249 lines
8.2 KiB
Python
249 lines
8.2 KiB
Python
from __future__ import print_function
|
|
import tensorflow.keras as keras
|
|
from tensorflow.keras.datasets import mnist
|
|
from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Activation, Concatenate
|
|
from tensorflow.keras import backend as K
|
|
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
|
|
from tensorflow.keras.models import Model
|
|
from tensorflow.keras.layers import Input, Flatten, Dense
|
|
|
|
from siamese import SiameseNetwork
|
|
|
|
import pdb
|
|
|
|
import os, math, numpy as np
|
|
from PIL import Image
|
|
|
|
batch_size = 128
|
|
num_classes = 131
|
|
|
|
# input image dimensions
|
|
img_rows, img_cols = 100, 100
|
|
|
|
def createTrainingData():
|
|
base_dir = './data/COCO/products/'
|
|
train_test_split = 0.7
|
|
no_of_files_in_each_class = 20
|
|
|
|
#Read all the folders in the directory
|
|
folder_list = os.listdir(base_dir)
|
|
print( len(folder_list), "categories found in the dataset")
|
|
|
|
#Declare training array
|
|
cat_list = []
|
|
x = []
|
|
names = []
|
|
y = []
|
|
y_label = 0
|
|
|
|
#Using just 5 images per category
|
|
for folder_name in folder_list:
|
|
files_list = os.listdir(os.path.join(base_dir, folder_name))
|
|
if len(files_list) < no_of_files_in_each_class:
|
|
continue
|
|
temp=[]
|
|
for file_name in files_list[:no_of_files_in_each_class]:
|
|
temp.append(len(x))
|
|
x.append(np.asarray(Image.open(os.path.join(base_dir, folder_name, file_name)).convert('RGB').resize((img_rows, img_cols))))
|
|
names.append(folder_name + "/" + file_name)
|
|
y.append(y_label)
|
|
y_label+=1
|
|
cat_list.append(temp)
|
|
|
|
cat_list = np.asarray(cat_list)
|
|
x = np.asarray(x)/255.0
|
|
y = np.asarray(y)
|
|
print('X, Y shape',x.shape, y.shape, cat_list.shape)
|
|
|
|
|
|
#Training Split
|
|
x_train, y_train, cat_train, x_val, y_val, cat_test = [], [], [], [], [], []
|
|
|
|
train_split = math.floor((train_test_split) * no_of_files_in_each_class)
|
|
test_split = math.floor((1-train_test_split) * no_of_files_in_each_class)
|
|
|
|
train_count = 0
|
|
test_count = 0
|
|
for i in range(len(x)-1):
|
|
if i % no_of_files_in_each_class == 0:
|
|
cat_train.append([])
|
|
cat_test.append([])
|
|
class_train_count = 1
|
|
class_test_count = 1
|
|
|
|
if i % math.floor(1/train_test_split) == 0 and class_test_count < test_split:
|
|
x_val.append(x[i])
|
|
y_val.append(y[i])
|
|
cat_test[-1].append(test_count)
|
|
test_count += 1
|
|
class_test_count += 1
|
|
|
|
elif class_train_count < train_split:
|
|
x_train.append(x[i])
|
|
y_train.append(y[i])
|
|
cat_train[-1].append(train_count)
|
|
train_count += 1
|
|
class_train_count += 1
|
|
|
|
|
|
x_val = np.array(x_val)
|
|
y_val = np.array(y_val)
|
|
x_train = np.array(x_train)
|
|
y_train = np.array(y_train)
|
|
cat_train = np.array(cat_train)
|
|
cat_test = np.array(cat_test)
|
|
|
|
|
|
print('X&Y shape of training data :',x_train.shape, 'and',
|
|
y_train.shape, cat_train.shape)
|
|
print('X&Y shape of testing data :' , x_val.shape, 'and',
|
|
y_val.shape, cat_test.shape)
|
|
|
|
return (x_train, y_train), (x_val, y_val), cat_train
|
|
|
|
|
|
# the data, split between train and test sets
|
|
# (x_train, y_train), (x_test, y_test) = mnist.load_data()
|
|
# channels = 1
|
|
|
|
(x_train, y_train), (x_test, y_test), cat_train = createTrainingData()
|
|
|
|
channels = 3
|
|
|
|
if K.image_data_format() == 'channels_first':
|
|
x_train = x_train.reshape(x_train.shape[0], channels, img_rows, img_cols)
|
|
x_test = x_test.reshape(x_test.shape[0], channels, img_rows, img_cols)
|
|
input_shape = (channels, img_rows, img_cols)
|
|
else:
|
|
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, channels)
|
|
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, channels)
|
|
input_shape = (img_rows, img_cols, channels)
|
|
|
|
x_train = x_train.astype('float32')
|
|
x_test = x_test.astype('float32')
|
|
|
|
def create_own_base_model(input_shape):
|
|
return keras.applications.vgg16.VGG16(include_top=False, input_tensor=Input(shape=input_shape), weights='imagenet',
|
|
classes=1)
|
|
|
|
def create_base_model(input_shape):
|
|
model_input = Input(shape=input_shape)
|
|
|
|
