def run_Affectnet_training():
config = {
'batch_size':
64,
'val_batch_size':
40,
'img_size': (112, 112), # (128, 128),
'metric_update_period':
50,
'layers':
50,
'load_epoch':
0,
#'load_path': '/media/nlab/data/test/resnext50-valence-llr',
'save_model_prefix':
'/media/nlab/data/SF/mbnet-singleframe',
'emotions_list': [
'Neutral', 'Happy', 'Sad', 'Surprise', 'Fear', 'Anger', 'Disgust',
'Contempt'
],
# 'multiply_basic_ratio': 4
}
train_iter = AffectnetIter(data_json_path='../training.csv',
batch_size=config['batch_size'],
train=True,
img_size=config['img_size'],
detector=None)
train_iter.global_num_inst = int(
train_iter.n_objects /
config['batch_size']) * config['batch_size'] * config['load_epoch']
fc1 = fmobilefacenet.get_symbol()
module = mx.mod.Module(fc1, context=mx.gpu(0))
module.bind(data_shapes=train_iter.provide_data,
label_shapes=train_iter.provide_label)
module.init_params(arg_params=None,
aux_params=None,
initializer=mx.init.MSRAPrelu(),
allow_missing=True)
val_iter = AffectnetIter(data_json_path='../validation.csv',
batch_size=config['val_batch_size'],
train=False,
img_size=config['img_size'],
detector=None)
train_Affectnet(module, train_iter, val_iter, config)
def get_symbol(args, arg_params, aux_params):
data_shape = (args.image_channel, args.image_h, args.image_w)
image_shape = ",".join([str(x) for x in data_shape])
margin_symbols = []
if args.network[0] == 'd':
embedding = fdensenet.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)
elif args.network[0] == 'm':
print('init mobilenet', args.num_layers)
if args.num_layers == 1:
embedding = fmobilenet.get_symbol(
args.emb_size,
version_se=args.version_se,
version_input=args.version_input,
version_output=args.version_output,
version_unit=args.version_unit)
else:
embedding = fmobilenetv2.get_symbol(args.emb_size)
elif args.network[0] == 'i':
print('init inception-resnet-v2', args.num_layers)
embedding = finception_resnet_v2.get_symbol(
args.emb_size,
version_se=args.version_se,
version_input=args.version_input,
version_output=args.version_output,
version_unit=args.version_unit)
elif args.network[0] == 'x':
print('init xception', args.num_layers)
embedding = fxception.get_symbol(args.emb_size,
version_se=args.version_se,
version_input=args.version_input,
version_output=args.version_output,
version_unit=args.version_unit)
elif args.network[0] == 'p':
print('init dpn', args.num_layers)
embedding = fdpn.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)
elif args.network[0] == 'n':
print('init nasnet', args.num_layers)
embedding = fnasnet.get_symbol(args.emb_size)
elif args.network[0] == 's':
print('init spherenet', args.num_layers)
embedding = spherenet.get_symbol(args.emb_size, args.num_layers)
elif args.network[0] == 'y':
print('init mobilefacenet', args.num_layers)
embedding = fmobilefacenet.get_symbol(
args.emb_size,
bn_mom=args.bn_mom,
version_output=args.version_output)
else:
print('init resnet', args.num_layers)
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,
version_act=args.version_act)
all_label = mx.symbol.Variable('softmax_label')
gt_label = all_label
extra_loss = None
_weight = mx.symbol.Variable("fc7_weight",
shape=(args.num_classes, args.emb_size),
lr_mult=args.fc7_lr_mult,
wd_mult=args.fc7_wd_mult)
if args.loss_type == 0: #softmax
if args.fc7_no_bias:
fc7 = mx.sym.FullyConnected(data=embedding,
weight=_weight,
no_bias=True,
num_hidden=args.num_classes,
name='fc7')
else:
_bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
fc7 = mx.sym.FullyConnected(data=embedding,
weight=_weight,
bias=_bias,
num_hidden=args.num_classes,
name='fc7')
elif args.loss_type == 1: #sphere
_weight = mx.symbol.L2Normalization(_weight, mode='instance')
fc7 = mx.sym.LSoftmax(data=embedding,
label=gt_label,
num_hidden=args.num_classes,
weight=_weight,
beta=args.beta,
margin=args.margin,
scale=args.scale,
beta_min=args.beta_min,
verbose=1000,
name='fc7')
elif args.loss_type == 2:
s = args.margin_s
m = args.margin_m
assert (s > 0.0)
assert (m > 0.0)
_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')
s_m = s * m
gt_one_hot = mx.sym.one_hot(gt_label,
depth=args.num_classes,
on_value=s_m,
off_value=0.0)
fc7 = fc7 - gt_one_hot
elif args.loss_type == 4:
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)
if args.finetune:
print("{}finetuning from trained model".format('-' * 10))
_weight = mx.symbol.Variable("finetune_weight",
shape=(args.num_classes,
args.emb_size),
lr_mult=10.0)
_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
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.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)
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 == 5:
s = args.margin_s
m = args.margin_m
assert s > 0.0
_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')
if args.margin_a != 1.0 or args.margin_m != 0.0 or args.margin_b != 0.0:
