def finalize_metrics(self, ks=(1, 5)):
"""
Calculate and log the final ensembled metrics.
ks (tuple): list of top-k values for topk_accuracies. For example,
ks = (1, 5) correspods to top-1 and top-5 accuracy.
"""
if not all(self.clip_count == self.num_clips):
logger.warning("clip count {} ~= num clips {}".format(
self.clip_count, self.num_clips))
logger.warning(self.clip_count)
verb_topks = metrics.topk_accuracies(self.verb_video_preds,
self.verb_video_labels, ks)
noun_topks = metrics.topk_accuracies(self.noun_video_preds,
self.noun_video_labels, ks)
assert len({len(ks), len(verb_topks)}) == 1
assert len({len(ks), len(noun_topks)}) == 1
stats = {"split": "test_final"}
for k, verb_topk in zip(ks, verb_topks):
stats["verb_top{}_acc".format(k)] = "{:.{prec}f}".format(verb_topk,
prec=2)
for k, noun_topk in zip(ks, noun_topks):
stats["noun_top{}_acc".format(k)] = "{:.{prec}f}".format(noun_topk,
prec=2)
logging.log_json_stats(stats)
return (self.verb_video_preds.numpy().copy(), self.noun_video_preds.numpy().copy()), \
(self.verb_video_labels.numpy().copy(), self.noun_video_labels.numpy().copy()), \
self.metadata.copy()
def train_epoch(train_loader, model, optimizer, train_meter, cur_epoch, cfg):
"""
Perform the video training for one epoch.
Args:
train_loader (loader): video training loader.
model (model): the video model to train.
optimizer (optim): the optimizer to perform optimization on the model's
parameters.
train_meter (TrainMeter): training meters to log the training performance.
cur_epoch (int): current epoch of training.
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Enable train mode.
model.train()
if cfg.BN.FREEZE:
model.freeze_fn('bn_statistics')
train_meter.iter_tic()
data_size = len(train_loader)
for cur_iter, (inputs, labels, _, meta) in enumerate(train_loader):
# Transfer the data to the current GPU device.
if isinstance(inputs, (list, )):
for i in range(len(inputs)):
inputs[i] = inputs[i].cuda(non_blocking=True)
else:
inputs = inputs.cuda(non_blocking=True)
if isinstance(labels, (dict, )):
labels = {k: v.cuda() for k, v in labels.items()}
else:
labels = labels.cuda()
# for key, val in meta.items():
# if isinstance(val, (list,)):
# for i in range(len(val)):
# val[i] = val[i].cuda(non_blocking=True)
# else:
# meta[key] = val.cuda(non_blocking=True)
# Update the learning rate.
lr = optim.get_epoch_lr(cur_epoch + float(cur_iter) / data_size, cfg)
optim.set_lr(optimizer, lr)
if cfg.DETECTION.ENABLE:
# Compute the predictions.
preds = model(inputs, meta["boxes"])
else:
# Perform the forward pass.
preds = model(inputs)
if isinstance(labels, (dict, )):
# Explicitly declare reduction to mean.
loss_fun = losses.get_loss_func(
cfg.MODEL.LOSS_FUNC)(reduction="mean")
# Compute the loss.
loss_verb = loss_fun(preds[0], labels['verb'])
loss_noun = loss_fun(preds[1], labels['noun'])
loss = 0.5 * (loss_verb + loss_noun)
# check Nan Loss.
misc.check_nan_losses(loss)
else:
# Explicitly declare reduction to mean.
loss_fun = losses.get_loss_func(
cfg.MODEL.LOSS_FUNC)(reduction="mean")
# Compute the loss.
loss = loss_fun(preds, labels)
# check Nan Loss.
misc.check_nan_losses(loss)
# Perform the backward pass.
optimizer.zero_grad()
loss.backward()
# Update the parameters.
optimizer.step()
if cfg.DETECTION.ENABLE:
if cfg.NUM_GPUS > 1:
loss = du.all_reduce([loss])[0]
loss = loss.item()
train_meter.iter_toc()
