def opset_7(cls, graph, node, **kw):
scale_node = graph.make_node(
'Constant',
attrs={'dtype': dtypes.ONNX.FLOAT,
'value': node.attr('scale')})
offset_node = graph.make_node(
'Constant',
attrs={'dtype': dtypes.ONNX.FLOAT,
'value': node.attr('offset')})
node0 = graph.make_node('Add', inputs=[node.input('X')[0], offset_node])
node1 = mapper_helper.clip_helper(graph, node, node0,
node.attr('threshold'), 0.0)
node2 = graph.make_node('Mul', inputs=[node.input('X')[0], node1])
node3 = graph.make_node(
'Div', inputs=[node2, scale_node], outputs=node.output('Out'))
def opset_9(cls, graph, node, **kw):
model_name = node.output('Boxes', 0)
input_shape = node.input_shape('X', 0)
mapper_helper.is_static_shape(input_shape)
image_size = node.input('ImgSize')
input_height = input_shape[2]
input_width = input_shape[3]
class_num = node.attr('class_num')
anchors = node.attr('anchors')
num_anchors = int(len(anchors)) // 2
scale_x_y = node.attr('scale_x_y')
downsample_ratio = node.attr('downsample_ratio')
input_size = input_height * downsample_ratio
conf_thresh = node.attr('conf_thresh')
conf_thresh_mat = [conf_thresh
] * num_anchors * input_height * input_width
cls.score_shape = [
1, input_height * input_width * int(num_anchors), class_num
]
im_outputs = []
x_shape = [1, num_anchors, 5 + class_num, input_height, input_width]
node_x_shape = graph.make_node('Constant',
attrs={
'dtype': dtypes.ONNX.INT64,
'value': x_shape
})
node_x_reshape = graph.make_node(
'Reshape', inputs=[node.input('X')[0], node_x_shape])
node_x_transpose = graph.make_node('Transpose',
inputs=[node_x_reshape],
perm=[0, 1, 3, 4, 2])
range_x = []
range_y = []
for i in range(0, input_width):
range_x.append(i)
for j in range(0, input_height):
range_y.append(j)
node_range_x = graph.make_node(
'Constant',
attrs={
'dtype': dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X',
0)],
'value': range_x,
})
node_range_y = graph.make_node(
'Constant',
inputs=[],
attrs={
'dtype': dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X',
0)],
'value': range_y,
})
range_x_new_shape = [1, input_width]
range_y_new_shape = [input_height, 1]
node_range_x_new_shape = graph.make_node('Constant',
inputs=[],
dtype=dtypes.ONNX.INT64,
value=range_x_new_shape)
node_range_y_new_shape = graph.make_node('Constant',
inputs=[],
dtype=dtypes.ONNX.INT64,
value=range_y_new_shape)
node_range_x_reshape = graph.make_node(
'Reshape', inputs=[node_range_x, node_range_x_new_shape])
node_range_y_reshape = graph.make_node(
'Reshape', inputs=[node_range_y, node_range_y_new_shape])
node_grid_x = graph.make_node(
"Tile", inputs=[node_range_x_reshape, node_range_y_new_shape])
node_grid_y = graph.make_node(
"Tile", inputs=[node_range_y_reshape, node_range_x_new_shape])
node_box_x = model_name + "@box_x"
node_box_y = model_name + "@box_y"
node_box_w = model_name + "@box_w"
node_box_h = model_name + "@box_h"
node_conf = model_name + "@conf"
node_prob = model_name + "@prob"
output = [
node_box_x, node_box_y, node_box_w, node_box_h, node_conf,
node_prob
]
node_split_input = mapper_helper.split_helper(
graph, [node_x_transpose],
output,
-1, [1, 1, 1, 1, 1, class_num],
dtype=node.input_dtype('X', 0))
node_box_x_sigmoid = graph.make_node("Sigmoid", inputs=[node_box_x])
node_box_y_sigmoid = graph.make_node("Sigmoid", inputs=[node_box_y])
if scale_x_y is not None:
bias_x_y = -0.5 * (scale_x_y - 1.0)
scale_x_y_node = graph.make_node(
'Constant',
attrs={
'dtype':
dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
'value': scale_x_y
