New approach for Proposal sub-graph conversion for TF OD API models (#5847)
* Draft version of new approach for Proposal sub-graph conversion for TF OD API models * Added clip_after_nms = True for Proposal sub-graph being replaced with DetectionOutput * Refactored code to insert DetectionOutput instead of Proposal operation * Code cleanup * Added separate function to insert DetectionOutput instead of Proposal operation * Updated transformation configuration files for the TF OD API models with Proposal transformation * Code refactoring * Code refactoring * Fix for the condition * Fixed transformation * Fixed transformation. One more time * Updated document about conversion of the TF OD API models * Update code comments
This commit is contained in:
Evgeny Lazarev
GitHub
parent
c38f08b777
commit
d28ecc4f2f
@ -1,6 +1,7 @@
|
||||
# Converting TensorFlow* Object Detection API Models {#openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models}
|
||||
|
||||
> **NOTES**:
|
||||
> * Starting with the 2022.1 release, the Model Optimizer can convert the TensorFlow\* Object Detection API Faster and Mask RCNNs topologies differently. By default, the Model Optimizer adds operation "Proposal" to the generated IR. This operation needs an additional input to the model with name "image_info" which should be fed with several values describing the pre-processing applied to the input image (refer to the [Proposal](../../../../ops/detection/Proposal_4.md) operation specification for more information). However, this input is redundant for the models trained and inferred with equal size images. Model Optimizer can generate IR for such models and insert operation [DetectionOutput](../../../../ops/detection/DetectionOutput_1.md) instead of `Proposal`. The `DetectionOutput` operation does not require additional model input "image_info" and moreover, for some models the produced inference results are closer to the original TensorFlow\* model. In order to trigger new behaviour the attribute "operation_to_add" in the corresponding JSON transformation configuration file should be set to value "DetectionOutput" instead of default one "Proposal".
|
||||
> * Starting with the 2021.1 release, the Model Optimizer converts the TensorFlow\* Object Detection API SSDs, Faster and Mask RCNNs topologies keeping shape-calculating sub-graphs by default, so topologies can be re-shaped in the Inference Engine using dedicated reshape API. Refer to [Using Shape Inference](../../../../IE_DG/ShapeInference.md) for more information on how to use this feature. It is possible to change the both spatial dimensions of the input image and batch size.
|
||||
> * To generate IRs for SSD topologies, the Model Optimizer creates a number of `PriorBoxClustered` layers instead of a constant node with prior boxes calculated for the particular input image size. This change allows you to reshape the topology in the Inference Engine using dedicated Inference Engine API. The reshaping is supported for all SSD topologies except FPNs which contain hardcoded shapes for some operations preventing from changing topology input shape.
|
||||
|
||||
@ -29,14 +30,16 @@ To convert a TensorFlow\* Object Detection API model, go to the `<INSTALL_DIR>/d
|
||||
* `faster_rcnn_support_api_v1.13.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.13.X
|
||||
* `faster_rcnn_support_api_v1.14.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.14.0 up to 1.14.X inclusively
|
||||
* `faster_rcnn_support_api_v1.15.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.15.0 up to 2.0
|
||||
* `faster_rcnn_support_api_v2.0.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.0 or higher
|
||||
* `faster_rcnn_support_api_v2.0.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.0 up to 2.3.X inclusively
|
||||
* `faster_rcnn_support_api_v2.4.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.4 or higher
|
||||
* `mask_rcnn_support.json` --- for Mask R-CNN topologies from the TF 1.X models zoo trained with TensorFlow\* version 1.9.0 or lower.
