def test_gModule(): # test input layer inputlayer1 = Input(name='input', inputshape=(384, 256, 3), datatype='uint') model = gModule(inputlayers=inputlayer1) assert model.layers['input'].top is None assert model.layers['input'].bottom is None model.compile(np.ndarray((16, 384, 256, 3))) assert_allclose(model.batch_size, 16) conv1 = model.add(Convolution(name='conv1', bottom=inputlayer1, nOutputPlane=64, kernal_size=3, padding=1)) assert model.layers['input'].top == [model.layers['conv1']] assert model.layers['conv1'].bottom == [inputlayer1] model.add(Convolution(name='conv2_1', bottom=conv1, nOutputPlane=128, kernal_size=3, padding=1)) assert model.layers['conv1'].top == [model.layers['conv2_1']] assert model.layers['conv2_1'].bottom == [model.layers['conv1']] model.add(Convolution(name='conv2_2', bottom=conv1, nOutputPlane=128, kernal_size=3, padding=1)) assert model.layers['conv1'].top == [model.layers['conv2_1'], model.layers['conv2_2']] assert model.layers['conv2_2'].bottom == [model.layers['conv1']] model.compile(np.ndarray((16, 384, 256, 3))) assert_allclose(model.nb_entries, 4) assert_allclose(model.batch_size, 16)
def main():
# test input layer
inputlayer1 = Input(name='input',
inputshape=(384, 256, 3),
datatype='uint')
model = gModule(inputlayers=inputlayer1)
conv1 = model.add(
Convolution(name='conv1',
bottom=inputlayer1,
nOutputPlane=64,
kernal_size=3,
padding=1))
model.add(
Convolution(name='conv2_1',
bottom=conv1,
nOutputPlane=128,
kernal_size=3,
padding=1))
model.add(
Convolution(name='conv2_2',
bottom=conv1,
nOutputPlane=128,
kernal_size=3,
padding=1))
model.compile(np.ndarray((16, 384, 256, 3)))
# model.remove('conv2_2')
# assert not model.layers.has_key('conv1')
# assert not model.blobs.has_key('conv1')
# model.compile(np.ndarray((16, 384, 256, 3)))
# assert_allclose(model.nb_entries, 3)
model.summary()
def main():
# create network
input_data = np.ndarray((1, 5120, 3840, 3))
inputlayer = Input(name='input',
inputshape=input_data.shape[1:],
datatype='uint')
model = gModule(inputlayer)
# define the head
conv1_1 = model.add(
Convolution(name='conv1_1',
bottom=inputlayer,
nOutputPlane=64,
kernal_size=3,
padding=1,
datatype='boolean'))
conv1_2 = model.add(
Convolution(name='conv1_2',
bottom=conv1_1,
nOutputPlane=64,
kernal_size=3,
padding=1,
datatype='boolean'))
# pool1 = model.add(Pooling(name='pool1', bottom=conv1_2, kernal_size=2, stride=2))
conv2_1 = model.add(
Convolution(name='conv2_1',
bottom=conv1_2,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='boolean'))
conv2_2 = model.add(
Convolution(name='conv2_2',
bottom=conv2_1,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='boolean'))
# pool2 = model.add(Pooling(name='pool2', bottom=conv2_2, kernal_size=2, stride=2))
conv3_1 = model.add(
Convolution(name='conv3_1',
bottom=conv2_2,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='boolean'))
conv3_2 = model.add(
Convolution(name='conv3_2',
bottom=conv3_1,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='boolean'))
conv3_3 = model.add(
Convolution(name='conv3_3',
bottom=conv3_2,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='boolean'))
conv3_4 = model.add(
Convolution(name='conv3_4',
bottom=conv3_3,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='boolean'))
# pool3 = model.add(Pooling(name='pool3', bottom=conv3_4, kernal_size=2, stride=2))
conv4_1 = model.add(
Convolution(name='conv4_1',
bottom=conv3_4,
nOutputPlane=512,
kernal_size=3,
padding=1,
datatype='boolean'))
conv4_2 = model.add(
Convolution(name='conv4_2',