# Update and log stats.
train_meter.update_stats(None, None, None, loss, lr)
else:
if isinstance(labels, (dict, )):
# Compute the verb accuracies.
verb_top1_acc, verb_top5_acc = metrics.topk_accuracies(
preds[0], labels['verb'], (1, 5))
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
loss_verb, verb_top1_acc, verb_top5_acc = du.all_reduce(
[loss_verb, verb_top1_acc, verb_top5_acc])
# Copy the stats from GPU to CPU (sync point).
loss_verb, verb_top1_acc, verb_top5_acc = (
loss_verb.item(),
verb_top1_acc.item(),
verb_top5_acc.item(),
)
# Compute the noun accuracies.
noun_top1_acc, noun_top5_acc = metrics.topk_accuracies(
preds[1], labels['noun'], (1, 5))
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
loss_noun, noun_top1_acc, noun_top5_acc = du.all_reduce(
[loss_noun, noun_top1_acc, noun_top5_acc])
# Copy the stats from GPU to CPU (sync point).
loss_noun, noun_top1_acc, noun_top5_acc = (
loss_noun.item(),
noun_top1_acc.item(),
noun_top5_acc.item(),
)
# Compute the action accuracies.
action_top1_acc, action_top5_acc = metrics.multitask_topk_accuracies(
(preds[0], preds[1]), (labels['verb'], labels['noun']),
(1, 5))
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
loss, action_top1_acc, action_top5_acc = du.all_reduce(
[loss, action_top1_acc, action_top5_acc])
# Copy the stats from GPU to CPU (sync point).
loss, action_top1_acc, action_top5_acc = (
loss.item(),
action_top1_acc.item(),
action_top5_acc.item(),
)
train_meter.iter_toc()
# Update and log stats.
train_meter.update_stats(
(verb_top1_acc, noun_top1_acc, action_top1_acc),
(verb_top5_acc, noun_top5_acc, action_top5_acc),
(loss_verb, loss_noun, loss), lr,
inputs[0].size(0) * cfg.NUM_GPUS)
else:
# Compute the errors.
num_topks_correct = metrics.topks_correct(
preds, labels, (1, 5))
top1_err, top5_err = [(1.0 - x / preds.size(0)) * 100.0
for x in num_topks_correct]
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
loss, top1_err, top5_err = du.all_reduce(
[loss, top1_err, top5_err])
# Copy the stats from GPU to CPU (sync point).
loss, top1_err, top5_err = (
loss.item(),
top1_err.item(),
top5_err.item(),
)
train_meter.iter_toc()
# Update and log stats.
train_meter.update_stats(top1_err, top5_err, loss, lr,
inputs[0].size(0) * cfg.NUM_GPUS)
train_meter.log_iter_stats(cur_epoch, cur_iter)
train_meter.iter_tic()
# Log epoch stats.
train_meter.log_epoch_stats(cur_epoch)
train_meter.reset()
def eval_epoch(val_loader, model, val_meter, cur_epoch, cfg):
"""
Evaluate the model on the val set.
Args:
val_loader (loader): data loader to provide validation data.
model (model): model to evaluate the performance.
val_meter (ValMeter): meter instance to record and calculate the metrics.
cur_epoch (int): number of the current epoch of training.
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Evaluation mode enabled. The running stats would not be updated.
model.eval()
val_meter.iter_tic()
for cur_iter, (inputs, labels, _, meta) in enumerate(val_loader):
# Transferthe data to the current GPU device.
if isinstance(inputs, (list, )):
for i in range(len(inputs)):
inputs[i] = inputs[i].cuda(non_blocking=True)
else:
inputs = inputs.cuda(non_blocking=True)
if isinstance(labels, (dict, )):
labels = {k: v.cuda() for k, v in labels.items()}
else:
labels = labels.cuda()
for key, val in meta.items():
if isinstance(val, (list, )):
for i in range(len(val)):
val[i] = val[i].cuda(non_blocking=True)
else:
meta[key] = val.cuda(non_blocking=True)
if cfg.DETECTION.ENABLE:
