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 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(self):
'''
Create singa net based on caffe proto files.
net_proto: caffe prototxt that describes net
solver_proto: caffe prototxt that describe solver
input_sample_shape: shape of input data tensor
return:
a FeedForwardNet object
'''
caffe_net = self.read_net_proto()
caffe_solver = None
if self.caffe_solver_path is not None:
caffe_solver = self.read_solver_proto()
layer_confs = ''
flatten_id = 0
# If the net proto has the input shape
if len(caffe_net.input_dim) > 0:
self.input_sample_shape = caffe_net.input_dim
if len(caffe_net.layer):
layer_confs = caffe_net.layer
elif len(caffe_net.layers):
layer_confs = caffe_net.layers
else:
raise Exception('Invalid proto file!')
net = ffnet.FeedForwardNet()
for i in range(len(layer_confs)):
if layer_confs[i].type == 'Data' or layer_confs[i].type == 5:
continue
elif layer_confs[i].type == 'Input':
self.input_sample_shape = layer_confs[i].input_param.shape[0].dim[1:]
elif layer_confs[i].type == 'SoftmaxWithLoss' or layer_confs[i].type == 21:
net.loss = loss.SoftmaxCrossEntropy()
elif layer_confs[i].type == 'EuclideanLoss' or layer_confs[i].type == 7:
net.loss = loss.SquareError()
elif layer_confs[i].type == 'Accuracy' or layer_confs[i].type == 1:
net.metric = metric.Accuracy()
else:
strConf = layer_confs[i].SerializeToString()
conf = model_pb2.LayerConf()
conf.ParseFromString(strConf)
if caffe_solver:
layer.engine = self.convert_engine(
layer_confs[i], caffe_solver.solver_mode)
else:
# if caffe_solver is None,
layer.engine = self.convert_engine(layer_confs[i], 0)
lyr = layer.Layer(conf.name, conf)
if len(net.layers) == 0:
print('input sample shape: ', self.input_sample_shape)
lyr.setup(self.input_sample_shape)
print(lyr.name, lyr.get_output_sample_shape())
if layer_confs[i].type == 'InnerProduct' or layer_confs[i].type == 14:
net.add(layer.Flatten('flat' + str(flatten_id)))
flatten_id += 1
net.add(lyr)
return net
def create_net(use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
ConvBnReLU(net, 'conv1_1', 64, (3, 32, 32))
net.add(layer.Dropout('drop1', 0.3))
ConvBnReLU(net, 'conv1_2', 64)
net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv2_1', 128)
net.add(layer.Dropout('drop2_1', 0.4))
ConvBnReLU(net, 'conv2_2', 128)
net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv3_1', 256)
net.add(layer.Dropout('drop3_1', 0.4))
ConvBnReLU(net, 'conv3_2', 256)
net.add(layer.Dropout('drop3_2', 0.4))
ConvBnReLU(net, 'conv3_3', 256)
net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv4_1', 512)
net.add(layer.Dropout('drop4_1', 0.4))
ConvBnReLU(net, 'conv4_2', 512)
net.add(layer.Dropout('drop4_2', 0.4))
ConvBnReLU(net, 'conv4_3', 512)
net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv5_1', 512)
net.add(layer.Dropout('drop5_1', 0.4))
ConvBnReLU(net, 'conv5_2', 512)
net.add(layer.Dropout('drop5_2', 0.4))
ConvBnReLU(net, 'conv5_3', 512)
net.add(layer.MaxPooling2D('pool5', 2, 2, border_mode='valid'))
net.add(layer.Flatten('flat'))
net.add(layer.Dropout('drop_flat', 0.5))
net.add(layer.Dense('ip1', 512))
net.add(layer.BatchNormalization('batchnorm_ip1'))
net.add(layer.Activation('relu_ip1'))
net.add(layer.Dropout('drop_ip2', 0.5))
net.add(layer.Dense('ip2', 10))
