def print_library_version():
print(os.getcwd())
version_pandas = pkg_resources.get_distribution("pandas").version
print("Version pandas: {}".format(version_pandas))
print("Version OpenCV: {}".format(cv2.__version__))
version_cntk = pkg_resources.get_distribution("cntk").version
print("Version CNTK: {}".format(version_cntk))
cntk.logging.set_trace_level(2)
print("Devices used by CNTK: {}".format(cntk.all_devices()))
import cntk
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras import backend as K
from scipy.io import wavfile
import numpy as np
from random import shuffle
# ## FRAMEWORK
# In[2]:
cntk.try_set_default_device(cntk.all_devices()[0])
# ## DATA PREPARATION
# In[3]:
sec = 3
img_rows = 28
img_cols = 28
input_shape = (img_rows, img_cols, 1)
num_classes = 2
root, _dirs, files = next(
os.walk(os.path.join(os.getcwd(), os.path.join("dataset", "train"))))
train_paths = [os.path.join(root, file) for file in files]
# for AlexNet base model use: from utils.configs.AlexNet_config import cfg as network_cfg
from utils.configs.AlexNet_config import cfg as network_cfg
# for Pascal VOC 2007 data set use: from utils.configs.Pascal_config import cfg as dataset_cfg
# for the Grocery data set use: from utils.configs.Grocery_config import cfg as dataset_cfg
# from utils.configs.Grocery_config import cfg as dataset_cfg
from utils.configs.BU_config import cfg as dataset_cfg
return merge_configs([detector_cfg, network_cfg, dataset_cfg])
# trains and evaluates a Fast R-CNN model.
if __name__ == '__main__':
cfg = get_configuration()
prepare(cfg, False)
cntk.logging.set_trace_level(2)
cntk.all_devices()
cntk.device.try_set_default_device(cntk.device.gpu(cfg.GPU_ID))
# train and test
trained_model = train_faster_rcnn(cfg)
eval_results = compute_test_set_aps(trained_model, cfg)
# write AP results to output
for class_name in eval_results:
print('AP for {:>15} = {:.4f}'.format(class_name,
eval_results[class_name]))
print('Mean AP = {:.4f}'.format(np.nanmean(list(eval_results.values()))))
# Plot results on test set images
if cfg.VISUALIZE_RESULTS:
num_eval = min(cfg["DATA"].NUM_TEST_IMAGES, 100)
# import the necessary packages
# from keras.applications import ResNet50
from keras.models import load_model
from keras.preprocessing.image import img_to_array
from keras.applications import imagenet_utils
from PIL import Image
import numpy as np
import flask
import io
from keras import backend as K
import os
from importlib import reload
import cntk
print(cntk.__version__)
print(cntk.all_devices())
# initialize our Flask application and the Keras model
app = flask.Flask(__name__)
model = None
model = load_model('my_model.h5')
def set_keras_backend(backend):
if K.backend() != backend:
os.environ['KERAS_BACKEND'] = backend
reload(K)
assert K.backend() == backend