add c2c label

2026-05-18 14:55:42 +00:00 · 2018-01-30 20:13:45 +08:00
parent 24c8b372ad
commit f0cb7f6b52
2 changed files with 88 additions and 43 deletions
--- a/src/data.py
+++ b/src/data.py
@@ -57,6 +57,7 @@ class FaceImageIter(io.DataIter):
                 path_imgrec = None,
                 shuffle=False, aug_list=None, mean = None,
                 rand_mirror = False,
+                 c2c_threshold = 0.0, output_c2c = 0,
                 ctx_num = 0, images_per_identity = 0, data_extra = None, hard_mining = False, 
                 triplet_params = None, coco_mode = False,
                 mx_model = None,
@@ -110,6 +111,8 @@ class FaceImageIter(io.DataIter):
        #self.cast_aug = mx.image.CastAug()
        #self.color_aug = mx.image.ColorJitterAug(0.4, 0.4, 0.4)
        self.ctx_num = ctx_num 
+        self.c2c_threshold = c2c_threshold
+        self.output_c2c = output_c2c
        self.per_batch_size = int(self.batch_size/self.ctx_num)
        self.images_per_identity = images_per_identity
        if self.images_per_identity>0:
@@ -131,7 +134,10 @@ class FaceImageIter(io.DataIter):
        self.triplet_mode = False
        self.coco_mode = coco_mode
        if len(label_name)>0:
-          self.provide_label = [(label_name, (batch_size,))]
+          if output_c2c:
+            self.provide_label = [(label_name, (batch_size,2))]
+          else:
+            self.provide_label = [(label_name, (batch_size,))]
        else:
          self.provide_label = []
        if self.coco_mode:
@@ -575,17 +581,24 @@ class FaceImageIter(io.DataIter):
        """Helper function for reading in next sample."""
        #set total batch size, for example, 1800, and maximum size for each people, for example 45
        if self.seq is not None:
-          if self.cur >= len(self.seq):
-              raise StopIteration
-          idx = self.seq[self.cur]
-          self.cur += 1
-          if self.imgrec is not None:
-            s = self.imgrec.read_idx(idx)
-            header, img = recordio.unpack(s)
-            return header.label, img, None, None
-          else:
-            label, fname, bbox, landmark = self.imglist[idx]
-            return label, self.read_image(fname), bbox, landmark
+          while True:
+            if self.cur >= len(self.seq):
+                raise StopIteration
+            idx = self.seq[self.cur]
+            self.cur += 1
+            if self.imgrec is not None:
+              s = self.imgrec.read_idx(idx)
+              header, img = recordio.unpack(s)
+              label = header.label
+              if not isinstance(header.label, numbers.Number):
+                label = header.label[0]
+                c = header.label[1]
+                if c<self.c2c_threshold:
+                  continue
+              return header.label, img, None, None
+            else:
+              label, fname, bbox, landmark = self.imglist[idx]
+              return label, self.read_image(fname), bbox, landmark
        else:
            s = self.imgrec.read()
            if s is None:
@@ -685,7 +698,18 @@ class FaceImageIter(io.DataIter):
                    batch_data[i][:] = self.postprocess_data(datum)
                    if self.provide_label is not None:
                      if not self.coco_mode:
-                        batch_label[i][:] = label
+                        if len(batch_label.shape)==1:
+                          batch_label[i][:] = label
+                        else:
+                          for ll in xrange(batch_label.shape[1]):
+                            v = label[ll]
+                            if ll>0:
+                              v = min(0.5, max(0.25,math.log(v+1)*4-1.85))
+                              v = math.cos(v)
+                              v = v*v
+                              print('c2c', i,v)
+
+                            batch_label[i][ll] = v
                      else:
                        batch_label[i][:] = (i%self.per_batch_size)//self.images_per_identity
                    i += 1
--- a/src/train_softmax.py
+++ b/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,