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import torch
import torchvision.transforms as T
from tqdm import tqdm
from time import sleep
from model.metrics import calculate_metrics_l, calculate_metrics_l_with_mask
from utils.utils_heatmap import get_keypoints_from_heatmap_batch_maxpool_l
def train_one_epoch(epoch_index, training_loader, optimizer, loss_fn, model, device, dataset="SoccerNet"):
model.train(True)
running_loss = 0.
samples = 0
with (tqdm(enumerate(training_loader), unit="batch", total=len(training_loader)) as tepoch):
for i, data in tepoch:
tepoch.set_description(f"Epoch {epoch_index}")
if dataset != "SoccerNet":
input, target, mask = data[0].to(device), data[1].to(device), data[2].to(device)
transform = T.Resize((540, 960))
input = transform(input)
optimizer.zero_grad()
outputs = model(input)
loss = loss_fn(outputs, target, mask.unsqueeze(-1).unsqueeze(-1))
else:
input, target = data[0].to(device), data[1].to(device)
optimizer.zero_grad()
outputs = model(input)
loss = loss_fn(outputs, target)
loss.backward()
optimizer.step()
# Gather data and report
running_loss += loss.item()
samples += input.size()[0]
tepoch.set_postfix(loss=running_loss / samples)
sleep(0.1)
avg_loss = running_loss / samples
return avg_loss
def validation_step(validation_loader, loss_fn, model, device, dataset="SoccerNet"):
running_vloss = 0.0
acc, prec, rec, f1 = 0, 0, 0, 0
samples = 0
model.eval()
with (torch.no_grad()):
for i, vdata in tqdm(enumerate(validation_loader), total=len(validation_loader)):
if dataset != "SoccerNet":
input, target, mask = vdata[0].to(device), vdata[1].to(device), vdata[2].to(device)
transform = T.Resize((540, 960))
input = transform(input)
voutputs = model(input)
vloss = loss_fn(voutputs, target, mask.unsqueeze(-1).unsqueeze(-1))
kp_gt = get_keypoints_from_heatmap_batch_maxpool_l(target[:,:-1,:,:], return_scores=True, max_keypoints=2)
kp_pred = get_keypoints_from_heatmap_batch_maxpool_l(voutputs[:,:-1,:,:], return_scores=True, max_keypoints=2)
metrics = calculate_metrics_l_with_mask(kp_gt, kp_pred, mask)
else:
input, target = vdata[0].to(device), vdata[1].to(device)
voutputs = model(input)
vloss = loss_fn(voutputs, target)
kp_gt = get_keypoints_from_heatmap_batch_maxpool_l(target[:,:-1,:,:], return_scores=True, max_keypoints=2)
kp_pred = get_keypoints_from_heatmap_batch_maxpool_l(voutputs[:,:-1,:,:], return_scores=True, max_keypoints=2)
metrics = calculate_metrics_l(kp_gt, kp_pred)
running_vloss += vloss
samples += input.size()[0]
acc += metrics[0]
prec += metrics[1]
rec += metrics[2]
f1 += metrics[3]
avg_vloss = running_vloss / samples
avg_acc = acc / (i+1)
avg_prec = prec / (i+1)
avg_rec = rec / (i+1)
avg_f1 = f1 / (i+1)
return avg_vloss, avg_acc, avg_prec, avg_rec, avg_f1
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