def getModel(x, num_output, K, stages, wd, is_training, transfer_mode= False):
with tf.variable_scope('conv1'):
x = common.spatialConvolution(x, 3, 1, 2*K, wd= wd)
# x = common.batchNormalization(x, is_training= is_training)
# x = tf.nn.relu(x)
# x = common.maxPool(x, 3, 2)
print x
with tf.variable_scope('block0'):
x = block(x, stages[0], K, is_training= is_training, wd= wd)
print x
with tf.variable_scope('trans1'):
x = transition(x, K, wd= wd, is_training= is_training)
print x
with tf.variable_scope('block2'):
x = block(x, stages[1], K, is_training= is_training, wd= wd)
print x
with tf.variable_scope('trans2'):
x = transition(x, K, wd= wd, is_training= is_training)
print x
with tf.variable_scope('block3'):
x = block(x, stages[2], K, is_training= is_training, wd= wd)
print x
x = common.avgPool(x,8,1, padding='VALID')
x= common.flatten(x)
if not transfer_mode:
with tf.variable_scope('output'):
x = common.fullyConnected(x, num_output, wd= wd)
else:
with tf.variable_scope('transfer_output'):
x = common.fullyConnected(x, num_output, wd= wd)
return x
def transition(x, K, wd, is_training):
with tf.variable_scope('conv'):
current = common.batchNormalization(x, is_training=is_training)
current = tf.nn.relu(current)
shape = current.get_shape().as_list()
dim = math.floor(shape[3] * 0.5)
current = common.spatialConvolution(current, 1, 1, dim, wd=wd)
current = common.avgPool(current, 2, 2)
return current
def inference(x, num_output, wd, is_training, transfer_mode=False):
with tf.variable_scope('block1'):
network = block(x, [11, 4, 96], wd, is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('block2'):
network = block(network, [5, 1, 256], wd, is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('block3'):
network = block(network, [3, 1, 384], wd, is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('block4'):
network = block(network, [3, 1, 1024], wd, is_training)
network = common.avgPool(network, 7, 1)
network = common.flatten(network)
output = [None] * len(num_output)
for o in xrange(0, len(num_output)):
with tf.variable_scope('output' + str(o)):
output[o] = common.fullyConnected(network, num_output[o], wd=wd)
return output
def inference(x, num_output, wd, is_training, transfer_mode=False):
with tf.variable_scope('block1'):
network = block(x, [11, 4, 96], wd, is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('block2'):
network = block(network, [5, 1, 256], wd, is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('block3'):
network = block(network, [3, 1, 384], wd, is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('block4'):
network = block(network, [3, 1, 1024], wd, is_training)
network = common.avgPool(network, 7, 1)
network = common.flatten(network)
if not transfer_mode:
with tf.variable_scope('output'):
network = common.fullyConnected(network, num_output, wd=wd)
else:
with tf.variable_scope('transfer_output'):
network = common.fullyConnected(network, num_output, wd=wd)
return network
def inference(x, num_output, wd, dropout_rate, is_training, transfer_mode= False):
with tf.variable_scope('features'):
with tf.variable_scope('conv1'):
network = common.spatialConvolution(x, 7, 2, 64, wd= wd)
network = common.batchNormalization(network, is_training= is_training)
network = tf.nn.relu (network)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('conv2'):
network = common.spatialConvolution(network, 1, 1, 64, wd= wd)
network = common.batchNormalization(network, is_training= is_training)
network = tf.nn.relu(network)
with tf.variable_scope('conv3'):
network = common.spatialConvolution(network, 3, 1, 192, wd= wd)
network = common.batchNormalization(network, is_training= is_training)
network = tf.nn.relu(network)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('inception3a'):
