class Runner:
def __init__(self, config):
self.config = config
def find_lr(self):
from torch_lr_finder import LRFinder
logger.info('finding the best learning rate')
cfg = self.config
if self.tsai_mode:
import sodium.tsai_model as module_arch
else:
import sodium.model.model as module_arch
# create a model instance
model = get_instance(module_arch, 'arch', cfg)
# setup the model with the device
model, device = setup_device(model, cfg['target_device'])
param_groups = setup_param_groups(model, cfg['optimizer'])
optimizer = get_instance(module_optimizer, 'optimizer', cfg,
param_groups)
criterion = getattr(module_loss, cfg['criterion'])()
self.lr_finder = LRFinder(model, optimizer, criterion, device="cuda")
lr_finder_epochs = cfg['lr_finder']['epochs']
logger.info(f'Running LR-Test for {lr_finder_epochs} epochs')
# my method
self.lr_finder.range_test(self.trainer.train_loader,
start_lr=1e-3,
end_lr=1,
num_iter=len(self.trainer.test_loader) *
lr_finder_epochs,
step_mode='linear')
# leslie smith method
# self.lr_finder.range_test(self.trainer.train_loader, val_loader = self.trainer.test_loader,
# end_lr=1, num_iter=len(self.trainer.train_loader), step_mode='linear')
# fast ai method
# self.lr_finder.range_test(
# self.trainer.train_loader, end_lr=100, num_iter=len(self.trainer.train_loader))
self.best_lr = self.lr_finder.history['lr'][
self.lr_finder.history['loss'].index(self.lr_finder.best_loss)]
sorted_lrs = [
x for _, x in sorted(
zip(self.lr_finder.history['loss'],
self.lr_finder.history['lr']))
]
logger.info(f'sorted lrs : {sorted_lrs[:10]}')
logger.info(f'found the best lr : {self.best_lr}')
logger.info('plotting lr_finder')
plt.style.use("dark_background")
self.lr_finder.plot()
# reset the model and the optimizer
self.lr_finder.reset()
plt.show()
del model, optimizer, criterion
def train(self, use_bestlr=False, lr_value=None):
# if the best lr was found use that value instead
if use_bestlr and self.best_lr is not None:
logger.info(f'using max_lr : {self.best_lr}')
logger.info(f'using min_lr : {self.best_lr/30}')
logger.info(f'using initial_lr : {self.best_lr/20}')
for param_group in self.trainer.optimizer.param_groups:
param_group['lr'] = self.best_lr / 10
param_group['max_lr'] = self.best_lr
param_group['min_lr'] = self.best_lr / 30
param_group['intial_lr'] = self.best_lr / 20
if not use_bestlr and (lr_value is not None):
for param_group in self.trainer.optimizer.param_groups:
param_group['lr'] = lr_value
self.trainer.train()
logger.info('Finished!')
def setup_train(self, tsai_mode=False):
cfg = self.config
self.tsai_mode = tsai_mode
if tsai_mode:
import sodium.tsai_model as module_arch
else:
import sodium.model.model as module_arch
logger.info('Training Config')
# display the config
for line in pprint.pformat(cfg).split('\n'):
logger.info(line)
# to get consistent results, seed everything
seed_everything(cfg['seed'])
# create a model instance
model = get_instance(module_arch, 'arch', cfg)
# setup the model with the device
model, device = setup_device(model, cfg['target_device'])
param_groups = setup_param_groups(model, cfg['optimizer'])
optimizer = get_instance(module_optimizer, 'optimizer', cfg,
param_groups)
self.transforms = get_instance(module_aug, 'augmentation', cfg)
# get the train and test loaders
self.data_loader = get_instance(module_data, 'data_loader', cfg,
self.transforms)
train_loader, test_loader = self.data_loader.get_loaders()
logger.info('Getting loss function handle')
criterion = getattr(module_loss, cfg['criterion'])()
batch_scheduler = False
if cfg['lr_scheduler']['type'] == 'OneCycleLR':
logger.info('Building: torch.optim.lr_scheduler.OneCycleLR')
max_at_epoch = cfg['lr_scheduler']['max_lr_at_epoch']
pct_start = (max_at_epoch) / \
cfg['training']['epochs'] if max_at_epoch else 0.8
sch_cfg = cfg['lr_scheduler']['args']
lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=sch_cfg['max_lr'],
steps_per_epoch=len(train_loader),
pct_start=pct_start,
epochs=cfg['training']['epochs'])
batch_scheduler = True
else:
lr_scheduler = get_instance(module_scheduler, 'lr_scheduler', cfg,
optimizer)
logger.info('Initializing trainer')
self.trainer = Trainer(model,
criterion,
optimizer,
cfg,
device,
train_loader,
test_loader,
lr_scheduler=lr_scheduler,
batch_scheduler=batch_scheduler)
def plot_metrics(self):
plt.style.use("dark_background")
logger.info('Plotting Metrics...')
plot.plot_metrics(self.trainer.train_metric, self.trainer.test_metric)
plot.plot_lr_metric(self.trainer.lr_metric)
def plot_gradcam(self, target_layers):
plt.style.use("dark_background")
logger.info('Plotting Grad-CAM...')
