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Ajout Augmented_datasetV2+trainUDA
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Harle, Antoine (Contracteur)
parent
a859db65c4
commit
48c3925d74
2 changed files with 114 additions and 14 deletions
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@ -66,10 +66,10 @@ if __name__ == "__main__":
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tasks={
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#'classic',
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#'aug_dataset',
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'aug_model'
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'aug_dataset',
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#'aug_model'
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}
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n_inner_iter = 0
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n_inner_iter = 1
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epochs = 150
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dataug_epoch_start=0
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@ -108,19 +108,34 @@ if __name__ == "__main__":
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t0 = time.process_time()
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data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
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data_train_aug.augement_data(aug_copy=30)
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print(data_train_aug)
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dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
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#data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
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#data_train_aug.augement_data(aug_copy=30)
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#print(data_train_aug)
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#dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
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xs, ys = next(iter(dl_train))
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viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
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#xs, ys = next(iter(dl_train))
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#viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
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#model = model.to(device)
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#print("{} on {} for {} epochs".format(str(model), device_name, epochs))
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#log= train_classic(model=model, epochs=epochs, print_freq=10)
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##log= train_classic_higher(model=model, epochs=epochs)
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data_train_aug = AugmentedDatasetV2("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
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data_train_aug.augement_data(aug_copy=10)
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print(data_train_aug)
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unsup_ratio = 5
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dl_unsup = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE*unsup_ratio, shuffle=True)
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unsup_xs, sup_xs, ys = next(iter(dl_unsup))
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viz_sample_data(imgs=sup_xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
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viz_sample_data(imgs=unsup_xs, labels=ys, fig_name='samples/data_sample_{}_unsup'.format(str(data_train_aug)))
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model = model.to(device)
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print("{} on {} for {} epochs".format(str(model), device_name, epochs))
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log= train_classic(model=model, epochs=epochs, print_freq=10)
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#log= train_classic_higher(model=model, epochs=epochs)
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log= train_UDA(model=model, dl_unsup=dl_unsup, epochs=epochs, print_freq=10)
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exec_time=time.process_time() - t0
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####
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@ -145,11 +160,11 @@ if __name__ == "__main__":
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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#aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=2, fixed_mag=True, shared_mag=True), model).to(device)
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aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.0, fixed_prob=True, fixed_mag=True, shared_mag=True), model).to(device)
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aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
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#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
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print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
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log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, KLdiv=True, loss_patience=None)
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log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, KLdiv=False, loss_patience=None)
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exec_time=time.process_time() - t0
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####
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@ -157,7 +172,7 @@ if __name__ == "__main__":
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times = [x["time"] for x in log]
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out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
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print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
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filename = "{}-{} epochs (dataug:{})- {} in_it (KLdiv)".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
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filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
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with open("res/log/%s.json" % filename, "w+") as f:
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json.dump(out, f, indent=True)
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print('Log :\"',f.name, '\" saved !')
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@ -305,6 +305,91 @@ def train_classic_tests(model, epochs=1):
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print("Copy ", countcopy)
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return log
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def train_UDA(model, dl_unsup, 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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optim = torch.optim.SGD(model.parameters(), 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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#print(unsup_logits.shape, sup_logits.shape)
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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.shape)
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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_simple_dataug(inner_it, epochs=1):
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device = next(model.parameters()).device
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dl_train_it = iter(dl_train)
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