network = inception( network, 64, [96, 128], [16, 32], 32, wd= wd, is_training= is_training)
with tf.variable_scope('inception3b'):
network = inception( network, 128, [128, 192], [32, 96], 64, wd= wd, is_training= is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('inception4a'):
network = inception( network, 192, [96, 208], [16, 48], 64, wd= wd, is_training= is_training)
with tf.variable_scope('inception4b'):
network = inception( network, 160, [112, 224], [24, 64], 64, wd= wd, is_training= is_training)
with tf.variable_scope('inception4c'):
network = inception( network, 128, [128, 256], [24, 64], 64, wd= wd, is_training= is_training)
with tf.variable_scope('inception4d'):
network = inception( network, 112, [144, 288], [32, 64], 64, wd= wd, is_training= is_training)
with tf.variable_scope('mainb'):
with tf.variable_scope('inception4e'):
main_branch = inception( network, 256, [160, 320], [32, 128], 128, wd= wd, is_training= is_training)
main_branch = common.maxPool(main_branch, 3, 2)
with tf.variable_scope('inception5a'):
main_branch= inception(main_branch, 256, [160, 320], [32, 128], 128, wd= wd, is_training= is_training)
with tf.variable_scope('inception5b'):
main_branch= inception(main_branch, 384, [192, 384], [48, 128], 128, wd= wd, is_training= is_training)
main_branch= common.avgPool(main_branch, 7, 1)
main_branch= common.flatten(main_branch)
main_branch= tf.nn.dropout(main_branch, dropout_rate)
if not transfer_mode:
with tf.variable_scope('output'):
main_branch= common.fullyConnected(main_branch, num_output, wd= wd)
else:
with tf.variable_scope('transfer_output'):
main_branch= common.fullyConnected(main_branch, num_output, wd= wd)
with tf.variable_scope('auxb'):
aux_classifier= common.avgPool(network, 5, 3)
with tf.variable_scope('conv1'):
aux_classifier= common.spatialConvolution(aux_classifier, 1, 1, 128, wd= wd)
aux_classifier= common.batchNormalization(aux_classifier, is_training= is_training)
aux_classifier= tf.nn.relu(aux_classifier)
aux_classifier= common.flatten(aux_classifier)
with tf.variable_scope('fc1'):
aux_classifier= common.fullyConnected(aux_classifier, 1024, wd= wd)
aux_classifier= tf.nn.dropout(aux_classifier, dropout_rate)
if not transfer_mode:
with tf.variable_scope('output'):
aux_classifier= common.fullyConnected(aux_classifier, num_output, wd= wd)
else:
with tf.variable_scope('transfer_output'):
aux_classifier= common.fullyConnected(aux_classifier, num_output, wd= wd)
return tf.concat([main_branch, aux_classifier],1)
def getModel(x,
num_output,
K,
stages,
dropout_rate,
wd,
is_training,
transfer_mode=False):
print("input", x)
with tf.variable_scope('conv1'):
x = common.spatialConvolution(x, 7, 2, 2 * K, wd=wd)
print("First Conv", x)
x = common.batchNormalization(x, is_training=is_training)
x = tf.nn.relu(x)
x = common.maxPool(x, 3, 2)
print("First Maxpool", x)
with tf.variable_scope('block1'):
x = block(x,
stages[0],
K,
is_training=is_training,
dropout_rate=dropout_rate,
wd=wd)
print("block1", x)
with tf.variable_scope('trans1'):
x = transition(x,
K,
dropout_rate=dropout_rate,
wd=wd,
is_training=is_training)
print("transition1", x)
with tf.variable_scope('block2'):
x = block(x,
stages[1],
K,
is_training=is_training,
dropout_rate=dropout_rate,
wd=wd)
print("block2", x)
with tf.variable_scope('trans2'):
x = transition(x,
K,
dropout_rate=dropout_rate,
wd=wd,
is_training=is_training)
print("transition2", x)
with tf.variable_scope('block3'):
x = block(x,
stages[2],
K,
is_training=is_training,
dropout_rate=dropout_rate,
wd=wd)
print("block3", x)
with tf.variable_scope('trans3'):
x = transition(x,
K,
dropout_rate=dropout_rate,
wd=wd,
is_training=is_training)
print("transition3", x)
with tf.variable_scope('block4'):
x = block(x,