# use the test images
data, target = next(iter(self.trainer.test_loader))
data, target = data.to(self.trainer.device), target.to(
self.trainer.device)
logger.info('Taking {5} samples')
# get 5 images
data = data[:5]
target = target[:5]
# get the generated grad cam
gcam_layers, predicted_probs, predicted_classes = get_gradcam(
data, target, self.trainer.model, self.trainer.device,
target_layers)
# get the denomarlization function
unorm = module_aug.UnNormalize(mean=self.transforms.mean,
std=self.transforms.std)
plot_gradcam(gcam_layers, data, target, predicted_classes,
self.data_loader.class_names, unorm)
def print_summary(self, input_size):
summary(self.trainer.model, input_size)
def print_visualization(self, input_size):
C, H, W = input_size
x = torch.zeros(1, C, H, W, dtype=torch.float, requires_grad=False)
x = x.to(self.trainer.device)
out = self.trainer.model(x)
# plot graph of variable, not of a nn.Module
dot_graph = torchviz.make_dot(out)
dot_graph.view()
return dot_graph
def plot_misclassifications(self, target_layers):
plt.style.use("dark_background")
assert (self.trainer.model is not None)
# get the data, target of only missclassified and do what you do for gradcam
logger.info('getting misclassifications')
misclassified = []
misclassified_target = []
misclassified_pred = []
model, device = self.trainer.model, self.trainer.device
# set the model to evaluation mode
model.eval()
# turn off gradients
with torch.no_grad():
for data, target in self.trainer.test_loader:
# move them to respective device
data, target = data.to(device), target.to(device)
# do inferencing
output = model(data)
# get the predicted output
pred = output.argmax(dim=1, keepdim=True)
# get the current misclassified in this batch
list_misclassified = (target.eq(pred.view_as(target)) == False)
batch_misclassified = data[list_misclassified]
batch_mis_pred = pred[list_misclassified]
batch_mis_target = target[list_misclassified]
# batch_misclassified =
misclassified.append(batch_misclassified)
misclassified_pred.append(batch_mis_pred)
misclassified_target.append(batch_mis_target)
# group all the batched together
misclassified = torch.cat(misclassified)
misclassified_pred = torch.cat(misclassified_pred)
misclassified_target = torch.cat(misclassified_target)
logger.info('Taking {25} samples')
# get 5 images
data = misclassified[:25]
target = misclassified_target[:25]
# get the generated grad cam
gcam_layers, predicted_probs, predicted_classes = get_gradcam(
data, target, self.trainer.model, self.trainer.device,
target_layers)
# get the denomarlization function
unorm = module_aug.UnNormalize(mean=self.transforms.mean,
std=self.transforms.std)
plot_gradcam(gcam_layers, data, target, predicted_classes,
self.data_loader.class_names, unorm)
def train_fully_supervised(model,n_epochs,train_loader,val_loader,criterion,optimizer,scheduler,auto_lr,\
save_folder,model_name,benchmark=False,save_all_ep=True, save_best=False, device='cpu',num_classes=21):
"""
A complete training of fully supervised model.
save_folder : Path to save the model, the courb of losses,metric...
benchmark : enable or disable backends.cudnn
save_all_ep : if True, the model is saved at each epoch in save_folder
scheduler : if True, the model will apply a lr scheduler during training
auto_lr : Auto lr finder
"""
torch.backends.cudnn.benchmark = benchmark
if auto_lr:
print('Auto finder for the Learning rate')
lr_finder = LRFinder(model,
optimizer,
criterion,
memory_cache=False,
cache_dir='/tmp',
device=device)
lr_finder.range_test(train_loader,
start_lr=10e-5,
end_lr=10,
num_iter=100)
if scheduler:
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lambda x: (1 - x / (len(train_loader) * n_epochs))**0.9)
loss_test = []
loss_train = []
iou_train = []
iou_test = []
accuracy_train = []
accuracy_test = []
model.to(device)
for ep in range(n_epochs):
print("EPOCH", ep)
model.train()
state = step_train_supervised(model,train_loader=train_loader,criterion=criterion,\
optimizer=optimizer,device=device,num_classes=num_classes)
iou = state.metrics['mean IoU']
acc = state.metrics['accuracy']
loss = state.metrics['CE Loss']
loss_train.append(loss)
iou_train.append(iou)
accuracy_train.append(acc)
print('TRAIN - EP:', ep, 'iou:', iou, 'Accuracy:', acc, 'Loss CE',
loss)
if scheduler:
lr_scheduler.step()
#Eval model
model.eval()
with torch.no_grad():
state = eval_model(model,
val_loader,
device=device,
num_classes=num_classes)
iou = state.metrics['mean IoU']
acc = state.metrics['accuracy']
loss = state.metrics['CE Loss']
loss_test.append(loss)
iou_test.append(iou)
accuracy_test.append(acc)
print('TEST - EP:', ep, 'iou:', iou, 'Accuracy:', acc, 'Loss CE',
loss)
## Save model
U.save_model(model,
save_all_ep,
save_best,
save_folder,
model_name,
ep=ep,
iou=iou,
iou_test=iou_test)
U.save_curves(path=save_folder,loss_train=loss_train,iou_train=iou_train,accuracy_train=accuracy_train\
,loss_test=loss_test,iou_test=iou_test,accuracy_test=accuracy_test)
def Interpol(N, neurons, iter, fun=0, a=1, b=1):
