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()