p4sjnt6ge
/
tensorlayer3
forked from p32761584/tensorlayer3
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
tensorlayer3/examples/model_zoo/yolo.py

392 lines
15 KiB
Python
Raw Permalink Blame History

#! /usr/bin/python
# -*- coding: utf-8 -*-
"""YOLOv4 for MS-COCO.
# Reference:
- [tensorflow-yolov4-tflite](
https://github.com/hunglc007/tensorflow-yolov4-tflite)
"""
import numpy as np
import tensorlayer as tl
from tensorlayer.layers.activation import Mish
from tensorlayer.layers import Conv2d, MaxPool2d, BatchNorm2d, ZeroPad2d, UpSampling2d, Concat, Elementwise
from tensorlayer.layers import Module, SequentialLayer
from tensorlayer import logging
__all__ = ['YOLOv4']
INPUT_SIZE = 416
weights_url = {'link': 'https://pan.baidu.com/s/1MC1dmEwpxsdgHO1MZ8fYRQ', 'password': 'idsz'}
class Convolutional(Module):
"""
Create Convolution layer
Because it is only a stack of reference layers, there is no build, so self._built=True
"""
def __init__(self, filters_shape, downsample=False, activate=True, bn=True, activate_type='leaky', name=None):
super(Convolutional, self).__init__()
self.act = activate
self.act_type = activate_type
self.downsample = downsample
self.bn = bn
self._built = True
if downsample:
padding = 'VALID'
strides = 2
else:
strides = 1
padding = 'SAME'
if bn:
b_init = None
else:
b_init = tl.initializers.constant(value=0.0)
self.zeropad = ZeroPad2d(((1, 0), (1, 0)))
self.conv = Conv2d(
n_filter=filters_shape[-1], in_channels=filters_shape[2], filter_size=(filters_shape[0], filters_shape[1]),
strides=(strides, strides), padding=padding, b_init=b_init, name=name
)
if bn:
if activate ==True:
if activate_type == 'leaky':
self.batchnorm2d = BatchNorm2d(act='leaky_relu0.1', num_features=filters_shape[-1])
elif activate_type == 'mish':
self.batchnorm2d = BatchNorm2d(act=Mish, num_features=filters_shape[-1])
else:
self.batchnorm2d = BatchNorm2d(act=None, num_features=filters_shape[-1])
def forward(self, input):
if self.downsample:
input = self.zeropad(input)
output = self.conv(input)
if self.bn:
output = self.batchnorm2d(output)
return output
class residual_block(Module):
def __init__(self, input_channel, filter_num1, filter_num2, activate_type='leaky'):
super(residual_block, self).__init__()
self.conv1 = Convolutional(filters_shape=(1, 1, input_channel, filter_num1), activate_type=activate_type)
self.conv2 = Convolutional(filters_shape=(3, 3, filter_num1, filter_num2), activate_type=activate_type)
self.add = Elementwise(tl.add)
def forward(self, inputs):
output = self.conv1(inputs)
output = self.conv2(output)
output = self.add([inputs, output])
return output
def residual_block_num(num, input_channel, filter_num1, filter_num2, activate_type='leaky'):
residual_list = []
for i in range(num):
residual_list.append(residual_block(input_channel, filter_num1, filter_num2, activate_type=activate_type))
return SequentialLayer(residual_list)
class cspdarknet53(Module):
def __init__(self):
super(cspdarknet53, self).__init__()
self._built = True
self.conv1_1 = Convolutional((3, 3, 3, 32), activate_type='mish')
self.conv1_2 = Convolutional((3, 3, 32, 64), downsample=True, activate_type='mish')
self.conv1_3 = Convolutional((1, 1, 64, 64), activate_type='mish', name='conv_rote_block_1')
