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提交 b2389e38 编写于 作者: D dengkaipeng
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fit for train.

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fluid/PaddleCV/yolov3/config.py
浏览文件 @ b2389e38
......@@ -24,10 +24,6 @@ cfg = _C
# Training options
#
# batch
_C.batch = 8
# Snapshot period
_C.snapshot_iter = 2000
......@@ -72,6 +68,9 @@ _C.pixel_stds = [0.229, 0.224, 0.225]
# SOLVER options
#
# batch size
_C.batch_size = 64
# derived learning rate the to get the final learning rate.
_C.learning_rate = 0.001
......@@ -92,9 +91,7 @@ _C.weight_decay = 0.0005
# momentum with SGD
_C.momentum = 0.9
# decay
_C.decay = 0.0005
#
# ENV options
#
......
fluid/PaddleCV/yolov3/config_parser.py → fluid/PaddleCV/yolov3/edict.py
浏览文件 @ b2389e38
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -14,40 +17,21 @@ from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
LAYER_TYPES = [
"net",
"convolutional",
"shortcut",
"route",
"upsample",
"maxpool",
"yolo",
]
class ConfigPaser(object):
def __init__(self, config_path):
self.config_path = config_path
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
def parse(self):
with open(self.config_path) as cfg_file:
model_defs = []
for line in cfg_file.readlines():
line = line.strip()
if len(line) == 0:
continue
if line.startswith('#'):
continue
if line.startswith('['):
layer_type = line[1:-1].strip()
if layer_type not in LAYER_TYPES:
print("Unknow config layer type: ", layer_type)
return None
model_defs.append({})
model_defs[-1]['type'] = layer_type
def __getattr__(self, name):
if name in self.__dict__:
return self.__dict__[name]
elif name in self:
return self[name]
else:
key, value = line.split('=')
model_defs[-1][key.strip()] = value.strip()
return model_defs
raise AttributeError(name)
def __setattr__(self, name, value):
if name in self.__dict__:
self.__dict__[name] = value
else:
self[name] = value
fluid/PaddleCV/yolov3/eval.py
浏览文件 @ b2389e38
......@@ -17,13 +17,13 @@ from __future__ import division
from __future__ import print_function
import os
import time
import json
import numpy as np
import paddle
import paddle.fluid as fluid
import reader
import models.yolov3 as models
from models.yolov3 import YOLOv3
from utility import print_arguments, parse_args
import json
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval, Params
from config import cfg
......@@ -39,11 +39,9 @@ def eval():
if not os.path.exists('output'):
os.mkdir('output')
model = models.YOLOv3(cfg.model_cfg_path, is_train=False)
model = YOLOv3(cfg.model_cfg_path, is_train=False)
model.build_model()
outputs = model.get_pred()
yolo_anchors = model.get_yolo_anchors()
yolo_classes = model.get_yolo_classes()
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# yapf: disable
......@@ -52,7 +50,7 @@ def eval():
return os.path.exists(os.path.join(cfg.weights, var.name))
fluid.io.load_vars(exe, cfg.weights, predicate=if_exist)
# yapf: enable
input_size = model.get_input_size()
input_size = cfg.input_size
test_reader = reader.test(input_size, 1)
label_names, label_ids = reader.get_label_infos()
if cfg.debug:
......
fluid/PaddleCV/yolov3/infer.py
浏览文件 @ b2389e38
......@@ -6,9 +6,7 @@ import paddle.fluid as fluid
import box_utils
import reader
from utility import print_arguments, parse_args
import models.yolov3 as models
# from coco_reader import load_label_names
import json
from models.yolov3 import YOLOv3
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval, Params
from config import cfg
......@@ -19,12 +17,10 @@ def infer():
if not os.path.exists('output'):
os.mkdir('output')
model = models.YOLOv3(cfg.model_cfg_path, is_train=False)
model = YOLOv3(cfg.model_cfg_path, is_train=False)
model.build_model()
outputs = model.get_pred()
input_size = model.get_input_size()
yolo_anchors = model.get_yolo_anchors()
yolo_classes = model.get_yolo_classes()
input_size = cfg.input_size
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# yapf: disable
......
fluid/PaddleCV/yolov3/learning_rate.py
浏览文件 @ b2389e38
......@@ -22,7 +22,7 @@ from paddle.fluid.layers import control_flow
def exponential_with_warmup_decay(learning_rate, boundaries, values,
warmup_iter, warmup_factor, start_step):
warmup_iter, warmup_factor):
global_step = lr_scheduler._decay_step_counter()
lr = fluid.layers.create_global_var(
......
