def create_preact_resnet(depth=200):
'''Resnet with the batchnorm and relu moved to before the conv layer for each block'''
net = ffnet.FeedForwardNet()
net.add(
Conv2D('input-conv',
64,
7,
2,
pad=3,
use_bias=False,
input_sample_shape=(3, 224, 224)))
net.add(BatchNormalization('input-bn'))
net.add(Activation('input_relu'))
net.add(MaxPooling2D('input_pool', 3, 2, pad=1))
conf = cfg[depth]
if depth > 34:
stage(0, net, conf[0], 64, 64, 256, 1, bottleneck, preact=True)
stage(1, net, conf[1], 256, 128, 512, 2, bottleneck, preact=True)
stage(2, net, conf[2], 512, 256, 1024, 2, bottleneck, preact=True)
stage(3, net, conf[3], 1024, 512, 2048, 2, bottleneck, preact=True)
else:
stage(0, net, conf[0], 64, 64, 64, 1, basicblock, preact=True)
stage(1, net, conf[1], 64, 128, 128, 2, basicblock, preact=True)
stage(2, net, conf[2], 128, 256, 256, 2, basicblock, preact=True)
stage(3, net, conf[3], 256, 512, 512, 2, basicblock, preact=True)
net.add(BatchNormalization('final-bn'))
net.add(Activation('final-relu'))
net.add(AvgPooling2D('avg', 7, 1, pad=0))
net.add(Flatten('flat'))
net.add(Dense('dense', 1000))
return net
def create_net(depth, nb_classes, batchnorm=False, use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet()
net = create_layers(net, cfg[depth], (3, 224, 224), batchnorm)
net.add(Flatten('flat'))
net.add(Dense('dense/classifier.0', 4096))
net.add(Activation('act/classifier.1'))
net.add(Dropout('dropout/classifier.2'))
net.add(Dense('dense/classifier.3', 4096))
net.add(Activation('act/classifier.4'))
net.add(Dropout('dropout/classifier.5'))
net.add(Dense('dense/classifier.6', nb_classes))
return net
def add_dense_connected_layers(name, net, growth_rate):
net.add(BatchNormalization('%s/bn1' % name))
net.add(Activation('%s/relu1' % name))
net.add(
Conv2D('%s/conv1' % name,
4 * growth_rate,
1,
1,
pad=0,
use_bias=conv_bias))
net.add(BatchNormalization('%s/bn2' % name))
net.add(Activation('%s/relu2' % name))
return net.add(
Conv2D('%s/conv2' % name, growth_rate, 3, 1, pad=1,
use_bias=conv_bias))
def create_resnet(depth=18):
'''Original resnet, where the there is a relue after the addition layer'''
net = ffnet.FeedForwardNet()
net.add(
Conv2D('input-conv',
64,
7,
2,
pad=3,
use_bias=False,
input_sample_shape=(3, 224, 224)))
net.add(BatchNormalization('input-bn'))
net.add(Activation('input_relu'))
net.add(MaxPooling2D('input_pool', 3, 2, pad=1))
conf = cfg[depth]
if depth > 34:
stage(0, net, conf[0], 64, 64, 256, 1, bottleneck)
stage(1, net, conf[1], 256, 128, 512, 2, bottleneck)
stage(2, net, conf[2], 512, 256, 1024, 2, bottleneck)
stage(3, net, conf[3], 1024, 512, 2048, 2, bottleneck)
else:
stage(0, net, conf[0], 64, 64, 64, 1, basicblock)
stage(1, net, conf[1], 64, 128, 128, 2, basicblock)
stage(2, net, conf[2], 128, 256, 256, 2, basicblock)
stage(3, net, conf[3], 256, 512, 512, 2, basicblock)
net.add(AvgPooling2D('avg', 7, 1, pad=0))
net.add(Flatten('flat'))
net.add(Dense('dense', 1000))
return net
def conv2d(net, name, nb_filter, k, s=1, border_mode='SAME', src=None):
if type(k) is list:
k = (k[0], k[1])
net.add(
Conv2D(name, nb_filter, k, s, border_mode=border_mode, use_bias=False),
src)
net.add(BatchNormalization('%s/BatchNorm' % name))
return net.add(Activation(name + '/relu'))
def bottleneck(name,
net,
inplane,
midplane,
outplane,
stride=1,
preact=False,
add_bn=False):
'''Add three conv layers, with a>=b<=c filters.
