def saveParams(net, classes, epoch):
log.i("EXPORTING MODEL PARAMS...", new_line=False)
net_filename = cfg.MODEL_PATH + cfg.RUN_NAME + "_model_params_epoch_" + str(
epoch) + ".pkl"
if not os.path.exists(cfg.MODEL_PATH):
os.makedirs(cfg.MODEL_PATH)
with open(net_filename,
'wb') as f: # 'wb' instead 'w' for binary file for python3
#We want to save the model params only and trained classes
params = l.get_all_param_values(net)
data = {
'params': params,
'classes': classes,
'run_name': cfg.RUN_NAME,
'epoch': epoch,
'im_size': cfg.IM_SIZE,
'im_dim': cfg.IM_DIM
}
pickle.dump(data, f,
-1) # -1 specifies highest binary protocol for python3
log.i("DONE!")
return os.path.split(net_filename)[-1]
def parseTestSet():
# Status
log.i('PARSING TEST SET...', new_line=False)
t = []
wav_files = [
os.path.join(cfg.TESTSET_PATH, f)
for f in sorted(os.listdir(cfg.TESTSET_PATH))
if os.path.splitext(f)[1] in ['.wav']
]
# Parse files
for f in wav_files:
t.append((f, os.path.splitext(f)[0].split('_RN')[-1]))
# Load class ids
codes = []
with open('metadata/labelset.txt', 'r') as lfile:
for line in lfile.readlines():
codes.append(line.replace('\r\n', '').replace('\n', ''))
labels = []
with open('metadata/labelset_latin.txt', 'r') as lfile:
for line in lfile.readlines():
labels.append(line.replace('\r\n', '').replace('\n', ''))
# Status
log.i(('Done!', len(t), 'TEST FILES'))
return t, codes, labels
def Process(handler, *args):
try:
handler.ssid = handler.get_cookie(etc.cookie_ssid)
handler.ss_ua = handler.request.headers['User-Agent']
handler.ss_ver = handler.get_cookie(etc.cookie_ver)
log.i(handler.request.cookies)
if not handler.ssid:
log.w('[U-A:%s] no_ssid' % handler.ss_ua)
res = {'idx': -1, 'ret': -1, 'msg': etc.err_op_fail, 'res': {}}
handler.write(json.dumps(res))
handler.finish()
return
postData = handler.get_argument('postData', default=None)
if postData:
paramsJson = json.loads(postData)
handler.ss_idx = paramsJson['idx'] if postData else 0
handler.ss_params = paramsJson['params'] if postData else None
log.i('[RIP:%s][U-A:%s][ssid:%s][idx:%s][ver:%s][params:-]' % (
handler.request.headers['X-Real-Ip'],
handler.ss_ua,
handler.ssid,
handler.ss_idx,
handler.ss_ver))
except Exception as e:
res = {'idx': -1, 'ret': -2, 'msg': etc.err_op_fail, 'res': {}}
handler.write(json.dumps(res))
handler.finish()
return
if handler.current_user is None:
log.w('[U-A:%s] signin_required' % handler.ss_ua)
res = {'idx': -1, 'ret': -3, 'msg': etc.err_signin_requied, 'res': {}}
handler.finish()
return
if handler.current_user['role'] != 1:
log.w('[U-A:%s] store_required' % handler.ss_ua)
res = {'idx': -1, 'ret': -5, 'msg': '无商家身份', 'res': {}}
handler.write(json.dumps(res))
handler.finish()
return
try:
request(handler, *args)
except Exception as e:
log.exp(e)
res = {'idx': -1, 'ret': -4, 'msg': etc.err_500, 'res': {}}
handler.write(json.dumps(res))
handler.finish()
return
try:
if handler.current_user != None:
handler.ss_store.replace(handler.ssid, handler.current_user)
except Exception as e:
log.exp(e)
res = {'idx': -1, 'ret': -4, 'msg': etc.err_500, 'res': {}}
handler.write(json.dumps(res))
handler.finish()
return
def test_function(net, hasTargets=True, layer_index=-1):
# We need the prediction function to calculate the validation accuracy
# this way we can test the net during/after training
# We need a version with targets and one without
prediction = l.get_output(l.get_all_layers(net)[layer_index],
deterministic=True)
log.i("COMPILING TEST FUNCTION...", new_line=False)
start = time.time()
if hasTargets:
# Theano variable for the class targets
targets = T.matrix('targets', dtype=theano.config.floatX)
loss = loss_function(net, prediction, targets)
accuracy = accuracy_function(net, prediction, targets)
test_net = theano.function(
[l.get_all_layers(net)[0].input_var, targets],
[prediction, loss, accuracy],
allow_input_downcast=True)
else:
test_net = theano.function([l.get_all_layers(net)[0].input_var],
prediction,
allow_input_downcast=True)
log.i(("DONE! (", int(time.time() - start), "s )"))
return test_net
def logout(handler):
handler.clear_all_cookies()
if handler.current_user:
log.i('userid=%s , ssid=%s' % (handler.current_user['userid'], handler.ssid))
handler.ss_store.delete(handler.ssid)
one_login_store = RdsOneLoginStore()
one_login_store.delete(handler.current_user['userid'])
handler.current_user = None
def loadModel(filename):
log.i(("IMPORTING MODEL...", filename.split(os.sep)[-1]), new_line=False)
net_filename = cfg.MODEL_PATH + filename
with open(net_filename, 'rb') as f:
model = pickle.load(f)
log.i("DONE!")