if args.margin_a == 1.0 and args.margin_m == 0.0:
s_m = s * args.margin_b
gt_one_hot = mx.sym.one_hot(gt_label,
depth=args.num_classes,
on_value=s_m,
off_value=0.0)
fc7 = fc7 - gt_one_hot
else:
zy = mx.sym.pick(fc7, gt_label, axis=1)
cos_t = zy / s
t = mx.sym.arccos(cos_t)
if args.margin_a != 1.0:
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
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
out_list = [mx.symbol.BlockGrad(embedding)]
softmax = mx.symbol.SoftmaxOutput(data=fc7,
label=gt_label,
name='softmax',
normalization='valid')
out_list.append(softmax)
out = mx.symbol.Group(out_list)
return (out, arg_params, aux_params)
model.forward(db, is_train=False)
embedding = model.get_outputs()[0].asnumpy()
#embedding = sklearn.preprocessing.normalize(embedding).flatten()
end_time = time.time()
embedding_time += end_time - start_time
#print('cost of generate features:' + str(end_time - start_time))
return read_img_time / loop_time, crop_time / loop_time, embedding_time / loop_time
ave_image_read_dict = {}
ave_crop_dict = {}
ave_embedding_dict = {}
# 原始模型
embedding = fmobilefacenet.get_symbol(128, bn_mom=0.9, version_output='GNAP')
detector = MtcnnDetector(model_folder=mtcnn_path,
ctx=mx.cpu(0),
num_worker=1,
accurate_landmark=True,
threshold=[0.6, 0.7, 0.8])
ave_read_image_time, ave_crop_time, ave_embedding_time = cal_time_cost(
embedding, detector, 50)
print(ave_read_image_time, ave_crop_time, ave_embedding_time)
ave_image_read_dict['orignal'] = ave_read_image_time
ave_crop_dict['orignal'] = ave_crop_time
ave_embedding_dict['orignal'] = ave_embedding_time
# 去掉45，5层
embedding = fmobilefacenet.get_symbol1(128, bn_mom=0.9, version_output='GNAP')
detector = MtcnnDetector(model_folder=mtcnn_path,
def get_symbol(args, arg_params, aux_params):
data_shape = (args.image_channel, args.image_h, args.image_w)
image_shape = ",".join([str(x) for x in data_shape])
margin_symbols = []
if args.network[0] == 'd':
embedding = fdensenet.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)
elif args.network[0] == 'm':
print('init mobilenet', args.num_layers)
if args.num_layers == 1:
embedding = fmobilenet.get_symbol(
args.emb_size,
version_input=args.version_input,
version_output=args.version_output,
version_multiplier=args.version_multiplier)
else:
embedding = fmobilenetv2.get_symbol(args.emb_size)
elif args.network[0] == 'i':
print('init inception-resnet-v2', args.num_layers)
embedding = finception_resnet_v2.get_symbol(
args.emb_size,
version_se=args.version_se,
version_input=args.version_input,
version_output=args.version_output,
version_unit=args.version_unit)
elif args.network[0] == 'x':
print('init xception', args.num_layers)
embedding = fxception.get_symbol(args.emb_size,
version_se=args.version_se,
version_input=args.version_input,
version_output=args.version_output,
version_unit=args.version_unit)
elif args.network[0] == 'p':
print('init dpn', args.num_layers)
embedding = fdpn.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)
elif args.network[0] == 'n':
print('init nasnet', args.num_layers)
embedding = fnasnet.get_symbol(args.emb_size)
elif args.network[0] == 's':
print('init spherenet', args.num_layers)
embedding = spherenet.get_symbol(args.emb_size, args.num_layers)
elif args.network[0] == 'y':
print('init mobilefacenet', args.num_layers)
embedding = fmobilefacenet.get_symbol(
args.emb_size,
bn_mom=args.bn_mom,
version_output=args.version_output)
else:
print('init resnet', args.num_layers)
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,
version_act=args.version_act)
all_label = mx.symbol.Variable('softmax_label')
gt_label = all_label
extra_loss = None
_weight = mx.symbol.Variable("fc7_weight",
shape=(args.num_classes, args.emb_size),
lr_mult=args.fc7_lr_mult,
wd_mult=args.fc7_wd_mult)
if args.loss_type == 0: #softmax
if args.fc7_no_bias:
fc7 = mx.sym.FullyConnected(data=embedding,
weight=_weight,
no_bias=True,
num_hidden=args.num_classes,
name='fc7')
else:
_bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
fc7 = mx.sym.FullyConnected(data=embedding,
weight=_weight,
bias=_bias,
num_hidden=args.num_classes,
name='fc7')
elif args.loss_type == 1: #sphere
_weight = mx.symbol.L2Normalization(_weight, mode='instance')
fc7 = mx.sym.LSoftmax(data=embedding,
label=gt_label,
num_hidden=args.num_classes,
weight=_weight,
beta=args.beta,
margin=args.margin,
scale=args.scale,
beta_min=args.beta_min,
verbose=1000,
name='fc7')
elif args.loss_type == 2:
s = args.margin_s
m = args.margin_m
assert (s > 0.0)
assert (m > 0.0)
_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')
s_m = s * m
gt_one_hot = mx.sym.one_hot(gt_label,
depth=args.num_classes,
on_value=s_m,
off_value=0.0)
fc7 = fc7 - gt_one_hot
elif args.loss_type == 4:
s = args.margin_s
m = args.margin_m
assert s > 0.0
assert m >= 0.0
assert m < (math.pi / 2)
_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