The default structure is
input
-split - conv1-bn1-relu1-conv2-bn2-relu2-conv3-bn3
- conv-bn or dummy
-add
-relu
Args:
inplane, num of feature maps of the input
midplane, num of featue maps of the middle layer
outplane, num of feature maps of the output
preact, if true, move the bn3 and relu before conv1, i.e., pre-activation ref identity mapping paper
add_bn, if true, move the last bn after the addition layer (for resnet-50)
'''
assert not (
preact and add_bn
), 'preact and batchnorm after addition cannot be true at the same time'
split = net.add(Split(name + '-split', 2))
if preact:
net.add(BatchNormalization(name + '-preact-bn'))
net.add(Activation(name + '-preact-relu'))
conv(net, name + '-0', midplane, 1, 1, 0, True, True)
conv(net, name + '-1', midplane, 3, stride, 1, True, True)
br0 = conv(net, name + '-2', outplane, 1, 1, 0, not (preact or add_bn),
False)
br1 = shortcut(net, name, inplane, outplane, stride, split, not add_bn)
ret = net.add(Merge(name + '-add'), [br0, br1])
if add_bn:
ret = net.add(BatchNormalization(name + '-add-bn'))
if not preact:
ret = net.add(Activation(name + '-add-relu'))
return ret
def densenet_base(depth, growth_rate=32, reduction=0.5):
'''
rewrite according to pytorch models
special case of densenet 161
'''
if depth == 121:
stages = [6, 12, 24, 16]
elif depth == 169:
stages = [6, 12, 32, 32]
elif depth == 201:
stages = [6, 12, 48, 32]
elif depth == 161:
stages = [6, 12, 36, 24]
else:
print('unknown depth: %d' % depth)
sys.exit(-1)
net = ffnet.FeedForwardNet()
growth_rate = 48 if depth == 161 else 32
n_channels = 2 * growth_rate
net.add(
Conv2D('input/conv',
n_channels,
7,
2,
pad=3,
use_bias=conv_bias,
input_sample_shape=(3, 224, 224)))
net.add(BatchNormalization('input/bn'))
net.add(Activation('input/relu'))
net.add(MaxPooling2D('input/pool', 3, 2, pad=1))
# Dense-Block 1 and transition (56x56)
n_channels = add_block('block1', net, n_channels, stages[0], growth_rate)
add_transition('trans1', net, int(math.floor(n_channels * reduction)))
n_channels = math.floor(n_channels * reduction)
# Dense-Block 2 and transition (28x28)
n_channels = add_block('block2', net, n_channels, stages[1], growth_rate)
add_transition('trans2', net, int(math.floor(n_channels * reduction)))
n_channels = math.floor(n_channels * reduction)
# Dense-Block 3 and transition (14x14)
n_channels = add_block('block3', net, n_channels, stages[2], growth_rate)
add_transition('trans3', net, int(math.floor(n_channels * reduction)))
n_channels = math.floor(n_channels * reduction)
# Dense-Block 4 and transition (7x7)
n_channels = add_block('block4', net, n_channels, stages[3], growth_rate)
add_transition('trans4', net, n_channels, True)
return net
def add_transition(name, net, n_channels, last=False):
net.add(BatchNormalization('%s/norm' % name))
lyr = net.add(Activation('%s/relu' % name))
if last:
net.add(
AvgPooling2D('%s/pool' % name,
lyr.get_output_sample_shape()[1:3],
pad=0))
net.add(Flatten('flat'))
else:
net.add(
Conv2D('%s/conv' % name,
n_channels,
1,
1,
pad=0,
use_bias=conv_bias))
net.add(AvgPooling2D('%s/pool' % name, 2, 2, pad=0))
def conv(net, prefix, n, ksize, stride=1, pad=0, bn=True, relu=True, src=None):
'''Add a convolution layer and optionally a batchnorm and relu layer.
