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2019-06-06 23:47:37 +08:00
parent 0d9ea44929
commit c8df372f63
8 changed files with 212 additions and 118 deletions
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1
.gitignore vendored
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dataset/__pycache__/casia_hwdb.cpython-36.pyc Executable file
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16
dataset/casia_hwdb.py
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@@ -7,13 +7,17 @@ we using this class to get .png and label from raw
"""
from alfred.dl.tf.common import mute_tf
mute_tf()
import struct
import numpy as np
import cv2
import tensorflow as tf
import os
this_dir = os.path.dirname(os.path.abspath(__file__))
class CASIAHWDBGNT(object):
"""
@@ -52,8 +56,9 @@ def parse_example(record):
tf.io.FixedLenFeature([], tf.string),
})
img = tf.io.decode_raw(features['image'], out_type=tf.uint8)
img = tf.cast(tf.reshape(img, (64, 64)), dtype=tf.float32)
label = tf.cast(features['label'], tf.int32)
return img, label
return {'image': img, 'label': label}
def load_ds():
@@ -63,14 +68,15 @@ def load_ds():
return ds
def load_charactors():
a = open('charactors.txt', 'r').readlines()
def load_characters():
a = open(os.path.join(this_dir, 'charactors.txt'), 'r').readlines()
return [i.strip() for i in a]
if __name__ == "__main__":
ds = load_ds()
charactors = load_charactors()
charactors = load_characters()
for img, label in ds.take(9):
# start training on model...
img = img.numpy()

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dataset/dataset_hwdb.py
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models/__pycache__/cnn_net.cpython-36.pyc Executable file
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38
models/cnn_net.py
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@@ -1,4 +1,3 @@
'''
@@ -21,9 +20,42 @@ accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.flo
'''
import tensorflow as tf
from tensorflow.keras import layers
# some simple models
def build_net_001(input_shape, n_classes):
assert len(input_shape) == 3, 'only support 3 channels'
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(
input_shape=input_shape, filters=32, kernel_size=(3, 3), strides=(1, 1),
padding='valid', activation='relu'))
model.add(tf.keras.layers.MaxPool2D(pool_size=(2, 2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(32, activation='relu'))
model.add(tf.keras.layers.Dense(n_classes, activation='softmax'))
return model
def build_net_002(input_shape, n_classes):
model = tf.keras.Sequential([
layers.Conv2D(input_shape=input_shape, filters=64, kernel_size=(3, 3), strides=(1, 1),
padding='same', activation='relu'),
layers.MaxPool2D(pool_size=(2, 2), padding='same'),
layers.Conv2D(filters=128, kernel_size=(3, 3), padding='same'),
layers.MaxPool2D(pool_size=(2, 2), padding='same'),
layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same'),
layers.MaxPool2D(pool_size=(2, 2), padding='same'),
layers.Flatten(),
layers.Dense(1024, activation='relu'),
layers.Dense(n_classes, activation='softmax')
])
return model
# some models wrapped into tf.keras.Model
class CNNNet(tf.keras.Model):
def __init__(self.):
pass
def __init__(self):
pass

48
train.py
View File

@@ -3,6 +3,7 @@ training HWDB Chinese charactors classification
on MobileNetV2
'''
from alfred.dl.tf.common import mute_tf
mute_tf()
import os
@@ -12,40 +13,41 @@ import tensorflow as tf
from alfred.utils.log import logger as logging
import tensorflow_datasets as tfds
from dataset.casia_hwdb import load_ds, load_charactors
from models.cnn_net import CNNNet
from dataset.casia_hwdb import load_ds, load_characters
from models.cnn_net import CNNNet, build_net_002
target_size = 224
target_size = 64
num_classes = 7356
use_keras_fit = False
# use_keras_fit = True
ckpt_path = './checkpoints/no_finetune/flowers_mbv2_scratch-{epoch}.ckpt'
# use_keras_fit = False
use_keras_fit = True
ckpt_path = './checkpoints/cn_ocr-{epoch}.ckpt'
def preprocess(x):
"""
minus mean pixel or normalize?
"""
x['image'] = tf.expand_dims(x['image'], axis=-1)
x['image'] = tf.image.resize(x['image'], (target_size, target_size))
x['image'] /= 255.
x['image'] = 2*x['image'] - 1
x['image'] = 2 * x['image'] - 1
return x['image'], x['label']
def train():
all_charactors = load_charactors()
num_classes = len(all_charactors)
# using mobilenetv2 classify tf_flowers dataset
all_characters = load_characters()
num_classes = len(all_characters)
logging.info('all characters: {}'.format(num_classes))
train_dataset = load_ds()
train_dataset = train_dataset.shuffle(100).map(preprocess).batch(4).repeat()
# init model
model = CNNNet()
# model.summary()
# model = tf.keras.models.load_model('flowers_mobilenetv2.h5')
model = build_net_002((64, 64, 1), num_classes)
model.summary()
logging.info('model loaded.')
start_epoch = 0
latest_ckpt = tf.train.latest_checkpoint(os.path.dirname(ckpt_path))
if latest_ckpt:
@@ -56,26 +58,24 @@ def train():
logging.info('passing resume since weights not there. training from scratch')
if use_keras_fit:
# todo: why keras fit converge faster than tf loop?
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
try:
model.fit(
train_dataset, epochs=50,
steps_per_epoch=700,)
train_dataset, epochs=50,
steps_per_epoch=700, )
except KeyboardInterrupt:
model.save_weights(ckpt_path.format(epoch=0))
logging.info('keras model saved.')
model.save_weights(ckpt_path.format(epoch=0))
model.save(os.path.join(os.path.dirname(ckpt_path), 'flowers_mobilenetv2.h5'))
model.save(os.path.join(os.path.dirname(ckpt_path), 'cn_ocr.h5'))
else:
loss_fn = tf.losses.SparseCategoricalCrossentropy()
optimizer = tf.optimizers.RMSprop()
train_loss = tf.metrics.Mean(name='train_loss')
# the accuracy calculation has some problems, seems not right?
train_accuracy = tf.metrics.SparseCategoricalAccuracy(name='train_accuracy')
for epoch in range(start_epoch, 120):
@@ -92,7 +92,7 @@ def train():
train_accuracy(labels, predictions)
if batch % 10 == 0:
logging.info('Epoch: {}, iter: {}, loss: {}, train_acc: {}'.format(
epoch, batch, train_loss.result(), train_accuracy.result()))
epoch, batch, train_loss.result(), train_accuracy.result()))
except KeyboardInterrupt:
logging.info('interrupted.')
model.save_weights(ckpt_path.format(epoch=epoch))
@@ -100,7 +100,5 @@ def train():
exit(0)
if __name__ == "__main__":
train()