def GetResnet101Features(): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") data_folder = 'C:/Users/paoca/Documents/UVA PHD/NLP/PROJECT/UnnecesaryDataFolder' # folder with data files saved by create_input_files.py data_name = 'coco_5_cap_per_img_5_min_word_freq' word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=5, shuffle=False, pin_memory=True) with torch.no_grad(): encoder = Encoder() encoder.fine_tune(False) emb_dim = 512 decoder_dim = 512 encoderVae_encoder = EncodeVAE_Encoder(embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map)) encoderVae_encoder.fine_tune(False) encoder.eval() encoderVae_encoder.eval() encoder = encoder.to(device) encoderVae_encoder = encoderVae_encoder.to(device) for i, (imgs, caps, caplens) in enumerate(train_loader): if i % 100 == 0: print(i) imgs = imgs.to(device) caps = caps.to(device) caplens = caplens.to(device) res = encoder(imgs) h = encoderVae_encoder(imgs, caps, caplens) pickle.dump( res[0].cpu().numpy(), open( "C:/Users/paoca/Documents/UVA PHD/NLP/PROJECT/UnnecesaryDataFolder/TrainResnet101Features/" + str(i) + ".p", "wb")) pickle.dump( h[0].cpu().numpy(), open( "C:/Users/paoca/Documents/UVA PHD/NLP/PROJECT/UnnecesaryDataFolder/TrainResnet101Features/VAE_" + str(i) + ".p", "wb"))
def main(imgurl): # Load word map (word2ix) with open('input_files/WORDMAP.json', 'r') as j: word_map = json.load(j) rev_word_map = {v: k for k, v in word_map.items()} # ix2word # Load model decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None decoder.load_state_dict( torch.load('output_files/BEST_checkpoint_decoder.pth.tar')) encoder.load_state_dict( torch.load('output_files/BEST_checkpoint_encoder.pth.tar')) decoder = decoder.to(device) decoder.eval() encoder = encoder.to(device) encoder.eval() # Encode, decode with attention and beam search seq, alphas = caption_image_beam_search(encoder, decoder, imgurl, word_map, beam_size=5) alphas = torch.FloatTensor(alphas) # Visualize caption and attention of best sequence # visualize_att(img, seq, alphas, rev_word_map, args.smooth) words = [rev_word_map[ind] for ind in seq] caption = ' '.join(words[1:-1]) visualize_att(imgurl, seq, alphas, rev_word_map)
def main(): global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') # Loading the wordmap file using dataname(flickr8k) with open(word_map_file, 'r') as j: word_map = json.load(j) if checkpoint is None: # if there is no checkpoint decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=embedded_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) # using the archi from models file decoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) # Adam optimizer encoder = Encoder() # using the archi from models file encoder.fine_tune(fine_tune_encoder) # finetune the encoder encoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None # Adam optimizer else: # load the checkpoint file to continue training checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) decoder = decoder.to(device) # converts tensors to CUDA variables if gpu is available encoder = encoder.to(device) # converts tensors to CUDA variables if gpu is available criterion = nn.CrossEntropyLoss().to(device) # Loss function normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], # normalizing the data std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Data loader for train set val_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Data loader for val set # Training Starts !!!!!!! for epoch in range(start_epoch, epochs): if epochs_since_improvement == 20: # Early stopping if the BLEU scores degrade for a long time break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) # learning rate decay to help the training process if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # learning rate decay to help the training process train(train_loader=train_loader, # Training using the encoder, decoder archi, input images, loss function and optimizers encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) recent_bleu4 = validate(val_loader=val_loader, # Validation after every epoch encoder=encoder, decoder=decoder, criterion=criterion) is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 # If no improvement print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,)) else: epochs_since_improvement = 0 save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, # Save checkpoint after every epoch decoder_optimizer, recent_bleu4, is_best)
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # Read word map (w2i) word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders (This page details the preprocessing or transformation we need to perform – # pixel values must be in the range [0,1] and we must then normalize the image by the mean and standard # deviation of the ImageNet images' RGB channels.) normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) initial_time = time.time() print("Initial time", initial_time) # Epochs for epoch in range(start_epoch, epochs): print("Starting epoch ", epoch) # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch, initial_time=initial_time) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)
def main(): """ Training and validation. """ # In Python, global keyword allows you to modify the variable outside of the current scope. # It is used to create a global variable and make changes to the variable in a local context. ''' The basic rules for global keyword in Python are: When we create a variable inside a function, it is local by default. When we define a variable outside of a function, it is global by default. You don't have to use global keyword. We use global keyword to read and write a global variable inside a function. Use of global keyword outside a function has no effect. ''' global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) ''' The filter() method constructs an iterator from elements of an iterable for which a function returns true. The filter() method takes two parameters: function - function that tests if elements of an iterable returns true or false If None, the function defaults to Identity function - which returns false if any elements are false iterable - iterable which is to be filtered, could be sets, lists, tuples, or containers of any iterators The filter() method returns an iterator that passed the function check for each element in the iterable. ''' decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # If there's no improvement in Bleu score for 20 epochs then stop training if epochs_since_improvement == 20: break # If there's no improvement in Bleu score for 8 epochs lower the lr if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map, rev_word_map # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) rev_word_map = {v: k for k, v in word_map.items()} # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) pretrained_embs, pretrained_embs_dim = load_embeddings( '/home/Iwamura/datasets/datasets/GloVe/glove.6B.300d.txt', word_map) assert pretrained_embs_dim == decoder.embed_dim decoder.load_pretrained_embeddings(pretrained_embs) decoder.fine_tune_embeddings(True) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder_opt = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_opt.fine_tune(fine_tune_encoder_opt) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None encoder_optimizer_opt = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder_opt.parameters()), lr=encoder_opt_lr) if fine_tune_encoder_opt else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder_opt = checkpoint['encoder_opt'] encoder_optimizer_opt = checkpoint['encoder_optimizer_opt'] # if fine_tune_encoder is True and encoder_optimizer is None and encoder_optimizer_opt is None if fine_tune_encoder_opt is True and encoder_optimizer_opt is None: encoder_opt.fine_tune(fine_tune_encoder_opt) encoder_optimizer_opt = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder_opt.parameters()), lr=encoder_opt_lr) # Move to GPU, if available decoder = decoder.to(device) encoder_opt = encoder_opt.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize_opt = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize_opt])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'VAL', transform=transforms.Compose([normalize_opt])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 10: break if epoch > 0 and epoch % 4 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder_opt: adjust_learning_rate(encoder_optimizer_opt, 0.8) # One epoch's training train(train_loader=train_loader, encoder_opt=encoder_opt, decoder=decoder, criterion=criterion, encoder_optimizer_opt=encoder_optimizer_opt, decoder_optimizer=decoder_optimizer, epoch=epoch) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder_opt=encoder_opt, decoder=decoder, criterion=criterion) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder_opt, decoder, encoder_optimizer_opt, decoder_optimizer, recent_bleu4, is_best)