print 'Start intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
initializer.gaussian(p, 0, 3 * 3 * p.shape[0])
else:
p.gaussian(0, 0.02)
else:
p.set_value(0)
print name, p.l1()
return net
def test_single_input_output(self):
ffn = net.FeedForwardNet(loss.SoftmaxCrossEntropy())
ffn.add(layer.Activation('relu1', input_sample_shape=(2,)))
ffn.add(layer.Activation('relu2'))
x = np.array([[-1, 1], [1, 1], [-1, -2]], dtype=np.float32)
x = tensor.from_numpy(x)
y = tensor.Tensor((3,))
y.set_value(0)
out, _ = ffn.evaluate(x, y)
self.assertAlmostEqual(out * 3,
- math.log(1.0/(1+math.exp(1))) - math.log(0.5) -math.log(0.5),
5);
def create_net(input_shape, use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
net.add(
layer.Conv2D('conv1',
nb_kernels=32,
kernel=7,
stride=3,
pad=1,
input_sample_shape=input_shape))
net.add(layer.Activation('relu1'))
net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid'))
net.add(layer.Conv2D('conv2', nb_kernels=64, kernel=5, stride=3))
net.add(layer.Activation('relu2'))
net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid'))
net.add(layer.Conv2D('conv3', nb_kernels=128, kernel=3, stride=1, pad=2))
net.add(layer.Activation('relu3'))
net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid'))
net.add(layer.Conv2D('conv4', nb_kernels=256, kernel=3, stride=1))
net.add(layer.Activation('relu4'))
net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid'))
net.add(layer.Flatten('flat'))
net.add(layer.Dense('ip5', 256))
net.add(layer.Activation('relu5'))
net.add(layer.Dense('ip6', 16))
net.add(layer.Activation('relu6'))
net.add(layer.Dense('ip7', 2))
print 'Parameter intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
initializer.gaussian(p, 0, p.size())
else:
p.gaussian(0, 0.02)
else:
p.set_value(0)
print name, p.l1()
return net
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 test_mult_inputs(self):
ffn = net.FeedForwardNet(loss.SoftmaxCrossEntropy())
s1 = ffn.add(layer.Activation('relu1', input_sample_shape=(2, )), [])
s2 = ffn.add(layer.Activation('relu2', input_sample_shape=(2, )), [])
ffn.add(layer.Merge('merge', input_sample_shape=(2, )), [s1, s2])
x1 = tensor.Tensor((2, 2))
x1.set_value(1.1)
x2 = tensor.Tensor((2, 2))
x2.set_value(0.9)
out = ffn.forward(False, {'relu1': x1, 'relu2': x2})
out = tensor.to_numpy(out)
self.assertAlmostEqual(np.average(out), 2)
def test_save_load(self):
ffn = net.FeedForwardNet(loss.SoftmaxCrossEntropy())
ffn.add(layer.Conv2D('conv', 4, 3, input_sample_shape=(3, 12, 12)))
ffn.add(layer.Flatten('flat'))
# ffn.add(layer.BatchNorm('bn'))
ffn.add(layer.Dense('dense', num_output=4))
for pname, pval in zip(ffn.param_names(), ffn.param_values()):
pval.set_value(0.1)
ffn.save('test_snaphost')
ffn.save('test_pickle', use_pickle=True)
ffn.load('test_snaphost')
ffn.load('test_pickle', use_pickle=True)
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 create_net(input_shape, use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
ConvBnReLUPool(net, 'conv1', 32, input_shape)
ConvBnReLUPool(net, 'conv2', 64)
ConvBnReLUPool(net, 'conv3', 128)
ConvBnReLUPool(net, 'conv4', 128)
ConvBnReLUPool(net, 'conv5', 256)
ConvBnReLUPool(net, 'conv6', 256)
ConvBnReLUPool(net, 'conv7', 512)
ConvBnReLUPool(net, 'conv8', 512)
net.add(layer.Flatten('flat'))
net.add(layer.Dense('ip1', 256))
net.add(layer.BatchNormalization('bn1'))