|
||||
* `mask_rcnn_support_api_v1.7.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.7.0 up to 1.9.X inclusively
|
||||
* `mask_rcnn_support_api_v1.11.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.11.0 up to 1.12.X inclusively
|
||||
* `mask_rcnn_support_api_v1.13.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.13.0 up to 1.13.X inclusively
|
||||
* `mask_rcnn_support_api_v1.14.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.14.0 up to 1.14.X inclusively
|
||||
* `mask_rcnn_support_api_v1.15.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.15.0 up to 2.0
|
||||
* `mask_rcnn_support_api_v2.0.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.0 or higher
|
||||
* `mask_rcnn_support_api_v2.0.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.0 up to 2.3.X inclusively
|
||||
* `mask_rcnn_support_api_v2.4.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.4 or higher
|
||||
* `rfcn_support.json` --- for RFCN topology from the models zoo trained with TensorFlow\* version up to 1.9.X inclusively
|
||||
* `rfcn_support_api_v1.10.json` --- for RFCN topology from the models zoo frozen with TensorFlow\* version 1.10.0 up to 1.12.X inclusively
|
||||
* `rfcn_support_api_v1.13.json` --- for RFCN topology from the models zoo frozen with TensorFlow\* version 1.13.X
|
||||
|
||||
@ -16,6 +16,7 @@ from extensions.front.tf.TFSliceToSlice import TFSliceToSliceReplacer
|
||||
from extensions.front.tf.pad_tf_to_pad import PadTFToPad
|
||||
from extensions.middle.InsertLayoutPropagationTransposes import mark_as_correct_data_layout, \
|
||||
mark_input_as_in_correct_layout, mark_output_as_in_correct_layout
|
||||
from extensions.ops.Cast import Cast
|
||||
from extensions.ops.DetectionOutput import DetectionOutput
|
||||
from extensions.ops.ReduceOps import ReduceMean
|
||||
from extensions.ops.activation_ops import Sigmoid
|
||||
@ -25,6 +26,7 @@ from extensions.ops.parameter import Parameter
|
||||
from extensions.ops.priorbox_clustered import PriorBoxClusteredOp
|
||||
from extensions.ops.proposal import ProposalOp
|
||||
from extensions.ops.psroipooling import PSROIPoolingOp
|
||||
from extensions.ops.split import Split
|
||||
from extensions.ops.transpose import Transpose
|
||||
from mo.front.common.layout import get_batch_dim, get_height_dim, get_width_dim
|
||||
from mo.front.common.partial_infer.utils import int64_array
|
||||
@ -37,6 +39,8 @@ from mo.front.tf.graph_utils import add_activation_function_after_node, add_conv
|
||||
create_op_with_const_inputs
|
||||
from mo.front.tf.replacement import FrontReplacementFromConfigFileSubGraph, FrontReplacementFromConfigFileGeneral
|
||||
from mo.graph.graph import Graph, Node
|
||||
from mo.middle.passes.convert_data_type import data_type_str_to_np
|
||||
from mo.ops.clamp import AttributedClamp
|
||||
from mo.ops.concat import Concat
|
||||
from mo.ops.const import Const
|
||||
from mo.ops.crop import Crop
|
||||
@ -46,9 +50,12 @@ from mo.ops.result import Result
|
||||
from mo.ops.roipooling import ROIPooling
|
||||
from mo.ops.shape import Shape
|
||||
from mo.ops.softmax import Softmax
|
||||
from mo.ops.squeeze import Squeeze
|
||||
from mo.ops.tile import Tile
|
||||
from mo.utils.error import Error
|
||||
from mo.utils.graph import backward_bfs_for_operation, bfs_search, clear_tensor_names_info, sub_graph_between_nodes
|
||||
from mo.utils.pipeline_config import PipelineConfig
|
||||
from mo.utils.shape import node_to_get_shape_value_of_indices
|
||||
|
||||
missing_param_error = 'To convert the model specify path to the pipeline configuration file which was used to ' \
|
||||
'generate the model. Please use "--tensorflow_object_detection_api_pipeline_config" option:\n' \
|
||||
@ -1074,6 +1081,7 @@ class ObjectDetectionAPIProposalReplacement(FrontReplacementFromConfigFileSubGra
