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TaskAmenability_GA.py
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import gym
import numpy as np
from gym import spaces
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
from torch.utils.data import Dataset, DataLoader,Subset
import os
import pandas as pd
class TaskAmenability_GA(gym.Env):
def __init__(self, x_train, x_val, task_predictors,device,log_dir,tensorboard=None):
self.x_train = x_train
self.x_val = x_val
self.device = device
self.indexes_of_seen_data = np.zeros(len(self.x_train))
self.batch_indices = None
# self.tensorboard = tensorboard
self.img_shape = self.x_train.__getitem__(0)[0].shape
self.task_predictors = task_predictors
self.controller_batch_size = 1000
self.task_evaluate_batch_size = 100
self.task_predictor_batch_size = 100
self.epochs_per_batch = 50
self.n_train = 0
self.x_val_loader = torch.utils.data.DataLoader(dataset=self.x_val,
batch_size=self.task_evaluate_batch_size,
shuffle=False)
self.x_train_loader = None
self.num_val = len(self.x_val)
self.observation_space = spaces.Box(low=0, high=1, shape=self.img_shape, dtype=np.float32)
self.action_space = spaces.Discrete(2)
self.actions_list = []
self.val_metric_list = [0.5]*10
self.sample_num_count = 0
self.total_epoch = 0
self.total_num_seen_data = 0
self.total_num_choosen_data = 0
self.criterion = nn.CrossEntropyLoss()
#### log files for multiple runs are NOT overwritten
self.log_dir = log_dir
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
self.log_dir = self.log_dir + '/' + 'rl_loss' + '/'
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
# self.model = w #TODO
#### get number of log files in log directory
self.run_num = 0
self.current_num_files = next(os.walk(self.log_dir))[2]
self.run_num = len(self.current_num_files)
#### create new log file for each run
self.log_f_name_train = self.log_dir + '/Train_' + 'rl' + "_log_" + str(self.run_num) + ".csv"
self.log_f_name_val = self.log_dir + '/Valid_' + 'rl' + "_log_" + str(self.run_num) + ".csv"
# logging file
self.log_f_train = open(self.log_f_name_train,"w+")
self.log_f_train.write('num_trained_data,epoch,total_num_choosen_data,loss,acc\n')
# logging file
self.log_f_valid = open(self.log_f_name_val,"w+")
self.log_f_valid.write('num_trained_data,epoch,total_num_choosen_data,acc\n')
self.best_valid_metric = 0
#### get number of log files in save_path directory
self.saving_path = log_dir+"/checkpoints/"
if not os.path.exists(self.saving_path):
os.makedirs(self.saving_path)
self.run_num = 0
self.current_num_files = next(os.walk(self.saving_path))[2]
self.run_num = len(self.current_num_files)
self.saving_path = self.saving_path + "/predictor_resnet_18_" +str(self.run_num) + ".ckpt"
print(self.saving_path)
self.seen_indices_path = log_dir+"/seen_indices"
if not os.path.exists(self.seen_indices_path):
os.makedirs(self.seen_indices_path)
self.run_num = 0
self.current_num_files = next(os.walk(self.seen_indices_path))[2]
self.run_num = len(self.current_num_files)
self.seen_indices_path = self.seen_indices_path + "/df" +str(self.run_num) +".csv"
print(self.seen_indices_path)
self.portion_of_none_noisy_selection = 0
self.portion_of_noisy_reduction = 0
def get_batch(self):
shuffle_inds = np.random.permutation(len(self.x_train))
self.x_train = Subset(self.x_train,shuffle_inds)
self.batch_indices = shuffle_inds[0:self.controller_batch_size]
return Subset(self.x_train,np.arange(self.controller_batch_size))
def compute_moving_avg(self):
self.val_metric_list = self.val_metric_list[-10:]
moving_avg = np.mean(self.val_metric_list)
return moving_avg
def select_samples(self, actions_list):
actions_list = np.clip(actions_list, 0, 1)
selection_vector = np.random.binomial(1, actions_list)
logical_inds = []
selected = 0
none_noisy_selected = 0
none_selected = 0
noisy_none_selected = 0
for i in range(len(selection_vector)):
if selection_vector[i] == 1:
logical_inds.append(i)
self.indexes_of_seen_data[self.batch_indices[i]] = 1
selected += 1
if self.x_train[self.batch_indices[i]][2] == 1:
none_noisy_selected += 1
else:
none_selected += 1
if self.x_train[self.batch_indices[i]][2] == 0:
noisy_none_selected += 1
self.portion_of_none_noisy_selection = none_noisy_selected/selected
self.portion_of_noisy_reduction = noisy_none_selected/none_selected
return Subset(self.x_train_batch,logical_inds)
def update_lr(self,optimizer, lr):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return
def get_val_acc_vec(self,num):
self.task_predictors[num].eval()
with torch.no_grad():
correct = []
# total = 0
for images, labels,if_noisy in self.x_val_loader:
images = images.to(self.device)
labels = labels.to(self.device)
outputs = self.task_predictors[num](images)
_, predicted = torch.max(outputs.data, 1)
correct += (predicted == labels).cpu()
return np.array(correct)
