run_networks.py

import os
import copy
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from utils import *
import time
import numpy as np
import warnings
import pdb

class model ():
    
    def __init__(self, config, data, test=False):

        self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
        self.config = config
        self.training_opt = self.config['training_opt']
        self.memory = self.config['memory']
        self.data = data
        self.test_mode = test
        
        # Initialize model
        self.init_models()

        # Under training mode, initialize training steps, optimizers, schedulers, criterions, and centroids
        if not self.test_mode:

            # If using steps for training, we need to calculate training steps 
            # for each epoch based on actual number of training data instead of 
            # oversampled data number 
            print('Using steps for training.')
            self.training_data_num = len(self.data['train'].dataset)
            self.epoch_steps = int(self.training_data_num  \
                                   / self.training_opt['batch_size'])

            # Initialize model optimizer and scheduler
            print('Initializing model optimizer.')
            self.scheduler_params = self.training_opt['scheduler_params']
            self.model_optimizer, \
            self.model_optimizer_scheduler = self.init_optimizers(self.model_optim_params_list)
            self.init_criterions()
            if self.memory['init_centroids']:
                self.criterions['FeatureLoss'].centroids.data = \
                    self.centroids_cal(self.data['train_plain'])
            
        # Set up log file
        self.log_file = os.path.join(self.training_opt['log_dir'], 'log.txt')
        if os.path.isfile(self.log_file):
            os.remove(self.log_file)
        
    def init_models(self, optimizer=True):

        networks_defs = self.config['networks']
        self.networks = {}
        self.model_optim_params_list = []

        print("Using", torch.cuda.device_count(), "GPUs.")
        
        for key, val in networks_defs.items():

            # Networks
            def_file = val['def_file']
            model_args = list(val['params'].values())
            model_args.append(self.test_mode)

            self.networks[key] = source_import(def_file).create_model(*model_args)
            self.networks[key] = nn.DataParallel(self.networks[key]).to(self.device)
            
            if 'fix' in val and val['fix']:
                print('Freezing feature weights except for modulated attention weights (if exist).')
                for param_name, param in self.networks[key].named_parameters():
                    # Freeze all parameters except self attention parameters
                    if 'modulatedatt' not in param_name and 'fc' not in param_name:
                        param.requires_grad = False

            # Optimizer list
            optim_params = val['optim_params']
            self.model_optim_params_list.append({'params': self.networks[key].parameters(),
                                                 'lr': optim_params['lr'],
                                                 'momentum': optim_params['momentum'],
                                                 'weight_decay': optim_params['weight_decay']})

    def init_criterions(self):

        criterion_defs = self.config['criterions']
        self.criterions = {}
        self.criterion_weights = {}

        for key, val in criterion_defs.items():
            def_file = val['def_file']
            loss_args = val['loss_params'].values()
            self.criterions[key] = source_import(def_file).create_loss(*loss_args).to(self.device)
            self.criterion_weights[key] = val['weight']
          
            if val['optim_params']:
                print('Initializing criterion optimizer.')
                optim_params = val['optim_params']
                optim_params = [{'params': self.criterions[key].parameters(),
                                'lr': optim_params['lr'],
                                'momentum': optim_params['momentum'],
                                'weight_decay': optim_params['weight_decay']}]
                # Initialize criterion optimizer and scheduler
                self.criterion_optimizer, \
                self.criterion_optimizer_scheduler = self.init_optimizers(optim_params)
            else:
                self.criterion_optimizer = None

    def init_optimizers(self, optim_params):
        optimizer = optim.SGD(optim_params)
        scheduler = optim.lr_scheduler.StepLR(optimizer,
                                              step_size=self.scheduler_params['step_size'],
                                              gamma=self.scheduler_params['gamma'])
        return optimizer, scheduler

    def batch_forward (self, inputs, labels=None, centroids=False, feature_ext=False, phase='train'):
        '''
        This is a general single batch running function. 
        '''

        # Calculate Features
        self.features, self.feature_maps = self.networks['feat_model'](inputs)

        # If not just extracting features, calculate logits
        if not feature_ext:

            # During training, calculate centroids if needed to 
            if phase != 'test':
                if centroids and 'FeatureLoss' in self.criterions.keys():
                    self.centroids = self.criterions['FeatureLoss'].centroids.data
                else:
                    self.centroids = None

            # Calculate logits with classifier
            self.logits, self.direct_memory_feature = self.networks['classifier'](self.features, self.centroids)

    def batch_backward(self):
        # Zero out optimizer gradients
        self.model_optimizer.zero_grad()
        if self.criterion_optimizer:
            self.criterion_optimizer.zero_grad()
        # Back-propagation from loss outputs
        self.loss.backward()
        # Step optimizers
        self.model_optimizer.step()
        if self.criterion_optimizer:
            self.criterion_optimizer.step()

    def batch_loss(self, labels):

