Why is the outputs's box less and badly offset in openvino ?(tiny-yolov3, keras, NCS2)

chen__rookie · ‎07-17-2019

Here's what I did

Downloaded keras-tiny-yolov3 model
Changed leaky_relu to relu and retrained, then I got the h5 file
Used h5_to_pb.py to get the pb file

Converted pb file to IR

sudo python mo_tf.py --input_model='/home/rookiechen/model/car/yolov3-tiny.pb' --output_dir='/home/rookiechen/yolo_test_openvino/' --input_shape=[1,416,416,3] --scale_values=[255,255,255] --data_type=FP16

Infered

def main_IE_infer():
    image_dir = './test_image'

    images_list = os.listdir(image_dir)
    images_file_list = []
    for i, image_file_i in enumerate(images_list):
        if image_file_i.split('.')[-1] in ['jpg', 'png']:
            images_file_list.append(os.path.join(image_dir, image_file_i))

    inputs_size=416
    channel_num=3
    input_shape = [416, 416]
    args = build_argparser().parse_args()
    model_xml = "yolov3-tiny.xml"  # <--- MYRIAD
    model_bin = os.path.splitext(model_xml)[0] + ".bin"
    namewindows = "car detect"
    cv2.namedWindow(namewindows, cv2.WINDOW_NORMAL)
    time.sleep(1)
    plugin = IEPlugin(device=args.device)
    if "CPU" in args.device:
        plugin.add_cpu_extension("lib/libcpu_extension.so")
    net = IENetwork(model=model_xml, weights=model_bin)
    input_blob = next(iter(net.inputs))
    exec_net = plugin.load(network=net)

    # for image_file in images_file_list:
    image_file='1.jpg'
    frame = cv2.imread(image_file)
    image = np.array(frame, dtype='float32')
    shape=image.shape
    image_re = cv2.resize(image, (inputs_size, inputs_size))
    image_re = cv2.cvtColor(image_re, cv2.COLOR_BGR2RGB)
    image_re /= 255.
    image_re = image_re.reshape(1, inputs_size, inputs_size, channel_num)#此时通道3,在最后面
    prepimg = image_re.transpose((0, 3, 1, 2))  # NHWC to NCHW,C 代表通道数,神经棒推理时,要求通道说写在前面第2个位置(1,3,416,416)
    outs= exec_net.infer(inputs={input_blob: prepimg})#2
    anchor_mask = [[3, 4, 5], [1, 2, 3]]  # default setting
    boxes, classes, scores = [], [], []
    i = 0
    for out in outs.values():  # out.shape=(1, 18,13, 13)，(1, 18,26, 26),batch,num_anchors,height,width
        b, c, s = _process_feats(out, anchors, anchor_mask, input_shape)
        b, c, s = _filter_boxes(b, c, s)
        boxes.append(b)
        classes.append(c)
        scores.append(s)
        i += 1

    boxes = np.concatenate(boxes)
    classes = np.concatenate(classes)
    scores = np.concatenate(scores)

    # Scale boxes back to original image shape.
    width, height = shape[1], shape[0]
    image_dims = [width, height, width, height]
    boxes = boxes * image_dims
    nboxes, nclasses, nscores = [], [], []
    for c in set(classes):
        inds = np.where(classes == c)
        b = boxes[inds]
        c = classes[inds]
        s = scores[inds]

        keep = _nms_boxes(b, s)

        nboxes.append(b[keep])
        nclasses.append(c[keep])
        nscores.append(s[keep])

    if not nclasses and not nscores:
        boxes=None
        classes=None
        scores=None
    else:
        boxes = np.concatenate(nboxes)
        classes = np.concatenate(nclasses)
        scores = np.concatenate(nscores)

    if boxes is not None:
        draw(frame, boxes, scores, classes,all_classes)
        # image=cv2.resize(image, (1280, 720))
    cv2.imshow(namewindows,frame)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    del exec_net
    del plugin

But the results is so bad.You can see in the attachment, the left is not using NCS2

