import tensorflow as tf
from tensorflow.python.ops import init_ops
from odin import training
from odin.utils import (args_parse, ctext, Progbar, as_tuple_of_shape,
crypto, stdio)
from odin import fuel as F, visual as V, nnet as N, backend as K
from helpers import (get_model_path, prepare_dnn_data,
BATCH_SIZE, EPOCH, LEARNING_RATE,
SCORE_SYSTEM_ID, GRADIENT_CLIPPING)
# ===========================================================================
# Create data feeder
# ===========================================================================
(EXP_DIR, MODEL_PATH, LOG_PATH) = get_model_path(system_name='xvec')
stdio(LOG_PATH)
# ====== load the data ====== #
(train, valid,
all_speakers) = prepare_dnn_data(save_dir=EXP_DIR)
n_speakers = len(all_speakers)
# ====== print some log ====== #
print("Training info:")
print(' ', "Batch size :", ctext(BATCH_SIZE, 'cyan'))
print(' ', "Epoch :", ctext(EPOCH, 'cyan'))
# ===========================================================================
# Create the network
# ===========================================================================
inputs = [K.placeholder(shape=(None,) + shape[1:],
dtype='float32',
name='input%d' % i)
# We add 1 class for background
hyper_params["total_labels"] = len(labels) + 1
# If you want to use different dataset and don't know max height and width values
# You can use calculate_max_height_width method in helpers
max_height, max_width = helpers.VOC["max_height"], helpers.VOC["max_width"]
VOC_test_data = VOC_test_data.map(lambda x : helpers.preprocessing(x, max_height, max_width))
padded_shapes, padding_values = helpers.get_padded_batch_params()
VOC_test_data = VOC_test_data.padded_batch(batch_size, padded_shapes=padded_shapes, padding_values=padding_values)
base_model = VGG16(include_top=False)
if hyper_params["stride"] == 16:
base_model = Sequential(base_model.layers[:-1])
rpn_model = rpn.get_model(base_model, hyper_params)
frcnn_model_path = helpers.get_model_path("frcnn", hyper_params["stride"])
rpn_model_path = helpers.get_model_path("rpn", hyper_params["stride"])
model_path = frcnn_model_path if load_weights_from_frcnn else rpn_model_path
rpn_model.load_weights(model_path, by_name=True)
for image_data in VOC_test_data:
img, gt_boxes, gt_labels = image_data
input_img, anchors = rpn.get_step_data(image_data, hyper_params, preprocess_input, mode="inference")
rpn_bbox_deltas, rpn_labels = rpn_model.predict_on_batch(input_img)
#
anchors_shape = tf.shape(anchors)
batch_size, anchor_row_size = anchors_shape[0], anchors_shape[1]
rpn_bbox_deltas = tf.reshape(rpn_bbox_deltas, (batch_size, anchor_row_size, 4))
rpn_labels = tf.reshape(rpn_labels, (batch_size, anchor_row_size, 1))
#
rpn_bboxes = helpers.get_bboxes_from_deltas(anchors, rpn_bbox_deltas)
n_indices = len(indices)
indices.close()
except Exception as e:
import traceback
traceback.print_exc()
print("Loading indices error: '%s'" % str(e), "at:", indices_path)
return True
if n_indices != n_files:
return True
return False
# ===========================================================================
# Searching for trained system
# ===========================================================================
sys_dir, _, _ = get_model_path(system_name=SCORE_SYSTEM_NAME, logging=False)
sys_name = os.path.basename(sys_dir)
all_sys = []
for path in os.listdir(sys_dir):
path = os.path.join(sys_dir, path)
if 'model.ai.' in path:
all_sys.append(path)
# ====== get the right model based on given system index ====== #
if len(all_sys) == 0:
final_sys = os.path.join(sys_dir, 'model.ai')
sys_index = ''
assert os.path.exists(final_sys), \
"Cannot find pre-trained model at path: %s" % sys_dir
else:
all_sys = sorted(all_sys, key=lambda x: int(x.split('.')[-1]))
final_sys = all_sys[SCORE_SYSTEM_ID]