|
||||
"""
|
||||
replacement_id = 'ObjectDetectionAPIProposalReplacement'
|
||||
run_not_recursively = True
|
||||
matched_input_nodes_to_keep = 2 # number of matched input nodes to keep
|
||||
|
||||
def run_after(self):
|
||||
return [ObjectDetectionAPIPreprocessorReplacement, ObjectDetectionAPIPreprocessor2Replacement]
|
||||
@ -1086,9 +1094,9 @@ class ObjectDetectionAPIProposalReplacement(FrontReplacementFromConfigFileSubGra
|
||||
|
||||
def nodes_to_remove(self, graph: Graph, match: SubgraphMatch):
|
||||
new_list = match.matched_nodes_names().copy()
|
||||
# do not remove nodes that produce box predictions and class predictions
|
||||
new_list.remove(match.single_input_node(0)[0].id)
|
||||
new_list.remove(match.single_input_node(1)[0].id)
|
||||
# do not remove nodes that produce box predictions and class predictions and optionally generated anchors
|
||||
for port in range(self.matched_input_nodes_to_keep):
|
||||
new_list.remove(match.single_input_node(port)[0].id)
|
||||
return new_list
|
||||
|
||||
def generate_sub_graph(self, graph: Graph, match: SubgraphMatch):
|
||||
@ -1097,6 +1105,11 @@ class ObjectDetectionAPIProposalReplacement(FrontReplacementFromConfigFileSubGra
|
||||
raise Error(missing_param_error)
|
||||
pipeline_config = PipelineConfig(argv.tensorflow_object_detection_api_pipeline_config)
|
||||
|
||||
# the transformation configuration file specifies what operations should be included with this transformation
|
||||
if match.custom_replacement_desc.custom_attributes.get('operation_to_add', 'Proposal') == 'DetectionOutput':
|
||||
self.matched_input_nodes_to_keep = 3 # keep the third input with prior boxes (anchors)
|
||||
return self.insert_detection_output_instead_of_proposal(graph, match, pipeline_config)
|
||||
|
||||
max_proposals = _value_or_raise(match, pipeline_config, 'first_stage_max_proposals')
|
||||
proposal_ratios = _value_or_raise(match, pipeline_config, 'anchor_generator_aspect_ratios')
|
||||
proposal_scales = _value_or_raise(match, pipeline_config, 'anchor_generator_scales')
|
||||
@ -1180,14 +1193,175 @@ class ObjectDetectionAPIProposalReplacement(FrontReplacementFromConfigFileSubGra
|
||||
|
||||
proposal_node = proposal_op.create_node([reshape_permute_node, anchors_node, input_with_image_size_node],
|
||||
dict(name='proposals'))
|
||||
|
||||
# models with use_matmul_crop_and_resize = True should not swap order of elements (YX to XY) after the Proposal
|
||||
swap_proposals = not match.custom_replacement_desc.custom_attributes.get('do_not_swap_proposals', False) and \
|
||||
not pipeline_config.get_param('use_matmul_crop_and_resize')
|
||||
|
||||
if swap_proposals:
|
||||
proposal_node = add_convolution_to_swap_xy_coordinates(graph, proposal_node, 5)
|
||||
|
||||
return {'proposal_node': ObjectDetectionAPIProposalReplacement.ie_to_tf_proposals(graph, proposal_node, match,
|
||||
max_proposals,
|
||||
swap_proposals)}
|
||||
|
||||
@staticmethod
|
||||
def insert_detection_output_instead_of_proposal(graph: Graph, match: SubgraphMatch,
|
||||
pipeline_config: PipelineConfig):
|
||||
"""
|
||||
The function inserts DetectionOutput operation instead of Proposal operation which may result in an increase of
|
||||
the accuracy for some models. The function is enabled with the custom attribute "operation_to_insert" with
|
||||
value "DetectionOutput" in the transformation configuration file section for the
|
||||
"ObjectDetectionAPIProposalReplacement" transformation. However, this transformation should not be applied in
|
||||
case when an input image should be scaled before feeding the IR because the DetectionOutput operation does not
|
||||
have information about the original input image size, whilst the Proposal operation has an input which contains
|
||||
this information.
