I'm using a script to run pose estimation on the video stream from a camera,
and I wanted to try improving the inference speed. Currently, I'm using:
* an SSD model, pre-trained on COCO (ssd_512_mobilenet1.0_coco), to detect
persons in the frame;
* simple_pose_resnet18_v1b for pose estiamtion, suing the detections from the
previous model;
And this the code I'm using.
```
import mxnet as mx
from gluoncv.data import mscoco
from gluoncv.model_zoo import get_model
from gluoncv.data.transforms.pose import detector_to_simple_pose,
heatmap_to_coord
from gluoncv.utils.viz import cv_plot_image, cv_plot_keypoints
from mxnet.contrib.quantization import *
def main():
ctx = mx.cpu()
net_obj_det = gcv.model_zoo.get_model('ssd_512_mobilenet1.0_coco',
pretrained=True, ctx=ctx)
net_pose_est = get_model('simple_pose_resnet18_v1b',
pretrained='ccd24037', ctx=ctx)
net_obj_det.hybridize()
net_pose_est.hybridize()
cap = cv2.VideoCapture(0)
while(True):
# Load frame from the camera
ret, frame_np_orig = cap.read()
key = cv2.waitKey(1)
if (key == ord('q')) or (ret == False):
cv2.destroyAllWindows()
cap.release()
break
## Image pre-processing
frame_nd_orig = mx.nd.array(cv2.cvtColor(frame_np_orig,
cv2.COLOR_BGR2RGB)).astype('uint8')
frame_nd_new, frame_np_new =
gcv.data.transforms.presets.ssd.transform_test(frame_nd_orig, short=512,
max_size=700)
frame_nd_new = frame_nd_new.as_in_context(ctx)
## run frame through network, detect keypoints for persons
frame_nd_new = frame_nd_new.as_in_context(ctx)
class_IDs, scores, bounding_boxes = net_obj_det(frame_nd_new)
## select only one class (person)
selected_indices_person = np.where( ((class_IDs[0].asnumpy() ==
0) & (scores[0].asnumpy() >= 0.35)))[0]
selected_bboxes_person =
bounding_boxes[0].asnumpy()[selected_indices_person]
selected_class_IDs =
class_IDs[0].asnumpy()[selected_indices_person] # unused so far
selected_scores = scores[0].asnumpy()[selected_indices_person]
# unused so far
## detection of body keypoints
pose_input, upscale_bbox =
detector_to_simple_pose(frame_np_new, class_IDs, scores, bounding_boxes,
output_shape=(256, 192), ctx=ctx)
if len(upscale_bbox) > 0:
predicted_heatmap = net_pose_est(pose_input)
pred_coords, confidence =
heatmap_to_coord(predicted_heatmap, upscale_bbox)
img = cv_plot_keypoints(frame_np_new, pred_coords,
confidence, class_IDs, bounding_boxes, scores, box_thresh=0.5,
keypoint_thresh=0.2)
cv_plot_image(img)
cap.release()
if __name__ == "__main__":
main()
```
I wanted to try a quantization of these models and I was referring to [this
page.](https://cv.gluon.ai/build/examples_deployment/int8_inference.html#demo-usage-for-ssd).
So, I tried something like this:
```
net_obj_det = net_obj_det = quantize_net(net_obj_det, quantized_dtype='auto',
exclude_layers=None,
exclude_layers_match=None,
calib_data=None,
data_shapes=None,
calib_mode='naive',
num_calib_examples=None,
ctx=mx.cpu(),
logger=logging)
```
But it's returning the error:
```
File
"/home/lews/anaconda3/envs/gluon/lib/python3.8/site-packages/mxnet/contrib/quantization.py",
line 820, in quantize_net
data_shapes = dshapes
UnboundLocalError: local variable 'dshapes' referenced before assignment
```
In the page I linked, it's specified that 'data_shapes' should be a "List of
DataDesc, required if calib_data is not provided". How am I supposed to use it
precisely?
---
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Topic](https://discuss.mxnet.apache.org/t/quantized-pose-estimation-object-detection-models/7102/1)
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