Args:
prefix, a string for the prefix of the layer name
n, num of filters for the conv layer
bn, if true add batchnorm
relu, if true add relu
Returns:
the last added layer
'''
ret = net.add(
Conv2D(prefix + '-conv', n, ksize, stride, pad=pad,
use_bias=conv_bias), src)
if bn:
ret = net.add(BatchNormalization(prefix + '-bn'))
if relu:
ret = net.add(Activation(prefix + '-relu'))
return ret
def create_layers(net, cfg, sample_shape, batch_norm=False):
lid = 0
for idx, v in enumerate(cfg):
if v == 'M':
net.add(MaxPooling2D('pool/features.%d' % lid, 2, 2, pad=0))
lid += 1
else:
net.add(
Conv2D('conv/features.%d' % lid,
v,
3,
pad=1,
input_sample_shape=sample_shape))
lid += 1
if batch_norm:
net.add(BatchNormalization('bn/features.%d' % lid))
lid += 1
net.add(Activation('act/features.%d' % lid))
lid += 1
sample_shape = None
return net
def create_net(shape, weight_path='bvlc_googlenet.pickle'):
net = ffnet.FeedForwardNet()
net.add(Conv2D('conv1/7x7_s2', 64, 7, 2, pad=3, input_sample_shape=shape))
c1 = net.add(Activation('conv1/relu_7x7'))
pool1 = pool(net, c1, 'pool1/3x3_s2', 3, 2)
norm1 = net.add(LRN('pool1/norm1', 5, 0.0001, 0.75))
c3x3r = conv(net, norm1, 'conv2', 64, 1, suffix='3x3_reduce')
c3x3 = conv(net, c3x3r, 'conv2', 192, 3, pad=1, suffix='3x3')
norm2 = net.add(LRN('conv2/norm2', 5, 0.0001, 0.75))
pool2 = pool(net, norm2, 'pool2/3x3_s2', 3, 2)
i3a = inception(net, pool2, 'inception_3a', 64, 96, 128, 16, 32, 32)
i3b = inception(net, i3a, 'inception_3b', 128, 128, 192, 32, 96, 64)
pool3 = pool(net, i3b, 'pool3/3x3_s2', 3, 2)
i4a = inception(net, pool3, 'inception_4a', 192, 96, 208, 16, 48, 64)
i4b = inception(net, i4a, 'inception_4b', 160, 112, 224, 24, 64, 64)
i4c = inception(net, i4b, 'inception_4c', 128, 128, 256, 24, 64, 64)
i4d = inception(net, i4c, 'inception_4d', 112, 144, 288, 32, 64, 64)
i4e = inception(net, i4d, 'inception_4e', 256, 160, 320, 32, 128, 128)
pool4 = pool(net, i4e, 'pool4/3x3_s2', 3, 2)
i5a = inception(net, pool4, 'inception_5a', 256, 160, 320, 32, 128, 128)
i5b = inception(net, i5a, 'inception_5b', 384, 192, 384, 48, 128, 128)
pool5 = net.add(AvgPooling2D('pool5/7x7_s1', 7, 1, pad=0))
drop5 = net.add(Dropout('drop', 0.4))
flat = net.add(Flatten('flat'))
dense = net.add(Dense('loss3/classifier', 1000))
# prob=net.add(Softmax('softmax'))
net.load(weight_path, use_pickle=True)
print('total num of params %d' % (len(net.param_names())))
# SINGA and Caffe have different layout for the weight matrix of the dense
# layer
for key, val in zip(net.param_names(), net.param_values()):
# print key
if key == 'loss3/classifier_weight' or key == 'loss3/classifier/weight':
tmp = tensor.to_numpy(val)
tmp = tmp.reshape(tmp.shape[::-1])
val.copy_from_numpy(np.transpose(tmp))
return net
def create_wide_resnet(depth=50):
'''Similar original resnet except that a<=b<=c for the bottleneck block'''