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # Read word map #word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') #with open(word_map_file, 'r') as j: # word_map = json.load(j) with open("/content/image_captioning/Image-Captioning-Codebase/vocab.pkl", "rb") as f: vocab = pickle.load(f) word_map = vocab.word2idx # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) transform_train = transforms.Compose( [ # smaller edge of image resized to 256 transforms.Resize( (224, 224)), # get 224x224 crop from random location transforms.RandomHorizontalFlip( ), # horizontally flip image with probability=0.5 transforms.ToTensor(), # convert the PIL Image to a tensor transforms.Normalize( (0.485, 0.456, 0.406), # normalize image for pre-trained model (0.229, 0.224, 0.225)) ]) """ train_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) """ train_loader = torch.utils.data.DataLoader( Flickr8kDataset(annot_path="/content/", img_path="/content/Flicker8k_Dataset/", \ split="train", transform=transform_train), \ batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader( Flickr8kDataset(annot_path="/content/", img_path="/content/Flicker8k_Dataset/", \ split="dev", transform=transform_train), \ batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map, glove_path, emb_dim, rev_word_map # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) rev_word_map = {v: k for k, v in word_map.items()} #get glove vectors = bcolz.open(f'{glove_path}/6B.300.dat')[:] words = pickle.load(open(f'{glove_path}/6B.300_words.pkl', 'rb')) word2idx = pickle.load(open(f'{glove_path}/6B.300_idx.pkl', 'rb')) glove = {w: vectors[word2idx[w]] for w in words} matrix_len = len(word_map) weights_matrix = np.zeros((matrix_len, emb_dim)) words_found = 0 for i, word in enumerate(word_map.keys()): try: weights_matrix[i] = glove[word] words_found += 1 except KeyError: weights_matrix[i] = np.random.normal(scale=0.6, size=(emb_dim, )) # weights_matrix = np.float64(weights_matrix) # weights_matrix = torch.from_numpy(weights_matrix) # pretrained_embedding = weights_matrix.to(dtype=torch.float) # print(pretrained_embedding.dtype) # if device.type == 'cpu' : # pretrained_embedding = torch.FloatTensor(weights_matrix) # else: # pretrained_embedding = torch.cuda.FloatTensor(weights_matrix) pretrained_embedding = torch.FloatTensor(weights_matrix) # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) decoder.load_pretrained_embeddings( pretrained_embedding ) # pretrained_embeddings should be of dimensions (len(word_map), emb_dim) decoder.fine_tune_embeddings(True) # or False decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training # train(train_loader=train_loader, # encoder=encoder, # decoder=decoder, # criterion=criterion, # encoder_optimizer=encoder_optimizer, # decoder_optimizer=decoder_optimizer, # epoch=epoch) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)
def main(): """ Training and validation. """ global best, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder if checkpoint is None: decoder = DecoderWithAttention( attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, # vocab_size=len(word_map), vocab_size=2, # X, Y coordinates and use it for regression dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) decoderMulti = network.__dict__['MultiTask'](output_size) decoderMulti_optimizer = torch.optim.Adam( decoderMulti.parameters(), lr=decoderMulti_lr, weight_decay=decoderMulti_lr_weight_decay) encoder = Encoder() encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best = checkpoint['b4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] decoderMulti = checkpoint['decoderMulti'] decoderMulti_optimizer = checkpoint['decoderMulti_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available if multiGpu: decoder = torch.nn.DataParallel(decoder).to(device) encoder = torch.nn.DataParallel(encoder).to(device) decoderMulti = torch.nn.DataParallel(decoderMulti).to(device) else: decoder = decoder.to(device) encoder = encoder.to(device) decoderMulti = decoderMulti.to(device) # Loss function criterionBinary = nn.BCELoss().to(device) criterionMse = nn.MSELoss().to(device) criterion = [criterionBinary, criterionMse] # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader_p = torch.utils.data.DataLoader(fiberDataset_COCO( data_folder_p, jason_file_p, image_folder, offset_folder, transforms.Compose([transforms.ToTensor(), normalize]), True), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True, drop_last=True) val_loader_p = torch.utils.data.DataLoader(fiberDataset_COCO( data_folder_val, jason_file_val, image_folder_val, offset_folder_val, transforms.Compose([transforms.ToTensor(), normalize]), True), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True, drop_last=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) train(train_loader=train_loader_p, encoder=encoder, decoder=decoder, decoderMulti=decoderMulti, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, decoderMulti_optimizer=decoderMulti_optimizer, epoch=epoch) recent = validate(val_loader=val_loader_p, encoder=encoder, decoder=decoder, decoderMulti=decoderMulti, criterion=criterion) # # # Check if there was an improvement is_best = recent < best best = min(recent, best) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 epochs_since_improvement = 0 # Save checkpoint save_checkpoint(save_weights_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, decoderMulti, decoderMulti_optimizer, best, is_best)
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map, lowest_loss_val # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Train N models and save them to each directory for n in range(1, args.num_models + 1): # Directory where the model will be saved model_out = os.path.join(args.model, "model_{}".format(n)) try: os.mkdir(model_out) except: pass # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 20 consecutive epochs # and terminate training after 50 consecutive epochs if epochs_since_improvement == 50: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # One epoch's validation recent_loss, recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # Check if there was an improvement using bleu is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) # Check if there was an improvement using loss #is_best = recent_loss < lowest_loss_val #lowest_loss_val = min(recent_loss, lowest_loss_val) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 #save_checkpoint_with_dir(model_out, data_name, epoch, epochs_since_improvement, # encoder, decoder, encoder_optimizer, # decoder_optimizer, lowest_loss_val, is_best) save_checkpoint_with_dir(model_out, data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best) # Delete encoder&decoder objects and reset memory del decoder del encoder torch.cuda.empty_cache() # Reset epochs since improvement to 0 for a new round of training epochs_since_improvement = 0 best_bleu4, start_epoch = 0, 0 check_point = None fine_tune_encoder = False
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Load Pretrained Embeddings and compare to Wordmap if True, otherwise reload the pickle file if reload_pretrained_embed == True: embeddings_index = dict() fid = open(pretrained_embeddings_file, encoding="utf8") for line in fid: values = line.split() word = values[0] coefs = np.asarray(values[1:], dtype='float32') embeddings_index[word] = coefs fid.close() pretrained_embeddings = torch.zeros((len(word_map) + 1, emb_dim)) for word, idx in word_map.items(): embed_vector = embeddings_index.get(word) if embed_vector is not None: # words not found in embedding index will be all-zeros. pretrained_embeddings[idx] = torch.from_numpy(embed_vector) else: pretrained_embeddings[idx] = torch.from_numpy( np.random.uniform(-1, 1, emb_dim)) # print(pretrained_embeddings[0:2, :]) # fid = open("embedding_matrix.pkl","wb") # dump(pretrained_embeddings, fid) # fid.close() # else: # pretrained_embeddings = open(pretrained_embedding_matrix, "wb") # print('Successfully Loaded Pretrained Embeddings Pickle') # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) # decoder.load_pretrained_embeddings(pretrained_embeddings) # pretrained_embeddings should be of dimensions (len(word_map), emb_dim) # decoder.fine_tune_embeddings(True) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=args['decoder_lr']) # encoder = vgg_face_dag() #VGG Face encoder = Encoder() #OG Encoder # encoder.cuda() # print(summary(encoder, (3, 224, 224))) # print('ENCODER SUMMARY') encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=args['encoder_lr']) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=args['encoder_lr']) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) #OG figures # normalize = transforms.Normalize(mean= [129.186279296875, 104.76238250732422, 93.59396362304688], #VGG Face figures # std= [1, 1, 1]) train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=args['batch_size'], shuffle=True, num_workers=workers, pin_memory=True) # print('validation_loader') val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=args['batch_size'], shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, args['epochs']): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 15 if epochs_since_improvement == 15: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # One epoch's validation # print('validation_loader_2') recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion, epoch=epoch) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 tensorboard_writer.add_scalar('BLEU-4/epoch', recent_bleu4, epoch) # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best) PATH = './cifar_net.pth' tensorboard_writer.close() print('Task ID number is: {}'.format(task.id))
