def build_baseline_model():
log.i('BUILDING BASELINE MODEL...')
# Random Seed
lasagne_random.set_rng(cfg.getRandomState())
# Input layer for images
net = l.InputLayer((None, cfg.IM_DIM, cfg.IM_SIZE[1], cfg.IM_SIZE[0]))
# Stride size (as an alternative to max pooling)
if cfg.MAX_POOLING:
s = 1
else:
s = 2
# Convolutinal layer groups
for i in range(len(cfg.FILTERS)):
# 3x3 Convolution + Stride
net = batch_norm(
l.Conv2DLayer(net,
num_filters=cfg.FILTERS[i],
filter_size=cfg.KERNEL_SIZES[i],
num_groups=cfg.NUM_OF_GROUPS[i],
pad='same',
stride=s,
W=initialization(cfg.NONLINEARITY),
nonlinearity=nonlinearity(cfg.NONLINEARITY)))
# Pooling layer
if cfg.MAX_POOLING:
net = l.MaxPool2DLayer(net, pool_size=2)
# Dropout Layer (we support different types of dropout)
if cfg.DROPOUT_TYPE == 'channels' and cfg.DROPOUT > 0.0:
net = l.dropout_channels(net, p=cfg.DROPOUT)
elif cfg.DROPOUT_TYPE == 'location' and cfg.DROPOUT > 0.0:
net = l.dropout_location(net, p=cfg.DROPOUT)
elif cfg.DROPOUT > 0.0:
net = l.DropoutLayer(net, p=cfg.DROPOUT)
log.i(('\tGROUP', i + 1, 'OUT SHAPE:', l.get_output_shape(net)))
# Final 1x1 Convolution
net = batch_norm(
l.Conv2DLayer(net,
num_filters=cfg.FILTERS[i] * 2,
filter_size=1,
W=initialization('identity'),
nonlinearity=nonlinearity('identity')))
log.i(('\tFINAL CONV OUT SHAPE:', l.get_output_shape(net)))
# Global Pooling layer (default mode = average)
net = l.GlobalPoolLayer(net)
log.i(("\tFINAL POOLING SHAPE:", l.get_output_shape(net)))
# Classification Layer (Softmax)
net = l.DenseLayer(net,
len(cfg.CLASSES),
nonlinearity=nonlinearity('softmax'),
W=initialization('softmax'))
log.i(("\tFINAL NET OUT SHAPE:", l.get_output_shape(net)))
log.i("...DONE!")
# Model stats
log.i(("MODEL HAS",
(sum(hasattr(layer, 'W')
for layer in l.get_all_layers(net))), "WEIGHTED LAYERS"))
log.i(("MODEL HAS", l.count_params(net), "PARAMS"))
return net
def resblock(net_in,
filters,
kernel_size,
stride=1,
num_groups=1,
preactivated=True):
# Preactivation
net_pre = batch_norm(net_in)
net_pre = l.NonlinearityLayer(net_pre,
nonlinearity=nonlinearity(cfg.NONLINEARITY))
# Preactivated shortcut?
if preactivated:
net_sc = net_pre
else:
net_sc = net_in
# Stride size
if cfg.MAX_POOLING:
s = 1
else:
s = stride
# First Convolution (alwys has preactivated input)
net = batch_norm(
l.Conv2DLayer(net_pre,
num_filters=filters,
filter_size=kernel_size,
pad='same',
stride=s,
num_groups=num_groups,
W=initialization(cfg.NONLINEARITY),
nonlinearity=nonlinearity(cfg.NONLINEARITY)))
# Optional pooling layer
if cfg.MAX_POOLING and stride > 1:
net = l.MaxPool2DLayer(net, pool_size=stride)
# Dropout Layer (we support different types of dropout)
if cfg.DROPOUT_TYPE == 'channels' and cfg.DROPOUT > 0.0:
net = l.dropout_channels(net, p=cfg.DROPOUT)
elif cfg.DROPOUT_TYPE == 'location' and cfg.DROPOUT > 0.0:
net = l.dropout_location(net, p=cfg.DROPOUT)
elif cfg.DROPOUT > 0.0:
net = l.DropoutLayer(net, p=cfg.DROPOUT)
# Second Convolution
net = l.Conv2DLayer(net,
num_filters=filters,
filter_size=kernel_size,
pad='same',
stride=1,
num_groups=num_groups,
W=initialization(cfg.NONLINEARITY),
nonlinearity=None)
# Shortcut Layer
if not l.get_output_shape(net) == l.get_output_shape(net_sc):
shortcut = l.Conv2DLayer(net_sc,
num_filters=filters,
filter_size=1,
pad='same',
stride=s,
W=initialization(cfg.NONLINEARITY),
nonlinearity=None,
b=None)
# Optional pooling layer
if cfg.MAX_POOLING and stride > 1:
shortcut = l.MaxPool2DLayer(shortcut, pool_size=stride)
else:
shortcut = net_sc
# Merge Layer
out = l.ElemwiseSumLayer([net, shortcut])
return out