g5_input_tensors = [
'Preprocessor/map/TensorArrayStack_1/TensorArrayGatherV3:0',
'BatchMultiClassNonMaxSuppression/map/TensorArrayStack_4/TensorArrayGatherV3:0',
'map_1/TensorArrayStack/TensorArrayGatherV3:0', 'Squeeze_2:0',
'Squeeze_3:0'
]
g5_output_tensors = [
'num_detections:0', 'detection_classes:0', 'detection_boxes:0',
'detection_scores:0'
]
g1_input_values = [img]
g1_output_values, t1 = run_tf_pb(PB1, g1_input_tensors, g1_input_values,
g1_output_tensors, warm_loop1, loop1)
g2_input_values = [g1_output_values[0]]
g3_input_values = [g1_output_values[0]]
g5_input_values = [g1_output_values[1]]
g2_output_values, t2 = run_tf_pb(PB2, g2_input_tensors, g2_input_values,
g2_output_tensors, warm_loop2, loop2)
for v in g2_output_values:
g3_input_values.append(v)
g3_output_values, t3 = run_tf_pb(PB3, g3_input_tensors, g3_input_values,
g3_output_tensors, warm_loop3, loop3)
g4_input_values = [g3_output_values[2]]
loop = int(sys.argv[2])
results_save_path = os.path.join(
root_path, 'results/' + model_flag + '/time_wholepb_warm' +
str(warm_loop) + '_loop' + str(loop) + '.txt')
PATH_TO_FROZEN_GRAPH = os.path.join(
root_path, 'model_whole/' + model_flag + '/frozen_inference_graph.pb')
itensor_names = ['image_tensor:0']
otensor_names = [
'num_detections:0', 'detection_classes:0', 'detection_boxes:0',
'detection_scores:0'
]
img = cv2.imread(os.path.join(root_path, "data/aa.JPEG"))
img = np.expand_dims(img, 0)
rets, t = run_tf_pb(PATH_TO_FROZEN_GRAPH, itensor_names, [img], otensor_names,
warm_loop, loop)
print('num_detections: ', rets[0])
print('detection_classes: ', rets[1])
print('detection_boxes: ', rets[2])
print('detection_scores: ', rets[3])
print('time: ', t)
with open(results_save_path, 'w+') as rsp:
rsp.write(str(t) + '\n')
PB2 = os.path.join(root_path,'model_parts/'+model_flag+'/part_2.pb')
PB3 = os.path.join(root_path,'model_parts/'+model_flag+'/part_3.pb')
img = cv2.imread(os.path.join(root_path,"data/aa.JPEG"))
img = np.expand_dims(img,0)
g1_input_tensors = ['image_tensor:0']
g1_output_tensors = ['Preprocessor/sub:0','Preprocessor/map/TensorArrayStack_1/TensorArrayGatherV3:0']
g2_input_tensors = ['Preprocessor/sub:0']
g2_output_tensors = ['Squeeze:0','concat_1:0']
g3_input_tensors = ['Preprocessor/sub:0','Preprocessor/map/TensorArrayStack_1/TensorArrayGatherV3:0','Squeeze:0','concat_1:0']
g3_output_tensors = ['num_detections:0','detection_classes:0','detection_boxes:0','detection_scores:0']
g1_input_values = [img]
g1_output_values,t1 = run_tf_pb(PB1,g1_input_tensors,g1_input_values,g1_output_tensors,warm_loop1,loop1)
g2_input_values = [g1_output_values[0]]
g3_input_values = g1_output_values
g2_output_values,t2 = run_tf_pb(PB2,g2_input_tensors,g2_input_values,g2_output_tensors,warm_loop2,loop2)
for value in g2_output_values:
g3_input_values.append(value)
g3_output_values,t3 = run_tf_pb(PB3,g3_input_tensors,g3_input_values,g3_output_tensors,warm_loop3,loop3)
print('num_detections: ', g3_output_values[0])
print('detection_classes: ', g3_output_values[1])
print('detection_boxes: ', g3_output_values[2])
print('detection_scores: ', g3_output_values[3])