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.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)
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 == 5:
s = args.margin_s
m = args.margin_m
assert s > 0.0
_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')
if args.margin_a != 1.0 or args.margin_m != 0.0 or args.margin_b != 0.0:
if args.margin_a == 1.0 and args.margin_m == 0.0:
s_m = s * args.margin_b
gt_one_hot = mx.sym.one_hot(gt_label,
depth=args.num_classes,
on_value=s_m,
off_value=0.0)
fc7 = fc7 - gt_one_hot
else:
zy = mx.sym.pick(fc7, gt_label, axis=1)
cos_t = zy / s
t = mx.sym.arccos(cos_t)
if args.margin_a != 1.0:
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
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
assert s > 0.0
assert args.margin_b > 0.0
_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
t = mx.sym.arccos(cos_t)
intra_loss = t / np.pi
intra_loss = mx.sym.mean(intra_loss)
#intra_loss = mx.sym.exp(cos_t*-1.0)
intra_loss = mx.sym.MakeLoss(intra_loss,
name='intra_loss',
grad_scale=args.margin_b)
if m > 0.0:
t = t + m
body = mx.sym.cos(t)
new_zy = body * s
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 == 7:
s = args.margin_s
m = args.margin_m
assert s > 0.0
assert args.margin_b > 0.0
assert args.margin_a > 0.0
_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
t = mx.sym.arccos(cos_t)
#counter_weight = mx.sym.take(_weight, gt_label, axis=1)
#counter_cos = mx.sym.dot(counter_weight, _weight, transpose_a=True)
counter_weight = mx.sym.take(_weight, gt_label, axis=0)
counter_cos = mx.sym.dot(counter_weight, _weight, transpose_b=True)
#counter_cos = mx.sym.minimum(counter_cos, 1.0)
#counter_angle = mx.sym.arccos(counter_cos)
#counter_angle = counter_angle * -1.0
#counter_angle = counter_angle/np.pi #[0,1]
#inter_loss = mx.sym.exp(counter_angle)
#counter_cos = mx.sym.dot(_weight, _weight, transpose_b=True)
#counter_cos = mx.sym.minimum(counter_cos, 1.0)
#counter_angle = mx.sym.arccos(counter_cos)
#counter_angle = mx.sym.sort(counter_angle, axis=1)
#counter_angle = mx.sym.slice_axis(counter_angle, axis=1, begin=0,end=int(args.margin_a))
#inter_loss = counter_angle*-1.0 # [-1,0]
#inter_loss = inter_loss+1.0 # [0,1]
inter_loss = counter_cos
inter_loss = mx.sym.mean(inter_loss)
inter_loss = mx.sym.MakeLoss(inter_loss,
name='inter_loss',
grad_scale=args.margin_b)
if m > 0.0:
t = t + m
body = mx.sym.cos(t)
new_zy = body * s
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
out_list = [mx.symbol.BlockGrad(embedding)]
softmax = mx.symbol.SoftmaxOutput(data=fc7,
label=gt_label,
name='softmax',
normalization='valid')
out_list.append(softmax)
if args.loss_type == 6:
out_list.append(intra_loss)
if args.loss_type == 7:
out_list.append(inter_loss)
#out_list.append(mx.sym.BlockGrad(counter_weight))
#out_list.append(intra_loss)
if args.ce_loss:
#ce_loss = mx.symbol.softmax_cross_entropy(data=fc7, label = gt_label, name='ce_loss')/args.per_batch_size
body = mx.symbol.SoftmaxActivation(data=fc7)
body = mx.symbol.log(body)
_label = mx.sym.one_hot(gt_label,
depth=args.num_classes,
on_value=-1.0,
off_value=0.0)
body = body * _label
ce_loss = mx.symbol.sum(body) / args.per_batch_size
out_list.append(mx.symbol.BlockGrad(ce_loss))
out = mx.symbol.Group(out_list)
return (out, arg_params, aux_params)
def get_symbol(args, arg_params, aux_params):
data_shape = (args.image_channel,args.image_h,args.image_w)
image_shape = ",".join([str(x) for x in data_shape])
margin_symbols = []
if args.network[0]=='d':
embedding = fdensenet.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)
elif args.network[0]=='m':
print('init mobilenet', args.num_layers)
if args.num_layers==1:
embedding = fmobilenet.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
else:
embedding = fmobilenetv2.get_symbol(args.emb_size)
elif args.network[0]=='i':
print('init inception-resnet-v2', args.num_layers)
embedding = finception_resnet_v2.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0]=='x':
print('init xception', args.num_layers)
embedding = fxception.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0]=='p':
print('init dpn', args.num_layers)
embedding = fdpn.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)
elif args.network[0]=='n':
print('init nasnet', args.num_layers)
embedding = fnasnet.get_symbol(args.emb_size)
elif args.network[0]=='s':
print('init spherenet', args.num_layers)
embedding = spherenet.get_symbol(args.emb_size, args.num_layers)
elif args.network[0]=='y':
print('init mobilefacenet', args.num_layers)
embedding = fmobilefacenet.get_symbol(args.emb_size, bn_mom = args.bn_mom, wd_mult = args.fc7_wd_mult)
else:
print('init resnet', args.num_layers)
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,
version_act=args.version_act)
all_label = mx.symbol.Variable('softmax_label')