|
||||
:param graph: the graph to operate on
|
||||
:param match: the object containing information about the matched sub-graph
|
||||
:param pipeline_config: object containing information from the pipeline.config file of the model
|
||||
:return: the dictionary with mapping information needed for other transformations
|
||||
"""
|
||||
max_proposals = _value_or_raise(match, pipeline_config, 'first_stage_max_proposals')
|
||||
|
||||
# Convolution/matmul node that produces classes predictions
|
||||
# Transpose result of the tensor with classes permissions so it will be in a correct layout for Softmax
|
||||
predictions_nodes = backward_bfs_for_operation(match.single_input_node(1)[0], ['Add'])
|
||||
assert len(predictions_nodes) >= 1, 'Expected to find nodes of type "Add" starting from the node "{}" in ' \
|
||||
'backward direction'.format(match.single_input_node(1)[0].id)
|
||||
predictions_node = predictions_nodes[0]
|
||||
|
||||
# prepare input with class probabilities. The DetectionOutput operation which will consume this tensor as a
|
||||
# second input expects probabilities to be normalized with SoftMax operation per each bounding box class. In
|
||||
# order to do this we first reshape the tensor so the last dimension contain probability for 2 classes
|
||||
# (background and foreground) for each bounding box. Before feeding this tensor to the DO operation the tensor
|
||||
# is flattened to the shape [num_batches, num_classes * num_bounding_boxes]
|
||||
reshape_classes_node = create_op_node_with_second_input(graph, Reshape, int64_array([0, -1, 2]),
|
||||
dict(name='predictions/Reshape'))
|
||||
# transpose from NCHW to NHWC will be inserted as input to the Reshape automatically. This is expected
|
||||
predictions_node.out_port(0).disconnect()
|
||||
predictions_node.out_port(0).connect(reshape_classes_node.in_port(0))
|
||||
softmax_conf_node = Softmax(graph, dict(axis=2, name=reshape_classes_node.id + '/Softmax')).create_node([
|
||||
reshape_classes_node])
|
||||
flattened_conf = create_op_node_with_second_input(graph, Reshape, int64_array([0, -1]),
|
||||
dict(name=softmax_conf_node.name + '/Flatten'),
|
||||
softmax_conf_node)
|
||||
|
||||
# prepare input with box logits
|
||||
boxes_logit = backward_bfs_for_operation(match.single_input_node(0)[0], ['Add'])[0]
|
||||
reshape_box_logits = create_op_node_with_second_input(
|
||||
graph, Reshape, int64_array([0, -1]), dict(name=boxes_logit.soft_get('name', boxes_logit.id) + '/Flatten'),
|
||||
boxes_logit)
|
||||
|
||||
yxyx_anchors = match.single_input_node(2)[0]
|
||||
|
||||
variance_height = pipeline_config.get_param('frcnn_variance_height')
|
||||
variance_width = pipeline_config.get_param('frcnn_variance_width')
|
||||
variance_x = pipeline_config.get_param('frcnn_variance_x')
|
||||
variance_y = pipeline_config.get_param('frcnn_variance_y')
|
||||
|
||||
# get the input image height and width to divide the anchors values by it
|
||||
initial_input_node_name = 'input_tensor' if 'input_tensor' in graph.nodes else 'image_tensor'
|
||||
if initial_input_node_name not in graph.nodes():
|
||||
raise Error('Input node "{}" of the graph is not found. Do not run the Model Optimizer with '
|
||||
'"--input" command line parameter.'.format(initial_input_node_name))
|
||||