self.conv1_4 = Convolutional((1, 1, 64, 64), activate_type='mish')
self.residual_1 = residual_block_num(1, 64, 32, 64, activate_type="mish")
self.conv2_1 = Convolutional((1, 1, 64, 64), activate_type='mish')
self.concat = Concat()
self.conv2_2 = Convolutional((1, 1, 128, 64), activate_type='mish')
self.conv2_3 = Convolutional((3, 3, 64, 128), downsample=True, activate_type='mish')
self.conv2_4 = Convolutional((1, 1, 128, 64), activate_type='mish', name='conv_rote_block_2')
self.conv2_5 = Convolutional((1, 1, 128, 64), activate_type='mish')
self.residual_2 = residual_block_num(2, 64, 64, 64, activate_type='mish')
self.conv3_1 = Convolutional((1, 1, 64, 64), activate_type='mish')
self.conv3_2 = Convolutional((1, 1, 128, 128), activate_type='mish')
self.conv3_3 = Convolutional((3, 3, 128, 256), downsample=True, activate_type='mish')
self.conv3_4 = Convolutional((1, 1, 256, 128), activate_type='mish', name='conv_rote_block_3')
self.conv3_5 = Convolutional((1, 1, 256, 128), activate_type='mish')
self.residual_3 = residual_block_num(8, 128, 128, 128, activate_type="mish")
self.conv4_1 = Convolutional((1, 1, 128, 128), activate_type='mish')
self.conv4_2 = Convolutional((1, 1, 256, 256), activate_type='mish')
self.conv4_3 = Convolutional((3, 3, 256, 512), downsample=True, activate_type='mish')
self.conv4_4 = Convolutional((1, 1, 512, 256), activate_type='mish', name='conv_rote_block_4')
self.conv4_5 = Convolutional((1, 1, 512, 256), activate_type='mish')
self.residual_4 = residual_block_num(8, 256, 256, 256, activate_type="mish")
self.conv5_1 = Convolutional((1, 1, 256, 256), activate_type='mish')
self.conv5_2 = Convolutional((1, 1, 512, 512), activate_type='mish')
self.conv5_3 = Convolutional((3, 3, 512, 1024), downsample=True, activate_type='mish')
self.conv5_4 = Convolutional((1, 1, 1024, 512), activate_type='mish', name='conv_rote_block_5')
self.conv5_5 = Convolutional((1, 1, 1024, 512), activate_type='mish')
self.residual_5 = residual_block_num(4, 512, 512, 512, activate_type="mish")
self.conv6_1 = Convolutional((1, 1, 512, 512), activate_type='mish')
self.conv6_2 = Convolutional((1, 1, 1024, 1024), activate_type='mish')
self.conv6_3 = Convolutional((1, 1, 1024, 512))
self.conv6_4 = Convolutional((3, 3, 512, 1024))
self.conv6_5 = Convolutional((1, 1, 1024, 512))
self.maxpool1 = MaxPool2d(filter_size=(13, 13), strides=(1, 1))
self.maxpool2 = MaxPool2d(filter_size=(9, 9), strides=(1, 1))
self.maxpool3 = MaxPool2d(filter_size=(5, 5), strides=(1, 1))
self.conv7_1 = Convolutional((1, 1, 2048, 512))
self.conv7_2 = Convolutional((3, 3, 512, 1024))
self.conv7_3 = Convolutional((1, 1, 1024, 512))
def forward(self, input_data):
input_data = self.conv1_1(input_data)
input_data = self.conv1_2(input_data)
route = input_data
route = self.conv1_3(route)
input_data = self.conv1_4(input_data)
input_data = self.residual_1(input_data)
input_data = self.conv2_1(input_data)
input_data = self.concat([input_data, route])
input_data = self.conv2_2(input_data)
input_data = self.conv2_3(input_data)
route = input_data
route = self.conv2_4(route)
input_data = self.conv2_5(input_data)
input_data = self.residual_2(input_data)
input_data = self.conv3_1(input_data)
input_data = self.concat([input_data, route])
input_data = self.conv3_2(input_data)
input_data = self.conv3_3(input_data)
