def get_symbol_train_job():
if args.use_synthetic_data:
(labels, images) = ofrecord_util.load_synthetic(args)
else:
labels, images = ofrecord_util.load_train_dataset(args)
image_size = images.shape[1:-1]
assert len(
image_size) == 2, "The length of image size must be equal to 2."
assert image_size[0] == image_size[
1], "image_size[0] should be equal to image_size[1]."
print("train image_size: ", image_size)
embedding = eval(config.net_name).get_symbol(images)
def _get_initializer():
return flow.random_normal_initializer(mean=0.0, stddev=0.01)
trainable = True
if config.loss_name == "softmax":
if args.model_parallel:
print("Training is using model parallelism now.")
labels = labels.with_distribute(flow.distribute.broadcast())
fc1_distribute = flow.distribute.broadcast()
fc7_data_distribute = flow.distribute.split(1)
fc7_model_distribute = flow.distribute.split(0)
else:
fc1_distribute = flow.distribute.split(0)
fc7_data_distribute = flow.distribute.split(0)
fc7_model_distribute = flow.distribute.broadcast()
fc7 = flow.layers.dense(
inputs=embedding.with_distribute(fc1_distribute),
units=config.num_classes,
activation=None,
use_bias=False,
kernel_initializer=_get_initializer(),
bias_initializer=None,
trainable=trainable,
name="fc7",
model_distribute=fc7_model_distribute,
)
fc7 = fc7.with_distribute(fc7_data_distribute)
elif config.loss_name == "margin_softmax":
if args.model_parallel:
print("Training is using model parallelism now.")
labels = labels.with_distribute(flow.distribute.broadcast())
fc1_distribute = flow.distribute.broadcast()
fc7_data_distribute = flow.distribute.split(1)
fc7_model_distribute = flow.distribute.split(0)
else:
fc1_distribute = flow.distribute.split(0)
fc7_data_distribute = flow.distribute.split(0)
fc7_model_distribute = flow.distribute.broadcast()
fc7_weight = flow.get_variable(
name="fc7-weight",
shape=(config.num_classes, embedding.shape[1]),
dtype=embedding.dtype,
initializer=_get_initializer(),
regularizer=None,
trainable=trainable,
model_name="weight",
distribute=fc7_model_distribute,
)
if args.partial_fc and args.model_parallel:
print(
"Training is using model parallelism and optimized by partial_fc now."
)
(
mapped_label,
sampled_label,
sampled_weight,
) = flow.distributed_partial_fc_sample(
weight=fc7_weight,
label=labels,
num_sample=args.total_num_sample,
)
labels = mapped_label
fc7_weight = sampled_weight
fc7_weight = flow.math.l2_normalize(input=fc7_weight,
axis=1,
epsilon=1e-10)
fc1 = flow.math.l2_normalize(input=embedding,
axis=1,
epsilon=1e-10)
fc7 = flow.matmul(a=fc1.with_distribute(fc1_distribute),
b=fc7_weight,
transpose_b=True)
fc7 = fc7.with_distribute(fc7_data_distribute)
fc7 = (flow.combined_margin_loss(fc7,
labels,
m1=config.loss_m1,
m2=config.loss_m2,
m3=config.loss_m3) *
config.loss_s)
fc7 = fc7.with_distribute(fc7_data_distribute)
else:
raise NotImplementedError
loss = flow.nn.sparse_softmax_cross_entropy_with_logits(
labels, fc7, name="softmax_loss")
lr_scheduler = flow.optimizer.PiecewiseScalingScheduler(
base_lr=args.lr,
boundaries=args.lr_steps,
scale=args.scales,
warmup=None)
flow.optimizer.SGDW(
lr_scheduler,
momentum=args.momentum if args.momentum > 0 else None,
weight_decay=args.weight_decay).minimize(loss)
return loss
def insightface_train_job():
if args.use_synthetic_data:
(labels, images) = ofrecord_util.load_synthetic(args)
else:
labels, images = ofrecord_util.load_train_dataset(args)
print("train batch data: ", images.shape)
embedding = insightface(images)
def _get_initializer():
return flow.random_normal_initializer(mean=0.0, stddev=0.01)
trainable = True
if args.loss_type == "arc_loss":
s = args.margin_s
m = args.margin
fc1 = flow.math.l2_normalize(input=embedding, axis=1, epsilon=1e-10)
fc1 = flow.math.multiply(fc1, s)
fc7 = flow.get_variable(
name="fc7-weight",
shape=(args.class_num, fc1.shape[1]),
dtype=fc1.dtype,
initializer=_get_initializer(),
trainable=trainable,
model_name="weight",
)
fc7 = flow.math.l2_normalize(input=fc7, axis=1, epsilon=1e-10)
matmul = flow.matmul(a=fc1, b=fc7, transpose_b=True)
labels_expand = flow.reshape(labels, (labels.shape[0], 1))