gt_label = all_label
extra_loss = None
_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0, wd_mult=args.fc7_wd_mult)
if args.loss_type==0: #softmax
_bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=args.num_classes, name='fc7')
elif args.loss_type==1: #sphere
_weight = mx.symbol.L2Normalization(_weight, mode='instance')
fc7 = mx.sym.LSoftmax(data=embedding, label=gt_label, num_hidden=args.num_classes,
weight = _weight,
beta=args.beta, margin=args.margin, scale=args.scale,
beta_min=args.beta_min, verbose=1000, name='fc7')
elif args.loss_type==2:
s = args.margin_s
m = args.margin_m
assert(s>0.0)
assert(m>0.0)
_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')
s_m = s*m
gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = s_m, off_value = 0.0)
fc7 = fc7-gt_one_hot
elif args.loss_type==4:
s = args.margin_s
m = args.margin_m
assert s>0.0
assert m>=0.0
assert m<(math.pi/2)
_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
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.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)
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==5:
s = args.margin_s
m = args.margin_m
assert s>0.0
_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')
if args.margin_a!=1.0 or args.margin_m!=0.0 or args.margin_b!=0.0:
if args.margin_a==1.0 and args.margin_m==0.0:
s_m = s*args.margin_b
gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = s_m, off_value = 0.0)
fc7 = fc7-gt_one_hot
else:
zy = mx.sym.pick(fc7, gt_label, axis=1)
cos_t = zy/s
t = mx.sym.arccos(cos_t)
if args.margin_a!=1.0:
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
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
out_list = [mx.symbol.BlockGrad(embedding)]
softmax = mx.symbol.SoftmaxOutput(data=fc7, label = gt_label, name='softmax', normalization='valid')
out_list.append(softmax)
out = mx.symbol.Group(out_list)
return (out, arg_params, aux_params)
def get_symbol(args, arg_params, aux_params):
data_shape = (args.image_channel,args.image_h,args.image_w)
image_shape = ",".join([str(x) for x in data_shape])
margin_symbols = []
if args.network[0]=='d':
embedding = fdensenet.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)
elif args.network[0]=='m':
print('init mobilenet', args.num_layers)
if args.num_layers==1:
embedding = fmobilenet.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
else:
embedding = fmobilenetv2.get_symbol(args.emb_size)
elif args.network[0]=='i':
print('init inception-resnet-v2', args.num_layers)
embedding = finception_resnet_v2.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0]=='x':
print('init xception', args.num_layers)
embedding = fxception.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0]=='p':
print('init dpn', args.num_layers)
embedding = fdpn.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)
elif args.network[0]=='n':
print('init nasnet', args.num_layers)
embedding = fnasnet.get_symbol(args.emb_size)
elif args.network[0]=='s':
print('init spherenet', args.num_layers)
embedding = spherenet.get_symbol(args.emb_size, args.num_layers)
elif args.network[0]=='y':
print('init mobilefacenet', args.num_layers)
embedding = fmobilefacenet.get_symbol(args.emb_size)
else:
print('init resnet', args.num_layers)
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,
version_act=args.version_act)
all_label = mx.symbol.Variable('softmax_label')
gt_label = all_label
extra_loss = None
s = args.margin_s
#m = args.margin_m
assert s>0.0
nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
out_list = [mx.symbol.BlockGrad(embedding)]
_args = copy.deepcopy(args)
if USE_FR:
_args.grad_scale = 1.0
fr_label = mx.symbol.slice_axis(all_label, axis=1, begin=0, end=1)
fr_label = mx.symbol.reshape(fr_label, (args.per_batch_size,))
fr_softmax = get_softmax(_args, embedding, nembedding, fr_label, 'fc7')
out_list.append(fr_softmax)
if USE_GENDER:
_args.grad_scale = 0.2
_args.margin_a = 0.0
_args.num_classes = 2
gender_label = mx.symbol.slice_axis(all_label, axis=1, begin=1, end=2)
gender_label = mx.symbol.reshape(gender_label, (args.per_batch_size,))
gender_softmax = get_softmax(_args, embedding, nembedding, gender_label, 'fc8')
out_list.append(gender_softmax)
if USE_AGE:
_args.grad_scale = 0.01
_args.margin_a = 0.0
_args.num_classes = 2
for i in xrange(AGE):
age_label = mx.symbol.slice_axis(all_label, axis=1, begin=2+i, end=3+i)
age_label = mx.symbol.reshape(age_label, (args.per_batch_size,))
age_softmax = get_softmax(_args, embedding, nembedding, age_label, 'fc9_%d'%(i))
out_list.append(age_softmax)
out = mx.symbol.Group(out_list)
return (out, arg_params, aux_params)
def get_symbol(args, arg_params, aux_params):
# define network
data_shape = (args.image_channel, args.image_h, args.image_w)
image_shape = ",".join([str(x) for x in data_shape])
margin_symbols = []
args.num_layers = 1
