def _train(model, optimizer, scheduler, checkpointer, epochs, train_loader,
test_loader, stat_tracker, log_dir, device):
'''
Training loop to train classifiers on top of an encoder with fixed weights.
-- e.g., use this for eval or running on new data
'''
# If mixed precision is on, will add the necessary hooks into the model and
# optimizer for half precision conversions
model, optimizer = mixed_precision.initialize(model, optimizer)
# ...
time_start = time.time()
total_updates = 0
next_epoch, total_updates = checkpointer.get_current_position(
classifier=True)
for epoch in range(next_epoch, epochs):
epoch_updates = 0
epoch_stats = AverageMeterSet()
for _, ((images1, images2), labels) in enumerate(train_loader):
# get data and info about this minibatch
images1 = images1.to(device)
images2 = images2.to(device)
labels = labels.to(device)
# run forward pass through model and collect activations
res_dict = model(x1=images1, x2=images2, class_only=True)
lgt_glb_mlp, lgt_glb_lin = res_dict['class']
# compute total loss for optimization
loss = (loss_xent(lgt_glb_mlp, labels) +
loss_xent(lgt_glb_lin, labels))
# do optimizer step for encoder
optimizer.zero_grad()
mixed_precision.backward(
loss, optimizer) # special mixed precision stuff
optimizer.step()
# record loss and accuracy on minibatch
epoch_stats.update('loss', loss.item(), n=1)
update_train_accuracies(epoch_stats, labels, lgt_glb_mlp,
lgt_glb_lin)
# shortcut diagnostics to deal with long epochs
total_updates += 1
epoch_updates += 1
if (total_updates % 100) == 0:
time_stop = time.time()
spu = (time_stop - time_start) / 100.
print(
'Epoch {0:d}, {1:d} updates -- {2:.4f} sec/update'.format(
epoch, epoch_updates, spu))
time_start = time.time()
# step learning rate scheduler
scheduler.step(epoch)
# record diagnostics
test_model(model, test_loader, device, epoch_stats, max_evals=500000)
epoch_str = epoch_stats.pretty_string(ignore=model.tasks)
diag_str = '{0:d}: {1:s}'.format(epoch, epoch_str)
print(diag_str)
sys.stdout.flush()
stat_tracker.record_stats(epoch_stats.averages(epoch, prefix='eval/'))
checkpointer.update(epoch + 1, total_updates, classifier=True)
def _train(model, optimizer, scheduler, checkpointer, epochs, train_loader,
test_loader, stat_tracker, log_dir, device):
'''
Training loop to train classifiers on top of an encoder with fixed weights.
-- e.g., use this for eval or running on new data
'''
# If mixed precision is on, will add the necessary hooks into the model and
# optimizer for half precision conversions
model, optimizer = mixed_precision.initialize(model, optimizer)
# ...
time_start = time.time()
total_updates = 0
next_epoch, total_updates = checkpointer.get_current_position(
decoder=True
) # Nawid - I think if I am continuing training, then it finds the current epoch it is on
for epoch in range(next_epoch, epochs):
epoch_updates = 0
epoch_stats = AverageMeterSet()
#for _, ((images1, images2), labels) in enumerate(test_loader): # Nawid - loads the two different images
for _, ((images1, images2), labels) in enumerate(
train_loader): # Nawid - loads the two different images
# get data and info about this minibatch
images1 = images1.to(device)
images2 = images2.to(device)
labels = labels.to(device)
# run forward pass through model and collect activations
res_dict = model(
x1=images1, x2=images2, decoder_only=True
) # Nawid - Only requires the first input, produces the dictionary of outputs which should be the encoder value and a decoded output
image_reconstructions = res_dict[
'decoder_output'] # Nawid- Obtains the logits from the mlp and the linear
# compute total loss for optimization
loss = loss_MSE(
images1, image_reconstructions
) # Nawid- Compute the loss using the mlp and the linear layer - there is no loss term related to the encoder and the input to the loss term is the logits which
# do optimizer step for encoder
optimizer.zero_grad()
mixed_precision.backward(
loss, optimizer) # special mixed precision stuff
optimizer.step() # Nawid - Gradient step
# record loss and accuracy on minibatch
epoch_stats.update('loss', loss.item(), n=1)
# - NEED TO CHANGE THIS FOR THE DECODER update_train_accuracies(epoch_stats, labels, lgt_glb_mlp, lgt_glb_lin) # Nawid - updates the accuracies
# shortcut diagnostics to deal with long epochs
total_updates += 1
epoch_updates += 1
if (total_updates % 100) == 0:
save_reconstructions(images1, image_reconstructions)
time_stop = time.time()
spu = (time_stop - time_start) / 100.
print(
'Epoch {0:d}, {1:d} updates -- {2:.4f} sec/update'.format(
epoch, epoch_updates, spu))
time_start = time.time()
# step learning rate scheduler
scheduler.step(epoch)
# record diagnostics
test_decoder_model(
model, test_loader, device, epoch_stats,
max_evals=500000) # Nawid - NEED TO CHANGE FOR DECODER I BELIEVE
epoch_str = epoch_stats.pretty_string(ignore=model.tasks)
diag_str = '{0:d}: {1:s}'.format(epoch, epoch_str)
print(diag_str)
sys.stdout.flush()
stat_tracker.record_stats(
epoch_stats.averages(
epoch,
prefix='decoder/')) # NAWID - NEED TO CHANGE FOR DECODER
checkpointer.update(epoch + 1, total_updates,
decoder=True) # Nawid - Updates the decoder