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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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Showing with 405 addition and 1025 deletion
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 ...@@ -13,7 +13,7 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
from __future__ import unicode_literals from __future__ import unicode_literals
from .edict import AttrDict from edict import AttrDict
import six import six
import numpy as np import numpy as np
...@@ -24,6 +24,10 @@ cfg = _C ...@@ -24,6 +24,10 @@ cfg = _C
# Training options # Training options
# #
# batch
_C.batch = 8
# Snapshot period # Snapshot period
_C.snapshot_iter = 2000 _C.snapshot_iter = 2000
...@@ -88,7 +92,9 @@ _C.weight_decay = 0.0005 ...@@ -88,7 +92,9 @@ _C.weight_decay = 0.0005
# momentum with SGD # momentum with SGD
_C.momentum = 0.9 _C.momentum = 0.9
# # decay
_C.decay = 0.0005
# ENV options # 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 ...@@ -21,12 +21,12 @@ import numpy as np
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
import reader import reader
import models import models.yolov3 as models
from utility import print_arguments, parse_args from utility import print_arguments, parse_args
import json import json
from pycocotools.coco import COCO from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval, Params from pycocotools.cocoeval import COCOeval, Params
from config.config import cfg from config import cfg
def eval(): def eval():
...@@ -42,7 +42,6 @@ def eval(): ...@@ -42,7 +42,6 @@ def eval():
model = models.YOLOv3(cfg.model_cfg_path, is_train=False) model = models.YOLOv3(cfg.model_cfg_path, is_train=False)
model.build_model() model.build_model()
outputs = model.get_pred() outputs = model.get_pred()
hyperparams = model.get_hyperparams()
yolo_anchors = model.get_yolo_anchors() yolo_anchors = model.get_yolo_anchors()
yolo_classes = model.get_yolo_classes() yolo_classes = model.get_yolo_classes()
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace() 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 ...@@ -6,12 +6,12 @@ import paddle.fluid as fluid
import box_utils import box_utils
import reader import reader
from utility import print_arguments, parse_args from utility import print_arguments, parse_args
import models import models.yolov3 as models
# from coco_reader import load_label_names # from coco_reader import load_label_names
import json import json
from pycocotools.coco import COCO from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval, Params from pycocotools.cocoeval import COCOeval, Params
from config.config import cfg from config import cfg
def infer(): 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 ...@@ -28,7 +28,7 @@ import box_utils
import image_utils import image_utils
from pycocotools.coco import COCO from pycocotools.coco import COCO
from data_utils import GeneratorEnqueuer from data_utils import GeneratorEnqueuer
from config.config import cfg from config import cfg
class DataSetReader(object): 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 ...@@ -26,9 +26,9 @@ from utility import parse_args, print_arguments, SmoothedValue
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
import reader import reader
import models import models.yolov3 as models
from learning_rate import exponential_with_warmup_decay from learning_rate import exponential_with_warmup_decay
from config.config import cfg from config import cfg
def train(): def train():
...@@ -48,12 +48,14 @@ def train(): ...@@ -48,12 +48,14 @@ def train():
loss = model.loss() loss = model.loss()
loss.persistable = True 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 = os.getenv("CUDA_VISIBLE_DEVICES") or ""
devices_num = len(devices.split(",")) devices_num = len(devices.split(","))
print("Found {} CUDA devices.".format(devices_num)) print("Found {} CUDA devices.".format(devices_num))
learning_rate = float(hyperparams['learning_rate']) learning_rate = float(cfg.learning_rate)
boundaries = cfg.lr_steps boundaries = cfg.lr_steps
gamma = cfg.lr_gamma gamma = cfg.lr_gamma
step_num = len(cfg.lr_steps) step_num = len(cfg.lr_steps)
...@@ -70,8 +72,8 @@ def train(): ...@@ -70,8 +72,8 @@ def train():
warmup_iter=cfg.warm_up_iter, warmup_iter=cfg.warm_up_iter,
warmup_factor=cfg.warm_up_factor, warmup_factor=cfg.warm_up_factor,
start_step=cfg.start_iter), start_step=cfg.start_iter),
regularization=fluid.regularizer.L2Decay(float(hyperparams['decay'])), regularization=fluid.regularizer.L2Decay(float(cfg.decay)),
momentum=float(hyperparams['momentum'])) momentum=float(cfg.momentum))
optimizer.minimize(loss) optimizer.minimize(loss)
fluid.memory_optimize(fluid.default_main_program()) fluid.memory_optimize(fluid.default_main_program())
...@@ -96,11 +98,11 @@ def train(): ...@@ -96,11 +98,11 @@ def train():
mixup_iter = cfg.max_iter - cfg.start_iter - cfg.no_mixup_iter mixup_iter = cfg.max_iter - cfg.start_iter - cfg.no_mixup_iter
if cfg.use_pyreader: 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 = model.py_reader
py_reader.decorate_paddle_reader(train_reader) py_reader.decorate_paddle_reader(train_reader)
else: 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()) feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
def save_model(postfix): def save_model(postfix):
......
fluid/PaddleCV/yolov3/utility.py
浏览文件 @ 9da63d61
...@@ -26,7 +26,7 @@ from collections import deque ...@@ -26,7 +26,7 @@ from collections import deque
from paddle.fluid import core from paddle.fluid import core
import argparse import argparse
import functools import functools
from config.config import * from config import *
def print_arguments(args): def print_arguments(args):
......
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