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train_VAE_EMG.py
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from wsgiref import validate
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 utils
import numpy as np
import os
import models as model_list
import wandb
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)
# 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.environ['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
# recover valid paths, domains, classes
# this will output the domain conversion (D1 -> 8, et cetera) and the label list
num_classes, valid_labels, source_domain, target_domain = utils.utils.get_domains_and_labels(args)
# device where everything is run
device = torch.device("cpu" 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)(16, 512, 16)
if args.action == "train":
# resume_from argument is adopted in case of restoring from a checkpoint
# if args.resume_from is not None:
# action_classifier.load_last_model(args.resume_from)
# i.e. number of batches passed
# notice, here it is multiplied by tot_batch/batch_size since gradient accumulation technique is adopted
# training_iterations = args.train.num_iter * (args.total_batch // args.batch_size)
# all dataloaders are generated here
train_loader = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'train', args.dataset, {'EMG': 32}, 5, {'EMG': False},
None, load_feat=False),
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},
None, load_feat=False),
batch_size=args.batch_size, shuffle=True,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
ae = train(models, train_loader, val_loader, device, args.models.EMG)
logger.info(f"TRAINING VAE FINISHED, SAVING THE MODELS...")
save_model(ae['EMG'], f"{args.name}_lr{args.models.EMG.lr}_{datetime.now()}.pth")
logger.info(f"Model saved in {args.name}_lr{args.models.EMG.lr}_{datetime.now()}.pth")
elif args.action == "save":
loader = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'train', args.dataset, {'EMG': 32}, 5, {'EMG': False},
None, load_feat=False),
batch_size=1, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=True)
loader_test = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'test', args.dataset, {'EMG': 32}, 5, {'EMG': False},
None, load_feat=False),
batch_size=1, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=True)
last_model = args.resume_from
logger.info(f"Loading last model from {last_model}")
load_model(models['EMG'], last_model)
logger.info(f"Reconstructing features...")
filename = f"./saved_features/reconstructed/VAE_{args.models.EMG.lr}_{datetime.now()}"
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":
if args.augmentation:
T_train_loaders = {}
T_val_loaders = {}
train_loaders = {}
val_loaders = {}
_features= {
'WD-MW': '../drive/MyDrive/actionnet_aug/Augmented_dataset_clip_WD-MW',
'MW': '../drive/MyDrive/actionnet_aug/Augmented_dataset_clip_MW',
'WD': '../drive/MyDrive/actionnet_aug/Augmented_dataset_clip_WD',
'MW-WD': '../drive/MyDrive/actionnet_aug/Augmented_dataset_clip_MW-WD',
}
for a in _features.keys():
args.dataset.EMG.features_name = _features[a]
T_train_loaders[a] = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'train', args.dataset, {'EMG': 32}, 5, {'EMG': False},
None, load_feat=True, additional_info=False, kwargs={'aug': True}),
batch_size=args.batch_size, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
T_val_loaders[a] = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'test', args.dataset, {'EMG': 32}, 5, {'EMG': False},
None, load_feat=True, additional_info=False, kwargs={'aug': True}),
batch_size=args.batch_size, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
train_loader = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'train', args.dataset, {'EMG': 32}, 5, {'EMG': False},
None, load_feat=False),
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("-")[-1], modalities,
'test', args.dataset, {'EMG': 32}, 5, {'EMG': False},
None, load_feat=False),
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},
None, load_feat=False),
batch_size=1, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=True)
loader_test = torch.utils.data.DataLoader(ActionNetDataset(args.dataset.shift.split("-")[0], modalities,
'test', args.dataset, {'EMG': 32}, 5, {'EMG': False},
None, load_feat=False),
batch_size=1, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=True)
ae = train(models, train_loader, train_loader, val_loader, device, args.models.EMG)
logger.info(f'Finished training original , now augmentated...')
for a in train_loaders.keys():
ae = train(ae, T_train_loaders[a], train_loader, T_val_loaders[a], device, num_classes)
logger.info(f'Finished training {a}')
timestamp = datetime.now()
save_model(ae['EMG'], f"{args.name}_lr{args.models.EGM.lr}_{timestamp}.pth")
logger.info(f"Model saved in {args.name}_lr{args.models.EMG.lr}_{timestamp}.pth")
logger.info(f"TRAINING VAE FINISHED, RECONSTUCTING FEATURES...")