bottom=conv4_1,
nOutputPlane=512,
kernal_size=3,
padding=1,
datatype='boolean'))
conv4_3 = model.add(
Convolution(name='conv4_3',
bottom=conv4_2,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='boolean'))
conv4_4 = model.add(
Convolution(name='conv4_4',
bottom=conv4_3,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='boolean'))
# define stage 1
conv5_1_l = model.add(
Convolution(name='conv5_1_l',
bottom=conv4_4,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='boolean'))
conv5_2_l = model.add(
Convolution(name='conv5_2_l',
bottom=conv5_1_l,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='boolean'))
conv5_3_l = model.add(
Convolution(name='conv5_3_l',
bottom=conv5_2_l,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='boolean'))
conv5_4_l = model.add(
Convolution(name='conv5_4_l',
bottom=conv5_3_l,
nOutputPlane=512,
kernal_size=1,
padding=0,
datatype='boolean'))
conv5_5_l = model.add(
Convolution(name='conv5_5_l',
bottom=conv5_4_l,
nOutputPlane=26,
kernal_size=1,
padding=0,
datatype='boolean'))
conv5_1_r = model.add(
Convolution(name='conv5_1_r',
bottom=conv4_4,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='boolean'))
conv5_2_r = model.add(
Convolution(name='conv5_2_r',
bottom=conv5_1_r,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='boolean'))
conv5_3_r = model.add(
Convolution(name='conv5_3_r',
bottom=conv5_2_r,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='boolean'))
conv5_4_r = model.add(
Convolution(name='conv5_4_r',
bottom=conv5_3_r,
nOutputPlane=512,
kernal_size=1,
padding=0,
datatype='boolean'))
conv5_5_r = model.add(
Convolution(name='conv5_5_r',
bottom=conv5_4_r,
nOutputPlane=13,
kernal_size=1,
padding=0,
datatype='boolean'))
concat_stage2 = model.add(
Concat(name='concat_stage2',
bottom=[conv4_4, conv5_5_l, conv5_5_r],
axis=2))
# concat_stage3 = stage_network(model, conv4_4, concat_stage2, 2)
# concat_stage4 = stage_network(model, conv4_4, concat_stage3, 3)
# concat_stage5 = stage_network(model, conv4_4, concat_stage4, 4)
# concat_stage6 = stage_network(model, conv4_4, concat_stage5, 5)
# define stage 6 (output stage)
stage_number = 2
conv1_stage6_l = model.add(
Convolution(name='conv1_stage%d_l' % stage_number,
bottom=concat_stage2,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv2_stage6_l = model.add(
Convolution(name='conv2_stage%d_l' % stage_number,
bottom=conv1_stage6_l,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv3_stage6_l = model.add(
Convolution(name='conv3_stage%d_l' % stage_number,
bottom=conv2_stage6_l,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv4_stage6_l = model.add(
Convolution(name='conv4_stage%d_l' % stage_number,
bottom=conv3_stage6_l,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv5_stage6_l = model.add(
Convolution(name='conv5_stage%d_l' % stage_number,
bottom=conv4_stage6_l,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv6_stage6_l = model.add(
Convolution(name='conv6_stage%d_l' % stage_number,
bottom=conv5_stage6_l,
nOutputPlane=128,
kernal_size=1,
padding=0,
datatype='boolean'))
conv7_stage6_l = model.add(
Convolution(name='conv7_stage%d_l' % stage_number,
bottom=conv6_stage6_l,
nOutputPlane=13,
kernal_size=1,
padding=0,
datatype='boolean'))
conv1_stage6_r = model.add(
Convolution(name='conv1_stage%d_r' % stage_number,
bottom=concat_stage2,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv2_stage6_r = model.add(
Convolution(name='conv2_stage%d_r' % stage_number,
bottom=conv1_stage6_r,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv3_stage6_r = model.add(
