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train_VAE_EMG_features.py
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train_VAE_EMG_features.py
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"""
Versione per non sporcare l'altra, dovrebbe essere più aggiornata. I bug li ho risolti tutti qui
"""
from utils.logger import logger
import torch.nn.parallel
import torch.nn as nn
import torch.optim
import torch
from utils.loaders import ActionNetDataset
from utils.args import args
from utils.utils import pformat_dict
import numpy as np
import os
import models as model_list
import wandb
from utils.utils import costant_scheduler, frange_cycle_linear, frange_cycle_sigmoid
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import pickle
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
# global variables among training functions
training_iterations = 0
modalities = None
np.random.seed(13696641)
torch.manual_seed(13696641)
### NEW PARAMETERS
# - args.train.bottleneck_size
# - args.train[m].feature_size
#
#
##################
# with this script we trained and tested FC_VAE.VariationalAutoencoder to reconstruct features from the EMG modality
def init_operations():
"""
parse all the arguments, generate the logger, check gpus to be used and wandb
"""
logger.info("Running with parameters: " + pformat_dict(args, indent=1))
# this is needed for multi-GPUs systems where you just want to use a predefined set of GPUs
if args.gpus is not None:
logger.debug('Using only these GPUs: {}'.format(args.gpus))
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpus)
# wanbd logging configuration
if args.wandb_name is not None:
WANDB_KEY = "c87fa53083814af2a9d0ed46e5a562b9a5f8b3ec" # Salvatore's key
if os.getenv('WANDB_KEY') is not None:
WANDB_KEY = os.environ['WANDB_KEY']
logger.info("Using key retrieved from enviroment.")
wandb.login(key=WANDB_KEY)
run = wandb.init(project="FC-VAE(EMG)", entity="egovision-aml22")
wandb.run.name = f'{args.name}_{args.models.EMG.model}'
def main():
global training_iterations, modalities
init_operations()
modalities = args.modality
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# these dictionaries are for more multi-modal training/testing, each key is a modality used
models = {}
logger.info("Instantiating models per modality")
for m in modalities:
logger.info('{} Net\tModality: {}'.format(args.models[m].model, m))
# notice that here, the first parameter passed is the input dimension
# In our case it represents the feature dimensionality which is equivalent to 1024 for I3D
#print(getattr(model_list, args.models[m].model)())
models[m] = getattr(model_list, args.models[m].model)(args.train[m].feature_size,
args.train.bottleneck_size,
args.train[m].feature_size,
resume_from=args.last_model)
<<<<<<< HEAD
=======
print(models['EMG'])
>>>>>>> 17781dd2be6217f7dcd30ec17744abb47cbea9ac
if args.action == "train":
# TODO: fiX dataset_config passing during multimodal training
train_loader = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0],
modalities,
'train',
args.dataset,
args.train.num_frames_per_clip,
args.train.num_clips,
args.train.dense_sampling,
transform = None,
load_feat=True,
require_spectrogram=True),
batch_size=args.batch_size, shuffle=True,
num_workers=args.dataset.workers, pin_memory=True, drop_last=True)
val_loader = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0],
modalities,
'test',
args.dataset,
args.train.num_frames_per_clip,
args.train.num_clips,
args.train.dense_sampling,
transform = None,
load_feat=True),
batch_size=args.batch_size, shuffle=True,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
autoencoders = train(models, train_loader, val_loader, device, args.models.EMG)
timestamp = datetime.now()
model_filename = f"{args.name}_lr{args.models.EMG.lr}_{timestamp}.pth"
save_model(ae['EMG'], model_filename)
logger.info(f"Model saved in {model_filename}")
elif args.action == "save":
if args.last_model is None:
raise ValueError("You must specify a model to load from")
print(args.last_model)
models['EMG'].load_last_model()
models['EMG'].load_on(device)
loader = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0],
modalities,
'train',
args.dataset,
args.save.num_frames_per_clip,
args.save.num_clips,
args.save.dense_sampling,
load_feat=True,
additional_info=True),
batch_size=1, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
loader_test = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0],
modalities,
'test',
args.dataset,
args.save.num_frames_per_clip,
args.save.num_clips,
args.save.dense_sampling,
load_feat=True,
additional_info=True),
batch_size=1, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
timestamp = datetime.now()
logger.info(f"Reconstructing features...")
