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Commit 428108b

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lizlooneylizlooney
authored
Merge pull request #253 from FIRST-Tech-Challenge/pr_no_raise
Handle two harmless exceptions I saw in the logs
2 parents 9e3f881 + f17dace commit 428108b

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3 files changed

+117
-68
lines changed

3 files changed

+117
-68
lines changed

‎server/frame_extractor.py

+10-2
Original file line numberDiff line numberDiff line change
@@ -207,8 +207,16 @@ def extract_frames(action_parameters):
207207
# Store the frame as a jpg image, which are smaller/faster than png.
208208
success, buffer = cv2.imencode('.jpg', frame)
209209
if success:
210-
video_entity = storage.store_frame_image(team_uuid, video_uuid, frame_number,
211-
'image/jpg', buffer.tostring())
210+
try:
211+
video_entity = storage.store_frame_image(team_uuid, video_uuid, frame_number,
212+
'image/jpg', buffer.tostring())
213+
except:
214+
# Check if the video has been deleted.
215+
team_entity = storage.retrieve_team_entity(team_uuid)
216+
if 'video_uuids_deleted' in team_entity:
217+
if video_uuid in team_entity['video_uuids_deleted']:
218+
return
219+
raise
212220
if video_entity['delete_in_progress']:
213221
return
214222
frame_number += 1

‎server/storage.py

+33
Original file line numberDiff line numberDiff line change
@@ -99,6 +99,9 @@ def retrieve_team_uuid(program, team_number):
9999
'datasets_created_today': 0,
100100
'datasets_downloaded_today': 0,
101101
'video_uuids_tracking_now': [],
102+
'video_uuids_deleted': [],
103+
'dataset_uuids_deleted': [],
104+
'model_uuids_deleted': [],
102105
})
103106
need_put = True
104107
else:
@@ -182,6 +185,30 @@ def __remove_video_uuid_from_tracking_list(transaction, team_uuid, video_uuid):
182185
team_entity['video_uuids_tracking_now'].remove(video_uuid)
183186
transaction.put(team_entity)
184187

188+
def __add_video_uuid_to_deleted_list(transaction, team_uuid, video_uuid):
189+
team_entity = retrieve_team_entity(team_uuid)
190+
if 'video_uuids_deleted' not in team_entity:
191+
team_entity['video_uuids_deleted'] = [video_uuid]
192+
elif video_uuid not in team_entity['video_uuids_deleted']:
193+
team_entity['video_uuids_deleted'].append(video_uuid)
194+
transaction.put(team_entity)
195+
196+
def __add_dataset_uuid_to_deleted_list(transaction, team_uuid, dataset_uuid):
197+
team_entity = retrieve_team_entity(team_uuid)
198+
if 'dataset_uuids_deleted' not in team_entity:
199+
team_entity['dataset_uuids_deleted'] = [dataset_uuid]
200+
elif dataset_uuid not in team_entity['dataset_uuids_deleted']:
201+
team_entity['dataset_uuids_deleted'].append(dataset_uuid)
202+
transaction.put(team_entity)
203+
204+
def __add_model_uuid_to_deleted_list(transaction, team_uuid, model_uuid):
205+
team_entity = retrieve_team_entity(team_uuid)
206+
if 'model_uuids_deleted' not in team_entity:
207+
team_entity['model_uuids_deleted'] = [model_uuid]
208+
elif model_uuid not in team_entity['model_uuids_deleted']:
209+
team_entity['model_uuids_deleted'].append(model_uuid)
210+
transaction.put(team_entity)
211+
185212
# video - public methods
186213

187214
def prepare_to_upload_video(team_uuid, content_type):
@@ -465,6 +492,8 @@ def delete_video(team_uuid, video_uuid):
465492
# Also update the team entity in the same transaction.
466493
__remove_video_uuid_from_tracking_list(transaction, team_uuid, video_uuid)
467494
transaction.put(video_entity)
495+
# Also update the team entity in the same transaction.
496+
__add_video_uuid_to_deleted_list(transaction, team_uuid, video_uuid)
468497
action_parameters = action.create_action_parameters(
469498
team_uuid, action.ACTION_NAME_DELETE_VIDEO)
470499
action_parameters['team_uuid'] = team_uuid
@@ -1015,6 +1044,8 @@ def delete_dataset(team_uuid, dataset_uuid):
10151044
dataset_entity = dataset_entities[0]
10161045
dataset_entity['delete_in_progress'] = True
10171046
transaction.put(dataset_entity)
1047+
# Also update the team entity in the same transaction.
1048+
__add_dataset_uuid_to_deleted_list(transaction, team_uuid, dataset_uuid)
10181049
action_parameters = action.create_action_parameters(
10191050
team_uuid, action.ACTION_NAME_DELETE_DATASET)
10201051
action_parameters['team_uuid'] = team_uuid
@@ -1766,6 +1797,8 @@ def delete_model(team_uuid, model_uuid):
17661797
model_entity = model_entities[0]
17671798
model_entity['delete_in_progress'] = True
17681799
transaction.put(model_entity)
1800+
# Also update the team entity in the same transaction.
1801+
__add_model_uuid_to_deleted_list(transaction, team_uuid, model_uuid)
17691802
action_parameters = action.create_action_parameters(
17701803
team_uuid, action.ACTION_NAME_DELETE_MODEL)
17711804
action_parameters['team_uuid'] = team_uuid

