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main.py
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import os
import argparse
import numpy as np
import matplotlib.pyplot as plt
import logging as log
import tensorflow as tf
from PIL import Image
from solver import Solver
# suppress warning
#os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
####################
# global variables #
####################
# data types
DATA_TYPE_CUB = 'cub'
DATA_TYPE_COMPCARS = 'compcars'
DATA_TYPE_IMAGENET = 'imagenet'
# folders
WEIGHTS_FOLDER = 'weights'
LOG_FOLDER = 'log'
NETWORKS_FOLDER = 'networks'
# weight files
WEIGHT_VGG16_IMAGENET = 'vgg16.npy'
WEIGHT_VGG19_IMAGENET = 'vgg19.npy'
# directories
DEFAULT_DATASET_DIR = 'D:\\Jason_Folder\\Yonsei\\Research\\dataset'
DEFAULT_CODE_DIR = 'D:\\Jason_Folder\\Yonsei\\Research\\fine_grained\\code'
LOG_DIR = os.path.join(DEFAULT_CODE_DIR, LOG_FOLDER)
NETWORKS_DIR = os.path.join(DEFAULT_CODE_DIR, NETWORKS_FOLDER)
WEIGHTS_DIR = os.path.join(NETWORKS_DIR, WEIGHTS_FOLDER)
#############
# functions #
#############
def set_logging():
"""
Configure logging information.
"""
# tf.logging.set_verbosity(tf.logging.INFO)
log.basicConfig(format='%(levelname)s: %(message)s', level=log.DEBUG)
log.info('Logging set')
def my_network_func(mode='train'):
with tf.Session() as sess:
if mode is 'train':
weights_path = {'vgg': os.path.join(WEIGHTS_DIR, 'vgg19.npy'),
'stn_1': None,
'stn_2': None,
'classification': os.path.join(WEIGHTS_DIR, 'vgg19.npy')}
init_layers = {'vgg': [],
'stn_1': ['conv1', 'fc2', 'fc3'],
'stn_2': ['conv1', 'fc2', 'fc3'],
'classification': ['conv1', 'fc2', 'fc3', 'fc4']}
solver = Solver(
sess=sess,
dataset_type='cub',
dataset_dir=DEFAULT_DATASET_DIR,
resize=(256, 256),
crop_shape=(224, 224),
network_type='my_network',
log_dir=LOG_DIR,
weights_path=weights_path,
init_layers=init_layers)
lr_fast_vars=[
# stn_1
'stn_1/conv1/conv1_filters', 'stn_1/conv1/conv1_biases',
'stn_1/fc2/fc2_filters', 'stn_1/fc2/fc2_biases',
'stn_1/fc3/fc3_filters', 'stn_1/fc3/fc3_biases',
# stn_2
'stn_2/conv1/conv1_filters', 'stn_2/conv1/conv1_biases',
'stn_2/fc2/fc2_filters', 'stn_2/fc2/fc2_biases',
'stn_2/fc3/fc3_filters', 'stn_2/fc3/fc3_biases',
# classification
# 'classification/conv1/conv1_filters', 'classification/conv1/conv1_biases',
'classification/fc2/fc2_filters', 'classification/fc2/fc2_biases',
'classification/fc3/fc3_filters', 'classification/fc3/fc3_biases',
'classification/fc4/fc4_filters', 'classification/fc4/fc4_biases']
solver.trainer(
learning_rate=0.0001,
epochs=250,
learning_rate_fast=0.002,
lr_fast_vars=lr_fast_vars,
l2_regularization_decay=5e-4,
save_path=os.path.join(WEIGHTS_DIR, 'my_network.npy'),
save_scope=['vgg', 'stn_1', 'stn_2', 'classification'],
save_epoch=[0, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 249])
elif mode is 'test':
weights_path = {'vgg': os.path.join(WEIGHTS_DIR, 'my_network_vgg_epoch220.npy'),
'stn_1': os.path.join(WEIGHTS_DIR, 'my_network_stn_1_epoch220.npy'),
'stn_2': os.path.join(WEIGHTS_DIR, 'my_network_stn_2_epoch220.npy'),
'classification': os.path.join(WEIGHTS_DIR, 'my_network_classification_epoch220.npy')}
init_layers = {'vgg': [],
'stn_1': [],
'stn_2': [],
'classification': []}
solver = Solver(
sess=sess,
dataset_type='cub',
dataset_dir=DEFAULT_DATASET_DIR,
resize=(256, 256),
crop_shape=(224, 224),
network_type='my_network',
log_dir=LOG_DIR,
weights_path=weights_path,
init_layers=init_layers)
solver.tester()
else:
log.error('Invalid mode.')
