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New Model Implementation

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This commit is contained in:
ehpersonal38
2023-01-14 15:22:39 -05:00
parent e2062362b8
commit 84763bf5b7
13 changed files with 177 additions and 9 deletions
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scripts/server.py
+77 -6
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@@ -35,6 +35,7 @@ DISCONNECT_MESSAGE = "!DISCONNECT"
userDir = os.path.expanduser('~')
DB_PATH = userDir + '/BirdNET-Pi/scripts/birds.db'
PREDICTED_SPECIES_LIST = []
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
@@ -51,7 +52,7 @@ with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:
this_run = f.readlines()
audiofmt = "." + str(str(str([i for i in this_run if i.startswith('AUDIOFMT')]).split('=')[1]).split('\\')[0])
priv_thresh = float("." + str(str(str([i for i in this_run if i.startswith('PRIVACY_THRESHOLD')]).split('=')[1]).split('\\')[0])) / 10
model = str(str(str([i for i in this_run if i.startswith('MODEL')]).split('=')[1]).split('\\')[0])
def loadModel():
@@ -63,7 +64,8 @@ def loadModel():
print('LOADING TF LITE MODEL...', end=' ')
# Load TFLite model and allocate tensors.
modelpath = userDir + '/BirdNET-Pi/model/BirdNET_6K_GLOBAL_MODEL.tflite'
# model will either be BirdNET_GLOBAL_3K_V2.2_MData_Model_FP16 (new) or BirdNET_6K_GLOBAL_MODEL (old)
modelpath = userDir + '/BirdNET-Pi/model/'+model+'.tflite'
myinterpreter = tflite.Interpreter(model_path=modelpath, num_threads=2)
myinterpreter.allocate_tensors()
@@ -73,7 +75,8 @@ def loadModel():
# Get input tensor index
INPUT_LAYER_INDEX = input_details[0]['index']
MDATA_INPUT_INDEX = input_details[1]['index']
if model == "BirdNET_6K_GLOBAL_MODEL":
MDATA_INPUT_INDEX = input_details[1]['index']
OUTPUT_LAYER_INDEX = output_details[0]['index']
# Load labels
@@ -87,6 +90,68 @@ def loadModel():
return myinterpreter
def loadMetaModel():
global M_INTERPRETER
global M_INPUT_LAYER_INDEX
global M_OUTPUT_LAYER_INDEX
# Load TFLite model and allocate tensors.
M_INTERPRETER = tflite.Interpreter(model_path=userDir + '/BirdNET-Pi/model/BirdNET_GLOBAL_3K_V2.2_MData_Model_FP16.tflite')
M_INTERPRETER.allocate_tensors()
# Get input and output tensors.
input_details = M_INTERPRETER.get_input_details()
output_details = M_INTERPRETER.get_output_details()
# Get input tensor index
M_INPUT_LAYER_INDEX = input_details[0]['index']
M_OUTPUT_LAYER_INDEX = output_details[0]['index']
print("loaded META model")
def predictFilter(lat, lon, week):
global M_INTERPRETER
# Does interpreter exist?
try:
if M_INTERPRETER == None:
loadMetaModel()
except Exception as e:
loadMetaModel()
# Prepare mdata as sample
sample = np.expand_dims(np.array([lat, lon, week], dtype='float32'), 0)
# Run inference
M_INTERPRETER.set_tensor(M_INPUT_LAYER_INDEX, sample)
M_INTERPRETER.invoke()
return M_INTERPRETER.get_tensor(M_OUTPUT_LAYER_INDEX)[0]
def explore(lat, lon, week):
# Make filter prediction
l_filter = predictFilter(lat, lon, week)
# Apply threshold
l_filter = np.where(l_filter >= 0.03, l_filter, 0)
# Zip with labels
l_filter = list(zip(l_filter, CLASSES))
# Sort by filter value
l_filter = sorted(l_filter, key=lambda x: x[0], reverse=True)
return l_filter
def predictSpeciesList(lat, lon, week):
l_filter = explore(lat, lon, week)
for s in l_filter:
if s[0] >= 0.03:
PREDICTED_SPECIES_LIST.append(s[1])
def loadCustomSpeciesList(path):
@@ -162,7 +227,8 @@ def predict(sample, sensitivity):
global INTERPRETER
# Make a prediction
INTERPRETER.set_tensor(INPUT_LAYER_INDEX, np.array(sample[0], dtype='float32'))
INTERPRETER.set_tensor(MDATA_INPUT_INDEX, np.array(sample[1], dtype='float32'))
if model == "BirdNET_6K_GLOBAL_MODEL":
INTERPRETER.set_tensor(MDATA_INPUT_INDEX, np.array(sample[1], dtype='float32'))
INTERPRETER.invoke()
prediction = INTERPRETER.get_tensor(OUTPUT_LAYER_INDEX)[0]
@@ -197,6 +263,11 @@ def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
start = time.time()
print('ANALYZING AUDIO...', end=' ', flush=True)
if model == "BirdNET_GLOBAL_3K_V2.2_MData_Model_FP16":
if len(PREDICTED_SPECIES_LIST) == 0:
predictSpeciesList(lat,lon,week)
# Convert and prepare metadata
mdata = convertMetadata(np.array([lat, lon, week]))
mdata = np.expand_dims(mdata, 0)
@@ -242,7 +313,7 @@ def writeResultsToFile(detections, min_conf, path):
rfile.write('Start (s);End (s);Scientific name;Common name;Confidence\n')
for d in detections:
for entry in detections[d]:
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0) and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0)):
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0) and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0) and (entry[0] in PREDICTED_SPECIES_LIST or len(PREDICTED_SPECIES_LIST) == 0) ):
rfile.write(d + ';' + entry[0].replace('_', ';') + ';' + str(entry[1]) + '\n')
rcnt += 1
print('DONE! WROTE', rcnt, 'RESULTS.')
@@ -364,7 +435,7 @@ def handle_client(conn, addr):
species_apprised_this_run = []
for entry in detections[d]:
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0)
and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0)):
and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0) and (entry[0] in PREDICTED_SPECIES_LIST or len(PREDICTED_SPECIES_LIST) == 0) ):
# Write to text file.
rfile.write(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';'
+ str(entry[1]) + ";" + str(args.lat) + ';' + str(args.lon) + ';' + str(min_conf) + ';' + str(week) + ';'