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https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 12:10:45 +02:00
Commentaires + rangement
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Harle, Antoine (Contracteur)
parent
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commit
2e09f07f52
4 changed files with 336 additions and 279 deletions
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@ -1,3 +1,7 @@
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""" Utilities function for training.
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"""
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import torch
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#import torch.optim
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import torchvision
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@ -7,6 +11,14 @@ from datasets import *
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from utils import *
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def test(model):
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"""Evaluate a model on test data.
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Args:
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model (nn.Module): Model to test.
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Returns:
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(float, Tensor) Returns the accuracy and test loss of the model.
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"""
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device = next(model.parameters()).device
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model.eval()
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@ -35,6 +47,16 @@ def test(model):
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return accuracy, np.mean(loss)
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def compute_vaLoss(model, dl_it, dl):
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"""Evaluate a model on a batch of data.
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Args:
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model (nn.Module): Model to evaluate.
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dl_it (Iterator): Data loader iterator.
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dl (DataLoader): Data loader.
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Returns:
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(Tensor) Loss on a single batch of data.
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"""
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device = next(model.parameters()).device
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try:
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xs, ys = next(dl_it)
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@ -47,6 +69,17 @@ def compute_vaLoss(model, dl_it, dl):
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return F.cross_entropy(F.log_softmax(model(xs), dim=1), ys)
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def train_classic(model, opt_param, epochs=1, print_freq=1):
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"""Classic training of a model.
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Args:
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model (nn.Module): Model to train.
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opt_param (dict): Dictionnary containing optimizers parameters.
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epochs (int): Number of epochs to perform. (default: 1)
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print_freq (int): Number of epoch between display of the state of training. If set to None, no display will be done. (default:1)
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Returns:
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(list) Logs of training. Each items is a dict containing results of an epoch.
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"""
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device = next(model.parameters()).device
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#opt = torch.optim.Adam(model.parameters(), lr=1e-3)
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optim = torch.optim.SGD(model.parameters(), lr=opt_param['Inner']['lr'], momentum=opt_param['Inner']['momentum']) #lr=1e-2 / momentum=0.9
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@ -97,152 +130,30 @@ def train_classic(model, opt_param, epochs=1, print_freq=1):
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return log
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def train_classic_higher(model, epochs=1):
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device = next(model.parameters()).device
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#opt = torch.optim.Adam(model.parameters(), lr=1e-3)
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optim = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9)
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def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start=0, print_freq=1, KLdiv=1, hp_opt=False, save_sample_freq=None):
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"""Training of an augmented model with higher.
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model.train()
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dl_val_it = iter(dl_val)
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log = []
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This function is intended to be used with Augmented_model containing an Higher_model (see dataug.py).
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Ex : Augmented_model(Data_augV5(...), Higher_model(model))
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fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
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diffopt = higher.optim.get_diff_optim(optim, model.parameters(),fmodel=fmodel,track_higher_grads=False)
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#with higher.innerloop_ctx(model, optim, copy_initial_weights=True, track_higher_grads=False) as (fmodel, diffopt):
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Training loss can either be computed directly from augmented inputs (KLdiv=0).
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However, it is recommended to use the KLdiv loss computation, inspired from UDA, which combine original and augmented inputs to compute the loss (KLdiv>0).
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See : https://github.com/google-research/uda
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for epoch in range(epochs):
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#print_torch_mem("Start epoch "+str(epoch))
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#print("Fast param ",len(fmodel._fast_params))
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t0 = time.process_time()
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for i, (features, labels) in enumerate(dl_train):
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#print_torch_mem("Start iter")
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features,labels = features.to(device), labels.to(device)
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Args:
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model (nn.Module): Augmented model to train.
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opt_param (dict): Dictionnary containing optimizers parameters.
