from tensorlayer.layers import * import matplotlib.pyplot as plt import os from tqdm import tqdm from net import Gb_all_layer_out, ResLayer, RouteLayer, upsample, conv2d_unit, detection # net_out = [tf.zeros(shape=(1, 52, 52, 3, 85)), tf.zeros(shape=(1, 26, 26, 3, 85)), tf.zeros(shape=(1, 13, 13, 3, 85))] checkpoint_dir = './ckpt/' ckpt_name = 'ep373-step66866-loss1389.557' # label = ['knot'] n = 2 input_pb = tf.placeholder(tf.float32, [None, 416, 416, 3]) net = InputLayer(input_pb, name='input') net = conv2d_unit(net, filters=32, kernels=3, strides=1, bn=True, name='0') net = conv2d_unit(net, filters=64, kernels=3, strides=2, bn=True, name='1') net = conv2d_unit(net, filters=32, kernels=1, strides=1, bn=True, name='2') net = conv2d_unit(net, filters=64, kernels=3, strides=1, bn=True, name='3') net = ResLayer(net, res=1, name='4') net = conv2d_unit(net, filters=128, kernels=3, strides=2, bn=True, name='5') net = conv2d_unit(net, filters=64, kernels=1, strides=1, bn=True, name='6') net = conv2d_unit(net, filters=128, kernels=3, strides=1, bn=True, name='7') net = ResLayer(net, res=5, name='8') net = conv2d_unit(net, filters=64, kernels=1, strides=1, bn=True, name='9') net = conv2d_unit(net, filters=128, kernels=3, strides=1, bn=True, name='10') net = ResLayer(net, res=8, name='11') net = conv2d_unit(net, filters=256, kernels=3, strides=2, bn=True, name='12') net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='13') net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='14') net = ResLayer(net, res=12, name='15')
def infenence(input):
n = 3 * 6 * 6
net = InputLayer(input, name='input')
net = conv2d_unit(net, filters=32, kernels=3, strides=1, bn=True, name='0')
net = conv2d_unit(net, filters=64, kernels=3, strides=2, bn=True, name='1')
net = conv2d_unit(net, filters=32, kernels=1, strides=1, bn=True, name='2')
net = conv2d_unit(net, filters=64, kernels=3, strides=1, bn=True, name='3')
net = ResLayer(net, res=1, name='4')
net = conv2d_unit(net, filters=128, kernels=3, strides=2, bn=True, name='5')
net = conv2d_unit(net, filters=64, kernels=1, strides=1, bn=True, name='6')
net = conv2d_unit(net, filters=128, kernels=3, strides=1, bn=True, name='7')
net = ResLayer(net, res=5, name='8')
net = conv2d_unit(net, filters=64, kernels=1, strides=1, bn=True, name='9')
net = conv2d_unit(net, filters=128, kernels=3, strides=1, bn=True, name='10')
net = ResLayer(net, res=8, name='11')
net = conv2d_unit(net, filters=256, kernels=3, strides=2, bn=True, name='12')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='13')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='14')
net = ResLayer(net, res=12, name='15')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='16')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='17')
net = ResLayer(net, res=15, name='18')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='19')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='20')
net = ResLayer(net, res=18, name='21')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='22')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='23')
net = ResLayer(net, res=21, name='24')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='25')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='26')
net = ResLayer(net, res=24, name='27')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='28')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='29')
net = ResLayer(net, res=27, name='30')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='31')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='32')
net = ResLayer(net, res=30, name='33')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='34')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='35')
net = ResLayer(net, res=33, name='36')
net = conv2d_unit(net, filters=512, kernels=3, strides=2, bn=True, name='37')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='38')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='39')
net = ResLayer(net, res=37, name='40')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='41')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='42')
net = ResLayer(net, res=40, name='43')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='44')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='45')
net = ResLayer(net, res=43, name='46')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='47')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='48')