So, what's the problem? Please help me.The following is the xml content

This XML file does not appear to have any style information associated with it. The document tree is shown below.
<net batch="1" name="yolov3-tiny" version="5">
<layers>
<layer id="0" name="input_1" precision="FP16" type="Input">
<output>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>416</dim>
<dim>416</dim>
</port>
</output>
</layer>
<layer id="1" name="conv2d_1/convolution" precision="FP16" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="16" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>416</dim>
<dim>416</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>16</dim>
<dim>416</dim>
<dim>416</dim>
</port>
</output>
<blobs>
<weights offset="0" size="864"/>
<biases offset="864" size="32"/>
</blobs>
</layer>
<layer id="2" name="re_lu_1/Relu" precision="FP16" type="ReLU">
<input>
<port id="0">
<dim>1</dim>
<dim>16</dim>
<dim>416</dim>
<dim>416</dim>
</port>
</input>
<output>
<port id="1">
<dim>1</dim>
<dim>16</dim>
<dim>416</dim>
<dim>416</dim>
</port>
</output>
</layer>
<layer id="3" name="max_pooling2d_1/MaxPool" precision="FP16" type="Pooling">
<data auto_pad="same_upper" exclude-pad="true" kernel="2,2" pads_begin="0,0" pads_end="0,0" pool-method="max" strides="2,2"/>
<input>
<port id="0">
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<dim>416</dim>
</port>
</input>
<output>
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<dim>208</dim>
<dim>208</dim>
</port>
</output>
</layer>
<layer id="4" name="conv2d_2/convolution" precision="FP16" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="32" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
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<dim>208</dim>
<dim>208</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>32</dim>
<dim>208</dim>
<dim>208</dim>
</port>
</output>
<blobs>
<weights offset="896" size="9216"/>
<biases offset="10112" size="64"/>
</blobs>
</layer>
<layer id="5" name="re_lu_2/Relu" precision="FP16" type="ReLU">
<input>
<port id="0">
<dim>1</dim>
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<dim>208</dim>
<dim>208</dim>
</port>
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<dim>104</dim>
<dim>104</dim>
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<input>
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<dim>512</dim>
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</port>
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<meta_data>
<MO_version value="2019.1.1-83-g28dfbfd"/>
<cli_parameters>
<data_type value="FP16"/>
<disable_fusing value="False"/>
<disable_gfusing value="False"/>
<disable_nhwc_to_nchw value="False"/>
<disable_resnet_optimization value="False"/>
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<input_shape value="[1,416,416,3]"/>
<keep_shape_ops value="False"/>
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<mean_scale_values value="[(None, array([255., 255., 255.]))]"/>
<mean_values value="()"/>
<move_to_preprocess value="False"/>
<output_dir value="DIR"/>
<placeholder_shapes value="[ 1 416 416 3]"/>
<reverse_input_channels value="False"/>
<scale_values value="[255,255,255]"/>
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<version value="False"/>
<unset unset_cli_parameters="batch, finegrain_fusing, freeze_placeholder_with_value, input, input_checkpoint, input_meta_graph, model_name, output, saved_model_dir, saved_model_tags, scale, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_operation_patterns, tensorflow_subgraph_patterns, tensorflow_use_custom_operations_config"/>
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Shubha_R_Intel · ‎07-17-2019

Dear chen, rookie,

Is this a custom-trained tiny-yolo v3 model or did you simply use the default pre-trained model ?

Thanks,

Shubha

chen__rookie · ‎07-19-2019

custom-trained

JesusE_Intel · ‎07-22-2019

Hi chen, rookie,

I don't see any attachments on your original post, could you try adding them again?

Also, could you try to add following option to the model optimizer command (mo_tf.py)?

--reverse_input_channels

Regards,

Jesus

Shubha_R_Intel · ‎07-24-2019

Dear everyone,

kindly download OpenVino 2019R2 which was just released today. It fixes a lot of issues and introduces some wonderful new features !

Thanks,

Shubha