‎server/tflite_creator.py

+74-66
Original file line numberDiff line numberDiff line change
@@ -46,69 +46,77 @@ def create_tflite(action_parameters):
4646
team_uuid = action_parameters['team_uuid']
4747
model_uuid = action_parameters['model_uuid']
4848

49-
model_entity = storage.retrieve_model_entity(team_uuid, model_uuid)
50-
model_folder = model_entity['model_folder']
51-
52-
# The following code is inspired by
53-
# https://colab.sandbox.google.com/github/tensorflow/models/blob/master/research/object_detection/colab_tutorials/convert_odt_model_to_TFLite.ipynb
54-
# and
55-
# https://github.com/tensorflow/models/blob/b3483b3942ab9bddc94fcbc5bd00fc790d1ddfcb/research/object_detection/export_tflite_graph_tf2.py
56-
57-
if not blob_storage.tflite_saved_model_exists(model_folder):
58-
# Export TFLite inference graph.
59-
pipeline_config_path = blob_storage.get_pipeline_config_path(model_folder)
60-
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
61-
with tf.io.gfile.GFile(pipeline_config_path, 'r') as f:
62-
text_format.Parse(f.read(), pipeline_config)
63-
trained_checkpoint_path = model_entity['trained_checkpoint_path']
64-
if trained_checkpoint_path == '':
65-
message = 'Error: Trained checkpoint not found for model_uuid=%s.' % model_uuid
66-
logging.critical(message)
67-
raise exceptions.HttpErrorNotFound(message)
68-
trained_checkpoint_dir = trained_checkpoint_path[:trained_checkpoint_path.rindex('/')]
69-
output_directory = blob_storage.get_tflite_folder_path(model_folder)
70-
max_detections = 10 # This matches the default for TFObjectDetector.Parameters.maxNumDetections in the the FTC SDK.
71-
export_tflite_graph_lib_tf2.export_tflite_model(pipeline_config, trained_checkpoint_dir,
72-
output_directory, max_detections, use_regular_nms=False)
73-
74-
action.retrigger_if_necessary(action_parameters)
75-
76-
if not blob_storage.tflite_quantized_model_exists(model_folder):
77-
# Convert to a quantized tflite model
78-
saved_model_path = blob_storage.get_tflite_saved_model_path(model_folder)
79-
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
80-
converter.optimizations = [tf.lite.Optimize.DEFAULT] # DEFAULT means the tflite model will be quantized.
81-
tflite_quantized_model = converter.convert()
82-
blob_storage.store_tflite_quantized_model(model_folder, tflite_quantized_model)
83-
84-
action.retrigger_if_necessary(action_parameters)
85-
86-
if not blob_storage.tflite_label_map_txt_exists(model_folder):
87-
# Create the label map.
88-
blob_storage.store_tflite_label_map_txt(model_folder,
89-
'\n'.join(model_entity['sorted_label_list']))
90-
91-
action.retrigger_if_necessary(action_parameters)
92-
93-
if not blob_storage.tflite_model_with_metadata_exists(model_folder):
94-
# Add Metadata
95-
# Make a temporary directory
96-
folder = '/tmp/tflite_creater/%s' % str(uuid.uuid4().hex)
97-
os.makedirs(folder, exist_ok=True)
98-
try:
99-
quantized_model_filename = '%s/quantized_model' % folder
100-
blob_storage.write_tflite_quantized_model_to_file(model_folder, quantized_model_filename)
101-
label_map_txt_filename = '%s/label_map.txt' % folder
102-
blob_storage.write_tflite_label_map_txt_to_file(model_folder, label_map_txt_filename)
103-
model_with_metadata_filename = '%s/model_with_metadata.tflite' % folder
104-
105-
writer = object_detector.MetadataWriter.create_for_inference(
106-
writer_utils.load_file(quantized_model_filename),
107-
input_norm_mean=[127.5], input_norm_std=[127.5],
108-
label_file_paths=[label_map_txt_filename])
109-
writer_utils.save_file(writer.populate(), model_with_metadata_filename)
110-
111-
blob_storage.store_tflite_model_with_metadata(model_folder, model_with_metadata_filename)
112-
finally:
113-
# Delete the temporary directory.
114-
shutil.rmtree(folder)
49+
try:
50+