net = ResLayer(net, res=46, name='49')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='50')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='51')
net = ResLayer(net, res=49, name='52')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='53')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='54')
net = ResLayer(net, res=52, name='55')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='56')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='57')
net = ResLayer(net, res=55, name='58')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='59')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='60')
net = ResLayer(net, res=58, name='61')
net = conv2d_unit(net, filters=1024, kernels=3, strides=2, bn=True, name='62')
net = conv2d_unit(net, filters=512, kernels=1, strides=1, bn=True, name='63')
net = conv2d_unit(net, filters=1024, kernels=3, strides=1, bn=True, name='64')
net = ResLayer(net, res=62, name='65')
net = conv2d_unit(net, filters=512, kernels=1, strides=1, bn=True, name='66')
net = conv2d_unit(net, filters=1024, kernels=3, strides=1, bn=True, name='67')
net = ResLayer(net, res=65, name='68')
net = conv2d_unit(net, filters=512, kernels=1, strides=1, bn=True, name='69')
net = conv2d_unit(net, filters=1024, kernels=3, strides=1, bn=True, name='70')
net = ResLayer(net, res=68, name='71')
net = conv2d_unit(net, filters=512, kernels=1, strides=1, bn=True, name='72')
net = conv2d_unit(net, filters=1024, kernels=3, strides=1, bn=True, name='73')
net = ResLayer(net, res=71, name='74')
net = conv2d_unit(net, filters=512, kernels=1, strides=1, bn=True, name='75')
net = conv2d_unit(net, filters=1024, kernels=3, strides=1, bn=True, name='76')
net = conv2d_unit(net, filters=512, kernels=1, strides=1, bn=True, name='77')
net = conv2d_unit(net, filters=1024, kernels=3, strides=1, bn=True, name='78')
net = conv2d_unit(net, filters=512, kernels=1, strides=1, bn=True, name='79')
net = conv2d_unit(net, filters=1024, kernels=3, strides=1, bn=True, name='80')
net = conv2d_unit(net, filters=n, kernels=1, strides=1, act='liner', bn=False, name='81')
detection(net, '82')
net = RouteLayer(net, [79], name='83')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='84')
net = upsample(net, scale=2, name='85')
net = RouteLayer(net, [85, 61], name='86')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='87')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='88')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='89')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='90')
net = conv2d_unit(net, filters=256, kernels=1, strides=1, bn=True, name='91')
net = conv2d_unit(net, filters=512, kernels=3, strides=1, bn=True, name='92')
net = conv2d_unit(net, filters=n, kernels=1, strides=1, act='liner', bn=False, name='93')
detection(net, '94')
net = RouteLayer(net, [91], name='95')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='96')
net = upsample(net, scale=2, name='97')
net = RouteLayer(net, [97, 36], name='98')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='99')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='100')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='101')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='102')
net = conv2d_unit(net, filters=128, kernels=1, strides=1, bn=True, name='103')
net = conv2d_unit(net, filters=256, kernels=3, strides=1, bn=True, name='104')
net = conv2d_unit(net, filters=n, kernels=1, strides=1, act='liner', bn=False, name='105')
detection(net, '106')
net_out = [Gb_all_layer_out[106], Gb_all_layer_out[94], Gb_all_layer_out[82]]
return net_out
import numpy as np
import tensorflow as tf
from keras.layers import *
from keras import backend as K
from keras.models import Model
from net import Gb_all_layers, Gb_out_index, conv2d_unit, shortcut, route, detection, upsample
from net import weights_file
num_classes = 80
print('Creating Keras model.')
print('layer filters size input output')
input_layer = Input(shape=(416, 416, 3))
net = conv2d_unit(input_layer,
filters=32,
kernels=3,
strides=1,
bn=True,
activation='leaky') # 0
net = conv2d_unit(net,
filters=64,
kernels=3,
strides=2,
bn=True,
activation='leaky') # 1
net = conv2d_unit(net,
filters=32,
kernels=1,
strides=1,
bn=True,
activation='leaky') # 2
net = conv2d_unit(net,