fluid/PaddleCV/yolov3/models.py 已删除 100644 → 0
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
import box_utils
from config.config_parser import ConfigPaser
from config.config import cfg
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act=None,
bn=False,
name=None,
is_train=True):
if bn:
out = 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=name + "_weights"),
bias_attr=False,
name=name + '.conv2d.output.1')
bn_name = "bn" + name[4:]
out = fluid.layers.batch_norm(input=out,
act=None,
is_test=not is_train,
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',
name=bn_name+'.output')
else:
out = 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=name + "_weights"),
bias_attr=ParamAttr(initializer=fluid.initializer.Constant(0.0),
regularizer=L2Decay(0.),
name=name + "_bias"),
name=name + '.conv2d.output.1')
if act == 'relu':
out = fluid.layers.relu(x=out)
if act == 'leaky':
out = fluid.layers.leaky_relu(x=out, alpha=0.1)
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.boxes = []
self.scores = []
self.downsample = 32
def build_model(self):
model_defs = self.config_parser.parse()
if model_defs is None:
return None
self.hyperparams = model_defs.pop(0)
assert self.hyperparams['type'].lower() == "net", \
"net config params should be given in the first segment named 'net'"
self.img_height = cfg.input_size
self.img_width = cfg.input_size
self.build_input()
out = self.image
layer_outputs = []
self.yolo_layer_defs = []
self.yolo_anchors = []
self.yolo_classes = []
self.outputs = []
for i, layer_def in enumerate(model_defs):
if layer_def['type'] == 'convolutional':
bn = layer_def.get('batch_normalize', 0)
ch_out = int(layer_def['filters'])
filter_size = int(layer_def['size'])
stride = int(layer_def['stride'])
padding = (filter_size - 1) // 2 if int(layer_def['pad']) else 0
act = layer_def['activation']
out = conv_bn_layer(
input=out,
ch_out=ch_out,
filter_size=filter_size,
stride=stride,
padding=padding,
act=act,
bn=bool(bn),
name="conv"+str(i),
is_train=self.is_train)
elif layer_def['type'] == 'shortcut':
layer_from = int(layer_def['from'])
out = fluid.layers.elementwise_add(
x=out,
y=layer_outputs[layer_from],
name="res"+str(i))
elif layer_def['type'] == 'route':
layers = map(int, layer_def['layers'].split(","))
out = fluid.layers.concat(
input=[layer_outputs[i] for i in layers],
axis=1)
elif layer_def['type'] == 'upsample':
scale = int(layer_def['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="upsample"+str(i))
elif layer_def['type'] == 'maxpool':
pool_size = int(layer_def['size'])
pool_stride = int(layer_def['stride'])
pool_padding = 0
if pool_stride == 1 and pool_size == 2:
pool_padding = 1
out = fluid.layers.pool2d(
input=out,
pool_type='max',
pool_size=pool_size,
pool_stride=pool_stride,
pool_padding=pool_padding)
elif layer_def['type'] == 'yolo':
self.yolo_layer_defs.append(layer_def)
self.outputs.append(out)
anchor_mask = map(int, layer_def['mask'].split(','))
anchors = map(int, layer_def['anchors'].split(','))
mask_anchors = []
for m in anchor_mask:
mask_anchors.append(anchors[2 * m])
mask_anchors.append(anchors[2 * m + 1])
self.yolo_anchors.append(mask_anchors)
class_num = int(layer_def['classes'])
self.yolo_classes.append(class_num)
if self.is_train:
ignore_thresh = float(layer_def['ignore_thresh'])
loss = fluid.layers.yolov3_loss(
x=out,
gtbox=self.gtbox,
gtlabel=self.gtlabel,
gtscore=self.gtscore,
anchors=anchors,
anchor_mask=anchor_mask,
class_num=class_num,
ignore_thresh=ignore_thresh,
downsample_ratio=self.downsample,
use_label_smooth=cfg.label_smooth,
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
layer_outputs.append(out)
def loss(self):
return sum(self.losses)
def get_pred(self):
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_hyperparams(self):
return self.hyperparams
def get_input_size(self):
return cfg.input_size
fluid/PaddleCV/yolov3/models/darknet.py
浏览文件 @ b2389e38
......@@ -16,7 +16,6 @@ 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,
......@@ -24,7 +23,8 @@ def conv_bn_layer(input,
stride,
padding,
act='leaky',
i=0):
is_test=True,
name=None):
conv1 = fluid.layers.conv2d(
input=input,
num_filters=ch_out,
......@@ -33,70 +33,67 @@ def conv_bn_layer(input,
padding=padding,
act=None,