route = input_data
route = self.conv3_4(route)
input_data = self.conv3_5(input_data)
input_data = self.residual_3(input_data)
input_data = self.conv4_1(input_data)
input_data = self.concat([input_data, route])
input_data = self.conv4_2(input_data)
route_1 = input_data
input_data = self.conv4_3(input_data)
route = input_data
route = self.conv4_4(route)
input_data = self.conv4_5(input_data)
input_data = self.residual_4(input_data)
input_data = self.conv5_1(input_data)
input_data = self.concat([input_data, route])
input_data = self.conv5_2(input_data)
route_2 = input_data
input_data = self.conv5_3(input_data)
route = input_data
route = self.conv5_4(route)
input_data = self.conv5_5(input_data)
input_data = self.residual_5(input_data)
input_data = self.conv6_1(input_data)
input_data = self.concat([input_data, route])
input_data = self.conv6_2(input_data)
input_data = self.conv6_3(input_data)
input_data = self.conv6_4(input_data)
input_data = self.conv6_5(input_data)
maxpool1 = self.maxpool1(input_data)
maxpool2 = self.maxpool2(input_data)
maxpool3 = self.maxpool3(input_data)
input_data = self.concat([maxpool1, maxpool2, maxpool3, input_data])
input_data = self.conv7_1(input_data)
input_data = self.conv7_2(input_data)
input_data = self.conv7_3(input_data)
return route_1, route_2, input_data
class YOLOv4_model(Module):
def __init__(self, NUM_CLASS):
super(YOLOv4_model, self).__init__()
self.cspdarnnet = cspdarknet53()
self.conv1_1 = Convolutional((1, 1, 512, 256))
self.upsamle = UpSampling2d(scale=2)
self.conv1_2 = Convolutional((1, 1, 512, 256), name='conv_yolo_1')
self.concat = Concat()
self.conv2_1 = Convolutional((1, 1, 512, 256))
self.conv2_2 = Convolutional((3, 3, 256, 512))
self.conv2_3 = Convolutional((1, 1, 512, 256))
self.conv2_4 = Convolutional((3, 3, 256, 512))
self.conv2_5 = Convolutional((1, 1, 512, 256))
self.conv3_1 = Convolutional((1, 1, 256, 128))
self.conv3_2 = Convolutional((1, 1, 256, 128), name='conv_yolo_2')
self.conv4_1 = Convolutional((1, 1, 256, 128))
self.conv4_2 = Convolutional((3, 3, 128, 256))
self.conv4_3 = Convolutional((1, 1, 256, 128))
self.conv4_4 = Convolutional((3, 3, 128, 256))
self.conv4_5 = Convolutional((1, 1, 256, 128))
self.conv5_1 = Convolutional((3, 3, 128, 256), name='conv_route_1')
self.conv5_2 = Convolutional((1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
self.conv6_1 = Convolutional((3, 3, 128, 256), downsample=True, name='conv_route_2')
self.conv6_2 = Convolutional((1, 1, 512, 256))
self.conv6_3 = Convolutional((3, 3, 256, 512))
self.conv6_4 = Convolutional((1, 1, 512, 256))
self.conv6_5 = Convolutional((3, 3, 256, 512))
self.conv6_6 = Convolutional((1, 1, 512, 256))
self.conv7_1 = Convolutional((3, 3, 256, 512), name='conv_route_3')
self.conv7_2 = Convolutional((1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
self.conv7_3 = Convolutional((3, 3, 256, 512), downsample=True, name='conv_route_4')
self.conv8_1 = Convolutional((1, 1, 1024, 512))
self.conv8_2 = Convolutional((3, 3, 512, 1024))
self.conv8_3 = Convolutional((1, 1, 1024, 512))
self.conv8_4 = Convolutional((3, 3, 512, 1024))
self.conv8_5 = Convolutional((1, 1, 1024, 512))
self.conv9_1 = Convolutional((3, 3, 512, 1024))
self.conv9_2 = Convolutional((1, 1, 1024, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