return model
def post(self):
try:
name = self.get_argument("name")
password = self.get_argument("password")
if not name or not password:
self.render("login.html")
if sql.user_base(name, password):
self.redirect("/index/")
except Exception, e:
log.i(e)
def main(p_port):
if p_port == 0:
print 'port could not be set as 0'
log.e('port could not be set as 0')
exit(1)
log.c('www listening on port : %s' % p_port)
app = Application()
app.listen(p_port)
log.i(app.settings['template_path'])
log.i(app.settings['static_path'])
tornado.ioloop.IOLoop.instance().start()
def loadParams(net, params):
log.i("IMPORTING MODEL PARAMS...", new_line=False)
if cfg.LOAD_OUTPUT_LAYER:
l.set_all_param_values(net, params)
else:
l.set_all_param_values(l.get_all_layers(net)[:-2], params[:-2])
log.i("DONE!")
return net
def incubate(self, cmdline: Union[List[str], str]):
if isinstance(cmdline, str):
cmdline = cmdline.split(" ")
process = subprocess.Popen(cmdline,
shell=self.__shell,
stdin=subprocess.DEVNULL,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
log.i(__name__, "Incubated process. PID={}".format(process.pid))
with self.__lock:
self.__processes_appended.append(process)
def parseDataset():
CLASSES = [
c for c in sorted(os.listdir(os.path.join(cfg.TRAINSET_PATH, 'train')))
]
for c in CLASSES:
log.i(c)
index = []
afiles = [
f for f in sorted(
os.listdir(os.path.join(cfg.TRAINSET_PATH, 'train', c)))
]
max_specs = cfg.MAX_SPECS_PER_CLASS // len(afiles) + 1
for i in range(len(afiles)):
spec_cnt = 0
try:
print(i + 1, '/', len(afiles), c, afiles[i])
specs, noise = getSpecs(
os.path.join(cfg.TRAINSET_PATH, 'train', c, afiles[i]))
for s in range(len(specs)):
if np.isnan(noise[s]):
noise[s] = 0.0
if noise[s] >= cfg.SPEC_SIGNAL_THRESHOLD:
filepath = os.path.join(cfg.DATASET_PATH, c)
if not os.path.exists(filepath):
os.makedirs(filepath)
elif noise[s] <= cfg.NOISE_THRESHOLD:
if RANDOM.choice([True, False], p=[0.60, 0.40]):
filepath = os.path.join(cfg.NOISE_PATH)
if not os.path.exists(filepath):
os.makedirs(filepath)
else:
filepath = None
else:
filepath = None
if filepath:
filename = str(
int(noise[s] *
10000)).zfill(4) + '_' + afiles[i].split(
'.')[0] + '_' + str(s).zfill(3)
# Write to HDD
cv2.imwrite(os.path.join(filepath, filename + '.png'),
specs[s] * 255.0)
if spec_cnt >= max_specs:
break
except:
log.e((spec_cnt, 'specs', 'ERROR DURING SPEC EXTRACT'))
continue
def _generate_res(self, ret_num, msg, step_name='', res=None):
if res is None:
res = {}
log.w(step_name)
ret = {
'idx': self.ss_idx,
'ret': ret_num,
'res': res,
'msg': msg,
}
log.i(step_name + 'finish')
self.write(json.dumps(ret))
self.finish()
def login(handler, user_base, expires):
if True:
user_detail = data_user_detail_mysql\
.get_user_detail_by_userid(user_base['userid'])
user_brief = data_user_brief_mysql\
.get_user_brief_by_userid(user_base['userid'])
user_other = data_user_other_mysql\
.get_user_other_by_userid(user_base['userid'])
orig_dict = {
'name': '',
'sex': '',
'sexot': '',
'love': '',
'horo': '',
'intro': '',
'imglink': '',
'music': '',
'hobby': '',
}
handler.current_user = {
'ssid': handler.ssid,
'userid': user_base['userid'],
'phone': user_base['phone'],
}
log.i('before update(orig_dict)=========%s=========' % handler.current_user)
handler.current_user.update(orig_dict)
if user_detail:
handler.current_user.update(user_detail)
if user_brief:
handler.current_user.update(user_brief)
log.i('after update(brief)=========%s=========' % handler.current_user)
one_login_store = RdsOneLoginStore()
old_ssid = one_login_store.get(user_base['userid'])
log.i('refresh ssid in mc old_ssid=%s handler.ssid=%s' % (old_ssid, handler.ssid))
one_login_store.set(user_base['userid'], handler.ssid)
if handler.ssid != old_ssid:
log.i('delete old ssid if handler.ssid is not old ssid')
handler.ss_store.delete(old_ssid)
domain = util.get_domain_from_host(handler.request.host)
handler.ssid_hmac = generate_hmac(handler.ssid)
handler.set_secure_cookie(etc.cookie_check, handler.ssid, domain=domain,
expires=expires)
handler.set_secure_cookie(etc.cookie_verify, handler.ssid_hmac,
domain=domain, expires=expires)
handler.ss_store.set(handler.ssid, handler.current_user)
def saveModel(net, classes, epoch):
log.i("EXPORTING MODEL...", new_line=False)
net_filename = cfg.MODEL_PATH + cfg.RUN_NAME + "_model_epoch_" + str(
epoch) + ".pkl"
if not os.path.exists(cfg.MODEL_PATH):
os.makedirs(cfg.MODEL_PATH)
with open(net_filename, 'w') as f:
#We want to save the model architecture with all params and trained classes
data = {
'net': net,
'classes': classes,
'run_name': cfg.RUN_NAME,
'epoch': epoch,
'im_size': cfg.IM_SIZE,
'im_dim': cfg.IM_DIM
}
pickle.dump(data, f)
log.i("DONE!")
