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import os
import time
from typing import Union
import torch
from lib.metrics import bin_calculate_auc_ap_ar, get_acc_mesure_func
from logs.logger import board_writing
from numpy import arange
from package_utils.utils import debugging_panel
from tqdm import tqdm
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count if self.count != 0 else 0
def get_batch_data(batch_data: Union[dict]):
"""
Parsing data for model consumption (train/val/test)
"""
inputs = batch_data["img"] if isinstance(batch_data, dict) else batch_data[0]
labels = batch_data["label"] if isinstance(batch_data, dict) else batch_data[1]
heatmaps = None
cstency_heatmaps = None
offsets = None
targets = None
temp_locs = None
maskout_pes = None
if "heatmap" in batch_data:
heatmaps = batch_data["heatmap"]
if "target" in batch_data:
targets = batch_data["target"]
if "cstency" in batch_data:
cstency_heatmaps = batch_data["cstency"]
if "offset" in batch_data:
offsets = batch_data["offset"]
if "temp_loc" in batch_data:
temp_locs = batch_data["temp_loc"]
if "mask_out_p" in batch_data:
maskout_pes = batch_data["mask_out_p"]
return (
inputs,
labels,
targets,
heatmaps,
cstency_heatmaps,
offsets,
temp_locs,
maskout_pes,
)
def train(
cfg,
model,
critetion,
optimizer,
epoch,
data_loader,
logger,
writer,
devices,
trainIters,
metrics_base="combine",
scaler=None,
):
calculate_acc = get_acc_mesure_func(metrics_base)
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc = AverageMeter()
# Switch to train mode
model.train()
data_loader = tqdm(data_loader, dynamic_ncols=True)
accumulation_steps = cfg.TRAIN.accumulation_steps if cfg.TRAIN.use_amp else 1
start = time.time()
optimizer.zero_grad()
for i, batch_data in enumerate(data_loader):
(
inputs,
labels,
targets,
heatmaps,
cstency_heatmaps,
offsets,
temp_locs,
maskout_pes,
) = get_batch_data(batch_data)
inputs = inputs.cuda().to(non_blocking=True, dtype=torch.float)
labels = labels.cuda().to(non_blocking=True, dtype=torch.float)
maskout_pes = (
maskout_pes.cuda().to(non_blocking=True)
if maskout_pes is not None
else None
)
# additional_targets = {"maskout_pes": maskout_pes}
# Measuring data loading time
data_time.update(time.time() - start)
loop = arange(1) if cfg.TRAIN.optimizer != "SAM" else arange(2)
for idx in loop:
with torch.cuda.amp.autocast(enabled=cfg.TRAIN.use_amp):
# outputs = model(inputs, **additional_targets)
outputs = model(inputs)
if isinstance(outputs, list):
outputs = outputs[0]
# In case outputs contain a dict key
if isinstance(outputs, dict):
outputs_cls = outputs["cls"]
outputs_hm = outputs["hm"] if "hm" in outputs.keys() else None
outputs_offset = (
outputs["offset"] if "offset" in outputs.keys() else None
)
outputs_cstency = (
outputs["cstency"] if "cstency" in outputs.keys() else None
)
outputs_temp_loc = (
outputs["temp_loc"] if "temp_loc" in outputs.keys() else None
)
if idx == 0:
first_outputs_hm = outputs_hm
first_outputs_cls = outputs_cls
if "Combined" in cfg.TRAIN.loss.type:
# labels = labels.cuda().to(non_blocking=True).long()
if offsets is not None:
offsets = offsets.cuda().to(non_blocking=True)
if cstency_heatmaps is not None:
cstency_heatmaps = cstency_heatmaps.cuda().to(non_blocking=True)
if temp_locs is not None:
temp_locs = temp_locs.cuda().to(non_blocking=True)
if cfg.TRAIN.loss.type != "CombinedHeatmapBinaryLoss":
heatmaps = heatmaps.cuda().to(non_blocking=True)
else:
heatmaps = targets.cuda().to(non_blocking=True)
loss_ = critetion(
outputs_hm,
heatmaps,
outputs_cls,
labels,
offset_preds=outputs_offset,
offset_gts=offsets,
cstency_preds=outputs_cstency,
cstency_gts=cstency_heatmaps,
temp_loc_preds=outputs_temp_loc,
temp_loc_gts=temp_locs,
hm_mask=maskout_pes,
)
loss = loss_["hm"]
if "cls" in loss_.keys():
loss += loss_["cls"]
if "dst_hm_cls" in loss_.keys():
loss += loss_["dst_hm_cls"]
if "offset" in loss_.keys():
loss += loss_["offset"]
if "cstency" in loss_.keys():
loss += loss_["cstency"]
if "temp_loc" in loss_.keys():
loss += loss_["temp_loc"]