datasamp = datagen(N, neurons, fun, a, b, legendre)
val_inputs, val_labels = datasamp.get_val()
train_inputs, train_labels = datasamp.get_train()
train_loader = DataLoader(dataset=datasamp,
num_workers=0) # Initiate the data and labels
class LockedCybenko(torch.nn.Module
): # Cybenko with inner weight=1 and bias=-x[i]
def __init__(self):
super(LockedCybenko, self).__init__()
self.fc1 = torch.nn.Linear(1, neurons, bias=True)
self.fc1.weight.data = torch.ones(neurons).reshape(-1, 1)
self.fc1.bias.data = -torch.linspace(-1, 1, neurons).reshape(
1, -1).float()
self.fc1.weight.requires_grad_(False)
self.fc1.bias.requires_grad_(False)
self.fc2 = torch.nn.Linear(neurons, 1, bias=False)
self.relu = torch.nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
return self.fc2(x)
class SemilockedCybenko(
torch.nn.Module
): # Cybenko with inner weight=-1, one node less and free bias
def __init__(self):
super(SemilockedCybenko, self).__init__()
self.fc1 = torch.nn.Linear(1, neurons, bias=True)
self.fc1.weight.data = torch.ones(neurons - 1).reshape(-1, 1)
self.fc1.weight.requires_grad_(False)
self.fc1.bias.requires_grad_(True)
self.fc2 = torch.nn.Linear(neurons, 1, bias=False)
self.relu = torch.nn.Sigmoid()
def forward(self, x):
x = self.relu(self.fc1(x))
return self.fc2(x)
class UnlockedCybenko(torch.nn.Module
): # Cybenko with free inner weight or bias
def __init__(self):
super(UnlockedCybenko, self).__init__()
self.fc1 = torch.nn.Linear(1, neurons, bias=True)
self.fc2 = torch.nn.Linear(neurons, 1, bias=True)
self.relu = torch.nn.Sigmoid()
def forward(self, x):
x = self.relu(self.fc1(x))
return self.fc2(x)
class Network(torch.nn.Module): # Arbitrary network
def __init__(self):
super(Network, self).__init__()
self.fc1 = torch.nn.Linear(1, neurons, bias=True)
self.fc2 = torch.nn.Linear(neurons, 2 * neurons, bias=True)
self.fc3 = torch.nn.Linear(2 * neurons, 1, bias=True)
self.relu = torch.nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
return self.fc3(x)
model = Network()
criterion = torch.nn.MSELoss(reduction="sum")
optimizer = torch.optim.SGD(model.parameters(), lr=0.005)
lr_finder = LRFinder(model, optimizer, criterion)
lr_finder.range_test(train_loader,
start_lr=0.001,
end_lr=1.5,
num_iter=1000)
lr_finder.reset(
) # to reset the model and optimizer to their initial state
learning = lr_finder.history.get('lr')[np.argmin(
lr_finder.history.get('loss'))]
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
EL2Val = []
EL2train = []
ELinf = []
EL2 = [] # L2 integral between f and u_teta
for epoch in range(iter):
x = []
ytrue = []
ypred = []
for i, (inputs, labels) in enumerate(train_loader):
y_pred = model(inputs)
loss = criterion(y_pred, labels)
x.append(inputs.data.numpy())
ytrue.append(labels.data.numpy())
ypred.append(y_pred.data.numpy())
optimizer.zero_grad()
loss.backward()
optimizer.step()
def modelonx(x):
return model(
torch.tensor(x.reshape(-1, 1).tolist(),
requires_grad=False)).data.numpy().reshape(1, -1)
def L2error(x):
return (modelonx(x) - np.array(truef(x, fun)).reshape(1, -1))**2
ELinf.append(max(abs(val_labels - model(val_inputs))))
EL2.append(quadrature(L2error, -1, 1)[0][0])
EL2Val.append(criterion(val_labels, model(val_inputs)))
EL2train.append((criterion(train_labels, model(train_inputs))))
print(
f'Epoch: {epoch} L2 Error on training : {EL2train[-1]:.6e} | L2 Error on validation : {EL2Val[-1]:.6e} | L2 on [-1,1] : {EL2[-1]:.6e}'
)
if epoch % 5 == 0:
fig, ax = pl.subplots(nrows=1, ncols=2)
plotrange = np.linspace(a - 0.1, b + 0.1, 100)
""" Function and Model Plot"""
ax[0].scatter(val_inputs.data.numpy(),
val_labels.data.numpy(),
c='red',
s=15)
ax[0].scatter(train_inputs, train_labels, s=15)
ax[0].plot(
plotrange,
model(torch.linspace(a - 0.1, b + 0.1,
100).reshape(-1, 1)).data.numpy(), 'r')
""" # Code qui permet d'afficher la fonction linéaire par morceau
alpha = model.fc2.weight.data.numpy()[0]
X = -model.fc1.bias.data.numpy()[0]
ReLU = lambda t : np.where(t<=0,0,t)
ax[0].plot(xx,alpha[0]*ReLU(xx-X[0])+alpha[1]*ReLU(xx-X[1])+alpha[2]*ReLU(xx-X[2])+alpha[3]*ReLU(xx-X[3])+alpha[4]*ReLU(xx-X[4])+alpha[5]*ReLU(xx-X[5]))
"""
ax[0].plot(plotrange, truef(plotrange, fun), c='blue')
#ax[0].plot(np.linspace(a-0.1,b+0.1,100),np.polyval(np.polyfit(train_inputs.data.numpy().reshape(1,-1)[0],train_labels.data.numpy().reshape(1,-1)[0],10),np.linspace(a-0.1,b+0.1,100)),c='green')
if fun == 7:
ax[0].plot(plotrange, maclaurin(plotrange, 50), c='green')
ax[0].set_ylim(-0.1, 1.1)
""" Error Plot """
ax[1].semilogy(range(epoch + 1), EL2Val, color='red')
ax[1].semilogy(range(epoch + 1), EL2train, color='blue')
#ax[1].semilogy(range(epoch+1),EL2,color='magenta')
#ax[1].semilogy(range(epoch+1),ELinf,color='black')
pl.show()
return model
def run_lr_finder(
args,
model,
train_loader,
optimizer,
criterion,
val_loader=None,
verbose=True,
show=True,
figpth=None,
device=None,
recommender="logmean14",
fieldnames=None,
outfile_path=None,
hparams=None,
):
if verbose:
print("Running learning rate finder")
if args.mix_pre_apex:
model, optimizer = amp.initialize(model, optimizer, opt_level="O2")
lr_finder = LRFinder(model, optimizer, criterion, device=device)
min_lr = 1e-7 if args.model == 'mlp' else 1e-10
lr_finder.range_test(
train_loader,
val_loader=val_loader,
start_lr=min_lr,
end_lr=10,
num_iter=200,
diverge_th=3,
)
min_index = np.argmin(lr_finder.history["loss"])
lr_at_min = lr_finder.history["lr"][min_index]
min_loss = lr_finder.history["loss"][min_index]
max_index = np.argmax(lr_finder.history["loss"][:min_index])
lr_at_max = lr_finder.history["lr"][max_index]
max_loss = lr_finder.history["loss"][max_index]
# Outputting data to CSV at end of epoch
if fieldnames and outfile_path:
with open(outfile_path, mode='a') as out_file:
writer = csv.DictWriter(out_file,
fieldnames=fieldnames,
lineterminator='\n')
writer.writerow({
'hp_idx': args.hp_idx,
'hyperparam_set': hparams,
'seed': args.seed,
'lr': lr_finder.history["lr"],
'loss': lr_finder.history["loss"]
})
if not show and not figpth:
lr_steepest = None
else:
if verbose:
print("Plotting learning rate finder results")
hf = plt.figure(figsize=(15, 9))
ax = plt.axes()
_, lr_steepest = lr_finder.plot(skip_start=0,
skip_end=3,
log_lr=True,
ax=ax)
ylim = np.array([min_loss, max_loss])
ylim += 0.1 * np.diff(ylim) * np.array([-1, 1])
plt.ylim(ylim)
plt.tick_params(reset=True, color=(0.2, 0.2, 0.2))
plt.tick_params(labelsize=14)
ax.minorticks_on()
ax.tick_params(direction="out")
init_loss = lr_finder.history["loss"][0]
loss_12 = min_loss + 0.5 * (max_loss - min_loss)
index_12 = max_index + np.argmin(
np.abs(
np.array(lr_finder.history["loss"][max_index:min_index]) -
loss_12))
lr_12 = lr_finder.history["lr"][index_12]
loss_13 = min_loss + 1 / 3 * (max_loss - min_loss)
index_13 = max_index + np.argmin(
np.abs(
np.array(lr_finder.history["loss"][max_index:min_index]) -
loss_13))
lr_13 = lr_finder.history["lr"][index_13]
loss_23 = min_loss + 2 / 3 * (max_loss - min_loss)
index_23 = max_index + np.argmin(
np.abs(
np.array(lr_finder.history["loss"][max_index:min_index]) -
loss_23))
lr_23 = lr_finder.history["lr"][index_23]
loss_14 = min_loss + 1 / 4 * (max_loss - min_loss)
index_14 = max_index + np.argmin(
np.abs(
np.array(lr_finder.history["loss"][max_index:min_index]) -
loss_14))
lr_14 = lr_finder.history["lr"][index_14]
if recommender == "div10":
lr_recomend = np.exp(np.mean([np.log(lr_at_min / 10), np.log(lr_12)]))
elif recommender == "min12":
lr_recomend = np.min([lr_at_min / 10, lr_12])
elif recommender == "min13":
lr_recomend = np.min([lr_at_min / 10, lr_13])
elif recommender == "min14":
lr_recomend = np.min([lr_at_min / 10, lr_14])
elif recommender == "logmean12":
lr_recomend = np.exp(np.mean([np.log(lr_at_min / 10), np.log(lr_12)]))
elif recommender == "logmean13":
lr_recomend = np.exp(np.mean([np.log(lr_at_min / 10), np.log(lr_13)]))
elif recommender == "logmean14":
lr_recomend = np.exp(np.mean([np.log(lr_at_min / 10), np.log(lr_14)]))
if verbose:
if lr_steepest is not None:
print("LR at steepest grad: {:.3e} (red)".format(lr_steepest))
print("LR at minimum loss : {:.3e}".format(lr_at_min))
print("LR a tenth of min : {:.3e} (orange)".format(lr_at_min / 10))
print("LR when 1/4 up : {:.3e} (yellow)".format(lr_14))
print("LR when 1/3 up : {:.3e} (blue)".format(lr_13))
print("LR when 1/2 up : {:.3e} (cyan)".format(lr_12))
print("LR when 2/3 up : {:.3e} (green)".format(lr_23))
print("LR recommended : {:.3e} (black)".format(lr_recomend))
if show or figpth:
ax.axvline(x=lr_steepest, color="red")
ax.axvline(x=lr_at_min / 10, color="orange")
ax.axvline(x=lr_14, color="yellow")
ax.axvline(x=lr_13, color="blue")
ax.axvline(x=lr_12, color="cyan")
ax.axvline(x=lr_23, color="green")
ax.axvline(x=lr_recomend, color="black", ls=":")
if figpth:
# Save figure
os.makedirs(os.path.dirname(figpth), exist_ok=True)
plt.savefig(figpth)
if verbose:
print("LR Finder results saved to {}".format(figpth))
if show:
plt.show()
return lr_recomend
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed_all(SEED)
legend = []
fig = None
for wd in [0, .1, 1e-2, 1e-3, 1e-4]:
for dp in [.1, 0.2, .3]:
nerbert = BertForTokenClassificationCustom.from_pretrained(pretrained_model_name_or_path=MODEL_NAME,
num_labels=len(labels2ind),
hidden_dropout_prob=dp,
attention_probs_dropout_prob=dp)
# Prepare optimizer and schedule (linear warmup and decay)
optimizer = get_optimizer_with_weight_decay(model=nerbert,
optimizer=OPTIMIZER,
learning_rate=LEARNING_RATE,
weight_decay=wd)
lr_finder = LRFinder(nerbert, optimizer, nn.CrossEntropyLoss(), device='cuda')
lr_finder.range_test(train_loader=dataloader_tr, end_lr=1, num_iter=100)
fig = lr_finder.plot(ax=fig)
legend.append(f"wd: {wd}")
fig.figure.legend(legend, loc='best')
fig.figure.tight_layout()
fig.figure.show()
fig.figure.savefig('lr_finder.png')
