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提交 9da63d61 编写于 作者: T tink2123 提交者： dengkaipeng
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added darknet and modified models

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fluid/PaddleCV/yolov3/config/config.py → fluid/PaddleCV/yolov3/config.py
浏览文件 @ 9da63d61
......@@ -13,7 +13,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from .edict import AttrDict
from edict import AttrDict
import six
import numpy as np
......@@ -24,6 +24,10 @@ cfg = _C
# Training options
#
# batch
_C.batch = 8
# Snapshot period
_C.snapshot_iter = 2000
......@@ -88,7 +92,9 @@ _C.weight_decay = 0.0005
# momentum with SGD
_C.momentum = 0.9
#
# decay
_C.decay = 0.0005
# ENV options
#
......
fluid/PaddleCV/yolov3/config/edict.py 已删除 100644 → 0
浏览文件 @ b4bdcc5e
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
def __getattr__(self, name):
if name in self.__dict__:
return self.__dict__[name]
elif name in self:
return self[name]
else:
raise AttributeError(name)
def __setattr__(self, name, value):
if name in self.__dict__:
self.__dict__[name] = value
else:
self[name] = value
fluid/PaddleCV/yolov3/config/yolov3-tiny.cfg 已删除 100644 → 0
浏览文件 @ b4bdcc5e
[net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=2
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=80
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 8
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=80
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
fluid/PaddleCV/yolov3/config/yolov3.cfg 已删除 100644 → 0
浏览文件 @ b4bdcc5e
[net]
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=16
width=608
height=608
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
fluid/PaddleCV/yolov3/eval.py
浏览文件 @ 9da63d61
......@@ -21,12 +21,12 @@ import numpy as np
import paddle
import paddle.fluid as fluid
import reader
import models
import models.yolov3 as models
from utility import print_arguments, parse_args
import json
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval, Params
from config.config import cfg
from config import cfg
def eval():
......@@ -42,7 +42,6 @@ def eval():
model = models.YOLOv3(cfg.model_cfg_path, is_train=False)
model.build_model()
outputs = model.get_pred()
hyperparams = model.get_hyperparams()
yolo_anchors = model.get_yolo_anchors()
yolo_classes = model.get_yolo_classes()
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
......
fluid/PaddleCV/yolov3/infer.py
浏览文件 @ 9da63d61
......@@ -6,12 +6,12 @@ import paddle.fluid as fluid
import box_utils
import reader
from utility import print_arguments, parse_args
import models
import models.yolov3 as models
# from coco_reader import load_label_names
import json
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval, Params
from config.config import cfg
from config import cfg
def infer():
......
fluid/PaddleCV/yolov3/models/darknet.py 0 → 100644
浏览文件 @ 9da63d61
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.initializer import Constant
from paddle.fluid.regularizer import L2Decay
from config import cfg
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='leaky',
i=0):
conv1 = fluid.layers.conv2d(
input=input,
num_filters=ch_out,
filter_size=filter_size,
stride=stride,
padding=padding,
act=None,
param_attr=ParamAttr(initializer=fluid.initializer.Normal(0., 0.02),
name="conv" + str(i)+"_weights"),
bias_attr=False)
bn_name = "bn" + str(i)
out = fluid.layers.batch_norm(
input=conv1,
act=None,
is_test=True,
param_attr=ParamAttr(
initializer=fluid.initializer.Normal(0., 0.02),
regularizer=L2Decay(0.),
name=bn_name + '_scale'),
bias_attr=ParamAttr(
initializer=fluid.initializer.Constant(0.0),
regularizer=L2Decay(0.),
name=bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_var')
if act == 'leaky':
out = fluid.layers.leaky_relu(x=out, alpha=0.1)
return out
def basicblock(input, ch_out, stride,i):
"""
channel: convolution channels for 1x1 conv
"""
conv1 = conv_bn_layer(input, ch_out, 1, 1, 0, i=i)
conv2 = conv_bn_layer(conv1, ch_out*2, 3, 1, 1, i=i+1)