})
bias_x_y_node = graph.make_node(
'Constant',
attrs={
'dtype':
dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
'value': bias_x_y
})
node_box_x_sigmoid = graph.make_node(
"Mul", inputs=[node_box_x_sigmoid, scale_x_y_node])
node_box_x_sigmoid = graph.make_node(
"Add", inputs=[node_box_x_sigmoid, bias_x_y_node])
node_box_y_sigmoid = graph.make_node(
"Mul", inputs=[node_box_y_sigmoid, scale_x_y_node])
node_box_y_sigmoid = graph.make_node(
"Add", inputs=[node_box_y_sigmoid, bias_x_y_node])
node_box_x_squeeze = mapper_helper.squeeze_helper(
graph, node_box_x_sigmoid, [4])
node_box_y_squeeze = mapper_helper.squeeze_helper(
graph, node_box_y_sigmoid, [4])
node_box_x_add_grid = graph.make_node(
"Add", inputs=[node_grid_x, node_box_x_squeeze])
node_box_y_add_grid = graph.make_node(
"Add", inputs=[node_grid_y, node_box_y_squeeze])
node_input_h = graph.make_node(
'Constant',
inputs=[],
dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
value=[input_height])
node_input_w = graph.make_node(
'Constant',
inputs=[],
dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
value=[input_width])
node_box_x_encode = graph.make_node(
'Div', inputs=[node_box_x_add_grid, node_input_w])
node_box_y_encode = graph.make_node(
'Div', inputs=[node_box_y_add_grid, node_input_h])
node_anchor_tensor = graph.make_node(
"Constant",
inputs=[],
dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
value=anchors)
anchor_shape = [int(num_anchors), 2]
node_anchor_shape = graph.make_node("Constant",
inputs=[],
dtype=dtypes.ONNX.INT64,
value=anchor_shape)
node_anchor_tensor_reshape = graph.make_node(
"Reshape", inputs=[node_anchor_tensor, node_anchor_shape])
node_input_size = graph.make_node(
"Constant",
inputs=[],
dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
value=[input_size])
node_anchors_div_input_size = graph.make_node(
"Div", inputs=[node_anchor_tensor_reshape, node_input_size])
node_anchor_w = model_name + "@anchor_w"
node_anchor_h = model_name + "@anchor_h"
node_anchor_split = mapper_helper.split_helper(
graph,
inputs=node_anchors_div_input_size,
axis=1,
split=[1, 1],
outputs=[node_anchor_w, node_anchor_h],
dtype=node.input_dtype('X', 0))
new_anchor_shape = [1, int(num_anchors), 1, 1]
node_new_anchor_shape = graph.make_node('Constant',
inputs=[],
dtype=dtypes.ONNX.INT64,
value=new_anchor_shape)
node_anchor_w_reshape = graph.make_node(
'Reshape', inputs=[node_anchor_w, node_new_anchor_shape])
node_anchor_h_reshape = graph.make_node(
'Reshape', inputs=[node_anchor_h, node_new_anchor_shape])
node_box_w_squeeze = mapper_helper.squeeze_helper(
graph, node_box_w, [4])
node_box_h_squeeze = mapper_helper.squeeze_helper(
graph, node_box_h, [4])
node_box_w_exp = graph.make_node("Exp", inputs=[node_box_w_squeeze])
node_box_h_exp = graph.make_node("Exp", inputs=[node_box_h_squeeze])
node_box_w_encode = graph.make_node(
'Mul', inputs=[node_box_w_exp, node_anchor_w_reshape])
node_box_h_encode = graph.make_node(
'Mul', inputs=[node_box_h_exp, node_anchor_h_reshape])
node_conf_sigmoid = graph.make_node('Sigmoid', inputs=[node_conf])
node_conf_thresh = graph.make_node(
'Constant',
inputs=[],
dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
value=conf_thresh_mat)
conf_shape = [1, int(num_anchors), input_height, input_width, 1]
node_conf_shape = graph.make_node('Constant',
inputs=[],
dtype=dtypes.ONNX.INT64,
value=conf_shape)
node_conf_thresh_reshape = graph.make_node(
'Reshape', inputs=[node_conf_thresh, node_conf_shape])
node_conf_sub = graph.make_node(