zy = flow.gather(matmul, labels_expand, batch_dims=1)
cos_t = flow.math.multiply(zy, 1 / s)
cos_m = math.cos(m)
sin_m = math.sin(m)
mm = math.sin(math.pi - m) * m
threshold = math.cos(math.pi - m)
if args.easy_margin:
cond = flow.math.relu(cos_t)
else:
cond_v = cos_t - threshold
cond = flow.math.relu(cond_v)
body = flow.math.square(cos_t)
body = flow.math.multiply(body, -1.0)
body = flow.math.add(1, body)
sin_t = flow.math.sqrt(body)
new_zy = flow.math.multiply(cos_t, cos_m)
b = flow.math.multiply(sin_t, sin_m)
b = flow.math.multiply(b, -1.0)
new_zy = flow.math.add(new_zy, b)
new_zy = flow.math.multiply(new_zy, s)
if args.easy_margin:
zy_keep = zy
else:
zy_keep = flow.math.add(zy, -s * mm)
cond = flow.cast(cond, dtype=flow.int32)
new_zy = flow.where(cond, new_zy, zy_keep)
zy = flow.math.multiply(zy, -1.0)
diff = flow.math.add(new_zy, zy)
gt_one_hot = flow.one_hot(
labels, depth=args.class_num, dtype=flow.float
)
body = flow.math.multiply(gt_one_hot, diff)
fc7 = flow.math.add(matmul, body)
elif args.loss_type == "margin_softmax":
fc7_weight = flow.get_variable(
name="fc7-weight",
shape=(args.class_num, embedding.shape[1]),
dtype=embedding.dtype,
initializer=_get_initializer(),
trainable=trainable,
model_name="weight",
)
s = args.margin_s
fc7_weight = flow.math.l2_normalize(
input=fc7_weight, axis=1, epsilon=1e-10
)
fc1 = (
flow.math.l2_normalize(input=embedding, axis=1, epsilon=1e-10) * s
)
fc7 = flow.matmul(a=fc1, b=fc7_weight, transpose_b=True)
if args.loss_m1 != 1.0 or args.loss_m2 != 0.0 or args.loss_m3 != 0.0:
if args.loss_m1 == 1.0 and args.loss_m2 == 0.0:
s_m = s * args.loss_m3
gt_one_hot = flow.one_hot(
labels,
depth=args.class_num,
on_value=s_m,
off_value=0.0,
dtype=flow.float,
)
fc7 = fc7 - gt_one_hot
else:
labels_expand = flow.reshape(labels, (labels.shape[0], 1))
zy = flow.gather(fc7, labels_expand, batch_dims=1)
cos_t = zy * (1 / s)
t = flow.math.acos(cos_t)
if args.loss_m1 != 1.0:
t = t * args.loss_m1
if args.loss_m2 > 0.0:
t = t + args.loss_m2
body = flow.math.cos(t)
if args.loss_m3 > 0.0:
body = body - args.loss_m3
new_zy = body * s
diff = new_zy - zy
gt_one_hot = flow.one_hot(
labels,
depth=args.class_num,
on_value=1.0,
off_value=0.0,
dtype=flow.float,
)
body = gt_one_hot * diff
fc7 = fc7 + body
elif args.loss_type == "softmax":
if args.model_parallel:
labels = labels.with_distribute(flow.distribute.broadcast())
fc1_distribute = flow.distribute.broadcast()
fc7_data_distribute = flow.distribute.split(1)
fc7_model_distribute = flow.distribute.split(0)
else:
fc1_distribute = flow.distribute.split(0)
fc7_data_distribute = flow.distribute.split(0)
fc7_model_distribute = flow.distribute.broadcast()
print("loss 0")
fc7 = flow.layers.dense(
inputs=embedding.with_distribute(fc1_distribute),
units=args.class_num,
activation=None,
use_bias=False,
kernel_initializer=_get_initializer(),
bias_initializer=None,
trainable=trainable,
name=args.models_name,
model_distribute=fc7_model_distribute,
)
fc7 = fc7.with_distribute(fc7_data_distribute)
elif args.loss_type == "arc_loss_ms":
labels = labels.with_distribute(flow.distribute.broadcast())
fc7_model_distribute = flow.distribute.split(0)
fc7_data_distribute = flow.distribute.split(1)
fc7_weight = flow.get_variable(
name="fc7-weight",
shape=(args.class_num, embedding.shape[1]),
dtype=embedding.dtype,
initializer=_get_initializer(),
trainable=trainable,
model_name="weight",
distribute=fc7_model_distribute,
)
s = args.margin_s
fc7_weight = flow.math.l2_normalize(
input=fc7_weight, axis=1, epsilon=1e-10
)
fc1 = (
flow.math.l2_normalize(input=embedding, axis=1, epsilon=1e-10)
)
fc1 = flow.parallel_cast(fc1, distribute=flow.distribute.broadcast())
fc7 = flow.matmul(a=fc1, b=fc7_weight, transpose_b=True) #s1
fc7 = flow.arc_loss(fc7, labels, margin=args.loss_m2)*60
fc7 = fc7.with_distribute(fc7_data_distribute)
else:
raise NotImplementedError
loss = flow.nn.sparse_softmax_cross_entropy_with_logits(
labels, fc7, name="softmax_loss"
)
lr_scheduler = flow.optimizer.PiecewiseScalingScheduler(args.base_lr, [100000, 140000, 160000], [0.1, 0.01, 0.001])
flow.optimizer.SGD(lr_scheduler, momentum=0.9).minimize(loss)
return loss