__all__ = [
'mean',
'mul',
'scale',
'sigmoid_cross_entropy_with_logits',
'elementwise_add',
'elementwise_div',
'elementwise_sub',
'elementwise_mul',
'elementwise_max',
'elementwise_min',
'elementwise_pow',
'clip',
'clip_by_norm',
'logical_and',
'logical_or',
'logical_xor',
'logical_not',
'uniform_random',
'uniform_random_batch_size_like',
'gaussian_random',
'gaussian_random_batch_size_like',
'cumsum',
'scatter',
'sum',
] + __activations__
for _OP in set(__all__):
globals()[_OP] = generate_layer_fn(_OP)
'bipartite_match',
'target_assign',
'detection_output',
'ssd_loss',
'detection_map',
]
__auto__ = [
'iou_similarity',
'box_coder',
]
__all__ += __auto__
for _OP in set(__auto__):
globals()[_OP] = generate_layer_fn(_OP)
def detection_output(loc,
scores,
prior_box,
prior_box_var,
background_label=0,
nms_threshold=0.3,
nms_top_k=400,
keep_top_k=200,
score_threshold=0.01,
nms_eta=1.0):
"""
**Detection Output Layer for Single Shot Multibox Detector (SSD).**
'logical_xor',
'logical_not',
'uniform_random_batch_size_like',
'gaussian_random',
'gaussian_random_batch_size_like',
'scatter',
'sum',
'slice',
'polygon_box_transform',
'shape',
'iou_similarity',
'maxout',
] + __activations__
for _OP in set(__all__):
globals()[_OP] = generate_layer_fn(_OP)
__all__ += ["uniform_random"]
_uniform_random_ = generate_layer_fn('uniform_random')
def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
kwargs = dict()
for name in locals():
val = locals()[name]
if val is not None:
kwargs[name] = val
return _uniform_random_(**kwargs)