stages[3],
K,
is_training=is_training,
dropout_rate=dropout_rate,
wd=wd)
print("block4", x)
x = common.avgPool(x, 7, 1, padding='VALID')
print("Last Avg Pool", x)
x = common.flatten(x)
print("flatten", x)
output = [None] * 8
with tf.variable_scope('output0'):
output[0] = common.fullyConnected(x, 48, wd=wd)
with tf.variable_scope('output1'):
output[1] = common.fullyConnected(x, 12, wd=wd)
for o in xrange(2, 8):
with tf.variable_scope('output' + str(o)):
output[o] = common.fullyConnected(x, 2, wd=wd)
return output
def googlenet(dtype=tf.float32,
batch_size=16,
dev='gpu',
width=227,
height=227,
g=None):
x_images = var([batch_size, 3, width, height], "T_NORMAL",
dtype=dtype) if dev == 'gpu' else var(
[batch_size, width, height, 3], "T_NORMAL", dtype=dtype)
W_1 = var([7, 7, 3, 64], "T_NORMAL", dtype=dtype)
b_1 = var([64], "CONSTANT", 0.1, dtype=dtype)
h_conv1 = conv2D(x_images, W_1, b_1, [1, 1, 2, 2], 'SAME',
'NCHW') if dev == 'gpu' else conv2D(
x_images, W_1, b_1, [1, 2, 2, 1], 'SAME', 'NHWC')
print h_conv1
h_pool1 = mxPool(h_conv1, [1, 1, 3, 3], [1, 1, 2, 2], 'SAME',
'NCHW') if dev == 'gpu' else mxPool(
h_conv1, [1, 3, 3, 1], [1, 2, 2, 1], 'SAME', 'NHWC')
print h_pool1
W_2 = var([1, 1, 64, 64], "T_NORMAL", dtype=dtype)
b_2 = var([64], "CONSTANT", 0.1, dtype=dtype)
h_conv2 = conv2D(h_pool1, W_2, b_2, [1, 1, 1, 1], 'SAME',
'NCHW') if dev == 'gpu' else conv2D(
h_pool1, W_2, b_2, [1, 1, 1, 1], 'SAME', 'NHWC')
print h_conv2
W_3 = var([3, 3, 64, 192], "T_NORMAL", dtype=dtype)
b_3 = var([192], "CONSTANT", 0.1, dtype=dtype)
h_conv3 = conv2D(h_conv2, W_3, b_3, [1, 1, 1, 1], 'SAME',
'NCHW') if dev == 'gpu' else conv2D(
h_conv2, W_3, b_3, [1, 1, 1, 1], 'SAME', 'NHWC')
print h_conv3
h_pool3 = mxPool(h_conv3, [1, 1, 3, 3], [1, 1, 2, 2], 'SAME',
'NCHW') if dev == 'gpu' else mxPool(
h_conv3, [1, 3, 3, 1], [1, 2, 2, 1], 'SAME', 'NHWC')
print h_pool3
op_list = []
op_list.append(['conv', 192, 64, [1, 1], [1, 1], 'SAME'])
#op_list.append(['conv',192,96,[1,1],[1,1],'SAME'])
op_list.append(['conv', 192, 128, [3, 3], [1, 1], 'SAME'])
#op_list.append(['conv',192,16,[1,1],[1,1],'SAME'])
op_list.append(['conv', 192, 32, [5, 5], [1, 1], 'SAME'])
#op_list.append(['mxpool',192,None,[3,3],[1,1],'SAME'])
op_list.append(['conv', 192, 32, [1, 1], [1, 1], 'SAME'])
incept_1 = inception_("incept_v1", op_list, h_pool3, dtype, dev)
print incept_1
op_list = []
op_list.append(['conv', 256, 128, [1, 1], [1, 1], 'SAME'])
#op_list.append(['conv',256,128,[1,1],[1,1],'SAME'])
op_list.append(['conv', 256, 192, [3, 3], [1, 1], 'SAME'])
#op_list.append(['conv',256,32,[1,1],[1,1],'SAME'])
op_list.append(['conv', 256, 96, [5, 5], [1, 1], 'SAME'])
#op_list.append(['mxpool',256,None,[3,3],[1,1],'SAME'])
op_list.append(['conv', 256, 64, [1, 1], [1, 1], 'SAME'])
incept_2 = inception_("incept_v1", op_list, incept_1, dtype, dev)
print incept_2
h_pool5 = mxPool(incept_2, [1, 1, 3, 3], [1, 1, 2, 2], 'SAME',
'NCHW') if dev == 'gpu' else mxPool(
incept_2, [1, 3, 3, 1], [1, 2, 2, 1], 'SAME', 'NHWC')
print h_pool5
op_list = []
op_list.append(['conv', 480, 192, [1, 1], [1, 1], 'SAME'])
#op_list.append(['conv',480,96,[1,1],[1,1],'SAME'])
op_list.append(['conv', 480, 208, [3, 3], [1, 1], 'SAME'])
#op_list.append(['conv',480,16,[1,1],[1,1],'SAME'])
op_list.append(['conv', 480, 48, [5, 5], [1, 1], 'SAME'])
#op_list.append(['mxpool',480,None,[3,3],[1,1],'SAME'])
op_list.append(['conv', 480, 64, [1, 1], [1, 1], 'SAME'])
incept_3 = inception_("incept_v1", op_list, h_pool5, dtype, dev)
print incept_3
op_list = []
op_list.append(['conv', 512, 160, [1, 1], [1, 1], 'SAME'])
#op_list.append(['conv',512,112,[1,1],[1,1],'SAME'])
op_list.append(['conv', 512, 224, [3, 3], [1, 1], 'SAME'])
#op_list.append(['conv',512,24,[1,1],[1,1],'SAME'])
