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finetune.py
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#!/usr/bin/env python
# coding: utf-8
# Import necessary libraries
import tensorflow as tf
import tensorflow.keras.backend as K
import os
import json
import numpy as np
import datetime
from sklearn.metrics import confusion_matrix
from scipy.stats import hmean
#save image batches to numpy files for faster processing
from data.save_data_to_numpy import save_data
#feature extractor
from models.Feature_extractor import CDFSOSR_Model
#outlier prediction network
from models.outlier_network import outlier_nn
#domain prediction network
from models.domain_network import domain_nn
#low noise GANs
from models.low_noise_GAN import generator_nn_low_s
from models.low_noise_GAN import dis_nn_low_s
#high noise GANs
from models.high_noise_GAN import generator_nn_high_s
from models.high_noise_GAN import dis_nn_high_s
#custom metrics (Variance)
from utils.variance_metric import ComputeIterationVaraince
#save accuracy values
from utils.save_accuracy_values import save_accuracy_values
#proto train
from utils.proto_train import proto_train
#new train episode
from utils.train_episode import new_episode
#adamatch augmentation
# from utils.adamatch_aug import adamatch_aug
with open('./config.json') as json_file:
config = json.load(json_file)
base_dir = config['base_dir']
save_path_source = config['save_path_source']
save_path_target = config['save_path_target']
num_classes = config['num_classes']#train_classes+test_classes
num_images_per_class = config['num_images_per_class']
# Hyperparameters for Support and Query images
CS=config['CS']
CQ=config['CQ'] #Not necessarily equal
Ss=config['Ss']
Sd=config['Sd']
Qsk=config['Qsk']
Qdk=config['Qdk']#N
Qsu=config['Qsu']
Qdu=config['Qdu']#X
# Fintune Classes
test_classes = config['test_classes']
#embedding dimensions
emb_dim = config['emb_dim']
#load the saved arrays into the memory
source_domain = np.load(f'{save_path_source}/source_{num_classes}.npy')
target_domain = np.load(f'{save_path_target}/target_{num_classes}.npy')
# Numeric class labels
class_labels = []
for i in range(len(source_domain)):
class_labels.append(i)
print("source_domain shape: ", source_domain.shape)
print("target_domain shape: ", target_domain.shape)
print("class_labels: ", class_labels)
#====================================================================Optimizers================================================================
lr = config['finetune_lr']
initial_learning_rate = config['finetune_initial_learning_rate']
decay_rate = config['finetune_decay_rate']
decay_steps = config['finetune_decay_steps']
# Create the learning rate schedule
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=True)
optim_d_low_s=tf.keras.optimizers.legacy.Adam(lr)
optim_g_low_s=tf.keras.optimizers.legacy.Adam(lr)
optim_d_high_s=tf.keras.optimizers.legacy.Adam(lr)
optim_g_high_s=tf.keras.optimizers.legacy.Adam(lr)
optim_model_Outlier = tf.keras.optimizers.legacy.Adam(initial_learning_rate)
optim_model_Domain = tf.keras.optimizers.legacy.Adam(initial_learning_rate)
optim2 = tf.keras.optimizers.legacy.Adam(lr)##Multitask Outlier network
optim3 = tf.keras.optimizers.legacy.Adam(initial_learning_rate)##FE
#====================================================================Finetune checkpoint================================================================
# Specify the directory where the existing checkpoint is saved
existing_checkpoint_dir = config["existing_checkpoint_dir"]
existing_checkpoint_prefix = os.path.join(existing_checkpoint_dir, "ckpt")
# Define a variable to keep track of the current epoch
epoch_var = tf.Variable(0, dtype=tf.int64, name="epoch")
# Load the existing checkpoint variables into the current model
existing_checkpoint = tf.train.Checkpoint(epoch_var,
optim3=optim3, optim2=optim2,
optim_d_low_s=optim_d_low_s,optim_g_low_s=optim_g_low_s, optim_d_high_s=optim_d_high_s,optim_g_high_s=optim_g_high_s,
