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System information

• What is the top-level directory of the model you are using:
  Mobilenet V3 SSD small/large downloaded from
  https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
  Yes, have also tried stock example scripts
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
  Windows 10 Pro 1903
• TensorFlow installed from (source or binary):
  From pip binary
• TensorFlow version (use command below):
  Tensorflow 2.0
• Bazel version (if compiling from source):
• CUDA/cuDNN version:
  NA
• GPU model and memory:
  NA using CPU
• Exact command to reproduce:
  NA not from a command

Describe the problem

No matter what I try it seems the Mobilenet V3 SSD outputs all zeroes. I downloaded both the small and large models from the detection model zoo. I have tried using multiple images, both normalized and unnormalized, running both the provided .tflite and frozen .pbs for the small and large models, and using different installs of Tensorflow to no avail. I also tried using the object_detection_tutorial.ipynb provided but MBV3 SSD does not work with that script. What am I doing wrong? Or is there a problem with the provided model?

Source code / logs

Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.

tf.compat.v1.disable_v2_behavior()
import numpy as np
import cv2


def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the
    # unserialized graph_def
    with tf.io.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.compat.v1.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we import the graph_def into a new Graph and returns it
    with tf.Graph().as_default() as graph:
        # The name var will prefix every op/nodes in your graph
        # Since we load everything in a new graph, this is not needed
        tf.import_graph_def(graph_def, name="")
    return graph


model_graph = load_graph(r"C:\Users\iden\Downloads\ssd_mobilenet_v3_small_coco_2019_08_14\frozen_inference_graph.pb")
outputs = [model_graph.get_tensor_by_name("raw_outputs/class_predictions:0"),
           model_graph.get_tensor_by_name("raw_outputs/box_encodings:0")]

image = cv2.imread(r"C:\Users\iden\Pictures\test.png").astype(np.float32)
image = cv2.resize(image, (320, 320))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = (image - 128) / 127.5
image = np.expand_dims(image, 0)

with tf.compat.v1.Session(graph=model_graph) as sess:
    output_tensors = sess.run(outputs, feed_dict={'normalized_input_image_tensor:0': image})

scores = output_tensors[0]
print(scores)
locations = output_tensors[1]
print(locations)

This script gives output:

[[[0.5 0.5 0.5 ... 0.5 0.5 0.5]
  [0.5 0.5 0.5 ... 0.5 0.5 0.5]
  [0.5 0.5 0.5 ... 0.5 0.5 0.5]
  ...
  [0.5 0.5 0.5 ... 0.5 0.5 0.5]
  [0.5 0.5 0.5 ... 0.5 0.5 0.5]
  [0.5 0.5 0.5 ... 0.5 0.5 0.5]]]
[[[-1.2356030e-17  1.4101090e-17 -6.3667132e-18  3.1901107e-17]
  [ 7.7017563e-18  4.9004532e-17 -7.8113352e-17 -9.1179292e-19]
  [-6.6128603e-17  3.8230567e-17 -7.8168674e-17 -3.8976880e-17]
  ...
  [ 7.6168566e-32  2.0741278e-30 -1.5562906e-30  1.0528488e-30]
  [-5.0384362e-30 -1.5268065e-30 -2.2974830e-30  1.3904762e-30]
  [-2.2760488e-30 -1.1501334e-30 -2.2292421e-30  1.1195719e-31]]]

So the input to the sigmoid is all zero and the box outputs are all basically zero (I have checked the whole tensors for larger values).