Use batch size larger than 1 in models with dynamic output about open_model_zoo HOT 3 OPEN

import numpy as np
import openvino as ov
import cv2

core = ov.runtime.ie_api.Core()
detection_model_xml = r"C:/Users/vzlobin/.cache/omz/intel/face-detection-0204/FP16/face-detection-0204.xml"
detection_model = core.read_model(model=detection_model_xml)
detection_input_layer = detection_model.input(0) 

new_shape = (-1, 3, 448, 448)
detection_model.reshape({detection_input_layer.any_name: new_shape})
detection_compiled_model = core.compile_model(model=detection_model, device_name="CPU")

# to run the model:
images = np.stack([
    cv2.resize(cv2.imread(r"C:/Users/vzlobin/OneDrive - Intel Corporation/a/test-data/coco128/images/train2017/000000000036.jpg"), (new_shape[3], new_shape[2])),
    cv2.resize(cv2.imread(r"C:/Users/vzlobin/OneDrive - Intel Corporation/a/test-data/coco128/images/train2017/000000000086.jpg"), (new_shape[3], new_shape[2]))
])
input_data = images.transpose(0, 3, 1, 2)
output = detection_compiled_model(input_data)
BLUE = (255, 0, 0)
for image_id, _, conf, x_min, y_min, x_max, y_max in output['detection_out'][0, 0]:
    if image_id < 0:
        break
    if conf < 0.5:
        continue
    cv2.rectangle(images[int(image_id)], [round(x_min * new_shape[3]), round(y_min * new_shape[2])], [round(x_max * new_shape[3]), round(y_max * new_shape[2])], BLUE, 2)
cv2.imshow('winname',  np.concatenate(images, 1))
cv2.waitKey(0)