else:
loss = critetion(outputs_cls, labels)
loss /= accumulation_steps
# gradients accumulation for larger batch
if cfg.TRAIN.use_amp:
scaler(
cfg,
loss,
optimizer,
parameters=model.parameters(),
step=idx,
update_grad=(i + 1) % accumulation_steps == 0,
)
if (i + 1) % accumulation_steps == 0:
optimizer.zero_grad()
else:
loss.backward()
if cfg.TRAIN.optimizer != "SAM":
optimizer.step()
else:
if idx == 0:
optimizer.first_step(zero_grad=True)
else:
optimizer.second_step(zero_grad=True)
optimizer.zero_grad()
if cfg.TRAIN.use_amp:
torch.cuda.synchronize()
if cfg.TRAIN.debug.active:
debugging_panel(
cfg.TRAIN.debug,
inputs,
heatmaps,
first_outputs_hm,
i,
batch_cls_pred=first_outputs_cls,
)
if metrics_base == "binary":
acc_ = calculate_acc(first_outputs_cls, targets=targets, labels=labels)
elif metrics_base == "heatmap":
acc_ = calculate_acc(first_outputs_hm, targets=targets, labels=labels)
else:
acc_ = calculate_acc(
first_outputs_hm,
first_outputs_cls,
targets=targets,
labels=labels,
cls_lamda=critetion.cls_lmda,
)
if isinstance(inputs, list):
batch_size = inputs[0].size(0)
else:
batch_size = inputs.size(0)
# Measure accuracy and record loss
losses.update(loss.item() * accumulation_steps, n=batch_size)
acc.update(acc_, n=batch_size)
batch_time.update(time.time() - start)
start = time.time()
# Logging
if i % 5 == 0:
params = {}
if "Combined" in cfg.TRAIN.loss.type:
if (
hasattr(critetion, "dst_hm_cls_lmda")
and critetion.dst_hm_cls_lmda > 0
):
params["loss_dst"] = loss_["dst_hm_cls"].item()
if hasattr(critetion, "offset_lmda") and critetion.offset_lmda > 0:
params["loss_offset"] = loss_["offset"].item()
if "cstency" in loss_.keys():
params["loss_cstency"] = loss_["cstency"].item()
if "temp_loc" in loss_.keys():
params["loss_temp_loc"] = loss_["temp_loc"].item()
logger.epochInfor(
epoch,
i,
len(data_loader),
batch_time=batch_time,
data_time=data_time,
losses=losses,
acc=acc,
speed=batch_size / batch_time.val,
loss_cls=loss_["cls"].item(),
**params,
)
else:
logger.epochInfor(
epoch,
i,
len(data_loader),
batch_time=batch_time,
data_time=data_time,
losses=losses,
acc=acc,
speed=batch_size / batch_time.val,
)
trainIters += 1
if cfg.TRAIN.tensorboard:
board_writing(writer, losses.avg, acc.avg, trainIters, "Train")
return losses, acc, trainIters
def validate(
cfg,
model,
critetion,
epoch,
data_loader,
logger,
writer,
devices,
valIters,
metrics_base="combine",
):
calculate_acc = get_acc_mesure_func(metrics_base)
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc = AverageMeter()
# Switch to test mode
model.eval()
data_loader = tqdm(data_loader, dynamic_ncols=True)
start = time.time()
with torch.no_grad():
for i, batch_data in enumerate(data_loader):
(
inputs,
labels,
targets,
heatmaps,
cstency_heatmaps,
offsets,
temp_locs,
maskout_pes,
) = get_batch_data(batch_data)
inputs = inputs.to(devices, non_blocking=True, dtype=torch.float).cuda()
labels = labels.cuda().to(non_blocking=True, dtype=torch.float)
maskout_pes = (
maskout_pes.cuda().to(non_blocking=True)
if maskout_pes is not None
else None
)
# additional_targets = {"maskout_pes": maskout_pes}
# Measuring data loading time
data_time.update(time.time() - start)
# outputs = model(inputs, **additional_targets)
outputs = model(inputs)
if isinstance(outputs, list):
outputs = outputs[0]
# In case outputs contain a dict key
if isinstance(outputs, dict):
outputs_cls = outputs["cls"]
outputs_hm = outputs["hm"] if "hm" in outputs.keys() else None
outputs_offset = (
outputs["offset"] if "offset" in outputs.keys() else None
)
outputs_cstency = (
outputs["cstency"] if "cstency" in outputs.keys() else None
)
outputs_temp_loc = (
outputs["temp_loc"] if "temp_loc" in outputs.keys() else None
)
if "Combined" in cfg.TRAIN.loss.type:
# labels = labels.cuda().to(non_blocking=True).long()
if offsets is not None:
offsets = offsets.cuda().to(non_blocking=True)
if cstency_heatmaps is not None:
cstency_heatmaps = cstency_heatmaps.cuda().to(non_blocking=True)
if temp_locs is not None:
temp_locs = temp_locs.cuda().to(non_blocking=True)