parameter_node = Node(graph, initial_input_node_name)
|
||||
|
||||
input_shape = Shape(graph, {'name': parameter_node.name}).create_node([parameter_node])
|
||||
input_image_hw = node_to_get_shape_value_of_indices(input_shape, [1, 2]) # NHWC layout
|
||||
hwhw = create_op_with_const_inputs(graph, Tile, {1: int64_array([2])}, {'name': 'image_hwhw'}, input_image_hw)
|
||||
|
||||
hwhw_float = Cast(graph,
|
||||
{'dst_type': data_type_str_to_np(graph.graph['cmd_params'].data_type)}).create_node([hwhw])
|
||||
scaled_anchors = Div(graph, {'name': 'scaled_anchors'}).create_node([yxyx_anchors, hwhw_float])
|
||||
|
||||
flattened_anchors = create_op_with_const_inputs(graph, Reshape, {1: int64_array([1, 1, -1])},
|
||||
{'name': 'flattened_anchors'}, scaled_anchors)
|
||||
cropped_anchors = AttributedClamp(graph, {'min': 0.0, 'max': 1.0, 'name': 'clamped_xyxy',
|
||||
'nchw_layout': True}).create_node([flattened_anchors])
|
||||
# the input tensor "scaled_anchors" for the "flattened_anchors" may be 4D. In order to avoid inserting Transpose
|
||||
# operation mark the "flattened_anchors" with the correct data layout
|
||||
mark_as_correct_data_layout(flattened_anchors)
|
||||
|
||||
# create tensor of shape [4] with variance values which then are tiled by the number of boxes which is obtained
|
||||
# from the 'yxyx_anchors' node
|
||||
variances = Const(graph, {'value': np.float32([1.0 / variance_x, 1.0 / variance_y, 1.0 / variance_width,
|
||||
1.0 / variance_height])}).create_node()
|
||||
|
||||
anchors_shape = Shape(graph, {'name': 'anchors_shape'}).create_node([yxyx_anchors])
|
||||
anchors_count = node_to_get_shape_value_of_indices(anchors_shape, [0])
|
||||
tiled_variances = Tile(graph, {'name': 'tiled_variances'}).create_node([variances, anchors_count])
|
||||
reshaped_tiled_variances = create_op_with_const_inputs(graph, Reshape, {1: int64_array([1, 1, -1])},
|
||||
{'name': 'flattened_variances'}, tiled_variances)
|
||||
|
||||
# now we can merge actual anchors coordinates with a tensor with variances as it is expected by the
|
||||
# DetectionOutput operation
|
||||
duplicate_anchors = Concat(graph, {'axis': 1, 'name': 'anchors_with_variances'}).create_node(
|
||||
[cropped_anchors, reshaped_tiled_variances])
|
||||
|
||||
do = DetectionOutput(graph,
|
||||
{'background_label_id': 0,
|
||||
'clip_after_nms': True,
|
||||
'clip_before_nms': False,
|
||||
'code_type': 'caffe.PriorBoxParameter.CENTER_SIZE',
|
||||
'confidence_threshold': 0.0,
|
||||
'decrease_label_id': False,
|
||||
'input_height': 1,
|
||||
'input_width': 1,
|
||||
'keep_top_k': max_proposals,
|
||||
'normalized': True,
|
||||
'num_classes': 2,
|
||||
'objectness_score': 0,
|
||||
'share_location': True,
|
||||
'top_k': 6000,
|
||||
'variance_encoded_in_target': False,
|
||||
'nms_threshold': _value_or_raise(match, pipeline_config, 'first_stage_nms_iou_threshold'),
|
||||
'name': 'first_do',
|
||||
}).create_node([reshape_box_logits, flattened_conf, duplicate_anchors])
|
||||
# DetectionOutput output tensor has YXYX box coordinates order
|
||||
# switch to 3D to avoid issues that part of the model with 4D shapes should be inferred in NCHW layout
|
||||
do_3d = create_op_with_const_inputs(graph, Squeeze, {1: int64_array(0)}, {'name': do.name + '/SqueezeDO'}, do)
|
||||
mark_as_correct_data_layout(do_3d)