def fit(t_params, checkpoint=None, m_params=None): # info data_name = t_params['data_name'] imgs_path = t_params['imgs_path'] df_path = t_params['df_path'] vocab = t_params['vocab'] start_epoch = 0 epochs_since_improvement = 0 best_bleu4 = 0 epochs = t_params['epochs'] batch_size = t_params['batch_size'] workers = t_params['workers'] encoder_lr = t_params['encoder_lr'] decoder_lr = t_params['decoder_lr'] fine_tune_encoder = t_params['fine_tune_encoder'] # init / load checkpoint if checkpoint is None: # getting hyperparameters attention_dim = m_params['attention_dim'] embed_dim = m_params['embed_dim'] decoder_dim = m_params['decoder_dim'] encoder_dim = m_params['encoder_dim'] dropout = m_params['dropout'] decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=embed_dim, decoder_dim=decoder_dim, encoder_dim=encoder_dim, vocab_size=len(vocab), dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None # load checkpoint else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # move to gpu, if available decoder = decoder.to(device) encoder = encoder.to(device) # loss function criterion = nn.CrossEntropyLoss().to(device) # dataloaders transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) print('Loading Data') train_loader, val_loader = get_loaders(batch_size, imgs_path, df_path, transform, vocab, workers) print('_' * 50) print('-' * 20, 'Fitting', '-' * 20) for epoch in range(start_epoch, epochs): # decay lr is there is no improvement for 8 consecutive epochs and terminate after 20 if epochs_since_improvement == 20: print('No improvement for 20 consecutive epochs, terminating...') break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) print('_' * 50) print('-' * 20, 'Training', '-' * 20) # one epoch of training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # one epoch of validation print('-' * 20, 'Validation', '-' * 20) recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # check for improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print( f'\nEpochs since last improvement: {epochs_since_improvement,}' ) else: # reset epochs_since_improvement = 0 save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint if use_sam: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout, use_glove=use_glove, word_map=word_map) base_optimizer = torch.optim.SGD decoder_optimizer = SAM(filter(lambda p: p.requires_grad, decoder.parameters()), base_optimizer, lr=decoder_lr, momentum=0.9) checkpoint = torch.load(checkpoint) encoder = checkpoint['encoder'] encoder_optimizer = None print("Loading best encoder but random decoder and using SAM...") elif checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout, use_glove=use_glove, word_map=word_map) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 print(f"Continuing training from epoch {start_epoch}...") epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] if use_sam: lr = checkpoint['decoder_optimizer'].param_groups[0]['lr'] base_optimizer = torch.optim.SGD decoder_optimizer = SAM(filter(lambda p: p.requires_grad, decoder.parameters()), base_optimizer, lr=lr, momentum=0.9) else: decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] if use_sam and fine_tune_encoder is True: lr = checkpoint['encoder_optimizer'].param_groups[0]['lr'] base_optimizer = torch.optim.SGD encoder_optimizer = SAM(filter(lambda p: p.requires_grad, encoder.parameters()), base_optimizer, lr=lr, momentum=0.9) else: encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) if use_sam: base_optimizer = torch.optim.SGD encoder_optimizer = SAM(filter(lambda p: p.requires_grad, encoder.parameters()), base_optimizer, lr=encoder_lr, momentum=0.9) else: encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # initialize dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CocoCaptionDataset( data_folder, data_name, 'TRAIN', transforms=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CocoCaptionDataset( data_folder, data_name, 'VAL', transforms=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) print(f"Train dataloader len: {len(train_loader)}") print(f"Val dataloader len: {len(val_loader)}") # set up tensorbaord train_writer = SummaryWriter( os.path.join(log_directory, f"{log_name}/train")) val_writer = SummaryWriter(os.path.join(log_directory, f"{log_name}/val")) # Epochs for epoch in tqdm(range(start_epoch, epochs)): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch, train_writer=train_writer) # One epoch's validation recent_bleu4, val_loss, val_top5_acc = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) val_writer.add_scalar('Epoch loss', val_loss, epoch + 1) val_writer.add_scalar('Epoch top-5 accuracy', val_top5_acc, epoch + 1) val_writer.add_scalar('BLEU-4', recent_bleu4, epoch + 1) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint checkpoint_name = data_name if use_glove: checkpoint_name = f"glove_{checkpoint_name}" if use_sam: checkpoint_name = f"sam_{checkpoint_name}" save_checkpoint(checkpoint_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best, checkpoint_path)
def main(): """ Describe main process including train and validation. """ global start_epoch, checkpoint, fine_tune_encoder, best_bleu4, epochs_since_improvement, word_map # Read word map word_map_path = os.path.join(data_folder, 'WORDMAP_' + dataset_name + ".json") with open(word_map_path, 'r') as j: word_map = json.load(j) # Set checkpoint or read from checkpoint if checkpoint is None: # No pretrained model, set model from beginning decoder = Decoder(embed_dim=embed_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout_rate) decoder_param = filter(lambda p: p.requires_grad, decoder.parameters()) for param in decoder_param: tensor0 = param.data dist.all_reduce(tensor0, op=dist.reduce_op.SUM) param.data = tensor0 / np.sqrt(np.float(num_nodes)) decoder_optimizer = optim.Adam(params=decoder_param, lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_param = filter(lambda p: p.requires_grad, encoder.parameters()) if fine_tune_encoder: for param in encoder_param: tensor0 = param.data dist.all_reduce(tensor0, op=dist.reduce_op.SUM) param.data = tensor0 / np.sqrt(np.float(num_nodes)) encoder_optimizer = optim.Adam( params=encoder_param, lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint["epoch"] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] #decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] #encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) decoder = decoder.to(device) encoder = encoder.to(device) criterion = nn.CrossEntropyLoss() # Data loader normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_set = CaptionDataset(data_folder=h5data_folder, data_name=dataset_name, split="TRAIN", transform=transforms.Compose([normalize])) val_set = CaptionDataset(data_folder=h5data_folder, data_name=dataset_name, split="VAL", transform=transforms.Compose([normalize])) train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) total_start_time = datetime.datetime.now() print("Start the 1st epoch at: ", total_start_time) # Epoch for epoch in range(start_epoch, num_epochs): # Pre-check by epochs_since_improvement if epochs_since_improvement == 20: # If there are 20 epochs that no improvements are achieved break if epochs_since_improvement % 8 == 0 and epochs_since_improvement > 0: adjust_learning_rate(decoder_optimizer) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer) # For every batch batch_time = AverageMeter() # forward prop. + back prop. time data_time = AverageMeter() # data loading time losses = AverageMeter() # loss (per word decoded) top5accs = AverageMeter() # top5 accuracy decoder.train() encoder.train() start = time.time() start_time = datetime.datetime.now( ) # Initialize start time for this epoch # TRAIN for j, (images, captions, caplens) in