# Compute the predictions.
preds = model(inputs, meta["boxes"])
preds = preds.cpu()
ori_boxes = meta["ori_boxes"].cpu()
metadata = meta["metadata"].cpu()
if cfg.NUM_GPUS > 1:
preds = torch.cat(du.all_gather_unaligned(preds), dim=0)
ori_boxes = torch.cat(du.all_gather_unaligned(ori_boxes),
dim=0)
metadata = torch.cat(du.all_gather_unaligned(metadata), dim=0)
val_meter.iter_toc()
# Update and log stats.
val_meter.update_stats(preds.cpu(), ori_boxes.cpu(),
metadata.cpu())
else:
preds = model(inputs)
if isinstance(labels, (dict, )):
# Compute the verb accuracies.
verb_top1_acc, verb_top5_acc = metrics.topk_accuracies(
preds[0], labels['verb'], (1, 5))
# Combine the errors across the GPUs.
if cfg.NUM_GPUS > 1:
verb_top1_acc, verb_top5_acc = du.all_reduce(
[verb_top1_acc, verb_top5_acc])
# Copy the errors from GPU to CPU (sync point).
verb_top1_acc, verb_top5_acc = verb_top1_acc.item(
), verb_top5_acc.item()
# Compute the noun accuracies.
noun_top1_acc, noun_top5_acc = metrics.topk_accuracies(
preds[1], labels['noun'], (1, 5))
# Combine the errors across the GPUs.
if cfg.NUM_GPUS > 1:
noun_top1_acc, noun_top5_acc = du.all_reduce(
[noun_top1_acc, noun_top5_acc])
# Copy the errors from GPU to CPU (sync point).
noun_top1_acc, noun_top5_acc = noun_top1_acc.item(
), noun_top5_acc.item()
# Compute the action accuracies.
action_top1_acc, action_top5_acc = metrics.multitask_topk_accuracies(
(preds[0], preds[1]), (labels['verb'], labels['noun']),
(1, 5))
# Combine the errors across the GPUs.
if cfg.NUM_GPUS > 1:
action_top1_acc, action_top5_acc = du.all_reduce(
[action_top1_acc, action_top5_acc])
# Copy the errors from GPU to CPU (sync point).
action_top1_acc, action_top5_acc = action_top1_acc.item(
), action_top5_acc.item()
val_meter.iter_toc()
# Update and log stats.
val_meter.update_stats(
(verb_top1_acc, noun_top1_acc, action_top1_acc),
(verb_top5_acc, noun_top5_acc, action_top5_acc),
inputs[0].size(0) * cfg.NUM_GPUS)
else:
# Compute the errors.
num_topks_correct = metrics.topks_correct(
preds, labels, (1, 5))
# Combine the errors across the GPUs.
top1_err, top5_err = [(1.0 - x / preds.size(0)) * 100.0
for x in num_topks_correct]
if cfg.NUM_GPUS > 1:
top1_err, top5_err = du.all_reduce([top1_err, top5_err])
# Copy the errors from GPU to CPU (sync point).
top1_err, top5_err = top1_err.item(), top5_err.item()
val_meter.iter_toc()
# Update and log stats.
val_meter.update_stats(top1_err, top5_err,
inputs[0].size(0) * cfg.NUM_GPUS)
val_meter.log_iter_stats(cur_epoch, cur_iter)
val_meter.iter_tic()
# Log epoch stats.
is_best_epoch = val_meter.log_epoch_stats(cur_epoch)
val_meter.reset()
return is_best_epoch
def eval_epoch(val_loader,
model,
val_meter,
cur_epoch,
cfg,
writer=None,
wandb_log=False):
"""
Evaluate the model on the val set.
Args:
val_loader (loader): data loader to provide validation data.
model (model): model to evaluate the performance.
val_meter (ValMeter): meter instance to record and calculate the metrics.
cur_epoch (int): number of the current epoch of training.
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
writer (TensorboardWriter, optional): TensorboardWriter object
to writer Tensorboard log.