return os.path.split(net_filename)[-1]
def Process(handler, *args):
try:
handler.ssid = handler.get_cookie(etc.cookie_ssid)
handler.ss_ua = handler.request.headers['User-Agent']
handler.ss_ver = handler.get_cookie(etc.cookie_ver)
if not handler.ssid:
log.w('[U-A:%s] no_ssid' % handler.ss_ua)
res = {'idx': -1, 'ret': -1, 'msg': etc.err_op_fail, 'res': {}}
handler.write(json.dumps(res))
handler.finish()
return
postData = handler.get_argument('postData', default=None)
if postData:
paramsJson = json.loads(postData)
handler.ss_idx = paramsJson['idx'] if postData else 0
handler.ss_params = paramsJson['params'] if postData else None
log.i('[RIP:%s][U-A:%s][idx:%s][ver:%s][params:-]' % (
handler.request.handers['X-Real-Ip'],
handler.ss_ua,
handler.ss_ver))
log.i('[ssid:%s] [check:%s] [verify:%s]' % (
handler.ssid,
handler.get_secure_cookie(etc.cookie_check),
handler.get_secure_cookie(etc.cookie_verify)))
except Exception as e:
log.exp(e)
res = {'idx': -1, 'ret': -2, 'msg': etc.err_op_fail, 'res' {}}
handler.write(json.dumps(res))
handler.finish()
return
try:
request(handler, *args)
except Exception as e:
log.exp(e)
res = {'idx': -1, 'ret': -4, 'msg': etc.err_500, 'res': {}}
handler.write(json.dumps(res))
handler.finish()
return
def parseDataset():
# Random Seed
random = cfg.getRandomState()
# We use subfolders as class labels
classes = [folder for folder in sorted(os.listdir(cfg.DATASET_PATH)) if folder in cfg.CLASS_WHITELIST or len(cfg.CLASS_WHITELIST) == 0]
if not cfg.SORT_CLASSES_ALPHABETICALLY:
classes = shuffle(classes, random_state=random)
classes = classes[:cfg.MAX_CLASSES]
# Now we enlist all image paths for each class
images = []
tclasses = []
sample_count = {}
for c in classes:
c_images = [os.path.join(cfg.DATASET_PATH, c, path) for path in shuffle(os.listdir(os.path.join(cfg.DATASET_PATH, c)), random_state=random) if isValidClass(c, path)][:cfg.MAX_SAMPLES_PER_CLASS]
sample_count[c] = len(c_images)
images += c_images
# Do we want to correct class imbalance?
# This will affect validation scores as we use some samples in TRAIN and VAL
while sample_count[c] < cfg.MIN_SAMPLES_PER_CLASS:
images += [c_images[random.randint(0, len(c_images))]]
sample_count[c] += 1
# Add labels to image paths
for i in range(len(images)):
path = images[i]
label = images[i].split(os.sep)[-2]
images[i] = (path, label)
# Shuffle image paths
images = shuffle(images, random_state=random)
# Validation split
vsplit = int(len(images) * cfg.VAL_SPLIT)
train = images[:-vsplit]
val = images[-vsplit:]
# Show some stats
log.i(("CLASSES:", len(classes)))
log.i(( "CLASS LABELS:", sorted(sample_count.items(), key=operator.itemgetter(1))))
log.i(("TRAINING IMAGES:", len(train)))
log.i(("VALIDATION IMAGES:", len(val)))
return classes, train, val
def train_function(net):
# We use dynamic learning rates which change after some epochs
lr_dynamic = T.scalar(name='learning_rate')
# Theano variable for the class targets
targets = T.matrix('targets', dtype=theano.config.floatX)
# Get the network output
prediction = l.get_output(net)
# The theano train functions takes images and class targets as input
log.i("COMPILING TRAIN FUNCTION...", new_line=False)
start = time.time()
loss = loss_function(net, prediction, targets)
updates = net_updates(net, loss, lr_dynamic)
train_net = theano.function(
[l.get_all_layers(net)[0].input_var, targets, lr_dynamic],
loss,
updates=updates,
allow_input_downcast=True)
log.i(("DONE! (", int(time.time() - start), "s )"))
return train_net
def get_current_user(self):
try:
log.i('---------start get_current_user-------------------')
cookie_check = self.get_secure_cookie(etc.cookie_check)
cookie_verify = self.get_secure_cookie(etc.cookie_verify)
if not cookie_check or not cookie_verify:
log.i('no cookie check or verify')
self.clear_cookie(etc.cookie_check)
self.clear_cookie(etc.cookie_verify)
return None
check_verify = generate_hmac(cookie_check)
if cookie_verify != check_verify:
log.w("evil session : %s %s" % (cookie_check, cookie_verify))
self.clear_cookie(etc.cookie_check)
self.clear_cookie(etc.cookie_verify)
return None
old_current_user = self.ss_store.get(cookie_check)
if old_current_user is None:
log.i("session expired")
self.clear_cookie(etc.cookie_check)
self.clear_cookie(etc.cookie_verify)
return None
log.i("---------self.current_user=%s -------------" % old_current_user)
return old_current_user
except Exception as e:
log.exp(e)
self.clear_cookie(etc.cookie_check)
self.clear_cookie(etc.cookie_verify)
return None
def _generate_res(self, ret_num, msg, step_name='', res=None):
if res is None:
res = {}
log.w(step_name)
ret = {
'idx': self.ss_idx,
'ret': ret_num,
'res': res,
'msg': msg,
}
log.i(step_name + 'finish')
self.write(json.dumps(ret))
self.finish()
def parseTestSet():
# Random Seed
random = cfg.getRandomState()
# Status
log.i('PARSING TEST SET...', new_line=False)
TEST = []
# List of test files
fnames = []
for path, dirs, files in os.walk(cfg.TESTSET_PATH):
if path.split(os.sep)[-1] in cfg.CLASSES:
scnt = 0
for f in files:
fnames.append(os.path.join(path, f))
scnt += 1
if scnt >= cfg.MAX_TEST_SAMPLES_PER_CLASS and cfg.MAX_TEST_SAMPLES_PER_CLASS > 0:
break
fnames = sorted(shuffle(fnames, random_state=random)[:cfg.MAX_TEST_FILES])
# Get ground truth from metadata
for f in fnames:
# Metadata path
m_path = os.path.join(cfg.METADATA_PATH,
f.split(os.sep)[-1].split('.')[0] + '.json')
# Load JSON
with open(m_path) as jfile:
data = json.load(jfile)
# Get Species (+ background species)
# Only species present in the trained classes are relevant for the metric
# Still, we are adding anything we have right now and sort it out later
if cfg.TEST_WITH_BG_SPECIES:
bg = data['background']
else:
bg = []
species = [data['sci-name']] + bg
# Add data to test set
TEST.append((f, species))
# Status
log.i('DONE!')