out = fluid.layers.elementwise_add(x=input, y=conv2, act=None,name="res"+str(i+2))
return out
def layer_warp(block_func, input, ch_out, count, stride,i):
res_out = block_func(input, ch_out, stride, i=i)
for j in range(1, count):
res_out = block_func(res_out, ch_out, 1 ,i=i+j*3)
return res_out
DarkNet_cfg = {
53: ([1,2,8,8,4],basicblock)
}
# num_filters = [32, 64, 128, 256, 512, 1024]
def add_DarkNet53_conv_body(body_input):
stages, block_func = DarkNet_cfg[53]
stages = stages[0:5]
conv1 = conv_bn_layer(
body_input, ch_out=32, filter_size=3, stride=1, padding=1, act="leaky",i=0)
conv2 = conv_bn_layer(
conv1, ch_out=64, filter_size=3, stride=2, padding=1, act="leaky", i=1)
block3 = layer_warp(block_func, conv2, 32, stages[0], 1, i=2)
downsample3 = conv_bn_layer(
block3, ch_out=128, filter_size=3, stride=2, padding=1, i=5)
block4 = layer_warp(block_func, downsample3, 64, stages[1], 1, i=6)
downsample4 = conv_bn_layer(
block4, ch_out=256, filter_size=3, stride=2, padding=1, i=12)
block5 = layer_warp(block_func, downsample4, 128, stages[2], 1,i=13)
downsample5 = conv_bn_layer(
block5, ch_out=512, filter_size=3, stride=2, padding=1, i=37)
block6 = layer_warp(block_func, downsample5, 256, stages[3], 1, i=38)
downsample6 = conv_bn_layer(
block6, ch_out=1024, filter_size=3, stride=2, padding=1, i=62)
block7 = layer_warp(block_func, downsample6, 512, stages[4], 1,i=63)
return block7,block6,block5
fluid/PaddleCV/yolov3/models/yolov3.py 0 → 100644
浏览文件 @ 9da63d61
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import division
from __future__ import print_function
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.initializer import Constant
from paddle.fluid.initializer import Normal
from paddle.fluid.regularizer import L2Decay
from config_parser import ConfigPaser
from config import cfg
from darknet import add_DarkNet53_conv_body
from darknet import conv_bn_layer
def yolo_detection_block(input, channel,i):
assert channel % 2 == 0, "channel {} cannot be divided by 2".format(channel)
conv1 = input
for j in range(2):
conv1 = conv_bn_layer(conv1, channel, filter_size=1, stride=1, padding=0,i=i+j*2)
conv1 = conv_bn_layer(conv1, channel*2, filter_size=3, stride=1, padding=1,i=i+j*2+1)
route = conv_bn_layer(conv1, channel, filter_size=1, stride=1, padding=0,i=i+4)
tip = conv_bn_layer(route,channel*2, filter_size=3, stride=1, padding=1,i=i+5)
return route, tip
def upsample(out, stride=2,name=None):
out = out
scale = stride
# get dynamic upsample output shape
shape_nchw = fluid.layers.shape(out)
shape_hw = fluid.layers.slice(shape_nchw, axes=[0], starts=[2], ends=[4])
shape_hw.stop_gradient = True
in_shape = fluid.layers.cast(shape_hw, dtype='int32')
out_shape = in_shape * scale
out_shape.stop_gradient = True
# reisze by actual_shape
out = fluid.layers.resize_nearest(
input=out,
scale=scale,
actual_shape=out_shape,
name=name)
return out
class YOLOv3(object):
def __init__(self,
model_cfg_path,
is_train=True,
use_pyreader=True,
use_random=True):
self.model_cfg_path = model_cfg_path
self.config_parser = ConfigPaser(model_cfg_path)
self.is_train = is_train
self.use_pyreader = use_pyreader
self.use_random = use_random
self.outputs = []
self.losses = []
self.downsample = 32
self.ignore_thresh = .7
self.class_num = 80
def build_model(self):
self.img_height = cfg.input_size
self.img_width = cfg.input_size
self.build_input()
out = self.image
self.yolo_anchors = []
self.yolo_classes = []
self.outputs = []
self.boxes = []
self.scores = []
scale1,scale2,scale3 = add_DarkNet53_conv_body(out)
# 13*13 scale output
route1, tip1 = yolo_detection_block(scale1, channel=512,i=75)
# scale1 output
scale1_out = fluid.layers.conv2d(
input=tip1,
num_filters=255,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(initializer=fluid.initializer.Normal(0., 0.02),
name="conv81_weights"),
bias_attr=ParamAttr(initializer=fluid.initializer.Constant(0.0),
regularizer=L2Decay(0.),
name="conv81_bias"))
self.outputs.append(scale1_out)
route1 = conv_bn_layer(
input=route1,
ch_out=256,
filter_size=1,
stride=1,
