fix c2c

src/data.py

@@ -7,6 +7,7 @@ import random
 import logging
 import sys
 import numbers
+import math
 import sklearn
 import datetime
 import numpy as np
@@ -72,6 +73,19 @@ class FaceImageIter(io.DataIter):
             self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')  # pylint: disable=redefined-variable-type
             s = self.imgrec.read_idx(0)
             header, _ = recordio.unpack(s)
+            self.idx2cos = {}
+            self.idx2meancos = {}
+            self.c2c_auto = False
+            if output_c2c or c2c_threshold>0.0:
+              path_c2c = os.path.join(os.path.dirname(path_imgrec), 'c2c')
+              print(path_c2c)
+              if os.path.exists(path_c2c):
+                for line in open(path_c2c, 'r'):
+                  vec = line.strip().split(',')
+                  self.idx2cos[int(vec[0])] = float(vec[1])
+              else:
+                self.c2c_auto = True
+                self.c2c_step = 10000
             if header.flag>0:
               print('header0 label', header.label)
               self.header0 = (int(header.label[0]), int(header.label[1]))
@@ -82,11 +96,22 @@ class FaceImageIter(io.DataIter):
               for identity in self.seq_identity:
                 s = self.imgrec.read_idx(identity)
                 header, _ = recordio.unpack(s)
+                a,b = int(header.label[0]), int(header.label[1])
                 #print('flag', header.flag)
                 #print(header.label)
                 #assert(header.flag==2)
-                self.id2range[identity] = (int(header.label[0]), int(header.label[1]))
+                self.id2range[identity] = (a,b)
+                if len(self.idx2cos)>0:
+                  m = 0.0
+                  for ii in xrange(a,b):
+                    m+=self.idx2cos[ii]
+                  m/=(b-a)
+                  for ii in xrange(a,b):
+                    self.idx2meancos[ii] = m
+                  #self.idx2meancos[identity] = m
+
               print('id2range', len(self.id2range))
+              print(len(self.idx2cos), len(self.idx2meancos))
             else:
               self.imgidx = list(self.imgrec.keys)
             if shuffle:
@@ -159,6 +184,7 @@ class FaceImageIter(io.DataIter):
           self.triplet_oseq_reset()
           self.seq_min_size = self.batch_size*2
         self.cur = 0
+        self.nbatch = 0
         self.is_init = False
         self.times = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
         #self.reset()
@@ -538,10 +564,63 @@ class FaceImageIter(io.DataIter):
       #    for i in xrange(self.images_per_identity):
       #      _idx = _list[i%len(_list)]
       #      self.seq.append(_idx)
+    def reset_c2c(self):
+      self.select_triplets()
+      for identity,v in self.id2range.iteritems():
+        _list = range(*v)
+      
+        for idx in _list:
+          s = imgrec.read_idx(idx)
+          ocontents.append(s)
+        embeddings = None
+        #print(len(ocontents))
+        ba = 0
+        while True:
+          bb = min(ba+args.batch_size, len(ocontents))
+          if ba>=bb:
+            break
+          _batch_size = bb-ba
+          _batch_size2 = max(_batch_size, args.ctx_num)
+          data = nd.zeros( (_batch_size2,3, image_size[0], image_size[1]) )
+          label = nd.zeros( (_batch_size2,) )
+          count = bb-ba
+          ii=0
+          for i in xrange(ba, bb):
+            header, img = mx.recordio.unpack(ocontents[i])
+            img = mx.image.imdecode(img)
+            img = nd.transpose(img, axes=(2, 0, 1))
+            data[ii][:] = img
+            label[ii][:] = header.label
+            ii+=1
+          while ii<_batch_size2:
+            data[ii][:] = data[0][:]
+            label[ii][:] = label[0][:]
+            ii+=1
+          db = mx.io.DataBatch(data=(data,), label=(label,))
+          self.mx_model.forward(db, is_train=False)
+          net_out = self.mx_model.get_outputs()
+          net_out = net_out[0].asnumpy()
+          model.forward(db, is_train=False)
+          net_out = model.get_outputs()
+          net_out = net_out[0].asnumpy()
+          if embeddings is None:
+            embeddings = np.zeros( (len(ocontents), net_out.shape[1]))
+          embeddings[ba:bb,:] = net_out[0:_batch_size,:]
+          ba = bb
+        embeddings = sklearn.preprocessing.normalize(embeddings)
+        embedding = np.mean(embeddings, axis=0, keepdims=True)
+        embedding = sklearn.preprocessing.normalize(embedding)
+        sims = np.dot(embeddings, embedding).flatten()
+        assert len(sims)==len(_list)
+        for i in xrange(len(_list)):
+          _idx = _list[i]
+          self.idx2cos[_idx] = sims[i]
 