net.add(layer.Activation('relu1'))
net.add(layer.Dropout('dropout1', 0.2))
net.add(layer.Dense('ip2', 16))
net.add(layer.BatchNormalization('bn2'))
net.add(layer.Activation('relu2'))
net.add(layer.Dropout('dropout2', 0.2))
net.add(layer.Dense('ip3', 2))
print 'Parameter intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
initializer.gaussian(p, 0, p.size())
else:
p.gaussian(0, 0.02)
else:
p.set_value(0)
print name, p.l1()
return net
def create_net(use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
net.add(
layer.Conv2D("conv1", 16, 3, 1, pad=1, input_sample_shape=(3, 32, 32)))
net.add(layer.BatchNormalization("bn1"))
net.add(layer.Activation("relu1"))
Block(net, "2a", 16, 1)
Block(net, "2b", 16, 1)
Block(net, "2c", 16, 1)
Block(net, "3a", 32, 2)
Block(net, "3b", 32, 1)
Block(net, "3c", 32, 1)
Block(net, "4a", 64, 2)
Block(net, "4b", 64, 1)
Block(net, "4c", 64, 1)
net.add(layer.AvgPooling2D("pool4", 8, 8, border_mode='valid'))
net.add(layer.Flatten('flat'))
net.add(layer.Dense('ip5', 10))
print 'Start intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
# print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
# initializer.gaussian(p, 0, math.sqrt(2.0/p.shape[1]))
initializer.gaussian(p, 0, 9.0 * p.shape[0])
else:
initializer.uniform(p, p.shape[0], p.shape[1])
else:
p.set_value(0)
# print name, p.l1()
return net
def test_train_one_batch(self):
ffn = net.FeedForwardNet(loss.SoftmaxCrossEntropy())
ffn.add(layer.Conv2D('conv', 4, 3, input_sample_shape=(3, 12, 12)))
ffn.add(layer.Flatten('flat'))
ffn.add(layer.Dense('dense', num_output=4))
for pname, pval in zip(ffn.param_names(), ffn.param_values()):
pval.set_value(0.1)
x = tensor.Tensor((4, 3, 12, 12))
x.gaussian(0, 0.01)
y = np.asarray([[1, 0, 0], [0, 0, 1], [0, 0, 1], [0, 1, 0]],
dtype=np.int32)
y = tensor.from_numpy(y)
o = ffn.forward(True, x)
ffn.loss.forward(True, o, y)
g = ffn.loss.backward()
for pname, pvalue, pgrad in ffn.backward(g):
self.assertEqual(len(pvalue), len(pgrad))
for p, g in zip(pvalue, pgrad):
self.assertEqual(p.size(), g.size())
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 build_net(self):
if self.use_cpu:
layer.engine = 'singacpp'
else:
layer.engine = 'cudnn'
self.net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(),
metric.Accuracy())
self.net.add(
Reshape('reshape1', (self.vocab_size, ),
input_sample_shape=(self.maxlen, self.vocab_size)))
self.net.add(layer.Dense('embed',
self.embed_size)) # output: (embed_size, )
self.net.add(layer.Dropout('dropout'))
self.net.add(Reshape('reshape2', (1, self.maxlen, self.embed_size)))
self.net.add(
layer.Conv2D('conv',
self.filters, (self.kernel_size, self.embed_size),
border_mode='valid')) # output: (filter, embed_size)
if self.use_cpu == False:
self.net.add(layer.BatchNormalization('batchNorm'))
self.net.add(layer.Activation('activ')) # output: (filter, embed_size)
self.net.add(layer.MaxPooling2D('max', stride=self.pool_size))
self.net.add(layer.Flatten('flatten'))
self.net.add(layer.Dense('dense', 2))
def create_net(use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
W0_specs = {'init': 'gaussian', 'mean': 0, 'std': 0.0001}
W1_specs = {'init': 'gaussian', 'mean': 0, 'std': 0.01}
W2_specs = {'init': 'gaussian', 'mean': 0, 'std': 0.01, 'decay_mult': 250}
b_specs = {'init': 'constant', 'value': 0, 'lr_mult': 2, 'decay_mult': 0}