filename = f"features_lr{args.models.EMG.lr}_b{args.models.EMG.beta}_{timestamp}"
reconstructed_features, output = reconstruct(models, loader, device, "train", save = True, filename=filename, debug=True)
logger.debug(f"Train Output {output}")
reconstructed_features, output = reconstruct(models, loader_test, device, "test", save = True, filename=filename, debug=True)
logger.debug(f"Test Output {output}")
elif args.action == "train_and_save":
train_loader = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'train', args.dataset, {'EMG': 32}, 5, {'EMG': False},
load_feat=True, require_spectrogram=True),
batch_size=args.batch_size, shuffle=True,
num_workers=args.dataset.workers, pin_memory=True, drop_last=True)
val_loader = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'test', args.dataset, {'EMG': 32}, 5, {'EMG': False},
load_feat=True, require_spectrogram=True),
batch_size=args.batch_size, shuffle=True,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
loader = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'train', args.dataset, {'EMG': 32}, 5, {'EMG': False},
load_feat=True, additional_info=True, require_spectrogram=True),
batch_size=1, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
loader_test = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'test', args.dataset, {'EMG': 32}, 5, {'EMG': False},
load_feat=True, additional_info=True,require_spectrogram=True),
batch_size=1, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
timestamp = datetime.now()
ae = train(models, train_loader, val_loader, device, args.models.EMG)
model_filename = f"{args.name}_lr{args.models.EMG.lr}_b{args.models.EMG.beta}_{timestamp}.pth"
save_model(ae['EMG'], model_filename)
logger.info(f"Model saved in {model_filename}")
logger.info(f"TRAINING VAE FINISHED, RECONSTUCTING FEATURES...")
filename = f"features_lr{args.models.EMG.lr}_b{args.models.EMG.beta}_{timestamp}"
reconstructed_features, results = reconstruct(models, loader, device, "train", save = True, filename=filename, debug = True)
logger.debug(f"Results on train: {results}")
reconstructed_features = reconstruct(models, loader_test, device, "test", save = True, filename=filename)
else:
raise NotImplementedError(f"Action {args.action} not implemented")
def train(autoencoder, train_dataloader, val_dataloader, device, model_args):
logger.info(f"Start VAE training.")
for m in modalities:
autoencoder[m].load_on(device)
autoencoder[m].train(True)
opt = build_optimizer(autoencoder['EMG'], "adam", model_args.lr)
scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=model_args.lr_steps, gamma=model_args.lr_gamma)
reconstruction_loss = nn.MSELoss(reduction='mean')
beta = costant_scheduler(model_args.beta, model_args.epochs)
# beta = np.concatenate((costant_scheduler(1/(100 * 1024), (model_args.epochs//5)*4), frange_cycle_linear(1/(100 * 1024), .5, (model_args.epochs//5)*1, n_cycle=1, ratio=.001)))
for epoch in range(model_args.epochs):
# train_loop
total_loss = 0 # total loss for the epoch
for i, (data, _) in enumerate(train_dataloader):
opt.zero_grad() # reset the gradients
for m in modalities:
data[m] = data[m].permute(1, 0, 2) # Data is now in the form (clip, batch, features)
for i_c in range(args.test.num_clips):
clip_level_loss = 0 # loss for the clip
for m in modalities:
# extract the clip related to the modality
clip = data[m][i_c].to(device)
x_hat, _, mean, log_var = autoencoder[m](clip)
mse_loss = reconstruction_loss(x_hat, clip) # compute the reconstruction loss
kld_loss = - 0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp()) # compute the KLD loss
loss = mse_loss + beta[epoch] * kld_loss
# generate an error if loss is nan
if loss.isnan():
raise ValueError("Loss is NaN.")
clip_level_loss += loss
loss.backward()
wandb.log({"Beta": beta[epoch], "MSE LOSS": mse_loss, 'KLD_loss': kld_loss, 'loss': loss, 'lr': scheduler.get_last_lr()[0]})
# update the weights at the end of the batch
opt.step()
total_loss += clip_level_loss.item()
if epoch % 10 == 0:
wandb.log({"validation_loss": validate(autoencoder['EMG'], val_dataloader, device, reconstruction_loss)})
logger.info(f"[{epoch+1}/{model_args.epochs}] - Total loss: {total_loss}")
wandb.log({"train_loss": total_loss})
scheduler.step()
logger.info(f"VAE training finished.")