# PyTorch import torchvision from torchvision import transforms, datasets, models import torch from torch import optim, cuda from torch.utils.data import DataLoader, sampler import torch.nn as nn from torch_lr_finder import LRFinder from utils.model import get_model, get_dataloaders model = get_model() dataloaders = get_dataloaders() # we will be using negative log likelihood as the loss function criterion = nn.CrossEntropyLoss() # we will be using the SGD optimizer as our optimizer optimizer = optim.SGD(model.fc.parameters(), lr=1e-4) lr_finder = LRFinder(model, optimizer, criterion, device='cuda') lr_finder.range_test(dataloaders['train'], end_lr=1, num_iter=2500) lr_finder.plot() lr_finder.reset()
def lr_range_test(
model,
dataset,
loss_func,
optimizer="AdamW",
batch_size=32,
num_iter=None,
skip_start=10,
skip_end=10,
start_lr=1e-7,
end_lr=10,
plot=False,
):
if num_iter is None:
num_iter = 100 + int(np.log10(10 + len(dataset)) * 50)
n_train = min(len(dataset), num_iter * batch_size)
n_val = min(int(0.3 * len(dataset)), 2 * num_iter)
log.debug("num_iter: {}, n_val: {}".format(num_iter, n_val))
split_idx = int(0.7 * len(dataset))
idx_train = np.random.choice(split_idx, size=n_train)
idx_val = np.random.choice(np.arange(split_idx, len(dataset)), size=n_val)
train_data = Subset(dataset, idx_train)
val_data = Subset(dataset, idx_val)
lrtest_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
lrtest_loader_val = DataLoader(val_data, batch_size=1024, shuffle=True)
lrtest_optimizer = create_optimizer(optimizer, model.parameters(),
start_lr)
with utils.HiddenPrints():
lr_finder = LRFinder(model, lrtest_optimizer, loss_func)
lr_finder.range_test(
lrtest_loader,
val_loader=lrtest_loader_val,
end_lr=end_lr,
num_iter=num_iter,
smooth_f=0.2, # re-consider if lr-rate varies a lot
)
lrs = lr_finder.history["lr"]
losses = lr_finder.history["loss"]
if skip_end == 0:
lrs = lrs[skip_start:]
losses = losses[skip_start:]
else:
lrs = lrs[skip_start:-skip_end]
losses = losses[skip_start:-skip_end]
if plot:
with utils.HiddenPrints():
ax, steepest_lr = lr_finder.plot(
) # to inspect the loss-learning rate graph
max_lr = None
try:
steep_idx = (np.gradient(np.array(losses))).argmin()
min_idx = (np.array(losses)).argmin()
steep_lr = lrs[steep_idx]
min_lr = lrs[min_idx]
max_lr = 10**((np.log10(steep_lr) + 2.0 * np.log10(min_lr)) / 3.0)
log.info("lr-range-test results: steep: {:.2E}, min: {:.2E}".format(
steep_lr, min_lr))
except ValueError:
log.error(
"Failed to compute the gradients, there might not be enough points."
)
if max_lr is not None:
log.info("learning rate range test selected lr: {:.2E}".format(max_lr))
else:
max_lr = 0.1
log.error("lr range test failed. defaulting to lr: {}".format(max_lr))
with utils.HiddenPrints():
lr_finder.reset(
) # to reset the model and optimizer to their initial state
return max_lr
def main_worker(index, opt):
random.seed(opt.manual_seed)
np.random.seed(opt.manual_seed)
torch.manual_seed(opt.manual_seed)
if index >= 0 and opt.device.type == 'cuda':
opt.device = torch.device(f'cuda:{index}')
if opt.distributed:
opt.dist_rank = opt.dist_rank * opt.ngpus_per_node + index
dist.init_process_group(backend='nccl',
init_method=opt.dist_url,
world_size=opt.world_size,
rank=opt.dist_rank)
opt.batch_size = int(opt.batch_size / opt.ngpus_per_node)
opt.n_threads = int(
(opt.n_threads + opt.ngpus_per_node - 1) / opt.ngpus_per_node)
opt.is_master_node = not opt.distributed or opt.dist_rank == 0
model = generate_model(opt)
if opt.batchnorm_sync:
assert opt.distributed, 'SyncBatchNorm only supports DistributedDataParallel.'
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
if opt.pretrain_path:
model = load_pretrained_model(model, opt.pretrain_path, opt.model,
opt.n_finetune_classes)
if opt.dropout:
n_classes = opt.n_classes
if opt.pretrain_path is not None:
n_classes = opt.n_finetune_classes
model = replace_fc_layer(model=model,
dropout_factor=opt.dropout_factor,
n_classes=n_classes)
if opt.resume_path is not None:
model = resume_model(opt.resume_path, opt.arch, model)
model = make_data_parallel(model, opt.distributed, opt.device)
if opt.pretrain_path:
parameters = get_fine_tuning_parameters(model, opt.ft_begin_module)
else:
parameters = model.parameters()
if opt.is_master_node:
print(model)
if opt.labelsmoothing:
criterion = LabelSmoothingCrossEntropy().to(opt.device)
else:
criterion = CrossEntropyLoss().to(opt.device)
if not opt.no_train:
(train_loader, train_sampler, train_logger, train_batch_logger,
optimizer, scheduler) = get_train_utils(opt, parameters)
if opt.resume_path is not None:
opt.begin_epoch, optimizer, scheduler = resume_train_utils(
opt.resume_path, opt.begin_epoch, optimizer, scheduler)
if opt.overwrite_milestones:
scheduler.milestones = opt.multistep_milestones
if not opt.no_val:
val_loader, val_logger = get_val_utils(opt)
if opt.tensorboard and opt.is_master_node:
from torch.utils.tensorboard import SummaryWriter
if opt.begin_epoch == 1:
tb_writer = SummaryWriter(log_dir=opt.result_path)
else:
tb_writer = SummaryWriter(log_dir=opt.result_path,
purge_step=opt.begin_epoch)
else:
tb_writer = None
if opt.lr_finder and not opt.no_train and not opt.no_val:
print(
"Performing Learning Rate Search\nWith Leslie Smith's approach...")