net = ffnet.FeedForwardNet()
net.add(
Conv2D('input-conv',
64,
7,
2,
pad=3,
use_bias=False,
input_sample_shape=(3, 224, 224)))
net.add(BatchNormalization('input-bn'))
net.add(Activation('input_relu'))
net.add(MaxPooling2D('input_pool', 3, 2, pad=1))
stage(0, net, 3, 64, 128, 256, 1, bottleneck)
stage(1, net, 4, 256, 256, 512, 2, bottleneck)
stage(2, net, 6, 512, 512, 1024, 2, bottleneck)
stage(3, net, 3, 1024, 1024, 2048, 2, bottleneck)
net.add(AvgPooling2D('avg_pool', 7, 1, pad=0))
net.add(Flatten('flag'))
net.add(Dense('dense', 1000))
return net
def conv(net, src, name, num, kernel, stride=1, pad=0, suffix=''):
net.add(Conv2D('%s/%s' % (name, suffix), num, kernel, stride, pad=pad),
src)
return net.add(Activation('%s/relue_%s' % (name, suffix)))
def inception_v3_base(name,
sample_shape,
final_endpoint,
aux_endpoint,
depth_multiplier=1,
min_depth=16):
"""Creates the Inception V3 network up to the given final endpoint.
Args:
sample_shape: input image sample shape, 3d tuple
final_endpoint: specifies the endpoint to construct the network up to.
aux_endpoint: for aux loss.
Returns:
logits: the logits outputs of the model.
end_points: the set of end_points from the inception model.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values
"""
V3 = 'InceptionV3'
end_points = {}
net = ffnet.FeedForwardNet()
def final_aux_check(block_name):
if block_name == final_endpoint:
return True
if block_name == aux_endpoint:
aux = aux_endpoint + '-aux'
end_points[aux] = net.add(Split(aux, 2))
return False
def depth(d):
return max(int(d * depth_multiplier), min_depth)
blk = V3 + '/Conv2d_1a_3x3'
# 299 x 299 x 3
net.add(
Conv2D(blk,
depth(32),
3,
2,
border_mode='VALID',
use_bias=False,
input_sample_shape=sample_shape))
net.add(BatchNormalization(blk + '/BatchNorm'))
end_points[blk] = net.add(Activation(blk + '/relu'))
if final_aux_check(blk):
return net, end_points
# 149 x 149 x 32
conv2d(net, '%s/Conv2d_2a_3x3' % V3, depth(32), 3, border_mode='VALID')
# 147 x 147 x 32
conv2d(net, '%s/Conv2d_2b_3x3' % V3, depth(64), 3)
# 147 x 147 x 64
net.add(MaxPooling2D('%s/MaxPool_3a_3x3' % V3, 3, 2, border_mode='VALID'))
# 73 x 73 x 64
conv2d(net, '%s/Conv2d_3b_1x1' % V3, depth(80), 1, border_mode='VALID')
# 73 x 73 x 80.
conv2d(net, '%s/Conv2d_4a_3x3' % V3, depth(192), 3, border_mode='VALID')
# 71 x 71 x 192.
net.add(MaxPooling2D('%s/MaxPool_5a_3x3' % V3, 3, 2, border_mode='VALID'))
# 35 x 35 x 192.
blk = V3 + '/Mixed_5b'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(48), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_5x5' % blk, depth(64), 5)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(96), 3)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_3x3' % blk, depth(96), 3)
net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(32), 1)
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
if final_aux_check(blk):