        # First, apply performance loss
        self.loss_perf = self.criterions['PerformanceLoss'](self.logits, labels) \
                    * self.criterion_weights['PerformanceLoss']

        # Add performance loss to total loss
        self.loss = self.loss_perf

        # Apply loss on features if set up
        if 'FeatureLoss' in self.criterions.keys():
            self.loss_feat = self.criterions['FeatureLoss'](self.features, labels)
            self.loss_feat = self.loss_feat * self.criterion_weights['FeatureLoss']
            # Add feature loss to total loss
            self.loss += self.loss_feat

    def train(self):

        # When training the network
        print_str = ['Phase: train']
        print_write(print_str, self.log_file)
        time.sleep(0.25)

        # Initialize best model
        best_model_weights = {}
        best_model_weights['feat_model'] = copy.deepcopy(self.networks['feat_model'].state_dict())
        best_model_weights['classifier'] = copy.deepcopy(self.networks['classifier'].state_dict())
        best_acc = 0.0
        best_epoch = 0

        end_epoch = self.training_opt['num_epochs']

        # Loop over epochs
        for epoch in range(1, end_epoch + 1):

            for model in self.networks.values():
                model.train()
                
            torch.cuda.empty_cache()
            
            # Iterate over dataset
            for step, (inputs, labels, _) in enumerate(self.data['train']):

                # Break when step equal to epoch step
                if step == self.epoch_steps:
                    break

                inputs, labels = inputs.to(self.device), labels.to(self.device)

                # If on training phase, enable gradients
                with torch.set_grad_enabled(True):
                        
                    # If training, forward with loss, and no top 5 accuracy calculation
                    self.batch_forward(inputs, labels, 
                                       centroids=self.memory['centroids'],
                                       phase='train')
                    self.batch_loss(labels)
                    self.batch_backward()

                    # Output minibatch training results
                    if step % self.training_opt['display_step'] == 0:

                        minibatch_loss_feat = self.loss_feat.item() \
                            if 'FeatureLoss' in self.criterions.keys() else None
                        minibatch_loss_perf = self.loss_perf.item()
                        _, preds = torch.max(self.logits, 1)
                        minibatch_acc = mic_acc_cal(preds, labels)

                        print_str = ['Epoch: [%d/%d]' 
                                     % (epoch, self.training_opt['num_epochs']),
                                     'Step: %5d' 
                                     % (step),
                                     'Minibatch_loss_feature: %.3f' 
                                     % (minibatch_loss_feat) if minibatch_loss_feat else '',
                                     'Minibatch_loss_performance: %.3f' 
                                     % (minibatch_loss_perf),
                                     'Minibatch_accuracy_micro: %.3f'
                                      % (minibatch_acc)]
                        print_write(print_str, self.log_file)

            # Set model modes and set scheduler
            # In training, step optimizer scheduler and set model to train()
            self.model_optimizer_scheduler.step()
            if self.criterion_optimizer:
                self.criterion_optimizer_scheduler.step()

            # After every epoch, validation
            self.eval(phase='val')

            # Under validation, the best model need to be updated
            if self.eval_acc_mic_top1 > best_acc:
                best_epoch = copy.deepcopy(epoch)
                best_acc = copy.deepcopy(self.eval_acc_mic_top1)
                best_centroids = copy.deepcopy(self.centroids)
                best_model_weights['feat_model'] = copy.deepcopy(self.networks['feat_model'].state_dict())
                best_model_weights['classifier'] = copy.deepcopy(self.networks['classifier'].state_dict())

        print()
        print('Training Complete.')

        print_str = ['Best validation accuracy is %.3f at epoch %d' % (best_acc, best_epoch)]
        print_write(print_str, self.log_file)
        # Save the best model and best centroids if calculated
        self.save_model(epoch, best_epoch, best_model_weights, best_acc, centroids=best_centroids)
                
        print('Done')

    def eval(self, phase='val', openset=False):

        print_str = ['Phase: %s' % (phase)]
        print_write(print_str, self.log_file)
        time.sleep(0.25)

        if openset:
            print('Under openset test mode. Open threshold is %.1f' 
                  % self.training_opt['open_threshold'])
 
        torch.cuda.empty_cache()

        # In validation or testing mode, set model to eval() and initialize running loss/correct
        for model in self.networks.values():
            model.eval()

        self.total_logits = torch.empty((0, self.training_opt['num_classes'])).to(self.device)
        self.total_labels = torch.empty(0, dtype=torch.long).to(self.device)
        self.total_paths = np.empty(0)

