def predict(captcha_image):
from load import load_graph
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--frozen_model_filename", default="results/frozen_model.pb", type=str,
help="Frozen model file to import")
parser.add_argument("--gpu_memory", default=.2, type=float, help="GPU memory per process")
args = parser.parse_args()
graph = load_graph('/Users/alpha/github/flask/flasky/app/cnn/model/frozen_model.pb')
x = graph.get_tensor_by_name('prefix/p_x:0')
y = graph.get_tensor_by_name('prefix/p_y:0')
keep_prob = graph.get_tensor_by_name('prefix/keep_prob:0')
print(x, y, keep_prob)
print('Starting Session, setting the GPU memory usage to %f' % args.gpu_memory)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
persistent_sess = tf.Session(graph=graph, config=sess_config)
out_put = graph.get_tensor_by_name("prefix/out_put:0")
predict = tf.argmax(tf.reshape(out_put, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
text_list = persistent_sess.run(predict, feed_dict={x: [captcha_image], keep_prob: 1})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
return vec2text(vector)
def predict(captcha_image):
from load import load_graph
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--frozen_model_filename", default="results/frozen_model.pb", type=str,
help="Frozen model file to import")
parser.add_argument("--gpu_memory", default=.2, type=float, help="GPU memory per process")
args = parser.parse_args()
graph = load_graph('/Users/alpha/github/flask/flasky/app/cnn/model/frozen_model.pb')
x = graph.get_tensor_by_name('prefix/p_x:0')
y = graph.get_tensor_by_name('prefix/p_y:0')
keep_prob = graph.get_tensor_by_name('prefix/keep_prob:0')
print(x, y, keep_prob)
print('Starting Session, setting the GPU memory usage to %f' % args.gpu_memory)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
persistent_sess = tf.Session(graph=graph, config=sess_config)
out_put = graph.get_tensor_by_name("prefix/out_put:0")
predict = tf.argmax(tf.reshape(out_put, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
text_list = persistent_sess.run(predict, feed_dict={x: [captcha_image], keep_prob: 1})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
return vec2text(vector)
parser = argparse.ArgumentParser()
parser.add_argument("--frozen_model_filename",
default="results/frozen_model.pb",
type=str,
help="Frozen model file to import")
parser.add_argument("--gpu_memory",
default=.2,
type=float,
help="GPU memory per process")
args = parser.parse_args()
##################################################
# Tensorflow part
##################################################
print('Loading the model')
graph = load_graph(args.frozen_model_filename)
x = graph.get_tensor_by_name('prefix/Placeholder/inputs_placeholder:0')
y = graph.get_tensor_by_name('prefix/Accuracy/predictions:0')
print('Starting Session, setting the GPU memory usage to %f' %
args.gpu_memory)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_memory)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
persistent_sess = tf.Session(graph=graph, config=sess_config)
##################################################
# END Tensorflow part
##################################################
print('Starting the API')
app.run()
default='',
help=
"Path to image folder. This is where the images from the run will be saved."
)
parser.add_argument('-u',
action='store_true',
dest='update',
help="Overwrite/update the specified model file.")
args = parser.parse_args()
ioloop = asyncio.get_event_loop()
ioloop.run_until_complete(check_model_path(args))
ioloop.close()
print("Loading from {}".format(args.model))
graph = load_graph(os.path.join(args.model))
X = graph.get_tensor_by_name('prefix/X:0')
keep_prob = graph.get_tensor_by_name('prefix/keep_prob:0')
prediction = graph.get_tensor_by_name('prefix/output:0')
sess = tf.Session(graph=graph)
if args.image_folder != '':
print("Creating image folder at {}".format(args.image_folder))
if not os.path.exists(args.image_folder):
os.makedirs(args.image_folder)
else:
shutil.rmtree(args.image_folder)
os.makedirs(args.image_folder)
print("RECORDING THIS RUN ...")