"""
# Evaluation mode enabled. The running stats would not be updated.
model.eval()
val_meter.iter_tic()
for cur_iter, (inputs, labels, _, meta) in enumerate(val_loader):
if cfg.NUM_GPUS:
# Transferthe data to the current GPU device.
if isinstance(inputs, (list, )):
for i in range(len(inputs)):
inputs[i] = inputs[i].cuda(non_blocking=True)
else:
inputs = inputs.cuda(non_blocking=True)
if isinstance(labels, (dict, )):
labels = {k: v.cuda() for k, v in labels.items()}
else:
labels = labels.cuda()
for key, val in meta.items():
if isinstance(val, (list, )):
for i in range(len(val)):
val[i] = val[i].cuda(non_blocking=True)
else:
meta[key] = val.cuda(non_blocking=True)
val_meter.data_toc()
preds = model(inputs)
if isinstance(labels, (dict, )):
# Explicitly declare reduction to mean.
loss_fun = losses.get_loss_func(
cfg.MODEL.LOSS_FUNC)(reduction="mean")
# Compute the loss.
loss_verb = loss_fun(preds[0], labels['verb'])
loss_noun = loss_fun(preds[1], labels['noun'])
loss = 0.5 * (loss_verb + loss_noun)
# Compute the verb accuracies.
verb_top1_acc, verb_top5_acc = metrics.topk_accuracies(
preds[0], labels['verb'], (1, 5))
# Combine the errors across the GPUs.
if cfg.NUM_GPUS > 1:
loss_verb, verb_top1_acc, verb_top5_acc = du.all_reduce(
[loss_verb, verb_top1_acc, verb_top5_acc])
# Copy the errors from GPU to CPU (sync point).
loss_verb, verb_top1_acc, verb_top5_acc = (
loss_verb.item(),
verb_top1_acc.item(),
verb_top5_acc.item(),
)
# Compute the noun accuracies.
noun_top1_acc, noun_top5_acc = metrics.topk_accuracies(
preds[1], labels['noun'], (1, 5))
# Combine the errors across the GPUs.
if cfg.NUM_GPUS > 1:
loss_noun, noun_top1_acc, noun_top5_acc = du.all_reduce(
[loss_noun, noun_top1_acc, noun_top5_acc])
# Copy the errors from GPU to CPU (sync point).
loss_noun, noun_top1_acc, noun_top5_acc = (
loss_noun.item(),
noun_top1_acc.item(),
noun_top5_acc.item(),
)
# Compute the action accuracies.
action_top1_acc, action_top5_acc = metrics.multitask_topk_accuracies(
(preds[0], preds[1]), (labels['verb'], labels['noun']), (1, 5))
# Combine the errors across the GPUs.
if cfg.NUM_GPUS > 1:
loss, action_top1_acc, action_top5_acc = du.all_reduce(
[loss, action_top1_acc, action_top5_acc])
# Copy the errors from GPU to CPU (sync point).
loss, action_top1_acc, action_top5_acc = (
loss.item(),
action_top1_acc.item(),
action_top5_acc.item(),
)
val_meter.iter_toc()
# Update and log stats.
val_meter.update_stats(
(verb_top1_acc, noun_top1_acc, action_top1_acc),
(verb_top5_acc, noun_top5_acc, action_top5_acc),
inputs[0].size(0) * max(
cfg.NUM_GPUS, 1
), # If running on CPU (cfg.NUM_GPUS == 1), use 1 to represent 1 CPU.
)
# write to tensorboard format if available.
if writer is not None and not wandb_log:
writer.add_scalars(
{
"Val/loss": loss,
"Val/Top1_acc": action_top1_acc,
"Val/Top5_acc": action_top5_acc,
"Val/verb/loss": loss_verb,
"Val/verb/Top1_acc": verb_top1_acc,
"Val/verb/Top5_acc": verb_top5_acc,
"Val/noun/loss": loss_noun,
"Val/noun/Top1_acc": noun_top1_acc,
"Val/noun/Top5_acc": noun_top5_acc,
},
global_step=len(val_loader) * cur_epoch + cur_iter,
)
if wandb_log:
wandb.log(
{
"Val/loss": loss,
"Val/Top1_acc": action_top1_acc,
"Val/Top5_acc": action_top5_acc,
"Val/verb/loss": loss_verb,
"Val/verb/Top1_acc": verb_top1_acc,
"Val/verb/Top5_acc": verb_top5_acc,
"Val/noun/loss": loss_noun,
"Val/noun/Top1_acc": noun_top1_acc,
"Val/noun/Top5_acc": noun_top5_acc,
"val_step": len(val_loader) * cur_epoch + cur_iter,
}, )
val_meter.update_predictions((preds[0], preds[1]),
(labels['verb'], labels['noun']))
else:
