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experiment.py
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import os
from datetime import datetime, timedelta
import torch
from torch.utils.data import DataLoader, random_split
import numpy as np
from dataset_factory import get_datasets
from file_utils import *
import matplotlib.pyplot as plt
from constants import ROOT_STATS_DIR
from model_factory import get_model
class Experiment(object):
def __init__(self, name):
config_data = read_file_in_dir('./config/', name + '.json')
if config_data is None:
raise Exception("Configuration file doesn't exist: ", name)
# Load Datasets
self.__name = config_data['experiment_name']
self.__experiment_dir = os.path.join(ROOT_STATS_DIR, self.__name)
ds_train, ds_val = get_datasets(config_data)
self.__train_loader = DataLoader(ds_train, batch_size=config_data['experiment']['batch_size_train'],
shuffle=True,
num_workers=config_data['experiment']['num_workers'], pin_memory=True)
self.__val_loader = DataLoader(ds_val, batch_size=config_data['experiment']['batch_size_val'], shuffle=True,
num_workers=config_data['experiment']['num_workers'], pin_memory=True)
# Setup Experiment Stats
self.__epochs = config_data['experiment']['num_epochs']
self.__current_epoch = 0
self.__training_losses = []
self.__val_losses = []
# Init Model
self.__model = get_model(config_data)
# These can be made configurable or changed, if required.
self.__criterion = torch.nn.BCEWithLogitsLoss()
self.__optimizer = torch.optim.Adam(self.__model.parameters(), lr=config_data['experiment']['learning_rate'])
self.__init_model()
# Load Experiment Data if available
self.__load_experiment()
def __load_experiment(self):
os.makedirs(ROOT_STATS_DIR, exist_ok=True)
if os.path.exists(self.__experiment_dir):
self.__training_losses = read_file_in_dir(self.__experiment_dir, 'training_losses.txt')
self.__val_losses = read_file_in_dir(self.__experiment_dir, 'val_losses.txt')
self.__current_epoch = len(self.__training_losses)
state_dict = torch.load(os.path.join(self.__experiment_dir, 'latest_model.pt'))
self.__model.load_state_dict(state_dict['model'])
self.__optimizer.load_state_dict(state_dict['optimizer'])
else:
os.makedirs(self.__experiment_dir)
os.makedirs(os.path.join(self.__experiment_dir, 'models'))
def __init_model(self):
if torch.cuda.is_available():
self.__model = self.__model.cuda().float()
self.__criterion = self.__criterion.cuda()
# self.model = torch.nn.DataParallel(self.model)
def run(self):
start_epoch = self.__current_epoch
for epoch in range(start_epoch, self.__epochs): # loop over the dataset multiple times
start_time = datetime.now()
self.__current_epoch = epoch
train_loss = self.__train()
val_loss = self.__val()
self.__record_stats(train_loss, val_loss)
self.__log_epoch_stats(start_time)
self.__save_model()
def __train(self):
self.__model.__train()
train_loss_epoch = []
for i, data in enumerate(self.__train_loader):
inputs = data[0].cuda().float() if torch.cuda.is_available() else data[0].double()
labels = data[1].cuda().float() if torch.cuda.is_available() else data[1].double()
self.__optimizer.zero_grad()
outputs = self.__model.forward(inputs).squeeze()
loss = self.__criterion(outputs, labels)
loss.backward()
self.__optimizer.step()
train_loss_epoch.append(loss.item())
status_str = "Epoch: {}, Train, Batch {}/{}. Loss {}".format(self.__current_epoch + 1, i + 1,
len(self.__train_loader),
loss.item())
self.__log(status_str)
return np.mean(train_loss_epoch)
def __val(self):
self.__model.eval()
val_loss_epoch = []
for i, data in enumerate(self.__val_loader):
inputs = data[0].cuda().float() if torch.cuda.is_available() else data[0].double()
labels = data[1].cuda().float() if torch.cuda.is_available() else data[1].double()
with torch.no_grad():
outputs = self.__model.forward(inputs).squeeze()
loss = self.__criterion(outputs, labels)
val_loss_epoch.append(loss.item())
status_str = "Epoch: {}, Val, Batch {}/{}. Loss {}".format(self.__current_epoch + 1, i + 1,
len(self.__val_loader),
loss.item())
self.__log(status_str)
return np.mean(val_loss_epoch)
def __save_model(self):
epoch_model_path = os.path.join(self.__experiment_dir, 'models', 'model_{}.pt'.format(self.__current_epoch))
root_model_path = os.path.join(self.__experiment_dir, 'latest_model.pt')
if isinstance(self.__model, torch.nn.DataParallel):
model_dict = self.__model.module.state_dict()
else:
model_dict = self.__model.state_dict()
state_dict = {'model': model_dict, 'optimizer': self.__optimizer.state_dict()}
torch.save(self.__model.state_dict(), epoch_model_path)
torch.save(state_dict, root_model_path)
def __record_stats(self, train_loss, val_loss, val_dice):
self.__training_losses.append(train_loss)
self.__val_losses.append(val_loss)
self.plot_stats()
write_to_file_in_dir(self.__experiment_dir, 'training_losses.txt', self.__training_losses)
write_to_file_in_dir(self.__experiment_dir, 'val_losses.txt', self.__val_losses)
def __log(self, log_str, file_name=None):
print(log_str)
log_to_file_in_dir(self.__experiment_dir, 'all.log', log_str)
if file_name is not None:
log_to_file_in_dir(self.__experiment_dir, file_name, log_str)
def __log_epoch_stats(self, start_time):
time_elapsed = datetime.now() - start_time
time_to_completion = time_elapsed * (self.__epochs - self.__current_epoch - 1)
train_loss = self.__training_losses[self.__current_epoch]
val_loss = self.__val_losses[self.__current_epoch]
summary_str = "Epoch: {}, Train Loss: {}, Val Loss: {}, Took {}, ETA: {}\n"
summary_str = summary_str.format(self.__current_epoch + 1, train_loss, val_loss, str(time_elapsed),
str(time_to_completion))
self.__log(summary_str, 'epoch.log')
def plot_stats(self):
e = len(self.__training_losses)
x_axis = np.arange(1, e + 1, 1)
plt.figure()
plt.plot(x_axis, self.__training_losses, label="Training Loss")
plt.plot(x_axis, self.__val_losses, label="Validation Loss")
plt.xlabel("Epochs")
plt.legend(loc='best')
plt.title(self.__name + " Stats Plot")
plt.savefig(os.path.join(self.__experiment_dir, "stat_plot.png"))
plt.show()