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training: optional transformation to embeddings prior to pooling #31

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training: optional transformation to embeddings prior to pooling #31

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12 changes: 12 additions & 0 deletions src/training/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,8 @@
from training.loss import ClipLoss
from .distributed import is_master

from open_clip import tokenize


def train_one_epoch(model_video, model_text, logit_scale, data, epoch, optimizer, scheduler, args, tb_writer=None):
device = torch.device(args.device)
Expand All @@ -24,13 +26,18 @@ def train_one_epoch(model_video, model_text, logit_scale, data, epoch, optimizer
dataloader = data["train"].dataloader
num_batches_per_epoch = dataloader.num_batches

with torch.no_grad():
shutterstock_embs = model_text(tokenize(["shutterstock"]).to(device))
shutterstock_embs = torch.cat([shutterstock_embs]*args.sequence_length)

running_loss = 0.0
for i, batch in enumerate(dataloader):
step = num_batches_per_epoch * epoch + i
scheduler(step)

embeddings, toks = batch
embeddings = embeddings.to(device, non_blocking=True)
embeddings -= shutterstock_embs
toks = toks.to(device, non_blocking=True)

optimizer.zero_grad()
Expand Down Expand Up @@ -76,6 +83,10 @@ def evaluate(model_video, model_text, logit_scale, data, epoch, args, tb_writer=
dataloader = data["val"].dataloader
model_video.eval()

with torch.no_grad():
shutterstock_embs = model_text(tokenize(["shutterstock"]).to(device))
shutterstock_embs = torch.cat([shutterstock_embs]*args.sequence_length)

metrics["val_loss"] = 0.0
loss_func = ClipLoss(
local_loss=False,
Expand All @@ -93,6 +104,7 @@ def evaluate(model_video, model_text, logit_scale, data, epoch, args, tb_writer=

embeddings, toks = batch
embeddings = embeddings.to(device, non_blocking=True)
embeddings -= shutterstock_embs
toks = toks.to(device, non_blocking=True)

video_embeddings = model_video(embeddings, None)
Expand Down