log.i(('TEST FILES:', len(TEST)))
return TEST
def runTest(SNAPSHOTS, TEST):
# Do we have more than one snapshot?
if not isinstance(SNAPSHOTS, (list, tuple)):
SNAPSHOTS = [SNAPSHOTS]
# Load snapshots
test_functions = []
for s in SNAPSHOTS:
# Settings
NET = s['net']
cfg.CLASSES = s['classes']
cfg.IM_DIM = s['im_dim']
cfg.IM_SIZE = s['im_size']
# Compile test function
test_net = birdnet.test_function(NET, hasTargets=False, layer_index=-1)
test_functions.append(test_net)
# Status
log.i('START TESTING...')
# Make predictions
submission = ''
for spec_batch, mediaid, timestamp, filename in bg.threadedGenerator(
getSpecBatches(TEST)):
try:
# Prediction
prediction_batch = []
for test_func in test_functions:
if len(prediction_batch) == 0:
prediction_batch = test_func(spec_batch)
else:
prediction_batch += test_func(spec_batch)
prediction_batch /= len(test_functions)
# Eliminate the scores for 'Noise'
if 'Noise' in cfg.CLASSES:
prediction_batch[:, cfg.CLASSES.index('Noise')] = np.min(
prediction_batch)
# Prediction pooling
p_pool = test.predictionPooling(prediction_batch)
# Get class labels
p_labels = {}
for i in range(p_pool.shape[0]):
p_labels[cfg.CLASSES[i]] = p_pool[i]
# Sort by score
p_sorted = sorted(p_labels.items(),
key=operator.itemgetter(1),
reverse=True)
# Add scores to submission
for i in range(min(100, len(p_sorted))):
if getClassId(p_sorted[i][0]):
submission += mediaid + ';' + timestamp + ';' + getClassId(
p_sorted[i][0]) + ';' + str(p_sorted[i][1]) + '\n'
# Show sample stats
log.i((filename, timestamp), new_line=False)
log.i(('TOP PREDICTION:', p_sorted[0][0],
int(p_sorted[0][1] * 1000) / 10.0, '%'),
new_line=True)
except KeyboardInterrupt:
cfg.DO_BREAK = True
break
# Status
log.i('DONE TESTING!')
return submission
def build_baseline_model():
log.i('BUILDING BASELINE MODEL...')
# Random Seed
lasagne_random.set_rng(cfg.getRandomState())
# Input layer for images
net = l.InputLayer((None, cfg.IM_DIM, cfg.IM_SIZE[1], cfg.IM_SIZE[0]))
# Stride size (as an alternative to max pooling)
if cfg.MAX_POOLING:
s = 1
else:
s = 2
# Convolutinal layer groups
for i in range(len(cfg.FILTERS)):
# 3x3 Convolution + Stride
net = batch_norm(
l.Conv2DLayer(net,
num_filters=cfg.FILTERS[i],
filter_size=cfg.KERNEL_SIZES[i],
num_groups=cfg.NUM_OF_GROUPS[i],
pad='same',
stride=s,
W=initialization(cfg.NONLINEARITY),
nonlinearity=nonlinearity(cfg.NONLINEARITY)))
# Pooling layer
if cfg.MAX_POOLING:
net = l.MaxPool2DLayer(net, pool_size=2)
# Dropout Layer (we support different types of dropout)
if cfg.DROPOUT_TYPE == 'channels' and cfg.DROPOUT > 0.0:
net = l.dropout_channels(net, p=cfg.DROPOUT)
elif cfg.DROPOUT_TYPE == 'location' and cfg.DROPOUT > 0.0:
net = l.dropout_location(net, p=cfg.DROPOUT)
elif cfg.DROPOUT > 0.0:
net = l.DropoutLayer(net, p=cfg.DROPOUT)
log.i(('\tGROUP', i + 1, 'OUT SHAPE:', l.get_output_shape(net)))
# Final 1x1 Convolution
net = batch_norm(
l.Conv2DLayer(net,
num_filters=cfg.FILTERS[i] * 2,
filter_size=1,
W=initialization('identity'),
nonlinearity=nonlinearity('identity')))
log.i(('\tFINAL CONV OUT SHAPE:', l.get_output_shape(net)))
# Global Pooling layer (default mode = average)
net = l.GlobalPoolLayer(net)
log.i(("\tFINAL POOLING SHAPE:", l.get_output_shape(net)))
# Classification Layer (Softmax)
net = l.DenseLayer(net,
len(cfg.CLASSES),
nonlinearity=nonlinearity('softmax'),
W=initialization('softmax'))
log.i(("\tFINAL NET OUT SHAPE:", l.get_output_shape(net)))
log.i("...DONE!")