print('init mobilefacenet', args.num_layers)
embedding = fmobilefacenet.get_symbol(args.emb_size,
bn_mom=args.bn_mom,
wd_mult=args.fc7_wd_mult)
# define loss
all_label = mx.symbol.Variable('softmax_label')
gt_label = all_label
_weight = mx.symbol.Variable("fc7_weight",
shape=(args.num_classes, args.emb_size),
lr_mult=1.0,
wd_mult=args.fc7_wd_mult)
if args.loss_type == 0: #softmax
_bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
fc7 = mx.sym.FullyConnected(data=embedding,
weight=_weight,
bias=_bias,
num_hidden=args.num_classes,
name='fc7')
elif args.loss_type == 4:
s = args.margin_s
m = args.margin_m
assert s > 0.0
assert m >= 0.0
assert m < (math.pi / 2)
_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
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.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)
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
out_list = [mx.symbol.BlockGrad(embedding)]
logit_t_val = mx.symbol.Variable('logit_t')
softmax = mx.symbol.SoftmaxOutput(data=fc7,
label=gt_label,
name='softmax',
normalization='valid')
soft_loss = mx.symbol.mean(
mx.symbol.square(fc7 / args.tau - logit_t_val / args.tau))
log_softmax = mx.sym.log_softmax(fc7)
hard_loss = -mx.sym.sum(mx.sym.broadcast_mul(
gt_one_hot, log_softmax)) / args.batch_size
total_loss = soft_loss * args.lamda + hard_loss * (1 - args.lamda)
total_loss = mx.symbol.MakeLoss(total_loss)
out_list.append(mx.sym.BlockGrad(softmax))
out_list.append(total_loss)
out = mx.sym.Group(out_list)
# out = mx.sym.Group([mx.sym.BlockGrad(embedding), softmax])
return (out, arg_params, aux_params)
def get_symbol(args, arg_params, aux_params):
# data_shape = (args.image_channel, args.image_h, args.image_w) # (3L,112L,112L)
# image_shape = ",".join([str(x) for x in data_shape]) #3,112,112
# margin_symbols = []
print('***network: ', args.network) # r100
if args.network[0] == 'd': # densenet
embedding = fdensenet.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)
elif args.network[0] == 'm': # mobilenet
print('init mobilenet', args.num_layers)
if args.num_layers == 1:
embedding = fmobilenet.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
else:
embedding = fmobilenetv2.get_symbol(args.emb_size)
# elif args.network[0] == 'v':
# print('init MobileNet-V3', args.num_layers)
# embedding = fmobilenetv3.get_symbol(args.emb_size)
elif args.network[0] == 'i': # inception-resnet-v2
print('init inception-resnet-v2', args.num_layers)
embedding = finception_resnet_v2.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0] == 'x':
print('init xception', args.num_layers)
embedding = fxception.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0] == 'p':
print('init dpn', args.num_layers)
embedding = fdpn.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)
elif args.network[0] == 'n':
print('init nasnet', args.num_layers)
embedding = fnasnet.get_symbol(args.emb_size)
elif args.network[0] == 's':
print('init spherenet', args.num_layers)
embedding = spherenet.get_symbol(args.emb_size, args.num_layers)
elif args.network[0] == 'y':
print('init mobilefacenet', args.num_layers)
embedding = fmobilefacenet.get_symbol(args.emb_size, bn_mom=args.bn_mom, version_output=args.version_output)
else: # 执行resnet
print('init resnet, 层数: ', args.num_layers)
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,
version_act=args.version_act)
# get_symbol
all_label = mx.symbol.Variable('softmax_label')
gt_label = all_label
# extra_loss = None
# 重新定义fc7的权重
_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=args.fc7_lr_mult,
wd_mult=args.fc7_wd_mult)
if args.loss_type == 0: # softmax
_bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
fc7 = mx.sym.FullyConnected(data=embedding, weight=_weight, bias=_bias, num_hidden=args.num_classes, name='fc7')
elif args.loss_type == 1: # sphere
print('*******'*10)
_weight = mx.symbol.L2Normalization(_weight, mode='instance')
fc7 = mx.sym.LSoftmax(data=embedding, label=gt_label, num_hidden=args.num_classes,
weight=_weight,
beta=args.beta, margin=args.margin, scale=args.scale,
beta_min=args.beta_min, verbose=1000, name='fc7')
elif args.loss_type == 2: # CosineFace
s = args.margin_s
m = args.margin_m
assert (s > 0.0)
assert (m > 0.0)
_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')
s_m = s * m
gt_one_hot = mx.sym.one_hot(gt_label, depth=args.num_classes, on_value=s_m,
off_value=0.0) # onehot两个值最大值s_m,最小值0.0
fc7 = fc7 - gt_one_hot
elif args.loss_type == 4: # ArcFace
s = args.margin_s # 参数s， 64
m = args.margin_m # 参数m， 0.5
assert s > 0.0
assert m >= 0.0
assert m < (math.pi / 2)
# pdb.set_trace()
# 权重归一化
_weight = mx.symbol.L2Normalization(_weight, mode='instance') # shape = [(4253, 512)]
# 特征归一化，并放大到 s*x
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') # args.num_classes:8631
zy = mx.sym.pick(fc7, gt_label, axis=1) # fc7每一行找出gt_label对应的值, 即s*cos_t