return net, end_points
# mixed_1: 35 x 35 x 288.
blk = V3 + '/Mixed_5c'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x1' % blk, depth(48), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv_1_0c_5x5' % blk, depth(64), 5)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(96), 3)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_3x3' % blk, depth(96), 3)
br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1),
src=s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(64), 1)
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
if final_aux_check(blk):
return net, end_points
# mixed_2: 35 x 35 x 288.
blk = V3 + '/Mixed_5d'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(48), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_5x5' % blk, depth(64), 5)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(96), 3)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_3x3' % blk, depth(96), 3)
br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(64), 1)
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
if final_aux_check(blk):
return net, end_points
# mixed_3: 17 x 17 x 768.
blk = V3 + '/Mixed_6a'
s = net.add(Split('%s/Split' % blk, 3))
br0 = conv2d(net,
'%s/Branch_0/Conv2d_1a_1x1' % blk,
depth(384),
3,
2,
border_mode='VALID',
src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_3x3' % blk, depth(96), 3)
br1 = conv2d(net,
'%s/Branch_1/Conv2d_1a_1x1' % blk,
depth(96),
3,
2,
border_mode='VALID')
br2 = net.add(
MaxPooling2D('%s/Branch_2/MaxPool_1a_3x3' % blk,
3,
2,
border_mode='VALID'), s)
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1), [br0, br1, br2])
if final_aux_check(blk):
return net, end_points
# mixed4: 17 x 17 x 768.
blk = V3 + '/Mixed_6b'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(192), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(128), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(128), [1, 7])
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
br2 = conv2d(net,
'%s/Branch_2/Conv2d_0a_1x1' % blk,
depth(128), [1, 1],
src=s)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(128), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(128), [1, 7])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(128), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0e_1x7' % blk, depth(192), [1, 7])
br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
if final_aux_check(blk):
return net, end_points
# mixed_5: 17 x 17 x 768.
blk = V3 + '/Mixed_6c'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net,
'%s/Branch_0/Conv2d_0a_1x1' % blk,
depth(192), [1, 1],
src=s)
br1 = conv2d(net,
'%s/Branch_1/Conv2d_0a_1x1' % blk,
depth(160), [1, 1],
src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(160), [1, 7])
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
br2 = conv2d(net,
'%s/Branch_2/Conv2d_0a_1x1' % blk,
depth(160), [1, 1],
src=s)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(160), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(160), [1, 7])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(160), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0e_1x7' % blk, depth(192), [1, 7])
br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
if final_aux_check(blk):
return net, end_points
# mixed_6: 17 x 17 x 768.
blk = V3 + '/Mixed_6d'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net,
'%s/Branch_0/Conv2d_0a_1x1' % blk,
depth(192), [1, 1],
src=s)
br1 = conv2d(net,
'%s/Branch_1/Conv2d_0a_1x1' % blk,
depth(160), [1, 1],
src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(160), [1, 7])
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
br2 = conv2d(net,
'%s/Branch_2/Conv2d_0a_1x1' % blk,
depth(160), [1, 1],
src=s)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(160), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(160), [1, 7])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(160), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0e_1x7' % blk, depth(192), [1, 7])
br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
if final_aux_check(blk):
return net, end_points
blk = V3 + '/Mixed_6e'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net,
'%s/Branch_0/Conv2d_0a_1x1' % blk,
depth(192), [1, 1],
src=s)
br1 = conv2d(net,
'%s/Branch_1/Conv2d_0a_1x1' % blk,
depth(192), [1, 1],
src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(192), [1, 7])
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
br2 = conv2d(net,
'%s/Branch_2/Conv2d_0a_1x1' % blk,
depth(192), [1, 1],
src=s)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(192), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(192), [1, 7])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(192), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0e_1x7' % blk, depth(192), [1, 7])
br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
if final_aux_check(blk):