if cfg.TRAIN.loss.type != "CombinedHeatmapBinaryLoss":
heatmaps = heatmaps.cuda().to(non_blocking=True)
else:
heatmaps = targets.cuda().to(non_blocking=True)
loss_ = critetion(
outputs_hm,
heatmaps,
outputs_cls,
labels,
offset_preds=outputs_offset,
offset_gts=offsets,
cstency_preds=outputs_cstency,
cstency_gts=cstency_heatmaps,
temp_loc_preds=outputs_temp_loc,
temp_loc_gts=temp_locs,
hm_mask=maskout_pes,
)
loss = loss_["hm"]
if "cls" in loss_.keys():
loss += loss_["cls"]
if "dst_hm_cls" in loss_.keys():
loss += loss_["dst_hm_cls"]
if "offset" in loss_.keys():
loss += loss_["offset"]
if "cstency" in loss_.keys():
loss += loss_["cstency"]
if "temp_loc" in loss_.keys():
loss += loss_["temp_loc"]
else:
loss = critetion(outputs_cls, labels)
if cfg.TRAIN.debug.active:
debugging_panel(
cfg.TRAIN.debug,
inputs,
heatmaps,
outputs_hm,
i,
batch_cls_pred=outputs_cls,
split="val",
)
if metrics_base == "binary":
acc_ = calculate_acc(outputs_cls, targets=targets, labels=labels)
elif metrics_base == "heatmap":
acc_ = calculate_acc(outputs_hm, targets=targets, labels=labels)
else:
acc_ = calculate_acc(
outputs_hm,
outputs_cls,
targets=targets,
labels=labels,
cls_lamda=critetion.cls_lmda,
)
if isinstance(inputs, list):
batch_size = inputs[0].size(0)
else:
batch_size = inputs.size(0)
# Measure accuracy and record loss
losses.update(loss.item(), n=batch_size)
acc.update(acc_, n=batch_size)
batch_time.update(time.time() - start)
start = time.time()
valIters += 1
if cfg.TRAIN.tensorboard:
board_writing(writer, losses.avg, acc.avg, valIters, "Val")
# Logging
params = {}
if "Combined" in cfg.TRAIN.loss.type:
if (
hasattr(critetion, "dst_hm_cls_lmda")
and critetion.dst_hm_cls_lmda > 0
):
params["loss_dst"] = loss_["dst_hm_cls"].item()
if hasattr(critetion, "offset_lmda") and critetion.offset_lmda > 0:
params["loss_offset"] = loss_["offset"].item()
if "cstency" in loss_.keys():
params["loss_cstency"] = loss_["cstency"].item()
if "temp_loc" in loss_.keys():
params["loss_temp_loc"] = loss_["temp_loc"].item()
logger.epochInfor(
epoch,
i,
len(data_loader),
batch_time=batch_time,
data_time=data_time,
losses=losses,
acc=acc,
speed=batch_size / batch_time.val,
loss_cls=loss_["cls"].item(),
**params,
)
else:
logger.epochInfor(
epoch,
i,
len(data_loader),
batch_time=batch_time,
data_time=data_time,
losses=losses,
acc=acc,
speed=batch_size / batch_time.val,
)
return losses, acc, valIters
def test(
cfg,
model,
critetion,
epoch,
data_loader,
logger,
writer,
devices,
valIters,
metrics_base="combine",
):
calculate_acc = get_acc_mesure_func(metrics_base)
total_preds = torch.tensor([]).cuda().to(dtype=torch.float)
total_labels = torch.tensor([]).cuda().to(dtype=torch.float)
# Switch to test mode
model.eval()
test_dataloader = tqdm(data_loader, dynamic_ncols=True)
with torch.no_grad():
for b, (inputs, labels, vid_ids) in enumerate(test_dataloader):
inputs = inputs.to(dtype=torch.float).cuda()
labels = labels.to(dtype=torch.float).cuda()
outputs = model(inputs)
# Applying Flip test
if isinstance(outputs, list):
outputs = outputs[0]
# In case outputs contain a dict key
if isinstance(outputs, dict):
# hm_outputs = outputs['hm'] if 'hm' in outputs.keys() else None
cls_outputs = outputs["cls"]
# outputs_temp_loc = outputs['temp_loc'] if 'temp_loc' in outputs.keys() else None
total_preds = torch.cat((total_preds, cls_outputs), 0)
total_labels = torch.cat((total_labels, labels), 0)
acc_ = calculate_acc(
total_preds, targets=None, labels=total_labels, threshold=cfg.TEST.threshold
)
metrics = bin_calculate_auc_ap_ar(
total_preds,
total_labels,
metrics_base=metrics_base,
threshold=cfg.TEST.threshold,
)
auc_, ap_, ar_, mf1_ = (
metrics["auc"],
metrics["ap"],
metrics["ar"],
metrics["mf1"],
)
logger.info(
f"Current ACC, AUC, AP, AR, mF1 for {cfg.DATASET.DATA.TEST.FAKETYPE} --- {cfg.DATASET.DATA.TEST.LABEL_FOLDER} -- \
{acc_*100} -- {auc_*100} -- {ap_*100} -- {ar_*100} -- {mf1_*100}"
)
return acc_, auc_, ap_, ar_
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