def evalModel(args, imgs, model):
t_test = transforms.Compose([
DataUtil.ToPIL(),
# DataUtil.RandomFlips(),
DataUtil.TenCrop(args.cropSize, [0.59008044], np.sqrt([0.06342617])),
])
labels = torch.zeros(imgs.shape[0])
test = dataloader.npdataset(imgs, labels.view(-1), t_test)
loader = torch.utils.data.DataLoader(test,
batch_size=args.bSize,
shuffle=False,
num_workers=0)
outs = []
usegpu = torch.cuda.is_available()
if usegpu:
model.cuda()
#
# Iterate.
for data in loader:
gc.collect()
inputs, labels_cpu = data['img'], data['label']
if usegpu:
labels_cpu.squeeze_()
inputs, labels = Variable(inputs, requires_grad=False).cuda(
async=True), Variable(labels_cpu,
requires_grad=False).cuda(async=True)
else:
inputs, labels = Variable(inputs, requires_grad=False), Variable(
labels_cpu, requires_grad=False)
#
# The 5 crop from above takes the corners of the iamge and center.
# We must now average the contributions.
bs, ncrops, c, h, w = inputs.size()
result = model(inputs.view(-1, c, h, w)) # fuse batch size and ncrops
out = result.view(bs, ncrops, -1).mean(1) # avg over crops
# print(outs)
outs.append(copy.copy(out.cpu()))
outs = torch.cat(outs, dim=0)
return outs
def train(args, imgs, labels, img_val, label_val, modelConst):
#
# Create the image augmentation.
t = transforms.Compose([
DataUtil.ToPIL(),
DataUtil.RandomFlips(),
# DataUtil.RandomRotation(5),
# DataUtil.ColourJitter(0.1, 0.1, 0.1, 0),
DataUtil.RandomResizedCrop(args.cropSize, (0.5, 1.3)),
DataUtil.ToTensor(),
DataUtil.Normalize([0.59008044], np.sqrt([0.06342617])),
# DataUtil.TenCrop(140, [0.59008044], np.sqrt([0.06342617])),
#
])
t_test = transforms.Compose([
DataUtil.ToPIL(),
DataUtil.RandomFlips(),
DataUtil.TenCrop(args.cropSize, [0.59008044], np.sqrt([0.06342617])),
])
# RandomRotation
# FiveCrop
# RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=2)
topil = transforms.ToPILImage()
train = dataloader.npdataset(imgs, labels.view(-1), t)
validation = dataloader.npdataset(img_val, label_val.view(-1), t_test)
stages = {
'train':
torch.utils.data.DataLoader(train,
batch_size=args.bSize,
shuffle=True,
num_workers=0,
pin_memory=True),
'val':
torch.utils.data.DataLoader(validation,
batch_size=args.bSize,
shuffle=False,
num_workers=0,
pin_memory=True),
}
model = modelConst()
usegpu = torch.cuda.is_available()
criteria = nn.CrossEntropyLoss()
#
# Whether to use the GPU.
if usegpu:
model.cuda()
#
# Type of optimizer.
optimizer = torch.optim.Adam(model.parameters(),
lr=0.0001,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=1e-2)
bestModel = copy.deepcopy(model.state_dict())
bestAcc = 0
logger = None
logEpoch = doNothing
closeLogger = doNothing
if args.useTB:
logger = SummaryWriter()
logEpoch = logEpochTensorboard
closeLogger = closeTensorboard
#
# Iterate.
for epoch in range(args.numEpochs):
printColour('Epoch {}/{}'.format(epoch, args.numEpochs - 1),
colours.OKBLUE)
for stage in stages:
gc.collect()
print('Stage: ', stage)
#
# Switch on / off gradients.
model.train(stage == 'train')
#
# The current loss.
runningLoss = 0.0
runningCorrect = 0.0
loader = stages[stage]
#
# Progress bar.
numMini = len(loader)
pbar = progressbar.ProgressBar(max_value=numMini - 1)
#
# Train.
for i, data in enumerate(loader):
inputs_cpu, labels_cpu = data['img'], data['label']
if usegpu:
labels_cpu.squeeze_()
inputs, labels = Variable(
inputs_cpu,
requires_grad=False).cuda(async=True), Variable(
labels_cpu, requires_grad=False).cuda(async=True)
else:
inputs, labels = Variable(inputs_cpu,
requires_grad=False,
volatile=True), Variable(
labels_cpu,
requires_grad=False,
volatile=True)
#
# Forward through network.
if stage == 'train':
out = model(inputs)
else:
#
# The 5 crop from above takes the corners of the iamge and center.
# We must now average the contributions.
bs, ncrops, c, h, w = inputs_cpu.size()
inputs = inputs.view(-1, c, h, w)
result = model(inputs) # fuse batch size and ncrops
out = result.view(bs, ncrops, -1).mean(1) # avg over crops
#
# Backward pass.
optimizer.zero_grad()
_, preds = torch.max(out.data, 1)
loss = criteria(out, labels)
#
# Backwards pass.
if stage == 'train':
loss.backward()
optimizer.step()
dCorrect = torch.sum(preds == labels.data)
#
# Stats.
runningLoss += loss.data[0]
runningCorrect += dCorrect
pbar.update(i)
#
# Overall stats
epochLoss = runningLoss / len(stages[stage])
epochAcc = runningCorrect / (len(stages[stage]) * args.bSize)
#
# Check if we have the new best model.
isBest = False
if stage == 'val' and epochAcc > bestAcc:
isBest = True
bestAcc = epochAcc
bestModel = model.state_dict()
#
# Print per epoch results.
print('\n{} Loss: {:.4f} Acc: {:.4f}'.format(
stage, epochLoss, epochAcc))
#
# Summary for logging in TB.
summary = {
'phase': stage,
'epoch': epoch,
'loss': epochLoss,
'acc': epochAcc,
'data': data,
'pred': preds
}
logEpoch(logger, model, summary)
printColour('Best validation performance:%f' % (bestAcc), colours.OKGREEN)
closeLogger(logger)
retModel = copy.deepcopy(bestModel)
for key, val in retModel.items():
retModel[key] = val.cpu()
return retModel, bestAcc