net.add(
layer.Conv2D('conv1',
32,
5,
1,
W_specs=W0_specs.copy(),
b_specs=b_specs.copy(),
pad=2,
input_sample_shape=(
3,
32,
32,
)))
net.add(layer.MaxPooling2D('pool1', 3, 2, pad=1))
net.add(layer.Activation('relu1'))
net.add(layer.LRN(name='lrn1', size=3, alpha=5e-5))
net.add(
layer.Conv2D('conv2',
32,
5,
1,
W_specs=W1_specs.copy(),
b_specs=b_specs.copy(),
pad=2))
net.add(layer.Activation('relu2'))
net.add(layer.AvgPooling2D('pool2', 3, 2, pad=1))
net.add(layer.LRN('lrn2', size=3, alpha=5e-5))
net.add(
layer.Conv2D('conv3',
64,
5,
1,
W_specs=W1_specs.copy(),
b_specs=b_specs.copy(),
pad=2))
net.add(layer.Activation('relu3'))
net.add(layer.AvgPooling2D('pool3', 3, 2, pad=1))
net.add(layer.Flatten('flat'))
net.add(
layer.Dense('dense',
10,
W_specs=W2_specs.copy(),
b_specs=b_specs.copy()))
for (p, specs) in zip(net.param_values(), net.param_specs()):
filler = specs.filler
if filler.type == 'gaussian':
p.gaussian(filler.mean, filler.std)
else:
p.set_value(0)
print specs.name, filler.type, p.l1()
return net
def __init__(self,
dev,
rows=28,
cols=28,
channels=1,
noise_size=100,
hidden_size=128,
batch=128,
interval=1000,
learning_rate=0.001,
epochs=1000000,
d_steps=3,
g_steps=1,
dataset_filepath='mnist.pkl.gz',
file_dir='lsgan_images/'):
self.dev = dev
self.rows = rows
self.cols = cols
self.channels = channels
self.feature_size = self.rows * self.cols * self.channels
self.noise_size = noise_size
self.hidden_size = hidden_size
self.batch = batch
self.batch_size = self.batch // 2
self.interval = interval
self.learning_rate = learning_rate
self.epochs = epochs
self.d_steps = d_steps
self.g_steps = g_steps
self.dataset_filepath = dataset_filepath
self.file_dir = file_dir
self.g_w0_specs = {
'init': 'xavier',
}
self.g_b0_specs = {
'init': 'constant',
'value': 0,
}
self.g_w1_specs = {
'init': 'xavier',
}
self.g_b1_specs = {
'init': 'constant',
'value': 0,
}
self.gen_net = ffnet.FeedForwardNet(loss.SquaredError(), )
self.gen_net_fc_0 = layer.Dense(name='g_fc_0',
num_output=self.hidden_size,
use_bias=True,
W_specs=self.g_w0_specs,
b_specs=self.g_b0_specs,
input_sample_shape=(self.noise_size, ))
self.gen_net_relu_0 = layer.Activation(
name='g_relu_0',
mode='relu',
input_sample_shape=(self.hidden_size, ))
self.gen_net_fc_1 = layer.Dense(
name='g_fc_1',
num_output=self.feature_size,
use_bias=True,
W_specs=self.g_w1_specs,
b_specs=self.g_b1_specs,
input_sample_shape=(self.hidden_size, ))
self.gen_net_sigmoid_1 = layer.Activation(
name='g_relu_1',
mode='sigmoid',
input_sample_shape=(self.feature_size, ))
self.gen_net.add(self.gen_net_fc_0)
self.gen_net.add(self.gen_net_relu_0)
self.gen_net.add(self.gen_net_fc_1)
self.gen_net.add(self.gen_net_sigmoid_1)
for (p, specs) in zip(self.gen_net.param_values(),
self.gen_net.param_specs()):
filler = specs.filler
if filler.type == 'gaussian':
p.gaussian(filler.mean, filler.std)
elif filler.type == 'xavier':
initializer.xavier(p)
else:
p.set_value(0)
print(specs.name, filler.type, p.l1())
self.gen_net.to_device(self.dev)
self.d_w0_specs = {
'init': 'xavier',
}
self.d_b0_specs = {
'init': 'constant',
'value': 0,
}
self.d_w1_specs = {
'init': 'xavier',
}
self.d_b1_specs = {
'init': 'constant',
'value': 0,
}
self.dis_net = ffnet.FeedForwardNet(loss.SquaredError(), )
self.dis_net_fc_0 = layer.Dense(