cos_t = zy / s # 网络输出output = s*x/|x|*w/|w|*cos(theta), 这里将输出除以s，得到实际的cos值，即cos（theta)
cos_m = math.cos(m)
sin_m = math.sin(m)
mm = math.sin(math.pi - m) * m # sin(pi-m)*m = sin(m) * m 0.2397
# threshold = 0.0
threshold = math.cos(math.pi - m) # 这个阈值避免theta+m >= pi, 实际上threshold<0 -cos(m) -0.8775825618903726
if args.easy_margin: # 将0作为阈值，得到超过阈值的索引
cond = mx.symbol.Activation(data=cos_t, act_type='relu')
else:
cond_v = cos_t - threshold # 将负数作为阈值
cond = mx.symbol.Activation(data=cond_v, act_type='relu')
body = cos_t * cos_t # cos_t^2 + sin_t^2 = 1
body = 1.0 - body
sin_t = mx.sym.sqrt(body)
new_zy = cos_t * cos_m # cos(t+m) = cos(t)cos(m) - sin(t)sin(m)
b = sin_t * sin_m
new_zy = new_zy - b
new_zy = new_zy * s # s*cos(t + m)
if args.easy_margin:
zy_keep = zy # zy_keep为zy，即s*cos(theta)
else:
zy_keep = zy - s * mm # zy-s*sin(m)*m = s*cos(t)- s*m*sin(m)
new_zy = mx.sym.where(cond, new_zy,
zy_keep) # cond中>0的保持new_zy=s*cos(theta+m)不变，<0的裁剪为zy_keep= s*cos(theta) or s*cos(theta)-s*m*sin(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) # 对应yi处为new_zy - zy
fc7 = fc7 + body # 对应yi处，fc7=zy + (new_zy - zy) = new_zy，即cond中>0的为s*cos(theta+m)，<0的裁剪为s*cos(theta) or s*cos(theta)-s*m*sin(m)
elif args.loss_type == 5:
s = args.margin_s
m = args.margin_m
assert s > 0.0
_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')
if args.margin_a != 1.0 or args.margin_m != 0.0 or args.margin_b != 0.0:
if args.margin_a == 1.0 and args.margin_m == 0.0:
s_m = s * args.margin_b
gt_one_hot = mx.sym.one_hot(gt_label, depth=args.num_classes, on_value=s_m, off_value=0.0)
fc7 = fc7 - gt_one_hot
else:
zy = mx.sym.pick(fc7, gt_label, axis=1)
cos_t = zy / s
t = mx.sym.arccos(cos_t)
if args.margin_a != 1.0:
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
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
out_list = [mx.symbol.BlockGrad(embedding)]
softmax = mx.symbol.SoftmaxOutput(data=fc7, label=gt_label, name='softmax', normalization='valid')
out_list.append(softmax)
out = mx.symbol.Group(out_list)
# print(out)
# sys.exit()
return (out, arg_params, aux_params)
def get_symbol(args, arg_params, aux_params):
data_shape = (args.image_channel, args.image_h, args.image_w)
image_shape = ",".join([str(x) for x in data_shape])
margin_symbols = []
if args.network[0] == 'd':
embedding = fdensenet.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)
elif args.network[0] == 'm':
print('init mobilenet', args.num_layers)
if args.num_layers == 1:
embedding = fmobilenet.get_symbol(
args.emb_size,
version_se=args.version_se,
version_input=args.version_input,
version_output=args.version_output,
version_unit=args.version_unit)
else:
embedding = fmobilenetv2.get_symbol(args.emb_size)
elif args.network[0] == 'i':
print('init inception-resnet-v2', args.num_layers)
embedding = finception_resnet_v2.get_symbol(
args.emb_size,
version_se=args.version_se,
version_input=args.version_input,
version_output=args.version_output,
version_unit=args.version_unit)
elif args.network[0] == 'x':
print('init xception', args.num_layers)
embedding = fxception.get_symbol(args.emb_size,
version_se=args.version_se,
version_input=args.version_input,
version_output=args.version_output,
version_unit=args.version_unit)
elif args.network[0] == 'p':
print('init dpn', args.num_layers)
embedding = fdpn.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)
elif args.network[0] == 'n':
print('init nasnet', args.num_layers)
embedding = fnasnet.get_symbol(args.emb_size)
elif args.network[0] == 's':
print('init spherenet', args.num_layers)
embedding = spherenet.get_symbol(args.emb_size, args.num_layers)
elif args.network[0] == 'y':
print('init mobilefacenet', args.num_layers)
embedding = fmobilefacenet.get_symbol(args.emb_size,
bn_mom=args.bn_mom,
wd_mult=args.fc7_wd_mult)
else:
print('init resnet', args.num_layers)
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,
version_act=args.version_act)
all_label = mx.symbol.Variable('softmax_label')
gt_label = all_label
extra_loss = None
_weight = mx.symbol.Variable("fc7_weight",
shape=(args.num_classes, args.emb_size),
lr_mult=1.0,
wd_mult=args.fc7_wd_mult)
if args.loss_type == 0: #softmax
_bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
fc7 = mx.sym.FullyConnected(data=embedding,
weight=_weight,
bias=_bias,
num_hidden=args.num_classes,
name='fc7')
elif args.loss_type == 1: #sphere
_weight = mx.symbol.L2Normalization(_weight, mode='instance')
fc7 = mx.sym.LSoftmax(data=embedding,
label=gt_label,
num_hidden=args.num_classes,
weight=_weight,
beta=args.beta,
margin=args.margin,
scale=args.scale,
beta_min=args.beta_min,
verbose=1000,
name='fc7')
elif args.loss_type == 2:
s = args.margin_s
m = args.margin_m
assert (s > 0.0)
assert (m > 0.0)
_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')
s_m = s * m
gt_one_hot = mx.sym.one_hot(gt_label,
depth=args.num_classes,
on_value=s_m,
