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TFlite e2e FP32 Object detection model #5439

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TFlite e2e FP32 Object detection model
anijain2305 Apr 24, 2020
d4adff4
Fix test
anijain2305 Apr 29, 2020
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74 changes: 49 additions & 25 deletions tests/python/frontend/tflite/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1729,23 +1729,27 @@ def test_detection_postprocess():
tflite_output = run_tflite_graph(tflite_model, [box_encodings, class_predictions])
tvm_output = run_tvm_graph(tflite_model, [box_encodings, class_predictions],
["raw_outputs/box_encodings", "raw_outputs/class_predictions"], num_output=4)
# check valid count is the same

# Check all output shapes are equal
assert all([tvm_tensor.shape == tflite_tensor.shape \
for (tvm_tensor, tflite_tensor) in zip(tvm_output, tflite_output)])

# Check valid count is the same
assert tvm_output[3] == tflite_output[3]
# check all the output shapes are the same
assert tvm_output[0].shape == tflite_output[0].shape
assert tvm_output[1].shape == tflite_output[1].shape
assert tvm_output[2].shape == tflite_output[2].shape
valid_count = tvm_output[3][0]
# only check the valid detections are the same
# tvm has a different convention to tflite for invalid detections, it uses all -1s whereas
# tflite appears to put in nonsense data instead
tvm_boxes = tvm_output[0][0][:valid_count]
tvm_classes = tvm_output[1][0][:valid_count]
tvm_scores = tvm_output[2][0][:valid_count]
# check the output data is correct
tvm.testing.assert_allclose(np.squeeze(tvm_boxes), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-5)
tvm.testing.assert_allclose(np.squeeze(tvm_classes), np.squeeze(tflite_output[1]), rtol=1e-5, atol=1e-5)
tvm.testing.assert_allclose(np.squeeze(tvm_scores), np.squeeze(tflite_output[2]), rtol=1e-5, atol=1e-5)

# For boxes that do not have any detections, TFLite puts random values. Therefore, we compare
# tflite and tvm tensors for only valid boxes.
for i in range(0, valid_count):
# Check bounding box co-ords
tvm.testing.assert_allclose(np.squeeze(tvm_output[0][0][i]), np.squeeze(tflite_output[0][0][i]),
rtol=1e-5, atol=1e-5)
# Check the class
tvm.testing.assert_allclose(np.squeeze(tvm_output[1][0][i]), np.squeeze(tflite_output[1][0][i]),
rtol=1e-5, atol=1e-5)
# Check the score
tvm.testing.assert_allclose(np.squeeze(tvm_output[2][0][i]), np.squeeze(tflite_output[2][0][i]),
rtol=1e-5, atol=1e-5)


#######################################################################
Expand Down Expand Up @@ -1933,21 +1937,41 @@ def test_forward_qnn_mobilenet_v3_net():
# SSD Mobilenet
# -------------

def test_forward_ssd_mobilenet_v1():
"""Test the SSD Mobilenet V1 TF Lite model."""
# SSD MobilenetV1
def test_forward_coco_ssd_mobilenet_v1():
"""Test the quantized Coco SSD Mobilenet V1 TF Lite model."""
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@anijain2305 anijain2305 Apr 29, 2020

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remove quantized,

tflite_model_file = tf_testing.get_workload_official(
"https://raw.githubusercontent.com/dmlc/web-data/master/tensorflow/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28_nopp.tgz",
"ssd_mobilenet_v1_coco_2018_01_28_nopp.tflite")
"https://raw.githubusercontent.com/dmlc/web-data/master/tensorflow/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tgz",
"ssd_mobilenet_v1_coco_2018_01_28.tflite")

with open(tflite_model_file, "rb") as f:
tflite_model_buf = f.read()

np.random.seed(0)
data = np.random.uniform(size=(1, 300, 300, 3)).astype('float32')
tflite_output = run_tflite_graph(tflite_model_buf, data)
tvm_output = run_tvm_graph(tflite_model_buf, data, 'normalized_input_image_tensor', num_output=2)
for i in range(2):
tvm.testing.assert_allclose(np.squeeze(tvm_output[i]), np.squeeze(tflite_output[i]),
rtol=1e-5, atol=2e-5)
tvm_output = run_tvm_graph(tflite_model_buf, data, 'normalized_input_image_tensor', num_output=4)

# Check all output shapes are equal
assert all([tvm_tensor.shape == tflite_tensor.shape \
for (tvm_tensor, tflite_tensor) in zip(tvm_output, tflite_output)])

# Check valid count is the same
assert tvm_output[3] == tflite_output[3]
valid_count = tvm_output[3][0]

# For boxes that do not have any detections, TFLite puts random values. Therefore, we compare
# tflite and tvm tensors for only valid boxes.
for i in range(0, valid_count):
# Check bounding box co-ords
tvm.testing.assert_allclose(np.squeeze(tvm_output[0][0][i]), np.squeeze(tflite_output[0][0][i]),
rtol=1e-5, atol=1e-5)
# Check the class
tvm.testing.assert_allclose(np.squeeze(tvm_output[1][0][i]), np.squeeze(tflite_output[1][0][i]),
rtol=1e-5, atol=1e-5)
# Check the score
tvm.testing.assert_allclose(np.squeeze(tvm_output[2][0][i]), np.squeeze(tflite_output[2][0][i]),
rtol=1e-5, atol=1e-5)


#######################################################################
# MediaPipe
Expand Down Expand Up @@ -2045,7 +2069,7 @@ def test_forward_mediapipe_hand_landmark():
test_forward_mobilenet_v3()
test_forward_inception_v3_net()
test_forward_inception_v4_net()
test_forward_ssd_mobilenet_v1()
test_forward_coco_ssd_mobilenet_v1()
test_forward_mediapipe_hand_landmark()

# End to End quantized
Expand Down