return net, end_points
# mixed_8: 8 x 8 x 1280.
blk = V3 + '/Mixed_7a'
s = net.add(Split('%s/Split' % blk, 3))
br0 = conv2d(net,
'%s/Branch_0/Conv2d_0a_1x1' % blk,
depth(192), [1, 1],
src=s)
br0 = conv2d(net,
'%s/Branch_0/Conv2d_1a_3x3' % blk,
depth(320), [3, 3],
2,
border_mode='VALID')
br1 = conv2d(net,
'%s/Branch_1/Conv2d_0a_1x1' % blk,
depth(192), [1, 1],
src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(192), [1, 7])
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
br1 = conv2d(net,
'%s/Branch_1/Conv2d_1a_3x3' % blk,
depth(192), [3, 3],
2,
border_mode='VALID')
br2 = net.add(
MaxPooling2D('%s/Branch_2/MaxPool_1a_3x3' % blk,
3,
2,
border_mode='VALID'), s)
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1), [br0, br1, br2])
if final_aux_check(blk):
return net, end_points
# mixed_9: 8 x 8 x 2048.
blk = V3 + '/Mixed_7b'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(320), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(384), 1, src=s)
s1 = net.add(Split('%s/Branch_1/Split1' % blk, 2))
br11 = conv2d(net,
'%s/Branch_1/Conv2d_0b_1x3' % blk,
depth(384), [1, 3],
src=s1)
br12 = conv2d(net,
'%s/Branch_1/Conv2d_0b_3x1' % blk,
depth(384), [3, 1],
src=s1)
br1 = net.add(Concat('%s/Branch_1/Concat1' % blk, 1), [br11, br12])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(448), 1, src=s)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(384), 3)
s2 = net.add(Split('%s/Branch_2/Split2' % blk, 2))
br21 = conv2d(net,
'%s/Branch_2/Conv2d_0c_1x3' % blk,
depth(384), [1, 3],
src=s2)
br22 = conv2d(net,
'%s/Branch_2/Conv2d_0d_3x1' % blk,
depth(384), [3, 1],
src=s2)
br2 = net.add(Concat('%s/Branch_2/Concat2' % blk, 1), [br21, br22])
br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1),
src=s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
if final_aux_check(blk):
return net, end_points
# mixed_10: 8 x 8 x 2048.
blk = V3 + '/Mixed_7c'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(320), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(384), 1, src=s)
s1 = net.add(Split('%s/Branch_1/Split1' % blk, 2))
br11 = conv2d(net,
'%s/Branch_1/Conv2d_0b_1x3' % blk,
depth(384), [1, 3],
src=s1)
br12 = conv2d(net,
'%s/Branch_1/Conv2d_0c_3x1' % blk,
depth(384), [3, 1],
src=s1)
br1 = net.add(Concat('%s/Branch_1/Concat1' % blk, 1), [br11, br12])
br2 = conv2d(net,
'%s/Branch_2/Conv2d_0a_1x1' % blk,
depth(448), [1, 1],
src=s)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(384), [3, 3])
s2 = net.add(Split('%s/Branch_2/Split2' % blk, 2))
br21 = conv2d(net,
'%s/Branch_2/Conv2d_0c_1x3' % blk,
depth(384), [1, 3],
src=s2)
br22 = conv2d(net,
'%s/Branch_2/Conv2d_0d_3x1' % blk,
depth(384), [3, 1],
src=s2)
br2 = net.add(Concat('%s/Branch_2/Concat2' % blk, 1), [br21, br22])
br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1),
src=s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
assert final_endpoint == blk, \
'final_enpoint = %s is not in the net' % final_endpoint
return net, end_points
def inception_v4_base(sample_shape,
final_endpoint='Inception/Mixed_7d',
aux_endpoint='Inception/Mixed_6e'):
"""Creates the Inception V4 network up to the given final endpoint.
Endpoint name list: 'InceptionV4/' +
['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e',
'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c',
'Mixed_7d']
Args:
sample_shape: input image sample shape, 3d tuple
final_endpoint: specifies the endpoint to construct the network up to.
aux_endpoint: for aux loss.
Returns:
the neural net
the set of end_points from the inception model.