enumerate(train_loader): if fine_tune_encoder and (epoch - start_epoch > 0 or j > 10): for group in encoder_optimizer.param_groups: for p in group['params']: state = encoder_optimizer.state[p] if (state['step'] >= 1024): state['step'] = 1000 if (epoch - start_epoch > 0 or j > 10): for group in decoder_optimizer.param_groups: for p in group['params']: state = decoder_optimizer.state[p] if (state['step'] >= 1024): state['step'] = 1000 data_time.update(time.time() - start) images = images.to(device) captions = captions.to(device) caplens = caplens.to(device) # Forward enc_images = encoder(images) predictions, enc_captions, dec_lengths, sort_ind = decoder( enc_images, captions, caplens) # Define target as original captions excluding <start> target = enc_captions[:, 1:] # (batch_size, max_caption_length-1) target, _ = pack_padded_sequence( target, dec_lengths, batch_first=True ) # Delete all paddings and concat all other parts predictions, _ = pack_padded_sequence( predictions, dec_lengths, batch_first=True) # (batch_size, sum(dec_lengths)) loss = criterion(predictions, target) # Backward decoder_optimizer.zero_grad() if encoder_optimizer is not None: encoder_optimizer.zero_grad() loss.backward() ## Clip gradients if grad_clip is not None: clip_gradient(decoder_optimizer, grad_clip) if encoder_optimizer is not None: clip_gradient(encoder_optimizer, grad_clip) ## Update decoder_optimizer.step() if encoder_optimizer is not None: encoder_optimizer.step() # Update metrics (AverageMeter) acc_top5 = compute_accuracy(predictions, target, k=5) top5accs.update(acc_top5, sum(dec_lengths)) losses.update(loss.item(), sum(dec_lengths)) batch_time.update(time.time() - start) # Print current status if (j + 1) % print_freq == 0: print( 'Epoch: [{0}][{1}/{2}]\t' 'Current Batch Time: {batch_time.val:.3f} (Average: {batch_time.avg:.3f})\t' 'Current Data Load Time: {data_time.val:.3f} (Average: {data_time.avg:.3f})\t' 'Current Loss: {loss.val:.4f} (Average: {loss.avg:.4f})\t' 'Current Top-5 Accuracy: {top5.val:.3f} (Average: {top5.avg:.3f})' .format(epoch + 1, j + 1, len(train_loader), batch_time=batch_time, data_time=data_time, loss=losses, top5=top5accs)) now_time = datetime.datetime.now() print("Epoch Training Time: ", now_time - start_time) print("Total Time: ", now_time - total_start_time) start = time.time() # VALIDATION decoder.eval() encoder.eval() batch_time = AverageMeter() # forward prop. + back prop. time losses = AverageMeter() # loss (per word decoded) top5accs = AverageMeter() # top5 accuracy references = list( ) # references (true captions) for calculating BLEU-4 score hypotheses = list() # hypotheses (predictions) start_time = datetime.datetime.now() for j, (images, captions, caplens, all_caps) in enumerate(val_loader): start = time.time() images = images.to(device) captions = captions.to(device) caplens = caplens.to(device) # Forward enc_images = encoder(images) predictions, enc_captions, dec_lengths, sort_ind = decoder( enc_images, captions, caplens) # Define target as original captions excluding <start> predictions_copy = predictions.clone() target = enc_captions[:, 1:] # (batch_size, max_caption_length-1) target, _ = pack_padded_sequence( target, dec_lengths, batch_first=True ) # Delete all paddings and concat all other parts predictions, _ = pack_padded_sequence( predictions, dec_lengths, batch_first=True) # (batch_size, sum(dec_lengths)) loss = criterion(predictions, target) # Update metrics (AverageMeter) acc_top5 = compute_accuracy(predictions, target, k=5) top5accs.update(acc_top5, sum(dec_lengths)) losses.update(loss.item(), sum(dec_lengths)) batch_time.update(time.time() - start) # Print current status if (j + 1) % print_freq == 0: print( 'Epoch: [{0}][{1}/{2}]\t' 'Batch Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Data Load Time {data_time.val:.3f} ({data_time.avg:.3f})\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' 'Top-5 Accuracy {top5.val:.3f} ({top5.avg:.3f})'.format( epoch + 1, j, len(val_loader), batch_time=batch_time, data_time=data_time, loss=losses, top5=top5accs)) now_time = datetime.datetime.now() print("Epoch Validation Time: ", now_time - start_time) print("Total Time: ", now_time - total_start_time) ## Store references (true captions), and hypothesis (prediction) for each image ## If for n images, we have n hypotheses, and references a, b, c... for each image, we need - ## references = [[ref1a, ref1b, ref1c], [ref2a, ref2b], ...], hypotheses = [hyp1, hyp2, ...] # references all_caps = all_caps[sort_ind] for k in range(all_caps.shape[0]): img_caps = all_caps[k].tolist() img_captions = list( map( lambda c: [ w for w in c if w not in {word_map["<start>"], word_map["<pad>"]} ], img_caps)) references.append(img_captions) # hypotheses _, preds = torch.max(predictions_copy, dim=2) preds = preds.tolist() temp_preds = list() for i, p in enumerate(preds): temp_preds.append(preds[i][:dec_lengths[i]]) # remove pads preds = temp_preds hypotheses.extend(preds) assert len(references) == len(hypotheses) ## Compute BLEU-4 Scores #recent_bleu4 = corpus_bleu(references, hypotheses, emulate_multibleu=True) recent_bleu4 = corpus_bleu(references, hypotheses) print( '\n * LOSS - {loss.avg:.3f}, TOP-5 ACCURACY - {top5.avg:.3f}, BLEU-4 - {bleu}\n' .format(loss=losses, top5=top5accs, bleu=recent_bleu4)) # CHECK IMPROVEMENT is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement)) else: epochs_since_improvement = 0 # SAVE CHECKPOINT save_checkpoint(dataset_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best) print("Epoch {}, cost time: {}\n".format(epoch + 1, now_time - total_start_time))
def main(): print('Training parameters Initialized') training_parameters = TrainingParameters( start_epoch = 0, epochs = 120, # number of epochs to train for epochs_since_improvement = 0, # Epochs since improvement in BLEU score batch_size = 32, workers = 1, # for data-loading; right now, only 1 works with h5py fine_tune_encoder = True, # fine-tune encoder encoder_lr = 1e-4, # learning rate for encoder, if fine-tuning is used decoder_lr = 4e-4, # learning rate for decoder grad_clip = 5.0, # clip gradients at an absolute value of alpha_c = 1.0, # regularization parameter for 'doubly stochastic attention' best_bleu4 = 0.0, # BLEU-4 score right now print_freq = 100, # print training/validation stats every __ batches checkpoint = './Result/BEST_checkpoint_flickr8k_5_captions_per_image_5_minimum_word_frequency.pth.tar' # path to checkpoint, None if none # checkpoint = None ) print('Loading Word-Map') word_map_file = os.path.join(data_folder,'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) print('Creating Model') if training_parameters.checkpoint is None: encoder = Encoder() encoder.fine_tune(training_parameters.fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter(lambda p : p.requires_grad, encoder.parameters()), lr=training_parameters.encoder_lr) if training_parameters.fine_tune_encoder else None decoder = Decoder(attention_dimension = attention_dimension, embedding_dimension = embedding_dimension, hidden_dimension = hidden_dimension, vocab_size = len(word_map), device = device, dropout = dropout) decoder_optimizer = torch.optim.Adam(params=filter(lambda p : p.requires_grad, decoder.parameters()), lr=training_parameters.decoder_lr) else: checkpoint = torch.load(training_parameters.checkpoint) training_parameters.start_epoch = checkpoint['epoch'] + 1 training_parameters.epochs_since_improvement = checkpoint['epochs_since_improvement'] training_parameters.best_bleu4 = checkpoint['bleu4'] encoder = Encoder() encoder.load_state_dict(checkpoint['encoder_state_dict']) encoder_optimizer = checkpoint['encoder_optimizer'] decoder = Decoder(attention_dimension = attention_dimension, embedding_dimension = embedding_dimension, hidden_dimension = hidden_dimension, vocab_size = len(word_map), device = device, dropout = dropout) decoder.load_state_dict(checkpoint['decoder_state_dict']) decoder_optimizer = checkpoint['decoder_optimizer'] if training_parameters.fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(training_parameters.fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter(lambda p : p.requires_grad, encoder.parameters()), lr=training_parameters.encoder_lr) encoder.to(device) decoder.to(device) criterion = nn.CrossEntropyLoss().to(device) normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) print('Creating Data Loaders') train_dataloader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=training_parameters.batch_size, shuffle=True) validation_dataloader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'VALID', transform=transforms.Compose([normalize])), batch_size=training_parameters.batch_size, shuffle=True, pin_memory=True) for epoch in range(training_parameters.start_epoch, training_parameters.epochs): if training_parameters.epochs_since_improvement == 20: break if training_parameters.epochs_since_improvement > 0 and training_parameters.epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if training_parameters.fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) train(train_loader = train_dataloader, encoder = encoder, decoder = decoder, criterion = criterion, encoder_optimizer = encoder_optimizer, decoder_optimizer = decoder_optimizer, epoch = epoch, device = device, training_parameters = training_parameters) recent_bleu4_score = validate(validation_loader = validation_dataloader, encoder = encoder, decoder = decoder, criterion = criterion, word_map = word_map, device = device, training_parameters = training_parameters) is_best_score = recent_bleu4_score > training_parameters.best_bleu4 training_parameters.best_bleu4 = max(recent_bleu4_score, training_parameters.best_bleu4) if not is_best_score: training_parameters.epochs_since_improvement += 1 print('\nEpochs since last improvement : %d\n' % (training_parameters.epochs_since_improvement)) else: training_parameters.epochs_since_improvement = 0 save_checkpoint(data_name, epoch, training_parameters.epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4_score, is_best_score)