# Explicitly declare reduction to mean.
loss_fun = losses.get_loss_func(
cfg.MODEL.LOSS_FUNC)(reduction="mean")
# Compute the loss.
loss = loss_fun(preds, labels)
if cfg.DATA.MULTI_LABEL:
if cfg.NUM_GPUS > 1:
preds, labels = du.all_gather([preds, labels])
else:
# Compute the errors.
num_topks_correct = metrics.topks_correct(
preds, labels, (1, 5))
# Combine the errors across the GPUs.
top1_err, top5_err = [(1.0 - x / preds.size(0)) * 100.0
for x in num_topks_correct]
if cfg.NUM_GPUS > 1:
loss, top1_err, top5_err = du.all_reduce(
[loss, top1_err, top5_err])
# Copy the errors from GPU to CPU (sync point).
loss, top1_err, top5_err = (
loss.item(),
top1_err.item(),
top5_err.item(),
)
val_meter.iter_toc()
# Update and log stats.
val_meter.update_stats(
top1_err,
top5_err,
inputs[0].size(0) * max(
cfg.NUM_GPUS, 1
), # If running on CPU (cfg.NUM_GPUS == 1), use 1 to represent 1 CPU.
)
# write to tensorboard format if available.
if writer is not None and not wandb_log:
writer.add_scalars(
{
"Val/loss": loss,
"Val/Top1_err": top1_err,
"Val/Top5_err": top5_err,
},
global_step=len(val_loader) * cur_epoch + cur_iter,
)
if wandb_log:
wandb.log(
{
"Val/loss": loss,
"Val/Top1_err": top1_err,
"Val/Top5_err": top5_err,
"val_step": len(val_loader) * cur_epoch + cur_iter,
}, )
val_meter.update_predictions(preds, labels)
val_meter.log_iter_stats(cur_epoch, cur_iter)
val_meter.iter_tic()
# Log epoch stats.
is_best_epoch, top1_dict = val_meter.log_epoch_stats(cur_epoch)
# write to tensorboard format if available.
if writer is not None:
all_preds = [pred.clone().detach() for pred in val_meter.all_preds]
all_labels = [label.clone().detach() for label in val_meter.all_labels]
if cfg.NUM_GPUS:
all_preds = [pred.cpu() for pred in all_preds]
all_labels = [label.cpu() for label in all_labels]
writer.plot_eval(preds=all_preds,
labels=all_labels,
global_step=cur_epoch)
if writer is not None and not wandb_log:
if "top1_acc" in top1_dict.keys():
writer.add_scalars(
{
"Val/epoch/Top1_acc": top1_dict["top1_acc"],
"Val/epoch/verb/Top1_acc": top1_dict["verb_top1_acc"],
"Val/epoch/noun/Top1_acc": top1_dict["noun_top1_acc"],
},
global_step=cur_epoch,
)
else:
writer.add_scalars(
{"Val/epoch/Top1_err": top1_dict["top1_err"]},
global_step=cur_epoch,
)
if wandb_log:
if "top1_acc" in top1_dict.keys():
wandb.log(
{
"Val/epoch/Top1_acc": top1_dict["top1_acc"],
"Val/epoch/verb/Top1_acc": top1_dict["verb_top1_acc"],
"Val/epoch/noun/Top1_acc": top1_dict["noun_top1_acc"],
"epoch": cur_epoch,
}, )
else:
wandb.log({
"Val/epoch/Top1_err": top1_dict["top1_err"],
"epoch": cur_epoch
})
top1 = top1_dict["top1_acc"] if "top1_acc" in top1_dict.keys(
) else top1_dict["top1_err"]
val_meter.reset()
return is_best_epoch, top1
def train_epoch(train_loader,
model,
optimizer,
train_meter,
cur_epoch,
cfg,
writer=None,
wandb_log=False):
"""
Perform the audio training for one epoch.
