class LandmarkDataLayer(caffe.Layer):
def setup(self, bottom, top):
param = eval(self.param_str)
self.batch = int(param['batch'])
self.img_size = config.IMG_SIZE
self.batch_loader = BatchLoader(param, "train")
self.ohem_batch_loader = BatchLoader(param, 'ohem')
self.train_ratio = 1.
self.ohem_ratio = 0
if self.ohem_batch_loader.is_loaded():
self.train_ratio = 7 / 8.
self.ohem_ratio = 1. - self.train_ratio
top[0].reshape(self.batch, 3, self.img_size, self.img_size) # data
top[1].reshape(self.batch, config.LANDMARK_SIZE * 2) # landmark
top[2].reshape(self.batch, 1) # landmark
def reshape(self, bottom, top):
pass
def forward(self, bottom, top):
batch_data = self.batch_loader.next_batch(
self.batch * self.train_ratio, '')
batch_data += self.ohem_batch_loader.next_batch(
self.batch * self.ohem_ratio, '')
for i, datum in enumerate(batch_data):
img, pts, eye_dist = datum
top[0].data[i, ...] = img
top[1].data[i, ...] = pts
top[2].data[i, ...] = eye_dist
def backward(self, bottom, top):
pass
class LandmarkDataLayer(caffe.Layer):
def setup(self, bottom, top):
param = eval(self.param_str)
self.batch = int(param['batch'])
self.batch_loader = BatchLoader(param, "train")
self.ohem_batch_loader = BatchLoader(param, 'ohem')
self.train_ratio = 1.
self.ohem_ratio = 0
if self.ohem_batch_loader.is_loaded():
self.train_ratio = 7 / 8.
self.ohem_ratio = 1. - self.train_ratio
top[0].reshape(self.batch, 3, self.img_size, self.img_size) # data
top[1].reshape(self.batch, 72) # landmark
def reshape(self, bottom, top):
pass
def forward(self, bottom, top):
batch_data = self.batch_loader.next_batch(
self.batch * self.train_ratio, '')
batch_data += self.ohem_batch_loader.next_batch(
self.batch * self.ohem_ratio, '')
random.shuffle(batch_data)
for i, datum in enumerate(batch_data):
img, label, bbox, landm5 = datum
top[0].data[i, ...] = img
top[1].data[i, ...] = label
top[2].data[i, ...] = bbox
if self.net != 'pnet':
top[3].data[i, ...] = landm5
def backward(self, bottom, top):
pass
class DataLayer(caffe.Layer):
def setup(self, bottom, top):
param = eval(self.param_str)
self.batch = int(param['batch'])
self.net = param['net']
self.img_size = config.NET_IMG_SIZES[self.net]
self.batch_loader = BatchLoader(param, "train")
self.ohem_batch_loader = BatchLoader(param, 'ohem')
self.train_ratio = 1.
self.ohem_ratio = 0
if self.ohem_batch_loader.is_loaded():
self.train_ratio = 7 / 8.
self.ohem_ratio = 1 / 8.
top[0].reshape(self.batch, 3, self.img_size, self.img_size) # data
top[1].reshape(self.batch, 1) # label
top[2].reshape(self.batch, 4) # bbox
if self.net != 'pnet':
top[3].reshape(self.batch, config.LANDMARK_SIZE * 2)
def reshape(self, bottom, top):
pass
def forward(self, bottom, top):
task = random.choice(config.TRAIN_TASKS[self.net])
batch_data = self.batch_loader.next_batch(
self.batch * self.train_ratio, '')
batch_data += self.ohem_batch_loader.next_batch(
self.batch * self.ohem_ratio, '')
random.shuffle(batch_data)
for i, datum in enumerate(batch_data):
img, label, bbox, landm5 = datum
top[0].data[i, ...] = img
top[1].data[i, ...] = label
top[2].data[i, ...] = bbox
if self.net != 'pnet':
top[3].data[i, ...] = landm5
def backward(self, bottom, top):
pass