|
||||
|
||||
# DetectionOutput output tensor produces a tensor of tuples with the following 7 elements:
|
||||
# [batch_id, class_id, confidence, x1, y1, x2, y2]. Here we split the DetectionOutput result into the 7
|
||||
# tensors with each of these elements for predictions. Then we crop predicted box coordinates (scaled) to be
|
||||
# within [0, 1] range (as it is predicted in the TF model) and then combine tensors back to the Proposal
|
||||
# operation output format: [batch_id, x1, y1, x2, y2].
|
||||
do_split = create_op_node_with_second_input(graph, Split, int64_array(2), {'num_splits': 7, 'nchw_layout': True,
|
||||
'name': do.name + '/Split'}, do_3d)
|
||||
|
||||
xyxy_coord = Concat(graph, {'axis': -1, 'nchw_layout': True, 'in_ports_count': 4,
|
||||
'name': do_split.name + '/xyxy'}).create_node()
|
||||
# change output from YXYX to XYXY order
|
||||
do_split.out_port(3).connect(xyxy_coord.in_port(1))
|
||||
do_split.out_port(4).connect(xyxy_coord.in_port(0))
|
||||
do_split.out_port(5).connect(xyxy_coord.in_port(3))
|
||||
do_split.out_port(6).connect(xyxy_coord.in_port(2))
|
||||
|
||||
clamped_xyxy_coord = AttributedClamp(graph, {'min': 0.0, 'max': 1.0, 'name': 'clamped_xyxy',
|
||||
'nchw_layout': True}).create_node([xyxy_coord])
|
||||
|
||||
# prepare final proposal boxes [batch_id, x1, y1, x2, y2]
|
||||
proposal_node = Concat(graph, {'axis': -1, 'nchw_layout': True, 'in_ports_count': 2,
|
||||
'name': 'proposals'}).create_node()
|
||||
do_split.out_port(0).connect(proposal_node.in_port(0))
|
||||
clamped_xyxy_coord.out_port(0).connect(proposal_node.in_port(1))
|
||||
return {'proposal_node': ObjectDetectionAPIProposalReplacement.ie_to_tf_proposals(graph, proposal_node, match,
|
||||
max_proposals, True)}
|
||||
|
||||
@staticmethod
|
||||
def ie_to_tf_proposals(graph: Graph, proposal_node: Node, match: SubgraphMatch, max_proposals: int,
|
||||
swap_proposals: bool = False):
|
||||
"""
|
||||
Builds a graph which converts the proposals data in IE format to the format of TensorFlow. This includes
|
||||
swapping of XYXY to YXYX (if needed), and cropping the IE output of format [batch, x1, y1, x2, y2] to simply
|
||||
[x1, y1, x2, y2] and reshaping tensor to an appropriate shape.
|
||||
|
||||
:param graph: the graph to operate on
|
||||
:param proposal_node: the node producing IE proposals
|
||||
:param match: the object containing information about matched sub-graph
|
||||
:param max_proposals: maximum number of proposal boxes. Needed for the reshaping of the tensor
|
||||
:param swap_proposals: flag to force swapping proposals for CropAndResize op
|
||||
:return: the node producing output in the TF format.
|
||||
"""
|
||||
proposal_reshape_2d_node = create_op_node_with_second_input(graph, Reshape, int64_array([-1, 5]),
|
||||
dict(name="reshape_swap_proposals_2d"),
|
||||
proposal_node)
|
||||
@ -1220,7 +1394,7 @@ class ObjectDetectionAPIProposalReplacement(FrontReplacementFromConfigFileSubGra
|
||||
tf_proposals_crop_reshape_3d_node = create_op_node_with_second_input(graph, Reshape, int64_array([0, -1, 4]),
|
||||
dict(name="reshape_crop_3d"), crop_node)
|
||||
mark_input_as_in_correct_layout(tf_proposals_crop_reshape_3d_node, 0)
|
||||
return {'proposal_node': tf_proposals_crop_reshape_3d_node}
|
||||
return tf_proposals_crop_reshape_3d_node
|
||||
|
||||
|
||||
class ObjectDetectionAPISSDPostprocessorReplacement(FrontReplacementFromConfigFileSubGraph):
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": true,
|
||||
"clip_after_nms": false
|
||||
},
|
||||
|
||||
@ -10,6 +10,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -10,6 +10,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": true,
|
||||
"clip_after_nms": false
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -36,6 +36,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": true,
|
||||
"clip_after_nms": false
|
||||
},
|
||||
|
||||
@ -10,6 +10,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -10,6 +10,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -35,6 +35,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -35,6 +35,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
@ -35,6 +35,7 @@
|
||||
},
|
||||
{
|
||||
"custom_attributes": {
|
||||
"operation_to_add": "Proposal",
|
||||
"clip_before_nms": false,
|
||||
"clip_after_nms": true
|
||||
},
|
||||
|
||||
Loading…
Reference in New Issue
Block a user