def st_vgg_func(mode='train'):
with tf.Session() as sess:
if mode is 'train':
weights_path = {'localization': os.path.join(WEIGHTS_DIR, 'vgg16_cub_ft.npy'),
'classification': os.path.join(WEIGHTS_DIR, 'vgg16_cub_ft.npy')}
init_layers = {'localization': ['conv6', 'fc7', 'fc8'],
'classification': []}
solver = Solver(
sess=sess,
dataset_type='cub',
dataset_dir=DEFAULT_DATASET_DIR,
resize=(256, 256),
crop_shape=(224, 224),
network_type='st_vgg',
log_dir=LOG_DIR,
weights_path=weights_path,
init_layers=init_layers)
lr_fast_vars=[
'localization/conv6/conv6_filters', 'localization/conv6/conv6_biases',
'localization/fc7/fc7_filters', 'localization/fc7/fc7_biases',
'localization/fc8/fc8_filters', 'localization/fc8/fc8_biases',
'classification/fc8/fc8_filters', 'classification/fc8/fc8_biases']
solver.trainer(
learning_rate=0.0001,
epochs=100,
learning_rate_fast=0.001,
lr_fast_vars=lr_fast_vars,
l2_regularization_decay=5e-4,
save_path=os.path.join(WEIGHTS_DIR, 'st_vgg16_cub_ft_supervised.npy'),
save_scope=['localization', 'classification'],
save_epoch=[50, 60, 70, 80, 90, 99])
elif mode is 'test':
weights_path = {'localization': os.path.join(WEIGHTS_DIR, 'st_vgg16_cub_ft_supervised_localization.npy'),
'classification': os.path.join(WEIGHTS_DIR, 'st_vgg16_cub_ft_supervised_classification.npy')}
init_layers = {'localization': [],
'classification': []}
solver = Solver(
sess=sess,
dataset_type='cub',
dataset_dir=DEFAULT_DATASET_DIR,
resize=(256, 256),
crop_shape=(224, 224),
network_type='st_vgg',
log_dir=LOG_DIR,
weights_path=weights_path,
init_layers=init_layers)
solver.tester()
else:
log.error('Invalid mode.')
def vgg_cub_ft_func(mode='train'):
with tf.Session() as sess:
if mode is 'train':
solver = Solver(
sess=sess,
dataset_type='cub',
dataset_dir=DEFAULT_DATASET_DIR,
resize=(256, 256),
crop_shape=(224, 224),
network_type='vgg19',
log_dir=LOG_DIR,
weights_path=os.path.join(WEIGHTS_DIR, 'vgg19.npy'),
init_layers=['fc8'])
solver.trainer(
learning_rate=0.0001,
epochs=150,
learning_rate_fast=0.001,
lr_fast_vars=['fc8/fc8_filters', 'fc8/fc8_biases'],
l2_regularization_decay=5e-4,
save_path=os.path.join(WEIGHTS_DIR, 'vgg19_cub_ft.npy'),
save_epoch=[50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 149])
elif mode is 'test':
solver = Solver(
sess=sess,
dataset_type='cub',
dataset_dir=DEFAULT_DATASET_DIR,
resize=(256, 256),
crop_shape=(224, 224),
network_type='vgg19',
log_dir=LOG_DIR,
weights_path=os.path.join(WEIGHTS_DIR, 'vgg19_cub_ft.npy'),
init_layers=[])
solver.tester()
else:
log.error('Invalid mode.')
# main
if __name__ == '__main__':
# set logging
set_logging()
# call appropricate functions
my_network_func(mode='test')