"""
name = 'InceptionV4'
end_points = {}
net = ffnet.FeedForwardNet()
def final_aux_check(block_name):
if block_name == final_endpoint:
return True
if block_name == aux_endpoint:
aux = aux_endpoint + '-aux'
end_points[aux] = net.add(Split(aux, 2))
return False
# 299 x 299 x 3
blk = name + '/Conv2d_1a_3x3'
net.add(
Conv2D(blk,
32,
3,
2,
border_mode='VALID',
use_bias=False,
input_sample_shape=sample_shape))
net.add(BatchNormalization('%s/BatchNorm' % blk))
end_points[blk] = net.add(Activation('%s/relu' % blk))
if final_aux_check(blk):
return net, end_points
# 149 x 149 x 32
blk = name + '/Conv2d_2a_3x3'
end_points[blk] = conv2d(net, blk, 32, 3, border_mode='VALID')
if final_aux_check(blk):
return net, end_points
# 147 x 147 x 32
blk = name + '/Conv2d_2b_3x3'
end_points[blk] = conv2d(net, blk, 64, 3)
if final_aux_check(blk):
return net, end_points
# 147 x 147 x 64
blk = name + '/Mixed_3a'
s = net.add(Split('%s/Split' % blk, 2))
br0 = net.add(
MaxPooling2D('%s/Branch_0/MaxPool_0a_3x3' % blk,
3,
2,
border_mode='VALID'), s)
br1 = conv2d(net,
'%s/Branch_1/Conv2d_0a_3x3' % blk,
96,
3,
2,
border_mode='VALID',
src=s)
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1), [br0, br1])
if final_aux_check(blk):
return net, end_points
# 73 x 73 x 160
blk = name + '/Mixed_4a'
s = net.add(Split('%s/Split' % blk, 2))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, 64, 1, src=s)
br0 = conv2d(net,
'%s/Branch_0/Conv2d_1a_3x3' % blk,
96,
3,
border_mode='VALID')
br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 64, 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, 64, (1, 7))
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, 64, (7, 1))
br1 = conv2d(net,
'%s/Branch_1/Conv2d_1a_3x3' % blk,
96,
3,
border_mode='VALID')
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1), [br0, br1])
if final_aux_check(blk):
return net, end_points
# 71 x 71 x 192
blk = name + '/Mixed_5a'
s = net.add(Split('%s/Split' % blk, 2))
br0 = conv2d(net,
'%s/Branch_0/Conv2d_1a_3x3' % blk,
192,
3,
2,
border_mode='VALID',
src=s)
br1 = net.add(
MaxPooling2D('%s/Branch_1/MaxPool_1a_3x3' % blk,
3,
2,
border_mode='VALID'), s)
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1), [br0, br1])
if final_aux_check(blk):
return net, end_points
# 35 x 35 x 384
# 4 x Inception-A blocks
for idx in range(4):
blk = name + '/Mixed_5' + chr(ord('b') + idx)
end_points[blk] = block_inception_a(blk, net)
if final_aux_check(blk):
return net, end_points
# 35 x 35 x 384
# Reduction-A block
blk = name + '/Mixed_6a'
end_points[blk] = block_reduction_a(blk, net)
if final_aux_check(blk):
return net, end_points[blk], end_points
# 17 x 17 x 1024
# 7 x Inception-B blocks
for idx in range(7):
blk = name + '/Mixed_6' + chr(ord('b') + idx)
end_points[blk] = block_inception_b(blk, net)
if final_aux_check(blk):
return net, end_points
# 17 x 17 x 1024
# Reduction-B block
blk = name + '/Mixed_7a'
end_points[blk] = block_reduction_b(blk, net)
if final_aux_check(blk):
return net, end_points
# 8 x 8 x 1536
# 3 x Inception-C blocks
for idx in range(3):
blk = name + '/Mixed_7' + chr(ord('b') + idx)
end_points[blk] = block_inception_c(blk, net)
if final_aux_check(blk):
return net, end_points
assert final_endpoint == blk, \
'final_enpoint = %s is not in the net' % final_endpoint