def create_net(num_classes=1001,
sample_shape=(3, 299, 299),
final_endpoint='InceptionV3/Mixed_7c',
aux_endpoint='InceptionV3/Mixed_6e',
dropout_keep_prob=0.8):
"""Creates the Inception V4 model.
Args:
num_classes: number of predicted classes.
dropout_keep_prob: float, the fraction to keep before final layer.
final_endpoint: 'InceptionV3/Mixed_7d',
aux_endpoint:
Returns:
logits: the logits outputs of the model.
end_points: the set of end_points from the inception model.
"""
name = 'InceptionV3'
net, end_points = inception_v3_base(name, sample_shape, final_endpoint,
aux_endpoint)
# Auxiliary Head logits
if aux_endpoint is not None:
# 8 x 8 x 1280
aux_logits = end_points[aux_endpoint + '-aux']
blk = name + '/AuxLogits'
net.add(
AvgPooling2D('%s/AvgPool_1a_5x5' % blk,
5,
stride=3,
border_mode='VALID'), aux_logits)
t = conv2d(net, '%s/Conv2d_1b_1x1' % blk, 128, 1)
s = t.get_output_sample_shape()[1:3]
conv2d(net,
'%s/Conv2d_2a_%dx%d' % (blk, s[0], s[1]),
768,
s,
border_mode='VALID')
net.add(Conv2D('%s/Conv2d_2b_1x1' % blk, num_classes, 1))
net.add(Flatten('%s/flat' % blk))
# Final pooling and prediction
# 8 x 8 x 2048
blk = name + '/Logits'
last_layer = end_points[final_endpoint]
net.add(
AvgPooling2D('%s/AvgPool_1a' % blk,
last_layer.get_output_sample_shape()[1:3],
1,
border_mode='VALID'), last_layer)
# 1 x 1 x 2048
net.add(Dropout('%s/Dropout_1b' % blk, 1 - dropout_keep_prob))
net.add(Conv2D('%s/Conv2d_1c_1x1' % blk, num_classes, 1))
end_points[blk] = net.add(Flatten('%s/flat' % blk))
# 2048
return net, end_points
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_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_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 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