filename = f"../drive/MyDrive/reconstructed/VAE_{args.models.EMG.lr}_{timestamp}"
reconstructed_features, results = reconstruct(ae, 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)
def reconstruct(autoencoder, dataloader, device, split=None, save = False, filename = None, debug = False, aug = None):
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)
# logger.debug(f"Data shape(before squeeze): {data[m].shape}")
data[m] = data[m].reshape(-1,16,5,32,32)
data[m] = data[m].permute(2, 0, 1, 3,4 ) # clip level
# logger.debug(f"Data shape(after squeeze): {data[m].shape}")
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)
x_hat = x_hat.to(device).detach()
# logger.debug(f"Clip: {clip.shape}, x_hat: {x_hat.shape}")
# logger.debug(f"Reconstruction loss: {reconstruction_loss(clip, x_hat)}")
clip_loss += reconstruction_loss(clip, x_hat)
clips.append(x_hat)
# avg_video_level_loss += clip_loss
# logger.debug(f"clips è un array({type(clips)}, di dimensione 5({len(clips)})")
clips = torch.stack(clips, dim = 0)
# logger.debug(f"clips è un TENSORE({type(clips)}, che rappresenta il video {clips.shape})")
clips = clips.permute(1, 0, 2)
# logger.debug(f"clips è un TENSORE({type(clips)}, che rappresenta il video ({clips.shape})[ho eliminato la dimensione inutile]")
avg_video_level_loss += reconstruction_loss(data[m].permute(1, 0, 2), clips)
clips = clips.squeeze(0)
# logger.debug(f"Reconstruction loss: {reconstruction_loss(data[m], clips)}")
result['features'].append({'features_EMG': clips.numpy(), 'label': label.item(), 'uid': uid.item(), 'video_name': video_name})
if save:
if aug:
filename = str('../drive/MyDrive/EXTRACTED_FEATURES_AUG_1/' + 'Augmented_features_' + aug.split("/")[-1].split('_')[3] + "_" + ('train' if train else 'test') + ".pkl")
pickle.dump(result, open(filename, 'wb'))
else:
pickle.dump(result, open(os.path.join("../drive/MyDrive/ACTIONNET_EMG/", args.name + "_" +
('train' if train else 'test') + "_" +
args.split + ".pkl"), 'wb'))
if debug:
return result, {'total_loss': avg_video_level_loss, 'avg_loss': avg_video_level_loss/len(dataloader)}
else:
return result
def frange_cycle_linear(start, stop, n_epoch, n_cycle=4, ratio=0.5):
L = np.ones(n_epoch)
period = n_epoch/n_cycle
step = (stop-start)/(period*ratio) # linear schedule
for c in range(n_cycle):
v , i = start , 0
while v <= stop and (int(i+c*period) < n_epoch):
L[int(i+c*period)] = v
v += step
i += 1
return L
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:
data[m] = data[m].reshape(-1,16,5,32,32)
data[m] = data[m].permute(2, 0, 1, 3,4 )
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 train(autoencoder, train_dataloader, train_original_dataloader, val_dataloader, device, model_args):
logger.info(f"Start VAE training.")
for m in modalities:
autoencoder[m].load_on(device)
opt = build_optimizer(autoencoder['EMG'], "sgd", 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()
autoencoder['EMG'].train(True)
#beta = 200*[1]
for epoch in range(model_args.epochs):
total_loss = 0
for i, ((data, _) , (data_o, _))in enumerate(zip(train_dataloader, train_original_dataloader)):
opt.zero_grad()
for m in modalities:
data[m] = data[m].reshape(-1,16,5,32,32)
data[m] = data[m].permute(2, 0, 1, 3,4 )
data_o[m] = data_o[m].reshape(-1,16,5,32,32)
data_o[m] = data_o[m].permute(2, 0, 1, 3,4 )
for i_c in range(args.test.num_clips):
clip_level_loss = 0
for m in modalities:
# extract the clip related to the modality
clip = data[m][i_c].to(device)
clip_o = data_o[m][i_c].to(device)
#print(f"Clip shape: {clip.shape}")
x_hat_a, _, mean_a, log_var_a = autoencoder[m](clip)
x_hat_o, _, log_var_o, log_var_o = autoencoder[m](clip_o)
mse_loss = reconstruction_loss(x_hat_a, clip_o)
kld_loss = -0.5 * torch.sum(1 + log_var_a - mean_a.pow(2) - log_var_a.exp())
loss = mse_loss +(0.01*1/1024 )*kld_loss
loss.backward()
# generate an error if loss is nan
if loss.isnan():
raise ValueError("Loss is NaN.")
clip_level_loss += loss
wandb.log({ "MSE LOSS": mse_loss, 'KLD_loss': kld_loss, 'loss': loss, 'lr': scheduler.get_last_lr()[0]})
total_loss += clip_level_loss.item()
opt.step()
if epoch % 10 == 0:
wandb.log({"validation_loss": validate(autoencoder['EMG'], val_dataloader, device, reconstruction_loss)})
print(f"[{epoch+1}/{model_args.epochs}] - Loss: {total_loss/(args.test.num_clips * len(train_dataloader))}")
scheduler.step()
return autoencoder
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({'model_state_dict': model.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 plot_latent(autoencoder, dataloader, device, split = 'train'):
"""
encodes rgb features, saves them in a latent_split.pkl file and plots them ina img_VAE_split.png file
"""
output = []
labels = []
final_latents = []
with torch.no_grad():
#print(len(dataloader))
for i, (data, label) in enumerate(dataloader):
output = []
for m in modalities:
data[m] = data[m].permute(1, 0, 2)
#print(len(data[m]))
for i_c in range(args.test.num_clips):
clip = data[m][i_c].to(device)
z = autoencoder[m].encoder.encode(clip)
z = z.to(device).detach()
output.append(z)
output = torch.stack(output)
output = output.permute(1, 0, 2)
#print(f'[DEBUG], Batch finito, output: {output.size()}')
for j in range(len(output)):
final_latents.append(output[j])
for _ in range(5):
labels.append(label[j].item())
final_latents = torch.stack(final_latents).reshape(-1,512)
reduced = TSNE().fit_transform(final_latents)
x_l = reduced[:, 0]
y_l = reduced[:, 1]
with open(f"./latent_{split}.pkl", "wb") as file:
pickle.dump({'x': x_l, 'y': y_l, 'labels': labels}, file)
d = pd.read_pickle(f'./aml22-ego/latent_{split}.pkl')
colors= ['green', 'red', 'yellow', 'grey', 'green', 'blue', 'black', 'purple']
for x, y, l in zip(d['x'], d['y'], d['labels']):
plt.scatter(x, y, c=colors[l])
plt.savefig(f"./img_VAE_{split}.png")
plt.show()
# colors= ['green', 'red', 'yellow', 'grey', 'green', 'blu', 'black', 'purple']
# # for x, y, l in zip(x_l, y_l, labels):
# # print(colors[l])
# plt.scatter(x_l, y_l, c=colors, label=labels)
# plt.legend()
# plt.savefig("./img_VAE.png")
# plt.show()
def load_model(ae, path):
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()