     def reset(self):
         """Resets the iterator to the beginning of the data."""
         print('call reset()')
+        if self.c2c_auto:
+          self.reset_c2c()
         self.cur = 0
         if self.images_per_identity>0:
           if self.triplet_mode:
@@ -592,12 +671,14 @@ class FaceImageIter(io.DataIter):
               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:
+              if self.c2c_threshold>0.0:
+                cos = self.idx2cos[idx]
+                if cos<self.c2c_threshold:
                   continue
-              return header.label, img, None, None
+              if self.output_c2c:
+                meancos = self.idx2meancos[idx]
+                label = [label, meancos]
+              return label, img, None, None
             else:
               label, fname, bbox, landmark = self.imglist[idx]
               return label, self.read_image(fname), bbox, landmark
@@ -655,6 +736,7 @@ class FaceImageIter(io.DataIter):
           self.is_init = True
         """Returns the next batch of data."""
         #print('in next', self.cur, self.labelcur)
+        self.nbatch+=1
         batch_size = self.batch_size
         c, h, w = self.data_shape
         batch_data = nd.empty((batch_size, c, h, w))
@@ -706,10 +788,25 @@ class FaceImageIter(io.DataIter):
                           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)
+                              c2c = v
+                              #m = min(0.55, max(0.3,math.log(c2c+1)*4-1.85))
+                              #v = math.cos(m)
+                              #v = v*v
+                              #_param = [0.5, 0.3, 0.85, 0.7]
+                              _param = [0.5, 0.25, 0.85, 0.65]
+                              _a = (_param[1]-_param[0])/(_param[3]-_param[2])
+                              m = _param[1]+_a*(c2c-_param[3])
+                              m = min(_param[0], max(_param[1],m))
+                              #m = 0.5
+                              #if c2c<0.77:
+                              #  m = 0.3
+                              #elif c2c<0.82:
+                              #  m = 0.4
+                              #elif c2c>0.88:
+                              #  m = 0.55
+                              v = math.cos(m)
                               v = v*v
-                              print('c2c', i,v)
+                              #print('c2c', i,c2c,m,v)
 
                             batch_label[i][ll] = v
                       else:

src/eval/verification.py

@@ -185,7 +185,7 @@ def load_bin(path, image_size):
   print(data_list[0].shape)
   return (data_list, issame_list)
 
-def test(data_set, mx_model, batch_size, data_extra = None):
+def test(data_set, mx_model, batch_size, data_extra = None, label_shape = None):
   print('testing verification..')
   data_list = data_set[0]
   issame_list = data_set[1]
@@ -194,6 +194,10 @@ def test(data_set, mx_model, batch_size, data_extra = None):
   if data_extra is not None:
     _data_extra = nd.array(data_extra)
   time_consumed = 0.0
+  if label_shape is None:
+    _label = nd.ones( (batch_size,) )
+  else:
+    _label = nd.ones( label_shape )
   for i in xrange( len(data_list) ):
     data = data_list[i]
     embeddings = None
@@ -202,7 +206,6 @@ def test(data_set, mx_model, batch_size, data_extra = None):
       bb = min(ba+batch_size, data.shape[0])
       count = bb-ba
       _data = nd.slice_axis(data, axis=0, begin=bb-batch_size, end=bb)
-      _label = nd.ones( (batch_size,) )
       #print(_data.shape, _label.shape)
       time0 = datetime.datetime.now()
       if data_extra is None:

src/train_softmax.py

@@ -77,7 +77,10 @@ class AccMetric(mx.metric.EvalMetric):
         if pred_label.shape != label.shape:
             pred_label = mx.ndarray.argmax(pred_label, axis=self.axis)
         pred_label = pred_label.asnumpy().astype('int32').flatten()
-        label = label.asnumpy().astype('int32').flatten()
+        label = label.asnumpy()
+        if label.ndim==2:
+          label = label[:,0]
+        label = label.astype('int32').flatten()
         #print(label)
         #print('label',label)
         #print('pred_label', pred_label)
@@ -223,7 +226,9 @@ def get_symbol(args, arg_params, aux_params):
     gt_label = all_label
   else:
     gt_label = mx.symbol.slice_axis(all_label, axis=1, begin=0, end=1)
+    gt_label = mx.symbol.reshape(gt_label, (args.per_batch_size,))
     c2c_label = mx.symbol.slice_axis(all_label, axis=1, begin=1, end=2)
+    c2c_label = mx.symbol.reshape(c2c_label, (args.per_batch_size,))
   assert args.loss_type>=0
   extra_loss = None
   if args.loss_type==0: #softmax
@@ -319,7 +324,7 @@ def get_symbol(args, arg_params, aux_params):
     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 m>0.0:
+    if args.output_c2c==0:
       cos_m = math.cos(m)
       sin_m = math.sin(m)
       mm = math.sin(math.pi-m)*m
@@ -345,7 +350,6 @@ def get_symbol(args, arg_params, aux_params):
         zy_keep = zy - s*mm
       new_zy = mx.sym.where(cond, new_zy, zy_keep)
     else:
-      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
@@ -619,6 +623,9 @@ def train_net(args):
       coco_mode = True
 
     label_name = 'softmax_label'
+    label_shape = (args.batch_size,)
+    if args.output_c2c:
+      label_shape = (args.batch_size,2)
     if data_extra is None:
       model = mx.mod.Module(
           context       = ctx,
@@ -658,6 +665,7 @@ def train_net(args):
           rand_mirror          = args.rand_mirror,
           mean                 = mean,
           c2c_threshold        = args.c2c_threshold,
+          output_c2c           = args.output_c2c,
           ctx_num              = args.ctx_num,
           images_per_identity  = args.images_per_identity,
           data_extra           = data_extra,
@@ -679,6 +687,7 @@ def train_net(args):
             rand_mirror          = args.rand_mirror,
             mean                 = mean,
             c2c_threshold        = args.c2c_threshold,
+            output_c2c           = args.output_c2c,
             ctx_num              = args.ctx_num,
             images_per_identity  = args.images_per_identity,
             data_extra           = data_extra,
@@ -726,7 +735,7 @@ def train_net(args):
     def ver_test(nbatch):
       results = []
       for i in xrange(len(ver_list)):
-        acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(ver_list[i], model, args.batch_size, data_extra)
+        acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(ver_list[i], model, args.batch_size, data_extra, label_shape)
         print('[%s][%d]XNorm: %f' % (ver_name_list[i], nbatch, xnorm))
         #print('[%s][%d]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc1, std1))
         print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc2, std2))