From 507e644d522d472441d107d2e5b36fcbca375b3e Mon Sep 17 00:00:00 2001
From: nttstar <guojia@gmail.com>
Date: Mon, 19 Feb 2018 19:13:20 +0800
Subject: [PATCH] tiny

---
 src/data.py          | 36 ++++++++++++++++++------------------
 src/train_softmax.py | 25 ++++++++++++++-----------
 2 files changed, 32 insertions(+), 29 deletions(-)

diff --git a/src/data.py b/src/data.py
index af8d0c9..b7efff1 100644
--- a/src/data.py
+++ b/src/data.py
@@ -108,34 +108,34 @@ class FaceImageIter(io.DataIter):
                 self.imgidx = imgidx2
               elif c2c_mode==1:
                 imgidx2 = []
+                tmp = []
                 for idx in self.imgidx:
                   c = self.idx2cos[idx]
                   f = self.idx2flag[idx]
-                  if f==2 and c>=0.05:
-                    continue
-                  imgidx2.append(idx)
+                  if f==1:
+                    imgidx2.append(idx)
+                  else:
+                    tmp.append( (idx, c) )
+                tmp = sorted(tmp, key = lambda x:x[1])
+                tmp = tmp[250000:300000]
+                for _t in tmp:
+                  imgidx2.append(_t[0])
                 print('idx count', len(self.imgidx), len(imgidx2))
                 self.imgidx = imgidx2
               elif c2c_mode==2:
                 imgidx2 = []
+                tmp = []
                 for idx in self.imgidx:
                   c = self.idx2cos[idx]
                   f = self.idx2flag[idx]
-                  if f==2 and c>=0.1:
-                    continue
-                  imgidx2.append(idx)
-                print('idx count', len(self.imgidx), len(imgidx2))
-                self.imgidx = imgidx2
-              elif c2c_mode==-1:
-                imgidx2 = []
-                for idx in self.imgidx:
-                  c = self.idx2cos[idx]
-                  f = self.idx2flag[idx]
-                  if f==2:
-                    continue
-                  if c<0.7:
-                    continue
-                  imgidx2.append(idx)
+                  if f==1:
+                    imgidx2.append(idx)
+                  else:
+                    tmp.append( (idx, c) )
+                tmp = sorted(tmp, key = lambda x:x[1])
+                tmp = tmp[200000:300000]
+                for _t in tmp:
+                  imgidx2.append(_t[0])
                 print('idx count', len(self.imgidx), len(imgidx2))
                 self.imgidx = imgidx2
               elif c2c_mode==-2:
diff --git a/src/train_softmax.py b/src/train_softmax.py
index eec7ac3..df36a02 100644
--- a/src/train_softmax.py
+++ b/src/train_softmax.py
@@ -445,7 +445,7 @@ def get_symbol(args, arg_params, aux_params):
     triplet_loss = mx.symbol.mean(triplet_loss)
     #triplet_loss = mx.symbol.sum(triplet_loss)/(args.per_batch_size//3)
     extra_loss = mx.symbol.MakeLoss(triplet_loss)
-  elif args.loss_type==13: #triplet loss with insightface margin
+  elif args.loss_type==13: #triplet loss with angular margin
     m = args.margin_m
     sin_m = math.sin(m)
     cos_m = math.cos(m)
@@ -457,16 +457,19 @@ def get_symbol(args, arg_params, aux_params):
     an = anchor * negative
     ap = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1)
     an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1)
-    #ap = mx.symbol.arccos(ap)
-    #an = mx.symbol.arccos(an)
-    #triplet_loss = mx.symbol.Activation(data = (ap-an+args.margin_m), act_type='relu')
-    body = ap*ap
-    body = 1.0-body
-    body = mx.symbol.sqrt(body)
-    body = body*sin_m
-    ap = ap*cos_m
-    ap = ap-body
-    triplet_loss = mx.symbol.Activation(data = (an-ap), act_type='relu')
+
+    ap = mx.symbol.arccos(ap)
+    an = mx.symbol.arccos(an)
+    triplet_loss = mx.symbol.Activation(data = (ap-an+args.margin_m), act_type='relu')
+
+    #body = ap*ap
+    #body = 1.0-body
+    #body = mx.symbol.sqrt(body)
+    #body = body*sin_m
+    #ap = ap*cos_m
+    #ap = ap-body
+    #triplet_loss = mx.symbol.Activation(data = (an-ap), act_type='relu')
+
     triplet_loss = mx.symbol.mean(triplet_loss)
     extra_loss = mx.symbol.MakeLoss(triplet_loss)
   elif args.loss_type==9: #coco loss