170 lines
4.5 KiB
Python
170 lines
4.5 KiB
Python
import tensorflow as tf
|
|
import random
|
|
import pandas as pd
|
|
from tensorflow import keras
|
|
from tensorflow.data import AUTOTUNE
|
|
from keras import layers, losses, optimizers
|
|
import requests
|
|
|
|
class NotifyServerCallback(tf.keras.callbacks.Callback):
|
|
def on_epoch_end(self, epoch, log, *args, **kwargs):
|
|
requests.get(f'http://localhost:8000/model/epoch/update?model_id={{.Model.Id}}&epoch={epoch}&accuracy={log["accuracy"]}&definition={{.DefId}}')
|
|
|
|
|
|
DATA_DIR = "{{ .DataDir }}"
|
|
image_size = ({{ .Size }})
|
|
|
|
df = pd.read_csv("{{ .RunPath }}/train.csv", dtype=str)
|
|
keys = tf.constant(df['Id'].dropna())
|
|
values = tf.constant(list(map(int, df['Index'].dropna())))
|
|
|
|
table = tf.lookup.StaticHashTable(
|
|
initializer=tf.lookup.KeyValueTensorInitializer(
|
|
keys=keys,
|
|
values=values,
|
|
),
|
|
default_value=tf.constant(-1),
|
|
name="Indexes"
|
|
)
|
|
|
|
DATA_DIR_PREPARE = DATA_DIR + "/"
|
|
|
|
#based on https://www.tensorflow.org/tutorials/load_data/images
|
|
def pathToLabel(path):
|
|
path = tf.strings.regex_replace(path, DATA_DIR_PREPARE, "")
|
|
{{ if eq .Model.Format "png" }}
|
|
path = tf.strings.regex_replace(path, ".png", "")
|
|
{{ else if eq .Model.Format "jpeg" }}
|
|
path = tf.strings.regex_replace(path, ".jpg", "")
|
|
path = tf.strings.regex_replace(path, ".jpeg", "")
|
|
{{ else }}
|
|
ERROR
|
|
{{ end }}
|
|
return table.lookup(tf.strings.as_string([path]))
|
|
|
|
def decode_image(img):
|
|
{{ if eq .Model.Format "png" }}
|
|
img = tf.io.decode_png(img, channels={{.ColorMode}})
|
|
{{ else if eq .Model.Format "jpeg" }}
|
|
img = tf.io.decode_jpeg(img, channels={{.ColorMode}})
|
|
{{ else }}
|
|
ERROR
|
|
{{ end }}
|
|
return tf.image.resize(img, image_size)
|
|
|
|
def process_path(path):
|
|
label = pathToLabel(path)
|
|
|
|
img = tf.io.read_file(path)
|
|
img = decode_image(img)
|
|
|
|
return img, label
|
|
|
|
def configure_for_performance(ds: tf.data.Dataset) -> tf.data.Dataset:
|
|
#ds = ds.cache()
|
|
ds = ds.shuffle(buffer_size= 1000)
|
|
ds = ds.batch(batch_size)
|
|
ds = ds.prefetch(AUTOTUNE)
|
|
return ds
|
|
|
|
def prepare_dataset(ds: tf.data.Dataset) -> tf.data.Dataset:
|
|
ds = ds.map(process_path, num_parallel_calls=AUTOTUNE)
|
|
ds = configure_for_performance(ds)
|
|
return ds
|
|
|
|
def filterDataset(path):
|
|
path = tf.strings.regex_replace(path, DATA_DIR_PREPARE, "")
|
|
|
|
path = tf.strings.regex_replace(path, ".jpg", "")
|
|
path = tf.strings.regex_replace(path, ".jpeg", "")
|
|
|
|
return tf.reshape(table.lookup(tf.strings.as_string([path])), []) != -1
|
|
|
|
seed = random.randint(0, 100000000)
|
|
|
|
batch_size = 64
|
|
|
|
# Read all the files from the direcotry
|
|
list_ds = tf.data.Dataset.list_files(str(f'{DATA_DIR}/*'), shuffle=False)
|
|
list_ds = list_ds.filter(filterDataset)
|
|
|
|
image_count = len(list(list_ds.as_numpy_iterator()))
|
|
|
|
list_ds = list_ds.shuffle(image_count, seed=seed)
|
|
|
|
val_size = int(image_count * 0.3)
|
|
|
|
train_ds = list_ds.skip(val_size)
|
|
val_ds = list_ds.take(val_size)
|
|
|
|
dataset = prepare_dataset(train_ds)
|
|
dataset_validation = prepare_dataset(val_ds)
|
|
|
|
track = 0
|
|
|
|
def addBlock(
|
|
b_size: int,
|
|
filter_size: int,
|
|
kernel_size: int = 3,
|
|
top: bool = True,
|
|
pooling_same: bool = False,
|
|
pool_func=layers.MaxPool2D
|
|
):
|
|
global track
|
|
model = keras.Sequential(name=f"{track}-{b_size}-{filter_size}-{kernel_size}")
|
|
track += 1
|
|
for _ in range(b_size):
|
|
model.add(layers.Conv2D(
|
|
filter_size,
|
|
kernel_size,
|
|
padding="same"
|
|
))
|
|
model.add(layers.ReLU())
|
|
if top:
|
|
if pooling_same:
|
|
model.add(pool_func(padding="same", strides=(1, 1)))
|
|
else:
|
|
model.add(pool_func())
|
|
model.add(layers.BatchNormalization())
|
|
model.add(layers.LeakyReLU())
|
|
model.add(layers.Dropout(0.4))
|
|
return model
|
|
|
|
|
|
{{ if .LoadPrev }}
|
|
model = tf.keras.saving.load_model('{{.LastModelRunPath}}')
|
|
{{ else }}
|
|
model = keras.Sequential()
|
|
|
|
{{- range .Layers }}
|
|
{{- if eq .LayerType 1}}
|
|
model.add(layers.Rescaling(1./255))
|
|
{{- else if eq .LayerType 2 }}
|
|
model.add(layers.Dense({{ .Shape }}, activation="sigmoid"))
|
|
{{- else if eq .LayerType 3}}
|
|
model.add(layers.Flatten())
|
|
{{- else if eq .LayerType 4}}
|
|
model.add(addBlock(2, 128, 3, pool_func=layers.AveragePooling2D))
|
|
{{- else }}
|
|
ERROR
|
|
{{- end }}
|
|
{{- end }}
|
|
{{ end }}
|
|
|
|
model.compile(
|
|
loss=losses.SparseCategoricalCrossentropy(),
|
|
optimizer=tf.keras.optimizers.Adam(),
|
|
metrics=['accuracy'])
|
|
|
|
his = model.fit(dataset, validation_data= dataset_validation, epochs={{.EPOCH_PER_RUN}}, callbacks=[NotifyServerCallback()], use_multiprocessing = True)
|
|
|
|
acc = his.history["accuracy"]
|
|
|
|
f = open("accuracy.val", "w")
|
|
f.write(str(acc[-1]))
|
|
f.close()
|
|
|
|
|
|
tf.saved_model.save(model, "model")
|
|
model.save("model.keras")
|