def forward(self, inputs):
route_1, route_2, conv = self.cspdarnnet(inputs)
route = conv
conv = self.conv1_1(conv)
conv = self.upsamle(conv)
route_2 = self.conv1_2(route_2)
conv = self.concat([route_2, conv])
conv = self.conv2_1(conv)
conv = self.conv2_2(conv)
conv = self.conv2_3(conv)
conv = self.conv2_4(conv)
conv = self.conv2_5(conv)
route_2 = conv
conv = self.conv3_1(conv)
conv = self.upsamle(conv)
route_1 = self.conv3_2(route_1)
conv = self.concat([route_1, conv])
conv = self.conv4_1(conv)
conv = self.conv4_2(conv)
conv = self.conv4_3(conv)
conv = self.conv4_4(conv)
conv = self.conv4_5(conv)
route_1 = conv
conv = self.conv5_1(conv)
conv_sbbox = self.conv5_2(conv)
conv = self.conv6_1(route_1)
conv = self.concat([conv, route_2])
conv = self.conv6_2(conv)
conv = self.conv6_3(conv)
conv = self.conv6_4(conv)
conv = self.conv6_5(conv)
conv = self.conv6_6(conv)
route_2 = conv
conv = self.conv7_1(conv)
conv_mbbox = self.conv7_2(conv)
conv = self.conv7_3(route_2)
conv = self.concat([conv, route])
conv = self.conv8_1(conv)
conv = self.conv8_2(conv)
conv = self.conv8_3(conv)
conv = self.conv8_4(conv)
conv = self.conv8_5(conv)
conv = self.conv9_1(conv)
conv_lbbox = self.conv9_2(conv)
return conv_sbbox, conv_mbbox, conv_lbbox
def YOLOv4(NUM_CLASS, pretrained=False):
"""Pre-trained YOLOv4 model.
Parameters
------------
NUM_CLASS : int
Number of classes in final prediction.
pretrained : boolean
Whether to load pretrained weights. Default False.
Examples
---------
Object Detection with YOLOv4, see `computer_vision.py
<https://github.com/tensorlayer/tensorlayer/blob/master/tensorlayer/app/computer_vision.py>`__
With TensorLayer
>>> # get the whole model, without pre-trained YOLOv4 parameters
>>> yolov4 = YOLOv4(NUM_CLASS=80, pretrained=False)
>>> # get the whole model, restore pre-trained YOLOv4 parameters
>>> yolov4 = YOLOv4(NUM_CLASS=80, pretrained=True)
>>> # use for inferencing
>>> output = yolov4(img)
"""
network = YOLOv4_model(NUM_CLASS=NUM_CLASS)
if pretrained:
restore_params(network, model_path='model/yolov4_model.npz')
return network
def restore_params(network, model_path='models.npz'):
logging.info("Restore pre-trained weights")
try:
npz = np.load(model_path, allow_pickle=True)
except:
print("Download the model file, placed in the /model ")
print("Weights download: ", weights_url['link'], "password:", weights_url['password'])
txt_path = 'model/yolov4_weights3_config.txt'
f = open(txt_path, "r")
line = f.readlines()
for i in range(len(line)):
network.all_weights[i].assign(npz[line[i].strip()])
logging.info(" Loading weights %s in %s" % (network.all_weights[i].shape, network.all_weights[i].name))
def tl2_weights_to_tl3_weights(
weights_2_path='model/weights_2.txt', weights_3_path='model/weights_3.txt',
txt_path='model/yolov4_weights_config.txt'
):
weights_2_path = weights_2_path
weights_3_path = weights_3_path
txt_path = txt_path
f1 = open(weights_2_path, "r")
f2 = open(weights_3_path, "r")
f3 = open(txt_path, "r")
line1 = f1.readlines()
line2 = f2.readlines()
line3 = f3.readlines()
_dicts = {}
for i in range(len(line1)):
_dicts[line1[i].strip()] = line3[i].strip()
for j in range(len(line2)):
print(_dicts[line2[j].strip()])
Reference in New Issue View Git Blame Copy Permalink