def extract(node):
param = node.pb.python_param
attrs = CaffePythonFrontExtractorOp.parse_param_str(param.param_str)
update_attrs = {
'feat_stride': 16,
'base_size': 16,
'min_size': 16,
'ratio': [0.5, 1, 2],
'scale': [8, 16, 32],
'pre_nms_topn': 6000,
'post_nms_topn': 300,
'nms_thresh': 0.7
}
if 'ratios' in attrs and 'ratio' in attrs:
log.error(
'Both ratios and ratio found, value of ratios will be used',
extra={'is_warning': True})
if 'scales' in attrs and 'scale' in attrs:
log.error(
'Both scales and scale found, value of scales will be used',
extra={'is_warning': True})
if 'ratios' in attrs:
attrs['ratio'] = attrs['ratios']
del attrs['ratios']
if 'scales' in attrs:
attrs['scale'] = attrs['scales']
del attrs['scales']
update_attrs.update(attrs)
CaffePythonFrontExtractorOp.check_param(
Op.get_op_class_by_name('Proposal'), update_attrs)
Op.get_op_class_by_name('Proposal').update_node_stat(
node, update_attrs)
return __class__.enabled
def extract_proposal_params(node, defaults):
param = node.pb.python_param
attrs = CaffePythonFrontExtractorOp.parse_param_str(param.param_str)
update_attrs = defaults
if 'ratios' in attrs and 'ratio' in attrs:
log.error(
'Both ratios and ratio found, value of ratios will be used',
extra={'is_warning': True})
if 'scales' in attrs and 'scale' in attrs:
log.error(
'Both scales and scale found, value of scales will be used',
extra={'is_warning': True})
if 'ratios' in attrs:
attrs['ratio'] = attrs['ratios']
del attrs['ratios']
if 'scales' in attrs:
attrs['scale'] = attrs['scales']
del attrs['scales']
update_attrs.update(attrs)
CaffePythonFrontExtractorOp.check_param(
Op.get_op_class_by_name('Proposal'), update_attrs)
Op.get_op_class_by_name('Proposal').update_node_stat(
node, update_attrs)
def test_python_extractor_for_op(self):
module = 'test_module'
layer = 'test_layer'
CaffePythonFrontExtractorOp.registered_ops['{}.{}'.format(module, layer)] = \
lambda node: CaffePythonFrontExtractorOp.parse_param_str(node.pb.python_param.param_str)
params = FakeMultiParam({
'module': module,
'layer': layer,
'param_str': "'feat_stride': 16"
})
ext = PythonFrontExtractorOp.extract(FakeNode(FakePythonProtoLayer(params), None))
self.assertEqual({'feat_stride': 16}, ext)
def test_get_attrs(self):
exp_attrs = {"test_attr_1": 12, "test_attr_2": "sdf sdf"}
param_str = "'test_attr_1': 12, 'test_attr_2': 'sdf sdf'"
attrs = CaffePythonFrontExtractorOp.get_attrs(
FakePythonParam(FakeMultiParam({'param_str': param_str})))
self.assertEqual(exp_attrs, attrs)