commit 76a1580861210e0310db23acbc29e1064ae30ead
author Kshitij Sisodia <kshitij.sisodia@arm.com> Fri Dec 24 11:05:11 2021 +0000
committer Liam Barry <liam.barry@arm.com> Fri Dec 24 14:20:36 2021 +0000
tree f947145cffd944aa3724c90745fc0e9d8e2fb2f4
parent 871fcdc755173b9f7ecb8cf9dc8dc6306329958c

MLECO-2599: Replace DSCNN with MicroNet for KWS

Added SoftMax function to Mathutils to allow MicroNet
to output probability as it does not nativelu have this layer.
Minor refactoring to accommodate Softmax Calculations
Extensive renaming and updating of documentation and resource download script.
Added SoftMax function to Mathutils to allow MicroNet
to output probability.

Change-Id: I7cbbda1024d14b85c9ac1beea7ca8fbffd0b6eb5
Signed-off-by: Liam Barry <liam.barry@arm.com>

set_up_default_resources.py

diff --git a/set_up_default_resources.py b/set_up_default_resources.py
index 91007e4..d244213 100755
--- a/set_up_default_resources.py
+++ b/set_up_default_resources.py
@@ -56,12 +56,12 @@
     },
     {
         "use_case_name": "kws",
-        "resources": [{"name": "ds_cnn_clustered_int8.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite"},
-                      {"name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy"},
+        "resources": [{"name": "ifm0.npy",
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy"},
                       {"name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy"}]
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy"},
+                      {"name": "kws_micronet_m.tflite",
+                       "url": " https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite"}]
     },
      {
         "use_case_name": "vww",
@@ -80,12 +80,12 @@
                        "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_2_int8/0.npy"},
                       {"sub_folder": "asr", "name": "ofm0.npy",
                        "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy"},
-                      {"name": "ds_cnn_clustered_int8.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite"},
                       {"sub_folder": "kws", "name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy"},
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy"},
                       {"sub_folder": "kws", "name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy"}]
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy"},
+                      {"name": "kws_micronet_m.tflite",
+                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite"}]
     },
     {
         "use_case_name": "noise_reduction",
@@ -303,8 +303,8 @@
     # 3. Run vela on models in resources_downloaded
     # New models will have same name with '_vela' appended.
     # For example:
-    #   original model:  ds_cnn_clustered_int8.tflite
-    # after vela model:  ds_cnn_clustered_int8_vela_H128.tflite
+    # original model:    kws_micronet_m.tflite
+    # after vela model:  kws_micronet_m_vela_H128.tflite
     #
     # Note: To avoid to run vela twice on the same model, it's supposed that
     # downloaded model names don't contain the 'vela' word.