return autoencoder
def reconstruct(autoencoder, dataloader, device, split=None, **kwargs):
"""
Reconstruct the features using the trained autoencoder
- autoencoder: the trained autoencoder
- dataloader: the dataloader to use
- device: the device to use
- split: the split to use
- kwargs: additional arguments
"""
debug = kwargs.get('debug', False)
filename = kwargs.get('filename', "reconstructed_features_EMG")
result = {'features': []}
# for debugging purpose, I introduce also a loss in reconstruction
reconstruction_loss = nn.MSELoss()
avg_video_level_loss = 0
with torch.no_grad():
for i, (data, label, video_name, uid) in enumerate(dataloader):
for m in modalities:
autoencoder[m].train(False)
data[m] = data[m].squeeze(1).permute(1, 0, 2) # clip level
clips = []
clip_loss = 0
for i_c in range(args.test.num_clips): # iterate over the clips
clip = data[m][i_c].to(device) # retrieve the clip
x_hat, _, _, _ = autoencoder[m](clip)
clip = clip.cpu()
x_hat = x_hat.cpu()
clip_loss += reconstruction_loss(clip, x_hat)
clips.append(x_hat)
clips = torch.stack(clips, dim = 0)
clips = clips.permute(1, 0, 2)
avg_video_level_loss += reconstruction_loss(data[m].permute(1, 0, 2), clips)
clips = clips.squeeze(0)
result['features'].append({
'features_EMG': clips.numpy(),
'label': label.item(),
'uid': uid.item(),
'video_name': video_name
})
try:
date = str(datetime.now().date())
if not os.path.isdir(os.path.join('./saved_features/reconstructed_emg/', date)):
os.mkdir(os.path.join('./saved_features/reconstructed_emg/', date))
with open(os.path.join('./saved_features/reconstructed_emg/', date, f"{filename}_{'ActionNet'}_{split}.pkl"), "wb") as file:
pickle.dump(result, file)
logger.info(f"Saved {filename}_{'ActionNet'}_{split}.pkl")
except Exception as e:
logger.warning(f"Error while saving the file: {e}")
if debug:
return result, {'total_loss': avg_video_level_loss, 'avg_loss': avg_video_level_loss/len(dataloader)}
else:
return result
def validate(autoencoder, val_dataloader, device, reconstruction_loss):
total_loss = 0
autoencoder.train(False)
for i, (data, labels) in enumerate(val_dataloader):
for m in modalities:
# logger.info(f"Data size: {data[m].squeeze(1).shape}")
data[m] = data[m].squeeze(1).permute(1, 0, 2).to(device)
# print(f"Data after permutation: {data[m].size()}")
for i_c in range(args.test.num_clips):
for m in modalities:
# extract the clip related to the modality
clip = data[m][i_c].to(device)
x_hat, _, mean, log_var = autoencoder(clip)
mse_loss = reconstruction_loss(x_hat, clip)
kld_loss = -0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp())
loss = mse_loss + kld_loss
total_loss += loss
return total_loss/len(val_dataloader)
def save_model(model, filename):
try:
date = str(datetime.now().date())
if not os.path.isdir(os.path.join('./saved_models/VAE_EMG', date)):
os.mkdir(os.path.join('./saved_models/VAE_EMG', date))
torch.save({'encoder': model.encoder.state_dict(), 'decoder': model.decoder.state_dict()},
os.path.join('./saved_models/VAE_EMG', date, filename))
except Exception as e:
logger.info("An error occurred while saving the checkpoint:")
logger.info(e)
def load_model(ae, path):
# TODO: load the model separately
state_dict = torch.load(path)["model_state_dict"]
#print([x for x in state_dict.keys()])
ae.load_state_dict(state_dict, strict=False)
def build_optimizer(network, optimizer, learning_rate):
if optimizer == "sgd":
optimizer = torch.optim.SGD(network.parameters(),
lr=learning_rate, momentum=0.9)
elif optimizer == "adam":
optimizer = torch.optim.Adam(network.parameters(),
lr=learning_rate)
return optimizer
if __name__ == '__main__':
main()