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bnp_app.py
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bnp_app.py
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# -*- coding: utf-8 -*-
# USAGE
# Start the server:
# python app.py
# Submit a request via cURL:
# curl --data input_word="good" http://localhost:5000/predict
# curl --data input_word="am" http://localhost:5000/predict
# curl --data input_word="bad" http://localhost:5000/predict
# import the necessary packages
import numpy as np
import flask
import io
import tensorflow as tf
import os
import itertools
import codecs
import re
import json
import datetime
import cairocffi as cairo
import editdistance
import numpy as np
from scipy import ndimage
from keras import backend as K
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers import Input, Dense, Activation
from keras.layers import Reshape, Lambda
from keras.layers.merge import add, concatenate
from keras.models import Model
from keras.layers.recurrent import GRU
from keras.optimizers import SGD
from keras.utils.data_utils import get_file
from keras.preprocessing import image
import keras.callbacks
import argparse
from src.TextImageGenerator import TextImageGenerator
from src.utils import *
parser = argparse.ArgumentParser("bnp_app")
parser.add_argument("--weight_path", type=str, default="./model/weights24.h5", help="path for the model weight")
args = parser.parse_args()
# initialize our Flask application and the Keras model
app = flask.Flask(__name__)
model = None
np.random.seed(55)
def build_model(weight_path):
img_w = 128
# Input Parameters
img_h = 64
words_per_epoch = 16000
val_split = 0.2
val_words = int(words_per_epoch * (val_split))
# Network parameters
conv_filters = 16
kernel_size = (3, 3)
pool_size = 2
time_dense_size = 32
rnn_size = 512
minibatch_size = 32
if K.image_data_format() == 'channels_first':
input_shape = (1, img_w, img_h)
else:
input_shape = (img_w, img_h, 1)
fdir = os.path.dirname(get_file('wordlists.tgz',
origin='http://www.mythic-ai.com/datasets/wordlists.tgz', untar=True))
img_gen = TextImageGenerator(monogram_file=os.path.join(fdir, 'wordlist_mono_clean.txt'),
bigram_file=os.path.join(fdir, 'wordlist_bi_clean.txt'),
minibatch_size=minibatch_size,
img_w=img_w,
img_h=img_h,
downsample_factor=(pool_size ** 2),
val_split=words_per_epoch - val_words
)
act = 'relu'
input_data = Input(name='the_input', shape=input_shape, dtype='float32')
inner = Conv2D(conv_filters, kernel_size, padding='same',
activation=act, kernel_initializer='he_normal',
name='conv1')(input_data)
inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)
inner = Conv2D(conv_filters, kernel_size, padding='same',
activation=act, kernel_initializer='he_normal',
name='conv2')(inner)
inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)
conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)
inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)
# cuts down input size going into RNN:
inner = Dense(time_dense_size, activation=act, name='dense1')(inner)
# Two layers of bidirectional GRUs
# GRU seems to work as well, if not better than LSTM:
gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)
gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)
gru1_merged = add([gru_1, gru_1b])
gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
# transforms RNN output to character activations:
inner = Dense(img_gen.get_output_size(), kernel_initializer='he_normal',
name='dense2')(concatenate([gru_2, gru_2b]))
y_pred = Activation('softmax', name='softmax')(inner)
Model(inputs=input_data, outputs=y_pred).summary()
labels = Input(name='the_labels', shape=[img_gen.absolute_max_string_len], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')
# Keras doesn't currently support loss funcs with extra parameters
# so CTC loss is implemented in a lambda layer
loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])
# clipnorm seems to speeds up convergence
sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)
# the loss calc occurs elsewhere, so use a dummy lambda func for the loss
model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)
model.load_weights(weight_path)
# captures output of softmax so we can decode the output during visualization
#test_func = K.function([input_data], [y_pred])
#global model_p
model_p = Model(inputs=input_data, outputs=y_pred)
#global graph
graph = tf.get_default_graph()
return model_p, graph
def decode_predict_ctc(out, top_paths = 1):
results = []
beam_width = 5
if beam_width < top_paths:
beam_width = top_paths
for i in range(top_paths):
lables = K.get_value(K.ctc_decode(out, input_length=np.ones(out.shape[0])*out.shape[1],
greedy=False, beam_width=beam_width, top_paths=top_paths)[0][i])[0]
text = labels_to_text(lables)
results.append(text)
return results
def predict_a_image(mdoel, a, top_paths = 1):
c = np.expand_dims(a.T, axis=0)
net_out_value = mdoel.predict(c)
top_pred_texts = decode_predict_ctc(net_out_value, top_paths)
return top_pred_texts
model_p, graph = build_model(args.weight_path)
@app.route("/predict", methods=["POST"])
def predict():
# initialize the data dictionary that will be returned from the
# view
data = {}
if flask.request.method == "POST":
if flask.request.get_data():
input_word = flask.request.form.get('input_word', '')
h = 64
w = 128
a = paint_text(input_word,h = h, w = w)
b = a.reshape((h, w))
c = np.expand_dims(a.T, axis=0)
with graph.as_default():
net_out_value = model_p.predict(c)
pred_texts = decode_predict_ctc(net_out_value)
top_3_paths = predict_a_image(model_p, a, top_paths = 3)
data["pred_texts"] = pred_texts
data["top_3_paths"] = top_3_paths
# return the data dictionary as a JSON response
return flask.jsonify(data)
# if this is the main thread of execution first load the model and
# then start the server
if __name__ == "__main__":
print(("* Loading Keras model and Flask starting server..."
"please wait until server has fully started"))
app.run(host='0.0.0.0')