name='d_fc_0',
num_output=self.hidden_size,
use_bias=True,
W_specs=self.d_w0_specs,
b_specs=self.d_b0_specs,
input_sample_shape=(self.feature_size, ))
self.dis_net_relu_0 = layer.Activation(
name='d_relu_0',
mode='relu',
input_sample_shape=(self.hidden_size, ))
self.dis_net_fc_1 = layer.Dense(
name='d_fc_1',
num_output=1,
use_bias=True,
W_specs=self.d_w1_specs,
b_specs=self.d_b1_specs,
input_sample_shape=(self.hidden_size, ))
self.dis_net.add(self.dis_net_fc_0)
self.dis_net.add(self.dis_net_relu_0)
self.dis_net.add(self.dis_net_fc_1)
for (p, specs) in zip(self.dis_net.param_values(),
self.dis_net.param_specs()):
filler = specs.filler
if filler.type == 'gaussian':
p.gaussian(filler.mean, filler.std)
elif filler.type == 'xavier':
initializer.xavier(p)
else:
p.set_value(0)
print(specs.name, filler.type, p.l1())
self.dis_net.to_device(self.dev)
self.combined_net = ffnet.FeedForwardNet(loss.SquaredError(), )
for l in self.gen_net.layers:
self.combined_net.add(l)
for l in self.dis_net.layers:
self.combined_net.add(l)
self.combined_net.to_device(self.dev)
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
def create_net(input_shape, use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
ConvBnReLU(net, 'conv1_1', 64, input_shape)
#net.add(layer.Dropout('drop1', 0.3))
net.add(layer.MaxPooling2D('pool0', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv1_2', 128)
net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv2_1', 128)
net.add(layer.Dropout('drop2_1', 0.4))
ConvBnReLU(net, 'conv2_2', 128)
net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv3_1', 256)
net.add(layer.Dropout('drop3_1', 0.4))
ConvBnReLU(net, 'conv3_2', 256)
net.add(layer.Dropout('drop3_2', 0.4))
ConvBnReLU(net, 'conv3_3', 256)
net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv4_1', 256)
net.add(layer.Dropout('drop4_1', 0.4))
ConvBnReLU(net, 'conv4_2', 256)
net.add(layer.Dropout('drop4_2', 0.4))
ConvBnReLU(net, 'conv4_3', 256)
net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv5_1', 512)
net.add(layer.Dropout('drop5_1', 0.4))
ConvBnReLU(net, 'conv5_2', 512)
net.add(layer.Dropout('drop5_2', 0.4))
ConvBnReLU(net, 'conv5_3', 512)
net.add(layer.MaxPooling2D('pool5', 2, 2, border_mode='valid'))
#ConvBnReLU(net, 'conv6_1', 512)
#net.add(layer.Dropout('drop6_1', 0.4))
#ConvBnReLU(net, 'conv6_2', 512)
#net.add(layer.Dropout('drop6_2', 0.4))
#ConvBnReLU(net, 'conv6_3', 512)
#net.add(layer.MaxPooling2D('pool6', 2, 2, border_mode='valid'))
#ConvBnReLU(net, 'conv7_1', 512)
#net.add(layer.Dropout('drop7_1', 0.4))
#ConvBnReLU(net, 'conv7_2', 512)
#net.add(layer.Dropout('drop7_2', 0.4))
#ConvBnReLU(net, 'conv7_3', 512)
#net.add(layer.MaxPooling2D('pool7', 2, 2, border_mode='valid'))
net.add(layer.Flatten('flat'))
net.add(layer.Dense('ip1', 256))
net.add(layer.BatchNormalization('bn1'))
net.add(layer.Activation('relu1'))
net.add(layer.Dropout('dropout1', 0.2))
net.add(layer.Dense('ip2', 16))
net.add(layer.BatchNormalization('bn2'))
net.add(layer.Activation('relu2'))
net.add(layer.Dropout('dropout2', 0.2))
net.add(layer.Dense('ip3', 2))
print 'Parameter intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
initializer.gaussian(p, 0, p.size())
else:
p.gaussian(0, 0.02)
else:
p.set_value(0)
print name, p.l1()
return net
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