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # Read word map word_map_file = os.path.join(data_folder, "WORDMAP_" + data_name + ".json") with open(word_map_file, "r") as j: word_map = json.load(j) # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention( attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout, ) decoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr, ) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = (torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr, ) if fine_tune_encoder else None) else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint["epoch"] + 1 epochs_since_improvement = checkpoint["epochs_since_improvement"] best_bleu4 = checkpoint["bleu-4"] decoder = checkpoint["decoder"] decoder_optimizer = checkpoint["decoder_optimizer"] encoder = checkpoint["encoder"] encoder_optimizer = checkpoint["encoder_optimizer"] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr, ) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, "TRAIN", transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True, ) val_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, "VAL", transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True, ) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train( train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch, ) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint( data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best, )
def main(): """ Training and validation. """ global epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map, role_map #print('reading word map') # Read word map word_map_file = os.path.join(data_folder, 'token2id' + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) #print('reading role map') role_map_file = os.path.join(data_folder, 'roles2id' + '.json') with open(role_map_file, 'r') as j: role_map = json.load(j) #print('initializing..') # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), role_vocab_size=len(role_map), role_embed_dim=role_dim, dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] #best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) #print('creating encoder/decoder..') #encoder = nn.DataParallel(encoder,device_ids=[0,1]) #decoder = nn.DataParallel(decoder,device_ids=[0,1]) # Loss function criterion = nn.CrossEntropyLoss().to(device) #print('creating dataloader..') # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(FrameDataset( data_folder, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # val_loader = torch.utils.data.DataLoader( # FrameDataset(data_folder, 'VAL', transform=transforms.Compose([normalize])), # batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # decay learning rate somehow # One epoch's training #print('start training') train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) print('start validation..')
def train(args): cfg_from_file(args.cfg) cfg.WORKERS = args.num_workers pprint.pprint(cfg) # set the seed manually np.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) # define outputer outputer_train = Outputer(args.output_dir, cfg.IMAGETEXT.PRINT_EVERY, cfg.IMAGETEXT.SAVE_EVERY) outputer_val = Outputer(args.output_dir, cfg.IMAGETEXT.PRINT_EVERY, cfg.IMAGETEXT.SAVE_EVERY) # define the dataset split_dir, bshuffle = 'train', True # Get data loader imsize = cfg.TREE.BASE_SIZE * (2**(cfg.TREE.BRANCH_NUM - 1)) train_transform = transforms.Compose([ transforms.Scale(int(imsize * 76 / 64)), transforms.RandomCrop(imsize), ]) val_transform = transforms.Compose([ transforms.Scale(int(imsize * 76 / 64)), transforms.CenterCrop(imsize), ]) if args.dataset == 'bird': train_dataset = ImageTextDataset(args.data_dir, split_dir, transform=train_transform, sample_type='train') val_dataset = ImageTextDataset(args.data_dir, 'val', transform=val_transform, sample_type='val') elif args.dataset == 'coco': train_dataset = CaptionDataset(args.data_dir, split_dir, transform=train_transform, sample_type='train', coco_data_json=args.coco_data_json) val_dataset = CaptionDataset(args.data_dir, 'val', transform=val_transform, sample_type='val', coco_data_json=args.coco_data_json) else: raise NotImplementedError train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=cfg.IMAGETEXT.BATCH_SIZE, shuffle=bshuffle, num_workers=int(cfg.WORKERS)) val_dataloader = torch.utils.data.DataLoader( val_dataset, batch_size=cfg.IMAGETEXT.BATCH_SIZE, shuffle=False, num_workers=1) # define the model and optimizer if args.raw_checkpoint != '': encoder, decoder = load_raw_checkpoint(args.raw_checkpoint) else: encoder = Encoder() decoder = DecoderWithAttention( attention_dim=cfg.IMAGETEXT.ATTENTION_DIM, embed_dim=cfg.IMAGETEXT.EMBED_DIM, decoder_dim=cfg.IMAGETEXT.DECODER_DIM, vocab_size=train_dataset.n_words) # load checkpoint if cfg.IMAGETEXT.CHECKPOINT != '': outputer_val.log("load model from: {}".format( cfg.IMAGETEXT.CHECKPOINT)) encoder, decoder = load_checkpoint(encoder, decoder, cfg.IMAGETEXT.CHECKPOINT) encoder.fine_tune(False) # to cuda encoder = encoder.cuda() decoder = decoder.cuda() loss_func = torch.nn.CrossEntropyLoss() if args.eval: # eval only outputer_val.log("only eval the model...") assert cfg.IMAGETEXT.CHECKPOINT != '' val_rtn_dict, outputer_val = validate_one_epoch( 0, val_dataloader, encoder, decoder, loss_func, outputer_val) outputer_val.log("\n[valid]: {}\n".format(dict2str(val_rtn_dict))) return # define optimizer optimizer_encoder = torch.optim.Adam(encoder.parameters(), lr=cfg.IMAGETEXT.ENCODER_LR) optimizer_decoder = torch.optim.Adam(decoder.parameters(), lr=cfg.IMAGETEXT.DECODER_LR) encoder_lr_scheduler = torch.optim.lr_scheduler.StepLR( optimizer_encoder, step_size=10, gamma=cfg.IMAGETEXT.LR_GAMMA) decoder_lr_scheduler = torch.optim.lr_scheduler.StepLR( optimizer_decoder, step_size=10, gamma=cfg.IMAGETEXT.LR_GAMMA) print("train the model...") for epoch_idx in range(cfg.IMAGETEXT.EPOCH): # val_rtn_dict, outputer_val = validate_one_epoch(epoch_idx, val_dataloader, encoder, # decoder, loss_func, outputer_val) # outputer_val.log("\n[valid] epoch: {}, {}".format(epoch_idx, dict2str(val_rtn_dict))) train_rtn_dict, outputer_train = train_one_epoch( epoch_idx, train_dataloader, encoder, decoder, optimizer_encoder, optimizer_decoder, loss_func, outputer_train) # adjust lr scheduler encoder_lr_scheduler.step() decoder_lr_scheduler.step() outputer_train.log("\n[train] epoch: {}, {}\n".format( epoch_idx, dict2str(train_rtn_dict))) val_rtn_dict, outputer_val = validate_one_epoch( epoch_idx, val_dataloader, encoder, decoder, loss_func, outputer_val) outputer_val.log("\n[valid] epoch: {}, {}\n".format( epoch_idx, dict2str(val_rtn_dict))) outputer_val.save_step({ "encoder": encoder.state_dict(), "decoder": decoder.state_dict() }) outputer_val.save({ "encoder": encoder.state_dict(), "decoder": decoder.state_dict() })