lr_finder = LRFinder(model, optimizer, criterion, device=opt.device)
lr_finder.range_test(train_loader,
val_loader=val_loader,
start_lr=opt.learning_rate,
end_lr=opt.lrf_end_lr,
num_iter=opt.lrf_num_it,
step_mode=opt.lrf_mode)
lr_finder.plot(log_lr=False)
with (opt.result_path / 'lr_search.json').open('w') as results_file:
json.dump(lr_finder.history, results_file, default=json_serial)
lr_finder.reset()
return
prev_val_loss = None
for i in range(opt.begin_epoch, opt.n_epochs + 1):
if not opt.no_train:
if opt.distributed:
train_sampler.set_epoch(i)
#current_lr = get_lr(optimizer)
train_epoch(i, train_loader, model, criterion, optimizer,
opt.device, train_logger, train_batch_logger,
scheduler, opt.lr_scheduler, tb_writer,
opt.distributed)
if i % opt.checkpoint == 0 and opt.is_master_node:
save_file_path = opt.result_path / 'save_{}.pth'.format(i)
save_checkpoint(save_file_path, i, opt.arch, model, optimizer,
scheduler)
if not opt.no_val:
prev_val_loss = val_epoch(i, val_loader, model, criterion,
opt.device, val_logger, tb_writer,
opt.distributed)
if not opt.no_train and opt.lr_scheduler == 'multistep':
scheduler.step()
elif not opt.no_train and opt.lr_scheduler == 'plateau':
scheduler.step(prev_val_loss)
elif not opt.no_train and opt.lr_scheduler == 'cosineannealing':
scheduler.step()
if opt.inference:
inference_loader, inference_class_names = get_inference_utils(opt)
inference_result_path = opt.result_path / '{}.json'.format(
opt.inference_subset)
inference.inference(inference_loader, model, inference_result_path,
inference_class_names, opt.inference_no_average,
opt.output_topk)
from torch_lr_finder import LRFinder
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.0001, momentum=0.9)
lr_finder = LRFinder(model, optimizer, criterion, device=device)
lr_finder.range_test(trainloader, end_lr=10, num_iter=1564, step_mode='exp')
lr_finder.plot() # to inspect the loss-learning rate graph
lr_finder.reset() # to reset the model and optimizer to their initial state
a = zip(lr_finder.history['lr'], lr_finder.history['loss'])
best_lrloss = sorted(a, key=take_lr, reverse=False)[:50]
def take_lr(x):
# print(x)
return x[1]
tup = zip(lr_finder.history['loss'], lr_finder.history['lr'])
sorted(tup, key=take_lr, reverse=False)[:50]
class shrink:
def __init__(self, config):
self.config = config
def apply_augmentations(self):
pass
valloader = dataloaders['val']
class CustomTrainIter(TrainDataLoaderIter):
# My dataloader returns index, X, y
def inputs_labels_from_batch(self, batch_data):
return batch_data[1], batch_data[2]
class CustomValIter(ValDataLoaderIter):
# My dataloader returns index, X, y
def inputs_labels_from_batch(self, batch_data):
return batch_data[1], batch_data[2]
custom_train_iter = CustomTrainIter(trainloader)
custom_val_iter = CustomValIter(valloader)
lr_finder.range_test(custom_train_iter,
end_lr=10,
num_iter=params.num_epochs,
step_mode='exp')
# Val loader does not work
#lr_finder.range_test(custom_train_iter, val_loader=custom_val_iter, end_lr=10, num_iter=params.num_epochs, step_mode='exp')
mylrs = lr_finder.history['lr']
mylosses = lr_finder.history['loss']
min_grad_idx = np.gradient(np.array(mylosses)).argmin()
print(f'Suggested lr: {mylrs[min_grad_idx]}')
lr_metrics = {'lr': mylrs, 'loss': mylosses}
fname = os.path.join(args.model_dir, f'lr_metrics.json')
with open(fname, 'w') as f:
f.write(json.dumps(lr_metrics))
'''
# Train
print(f'Fold {fold}')
print('-'*10)
class Shrink:
'''Shrinks the code and gets the output'''
def __init__(self, in_config):
self.config = in_config
self.class_names = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
self.mean = (0.491, 0.482, 0.446)
self.std = (0.247, 0.243, 0.261)
self.device = "cuda" if torch.cuda.is_available else "cpu"
self.model_path = self.config['modelpath']['args']
plt.style.use("dark_background")
def seed_everything(self,seed: int) -> None:
'''Seeds the Code so that we get predictable outputs'''
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
def load_data(self, train_transforms, test_transforms, in_dir='./data'):
'''Downloads the dataset and returns train and testloaders after applying the Transformations'''
trainset = datasets.CIFAR10(in_dir, train=True, download=True, transform=train_transforms())
testset = datasets.CIFAR10(in_dir, train=False, download=True, transform=test_transforms())
self.trainloader = torch.utils.data.DataLoader(trainset, **self.config['train_data_loader']['args'])
self.testloader = torch.utils.data.DataLoader(testset, **self.config['test_data_loader']['args'])
return self.trainloader, self.testloader
def load_imagenet_data(self, train_transforms, test_transforms):
'''Loads the imagenet dataset'''
self.trainloader, self.testloader = get_imagenet_loader(
train_transforms, test_transforms,
self.config['train_data_loader']['args'],
self.config['test_data_loader']['args'])
def mean_std_dev(self):
pass
def show_data(self, mode='train', n=25):
'''Plots the images on a gridplot to show the images passed via dataloader'''
figure = plt.figure(figsize=(20,20))
images = None
labels = None
if mode.lower() == 'train':
images, labels = next(iter(self.trainloader))
labels = np.array(labels)
elif mode.lower() == 'test':
images, labels = next(iter(self.testloader))
labels = np.array(labels)
images = self.denormalize(images)
# images = self.denormalize(images)
for index in range(1,n+1):
plt.subplot(5,5,index)
plt.axis('off')
# Gets the first n images of the dataset
plt.imshow(np.transpose(images[index], (1,2,0))) # Plots the dataset