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epochs (int): Number of epochs to perform. (default: 1)
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inner_it (int): Number of inner iteration before a meta-step. 0 inner iteration means there's no meta-step. (default: 1)
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dataug_epoch_start (int): Epoch when to start data augmentation. (default: 0)
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print_freq (int): Number of epoch between display of the state of training. If set to None, no display will be done. (default:1)
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KLdiv (float): Proportion of the KLdiv loss added to the supervised loss. If set to 0, the loss is classicly computed on augmented inputs. (default: 1)
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hp_opt (bool): Wether to learn inner optimizer parameters. (default: False)
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save_sample_freq (int): Number of epochs between saves of samples of data. If set to None, only one save would be done at the end of the training. (default: None)
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#optim.zero_grad()
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logits = model.forward(features)
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pred = F.log_softmax(logits, dim=1)
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loss = F.cross_entropy(pred,labels)
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#.backward()
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#optim.step()
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diffopt.step(loss) #(opt.zero_grad, loss.backward, opt.step)
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model_copy(src=fmodel, dst=model, patch_copy=False)
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optim_copy(dopt=diffopt, opt=optim)
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fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
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diffopt = higher.optim.get_diff_optim(optim, model.parameters(),fmodel=fmodel,track_higher_grads=False)
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#### Tests ####
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tf = time.process_time()
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try:
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xs_val, ys_val = next(dl_val_it)
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except StopIteration: #Fin epoch val
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dl_val_it = iter(dl_val)
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xs_val, ys_val = next(dl_val_it)
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xs_val, ys_val = xs_val.to(device), ys_val.to(device)
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val_loss = F.cross_entropy(model(xs_val), ys_val)
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accuracy, _ =test(model)
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model.train()
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#### Log ####
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data={
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"epoch": epoch,
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"train_loss": loss.item(),
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"val_loss": val_loss.item(),
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"acc": accuracy,
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"time": tf - t0,
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"param": None,
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}
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log.append(data)
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return log
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def train_UDA(model, dl_unsup, opt_param, epochs=1, print_freq=1):
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device = next(model.parameters()).device
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#opt = torch.optim.Adam(model.parameters(), lr=1e-3)
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opt = torch.optim.SGD(model.parameters(), lr=opt_param['Inner']['lr'], momentum=opt_param['Inner']['momentum']) #lr=1e-2 / momentum=0.9
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model.train()
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dl_val_it = iter(dl_val)
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dl_unsup_it =iter(dl_unsup)
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log = []
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for epoch in range(epochs):
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#print_torch_mem("Start epoch")
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t0 = time.process_time()
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for i, (features, labels) in enumerate(dl_train):
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#print_torch_mem("Start iter")
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features,labels = features.to(device), labels.to(device)
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optim.zero_grad()
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#Supervised
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logits = model.forward(features)
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pred = F.log_softmax(logits, dim=1)
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sup_loss = F.cross_entropy(pred,labels)
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#Unsupervised
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try:
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aug_xs, origin_xs, ys = next(dl_unsup_it)
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except StopIteration: #Fin epoch val
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dl_unsup_it =iter(dl_unsup)
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aug_xs, origin_xs, ys = next(dl_unsup_it)
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aug_xs, origin_xs, ys = aug_xs.to(device), origin_xs.to(device), ys.to(device)
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#print(aug_xs.shape, origin_xs.shape, ys.shape)
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sup_logits = model.forward(origin_xs)
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unsup_logits = model.forward(aug_xs)
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log_sup=F.log_softmax(sup_logits, dim=1)
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log_unsup=F.log_softmax(unsup_logits, dim=1)
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#KL div w/ logits
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unsup_loss = F.softmax(sup_logits, dim=1)*(log_sup-log_unsup)
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unsup_loss=unsup_loss.sum(dim=-1).mean()
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#print(unsup_loss)
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unsupp_coeff = 1
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loss = sup_loss + unsup_loss * unsupp_coeff
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loss.backward()
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optim.step()
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#### Tests ####
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tf = time.process_time()
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try:
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xs_val, ys_val = next(dl_val_it)
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except StopIteration: #Fin epoch val
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dl_val_it = iter(dl_val)
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xs_val, ys_val = next(dl_val_it)
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xs_val, ys_val = xs_val.to(device), ys_val.to(device)
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val_loss = F.cross_entropy(model(xs_val), ys_val)
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accuracy, _ =test(model)
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model.train()
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#### Print ####
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if(print_freq and epoch%print_freq==0):
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print('-'*9)
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print('Epoch : %d/%d'%(epoch,epochs))
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print('Time : %.00f'%(tf - t0))
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print('Train loss :',loss.item(), '/ val loss', val_loss.item())
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print('Sup Loss :', sup_loss.item(), '/ unsup_loss :', unsup_loss.item())
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print('Accuracy :', accuracy)
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#### Log ####
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data={
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"epoch": epoch,
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"train_loss": loss.item(),
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"val_loss": val_loss.item(),
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"acc": accuracy,
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"time": tf - t0,
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"param": None,
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}
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log.append(data)
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return log
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def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start=0, print_freq=1, KLdiv=False, hp_opt=False, save_sample=False):
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Returns:
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(list) Logs of training. Each items is a dict containing results of an epoch.
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"""
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device = next(model.parameters()).device
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log = []
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dl_val_it = iter(dl_val)
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@ -282,7 +193,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
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for i, (xs, ys) in enumerate(dl_train):
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xs, ys = xs.to(device), ys.to(device)
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if(not KLdiv):
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if(KLdiv<=0):
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#Methode uniforme
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logits = model(xs) # modified `params` can also be passed as a kwarg
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loss = F.cross_entropy(F.log_softmax(logits, dim=1), ys, reduction='none') # no need to call loss.backwards()
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@ -317,8 +228,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
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aug_loss = (w_loss * aug_loss).mean()
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aug_loss += (F.cross_entropy(log_aug, ys , reduction='none') * w_loss).mean()
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unsupp_coeff = 1
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loss += aug_loss * unsupp_coeff
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loss += aug_loss * KLdiv
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#print_graph(loss) #to visualize computational graph
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@ -351,7 +261,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
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tf = time.process_time()
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if save_sample: #Data sample saving
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if (save_sample_freq and epoch%save_sample_freq==0): #Data sample saving
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try:
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viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
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viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
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@ -423,4 +333,4 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
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print("Couldn't save finals samples")
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pass
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return log
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return log
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