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add coco loss

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Jia Guo
2018-01-02 09:05:35 +08:00
parent 802a67b9e3
commit 5b65a9a028
2 changed files with 154 additions and 36 deletions
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37
src/train_softmax.py
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@@ -272,21 +272,18 @@ 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 II
nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')
anchor = mx.symbol.slice_axis(nembedding, axis=0, begin=0, end=args.per_batch_size//3)
positive = mx.symbol.slice_axis(nembedding, axis=0, begin=args.per_batch_size//3, end=2*args.per_batch_size//3)
negative = mx.symbol.slice_axis(nembedding, axis=0, begin=2*args.per_batch_size//3, end=args.per_batch_size)
ap = anchor - positive
an = anchor - negative
ap = ap*ap
an = an*an
ap = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1)
an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1)
triplet_loss = mx.symbol.Activation(data = (ap-an+args.triplet_alpha), act_type='relu')
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==9: #coco loss
centroids = []
for i in xrange(args.per_identities):
xs = mx.symbol.slice_axis(embedding, axis=0, begin=i*args.images_per_identity, end=(i+1)*args.images_per_identity)
mean = mx.symbol.mean(xs, axis=0, keepdims=True)
mean = mx.symbol.L2Normalization(mean, mode='instance')
centroids.append(mean)
centroids = mx.symbol.concat(*centroids, dim=0)
nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*args.coco_scale
fc7 = mx.symbol.dot(nembedding, centroids, transpose_b = True) #(batchsize, per_identities)
#extra_loss = mx.symbol.softmax_cross_entropy(fc7, gt_label, name='softmax_ce')/args.per_batch_size
#extra_loss = mx.symbol.BlockGrad(extra_loss)
else:
#embedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*float(args.loss_type)
embedding = embedding * 5
@@ -375,6 +372,7 @@ def train_net(args):
assert(args.num_classes>0)
print('num_classes', args.num_classes)
args.coco_scale = 0.5*math.log(float(args.num_classes-1))+3
#path_imglist = "/raid5data/dplearn/MS-Celeb-Aligned/lst2"
path_imgrec = os.path.join(data_dir, "train.rec")
@@ -394,13 +392,13 @@ def train_net(args):
args.beta_freeze = 5000
args.gamma = 0.06
if args.loss_type<10:
if args.loss_type<9:
assert args.images_per_identity==0
else:
if args.images_per_identity==0:
if args.loss_type==11:
args.images_per_identity = 2
elif args.loss_type==10:
elif args.loss_type==10 or args.loss_type==9:
args.images_per_identity = 16
elif args.loss_type==12:
args.images_per_identity = 5
@@ -445,6 +443,7 @@ def train_net(args):
data_extra = None
hard_mining = False
triplet_params = None
coco_mode = False
if args.loss_type==10:
hard_mining = True
_shape = (args.batch_size, args.per_batch_size)
@@ -467,6 +466,8 @@ def train_net(args):
c+=args.per_batch_size
elif args.loss_type==12:
triplet_params = [args.triplet_bag_size, args.triplet_alpha]
elif args.loss_type==9:
coco_mode = True
label_name = 'softmax_label'
if data_extra is None:
@@ -497,6 +498,7 @@ def train_net(args):
data_extra = data_extra,
hard_mining = hard_mining,
triplet_params = triplet_params,
coco_mode = coco_mode,
mx_model = model,
label_name = label_name,
)
@@ -516,6 +518,7 @@ def train_net(args):
data_extra = data_extra,
hard_mining = hard_mining,
triplet_params = triplet_params,
coco_mode = coco_mode,
mx_model = model,
label_name = label_name,
)