off_value=0.0)
fc7 = fc7 - gt_one_hot
elif args.loss_type == 4:
s = args.margin_s
m = args.margin_m
assert s > 0.0
assert m >= 0.0
assert m < (math.pi / 2)
_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')
# split fc7 into fc7(labeled part) and fc7_2(unlabeled part)
# calculate the likelihood function of the fc7_2 part
subtract = args.num_classes * math.log(
args.num_classes
) ############### This is calculated based on the number of labeled classes
num_unlabeled = args.num_unlabeled
num_labeled = args.per_batch_size - args.num_unlabeled
per_batch_size = args.per_batch_size
assert num_labeled > 0
fc7_2 = mx.sym.slice_axis(fc7,
axis=0,
begin=num_labeled,
end=per_batch_size)
fc7 = mx.sym.slice_axis(fc7, axis=0, begin=0, end=num_labeled)
fc7_2 = mx.sym.softmax(data=fc7_2, axis=1)
log_likelihood = -mx.sym.log_softmax(fc7_2, axis=1)
log_likelihood_loss = (mx.sym.sum(log_likelihood) / num_unlabeled)
log_likelihood_loss = log_likelihood_loss - subtract
gt_label = mx.sym.slice_axis(gt_label,
axis=0,
begin=0,
end=num_labeled)
############################################################
#last_to_append = mx.symbol.BlockGrad(mx.sym.softmax(fc7))
out_list = []
zy = mx.sym.pick(fc7, gt_label, axis=1)
cos_t = zy / s
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.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)
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 == 5:
s = args.margin_s
m = args.margin_m
assert s > 0.0
_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')
if args.margin_a != 1.0 or args.margin_m != 0.0 or args.margin_b != 0.0:
if args.margin_a == 1.0 and args.margin_m == 0.0:
s_m = s * args.margin_b
gt_one_hot = mx.sym.one_hot(gt_label,
depth=args.num_classes,
on_value=s_m,
off_value=0.0)
fc7 = fc7 - gt_one_hot
else:
zy = mx.sym.pick(fc7, gt_label, axis=1)
cos_t = zy / s
t = mx.sym.arccos(cos_t)
if args.margin_a != 1.0:
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
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
out_list.append(mx.symbol.BlockGrad(embedding))
if args.loss_type == 4:
#softmax = mx.sym.softmax_cross_entropy(data=fc7, label=gt_label)
cos_fc7 = mx.symbol.BlockGrad(
fc7) # TODO: maybe this softmax makes the accuracy inconsistent
gt_label_one_hot = mx.sym.one_hot(gt_label, args.num_classes)
fc7 = -mx.sym.log_softmax(fc7, axis=1)
softmax = gt_label_one_hot * fc7
softmax = mx.sym.sum(softmax) / (args.per_batch_size -
args.num_unlabeled)
scale = args.likelihood_scale_factor
loss = mx.sym.make_loss(data=(softmax + log_likelihood_loss * scale))
out_list.append(loss)
out_list.append(cos_fc7)
out_list.append(mx.sym.BlockGrad(softmax))
out_list.append(mx.sym.BlockGrad(log_likelihood_loss))
else:
softmax = mx.symbol.SoftmaxOutput(data=fc7,
label=gt_label,
name='softmax',
normalization='valid')
out_list.append(softmax)
out = mx.symbol.Group(out_list)
return (out, arg_params, aux_params)
def get_symbol(args, arg_params, aux_params):
data_shape = (args.image_channel,args.image_h,args.image_w)
image_shape = ",".join([str(x) for x in data_shape])
margin_symbols = []
if args.network[0]=='d':
embedding = fdensenet.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)
elif args.network[0]=='m':
print('init mobilenet', args.num_layers)
if args.num_layers==1:
embedding = fmobilenet.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
else:
embedding = fmobilenetv2.get_symbol(args.emb_size)
elif args.network[0]=='i':
print('init inception-resnet-v2', args.num_layers)
embedding = finception_resnet_v2.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0]=='x':
print('init xception', args.num_layers)
embedding = fxception.get_symbol(args.emb_size,
version_se=args.version_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0]=='p':
print('init dpn', args.num_layers)
embedding = fdpn.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)
elif args.network[0]=='n':
print('init nasnet', args.num_layers)
embedding = fnasnet.get_symbol(args.emb_size)
elif args.network[0]=='s':
print('init spherenet', args.num_layers)
embedding = spherenet.get_symbol(args.emb_size, args.num_layers)
elif args.network[0]=='y':
print('init mobilefacenet', args.num_layers)
embedding = fmobilefacenet.get_symbol(args.emb_size, bn_mom = args.bn_mom, wd_mult = args.fc7_wd_mult)
else:
print('init resnet', args.num_layers)
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,
version_act=args.version_act)
all_label = mx.symbol.Variable('softmax_label')
#center_label = mx.symbol.Variable('center_label')
gt_label = all_label
extra_loss = None
_weight = mx.symbol.Variable("fc7_weight", shape=(args.num_classes, args.emb_size), lr_mult=1.0, wd_mult=args.fc7_wd_mult)
if args.loss_type==0: #softmax
_bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)
fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=args.num_classes, name='fc7')