else:
print("NOT RECORDING THIS RUN ...")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--frozen_model_filename", default="results/frozen_model.pb", type=str,
help="Frozen model file to import")
parser.add_argument("--gpu_memory", default=.2, type=float, help="GPU memory per process")
args = parser.parse_args()
##################################################
# Tensorflow part
##################################################
print('Loading the model')
# graph = load_graph(args.frozen_model_filename)
# x = graph.get_tensor_by_name('prefix/Placeholder/inputs_placeholder:0')
# y = graph.get_tensor_by_name('prefix/Accuracy/predictions:0')
graph = load_graph('/Users/alpha/github/flask/flasky/app/cnn/model/frozen_model.pb')
# x = graph.get_tensor_by_name('prefix/inputs_placeholder:0')
# y = graph.get_tensor_by_name('prefix/predictions:0')
X = tf.placeholder(tf.float32, [None, 60 * 160])
Y = tf.placeholder(tf.float32, [None, 4 * 57])
keep_prob = tf.placeholder(tf.float32)
print('Starting Session, setting the GPU memory usage to %f' % args.gpu_memory)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
persistent_sess = tf.Session(graph=graph, config=sess_config)
##################################################
# END Tensorflow part
##################################################
type=int,
default=5,
help='number of topology suggestions to print out (sorted by depth).')
parser.add_argument(
'--print-only',
'-p',
action='store_true',
help='only read and print from existing CSV result file.')
args = parser.parse_args()
# Create folders if not exist
mkdirp(RESULT_FOLDER)
mkdirp(CACHE_FOLDER)
fname = os.path.join(CACHE_FOLDER, args.site + '-' + args.name + '.pick')
G = load.load_graph(args.site, args.name, fname, args.reload)
csvname = os.path.join(RESULT_FOLDER, args.site + "-" + args.name + '.csv')
if not args.print_only:
df = check_all(G,
args.site,
args.name,
kappa=args.kappa,
reduction=not args.no_reduction,
margin=args.margin)
df.to_csv(csvname)
elif not os.path.isfile(csvname):
print("No CSV result file to read from")
exit(1)
else:
df = pd.read_csv(csvname)
help="Frozen model file to import")
parser.add_argument("--gpu_memory",
default=.2,
type=float,
help="GPU memory per process")
args = parser.parse_args()
##################################################
# Tensorflow part
##################################################
print('Loading the model')
# graph = load_graph(args.frozen_model_filename)
# x = graph.get_tensor_by_name('prefix/Placeholder/inputs_placeholder:0')
# y = graph.get_tensor_by_name('prefix/Accuracy/predictions:0')
graph = load_graph(
'/Users/alpha/github/flask/flasky/app/cnn/model/frozen_model.pb')
# x = graph.get_tensor_by_name('prefix/inputs_placeholder:0')
# y = graph.get_tensor_by_name('prefix/predictions:0')
X = tf.placeholder(tf.float32, [None, 60 * 160])
Y = tf.placeholder(tf.float32, [None, 4 * 57])
keep_prob = tf.placeholder(tf.float32)
print('Starting Session, setting the GPU memory usage to %f' %
args.gpu_memory)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_memory)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
persistent_sess = tf.Session(graph=graph, config=sess_config)
##################################################
# END Tensorflow part
from load import load_graph
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
"""
Adapted from https://gist.github.com/morgangiraud/4a062f31e8a7b71a030c2ced3277cc20#file-medium-tffreeze-3-py
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-m",
dest='model_filename',
type=str,
help="frozen model .pb file to import")
args = parser.parse_args()
graph = load_graph(args.model_filename)
for op in graph.get_operations():
print(op.name)
X = graph.get_tensor_by_name('prefix/X:0')
keep_prob = graph.get_tensor_by_name('prefix/keep_prob:0')
output = graph.get_tensor_by_name('prefix/output:0')
with tf.Session(graph=graph) as sess:
prediction = sess.run(output,
feed_dict={
X: np.random.randn(40, 66, 200, 3) * 100,
keep_prob: 1.0
})
print(prediction)
return json_data
##################################################
# END API part
##################################################
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--frozen_model_filename", default="results/frozen_model.pb", type=str, help="Frozen model file to import")
parser.add_argument("--gpu_memory", default=.2, type=float, help="GPU memory per process")
args = parser.parse_args()
##################################################
# Tensorflow part
##################################################
print('Loading the model')
graph = load_graph(args.frozen_model_filename)
x = graph.get_tensor_by_name('prefix/Placeholder/inputs_placeholder:0')
y = graph.get_tensor_by_name('prefix/Accuracy/predictions:0')
print('Starting Session, setting the GPU memory usage to %f' % args.gpu_memory)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
persistent_sess = tf.Session(graph=graph, config=sess_config)
##################################################
# END Tensorflow part
##################################################
print('Starting the API')
app.run()