# Model stats
log.i(("MODEL HAS",
(sum(hasattr(layer, 'W')
for layer in l.get_all_layers(net))), "WEIGHTED LAYERS"))
log.i(("MODEL HAS", l.count_params(net), "PARAMS"))
return net
def build_pi_model():
log.i('BUILDING RASBPERRY PI MODEL...')
# Random Seed
lasagne_random.set_rng(cfg.getRandomState())
# Input layer for images
net = l.InputLayer((None, cfg.IM_DIM, cfg.IM_SIZE[1], cfg.IM_SIZE[0]))
# Convolutinal layer groups
for i in range(len(cfg.FILTERS)):
# 3x3 Convolution + Stride
net = batch_norm(
l.Conv2DLayer(net,
num_filters=cfg.FILTERS[i],
filter_size=cfg.KERNEL_SIZES[i],
num_groups=cfg.NUM_OF_GROUPS[i],
pad='same',
stride=2,
W=initialization(cfg.NONLINEARITY),
nonlinearity=nonlinearity(cfg.NONLINEARITY)))
log.i(('\tGROUP', i + 1, 'OUT SHAPE:', l.get_output_shape(net)))
# Fully connected layers + dropout layers
net = l.DenseLayer(net,
cfg.DENSE_UNITS,
nonlinearity=nonlinearity(cfg.NONLINEARITY),
W=initialization(cfg.NONLINEARITY))
net = l.DropoutLayer(net, p=cfg.DROPOUT)
net = l.DenseLayer(net,
cfg.DENSE_UNITS,
nonlinearity=nonlinearity(cfg.NONLINEARITY),
W=initialization(cfg.NONLINEARITY))
net = l.DropoutLayer(net, p=cfg.DROPOUT)
# Classification Layer (Softmax)
net = l.DenseLayer(net,
len(cfg.CLASSES),
nonlinearity=nonlinearity('softmax'),
W=initialization('softmax'))
log.i(("\tFINAL NET OUT SHAPE:", l.get_output_shape(net)))
log.i("...DONE!")
# Model stats
log.i(("MODEL HAS",
(sum(hasattr(layer, 'W')
for layer in l.get_all_layers(net))), "WEIGHTED LAYERS"))
log.i(("MODEL HAS", l.count_params(net), "PARAMS"))
return net
def build_resnet_model():
log.i('BUILDING RESNET MODEL...')
# Random Seed
lasagne_random.set_rng(cfg.getRandomState())
# Input layer for images
net = l.InputLayer((None, cfg.IM_DIM, cfg.IM_SIZE[1], cfg.IM_SIZE[0]))
# First Convolution
net = l.Conv2DLayer(net,
num_filters=cfg.FILTERS[0],
filter_size=cfg.KERNEL_SIZES[0],
pad='same',
W=initialization(cfg.NONLINEARITY),
nonlinearity=None)
log.i(("\tFIRST CONV OUT SHAPE:", l.get_output_shape(net), "LAYER:",
len(l.get_all_layers(net)) - 1))
# Residual Stacks
for i in range(0, len(cfg.FILTERS)):
net = resblock(net,
filters=cfg.FILTERS[i] * cfg.RESNET_K,
kernel_size=cfg.KERNEL_SIZES[i],
stride=2,
num_groups=cfg.NUM_OF_GROUPS[i])
for _ in range(1, cfg.RESNET_N):
net = resblock(net,
filters=cfg.FILTERS[i] * cfg.RESNET_K,
kernel_size=cfg.KERNEL_SIZES[i],
num_groups=cfg.NUM_OF_GROUPS[i],
preactivated=False)
log.i(("\tRES STACK", i + 1, "OUT SHAPE:", l.get_output_shape(net),
"LAYER:", len(l.get_all_layers(net)) - 1))
# Post Activation
net = batch_norm(net)
net = l.NonlinearityLayer(net, nonlinearity=nonlinearity(cfg.NONLINEARITY))
# Pooling
net = l.GlobalPoolLayer(net)
log.i(("\tFINAL POOLING SHAPE:", l.get_output_shape(net), "LAYER:",
len(l.get_all_layers(net)) - 1))
# Classification Layer
net = l.DenseLayer(net,
len(cfg.CLASSES),
nonlinearity=nonlinearity('identity'),
W=initialization('identity'))
net = l.NonlinearityLayer(net, nonlinearity=nonlinearity('softmax'))
log.i(("\tFINAL NET OUT SHAPE:", l.get_output_shape(net), "LAYER:",
len(l.get_all_layers(net))))
log.i("...DONE!")
# Model stats
log.i(("MODEL HAS",
(sum(hasattr(layer, 'W')
for layer in l.get_all_layers(net))), "WEIGHTED LAYERS"))
log.i(("MODEL HAS", l.count_params(net), "PARAMS"))
return net
def test(SNAPSHOTS):
# Do we have more than one snapshot?
if not isinstance(SNAPSHOTS, (list, tuple)):
SNAPSHOTS = [SNAPSHOTS]
# Load snapshots
test_functions = []
for s in SNAPSHOTS:
# Settings
NET = s['net']
cfg.CLASSES = s['classes']
cfg.IM_DIM = s['im_dim']
cfg.IM_SIZE = s['im_size']
# Compile test function
test_net = birdnet.test_function(NET, hasTargets=False, layer_index=-1)
test_functions.append(test_net)
# Parse Testset
TEST = parseTestSet()
# Status
log.i('START TESTING...')