'Sub', inputs=[node_conf_sigmoid, node_conf_thresh_reshape])
node_conf_clip = mapper_helper.clip_helper(graph, node, node_conf_sub,
float(MAX_FLOAT32), 0.0)
node_zeros = graph.make_node(
'Constant',
inputs=[],
dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
value=[0])
node_conf_clip_bool = graph.make_node(
'Greater', inputs=[node_conf_clip, node_zeros])
node_conf_clip_cast = graph.make_node(
'Cast',
inputs=[node_conf_clip_bool],
to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
node_conf_set_zero = graph.make_node(
'Mul', inputs=[node_conf_sigmoid, node_conf_clip_cast])
node_prob_sigmoid = graph.make_node('Sigmoid', inputs=[node_prob])
new_shape = [1, int(num_anchors), input_height, input_width, 1]
node_new_shape = graph.make_node('Constant',
inputs=[],
dtype=dtypes.ONNX.INT64,
dims=[len(new_shape)],
value=new_shape)
node_conf_new_shape = graph.make_node(
'Reshape', inputs=[node_conf_set_zero, node_new_shape])
cls.node_score = graph.make_node(
'Mul', inputs=[node_prob_sigmoid, node_conf_new_shape])
node_conf_bool = graph.make_node(
'Greater', inputs=[node_conf_new_shape, node_zeros])
node_box_x_new_shape = graph.make_node(
'Reshape', inputs=[node_box_x_encode, node_new_shape])
node_box_y_new_shape = graph.make_node(
'Reshape', inputs=[node_box_y_encode, node_new_shape])
node_box_w_new_shape = graph.make_node(
'Reshape', inputs=[node_box_w_encode, node_new_shape])
node_box_h_new_shape = graph.make_node(
'Reshape', inputs=[node_box_h_encode, node_new_shape])
node_pred_box = graph.make_node(
'Concat',
inputs=[node_box_x_new_shape, node_box_y_new_shape, \
node_box_w_new_shape, node_box_h_new_shape],
axis=4)
node_conf_cast = graph.make_node(
'Cast',
inputs=[node_conf_bool],
to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
node_pred_box_mul_conf = graph.make_node(
'Mul', inputs=[node_pred_box, node_conf_cast])
box_shape = [1, int(num_anchors) * input_height * input_width, 4]
node_box_shape = graph.make_node('Constant',
inputs=[],
dtype=dtypes.ONNX.INT64,
value=box_shape)
node_pred_box_new_shape = graph.make_node(
'Reshape', inputs=[node_pred_box_mul_conf, node_box_shape])
node_pred_box_x = model_name + "@_pred_box_x"
node_pred_box_y = model_name + "@_pred_box_y"
node_pred_box_w = model_name + "@_pred_box_w"
node_pred_box_h = model_name + "@_pred_box_h"
if node.input_dtype('X', 0) == paddle.float64:
node_pred_box_new_shape = graph.make_node(
'Cast', inputs=[node_pred_box_new_shape], to=TensorProto.FLOAT)
node_pred_box_split = mapper_helper.split_helper(
graph,
inputs=node_pred_box_new_shape,
axis=2,
split=[1, 1, 1, 1],
outputs=[
node_pred_box_x, node_pred_box_y, node_pred_box_w,
node_pred_box_h
],
dtype=node.input_dtype('X', 0))
if node.input_dtype('X', 0) == paddle.float64:
node_pred_box_x = graph.make_node(
'Cast',
inputs=[node_pred_box_x],
to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
node_pred_box_y = graph.make_node(
'Cast',
inputs=[node_pred_box_y],
to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
node_pred_box_w = graph.make_node(
'Cast',
inputs=[node_pred_box_w],
to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
node_pred_box_h = graph.make_node(
'Cast',
inputs=[node_pred_box_h],
to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
node_number_two = graph.make_node(
"Constant",
inputs=[],
dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
value=[2])
node_half_w = graph.make_node(
"Div", inputs=[node_pred_box_w, node_number_two])
node_half_h = graph.make_node(