optim_model_Outlier = optim_model_Outlier,
optim_model_Domain = optim_model_Domain,
CDFSOSR_Model = CDFSOSR_Model,
generator_nn_low_s=generator_nn_low_s, generator_nn_high_s=generator_nn_high_s, dis_nn_low_s=dis_nn_low_s, dis_nn_high_s=dis_nn_high_s,
outlier_nn=outlier_nn,
domain_nn=domain_nn)
existing_checkpoint.restore(tf.train.latest_checkpoint(existing_checkpoint_dir))
existing_ckpt_manager = tf.train.CheckpointManager(existing_checkpoint,existing_checkpoint_prefix, max_to_keep=5)
#New checkpoint to save the current values
current_checkpoint_dir = config["current_checkpoint_dir"]
current_checkpoint_prefix = os.path.join(current_checkpoint_dir, "ckpt")
current_checkpoint = tf.train.Checkpoint(epoch_var,
optim3=optim3, optim2=optim2,
optim_d_low_s=optim_d_low_s,optim_g_low_s=optim_g_low_s, optim_d_high_s=optim_d_high_s,optim_g_high_s=optim_g_high_s,
optim_model_Outlier = optim_model_Outlier,
optim_model_Domain = optim_model_Domain,
CDFSOSR_Model = CDFSOSR_Model,
generator_nn_low_s=generator_nn_low_s, generator_nn_high_s=generator_nn_high_s, dis_nn_low_s=dis_nn_low_s, dis_nn_high_s=dis_nn_high_s,
outlier_nn=outlier_nn,
domain_nn=domain_nn)
current_ckpt_manager = tf.train.CheckpointManager(current_checkpoint,current_checkpoint_prefix, max_to_keep=5)
#===============================================================Finetune Step========================================================
# Metrics to gather
finetune_loss = tf.metrics.Mean(name='finetune_loss')
finetune_acc = tf.metrics.Mean(name='finetune_accuracy')
finetune_closedoa_cm = tf.metrics.Mean(name='finetune_closedoa_cm')
finetune_openoa = tf.metrics.Mean(name='finetune_openoa')
finetune_outlier_acc = tf.metrics.Mean(name='finetune_outlier_acc')
finetune_domain_acc = tf.metrics.Mean(name='finetune_domain_acc')
finetune_closedoa_cm_variance = ComputeIterationVaraince(name="finetune_closedoa_cm_variance")
finetune_openoa_variance = ComputeIterationVaraince(name="finetune_openoa_variance")
ntimes=config['ntimes']
def finetune_step(tsupport_patches,tquery_patches,support_labels,query_labels,support_classes,CS, CQ, Ss, Sd, Qsk, Qdk, Qsu, Qdu, query_dom_labels, support_dom_labels):
# Forward & update gradients
with tf.GradientTape() as tape:
loss, accuracy, closed_oa_cm, outlier_det_acc, openoa, domain_acc = proto_train(tsupport_patches,tquery_patches,support_labels,query_labels,support_classes,CS, CQ, Ss, Sd, Qsk, Qdk, Qsu, Qdu, query_dom_labels, support_dom_labels, CDFSOSR_Model, outlier_nn, domain_nn, generator_nn_low_s, dis_nn_low_s,generator_nn_high_s, dis_nn_high_s, ntimes, lr_schedule, optim_model_Outlier, optim_model_Domain, optim_d_low_s,optim_g_low_s, optim_d_high_s,optim_g_high_s)
gradients = tape.gradient(loss, CDFSOSR_Model.trainable_variables)
# Compute the learning rate using the schedule
learning_rate = lr_schedule(optim3.iterations)
optim3.learning_rate = learning_rate
optim3.apply_gradients(zip(gradients, CDFSOSR_Model.trainable_variables))
finetune_loss(loss)
finetune_acc(accuracy)
finetune_closedoa_cm(closed_oa_cm)
finetune_openoa(openoa)
finetune_outlier_acc(outlier_det_acc)
finetune_domain_acc(domain_acc)
finetune_closedoa_cm_variance.add(closed_oa_cm)
finetune_openoa_variance.add(openoa)
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
finetune_log_dir = 'logs/gradient_tape/' + current_time + '/finetune'
finetune_summary_writer = tf.summary.create_file_writer(finetune_log_dir)
#===============================================================Train Loop========================================================
start_epoch = 0
if existing_ckpt_manager.latest_checkpoint:
start_epoch = int(existing_ckpt_manager.latest_checkpoint.split('-')[-1])
# restoring the latest checkpoint in checkpoint_path
existing_checkpoint.restore(existing_ckpt_manager.latest_checkpoint)
print("Restarted from: ", start_epoch)
else:
print("Training from scratch...")