stats.clearStats()
stats.tic('test_time')
# Make predictions
for spec_batch, labels, filename in bg.threadedGenerator(
getSpecBatches(TEST)):
try:
# Status
stats.tic('pred_time')
# Prediction
prediction_batch = []
for test_func in test_functions:
if len(prediction_batch) == 0:
prediction_batch = test_func(spec_batch)
else:
prediction_batch += test_func(spec_batch)
prediction_batch /= len(test_functions)
# Eliminate the scores for 'Noise'
if 'Noise' in cfg.CLASSES:
prediction_batch[:, cfg.CLASSES.index('Noise')] = np.min(
prediction_batch)
# Prediction pooling
p_pool = predictionPooling(prediction_batch)
# Get class labels
p_labels = {}
for i in range(p_pool.shape[0]):
p_labels[cfg.CLASSES[i]] = p_pool[i]
# Sort by score
p_sorted = sorted(p_labels.items(),
key=operator.itemgetter(1),
reverse=True)
# Calculate MLRAP (MRR for single labels)
targets = np.zeros(p_pool.shape[0], dtype='float32')
for label in labels:
if label in cfg.CLASSES:
targets[cfg.CLASSES.index(label)] = 1.0
lrap = metrics.lrap(np.expand_dims(p_pool, 0),
np.expand_dims(targets, 0))
stats.setValue('lrap', lrap, mode='append')
# Show sample stats
log.i((filename), new_line=True)
log.i(('\tLABELS:', labels), new_line=True)
log.i(('\tTOP PREDICTION:', p_sorted[0][0],
int(p_sorted[0][1] * 1000) / 10.0, '%'),
new_line=True)
log.i(('\tLRAP:', int(lrap * 1000) / 1000.0), new_line=False)
log.i(('\tMLRAP:',
int(np.mean(stats.getValue('lrap')) * 1000) / 1000.0),
new_line=True)
# Save some stats
if p_sorted[0][0] == labels[0]:
stats.setValue('top1_correct', 1, 'add')
stats.setValue('top1_confidence', p_sorted[0][1], 'append')
else:
stats.setValue('top1_incorrect', 1, 'add')
stats.toc('pred_time')
stats.setValue('time_per_batch', stats.getValue('pred_time'),
'append')
except KeyboardInterrupt:
cfg.DO_BREAK = True
break
except:
log.e('ERROR WHILE TRAINING!')
continue
# Stats
stats.toc('test_time')
log.i(('TESTING DONE!', 'TIME:', stats.getValue('test_time'), 's'))
log.r(('FINAL MLRAP:', np.mean(stats.getValue('lrap'))))
log.r(('TOP 1 ACCURACY:',
max(
0,
float(stats.getValue('top1_correct')) /
(stats.getValue('top1_correct') +
stats.getValue('top1_incorrect')))))
log.r(('TOP 1 MEAN CONFIDENCE:',
max(0, np.mean(stats.getValue('top1_confidence')))))
log.r(('TIME PER BATCH:',
int(np.mean(stats.getValue('time_per_batch')) * 1000), 'ms'))
return np.mean(stats.getValue('lrap')), int(
np.mean(stats.getValue('time_per_file')) * 1000)
def parseDataset():
filedata = {}
with open(r"flockfiles.json", 'r') as f:
filedata = json.load(f)
# List of classes, subfolders as class names
CLASSES = [c for c in sorted(os.listdir(r""))]
CLASSES = CLASSES[28:]
# Parse every class
for c in CLASSES:
log.i(c)
index = []
# List all audio files
print(c)
afiles = [f for f in sorted(os.listdir(os.path.join(r"", c)))]
print(len(afiles))
# Calculate maximum specs per file
for i in range(len(afiles)):
if (afiles[i][:-4] + '.WAV' in filedata[c]):
index.append(i)
print("del one")
index.reverse()
for i in index:
del afiles[i]
print(len(afiles))
max_specs = cfg.MAX_SPECS_PER_CLASS // len(afiles) + 1
# Get specs for every audio file
for i in range(len(afiles)):
spec_cnt = 0
try:
# Stats
print(i + 1, '/', len(afiles), c, afiles[i])
# Get specs and signal to noise ratios
specs, noise = getSpecs(os.path.join(r"", c, afiles[i]))
# Save specs if it contains signal
for s in range(len(specs)):
# NaN?
if np.isnan(noise[s]):
noise[s] = 0.0
# Above SIGNAL_THRESHOLD?
if noise[s] >= cfg.SPEC_SIGNAL_THRESHOLD:
# Create target path for accepted specs
filepath = os.path.join(cfg.DATASET_PATH, c)
if not os.path.exists(filepath):
os.makedirs(filepath)
elif noise[s] <= cfg.NOISE_THRESHOLD:
if RANDOM.choice([True, False], p=[0.60, 0.40]):
filepath = os.path.join(cfg.NOISE_PATH)
if not os.path.exists(filepath):
os.makedirs(filepath)
else:
filepath = None
else:
filepath = None
if filepath:
# Filename contains s2n-ratio
filename = str(
int(noise[s] *
10000)).zfill(4) + '_' + afiles[i].split(
'.')[0] + '_' + str(s).zfill(3)
# Write to HDD
cv2.imwrite(os.path.join(filepath, filename + '.png'),
specs[s] * 255.0)