Args:
train_loader (loader): audio training loader.
model (model): the audio model to train.
optimizer (optim): the optimizer to perform optimization on the model's
parameters.
train_meter (TrainMeter): training meters to log the training performance.
cur_epoch (int): current epoch of training.
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
writer (TensorboardWriter, optional): TensorboardWriter object
to writer Tensorboard log.
"""
# Enable train mode.
model.train()
if cfg.BN.FREEZE:
model.module.freeze_fn(
'bn_statistics') if cfg.NUM_GPUS > 1 else model.freeze_fn(
'bn_statistics')
train_meter.iter_tic()
data_size = len(train_loader)
for cur_iter, (inputs, labels, _, meta) in enumerate(train_loader):
# Transfer the data to the current GPU device.
if cfg.NUM_GPUS:
if isinstance(inputs, (list, )):
for i in range(len(inputs)):
inputs[i] = inputs[i].cuda(non_blocking=True)
else:
inputs = inputs.cuda(non_blocking=True)
if isinstance(labels, (dict, )):
labels = {k: v.cuda() for k, v in labels.items()}
else:
labels = labels.cuda()
for key, val in meta.items():
if isinstance(val, (list, )):
for i in range(len(val)):
val[i] = val[i].cuda(non_blocking=True)
else:
meta[key] = val.cuda(non_blocking=True)
# Update the learning rate.
lr = optim.get_epoch_lr(cur_epoch + float(cur_iter) / data_size, cfg)
optim.set_lr(optimizer, lr)
train_meter.data_toc()
# preds = model(inputs) #this is how model.forward() is called
preds = model(inputs)[
0] #this is the original output, the output of the last layer
linear_layer_output = model(inputs)[1]
if isinstance(labels, (dict, )):
# Explicitly declare reduction to mean.
loss_fun = losses.get_loss_func(
cfg.MODEL.LOSS_FUNC)(reduction="mean")
# Compute the loss.
loss_verb = loss_fun(preds[0], labels['verb'])
loss_noun = loss_fun(preds[1], labels['noun'])
loss = 0.5 * (loss_verb + loss_noun)
# check Nan Loss.
misc.check_nan_losses(loss)
else:
#I believe this is the VGG loss part, as the labels are not split into nouns and verbs
# Explicitly declare reduction to mean.
loss_fun = losses.get_loss_func(
cfg.MODEL.LOSS_FUNC)(reduction="mean")
# Embedding loss function.
emb_loss_fun = losses.get_loss_func(
cfg.MODEL.EMB_LOSS_FUNC)(reduction="mean")
# Compute the loss for the main model.
loss = loss_fun(preds, labels)
# Compute the loss for the embeddings.
emb_loss = emb_loss_fun(linear_layer_output, word_embedding)
# Add the losses together- use embeddings to fine tune the model's objective
loss = loss + emb_loss
# check Nan Loss.
misc.check_nan_losses(loss)
# Perform the backward pass.
optimizer.zero_grad()
loss.backward()
# Update the parameters.
optimizer.step()
if isinstance(labels, (dict, )):
# Compute the verb accuracies.
verb_top1_acc, verb_top5_acc = metrics.topk_accuracies(
preds[0], labels['verb'], (1, 5))
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
loss_verb, verb_top1_acc, verb_top5_acc = du.all_reduce(
[loss_verb, verb_top1_acc, verb_top5_acc])
# Copy the stats from GPU to CPU (sync point).
loss_verb, verb_top1_acc, verb_top5_acc = (
loss_verb.item(),
verb_top1_acc.item(),
verb_top5_acc.item(),
)
# Compute the noun accuracies.
noun_top1_acc, noun_top5_acc = metrics.topk_accuracies(
preds[1], labels['noun'], (1, 5))
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
loss_noun, noun_top1_acc, noun_top5_acc = du.all_reduce(
[loss_noun, noun_top1_acc, noun_top5_acc])