padding=0,
i=84)
# upsample
route1 = upsample(route1)
# concat
route1 = fluid.layers.concat(
input=[route1,scale2],
axis=1)
# 26*26 scale output
route2, tip2 = yolo_detection_block(route1, channel=256,i=87)
# scale2 output
scale2_out = fluid.layers.conv2d(
input=tip2,
num_filters=255,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(name="conv93_weights"),
bias_attr=ParamAttr(name="conv93_bias"))
self.outputs.append(scale2_out)
route2 = conv_bn_layer(
input=route2,
ch_out=128,
filter_size=1,
stride=1,
padding=0,
i=96)
# upsample
route2 = upsample(route2)
# concat
route2 = fluid.layers.concat(
input=[route2,scale3],
axis=1)
# 52*52 scale output
route3, tip3 = yolo_detection_block(route2, channel=128, i=99)
# scale3 output
scale3_out = fluid.layers.conv2d(
input=tip3,
num_filters=255,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(name="conv105_weights"),
bias_attr=ParamAttr(name="conv105_bias"))
self.outputs.append(scale3_out)
# yolo
anchor_mask = [6,7,8,3,4,5,0,1,2]
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
for i,out in enumerate(self.outputs):
mask = anchor_mask[i*3 : (i+1)*3]
mask_anchors=[]
for m in mask:
mask_anchors.append(anchors[2 * m])
mask_anchors.append(anchors[2 * m + 1])
self.yolo_anchors.append(mask_anchors)
class_num = int(self.class_num)
self.yolo_classes.append(class_num)
if self.is_train:
ignore_thresh = float(self.ignore_thresh)
loss = fluid.layers.yolov3_loss(
x=out,
gtbox=self.gtbox,
gtlabel=self.gtlabel,
# gtscore=self.gtscore,
anchors=anchors,
anchor_mask=mask,
class_num=class_num,
ignore_thresh=ignore_thresh,
downsample_ratio=self.downsample,
# use_label_smooth=False,
name="yolo_loss"+str(i))
self.losses.append(fluid.layers.reduce_mean(loss))
else:
boxes, scores = fluid.layers.yolo_box(
x=out,
img_size=self.im_shape,
anchors=mask_anchors,
class_num=class_num,
conf_thresh=cfg.valid_thresh,
downsample_ratio=self.downsample,
name="yolo_box"+str(i))
self.boxes.append(boxes)
self.scores.append(fluid.layers.transpose(scores, perm=[0, 2, 1]))
self.downsample //= 2
def loss(self):
return sum(self.losses)
def get_pred(self):
# return self.outputs
yolo_boxes = fluid.layers.concat(self.boxes, axis=1)
yolo_scores = fluid.layers.concat(self.scores, axis=2)
return fluid.layers.multiclass_nms(
bboxes=yolo_boxes,
scores=yolo_scores,
score_threshold=cfg.valid_thresh,
nms_top_k=cfg.nms_topk,
keep_top_k=cfg.nms_posk,
nms_threshold=cfg.nms_thresh,
background_label=-1,
name="multiclass_nms")
def get_yolo_anchors(self):
return self.yolo_anchors
def get_yolo_classes(self):
return self.yolo_classes
def build_input(self):
self.image_shape = [3, self.img_height, self.img_width]
if self.use_pyreader and self.is_train:
self.py_reader = fluid.layers.py_reader(
capacity=64,
shapes = [[-1] + self.image_shape, [-1, cfg.max_box_num, 4], [-1, cfg.max_box_num], [-1, cfg.max_box_num]],
lod_levels=[0, 0, 0, 0],
dtypes=['float32'] * 2 + ['int32'] + ['float32'],
use_double_buffer=True)
self.image, self.gtbox, self.gtlabel, self.gtscore = fluid.layers.read_file(self.py_reader)
else:
self.image = fluid.layers.data(
name='image', shape=self.image_shape, dtype='float32'
)
self.gtbox = fluid.layers.data(
name='gtbox', shape=[cfg.max_box_num, 4], dtype='float32'
)
self.gtlabel = fluid.layers.data(
name='gtlabel', shape=[cfg.max_box_num], dtype='int32'
)
self.gtscore = fluid.layers.data(
name='gtscore', shape=[cfg.max_box_num], dtype='float32'
)
self.im_shape = fluid.layers.data(
name="im_shape", shape=[2], dtype='int32')
self.im_id = fluid.layers.data(
name="im_id", shape=[1], dtype='int32')
def feeds(self):
if not self.is_train:
return [self.image, self.im_id, self.im_shape]
return [self.image, self.gtbox, self.gtlabel, self.gtscore]
def get_input_size(self):
return cfg.input_size
fluid/PaddleCV/yolov3/reader.py
浏览文件 @ 9da63d61
......@@ -28,7 +28,7 @@ import box_utils
import image_utils
from pycocotools.coco import COCO
from data_utils import GeneratorEnqueuer
from config.config import cfg
from config import cfg
class DataSetReader(object):
......