elif args.loss_type==1: #sphere
_weight = mx.symbol.L2Normalization(_weight, mode='instance')
fc7 = mx.sym.LSoftmax(data=embedding, label=gt_label, num_hidden=args.num_classes,
weight = _weight,
beta=args.beta, margin=args.margin, scale=args.scale,
beta_min=args.beta_min, verbose=1000, name='fc7')
elif args.loss_type==2:
s = args.margin_s
m = args.margin_m
assert(s>0.0)
assert(m>0.0)
_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')
s_m = s*m
gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = s_m, off_value = 0.0)
fc7 = fc7-gt_one_hot
elif args.loss_type==4:
s = args.margin_s
m = args.margin_m
assert s>0.0
assert m>=0.0
assert m<(math.pi/2)
_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
t = mx.sym.arccos(cos_t)
if args.margin_a!=1.0:
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
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==5:
s = args.margin_s
m = args.margin_m
assert s>0.0
_weight = mx.symbol.L2Normalization(_weight, mode='instance')
nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
nembedding = mx.symbol.Dropout(data=nembedding, p=0.4)
fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
if args.margin_a!=1.0 or args.margin_m!=0.0 or args.margin_b!=0.0:
if args.margin_a==1.0 and args.margin_m==0.0:
s_m = s*args.margin_b
gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = s_m, off_value = 0.0)
fc7 = fc7-gt_one_hot
else:
zy = mx.sym.pick(fc7, gt_label, axis=1)
cos_t = zy/s
t = mx.sym.arccos(cos_t)
if args.margin_a!=1.0:
t = t*args.margin_a
if args.margin_m>0.0:
t = t+args.margin_m
#threshold = math.cos(math.pi-m)
if args.easy_margin: # m<pi/2 so: pi-m > pi/2
cond = mx.symbol.Activation(data=cos_t, act_type='relu') # this means t > pi/2, when pi/2 <t < 3*pi/2, sin is decrease
#cond_v = cos_t - threshold
#cond = mx.symbol.Activation(data=cond_v, act_type='relu')
else:
cond_v = math.pi - t
cond = mx.symbol.Activation(data=cond_v, act_type='relu')
print("not easy margin: cond")
#cond = mx.symbol.Activation(data=cos_t, act_type='relu')
if args.easy_margin:
zy_keep = mx.sym.cos(t)
else:
print("not easy margin: sine")
zy_keep = mx.sym.sin(t)-1
new_zy = mx.sym.cos(t)
body = mx.sym.where(cond, new_zy, zy_keep)
#body = mx.sym.cos(t)
if args.margin_b>0.0:
body = body + args.margin_b
new_zy = body*s
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
assert s>0.0
_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')
if args.margin_a!=1.0 or args.margin_m!=0.0 or args.margin_b!=0.0:
if args.margin_a==1.0 and args.margin_m==0.0:
s_m = s*args.margin_b
gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = s_m, off_value = 0.0)
fc7 = fc7-gt_one_hot
else:
zy = mx.sym.pick(fc7, gt_label, axis=1)
cos_t = zy/s
t = mx.sym.arccos(cos_t)
if args.margin_a!=1.0:
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
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
out_list = [mx.symbol.BlockGrad(embedding)]
softmax = mx.symbol.SoftmaxOutput(data=fc7, label = gt_label, name='softmax', normalization='valid')
out_list.append(softmax)
#print("out shape",np.shape(softmax))
#center_in = mx.symbol.concat(embedding,fc7,dim=1)
#center_loss_data = mx.symbol.Custom(data=embedding, label=gt_label, name='center_loss_data', op_type='centerloss',\
# num_class=args.num_classes, alpha=0.5, scale=0.5,lamdb=0.1,batchsize=args.per_batch_size,emb_size=args.emb_size)
#extra_center_loss = mx.symbol.MakeLoss(name='extra_center_loss', data=center_loss_data)
#total_loss = mx.symbol.ElementWiseSum([softmax, extra_loss],name='total_loss')
#total_loss_op = mx.symbol.MakeLoss(name='total_loss_op',data=total_loss)
#out_list.append(extra_center_loss)
out = mx.symbol.Group(out_list)
return (out, arg_params, aux_params)
def get_symbol(args, arg_params, aux_params):
# data_shape = (args.image_channel, args.image_h, args.image_w)
data_shape = (3, 112, 112)
image_shape = ",".join([str(x) for x in data_shape])
margin_symbols = []
# print('init mobilefacenet', args.num_layers)
embedding = fmobilefacenet.get_symbol(args.emb_size,
bn_mom=args.bn_mom,
version_output=args.version_output)
all_label = mx.symbol.Variable('softmax_label')
gt_label = all_label
extra_loss = None
_weight = mx.symbol.Variable("fc7_weight",
shape=(args.num_classes, args.emb_size),
lr_mult=args.fc7_lr_mult,
wd_mult=args.fc7_wd_mult)
if args.loss_type == 4:
s = args.margin_s
m = args.margin_m
assert s > 0.0
assert m >= 0.0
assert m < (math.pi / 2)
_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
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.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)
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
out_list = [mx.symbol.BlockGrad(embedding)]
softmax = mx.symbol.SoftmaxOutput(data=fc7,
label=gt_label,
name='softmax',
normalization='valid')
out_list.append(softmax)
out = mx.symbol.Group(out_list)
return (out, arg_params, aux_params)