# Do we have enough specs already?
if spec_cnt >= max_specs:
break
except:
log.e((spec_cnt, 'specs', 'ERROR DURING SPEC EXTRACT'))
continue
def default_signal_handler(signal_, _):
log.i(__name__, "Receive signal {0}".format(signal_))
incubator.stop()
def parseDataset():
spec_all = []
# List of classes, subfolders as class names
CLASSES = [
c for c in sorted(os.listdir(os.path.join(cfg.TRAINSET_PATH, 'audio')))
]
stop = 0
#switch = 0
# Parse every class
for c in CLASSES:
#if stop==1:
#break
#if c == 'norfli':
# switch = 1
#if switch == 1:
# List all audio files
afiles = [
f for f in sorted(
os.listdir(os.path.join(cfg.TRAINSET_PATH, 'audio', c)))
]
# Calculate maximum specs per file
max_specs = cfg.MAX_SPECS_PER_CLASS // len(afiles) + 1
# Get specs for every audio file
for i in range(len(afiles)):
spec_cnt = 0
try:
# Stats
print(i + 1, '/', len(afiles), c, afiles[i])
# Get specs and signal to noise ratios
specs, noise = getSpecs(
os.path.join(cfg.TRAINSET_PATH, 'audio', c, afiles[i]))
# print('number of specs = ' + str(specs))
threshold = 0
max_index = 0
# Save specs if it contains signal
for s in range(len(specs)):
# NaN?
if np.isnan(noise[s]):
noise[s] = 0.0
# Above SIGNAL_THRESHOLD?
if noise[s] >= threshold:
threshold = noise[s]
max_index = s
#print(threshold)
# Create target path for accepted specs
spec_filepath = os.path.join(cfg.DATASET_PATH, c)
if not os.path.exists(spec_filepath):
os.makedirs(spec_filepath)
else:
# Create target path for rejected specs -
# but we don't want to save every sample (only 10%)
if RANDOM.choice([True, False], p=[0.1, 0.90]):
filepath = os.path.join(cfg.NOISE_PATH)
if not os.path.exists(filepath):
os.makedirs(filepath)
if filepath:
# Filename contains s2n-ratio
filename = str(int(
noise[s] *
10000)).zfill(4) + '_' + afiles[i].split(
'.')[0] + '_' + str(s).zfill(3)
# Write to HDD
cv2.imwrite(
os.path.join(filepath, filename + '.png'),
specs[s] * 255.0)
else:
filepath = None
if s == (len(specs) - 1):
# Filename contains s2n-ratio
filename = str(
int(noise[max_index] *
10000)).zfill(4) + '_' + afiles[i].split(
'.')[0] + '_' + str(s).zfill(3)
# Write to HDD
cv2.imwrite(
os.path.join(spec_filepath, filename + '.png'),
specs[max_index] * 255.0)
# Save all spec into a list
spec_all.append(specs[max_index])
# Count specs
spec_cnt += 1
# Do we have enough specs already?
if spec_cnt >= max_specs:
break
# Stats
log.i((spec_cnt, 'specs'))
except:
log.e((spec_cnt, 'specs', 'ERROR DURING SPEC EXTRACT'))
continue
stop += 1
print(spec_all)
spec_all = np.array(spec_all)
spec_mean = np.mean(spec_all)
spec_std = np.std(spec_all)
print(spec_mean)
print(spec_std)
except KeyboardInterrupt:
cfg.DO_BREAK = True
break
# Status
log.i('DONE TESTING!')
return submission
if __name__ == '__main__':
# Parse Testset
TEST, CODES, LABELS = parseTestSet()
# Load trained models
if not isinstance(cfg.TEST_MODELS, (list, tuple)):
cfg.TEST_MODELS = [cfg.TEST_MODELS]
SNAPSHOTS = []
for test_model in cfg.TEST_MODELS:
SNAPSHOTS.append(io.loadModel(test_model))
# Generate submission
submission = runTest(SNAPSHOTS, TEST)
# Write submission to file
log.i('WRITING SUBMISSION...', new_line=False)
with open(cfg.RUN_NAME + '_SOUNDSCAPE_SUBMISSION.txt', 'w') as sfile:
sfile.write(submission)
log.i('DONE!')
def train(NET, TRAIN, VAL):
# Random Seed
random = cfg.getRandomState()
image.resetRandomState()
# Load pretrained model
if cfg.PRETRAINED_MODEL_NAME:
snapshot = io.loadModel(cfg.PRETRAINED_MODEL_NAME)
NET = io.loadParams(NET, snapshot['params'])
# Load teacher models
teach_funcs = []
if len(cfg.TEACHER) > 0:
for t in cfg.TEACHER:
snapshot = io.loadModel(t)
TEACHER = snapshot['net']
teach_funcs.append(birdnet.test_function(TEACHER, hasTargets=False))
# Compile Theano functions
train_net = birdnet.train_function(NET)
test_net = birdnet.test_function(NET)
# Status
log.i("START TRAINING...")
# Train for some epochs...
for epoch in range(cfg.EPOCH_START, cfg.EPOCHS + 1):
try:
# Stop?
if cfg.DO_BREAK:
break
# Clear stats for every epoch
stats.clearStats()
stats.setValue('sample_count', len(TRAIN) + len(VAL))
# Start timer
stats.tic('epoch_time')
# Shuffle dataset (this way we get "new" batches every epoch)
TRAIN = shuffle(TRAIN, random_state=random)
# Iterate over TRAIN batches of images
for image_batch, target_batch in bg.nextBatch(TRAIN):
# Show progress
stats.showProgress(epoch)
# If we have a teacher, we use that model to get new targets
if len(teach_funcs) > 0:
target_batch = np.zeros((len(teach_funcs), target_batch.shape[0], target_batch.shape[1]), dtype='float32')
for i in range(len(teach_funcs)):
target_batch[i] = teach_funcs[i](image_batch)
target_batch = np.mean(target_batch, axis=0)
# Calling the training functions returns the current loss
loss = train_net(image_batch, target_batch, lr.dynamicLearningRate(cfg.LR_SCHEDULE, epoch))
stats.setValue('train loss', loss, 'append')
stats.setValue('batch_count', 1, 'add')