def main(): """ 训练和验证 """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # 读入词典 word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # 初始化/加载模型 if checkpoint is None: decoder = DecoderWithAttention(hidden_size=hidden_size, vocab_size=len(word_map), attention_dim=attention_dim, embed_size=emb_dim, dropout=dropout) decoder_optimizer = torch.optim.Adam(params=decoder.parameters(), lr=decoder_lr, betas=(0.8, 0.999)) encoder = Encoder(hidden_size=hidden_size, embed_size=emb_dim, dropout=dropout) # 是否微调 encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr, betas=(0.8, 0.999)) if fine_tune_encoder else None else: #载入checkpoint checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: # 如果此时要开始微调,需要定义优化器 encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr, betas=(0.8, 0.999)) # 移动到GPU decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) #ImageNet # pin_memory = True 驻留内存,不换进换出 train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): if epoch > 15: adjust_learning_rate(decoder_optimizer, epoch) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, epoch) # Early Stopping if the validation score does not imporive for 6 consecutive epochs if epochs_since_improvement == 6: break # 一个epoch的训练 train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch, vocab_size=len(word_map)) # 一个epoch的验证 recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # 检查是否有提升 is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # 保存模型 save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)
def main(): global checkpoint, start_epoch, fine_tune_encoder, word_map_structure, word_map_cell, epochs_since_improvement, hyper_loss, id2word_stucture, id2word_cell, teds, best_TED if checkpoint is None: decoder_structure = DecoderStuctureWithAttention( attention_dim=attention_dim, embed_dim=emb_dim_structure, decoder_dim=decoder_dim_structure, vocab=word_map_structure, dropout=dropout) decoder_cell = DecoderCellPerImageWithAttention( attention_dim=attention_dim, embed_dim=emb_dim_cell, decoder_dim=decoder_dim_cell, vocab_size=len(word_map_cell), dropout=0.2, decoder_structure_dim=decoder_dim_structure) decoder_structure_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder_structure.parameters()), lr=decoder_lr) decoder_cell_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder_structure.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] decoder_structure = checkpoint['decoder_structure'] decoder_structure_optimizer = checkpoint["decoder_structure_optimizer"] decoder_cell = checkpoint["decoder_cell"] decoder_cell_optimizer = checkpoint["decoder_cell_optimizer"] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] best_TED = checkpoint['ted_score'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder_structure = decoder_structure.to(device) decoder_cell = decoder_cell.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) print("loading train_loader and val_loader:") train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, 'train', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, 'val', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) print("Done train_loader and val_loader:") # train foreach epoch for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_structure, 0.8) adjust_learning_rate(decoder_cell, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) print("Starting train..............") train(train_loader=train_loader, encoder=encoder, decoder_structure=decoder_structure, decoder_cell=decoder_cell, criterion_structure=criterion, criterion_cell=criterion, encoder_optimizer=encoder_optimizer, decoder_structure_optimizer=decoder_structure_optimizer, decoder_cell_optimizer=decoder_cell_optimizer, epoch=epoch) print("Starting validation..............") recent_ted_score = val(val_loader=val_loader, encoder=encoder, decoder_structure=decoder_structure, decoder_cell=decoder_cell, criterion_structure=criterion, criterion_cell=criterion) # Check if there was an improvement is_best = recent_ted_score > best_TED best_TED = max(recent_ted_score, best_TED) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # save checkpoint save_checkpoint(epoch, epochs_since_improvement, encoder, decoder_structure, decoder_cell, encoder_optimizer, decoder_structure_optimizer, decoder_cell_optimizer, recent_ted_score, is_best)
def main(): """ Training and validation. """ global checkpoint, start_epoch, fine_tune_encoder # Initialize / load checkpoint if checkpoint is None: encoder = Encoder() print(encoder) encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=vocab_size, encoder_dim=encoder_dim, dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # customized dataloader MyDataset = DualLoadDatasets(imgsz, txt_folder, img_folder, bin_folder, split, Gfiltersz, Gblursigma) #drop the last batch since it is not divisible by batchsize train_loader = torch.utils.data.DataLoader(MyDataset, batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True, drop_last=True) # val_loader = torch.utils.data.DataLoader( # CaptionDataset(data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), # batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Save checkpoint epoch = 0 save_checkpoint(epoch, encoder, decoder, encoder_optimizer, decoder_optimizer) print('saving models to models/checkpoint') # Epochs for epoch in range(start_epoch, epochs): #print(image_transforms) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, transform=transform, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # Save checkpoint save_checkpoint(epoch, encoder, decoder, encoder_optimizer, decoder_optimizer) print('saving models to models/checkpoint')
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint if checkpoint is None: emb_dim=100 #remove if not usiong pretrained model decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) pretrained_embeddings = decoder.create_pretrained_embedding_matrix(word_map) decoder.load_pretrained_embeddings( pretrained_embeddings) # pretrained_embeddings should be of dimensions (len(word_map), emb_dim) decoder.fine_tune_embeddings(True) decoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # One epoch's validation recent_bleu4, val_loss_avg, val_accu_avg = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) #write to tensorboard writer.add_scalar('validation_loss', val_loss_avg, epoch) writer.add_scalar('validation_accuracy', val_accu_avg, epoch) writer.add_scalar('validation_bleu4', recent_bleu4, epoch) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,)) else: epochs_since_improvement = 0 # Save checkpoint print("Saving model to file",ckpt_name.format(epoch, bleu=recent_bleu4, loss=val_loss_avg, acc=val_accu_avg)) save_checkpoint(ckpt_name.format(epoch, bleu=recent_bleu4, loss=val_loss_avg, acc=val_accu_avg), epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best) #close tensorboard writer writer.close()
def main(): """ Training and validation. """ global best_bleu4, use_amp, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map #use_amp = True #print("Using amp for mized precision training") # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) # use mixed precision training using Nvidia Apex if use_amp: decoder, decoder_optimizer = amp.initialize( decoder, decoder_optimizer, opt_level="O2", keep_batchnorm_fp32=True, loss_scale="dynamic") encoder = encoder.to(device) if not encoder_optimizer: print("Encoder is not being optimized") elif use_amp: encoder, encoder_optimizer = amp.initialize( encoder, encoder_optimizer, opt_level="O2", keep_batchnorm_fp32=True, loss_scale="dynamic") # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,)) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint decoder = Fine_Tune_DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) val_decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None g_remover = RemoveGenderRegion() if checkpoint is not None: if is_cpu: checkpoint = torch.load(checkpoint, map_location='cpu') else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = load_parameter(checkpoint['decoder'], decoder) encoder = load_parameter(checkpoint['encoder'], encoder) # decoder_optimizer = checkpoint['decoder_optimizer'] decoder_optimizer = load_parameter(checkpoint['decoder_optimizer'], decoder_optimizer) #encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder_optimizer = load_parameter(checkpoint['encoder_optimizer'], encoder_optimizer) if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if freeze_decoder_lstm: decoder.freeze_LSTM(freeze=True) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) g_remover = g_remover.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # fix CUDA bug if not is_cpu: for state in decoder_optimizer.state.values(): for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.cuda() ''' train_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) ''' if not supervised_training: train_loader = torch.utils.data.DataLoader( Fine_Tune_CaptionDataset(data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=False, num_workers=workers, pin_memory=True) else: train_loader = torch.utils.data.DataLoader( Fine_Tune_CaptionDataset_With_Mask(data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=False, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader( CaptionDataset(data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) if not supervised_training: # One epoch's training self_guided_fine_tune_train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, g_remover=g_remover, epoch=epoch) else: supervised_guided_fine_tune_train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, g_remover=g_remover, epoch=epoch) # One epoch's validation val_decoder = load_parameter(decoder, val_decoder) val_decoder = val_decoder.to(device) recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=val_decoder, criterion=criterion) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,)) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best, checkpoint_savepath)
