class Classifier():
def __init__(self, nnet_prms_file, labellings_file, logbase=2, only_top=5):
with open(nnet_prms_file, 'rb') as nnet_prms_fp:
nnet_prms = pickle.load(nnet_prms_fp)
nnet_prms['training_params']['BATCH_SZ'] = 1
self.ntwk = NeuralNet(**nnet_prms)
self.tester = self.ntwk.get_data_test_model()
self.ht = nnet_prms['layers'][0][1]['img_sz']
self.logbase = logbase
self.only_top = only_top
self.unichars = LabelToUnicodeConverter(labellings_file)
self.nclasses = nnet_prms['layers'][-1][1]["n_out"]
logi("Network {}".format(self.ntwk))
logi("LogBase {}".format(self.logbase))
logi("OnlyTop {}".format(self.only_top))
def __call__(self, scaled_glp):
img = scaled_glp.pix.astype('float32').reshape((1, 1, self.ht, self.ht))
if self.ntwk.takes_aux():
dtopbot = scaled_glp.dtop, scaled_glp.dbot
aux_data = np.array([[dtopbot, dtopbot]], dtype='float32')
logprobs, preds = self.tester(img, aux_data)
else:
logprobs, preds = self.tester(img)
logprobs = logprobs[0]/self.logbase
if self.only_top:
decent = np.argpartition(logprobs, -self.only_top)[-self.only_top:]
if logger.isEnabledFor(logging.INFO):
decent = decent[np.argsort(-logprobs[decent])]
else:
decent = np.arange(self.nclasses)
return [(ch, logprobs[i])
for i in decent
for ch in self.unichars[i]]
class Classifier():
def __init__(self, nnet_prms_file, labellings_file, logbase=2, only_top=5):
with open(nnet_prms_file, 'rb') as nnet_prms_fp:
nnet_prms = pickle.load(nnet_prms_fp)
nnet_prms['training_params']['BATCH_SZ'] = 1
self.ntwk = NeuralNet(**nnet_prms)
self.tester = self.ntwk.get_data_test_model()
self.ht = nnet_prms['layers'][0][1]['img_sz']
self.logbase = logbase
self.only_top = only_top
self.unichars = LabelToUnicodeConverter(labellings_file)
self.nclasses = nnet_prms['layers'][-1][1]["n_out"]
logi("Network {}".format(self.ntwk))
logi("LogBase {}".format(self.logbase))
logi("OnlyTop {}".format(self.only_top))
def __call__(self, scaled_glp):
img = scaled_glp.pix.astype('float32').reshape((1, 1, self.ht, self.ht))
if self.ntwk.takes_aux():
dtopbot = scaled_glp.dtop, scaled_glp.dbot
aux_data = np.array([[dtopbot, dtopbot]], dtype='float32')
logprobs, preds = self.tester(img, aux_data)
else:
logprobs, preds = self.tester(img)
logprobs = logprobs[0]/self.logbase
if self.only_top:
decent = np.argpartition(logprobs, -self.only_top)[-self.only_top:]
if logger.isEnabledFor(logging.INFO):
decent = decent[np.argsort(-logprobs[decent])]
else:
decent = np.arange(self.nclasses)
return [(ch, logprobs[i])
for i in decent
for ch in self.unichars[i]]
with open(nnet_prms_file_name, 'rb') as nnet_prms_file:
nnet_prms = pickle.load(nnet_prms_file)
with open(labelings_file_name, encoding='utf-8') as labels_fp:
labellings = ast.literal_eval(labels_fp.read())
# print(labellings)
chars = LabelToUnicodeConverter(labellings).onecode
############################################# Init Network
Bantry.scaler = ScalerFactory(scaler_prms)
bf = BantryFile(banti_file_name)
nnet_prms['training_params']['BATCH_SZ'] = 1
ntwk = NeuralNet(**nnet_prms)
tester = ntwk.get_data_test_model(go_nuts=True)
############################################# Image saver
dir_name = os.path.basename(nnet_prms_file_name)[:-7] + '/'
if not os.path.exists(dir_name):
os.makedirs(dir_name)
namer = (dir_name + '{:03d}_{}_{:02d}.png').format
print("Look for me in :", dir_name)
def saver(outs, ch, debug=True):
saver.index += 1
for i, out in enumerate(outs):
global_normalize = False
if out.ndim == 2:
n_nodes = out.shape[1]
w = n_nodes // int(np.sqrt(n_nodes))