# Stop?
if cfg.DO_BREAK:
break
# Iterate over VAL batches of images
for image_batch, target_batch in bg.nextBatch(VAL, False, True):
# Calling the test function returns the net output, loss and accuracy
prediction_batch, loss, acc = test_net(image_batch, target_batch)
stats.setValue('val loss', loss, 'append')
stats.setValue('val acc', acc, 'append')
stats.setValue('batch_count', 1, 'add')
stats.setValue('lrap', [metrics.lrap(prediction_batch, target_batch)], 'add')
# Show progress
stats.showProgress(epoch)
# Stop?
if cfg.DO_BREAK:
break
# Show stats for epoch
stats.showProgress(epoch, done=True)
stats.toc('epoch_time')
log.r(('TRAIN LOSS:', np.mean(stats.getValue('train loss'))), new_line=False)
log.r(('VAL LOSS:', np.mean(stats.getValue('val loss'))), new_line=False)
log.r(('VAL ACC:', int(np.mean(stats.getValue('val acc')) * 10000) / 100.0, '%'), new_line=False)
log.r(('MLRAP:', int(np.mean(stats.getValue('lrap')) * 1000) / 1000.0), new_line=False)
log.r(('TIME:', stats.getValue('epoch_time'), 's'))
# Save snapshot?
if not epoch % cfg.SNAPSHOT_EPOCHS:
io.saveModel(NET, cfg.CLASSES, epoch)
print('vish')
io.saveParams(NET, cfg.CLASSES, epoch)
# New best net?
if np.mean(stats.getValue('lrap')) > stats.getValue('best_mlrap'):
stats.setValue('best_net', NET, static=True)
stats.setValue('best_epoch', epoch, static=True)
stats.setValue('best_mlrap', np.mean(stats.getValue('lrap')), static=True)
# Early stopping?
if epoch - stats.getValue('best_epoch') >= cfg.EARLY_STOPPING_WAIT:
log.i('EARLY STOPPING!')
break
# Stop?
if cfg.DO_BREAK:
break
except KeyboardInterrupt:
log.i('KeyboardInterrupt')
cfg.DO_BREAK = True
break
# Status
log.i('TRAINING DONE!')
log.r(('BEST MLRAP:', stats.getValue('best_mlrap'), 'EPOCH:', stats.getValue('best_epoch')))
# Save best model and return
io.saveParams(stats.getValue('best_net'), cfg.CLASSES, stats.getValue('best_epoch'))
print('in training vish')
return io.saveModel(stats.getValue('best_net'), cfg.CLASSES, stats.getValue('best_epoch'))
def parseDataset():
# List of classes, subfolders as class names
CLASSES = [
c for c in sorted(os.listdir(os.path.join(cfg.TRAINSET_PATH, 'train')))
]
# Parse every class
for c in CLASSES:
# List all audio files
afiles = [
f for f in sorted(
os.listdir(os.path.join(cfg.TRAINSET_PATH, 'train', c)))
]
# Calculate maximum specs per file
max_specs = cfg.MAX_SPECS_PER_CLASS // len(afiles) + 1
# Get specs for every audio file
for i in range(len(afiles)):
spec_cnt = 0
try:
# Stats
print i + 1, '/', len(afiles), c, afiles[i],
# Get specs and signal to noise ratios
specs, noise = getSpecs(
os.path.join(cfg.TRAINSET_PATH, 'train', c, afiles[i]))
# Save specs if it contains signal
for s in range(len(specs)):
# NaN?
if np.isnan(noise[s]):
noise[s] = 0.0
# Above SIGNAL_THRESHOLD?
if noise[s] >= cfg.SPEC_SIGNAL_THRESHOLD:
# Create target path for accepted specs
filepath = os.path.join(cfg.DATASET_PATH, c)
if not os.path.exists(filepath):
os.makedirs(filepath)
# Count specs
spec_cnt += 1
else:
# Create target path for rejected specs -
# but we don't want to save every sample (only 10%)
if RANDOM.choice([True, False], p=[0.1, 0.90]):
filepath = os.path.join(cfg.NOISE_PATH)
if not os.path.exists(filepath):
os.makedirs(filepath)
else:
filepath = None
if filepath:
# Filename contains s2n-ratio
filename = str(
int(noise[s] *
10000)).zfill(4) + '_' + afiles[i].split(
'.')[0] + '_' + str(s).zfill(3)
# Write to HDD
cv2.imwrite(os.path.join(filepath, filename + '.png'),
specs[s] * 255.0)
# Do we have enough specs already?
if spec_cnt >= max_specs:
break
# Stats
log.i((spec_cnt, 'specs'))
except:
log.e((spec_cnt, 'specs', 'ERROR DURING SPEC EXTRACT'))
continue
help="Target script name",
action="store",
default=None)
parser.add_argument("-c",
"--target-count",
help="Target instance count",
action="store",
type=int,
default=1)
parser.add_argument("--shell",
help="Run command as shell mode",
action="store_true",
default=False)
parser.add_argument("--socket",
help="Unix domain socket path",
action="store",
default=None)
args = parser.parse_args()
log.init(args.name)
log.i(__name__, "Start QB Incubator")
if args.script is None and args.target is None:
log.e(__name__, "--script and --target are not set.")
exit(1)
if args.script is not None:
script_main(args.shell, args.script, args.socket)
else:
target_main(args.shell, args.target, args.target_count, args.socket)