        # Iterate over dataset
        for inputs, labels, paths in tqdm(self.data[phase]):
            inputs, labels = inputs.to(self.device), labels.to(self.device)

            # If on training phase, enable gradients
            with torch.set_grad_enabled(False):

                # In validation or testing
                self.batch_forward(inputs, labels, 
                                   centroids=self.memory['centroids'],
                                   phase=phase)
                self.total_logits = torch.cat((self.total_logits, self.logits))
                self.total_labels = torch.cat((self.total_labels, labels))
                self.total_paths = np.concatenate((self.total_paths, paths))

        probs, preds = F.softmax(self.total_logits.detach(), dim=1).max(dim=1)

        if openset:
            preds[probs < self.training_opt['open_threshold']] = -1
            self.openset_acc = mic_acc_cal(preds[self.total_labels == -1],
                                            self.total_labels[self.total_labels == -1])
            print('\n\nOpenset Accuracy: %.3f' % self.openset_acc)

        # Calculate the overall accuracy and F measurement
        self.eval_acc_mic_top1= mic_acc_cal(preds[self.total_labels != -1],
                                            self.total_labels[self.total_labels != -1])
        self.eval_f_measure = F_measure(preds, self.total_labels, openset=openset,
                                        theta=self.training_opt['open_threshold'])
        self.many_acc_top1, \
        self.median_acc_top1, \
        self.low_acc_top1 = shot_acc(preds[self.total_labels != -1],
                                     self.total_labels[self.total_labels != -1], 
                                     self.data['train'])
        # Top-1 accuracy and additional string
        print_str = ['\n\n',
                     'Phase: %s' 
                     % (phase),
                     '\n\n',
                     'Evaluation_accuracy_micro_top1: %.3f' 
                     % (self.eval_acc_mic_top1),
                     '\n',
                     'Averaged F-measure: %.3f' 
                     % (self.eval_f_measure),
                     '\n',
                     'Many_shot_accuracy_top1: %.3f' 
                     % (self.many_acc_top1),
                     'Median_shot_accuracy_top1: %.3f' 
                     % (self.median_acc_top1),
                     'Low_shot_accuracy_top1: %.3f' 
                     % (self.low_acc_top1),
                     '\n']

        if phase == 'val':
            print_write(print_str, self.log_file)
        else:
            print(*print_str)
            
    def centroids_cal(self, data):

        centroids = torch.zeros(self.training_opt['num_classes'],
                                   self.training_opt['feature_dim']).cuda()

        print('Calculating centroids.')

        for model in self.networks.values():
            model.eval()

        # Calculate initial centroids only on training data.
        with torch.set_grad_enabled(False):
            
            for inputs, labels, _ in tqdm(data):
                inputs, labels = inputs.to(self.device), labels.to(self.device)
                # Calculate Features of each training data
                self.batch_forward(inputs, feature_ext=True)
                # Add all calculated features to center tensor
                for i in range(len(labels)):
                    label = labels[i]
                    centroids[label] += self.features[i]

        # Average summed features with class count
        centroids /= torch.tensor(class_count(data)).float().unsqueeze(1).cuda()

        return centroids

    def load_model(self):
            
        model_dir = os.path.join(self.training_opt['log_dir'], 
                                 'final_model_checkpoint.pth')
        
        print('Validation on the best model.')
        print('Loading model from %s' % (model_dir))
        
        checkpoint = torch.load(model_dir)          
        model_state = checkpoint['state_dict_best']
        
        self.centroids = checkpoint['centroids'] if 'centroids' in checkpoint else None
        
        for key, model in self.networks.items():

            weights = model_state[key]
            weights = {k: weights[k] for k in weights if k in model.state_dict()}
            # model.load_state_dict(model_state[key])
            model.load_state_dict(weights)
        
    def save_model(self, epoch, best_epoch, best_model_weights, best_acc, centroids=None):
        
        model_states = {'epoch': epoch,
                'best_epoch': best_epoch,
                'state_dict_best': best_model_weights,
                'best_acc': best_acc,
                'centroids': centroids}

        model_dir = os.path.join(self.training_opt['log_dir'], 
                                 'final_model_checkpoint.pth')

        torch.save(model_states, model_dir)
            
    def output_logits(self, openset=False):
        filename = os.path.join(self.training_opt['log_dir'], 
                                'logits_%s'%('open' if openset else 'close'))
        print("Saving total logits to: %s.npz" % filename)
        np.savez(filename, 
                 logits=self.total_logits.detach().cpu().numpy(), 
                 labels=self.total_labels.detach().cpu().numpy(),
                 paths=self.total_paths)