embedding = Conv2D(32, kernel_size=(3, 3), input_shape=input_shape)(model_input)
|
|
embedding = BatchNormalization()(embedding)
|
|
embedding = Activation(activation='relu')(embedding)
|
|
embedding = MaxPooling2D(pool_size=(2, 2))(embedding)
|
|
embedding = Conv2D(64, kernel_size=(3, 3))(embedding)
|
|
embedding = BatchNormalization()(embedding)
|
|
embedding = Activation(activation='relu')(embedding)
|
|
embedding = MaxPooling2D(pool_size=(2, 2))(embedding)
|
|
embedding = Flatten()(embedding)
|
|
embedding = Dense(128)(embedding)
|
|
embedding = BatchNormalization()(embedding)
|
|
embedding = Activation(activation='relu')(embedding)
|
|
|
|
return Model(model_input, embedding)
|
|
|
|
def create_own_head_model(embedding_shape):
|
|
embedding_a = Input(shape=embedding_shape[1:])
|
|
embedding_b = Input(shape=embedding_shape[1:])
|
|
|
|
embedding_a_mod = Flatten()(embedding_a)
|
|
embedding_a_mod = Dense(128)(embedding_a_mod)
|
|
embedding_a_mod = BatchNormalization()(embedding_a_mod)
|
|
embedding_a_mod = Activation(activation='relu')(embedding_a_mod)
|
|
|
|
embedding_b_mod = Flatten()(embedding_b)
|
|
embedding_b_mod = Dense(128)(embedding_b_mod)
|
|
embedding_b_mod = BatchNormalization()(embedding_b_mod)
|
|
embedding_b_mod = Activation(activation='relu')(embedding_b_mod)
|
|
|
|
head = Concatenate()([embedding_a_mod, embedding_b_mod])
|
|
head = Dense(8)(head)
|
|
head = BatchNormalization()(head)
|
|
head = Activation(activation='sigmoid')(head)
|
|
|
|
head = Dense(1)(head)
|
|
head = BatchNormalization()(head)
|
|
head = Activation(activation='sigmoid')(head)
|
|
|
|
return Model([embedding_a, embedding_b], head)
|
|
|
|
def create_head_model(embedding_shape):
|
|
embedding_a = Input(shape=embedding_shape[1:])
|
|
embedding_b = Input(shape=embedding_shape[1:])
|
|
|
|
head = Concatenate()([embedding_a, embedding_b])
|
|
head = Dense(8)(head)
|
|
head = BatchNormalization()(head)
|
|
head = Activation(activation='sigmoid')(head)
|
|
|
|
head = Dense(1)(head)
|
|
head = BatchNormalization()(head)
|
|
head = Activation(activation='sigmoid')(head)
|
|
|
|
return Model([embedding_a, embedding_b], head)
|
|
|
|
def get_batch(x_train, y_train, x_test, y_test, cat_train, batch_size=64):
|
|
|
|
temp_x = x_train
|
|
temp_cat_list = cat_train
|
|
start=0
|
|
batch_x=[]
|
|
|
|
batch_y = np.zeros(batch_size)
|
|
batch_y[int(batch_size/2):] = 1
|
|
np.random.shuffle(batch_y)
|
|
|
|
class_list = np.random.randint(start, len(cat_train), batch_size)
|
|
batch_x.append(np.zeros((batch_size, 100, 100, 3)))
|
|
batch_x.append(np.zeros((batch_size, 100, 100, 3)))
|
|
|
|
for i in range(0, batch_size):
|
|
batch_x[0][i] = temp_x[np.random.choice(temp_cat_list[class_list[i]])]
|
|
#If train_y has 0 pick from the same class, else pick from any other class
|
|
if batch_y[i]==0:
|
|
r = np.random.choice(temp_cat_list[class_list[i]])
|
|
batch_x[1][i] = temp_x[r]
|
|
|
|
else:
|
|
temp_list = np.append(temp_cat_list[:class_list[i]].flatten(), temp_cat_list[class_list[i]+1:].flatten())
|
|
batch_x[1][i] = temp_x[np.random.choice(temp_list)]
|
|
|
|
return(batch_x, batch_y)
|
|
|
|
|
|
num_classes = 131
|
|
epochs = 2000
|
|
|
|
base_model = create_own_base_model(input_shape)
|
|
head_model = create_own_head_model(base_model.output_shape)
|
|
|
|
siamese_network = SiameseNetwork(base_model, head_model)
|
|
siamese_network.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
|
|
|
|
siamese_checkpoint_path = "../siamese100_products"
|
|
model_path = "/variables/variables"
|
|
siamese_callbacks = [
|
|
# EarlyStopping(monitor='val_accuracy', patience=10, verbose=0),
|
|
ModelCheckpoint(siamese_checkpoint_path, monitor='val_accuracy', save_best_only=True, verbose=0)
|
|
]
|
|
|
|
try:
|
|
print("loading weights for model")
|
|
siamese_network.load_weights(siamese_checkpoint_path+model_path)
|
|
except Exception as e:
|
|
print(e)
|
|
|
|
|
|
siamese_network.fit(x_train, y_train,
|
|
validation_data=(x_test, y_test),
|
|
batch_size=45,
|
|
epochs=epochs,
|
|
callbacks=siamese_callbacks)
|
|
|
|
|
|
score = siamese_network.evaluate(x_test, y_test, batch_size=60, verbose=0)
|
|
print('Test loss:', score[0])
|
|
print('Test accuracy:', score[1])
|