# plt.title(self.class_names[labels[index]])
def get_batched_data(self,in_data):
'''Takes in the list data and outputs data, targets and preds'''
in_imgs = []
in_preds = []
in_targets = []
for index, i in enumerate(in_data):
in_imgs.append(i[0])
in_preds.append(i[1])
in_targets.append(i[2])
return torch.stack(in_imgs), torch.stack(in_preds), torch.stack(in_targets)
def plot_gradcam(self, target_layers, images, pred, target, nimgs):
'''Plot GradCam - '''
index = 0
in_data = None
# model.load_state_dict(torch.load(self.model_path))
images = images[index:nimgs].to(self.device)
target = target[index:nimgs]
pred = pred[index:nimgs]
gcam_layers, predicted_probs, predicted_classes = get_gradcam(images, target, self.model, self.device, target_layers)
# get the denomarlization function
unorm = UnNormalize(mean=self.mean, std=self.std)
plt_gradcam(gcam_layers=gcam_layers, images=images, target_labels=target, predicted_labels= predicted_classes, class_labels= self.class_names, denormalize= unorm)
def get_gradoutput(self, misclassified=False):
'''Outputs a gradcam output when Inputting an image'''
if misclassified:
in_data = self.misclassified
else:
in_data = self.correct_classified
target_layers = ["layer1", "layer2", "layer3", "layer4"]
imgs, preds, targets = self.get_batched_data(in_data)
self.plot_gradcam(target_layers, imgs, preds, targets, 25)
def denormalize(self,tensor):
'''Denormalize the data'''
if not tensor.ndimension() == 4:
raise TypeError('tensor should be 4D')
mean = torch.FloatTensor(self.mean).view(1, 3, 1, 1).expand_as(tensor).to(tensor.device)
std = torch.FloatTensor(self.std).view(1, 3, 1, 1).expand_as(tensor).to(tensor.device)
return tensor.mul(std).add(mean)
def get_model(self, train=True):
self.model = get_attributes(model_arch, 'model', self.config).to(self.device)
self.epochs = self.config['epochs']
if train:
'''Trains the model and sends the output'''
criterion = nn.CrossEntropyLoss(reduction='mean')
optimizer = optim.SGD(self.model.parameters(),lr = 0.01, momentum=0.9)# **self.config['optimizer']['args'])
max_at_epoch = 5
self.best_lr = self.config['best_lr']
pct_start_val = (max_at_epoch * len(self.trainloader)) / (self.epochs * len(self.trainloader))
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=self.best_lr,
total_steps = len(self.trainloader) *self.epochs,
steps_per_epoch=len(self.trainloader),
epochs=self.epochs,
pct_start=pct_start_val,
anneal_strategy='cos',
div_factor=10,
final_div_factor=10
)
self.train_acc = []
self.train_losses = []
self.test_acc = []
self.test_losses = []
self.lr_metric = []
EPOCHS = self.epochs
print(f'Starting Training for {EPOCHS} Epochs')
for i in range(EPOCHS):
lr_value = [group['lr']
for group in optimizer.param_groups][0]
self.lr_metric.append(lr_value)
print(f'EPOCHS : {i} Learning Rate: {lr_value}')
model_training(self.model, self.device, self.trainloader, optimizer, scheduler, self.train_acc, self.train_losses, criterion, l1_loss=False)
torch.save(self.model.state_dict(), self.model_path)
self.misclassified, self.correct_classified = model_testing(self.model, self.device, self.testloader, self.test_acc, self.test_losses, criterion)
else:
return self.model
def test_model(self):
'''Loads and saves the test model'''
test_losses = []
test_acc = []
model_path = 'latest_model.h5'
self.model.load_state_dict(torch.load(model_path))
self.misclassified, self.correct_classified = model_testing(self.model, self.device, self.testloader, test_acc, test_losses)
return self.misclassified, self.correct_classified
def findbestlr(self):
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(self.model.parameters(), lr= 0.01, momentum= 0.95, weight_decay= 0.0005)
self.lr_finder = LRFinder(self.model, optimizer, criterion, device=self.device)
self.lr_finder.range_test(self.trainloader, **self.config['range_test']['args'])
self.lr_finder.plot() # to inspect the loss-learning rate graph
self.lr_finder.reset() # to reset the model and optimizer to their initial state
return self.lr_finder
def model_metrics(self):
fig, axs = plt.subplots(2,2, figsize=(15,10))
axs[0,0].plot(self.train_losses)
axs[0,0].set_title('Train_Losses')
axs[0,1].plot(self.train_acc)
axs[0,1].set_title('Training_Accuracy')
axs[1,0].plot(self.test_losses)
axs[1,0].set_title('Test_Losses')
axs[1,1].plot(self.test_acc)
axs[1,1].set_title('Test_Accuracy')
def print_visualization(self, input_size):
'''Prints a visualization graph for Torch models'''
C, H, W = input_size
x = torch.zeros(1, C, H, W, dtype=torch.float, requires_grad=False)
x = x.to(self.device)
out = self.model(x)
# plot graph of variable, not of a nn.Module
dot_graph = torchviz.make_dot(out)
dot_graph.view()
return dot_graph
criterion = metrics.MyLossFunc()
optimiser = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimiser,
'min',
patience=5,
verbose=True)
# find learning rate
if find_LR and not torch.cuda.is_available():
if learning_rate > 0.0001:
print(
f"Selected initial learning rate too high.\nLearning rate changed to 0.0001"
)
optimiser = torch.optim.Adam(model.parameters(), lr=0.0001)
lr_finder = LRFinder(model, optimiser, criterion, device=device)
lr_finder.range_test(train_loader, end_lr=200, num_iter=200)
lr_finder.plot() # to inspect the loss-learning rate graph
lr_finder.reset(
) # to reset the model and optimizer to their initial state
subprocess.Popen(["kill", "-9", f"{TB_process.pid}"])
sys.exit("Learning rate plot finished")
# computational graph
if print_comp_graph:
for sample in train_loader:
writer.add_graph(model, sample[0].float())
writer.close()
# training loop
running_train_loss = 0.
