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"""OTXMaskRCNNModel & OTXSSDModel of OTX Detection."""
# Copyright (C) 2022 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions
# and limitations under the License.
from typing import Dict
import numpy as np
try:
from openvino.model_zoo.model_api.models.instance_segmentation import MaskRCNNModel
from openvino.model_zoo.model_api.models.ssd import SSD, find_layer_by_name
from openvino.model_zoo.model_api.models.utils import Detection
except ImportError as e:
import warnings
warnings.warn(f"{e}: ModelAPI was not found.")
class OTXMaskRCNNModel(MaskRCNNModel):
"""OpenVINO model wrapper for OTX MaskRCNN model."""
__model__ = "OTX_MaskRCNN"
def __init__(self, model_adapter, configuration, preload=False):
super().__init__(model_adapter, configuration, preload)
self.resize_mask = True
def _check_io_number(self, number_of_inputs, number_of_outputs):
"""Checks whether the number of model inputs/outputs is supported.
Args:
number_of_inputs (int, Tuple(int)): number of inputs supported by wrapper.
Use -1 to omit the check
number_of_outputs (int, Tuple(int)): number of outputs supported by wrapper.
Use -1 to omit the check
Raises:
WrapperError: if the model has unsupported number of inputs/outputs
"""
super()._check_io_number(number_of_inputs, -1)
def _get_outputs(self):
output_match_dict = {}
output_names = ["boxes", "labels", "masks", "feature_vector", "saliency_map"]
for output_name in output_names:
for node_name, node_meta in self.outputs.items():
if output_name in node_meta.names:
output_match_dict[output_name] = node_name
break
return output_match_dict
def postprocess(self, outputs, meta):
"""Post process function for OTX MaskRCNN model."""
# pylint: disable-msg=too-many-locals
# FIXME: here, batch dim of IR must be 1
boxes = outputs[self.output_blob_name["boxes"]]
if boxes.shape[0] == 1:
boxes = boxes.squeeze(0)
assert boxes.ndim == 2
masks = outputs[self.output_blob_name["masks"]]
if masks.shape[0] == 1:
masks = masks.squeeze(0)
assert masks.ndim == 3
classes = outputs[self.output_blob_name["labels"]].astype(np.uint32)
if classes.shape[0] == 1:
classes = classes.squeeze(0)
assert classes.ndim == 1
if self.is_segmentoly:
scores = outputs[self.output_blob_name["scores"]]
else:
scores = boxes[:, 4]
boxes = boxes[:, :4]
classes += 1
# Filter out detections with low confidence.
detections_filter = scores > self.confidence_threshold # pylint: disable=no-member
scores = scores[detections_filter]
boxes = boxes[detections_filter]
masks = masks[detections_filter]
classes = classes[detections_filter]
scale_x = meta["resized_shape"][1] / meta["original_shape"][1]
scale_y = meta["resized_shape"][0] / meta["original_shape"][0]
boxes[:, 0::2] /= scale_x
boxes[:, 1::2] /= scale_y
resized_masks = []
for box, cls, raw_mask in zip(boxes, classes, masks):
raw_cls_mask = raw_mask[cls, ...] if self.is_segmentoly else raw_mask
if self.resize_mask:
resized_masks.append(self._segm_postprocess(box, raw_cls_mask, *meta["original_shape"][:-1]))
else:
resized_masks.append(raw_cls_mask)
return scores, classes, boxes, resized_masks
def segm_postprocess(self, *args, **kwargs):
"""Post-process for segmentation masks."""
return self._segm_postprocess(*args, **kwargs)
def disable_mask_resizing(self):
"""Disable mask resizing.
There is no need to resize mask in tile as it will be processed at the end.
"""
self.resize_mask = False
class OTXSSDModel(SSD):
"""OpenVINO model wrapper for OTX SSD model."""
__model__ = "OTX_SSD"
def __init__(self, model_adapter, configuration=None, preload=False):
# pylint: disable-next=bad-super-call
super(SSD, self).__init__(model_adapter, configuration, preload)
self.image_info_blob_name = self.image_info_blob_names[0] if len(self.image_info_blob_names) == 1 else None
self.output_parser = BatchBoxesLabelsParser(
self.outputs,
self.inputs[self.image_blob_name].shape[2:][::-1],
)
def _get_outputs(self) -> Dict:
"""Match the output names with graph node index."""
output_match_dict = {}
output_names = ["boxes", "labels", "feature_vector", "saliency_map"]
for output_name in output_names:
for node_name, node_meta in self.outputs.items():
if output_name in node_meta.names:
output_match_dict[output_name] = node_name
break
return output_match_dict
class BatchBoxesLabelsParser:
"""Batched output parser."""
def __init__(self, layers, input_size, labels_layer="labels", default_label=0):
try:
self.labels_layer = find_layer_by_name(labels_layer, layers)
except ValueError:
self.labels_layer = None
self.default_label = default_label
try:
self.bboxes_layer = self.find_layer_bboxes_output(layers)
except ValueError:
self.bboxes_layer = find_layer_by_name("boxes", layers)
self.input_size = input_size
@staticmethod
def find_layer_bboxes_output(layers):
"""find_layer_bboxes_output."""
filter_outputs = [name for name, data in layers.items() if len(data.shape) == 3 and data.shape[-1] == 5]
if not filter_outputs:
raise ValueError("Suitable output with bounding boxes is not found")
if len(filter_outputs) > 1:
raise ValueError("More than 1 candidate for output with bounding boxes.")
return filter_outputs[0]
def __call__(self, outputs):
"""Parse bboxes."""
# FIXME: here, batch dim of IR must be 1
bboxes = outputs[self.bboxes_layer]
if bboxes.shape[0] == 1:
bboxes = bboxes.squeeze(0)
assert bboxes.ndim == 2
scores = bboxes[:, 4]
bboxes = bboxes[:, :4]
bboxes[:, 0::2] /= self.input_size[0]
bboxes[:, 1::2] /= self.input_size[1]
if self.labels_layer:
labels = outputs[self.labels_layer]
else:
labels = np.full(len(bboxes), self.default_label, dtype=bboxes.dtype)
if labels.shape[0] == 1:
labels = labels.squeeze(0)
detections = [Detection(*bbox, score, label) for label, score, bbox in zip(labels, scores, bboxes)]
return detections
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