for epoch in range(start_epoch, config["finetune_epochs"]): # 80 train + 80 tune + 100 train + 160 tune + 40 train
finetune_loss.reset_states()
finetune_acc.reset_states()
finetune_closedoa_cm.reset_states()
finetune_openoa.reset_states()
finetune_outlier_acc.reset_states()
finetune_domain_acc.reset_states()
finetune_closedoa_cm_variance.reset_states()
finetune_openoa_variance.reset_states()
epoch_var.assign_add(1)
for epi in range(10):
tquery_patches, tsupport_patches, query_labels, support_labels, support_classes, query_dom_labels, support_dom_labels = new_episode(source_domain[-test_classes:,:,:,:,:], target_domain[-test_classes:,:,:,:,:], CS, CQ, Ss, Sd, Qsk, Qdk, Qsu, Qdu, class_labels[-test_classes:])
# tsupport_patches_aug, support_labels_aug, Ss_aug, Sd_aug, support_dom_labels_aug = adamatch_aug(tsupport_patches, support_labels, CS, Ss, Sd, support_dom_labels)
finetune_step(tsupport_patches,tquery_patches,support_labels,query_labels,support_classes,CS, CQ, Ss, Sd, Qsk, Qdk, Qsu, Qdu, query_dom_labels, support_dom_labels)
with finetune_summary_writer.as_default():
tf.summary.scalar('loss', finetune_loss.result(), step=epoch)
tf.summary.scalar('accuracy', finetune_acc.result(), step=epoch)
tf.summary.scalar('closedoa_cm', finetune_closedoa_cm.result(), step=epoch)
tf.summary.scalar('openoa', finetune_openoa.result(), step=epoch)
tf.summary.scalar('outlier_det_acc', finetune_outlier_acc.result(), step=epoch)
tf.summary.scalar('domain_det_acc', finetune_domain_acc.result(), step=epoch)
template = 'Epoch {}, Finetune Loss: {:.2f}, Finetune Accuracy: {:.2f}, Finetune Closed OA: {:.2f}, Fintune Open OA: {:.2f}, Finetune Outlier Det. Acc: {:.2f}, Finetune Domain Det. Acc: {:.2f}'
print(template.format(epoch+1, finetune_loss.result(), finetune_acc.result()*100, finetune_closedoa_cm.result()*100, finetune_openoa.result()*100, finetune_outlier_acc.result()*100, finetune_domain_acc.result()*100))
#save mean accuracy values per epoch
save_accuracy_values(finetune_closedoa_cm.result(), finetune_openoa.result(), 'accuracy_values.npy')
#save variance for accuracys
save_accuracy_values(finetune_closedoa_cm_variance.compute_variance(), finetune_openoa_variance.compute_variance(), 'accuracy_variance_values.npy')
if epoch % config["save_every"] == 0 and epoch != 0 :
current_checkpoint.save(file_prefix = current_checkpoint_prefix)