running_valid_loss = 0.
def init_weights(m):
if type(m) == nn.Linear:
torch.nn.init.xavier_uniform(m.weight)
#%%
torch.manual_seed(42)
net = MyNet(50, 40, 20, 3, 5, 0.5)
criterion = nn.BCELoss()
optim = torch.optim.Adam(net.parameters(), lr=10**-2)
# Explicitly init weights!
net.apply(init_weights)
#%%
lrf = LRFinder(net, optim, criterion)
lrf.range_test(train_loader, start_lr=0.0001, end_lr=1)
lrf.plot()
lrf.reset()
#%%
# seemingly best: Adam + cyclical LR + exp_range decay of learning rate
N_EPOCHS = 30
scheduler = torch.optim.lr_scheduler.CyclicLR(
optim,
10**-4,
10**-2,
mode='exp_range',
step_size_up=(xtrain.size(0) / BATCHSIZE) * 2,
cycle_momentum=False)
history = {'train_loss': [], 'val_loss': []}
elif model_name == 'vgg': trans.insert(0, torchvision.transforms.Resize((244,244))) trans.insert(0, fancy_pca()) trans.insert(0, torchvision.transforms.RandomRotation(180)) trans.insert(0, torchvision.transforms.RandomHorizontalFlip(p=0.5)) train_dataset = torchvision.datasets.ImageFolder( root=data_path, transform=torchvision.transforms.Compose(trans) ) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=256, num_workers=0, shuffle=True ) return train_loader trainloader = train_loader(model_name='inception') model = Inception.inception_v3(img_size=256) criterion = nn.CrossEntropyLoss() #optimizer = torch.optim.SGD(model.parameters(), lr=1e-6, momentum = 0.9, weight_decay = 5e-3, nesterov=True) optimizer = torch.optim.AdamW(model.parameters(), lr=1e-7, weight_decay=1e-4) lr_finder = LRFinder(model, optimizer, criterion, device="cuda") lr_finder.range_test(trainloader, end_lr=1, num_iter=100) lr_finder.plot() # to inspect the loss-learning rate graph lr_finder.reset() # to reset the model and optimizer to their initial state
transforms = utils.build_transforms(second_stage=True)
loaders = utils.build_loaders(data_dir,
transforms,
batch_sizes,
num_workers,
second_stage=True)
model = utils.build_model(backbone,
second_stage=True,
num_classes=num_classes,
ckpt_pretrained=ckpt_pretrained).cuda()
optim = utils.build_optim(model, optimizer_params, scheduler_params,
criterion_params)
criterion, optimizer, scheduler = (
optim["criterion"],
optim["optimizer"],
optim["scheduler"],
)
lr_finder = LRFinder(model, optimizer, criterion, device="cuda")
lr_finder.range_test(loaders["train_features_loader"],
end_lr=1,
num_iter=300)
fig, ax = plt.subplots()
lr_finder.plot(ax=ax)
fig.savefig(
"lr_finder_plots/supcon_{}_{}_bs_{}_stage_{}_lr_finder.png".format(
optimizer_params["name"],
data_dir.split("/")[-1], batch_sizes["train_batch_size"],
'second'))
def train(model, device, train_loader, test_loader, EPOCH, FACTOR, PATIENCE,
MOMENTUM, LEARNING_RATE):
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),
lr=0.001,
momentum=0.9,
nesterov=True,
weight_decay=0.0001)
scheduler = optim.lr_scheduler.OneCycleLR(optimizer=optimizer,
max_lr=0.008,
pct_start=5 / 24,
epochs=24,
steps_per_epoch=len(trainloader))
train_losses = []
train_acc = []
test_losses = []
test_acc = []
for epoch in range(EPOCH):
correct = 0
processed = 0
pbar = tqdm(train_loader)
model.train()
for batch_idx, (data, target) in enumerate(pbar):
# get samples
data, target = data.to(device), target.to(device)
# Init
optimizer.zero_grad()
# In PyTorch, we need to set the gradients to zero before starting to do backpropragation because PyTorch accumulates the gradients on subsequent backward passes.
# Because of this, when you start your training loop, ideally you should zero out the gradients so that you do the parameter update correctly.
# Predict
y_pred = model(data)
# Calculate loss
# regularization_loss = 0
# for param in model.parameters():
# regularization_loss += torch.sum(abs(param))
# classify_loss = criterion(y_pred,target)
loss = F.nll_loss(y_pred, target)
#loss = classify_loss + LAMDA * regularization_loss
# train_losses.append(loss)
# Backpropagation
loss.backward()
optimizer.step()
scheduler.step()
# Update pbar-tqdm
pred = y_pred.argmax(
dim=1,
keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
processed += len(data)
pbar.set_description(
desc=
f'Loss={loss.item()} Batch_id={batch_idx} Accuracy={100*correct/processed:0.2f}'
)
# train_acc.append(100*correct/processed)
train_losses.append(loss.item())
train_acc.append(100 * correct / processed)
img, true_wrong, pred_wrong, tst_acc, tst_loss = test(
model, device, test_loader)
test_losses.append(tst_loss)
test_acc.append(tst_acc)
lr_finder = LRFinder(model, optimizer, criterion, device)
lr_finder.range_test(train_loader, end_lr=100, num_iter=100)
lr_finder.plot() # to inspect the loss-learning rate graph
# lr_finder.reset()
return train_losses, train_acc, model, img, true_wrong, pred_wrong, test_acc, test_losses, lr_finder