def main(): """ Training and validation. """ parser = argparse.ArgumentParser() parser.add_argument( "--data_folder", default='data/', type=str, help="folder with data files saved by create_input_files.py") parser.add_argument("--data_name", default='coco_5_cap_per_img_5_min_word_freq', type=str, help="base name shared by data files") parser.add_argument("--output_dir", default='saved_models/', type=str, help="path to save checkpoints") parser.add_argument("--checkpoint", default=None, type=str, help="path to checkpoint") parser.add_argument("--emb_dim", default=512, type=int, help="dimension of word embeddings") parser.add_argument("--attention_dim", default=512, type=int, help="dimension of attention linear layers") parser.add_argument("--decoder_dim", default=512, type=int, help="dimension of decoder RNN") parser.add_argument("--dropout", default=0.5, type=float, help="dimension of word embeddings") parser.add_argument("--start_epoch", default=0, type=int) parser.add_argument( "--epochs", default=120, type=int, help= "number of epochs to train for (if early stopping is not triggered)") parser.add_argument("--batch_size", default=128, type=int, help="batch size for training and testing") parser.add_argument("--workers", default=8, type=int, help="num of workers for data-loading") parser.add_argument("--encoder_lr", default=1e-4, type=float) parser.add_argument("--decoder_lr", default=5e-4, type=float) parser.add_argument("--grad_clip", default=5, type=float, help="clip gradients at an absolute value of") parser.add_argument( "--alpha_c", default=1, type=int, help= "regularization parameter for 'doubly stochastic attention', as in the paper" ) parser.add_argument( "--print_freq", default=100, type=int, help="print training/validation stats every __ batches") parser.add_argument("--fine_tune_encoder", action='store_true', help="Whether to finetune the encoder") args = parser.parse_args() if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") args.device = device best_bleu4 = 0 epochs_since_improvement = 0 # Read word map word_map_file = os.path.join(args.data_folder, 'WORDMAP_' + args.data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint if args.checkpoint is None: decoder = DecoderWithAttention(attention_dim=args.attention_dim, embed_dim=args.emb_dim, decoder_dim=args.decoder_dim, vocab_size=len(word_map), dropout=args.dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=args.decoder_lr) encoder = Encoder() encoder.fine_tune(args.fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=args.encoder_lr) if args.fine_tune_encoder else None else: checkpoint = torch.load(args.checkpoint) args.start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if args.fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(args.fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=args.encoder_lr) # Move to GPU, if available decoder = decoder.to(args.device) encoder = encoder.to(args.device) # Loss function criterion = nn.CrossEntropyLoss(ignore_index=0).to(args.device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CaptionDataset( args.data_folder, args.data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True) print(f'train dataset length {len(train_loader)}') val_loader = torch.utils.data.DataLoader(CaptionDataset( args.data_folder, args.data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True) print(f'val dataset length {len(val_loader)}') # Epochs for epoch in range(args.start_epoch, args.epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if args.fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch, args=args) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion, word_map=word_map, args=args) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(args.data_name, args.output_dir, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map best_bleu4 = config.best_bleu4 epochs_since_improvement = config.epochs_since_improvement checkpoint = config.checkpoint start_epoch = config.start_epoch fine_tune_encoder = config.fine_tune_encoder data_name = config.data_name checkpoint = config.checkpoint log_f = open(config.train_log_path, 'a+', encoding='utf-8') # Read word map word_map_file = os.path.join(config.data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint if checkpoint is None: print('no checkpoint, rebuild') log_f.write('\n\nno checkpoint, rebuild' + '\n') decoder = DecoderWithAttention(attention_dim=config.attention_dim, embed_dim=config.emb_dim, decoder_dim=config.decoder_dim, vocab_size=len(word_map), dropout=config.dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=config.decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=config.encoder_lr) if fine_tune_encoder else None else: print('checkpoint exist,continue.. \n{}'.format(checkpoint)) log_f.write('\n\ncheckpoint exist,continue.. \n{}'.format(checkpoint) + '\n') log_f.close() checkpoint = torch.load( checkpoint, map_location=config.device) # map_location=device for cpu start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: # check for fine tuning encoder.fine_tune( fine_tune_encoder) # change requires_grad for weights encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=config.encoder_lr) # Move to GPU, if available # decoder = decoder.to(config.device) # no GPU # encoder = encoder.to(config.device) # no GPU # Loss function # criterion = nn.CrossEntropyLoss().to(config.device) # no GPU criterion = nn.CrossEntropyLoss() # Custom batch dataloaders normalize = transforms.Normalize( mean=[0.485, 0.456, 0.406], # 这里是原ResNet的mean和std std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader( CaptionDataset(config.data_folder, data_name, 'TRAIN', transform=transforms.Compose( [normalize])), # CaptionDataset is in datasets.py batch_size=config.batch_size, shuffle=True, num_workers=config.workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( config.data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=config.batch_size, shuffle=True, num_workers=config.workers, pin_memory=True) # Epochs val_writer = SummaryWriter( log_dir=config.tensorboard_path + '/val/' + time.strftime('%m-%d_%H%M', time.localtime())) # for tensorboard for epoch in range(start_epoch, config.epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) # utils.py if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion, writer=val_writer, epoch=epoch) # Check if there was an improvement, check each epoch is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) log_f = open(config.train_log_path, 'a+', encoding='utf-8') if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) log_f.write("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, ) + '\n') else: epochs_since_improvement = 0 log_f.write('\n') log_f.close() # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best) val_writer.close()
def main(checkpoint, tienet): """ Training and validation. """ global best_bleu4, epochs_since_improvement, start_epoch, fine_tune_encoder, data_name, word_map if checkpoint: dest_dir = checkpoint checkpoint = os.path.join( dest_dir, 'checkpoint_mimiccxr_1_cap_per_img_5_min_word_freq.pth.tar' ) # path to checkpoint, None if none else: dest_dir = os.path.join( '/data/medg/misc/liuguanx/TieNet/models', datetime.datetime.now().strftime('%Y-%m-%d-%H%M%S-%f')) os.makedirs(dest_dir) checkpoint = None # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint if checkpoint is None: decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None if (tienet): jointlearner = JointLearning(num_global_att=num_global_att, s=s, decoder_dim=decoder_dim, label_size=label_size) jointlearner_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, jointlearner.parameters()), lr=jointlearning_lr) else: jointlearner = None jointlearner_optimizer = None else: checkpoint = torch.load(checkpoint) print('checkpoint loaded') start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['best_bleu'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] jointlearner = checkpoint['jointlearner'] jointlearner_optimizer = checkpoint['jointlearner_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available if torch.cuda.device_count() > 1: print('Using', torch.cuda.device_count(), 'GPUs') # decoder = nn.DataParallel(decoder) encoder = nn.DataParallel(encoder, device_ids=[1]) if tienet: jointlearner = nn.DataParallel(jointlearner, device_ids=[1]) decoder = decoder.to(device) encoder = encoder.to(device) if tienet: jointlearner = jointlearner.to(device) # Loss function criterion_R = nn.CrossEntropyLoss().to(device) criterion_C = nn.BCEWithLogitsLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if tienet: adjust_learning_rate(jointlearner_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, jointlearner=jointlearner, criterion_R=criterion_R, criterion_C=criterion_C, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, jointlearner_optimizer=jointlearner_optimizer, epoch=epoch, dest_dir=dest_dir, tienet=tienet) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, jointlearner=jointlearner, criterion_R=criterion_R, criterion_C=criterion_C, tienet=tienet) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, jointlearner, encoder_optimizer, decoder_optimizer, jointlearner_optimizer, recent_bleu4, best_bleu4, is_best, dest_dir)
