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Merge branch 'master' of https://github.com/deepinsight/insightface

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nttstar
2018-03-10 23:36:33 +08:00
parent 7dcc4b235a ba8662d04f
commit 05a7c05f08
2 changed files with 65 additions and 34 deletions
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8
src/data.py
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@@ -769,6 +769,14 @@ class FaceImageIter(io.DataIter):
v = numpy.random.normal(mu, sigma)
v = math.abs(v)*-1.0+mrange[1]
v = max(v, mrange[0])
elif self.output_c2c==5:
v = np.random.uniform(0.41, 0.51)
if count>=175:
v = np.random.uniform(0.37, 0.47)
elif self.output_c2c==6:
v = np.random.uniform(0.41, 0.51)
if count>=175:
v = np.random.uniform(0.38, 0.48)
else:
assert False

91
src/train_softmax.py
View File

@@ -133,12 +133,14 @@ def parse_args():
help='')
parser.add_argument('--per-batch-size', type=int, default=128,
help='')
parser.add_argument('--margin-m', type=float, default=0.35,
parser.add_argument('--margin-m', type=float, default=0.5,
help='')
parser.add_argument('--margin-s', type=float, default=64.0,
help='')
parser.add_argument('--margin-a', type=float, default=0.0,
help='')
parser.add_argument('--margin-b', type=float, default=0.0,
help='')
parser.add_argument('--easy-margin', type=int, default=0,
help='')
parser.add_argument('--margin-verbose', type=int, default=0,
@@ -172,7 +174,7 @@ def parse_args():
parser.add_argument('--triplet-alpha', type=float, default=0.3, help='')
parser.add_argument('--triplet-max-ap', type=float, default=0.0, help='')
parser.add_argument('--verbose', type=int, default=2000, help='')
parser.add_argument('--loss-type', type=int, default=1,
parser.add_argument('--loss-type', type=int, default=4,
help='')
parser.add_argument('--incay', type=float, default=0.0,
help='feature incay')
@@ -396,48 +398,69 @@ def get_symbol(args, arg_params, aux_params):
body = mx.sym.broadcast_mul(gt_one_hot, diff)
fc7 = fc7+body
elif args.loss_type==5:
#s = args.margin_s
#m = args.margin_m
#assert s>0.0
#assert m>=0.0
#assert m<(math.pi/2)
#_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
#_weight = mx.symbol.L2Normalization(_weight, mode='instance')
#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 m>0.0:
# a1 = args.margin_a
# r1 = ta-a1
# r1 = mx.symbol.Activation(data=r1, act_type='relu')
# r1 = r1+a1
# t = mx.sym.arccos(cos_t)
# cond = t-1.0
# cond = mx.symbol.Activation(data=cond, act_type='relu')
# r = mx.sym.where(cond, r2, r1)
# t = t+var_m
# body = mx.sym.cos(t)
# new_zy = body*s
# if args.margin_verbose>0:
# new_cos_t = new_zy/s
# margin_symbols.append(mx.symbol.mean(new_cos_t))
# #margin_symbols.append(mx.symbol.mean(var_m))
# 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)
# body = mx.sym.broadcast_mul(gt_one_hot, diff)
# fc7 = fc7+body
s = args.margin_s
m = args.margin_m
assert s>0.0
assert m>=0.0
assert m<(math.pi/2)
#assert m>=0.0
#assert m<(math.pi/2)
_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0)
_weight = mx.symbol.L2Normalization(_weight, mode='instance')
#nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')
#cos_a = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
#theta_a = mx.sym.arccos(cos_a)
#gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = m, off_value = 0.0)
#theta_a = theta_a+gt_one_hot
#fc7 = math.pi/2 - theta_a
#fc7 = fc7*s
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
t = mx.sym.arccos(cos_t)
if args.margin_verbose>0:
margin_symbols.append(mx.symbol.mean(cos_t))
if m>0.0:
a1 = args.margin_a
r1 = ta-a1
r1 = mx.symbol.Activation(data=r1, act_type='relu')
r1 = r1+a1
t = mx.sym.arccos(cos_t)
cond = t-1.0
cond = mx.symbol.Activation(data=cond, act_type='relu')
r = mx.sym.where(cond, r2, r1)
t = t+var_m
body = mx.sym.cos(t)
new_zy = body*s
if args.margin_verbose>0:
new_cos_t = new_zy/s
margin_symbols.append(mx.symbol.mean(new_cos_t))
#margin_symbols.append(mx.symbol.mean(var_m))
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)
body = mx.sym.broadcast_mul(gt_one_hot, diff)
fc7 = fc7+body
margin_symbols.append(mx.symbol.mean(t))
if args.margin_a>0.0:
t = t*args.margin_a
if args.margin_m>0.0:
t = t+args.margin_m
body = mx.sym.cos(t)
if args.margin_b>0.0:
body = body - args.margin_b
new_zy = body*s
if args.margin_verbose>0:
margin_symbols.append(mx.symbol.mean(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)
body = mx.sym.broadcast_mul(gt_one_hot, diff)
fc7 = fc7+body
elif args.loss_type==6:
s = args.margin_s
m = args.margin_m