Convolution(name='conv3_stage%d_r' % stage_number,
bottom=conv2_stage6_r,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv4_stage6_r = model.add(
Convolution(name='conv4_stage%d_r' % stage_number,
bottom=conv3_stage6_r,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv5_stage6_r = model.add(
Convolution(name='conv5_stage%d_r' % stage_number,
bottom=conv4_stage6_r,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='boolean'))
conv6_stage6_r = model.add(
Convolution(name='conv6_stage%d_r' % stage_number,
bottom=conv5_stage6_r,
nOutputPlane=128,
kernal_size=1,
padding=0,
datatype='boolean'))
conv7_stage6_r = model.add(
Convolution(name='conv7_stage%d_r' % stage_number,
bottom=conv6_stage6_r,
nOutputPlane=26,
kernal_size=1,
padding=0,
datatype='boolean'))
model.compile(input_data)
model.summary()
def main():
# create network
input_data = np.ndarray((1, 512, 384, 3))
inputlayer = Input(name='input',
inputshape=input_data.shape[1:],
datatype='uint')
model = gModule(inputlayer)
# define the head
conv1_1 = model.add(
Convolution(name='conv1_1',
bottom=inputlayer,
nOutputPlane=64,
kernal_size=3,
padding=1,
datatype='single'))
conv1_2 = model.add(
Convolution(name='conv1_2',
bottom=conv1_1,
nOutputPlane=64,
kernal_size=3,
padding=1,
datatype='single'))
pool1 = model.add(
Pooling(name='pool1', bottom=conv1_2, kernal_size=2, stride=2))
conv2_1 = model.add(
Convolution(name='conv2_1',
bottom=pool1,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='single'))
conv2_2 = model.add(
Convolution(name='conv2_2',
bottom=conv2_1,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='single'))
pool2 = model.add(
Pooling(name='pool2', bottom=conv2_2, kernal_size=2, stride=2))
conv3_1 = model.add(
Convolution(name='conv3_1',
bottom=pool2,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='single'))
conv3_2 = model.add(
Convolution(name='conv3_2',
bottom=conv3_1,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='single'))
conv3_3 = model.add(
Convolution(name='conv3_3',
bottom=conv3_2,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='single'))
conv3_4 = model.add(
Convolution(name='conv3_4',
bottom=conv3_3,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='single'))
pool3 = model.add(
Pooling(name='pool3', bottom=conv3_4, kernal_size=2, stride=2))
conv4_1 = model.add(
Convolution(name='conv4_1',
bottom=pool3,
nOutputPlane=512,
kernal_size=3,
padding=1,
datatype='single'))
conv4_2 = model.add(
Convolution(name='conv4_2',
bottom=conv4_1,
nOutputPlane=512,
kernal_size=3,
padding=1,
datatype='single'))
conv4_3 = model.add(
Convolution(name='conv4_3',
bottom=conv4_2,
nOutputPlane=256,
kernal_size=3,
padding=1,
datatype='single'))
conv4_4 = model.add(
Convolution(name='conv4_4',
bottom=conv4_3,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='single'))
# define stage 1
conv5_1_l = model.add(
Convolution(name='conv5_1_l',
bottom=conv4_4,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='single'))
conv5_2_l = model.add(
Convolution(name='conv5_2_l',
bottom=conv5_1_l,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='single'))
conv5_3_l = model.add(
Convolution(name='conv5_3_l',
bottom=conv5_2_l,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='single'))
conv5_4_l = model.add(
Convolution(name='conv5_4_l',
bottom=conv5_3_l,
nOutputPlane=512,
kernal_size=1,
padding=0,
datatype='single'))
conv5_5_l = model.add(
Convolution(name='conv5_5_l',
bottom=conv5_4_l,
nOutputPlane=38,
kernal_size=1,