def train_predictor(self,train_data, num,mode = 'PPO'):
train_loader = torch.utils.data.DataLoader(dataset=train_data,
batch_size=self.task_predictor_batch_size, shuffle=True)
self.learning_rate = 0.001
self.optimizer = torch.optim.Adam(self.task_predictors[num].parameters(), lr=self.learning_rate)
curr_lr = self.learning_rate
self.task_predictors[num].train()
for epoch in range(self.epochs_per_batch):
correct = []
self.total_epoch += 1
for i, (images, labels,if_noisy) in enumerate(train_loader):
images = images.to(self.device)
labels = labels.to(self.device)
self.total_num_seen_data += len(labels)
# Forward pass
outputs = self.task_predictors[num](images)
loss = self.criterion(outputs, labels)
_, predicted = torch.max(outputs.data, 1)
correct += (predicted == labels).cpu()
# Backward and optimizea
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if (self.n_train) % 500 == 0:
pass
# self.tensorboard.writer.add_scalar('Train/loss_'+mode, loss.item(), self.n_train)
train_metric = np.mean(np.multiply(np.array(correct),1))
if (epoch+1) % 10 == 0:
self.log_f_train.write('{},{},{},{},{}\n'.format(self.total_num_seen_data,self.total_epoch, sum(self.indexes_of_seen_data),
loss.item(),np.mean(np.multiply(np.array(correct),1))))
self.log_f_train.flush()
# self.tensorboard.writer.add_scalar('Train/Acc_'+mode, train_metric, self.n_train)
# Decay learning rate
if (epoch+1) % 10 == 0:
curr_lr /= 3
self.update_lr(self.optimizer, curr_lr)
def step(self, action,num):
self.actions_list.append(action)
self.sample_num_count += 1
# print(self.sample_num_count)
if self.sample_num_count < self.controller_batch_size:
reward = 0
done = False
return torch.unsqueeze(self.x_train_batch.__getitem__(self.sample_num_count)[0],axis = 0), reward, done, {}
else:
x_train_selected = self.select_samples(self.actions_list[:self.controller_batch_size])
if len(x_train_selected) < 1:
reward = -1
done = True
else:
moving_avg = self.compute_moving_avg()
self.train_predictor(x_train_selected,num,'PPO')
val_acc_vec = self.get_val_acc_vec(num)
# print(val_acc_vec.shape,len(self.actions_list),self.controller_batch_size)
# val_sel_vec = self.actions_list[self.controller_batch_size:]
# print(len(self.actions_list))
# val_sel_vec_normalised = np.array(val_sel_vec) / np.mean(val_sel_vec)
# print(val_sel_vec_normalised.shape)
val_metric = np.mean(np.multiply(np.array(val_acc_vec),1))
if val_metric > self.best_valid_metric:
self.best_valid_metric = val_metric
torch.save(self.task_predictors[num].state_dict(), self.saving_path)
pd.DataFrame(self.indexes_of_seen_data).to_csv(self.seen_indices_path)
# self.tensorboard.writer.add_scalar('Val/Acc_PPO', val_metric, self.total_step)
self.log_f_valid.write('{},{},{},{}\n'.format(self.total_num_seen_data,self.total_epoch, sum(self.indexes_of_seen_data),val_metric))
self.log_f_valid.flush()
self.val_metric_list.append(val_metric)
reward = val_metric - moving_avg
# print(reward)
done = True
return np.random.rand(self.img_shape[0], self.img_shape[1], self.img_shape[2]), reward, done, {}
def reset(self):
self.x_train_batch = self.get_batch()
self.actions_list = []
self.sample_num_count = 0
return torch.unsqueeze(self.x_train_batch.__getitem__(self.sample_num_count)[0],axis = 0)
def save_task_predictor(self, task_predictor_save_path):
self.task_predictors[num].save(task_predictor_save_path)
# def compute_random(self):
# actions_list = np.random.randint(2, size=self.controller_batch_size)
# x_train_selected = self.select_samples(actions_list)
# moving_avg = self.compute_moving_avg()
# self.train_predictor(x_train_selected,'Rand')
# val_acc_vec = self.get_val_acc_vec()
# # print(val_acc_vec.shape,len(self.actions_list),self.controller_batch_size)
# # val_sel_vec = self.actions_list[self.controller_batch_size:]
# # print(len(self.actions_list))
# # val_sel_vec_normalised = np.array(val_sel_vec) / np.mean(val_sel_vec)
# # print(val_sel_vec_normalised.shape)
# val_metric = np.mean(np.multiply(np.array(val_acc_vec),1))
# self.tensorboard.writer.add_scalar('Val/Acc_Rand', val_metric, self.total_step)
# self.val_metric_list.append(val_metric)
# reward = val_metric - moving_avg
# done = True
# return
# def compute_whole(self):
# moving_avg = self.compute_moving_avg()
# self.train_predictor(x_train_selected,'Rand')
# val_acc_vec = self.get_val_acc_vec()
# # print(val_acc_vec.shape,len(self.actions_list),self.controller_batch_size)
# # val_sel_vec = self.actions_list[self.controller_batch_size:]
# # print(len(self.actions_list))
# # val_sel_vec_normalised = np.array(val_sel_vec) / np.mean(val_sel_vec)
# # print(val_sel_vec_normalised.shape)
# val_metric = np.mean(np.multiply(np.array(val_acc_vec),1))
# self.tensorboard.writer.add_scalar('Val/Acc_Rand', val_metric, self.total_step)
# self.val_metric_list.append(val_metric)
# reward = val_metric - moving_avg
# done = True
# return