def fit(t_params, checkpoint=None, m_params=None, logger=None): # info data_name = t_params['data_name'] imgs_path = t_params['imgs_path'] df_path = t_params['df_path'] vocab = t_params['vocab'] start_epoch = 0 epochs_since_improvement = 0 best_bleu4 = 0 epochs = t_params['epochs'] batch_size = t_params['batch_size'] workers = t_params['workers'] encoder_lr = t_params['encoder_lr'] decoder_lr = t_params['decoder_lr'] fine_tune_encoder = t_params['fine_tune_encoder'] # pretrained word embeddings pretrained_embeddings = t_params['pretrained_embeddings'] if pretrained_embeddings: fine_tune_embeddings = t_params['fine_tune_embeddings'] embeddings_matrix = m_params['embeddings_matrix'] # init / load checkpoint if checkpoint is None: # getting hyperparameters attention_dim = m_params['attention_dim'] embed_dim = m_params['embed_dim'] decoder_dim = m_params['decoder_dim'] encoder_dim = m_params['encoder_dim'] dropout = m_params['dropout'] decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=embed_dim, decoder_dim=decoder_dim, encoder_dim=encoder_dim, vocab_size=len(vocab), dropout=dropout) if pretrained_embeddings: decoder.load_pretrained_embeddings( torch.tensor(embeddings_matrix, dtype=torch.float32)) decoder.fine_tune_embeddings(fine_tune=fine_tune_embeddings) decoder_optimizer = torch.optim.RMSprop(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.RMSprop( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None # load checkpoint else: checkpoint = torch.load(checkpoint) print('Loaded Checkpoint!!') start_epoch = checkpoint['epoch'] + 1 print(f"Starting Epoch: {start_epoch}") epochs_since_improvement = checkpoint['epochs_since_imrovment'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['deocder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.RMSprop(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Schedulers decoder_scheduler = ReduceLROnPlateau(decoder_optimizer, patience=2, verbose=True) if fine_tune_encoder: encoder_scheduler = ReduceLROnPlateau(encoder_optimizer, patience=2, verbose=True) # move to gpu, if available decoder = decoder.to(device) encoder = encoder.to(device) # loss function criterion = nn.CrossEntropyLoss().to(device) # dataloaders transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) print('Loading Data') train_loader, val_loader = get_loaders(batch_size, imgs_path, df_path, transform, vocab, False, workers) print('_' * 50) print('-' * 20, 'Fitting', '-' * 20) for epoch in range(start_epoch, epochs): # if epochs_since_improvement > 0 and epochs_since_improvement % 2 == 0: # adjust_learning_rate(decoder_optimizer, 0.8) # if fine_tune_encoder: # adjust_learning_rate(encoder_optimizer, 0.8) print('_' * 50) print('-' * 20, 'Training', '-' * 20) # one epoch of training epoch_time = AverageMeter() start_time = time.time() train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch, logger=logger) epoch_time.update(time.time() - start_time) print(f"Epoch train time {epoch_time.val:.3f} (epoch_time.avg:.3f)") # one epoch of validation epoch_time = AverageMeter() start_time = time.time() print('-' * 20, 'Validation', '-' * 20) b1, b2, b3, recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion, vocab=vocab, epoch=epoch, logger=logger) epoch_time.update(time.time() - start_time) # tensorboard logger.add_scalar(f'b-1/valid', b1, epoch) logger.add_scalar(f'b-2/valid', b2, epoch) logger.add_scalar(f'b-3/valid', b3, epoch) logger.add_scalar(f'b-4/valid', recent_bleu4, epoch) # logger.add_scalar(f'Meteor/valid', m, epoch) print( f"Epoch validation time {epoch_time.val:.3f} (epoch_time.avg:.3f)") # check for improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print( f'\nEpochs since last improvement: {epochs_since_improvement,}' ) else: # reset epochs_since_improvement = 0 # stop training if no improvement for 5 epochs if epochs_since_improvement == 5: print('No improvement for 5 consecutive epochs, terminating...') break # learning rate schedular decoder_scheduler.step(recent_bleu4) if fine_tune_encoder: encoder_scheduler.step(recent_bleu4) save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)
encoder_lr = 5e-4 # learning rate for encoder if fine-tuning decoder_lr = 5e-4 # learning rate for decoder grad_clip = 5. # clip gradients at an absolute value of alpha_c = 1. # regularization parameter for 'doubly stochastic attention', as in the paper fine_tune_encoder = True # fine-tune encoder? decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(vocab), dropout=dropout) decoder.fine_tune_embeddings(True) decoder_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) encoder = Encoder() encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None decoder_sched = torch.optim.lr_scheduler.CosineAnnealingLR(decoder_optimizer, 5, eta_min=1e-5, last_epoch=-1) encoder_sched = torch.optim.lr_scheduler.CosineAnnealingLR(encoder_optimizer, 5, eta_min=1e-5, last_epoch=-1) encoder = encoder.cuda() decoder = decoder.cuda()
def main(): """ Training and validation. """ global best_bleu4, epochs_since_improvement, checkpoint, start_epoch, fine_tune_encoder, data_name, word_map # Read word map word_map_file = os.path.join(data_folder, 'WORDMAP_' + data_name + '.json') with open(word_map_file, 'r') as j: word_map = json.load(j) # Initialize / load checkpoint if checkpoint is None: # resnet encoder = Encoder(model_name="resnet") encoder_dim = 2048 # squeezenet # encoder = Encoder(model_name="squeezenet") # encoder_dim = 1000 # vgg # encoder = Encoder(model_name="vgg") # encoder_dim = 512 # # mobileNet # encoder = Encoder(model_name="mobileNet") # encoder_dim = 1024 encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam( params=filter(lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) if fine_tune_encoder else None decoder = DecoderWithAttention(attention_dim=attention_dim, embed_dim=emb_dim, decoder_dim=decoder_dim, vocab_size=len(word_map), dropout=dropout, encoder_dim=encoder_dim) decoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, decoder.parameters()), lr=decoder_lr) else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_bleu4 = checkpoint['bleu-4'] decoder = checkpoint['decoder'] decoder_optimizer = checkpoint['decoder_optimizer'] encoder = checkpoint['encoder'] encoder_optimizer = checkpoint['encoder_optimizer'] if fine_tune_encoder is True and encoder_optimizer is None: encoder.fine_tune(fine_tune_encoder) encoder_optimizer = torch.optim.Adam(params=filter( lambda p: p.requires_grad, encoder.parameters()), lr=encoder_lr) # Move to GPU, if available decoder = decoder.to(device) encoder = encoder.to(device) # Loss function criterion = nn.CrossEntropyLoss().to(device) # Custom dataloaders normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'TRAIN', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) val_loader = torch.utils.data.DataLoader(CaptionDataset( data_folder, data_name, 'VAL', transform=transforms.Compose([normalize])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20 if epochs_since_improvement == 20: break if epochs_since_improvement > 0 and epochs_since_improvement % 2 == 0: adjust_learning_rate(decoder_optimizer, 0.8) if fine_tune_encoder: adjust_learning_rate(encoder_optimizer, 0.8) # start fine-tuning after bleu4 reaches 23, so break this loop if best_bleu4 >= 23: break count_parameters(encoder) count_parameters(decoder) # One epoch's training train(train_loader=train_loader, encoder=encoder, decoder=decoder, criterion=criterion, encoder_optimizer=encoder_optimizer, decoder_optimizer=decoder_optimizer, epoch=epoch) # One epoch's validation recent_bleu4 = validate(val_loader=val_loader, encoder=encoder, decoder=decoder, criterion=criterion) # Check if there was an improvement is_best = recent_bleu4 > best_bleu4 best_bleu4 = max(recent_bleu4, best_bleu4) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement, )) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, recent_bleu4, is_best)