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Added SSD-Resnet-34 model
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‎demos/python_demos/object_detection_demo_ssd_async/object_detection_demo_ssd_async.py

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@@ -54,6 +54,53 @@ def build_argparser():
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return parser
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class SingleOutputPostprocessor:
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def __init__(self, output_layer):
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self.output_layer = output_layer
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def __call__(self, outputs):
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return outputs[self.output_layer][0][0]
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class MultipleOutputPostprocessor:
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def __init__(self, bboxes_layer='bboxes', scores_layer='scores', labels_layer='labels'):
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self.bboxes_layer = bboxes_layer
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self.scores_layer = scores_layer
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self.labels_layer = labels_layer
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def __call__(self, outputs):
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bboxes = outputs[self.bboxes_layer][0]
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scores = outputs[self.scores_layer][0]
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labels = outputs[self.labels_layer][0]
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result = [[0, label, score, *bbox] for label, score, bbox in zip(labels, scores, bboxes)]
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return result
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def get_output_postprocessor(net, bboxes='bboxes', labels='labels', scores='scores'):
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if len(net.outputs) == 1:
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output_blob = next(iter(net.outputs))
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return SingleOutputPostprocessor(output_blob)
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elif len(net.outputs) >= 3:
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print(*(net.outputs))
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def find_layer(name, output_name, all_outputs):
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suitable_layers = [layer_name for layer_name in all_outputs if name in layer_name]
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if not suitable_layers:
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raise ValueError('suitable layer for {} output is not found'.format(output_name))
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if len(suitable_layers) > 1:
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raise ValueError('more than 1 layers matched to regular expression, please specify more detailed regex')
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return suitable_layers[0]
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labels_out = find_layer(labels, 'labels', net.outputs)
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scores_out = find_layer(scores, 'scores', net.outputs)
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bboxes_out = find_layer(bboxes, 'bboxes', net.outputs)
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return MultipleOutputPostprocessor(bboxes_out, scores_out, labels_out)
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raise RuntimeError("Unsupported models outputs")
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def main():
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log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
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args = build_argparser().parse_args()
@@ -89,9 +136,8 @@ def main():
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raise RuntimeError("Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported"
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.format(len(net.inputs[blob_name].shape), blob_name))
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assert len(net.outputs) == 1, "Demo supports only single output topologies"
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output_postprocessor = get_output_postprocessor(net)
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out_blob = next(iter(net.outputs))
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log.info("Loading IR to the plugin...")
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exec_net = ie.load_network(network=net, num_requests=2, device_name=args.device)
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# Read and pre-process input image
@@ -158,8 +204,8 @@ def main():
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det_time = inf_end - inf_start
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# Parse detection results of the current request
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res = exec_net.requests[cur_request_id].outputs[out_blob]
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for obj in res[0][0]:
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res = output_postprocessor(exec_net.requests[cur_request_id].outputs)
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for obj in res:
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# Draw only objects when probability more than specified threshold
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if obj[2] > args.prob_threshold:
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xmin = int(obj[3] * frame_w)

‎models/public/licenses/APACHE-2.0-MLPerf.txt

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‎models/public/ssd-resnet34-1200-onnx/model.yml

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# Copyright (c) 2019 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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description: >-
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" "
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task_type: detection
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files:
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- name: resnet34-ssd1200.onnx
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size: 80363696
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sha256: b417a8186967f1e78670b0075aa72a10e5c7d1a6d5c08af9d13dc97f3d29ef0d
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source: https://zenodo.org/record/3228411/files/resnet34-ssd1200.onnx
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model_optimizer_args:
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- --reverse_input_channels
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- --input_shape=[1,3,1200,1200]
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- --input=image
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- --mean_values=[123.675,116.28,103.53]
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- --scale_values=[58.395,57.12,57.375]
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- --input_model=$dl_dir/resnet34-ssd1200.onnx
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framework: onnx
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license: https://raw.githubusercontent.com/mlperf/inference/master/LICENSE.md

‎models/public/ssd-resnet34-1200-onnx/ssd-resnet34-1200-onnx.md

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# ssd-resnet-34-1200-onnx
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## Use Case and High-Level Description
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The `ssd-resnet-34-1200-onnx` model is a multiscale SSD based on ResNet-34 backbone network intended to perform object detection. The model has been trained from the Common Objects in Context (COCO) image dataset. This model is pretrained in PyTorch\* framework and converted to ONNX\* format.
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The model input is a blob that consists of a single image of 1x3x1200x1200 in RGB order.
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The model output is a typical vector containing the tracked object data, as previously described. Note that the "class_id" data is now significant and should be used to determine the classification for any detected object.
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## Example
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## Specification
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| Metric | Value |
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|-------------------|---------------|
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| Type | Detection |
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| GFLOPs | |
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| MParams | |
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| Source framework | PyTorch\* |
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## Accuracy
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## Performance
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## Input
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Note that original model expects image in `RGB` format, converted model - in `BGR` format.
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### Original model
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Image, shape - `1,3,1200,1200,`, format is `B,C,H,W` where:
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- `B` - batch size
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- `C` - channel
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- `H` - height
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- `W` - width
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Channel order is `RGB`.
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### Converted model
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Image, shape - `1,3,1200,1200,`, format is `B,C,H,W` where:
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- `B` - batch size
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- `C` - channel
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- `H` - height
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- `W` - width
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Channel order is `BGR`.
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## Output
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> **NOTE** output format changes after Model Optimizer conversion. To find detailed explanation of changes, go to [Model Optimizer development guide](http://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html)
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### Original model
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1. Classifier, name - `labels`, shape - `1,N`, contains predicted classes for each detected bounding box. The model was trained on Microsoft\* COCO dataset version with 80 categories of object.
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2. Probability, name - `scores`, shape - `1,N`, contains confidence of each detected bounding boxes.
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3. Detection boxes, name - `bboxes`, shape - `1,N,4`, contains detection boxes coordinates in format `[y_min, x_min, y_max, x_max]`, where (`x_min`, `y_min`) are coordinates top left corner, (`x_max`, `y_max`) are coordinates right bottom corner. Coordinates are rescaled to input image size.
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### Converted model
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1. Classifier, shape - `1,200`, contains predicted class ID for each detected bounding box. The model was trained on Microsoft\* COCO dataset version with 80 categories of object.
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2. Probability, shape - `1,200`, contains confidence of each detected bounding boxes.
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3. Detection boxes, shape - `1,200,4`, contains detection boxes coordinates in format `[y_min, x_min, y_max, x_max]`, where (`x_min`, `y_min`) are coordinates top left corner, (`x_max`, `y_max`) are coordinates right bottom corner. Coordinates are in normalized format, in range [0, 1].
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## Legal Information
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The original model is distributed under the
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[Apache License, Version 2.0](https://raw.githubusercontent.com/mlperf/inference/master/LICENSE.md).
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A copy of the license is provided in [APACHE-2.0-MLPerf.txt](../licenses/APACHE-2.0-MLPerf.txt).

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