def test_psroipool_infer_nhwc(self):
graph = build_graph(
nodes_attributes,
[('node_1', 'psroipool'), ('node_2', 'psroipool'),
('psroipool', 'node_3'), ('node_3', 'op_output')], {
'node_3': {
'shape': None
},
'node_1': {
'shape': np.array([1, 227, 227, 3])
},
'node_2': {
'shape': np.array([100, 5])
},
'psroipool': {
'output_dim': 4,
'group_size': 15
}
})
graph.graph['layout'] = 'NHWC'
psroipool_node = Node(graph, 'psroipool')
PSROIPoolingOp.psroipooling_infer(psroipool_node)
exp_shape = np.array([100, 15, 15, 4])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def test_psroipool_infer_no_shape(self):
graph = build_graph(
nodes_attributes,
[('node_1', 'psroipool'), ('node_2', 'psroipool'),
('psroipool', 'node_3'), ('node_3', 'op_output')], {
'node_3': {
'shape': None
},
'node_1': {
'shape': None
},
'node_2': {
'shape': np.array([100, 5])
},
'psroipool': {
'output_dim': 4,
'group_size': 224
}
})
graph.graph['layout'] = 'NCHW'
psroipool_node = Node(graph, 'psroipool')
PSROIPoolingOp.psroipooling_infer(psroipool_node)
res_shape = graph.node['node_3']['shape']
self.assertIsNone(res_shape)
def extract(node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
spatial_scale = attrs.float('spatial_scale', None)
group_size = attrs.int('group_size', 0)
no_trans = attrs.bool('no_trans', False)
trans_std = attrs.float('trans_std', 0)
output_dim = attrs.int('output_dim', 0)
part_size = attrs.int('part_size', 0)
sample_per_part = attrs.int('sample_per_part', 1)
pooled_size = attrs.int('pooled_size', 0)
data = {
'spatial_scale': spatial_scale,
'mode': 'bilinear_deformable',
'group_size': group_size,
'output_dim': output_dim,
'no_trans': no_trans,
'trans_std': trans_std,
'part_size': part_size,
'spatial_bins_x': sample_per_part,
'spatial_bins_y': sample_per_part,
'pooled_width': pooled_size,
'pooled_height': pooled_size,
}
# update the attributes of the node
PSROIPoolingOp.update_node_stat(node, data)
return __class__.enabled
def extract(cls, node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
spatial_scale = attrs.float('spatial_scale', None)
group_size = attrs.int('group_size', 0)
no_trans = attrs.bool('no_trans', False)
trans_std = attrs.float('trans_std', 0)
output_dim = attrs.int('output_dim', 0)
part_size = attrs.int('part_size', 0)
sample_per_part = attrs.int('sample_per_part', 1)
pooled_size = attrs.int('pooled_size', 0)
data = {
'spatial_scale': spatial_scale,
'mode': 'bilinear_deformable',
'group_size': group_size,
'output_dim': output_dim,
'trans_std': trans_std,
'part_size': part_size,
'spatial_bins_x': sample_per_part,
'spatial_bins_y': sample_per_part,
'pooled_width': pooled_size,
'pooled_height': pooled_size,
}
# update the attributes of the node
if not node.graph.graph['cmd_params'].generate_experimental_IR_V10:
data.update({'no_trans': no_trans})
PSROIPoolingOp.update_node_stat(node, data)
else:
DeformablePSROIPoolingOp.update_node_stat(node, data)
return cls.enabled
def extract(cls, node):
proto_layer = node.pb
param = proto_layer.psroi_pooling_param
update_attrs = {
'spatial_scale': param.spatial_scale,
'output_dim': param.output_dim,
'group_size': param.group_size,
}
mapping_rule = merge_attrs(param, update_attrs)
mapping_rule.update(layout_attrs())
# update the attributes of the node
PSROIPoolingOp.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(cls, node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
spatial_scale = attrs.float("spatial_scale", None)
pooled_size = attrs.int("pooled_size", None)
output_dim = attrs.int("output_dim", None)
group_size = attrs.int("group_size", 0)
if group_size == 0:
group_size = pooled_size
data = {
'spatial_scale': spatial_scale,
'output_dim': output_dim,
'group_size': group_size,
}
# update the attributes of the node
PSROIPoolingOp.update_node_stat(node, data)
return cls.enabled