model_entity = storage.retrieve_model_entity(team_uuid, model_uuid)
51+
model_folder = model_entity['model_folder']
52+
53+
# The following code is inspired by
54+
# https://colab.sandbox.google.com/github/tensorflow/models/blob/master/research/object_detection/colab_tutorials/convert_odt_model_to_TFLite.ipynb
55+
# and
56+
# https://github.com/tensorflow/models/blob/b3483b3942ab9bddc94fcbc5bd00fc790d1ddfcb/research/object_detection/export_tflite_graph_tf2.py
57+
58+
if not blob_storage.tflite_saved_model_exists(model_folder):
59+
# Export TFLite inference graph.
60+
pipeline_config_path = blob_storage.get_pipeline_config_path(model_folder)
61+
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
62+
with tf.io.gfile.GFile(pipeline_config_path, 'r') as f:
63+
text_format.Parse(f.read(), pipeline_config)
64+
trained_checkpoint_path = model_entity['trained_checkpoint_path']
65+
if trained_checkpoint_path == '':
66+
message = 'Error: Trained checkpoint not found for model_uuid=%s.' % model_uuid
67+
logging.critical(message)
68+
raise exceptions.HttpErrorNotFound(message)
69+
trained_checkpoint_dir = trained_checkpoint_path[:trained_checkpoint_path.rindex('/')]
70+
output_directory = blob_storage.get_tflite_folder_path(model_folder)
71+
max_detections = 10 # This matches the default for TFObjectDetector.Parameters.maxNumDetections in the the FTC SDK.
72+
export_tflite_graph_lib_tf2.export_tflite_model(pipeline_config, trained_checkpoint_dir,
73+
output_directory, max_detections, use_regular_nms=False)
74+
75+
action.retrigger_if_necessary(action_parameters)
76+
77+
if not blob_storage.tflite_quantized_model_exists(model_folder):
78+
# Convert to a quantized tflite model
79+
saved_model_path = blob_storage.get_tflite_saved_model_path(model_folder)
80+
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
81+
converter.optimizations = [tf.lite.Optimize.DEFAULT] # DEFAULT means the tflite model will be quantized.
82+
tflite_quantized_model = converter.convert()
83+
blob_storage.store_tflite_quantized_model(model_folder, tflite_quantized_model)
84+
85+
action.retrigger_if_necessary(action_parameters)
86+
87+
if not blob_storage.tflite_label_map_txt_exists(model_folder):
88+
# Create the label map.
89+
blob_storage.store_tflite_label_map_txt(model_folder,
90+
'\n'.join(model_entity['sorted_label_list']))
91+
92+
action.retrigger_if_necessary(action_parameters)
93+
94+
if not blob_storage.tflite_model_with_metadata_exists(model_folder):
95+
# Add Metadata
96+
# Make a temporary directory
97+
folder = '/tmp/tflite_creater/%s' % str(uuid.uuid4().hex)
98+
os.makedirs(folder, exist_ok=True)
99+
try:
100+
quantized_model_filename = '%s/quantized_model' % folder
101+
blob_storage.write_tflite_quantized_model_to_file(model_folder, quantized_model_filename)
102+
label_map_txt_filename = '%s/label_map.txt' % folder
103+
blob_storage.write_tflite_label_map_txt_to_file(model_folder, label_map_txt_filename)
104+
model_with_metadata_filename = '%s/model_with_metadata.tflite' % folder
105+
106+
writer = object_detector.MetadataWriter.create_for_inference(
107+
writer_utils.load_file(quantized_model_filename),
108+
input_norm_mean=[127.5], input_norm_std=[127.5],
109+
label_file_paths=[label_map_txt_filename])
110+
writer_utils.save_file(writer.populate(), model_with_metadata_filename)
111+
112+
blob_storage.store_tflite_model_with_metadata(model_folder, model_with_metadata_filename)
113+
finally:
114+
# Delete the temporary directory.
115+
shutil.rmtree(folder)
116+
except:
117+
# Check if the model has been deleted.
118+
team_entity = storage.retrieve_team_entity(team_uuid)
119+
if 'model_uuids_deleted' in team_entity:
120+
if model_uuid in team_entity['model_uuids_deleted']:
121+
return
122+
raise

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