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train_TRANSFORMER_ENC.py
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train_TRANSFORMER_ENC.py
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import torch
import wandb
from data.vocaset import *
from utils import *
from transformers import *
import tqdm
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
hyper = {
'LANDMARK_DIM' : 68,
'INPUT_DIM' : 68*3,
'HID_DIM' : 64,
'BATCH_SIZE': 1,
'EPOCHS': 5000,
'NUM_LAYERS': 2,
'LR': 3e-4,
'SERVER':'Yoda'
}
def model_pipeline():
with wandb.init(project="Lip-Reading-3D", config=hyper, mode="disabled"):
#access all HPs through wandb.config, so logging matches executing
config = wandb.config
#make the model, data and optimization problem
model, ctc_loss, optimizer,trainloader, valloader, vocabulary = create(config)
#train the model
mean_loss = train(model, ctc_loss, optimizer,trainloader, vocabulary,config,valloader )
#test the model
#print("Accuracy test: ",test(model, valloader, vocabulary))
return model
def create(config):
#get dataloader
#trainset = vocadataset("train", landmark=True)
trainset = vocadataset("train", landmark=True)
#valset = vocadataset("val", landmark=True)
valset = vocadataset("val", landmark=True)
trainloader = DataLoader(trainset, batch_size=config.BATCH_SIZE, collate_fn=collate_fn, num_workers=8, shuffle=True, pin_memory=True)
valloader = DataLoader(valset, batch_size=config.BATCH_SIZE, collate_fn=collate_fn, num_workers=8, pin_memory=True)
#define the vocabulary
vocabulary = create_vocabulary(blank='@')
# define the models
model = Transformer_Encoder(len(vocabulary)).to(device)
# Define the CTC loss function
ctc_loss = nn.CTCLoss()
# Define the optimizer
optimizer = optim.Adam(model.parameters(), lr=config.LR)
return model, ctc_loss, optimizer,trainloader, valloader, vocabulary
# Function to train a model.
def train(model, ctc_loss, optimizer,trainloader, vocabulary, config,valloader, modeltitle= "_AV"):
#telling wand to watch
#if wandb.run is not None:
wandb.watch(model, optimizer, log="all", log_freq=320)
model.train()
#model.load_state_dict(torch.load("/home/hsilva/lipreading/models/model_AV_500_4.pt"))
# Training loop
for epoch in range(config.EPOCHS):
#list to save sentences
real_sentences = []
pred_sentences = []
losses = []
progress_bar = tqdm.tqdm(total=len(trainloader), unit='step')
for landmarks, len_landmark, label, len_label in trainloader:
#print("landmark",landmarks.shape,"len_landmark",len_landmark.shape,"label",label,"len_label",len_label.shape)
#break
# reshape the batch from [batch_size, frame_size, num_landmark, 3] to [batch_size, frame_size, num_landmark * 3]
landmarks = torch.reshape(landmarks, (landmarks.shape[0], landmarks.shape[1], landmarks.shape[2]*landmarks.shape[3]))
#variable to recover later the target sequences
label_list = label
# label char to index
label = char_to_index_batch(label, vocabulary)
# move data to GPU!
landmarks = landmarks.to(device)
len_landmark = len_landmark.to(device)
label = label.to(device)
len_label = len_label.to(device)
optimizer.zero_grad()
output = model(landmarks)
output = output.permute(1, 0, 2)#had to permute for the ctc loss. it acceprs [seq_len, batch_size, "num_class"]
loss = ctc_loss(torch.nn.functional.log_softmax(output, dim=2), label, len_landmark, len_label)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
losses.append(loss.item())
#progress bar stuff
progress_bar.set_description(f"Epoch {epoch+1}/{config.EPOCHS}")
#progress_bar.set_postfix(loss=loss.item()) # Update the loss value
progress_bar.set_postfix(loss=np.mean(losses)) # Update the loss value
progress_bar.update(1)
if epoch%500 == 0:
real_sentences, pred_sentences = write_results(len_label, label_list, output.detach(), trainloader.batch_size, vocabulary, real_sentences, pred_sentences)
# endfor batch
#if wandb.run is not None:
wandb.log({"epoch":epoch, "loss":np.mean(losses)})
# save the model
if epoch%10 == 0:
val_accuracy = test(model, valloader, vocabulary, ctc_loss)
wandb.log({"val_loss":val_accuracy})
if epoch%100 == 0:
torch.save(model.state_dict(), "models/model"+str(modeltitle)+"5.pt")
if epoch%500 == 0:
save_results(f"./results/results_{epoch}_4.txt", real_sentences, pred_sentences, overwrite=True)
return
def test(model, valloader, vocabulary, ctc_loss):
model.eval()
real_sentences = []
pred_sentences = []
losses = []
with torch.no_grad():
for landmarks, len_landmark, label, len_label in valloader:
# reshape the batch from [batch_size, frame_size, num_landmark, 3] to [batch_size, frame_size, num_landmark * 3]
landmarks = torch.reshape(landmarks, (landmarks.shape[0], landmarks.shape[1], landmarks.shape[2]*landmarks.shape[3]))
#variable to recover later the target sequences
label_list = label
# label char to index
label = char_to_index_batch(label, vocabulary)
# move data to GPU!
landmarks = landmarks.to(device)
len_landmark = len_landmark.to(device)
label = label.to(device)
len_label = len_label.to(device)
output = model(landmarks)
output = output.permute(1, 0, 2)
# scrittura nel file del outuput e della frase originale
loss = ctc_loss(torch.nn.functional.log_softmax(output, dim=2), label, len_landmark, len_label)
losses.append(loss.item())
real_sentences, pred_sentences = write_results(len_label, label_list, output.detach(), valloader.batch_size, vocabulary, real_sentences, pred_sentences)
print(":>",np.mean(losses))
pred_sentences = list(map(lambda x:process_string(x),pred_sentences))
save_results(f"./results/validation_4.txt", real_sentences, pred_sentences, overwrite=True)
model.train()
return np.mean(losses)
model_pipeline()