# Copy the stats from GPU to CPU (sync point).
loss_noun, noun_top1_acc, noun_top5_acc = (
loss_noun.item(),
noun_top1_acc.item(),
noun_top5_acc.item(),
)
# Compute the action accuracies.
action_top1_acc, action_top5_acc = metrics.multitask_topk_accuracies(
(preds[0], preds[1]), (labels['verb'], labels['noun']), (1, 5))
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
loss, action_top1_acc, action_top5_acc = du.all_reduce(
[loss, action_top1_acc, action_top5_acc])
# Copy the stats from GPU to CPU (sync point).
loss, action_top1_acc, action_top5_acc = (
loss.item(),
action_top1_acc.item(),
action_top5_acc.item(),
)
# Update and log stats.
train_meter.update_stats(
(verb_top1_acc, noun_top1_acc, action_top1_acc),
(verb_top5_acc, noun_top5_acc, action_top5_acc),
(loss_verb, loss_noun, loss),
lr,
inputs[0].size(0) * max(
cfg.NUM_GPUS, 1
), # If running on CPU (cfg.NUM_GPUS == 1), use 1 to represent 1 CPU.
)
# write to tensorboard format if available.
if writer is not None and not wandb_log:
writer.add_scalars(
{
"Train/loss": loss,
"Train/lr": lr,
"Train/Top1_acc": action_top1_acc,
"Train/Top5_acc": action_top5_acc,
"Train/verb/loss": loss_verb,
"Train/noun/loss": loss_noun,
"Train/verb/Top1_acc": verb_top1_acc,
"Train/verb/Top5_acc": verb_top5_acc,
"Train/noun/Top1_acc": noun_top1_acc,
"Train/noun/Top5_acc": noun_top5_acc,
},
global_step=data_size * cur_epoch + cur_iter,
)
if wandb_log:
wandb.log(
{
"Train/loss": loss,
"Train/lr": lr,
"Train/Top1_acc": action_top1_acc,
"Train/Top5_acc": action_top5_acc,
"Train/verb/loss": loss_verb,
"Train/noun/loss": loss_noun,
"Train/verb/Top1_acc": verb_top1_acc,
"Train/verb/Top5_acc": verb_top5_acc,
"Train/noun/Top1_acc": noun_top1_acc,
"Train/noun/Top5_acc": noun_top5_acc,
"train_step": data_size * cur_epoch + cur_iter,
}, )
else:
top1_err, top5_err = None, None
if cfg.DATA.MULTI_LABEL:
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
[loss] = du.all_reduce([loss])
loss = loss.item()
else:
# Compute the errors.
num_topks_correct = metrics.topks_correct(
preds, labels, (1, 5))
top1_err, top5_err = [(1.0 - x / preds.size(0)) * 100.0
for x in num_topks_correct]
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
loss, top1_err, top5_err = du.all_reduce(
[loss, top1_err, top5_err])
# Copy the stats from GPU to CPU (sync point).
loss, top1_err, top5_err = (
loss.item(),
top1_err.item(),
top5_err.item(),
)
# Update and log stats.
train_meter.update_stats(
top1_err,
top5_err,
loss,
lr,
inputs[0].size(0) * max(
cfg.NUM_GPUS, 1
), # If running on CPU (cfg.NUM_GPUS == 1), use 1 to represent 1 CPU.
)
# write to tensorboard format if available.
if writer is not None and not wandb_log:
writer.add_scalars(
{
"Train/loss": loss,
"Train/lr": lr,
"Train/Top1_err": top1_err,
"Train/Top5_err": top5_err,
},
global_step=data_size * cur_epoch + cur_iter,
)
if wandb_log:
wandb.log(
{
"Train/loss": loss,
"Train/lr": lr,
"Train/Top1_err": top1_err,
"Train/Top5_err": top5_err,
"train_step": data_size * cur_epoch + cur_iter,
}, )
train_meter.iter_toc() # measure allreduce for this meter
train_meter.log_iter_stats(cur_epoch, cur_iter)
train_meter.iter_tic()
# Log epoch stats.
train_meter.log_epoch_stats(cur_epoch)
train_meter.reset()