input=args.version_input, 
         version_output=args.version_output, version_unit=args.version_unit)
-  gt_label = mx.symbol.Variable('softmax_label')
+  all_label = mx.symbol.Variable('softmax_label')
+  if not args.output_c2c:
+    gt_label = all_label
+  else:
+    gt_label = mx.symbol.slice_axis(all_label, axis=1, begin=0, end=1)
+    c2c_label = mx.symbol.slice_axis(all_label, axis=1, begin=1, end=2)
   assert args.loss_type>=0
   extra_loss = None
   if args.loss_type==0: #softmax
@@ -302,46 +311,56 @@ def get_symbol(args, arg_params, aux_params):
     s = args.margin_s
     m = args.margin_m
     assert s>0.0
-    assert m>0.0
+    assert m>=0.0
     assert m<(math.pi/2)
-    cos_m = math.cos(m)
-    sin_m = math.sin(m)
-    mm = math.sin(math.pi-m)*m
     _weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
     _weight = mx.symbol.L2Normalization(_weight, mode='instance')
-    #threshold = 0.0
-    threshold = math.cos(math.pi-m)
     nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
     fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
     zy = mx.sym.pick(fc7, gt_label, axis=1)
     cos_t = zy/s
-    if args.margin_verbose>0:
-      margin_symbols.append(mx.symbol.mean(cos_t))
-    if args.easy_margin:
-      cond = mx.symbol.Activation(data=cos_t, act_type='relu')
-      #cond_v = cos_t - 0.4
-      #cond = mx.symbol.Activation(data=cond_v, act_type='relu')
+    if m>0.0:
+      cos_m = math.cos(m)
+      sin_m = math.sin(m)
+      mm = math.sin(math.pi-m)*m
+      #threshold = 0.0
+      threshold = math.cos(math.pi-m)
+      if args.margin_verbose>0:
+        margin_symbols.append(mx.symbol.mean(cos_t))
+      if args.easy_margin:
+        cond = mx.symbol.Activation(data=cos_t, act_type='relu')
+      else:
+        cond_v = cos_t - threshold
+        cond = mx.symbol.Activation(data=cond_v, act_type='relu')
+      body = cos_t*cos_t
+      body = 1.0-body
+      sin_t = mx.sym.sqrt(body)
+      new_zy = cos_t*cos_m
+      b = sin_t*sin_m
+      new_zy = new_zy - b
+      new_zy = new_zy*s
+      if args.easy_margin:
+        zy_keep = zy
+      else:
+        zy_keep = zy - s*mm
+      new_zy = mx.sym.where(cond, new_zy, zy_keep)
     else:
-      cond_v = cos_t - threshold
-      cond = mx.symbol.Activation(data=cond_v, act_type='relu')
-    #theta = mx.sym.arccos(costheta)
-    #sintheta = mx.sym.sin(theta)
-    body = cos_t*cos_t
-    body = 1.0-body
-    sin_t = mx.sym.sqrt(body)
-    new_zy = cos_t*cos_m
-    b = sin_t*sin_m
-    new_zy = new_zy - b
-    new_zy = new_zy*s
-    if args.easy_margin:
-      zy_keep = zy
-    else:
-      zy_keep = zy - s*mm
-    new_zy = mx.sym.where(cond, new_zy, zy_keep)
+      assert args.output_c2c
+      #set c2c as cosm^2 in data.py
+      cos_m = mx.sym.sqrt(c2c_label)
+      sin_m = 1.0-c2c_label
+      sin_m = mx.sym.sqrt(sin_m)
+      body = cos_t*cos_t
+      body = 1.0-body
+      sin_t = mx.sym.sqrt(body)
+      new_zy = cos_t*cos_m
+      b = sin_t*sin_m
+      new_zy = new_zy - b
+      new_zy = new_zy*s
+
     if args.margin_verbose>0:
       new_cos_t = new_zy/s
       margin_symbols.append(mx.symbol.mean(new_cos_t))
-    
     diff = new_zy - zy
     diff = mx.sym.expand_dims(diff, 1)
     gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
@@ -638,6 +657,7 @@ def train_net(args):
           shuffle              = True,
           rand_mirror          = args.rand_mirror,
           mean                 = mean,
+          c2c_threshold        = args.c2c_threshold,
           ctx_num              = args.ctx_num,
           images_per_identity  = args.images_per_identity,
           data_extra           = data_extra,
@@ -658,6 +678,7 @@ def train_net(args):
             shuffle              = True,
             rand_mirror          = args.rand_mirror,
             mean                 = mean,
+            c2c_threshold        = args.c2c_threshold,
             ctx_num              = args.ctx_num,
             images_per_identity  = args.images_per_identity,
             data_extra           = data_extra,