padding=0,
datatype='single'))
conv5_1_r = model.add(
Convolution(name='conv5_1_r',
bottom=conv4_4,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='single'))
conv5_2_r = model.add(
Convolution(name='conv5_2_r',
bottom=conv5_1_r,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='single'))
conv5_3_r = model.add(
Convolution(name='conv5_3_r',
bottom=conv5_2_r,
nOutputPlane=128,
kernal_size=3,
padding=1,
datatype='single'))
conv5_4_r = model.add(
Convolution(name='conv5_4_r',
bottom=conv5_3_r,
nOutputPlane=512,
kernal_size=1,
padding=0,
datatype='single'))
conv5_5_r = model.add(
Convolution(name='conv5_5_r',
bottom=conv5_4_r,
nOutputPlane=19,
kernal_size=1,
padding=0,
datatype='single'))
concat_stage2 = model.add(
Concat(name='concat_stage2',
bottom=[conv4_4, conv5_5_l, conv5_5_r],
axis=2))
concat_stage3 = stage_network(model, conv4_4, concat_stage2, 2)
concat_stage4 = stage_network(model, conv4_4, concat_stage3, 3)
concat_stage5 = stage_network(model, conv4_4, concat_stage4, 4)
concat_stage6 = stage_network(model, conv4_4, concat_stage5, 5)
# define stage 6 (output stage)
stage_number = 6
conv1_stage6_l = model.add(
Convolution(name='conv1_stage%d_l' % stage_number,
bottom=concat_stage6,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv2_stage6_l = model.add(
Convolution(name='conv2_stage%d_l' % stage_number,
bottom=conv1_stage6_l,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv3_stage6_l = model.add(
Convolution(name='conv3_stage%d_l' % stage_number,
bottom=conv2_stage6_l,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv4_stage6_l = model.add(
Convolution(name='conv4_stage%d_l' % stage_number,
bottom=conv3_stage6_l,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv5_stage6_l = model.add(
Convolution(name='conv5_stage%d_l' % stage_number,
bottom=conv4_stage6_l,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv6_stage6_l = model.add(
Convolution(name='conv6_stage%d_l' % stage_number,
bottom=conv5_stage6_l,
nOutputPlane=128,
kernal_size=1,
padding=0,
datatype='single'))
conv7_stage6_l = model.add(
Convolution(name='conv7_stage%d_l' % stage_number,
bottom=conv6_stage6_l,
nOutputPlane=38,
kernal_size=1,
padding=0,
datatype='single'))
conv1_stage6_r = model.add(
Convolution(name='conv1_stage%d_r' % stage_number,
bottom=concat_stage6,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv2_stage6_r = model.add(
Convolution(name='conv2_stage%d_r' % stage_number,
bottom=conv1_stage6_r,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv3_stage6_r = model.add(
Convolution(name='conv3_stage%d_r' % stage_number,
bottom=conv2_stage6_r,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv4_stage6_r = model.add(
Convolution(name='conv4_stage%d_r' % stage_number,
bottom=conv3_stage6_r,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv5_stage6_r = model.add(
Convolution(name='conv5_stage%d_r' % stage_number,
bottom=conv4_stage6_r,
nOutputPlane=128,
kernal_size=7,
padding=3,
datatype='single'))
conv6_stage6_r = model.add(
Convolution(name='conv6_stage%d_r' % stage_number,
bottom=conv5_stage6_r,
nOutputPlane=128,
kernal_size=1,
padding=0,
datatype='single'))
conv7_stage6_r = model.add(
Convolution(name='conv7_stage%d_r' % stage_number,
bottom=conv6_stage6_r,
nOutputPlane=19,
kernal_size=1,
padding=0,
datatype='single'))
model.compile(input_data)
model_path = 'cpm_model.pdf'
chart_path = 'cpm_memory.png'
table_path = 'cpm_table.txt'
model.summary(model_path=model_path,
chart_path=chart_path,
table_path=table_path,
visualize=False)