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trainer.py
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import torch
import numpy as np
def fit(train_loader, val_loader, model, loss_fn, optimizer, scheduler, n_epochs, cuda, log_interval, metrics=[],
start_epoch=0):
"""
Loaders, model, loss function and metrics should work together for a given task,
i.e. The model should be able to process data output of loaders,
loss function should process target output of loaders and outputs from the model
Examples: Classification: batch loader, classification model, NLL loss, accuracy metric
Siamese network: Siamese loader, siamese model, contrastive loss
Online triplet learning: batch loader, embedding model, online triplet loss
"""
for epoch in range(0, start_epoch):
scheduler.step()
for epoch in range(start_epoch, n_epochs):
scheduler.step()
# Train stage
train_loss, metrics = train_epoch(train_loader, model, loss_fn, optimizer, cuda, log_interval, metrics)
message = 'Epoch: {}/{}. Train set: Average loss: {:.4f}'.format(epoch + 1, n_epochs, train_loss)
for metric in metrics:
message += '\t{}: {}'.format(metric.name(), metric.value())
val_loss, metrics = test_epoch(val_loader, model, loss_fn, cuda, metrics)
val_loss /= len(val_loader)
message += '\nEpoch: {}/{}. Validation set: Average loss: {:.4f}'.format(epoch + 1, n_epochs,
val_loss)
for metric in metrics:
message += '\t{}: {}'.format(metric.name(), metric.value())
print(message)
def train_epoch(train_loader, model, loss_fn, optimizer, cuda, log_interval, metrics):
for metric in metrics:
metric.reset()
model.train()
losses = []
total_loss = 0
for batch_idx, (data, target) in enumerate(train_loader):
target = target if len(target) > 0 else None
if not type(data) in (tuple, list):
data = (data,)
if cuda:
data = tuple(d.cuda() for d in data)
if target is not None:
target = target.cuda()
optimizer.zero_grad()
outputs = model(*data)
if type(outputs) not in (tuple, list):
outputs = (outputs,)
loss_inputs = outputs
if target is not None:
target = (target,)
loss_inputs += target
loss_outputs = loss_fn(*loss_inputs)
loss = loss_outputs[0] if type(loss_outputs) in (tuple, list) else loss_outputs
losses.append(loss.item())
total_loss += loss.item()
loss.backward()
optimizer.step()
for metric in metrics:
metric(outputs, target, loss_outputs)
if batch_idx % log_interval == 0:
message = 'Train: [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
batch_idx * len(data[0]), len(train_loader.dataset),
100. * batch_idx / len(train_loader), np.mean(losses))
for metric in metrics:
message += '\t{}: {}'.format(metric.name(), metric.value())
print(message)
losses = []
total_loss /= (batch_idx + 1)
return total_loss, metrics
def test_epoch(val_loader, model, loss_fn, cuda, metrics):
with torch.no_grad():
for metric in metrics:
metric.reset()
model.eval()
val_loss = 0
for batch_idx, (data, target) in enumerate(val_loader):
target = target if len(target) > 0 else None
if not type(data) in (tuple, list):
data = (data,)
if cuda:
data = tuple(d.cuda() for d in data)
if target is not None:
target = target.cuda()
outputs = model(*data)
if type(outputs) not in (tuple, list):
outputs = (outputs,)
loss_inputs = outputs
if target is not None:
target = (target,)
loss_inputs += target
loss_outputs = loss_fn(*loss_inputs)
loss = loss_outputs[0] if type(loss_outputs) in (tuple, list) else loss_outputs
val_loss += loss.item()
for metric in metrics:
metric(outputs, target, loss_outputs)
return val_loss, metrics
def semantic(train_loader, model, cuda):
classes_semantic = {}
classes_num = {}
with torch.no_grad():
model.eval()
for batch_idx, (data, target) in enumerate(train_loader):
target = target if len(target) > 0 else None
if cuda:
data = data.cuda()
if target is not None:
target = target.cuda()
target = target.cpu().numpy()
outputs = model(data).cpu().numpy()
for i in range(len(data)):
if target[i] not in classes_semantic.keys():
classes_semantic[target[i]] = outputs[i]
classes_num[target[i]] = 1
else:
classes_semantic[target[i]] += outputs[i]
classes_num[target[i]] += 1
for i in classes_semantic.keys():
classes_semantic[i] = classes_semantic[i] / classes_num[i]
return classes_semantic
def eval_precious(test_loader, classes_semantic, model, epsilon, cuda):
test_precious = {}
test_num = {}
with torch.no_grad():
model.eval()
for batch_idx, (data, target) in enumerate(test_loader):
target = target if len(target) > 0 else None
if cuda:
data = data.cuda()
if target is not None:
target = target.cuda()
target = target.cpu().numpy()
outputs = model(data).cpu().numpy()
for i in range(len(data)):
minn = 1e9
minn_target = 0
for j in classes_semantic.keys():
dis = outputs[i]-classes_semantic[j]
tem = np.dot(dis, dis)
if tem < minn:
minn = tem
minn_target = j
#print(tem, j, target[i])
#print(minn, minn_target, target[i])
if (minn > epsilon and target[i] not in classes_semantic.keys()) \
or (minn <= epsilon and target[i] == minn_target):
if target[i] not in test_precious.keys():
test_precious[target[i]] = 1
else:
test_precious[target[i]] += 1
if target[i] not in test_num.keys():
test_num[target[i]] = 1
else:
test_num[target[i]] += 1
for i in test_precious.keys():
test_precious[i] = test_precious[i] / test_num[i]
return test_precious