"Div", inputs=[node_pred_box_h, node_number_two])
node_pred_box_x1 = graph.make_node(
'Sub', inputs=[node_pred_box_x, node_half_w])
node_pred_box_y1 = graph.make_node(
'Sub', inputs=[node_pred_box_y, node_half_h])
node_pred_box_x2 = graph.make_node(
'Add', inputs=[node_pred_box_x, node_half_w])
node_pred_box_y2 = graph.make_node(
'Add', inputs=[node_pred_box_y, node_half_h])
node_sqeeze_image_size = mapper_helper.squeeze_helper(
graph, image_size[0], [0])
node_img_height = model_name + "@img_height"
node_img_width = model_name + "@img_width"
node_image_size_split = mapper_helper.split_helper(
graph, [node_sqeeze_image_size], [node_img_height, node_img_width],
-1, [1, 1],
dtype=node.input_dtype('X', 0))
node_img_width_cast = graph.make_node(
'Cast',
inputs=[node_img_width],
to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
node_img_height_cast = graph.make_node(
'Cast',
inputs=[node_img_height],
to=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)])
cls.node_pred_box_x1_decode = graph.make_node(
'Mul', inputs=[node_pred_box_x1,
node_img_width_cast]) #boxes[box_idx]
cls.node_pred_box_y1_decode = graph.make_node(
'Mul', inputs=[node_pred_box_y1,
node_img_height_cast]) #boxes[box_idx + 1]
cls.node_pred_box_x2_decode = graph.make_node(
'Mul', inputs=[node_pred_box_x2,
node_img_width_cast]) #boxes[box_idx + 2]
cls.node_pred_box_y2_decode = graph.make_node(
'Mul', inputs=[node_pred_box_y2,
node_img_height_cast]) #boxes[box_idx + 3]
if node.attr('clip_bbox'):
node_number_one = graph.make_node(
'Constant',
inputs=[],
dtype=dtypes.DTYPE_PADDLE_ONNX_MAP[node.input_dtype('X', 0)],
value=[1])
node_new_img_height = graph.make_node(
'Sub', inputs=[node_img_height_cast, node_number_one])
node_new_img_width = graph.make_node(
'Sub', inputs=[node_img_width_cast, node_number_one])
cls.node_pred_box_x2_sub_w = graph.make_node(
'Sub',
inputs=[cls.node_pred_box_x2_decode, node_new_img_width])
cls.node_pred_box_y2_sub_h = graph.make_node(
'Sub',
inputs=[cls.node_pred_box_y2_decode, node_new_img_height])
node_pred_box_x1_clip = mapper_helper.clip_helper(
graph, node, cls.node_pred_box_x1_decode, float(MAX_FLOAT32),
0.0)
node_pred_box_y1_clip = mapper_helper.clip_helper(
graph, node, cls.node_pred_box_y1_decode, float(MAX_FLOAT32),
0.0)
node_pred_box_x2_clip = mapper_helper.clip_helper(
graph, node, cls.node_pred_box_x2_sub_w, float(MAX_FLOAT32),
0.0)
node_pred_box_y2_clip = mapper_helper.clip_helper(
graph, node, cls.node_pred_box_y2_sub_h, float(MAX_FLOAT32),
0.0)
node_pred_box_x2_res = graph.make_node(
'Sub',
inputs=[cls.node_pred_box_x2_decode, node_pred_box_x2_clip])
node_pred_box_y2_res = graph.make_node(
'Sub',
inputs=[cls.node_pred_box_y2_decode, node_pred_box_y2_clip])
node_pred_box_result = graph.make_node(
'Concat',
inputs=[
node_pred_box_x1_clip, node_pred_box_y1_clip,
node_pred_box_x2_res, node_pred_box_y2_res
],
outputs=node.output('Boxes'),
axis=-1)
else:
node_pred_box_result = graph.make_node(
'Concat',
inputs=[
cls.node_pred_box_x1_decode, cls.node_pred_box_y1_decode,
cls.node_pred_box_x2_decode, cls.node_pred_box_y2_decode
],
outputs=node.output('Boxes'),
axis=-1)
node_score_shape = graph.make_node("Constant",
inputs=[],
dtype=dtypes.ONNX.INT64,
value=cls.score_shape)
node_score_new_shape = graph.make_node(
'Reshape',
inputs=[cls.node_score, node_score_shape],
outputs=node.output('Scores'))
def opset_1(cls, graph, node, **kw):
mapper_helper.clip_helper(graph, node,
node.input('X', 0),
node.attr('threshold'), 0.0,
node.output('Out', 0))