fluid/PaddleCV/yolov3/train.py
浏览文件 @ 9da63d61
......@@ -26,9 +26,9 @@ from utility import parse_args, print_arguments, SmoothedValue
import paddle
import paddle.fluid as fluid
import reader
import models
import models.yolov3 as models
from learning_rate import exponential_with_warmup_decay
from config.config import cfg
from config import cfg
def train():
......@@ -48,12 +48,14 @@ def train():
loss = model.loss()
loss.persistable = True
hyperparams = model.get_hyperparams()
print("cfg.learning",cfg.learning_rate)
print("cfg.decay",cfg.decay)
devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
devices_num = len(devices.split(","))
print("Found {} CUDA devices.".format(devices_num))
learning_rate = float(hyperparams['learning_rate'])
learning_rate = float(cfg.learning_rate)
boundaries = cfg.lr_steps
gamma = cfg.lr_gamma
step_num = len(cfg.lr_steps)
......@@ -70,8 +72,8 @@ def train():
warmup_iter=cfg.warm_up_iter,
warmup_factor=cfg.warm_up_factor,
start_step=cfg.start_iter),
regularization=fluid.regularizer.L2Decay(float(hyperparams['decay'])),
momentum=float(hyperparams['momentum']))
regularization=fluid.regularizer.L2Decay(float(cfg.decay)),
momentum=float(cfg.momentum))
optimizer.minimize(loss)
fluid.memory_optimize(fluid.default_main_program())
......@@ -96,11 +98,11 @@ def train():
mixup_iter = cfg.max_iter - cfg.start_iter - cfg.no_mixup_iter
if cfg.use_pyreader:
train_reader = reader.train(input_size, batch_size=int(hyperparams['batch'])/devices_num, shuffle=True, mixup_iter=mixup_iter*devices_num, random_sizes=random_sizes, interval=10, pyreader_num=devices_num, use_multiprocessing=cfg.use_multiprocess)
train_reader = reader.train(input_size, batch_size=int(cfg.batch)/devices_num, shuffle=True, mixup_iter=mixup_iter*devices_num, random_sizes=random_sizes, interval=10, pyreader_num=devices_num, use_multiprocessing=cfg.use_multiprocess)
py_reader = model.py_reader
py_reader.decorate_paddle_reader(train_reader)
else:
train_reader = reader.train(input_size, batch_size=int(hyperparams['batch']), shuffle=True, mixup_iter=mixup_iter, random_sizes=random_sizes, use_multiprocessing=cfg.use_multiprocess)
train_reader = reader.train(input_size, batch_size=int(cfg.batch), shuffle=True, mixup_iter=mixup_iter, random_sizes=random_sizes, use_multiprocessing=cfg.use_multiprocess)
feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
def save_model(postfix):
......
fluid/PaddleCV/yolov3/utility.py
浏览文件 @ 9da63d61
......@@ -26,7 +26,7 @@ from collections import deque
from paddle.fluid import core
import argparse
import functools
from config.config import *
from config import *
def print_arguments(args):
......
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