op_list.append(['conv', 512, 64, [5, 5], [1, 1], 'SAME'])
#op_list.append(['mxpool',512,None,[3,3],[1,1],'SAME'])
op_list.append(['conv', 512, 64, [1, 1], [1, 1], 'SAME'])
incept_4 = inception_("incept_v1", op_list, incept_3, dtype, dev)
print incept_4
op_list = []
op_list.append(['conv', 512, 128, [1, 1], [1, 1], 'SAME'])
#op_list.append(['conv',512,128,[1,1],[1,1],'SAME'])
op_list.append(['conv', 512, 256, [3, 3], [1, 1], 'SAME'])
#op_list.append(['conv',512,24,[1,1],[1,1],'SAME'])
op_list.append(['conv', 512, 64, [5, 5], [1, 1], 'SAME'])
#op_list.append(['mxpool',512,None,[3,3],[1,1],'SAME'])
op_list.append(['conv', 512, 64, [1, 1], [1, 1], 'SAME'])
incept_5 = inception_("incept_v1", op_list, incept_4, dtype, dev)
print incept_5
op_list = []
op_list.append(['conv', 512, 112, [1, 1], [1, 1], 'SAME'])
#op_list.append(['conv',512,144,[1,1],[1,1],'SAME'])
op_list.append(['conv', 512, 288, [3, 3], [1, 1], 'SAME'])
#op_list.append(['conv',512,32,[1,1],[1,1],'SAME'])
op_list.append(['conv', 512, 64, [5, 5], [1, 1], 'SAME'])
#op_list.append(['mxpool',512,None,[3,3],[1,1],'SAME'])
op_list.append(['conv', 512, 64, [1, 1], [1, 1], 'SAME'])
incept_6 = inception_("incept_v1", op_list, incept_5, dtype, dev)
print incept_6
op_list = []
op_list.append(['conv', 528, 256, [1, 1], [1, 1], 'SAME'])
#op_list.append(['conv',528,160,[1,1],[1,1],'SAME'])
op_list.append(['conv', 528, 320, [3, 3], [1, 1], 'SAME'])
#op_list.append(['conv',528,32,[1,1],[1,1],'SAME'])
op_list.append(['conv', 528, 128, [5, 5], [1, 1], 'SAME'])
#op_list.append(['mxpool',528,None,[3,3],[1,1],'SAME'])
op_list.append(['conv', 528, 128, [1, 1], [1, 1], 'SAME'])
incept_7 = inception_("incept_v1", op_list, incept_6, dtype, dev)
print incept_7
h_pool6 = mxPool(incept_7, [1, 1, 3, 3], [1, 1, 2, 2], 'SAME',
'NCHW') if dev == 'gpu' else mxPool(
incept_7, [1, 3, 3, 1], [1, 2, 2, 1], 'SAME', 'NHWC')
print h_pool6
op_list = []
op_list.append(['conv', 832, 256, [1, 1], [1, 1], 'SAME'])
#op_list.append(['conv',832,160,[1,1],[1,1],'SAME'])
op_list.append(['conv', 832, 320, [3, 3], [1, 1], 'SAME'])
#op_list.append(['conv',832,32,[1,1],[1,1],'SAME'])
op_list.append(['conv', 832, 128, [5, 5], [1, 1], 'SAME'])
#op_list.append(['mxpool',528,None,[3,3],[1,1],'SAME'])
op_list.append(['conv', 832, 128, [1, 1], [1, 1], 'SAME'])
incept_8 = inception_("incept_v1", op_list, h_pool6, dtype, dev)
print incept_8
op_list = []
op_list.append(['conv', 832, 384, [1, 1], [1, 1], 'SAME'])
#op_list.append(['conv',832,192,[1,1],[1,1],'SAME'])
op_list.append(['conv', 832, 384, [3, 3], [1, 1], 'SAME'])
#op_list.append(['conv',832,48,[1,1],[1,1],'SAME'])
op_list.append(['conv', 832, 128, [5, 5], [1, 1], 'SAME'])
#op_list.append(['mxpool',528,None,[3,3],[1,1],'SAME'])
op_list.append(['conv', 832, 128, [1, 1], [1, 1], 'SAME'])
incept_9 = inception_("incept_v1", op_list, incept_8, dtype, dev)
print incept_9
h_pool7 = avgPool(incept_9, [1, 1, 7, 7], [1, 1, 1, 1], 'VALID',
'NCHW') if dev == 'gpu' else mxPool(
incept_9, [1, 7, 7, 1], [1, 1, 1, 1], 'VALID',
'NHWC')
print h_pool7
y = tf.reshape(h_pool7, [-1, 1024])
print y
op_list = [op.name for op in g.get_operations()]
flop_list = [
ops.get_stats_for_node_def(g, op.node_def, 'flops').value
if ops.get_stats_for_node_def(g, op.node_def, 'flops').value != None
else 0 for op in g.get_operations()
]
if dev == 'cpu':
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L0)),
device_count={'GPU': 0})
else:
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L0)))
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(y.op)
iter = 100
start = time()
for i in range(iter):
if (i + 1) % 10 == 0:
print "<", i / 10, ">"
sess.run(y.op)
elapsed = time() - start
print "frames/sec:", iter * batch_size / elapsed
print "flops:", sum(flop_list)