param_attr=ParamAttr(initializer=fluid.initializer.Normal(0., 0.02),
name="conv" + str(i)+"_weights"),
name=name+".conv.weights"),
bias_attr=False)
bn_name = "bn" + str(i)
bn_name = name + ".bn"
out = fluid.layers.batch_norm(
input=conv1,
act=None,
is_test=True,
is_test=is_test,
param_attr=ParamAttr(
initializer=fluid.initializer.Normal(0., 0.02),
regularizer=L2Decay(0.),
name=bn_name + '_scale'),
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')
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))
def downsample(input, ch_out, filter_size=3, stride=2, padding=1, is_test=True, name=None):
return conv_bn_layer(input,
ch_out=ch_out,
filter_size=filter_size,
stride=stride,
padding=padding,
is_test=is_test,
name=name)
def basicblock(input, ch_out, is_test=True, name=None):
conv1 = conv_bn_layer(input, ch_out, 1, 1, 0, is_test=is_test, name=name+".0")
conv2 = conv_bn_layer(conv1, ch_out*2, 3, 1, 1, is_test=is_test, name=name+".1")
out = fluid.layers.elementwise_add(x=input, y=conv2, act=None)
return out
def layer_warp(block_func, input, ch_out, count, stride,i):
res_out = block_func(input, ch_out, stride, i=i)
def layer_warp(block_func, input, ch_out, count, is_test=True, name=None):
res_out = block_func(input, ch_out, is_test=is_test, name='{}.0'.format(name))
for j in range(1, count):
res_out = block_func(res_out, ch_out, 1 ,i=i+j*3)
res_out = block_func(res_out, ch_out, is_test=is_test, name='{}.{}'.format(name, j))
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):
def add_DarkNet53_conv_body(body_input, is_test=True):
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
body_input, ch_out=32, filter_size=3, stride=1, padding=1, is_test=is_test, name="yolo_input")
downsample_ = downsample(conv1, ch_out=conv1.shape[1]*2, is_test=is_test, name="yolo_input.downsample")
index = 2
blocks = []
for i, stage in enumerate(stages):
block = layer_warp(block_func, downsample_, 32 *(2**i), stage, is_test=is_test, name="stage.{}".format(i))
blocks.append(block)
index += 3 * stage
if i < len(stages) - 1: # do not downsaple in the last stage
downsample_ = downsample(block, ch_out=block.shape[1]*2, is_test=is_test, name="stage.{}.downsample".format(i))
index += 1
return blocks[-1:-4:-1]
fluid/PaddleCV/yolov3/models/yolov3.py
浏览文件 @ b2389e38
......@@ -22,27 +22,24 @@ 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
from .darknet import add_DarkNet53_conv_body
from .darknet import conv_bn_layer
def yolo_detection_block(input, channel,i):
def yolo_detection_block(input, channel, is_test=True, name=None):
assert channel % 2 == 0, "channel {} cannot be divided by 2".format(channel)
conv1 = input
conv = 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)
conv = conv_bn_layer(conv, channel, filter_size=1, stride=1, padding=0, is_test=is_test, name='{}.{}.0'.format(name, j))
conv = conv_bn_layer(conv, channel*2, filter_size=3, stride=1, padding=1, is_test=is_test, name='{}.{}.1'.format(name, j))
route = conv_bn_layer(conv, channel, filter_size=1, stride=1, padding=0, is_test=is_test, name='{}.2'.format(name))
tip = conv_bn_layer(route,channel*2, filter_size=3, stride=1, padding=1, is_test=is_test, name='{}.tip'.format(name))
return route, tip
def upsample(out, stride=2,name=None):
out = out
scale = stride
def upsample(input, scale=2,name=None):
# get dynamic upsample output shape
shape_nchw = fluid.layers.shape(out)
shape_nchw = fluid.layers.shape(input)
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')
......@@ -51,7 +48,7 @@ def upsample(out, stride=2,name=None):
# reisze by actual_shape
out = fluid.layers.resize_nearest(
input=out,
input=input,
scale=scale,
actual_shape=out_shape,
name=name)
......@@ -64,7 +61,6 @@ class YOLOv3(object):
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
......@@ -81,98 +77,45 @@ class YOLOv3(object):
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,
blocks = add_DarkNet53_conv_body(self.image, not self.is_train)
for i, block in enumerate(blocks):
if i > 0:
block = fluid.layers.concat(
input=[route, block],
axis=1)
route, tip = yolo_detection_block(block, channel=512//(2**i),
is_test=(not self.is_train),
name="yolo_block.{}".format(i))
block_out = fluid.layers.conv2d(
input=tip,
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"),
name="yolo_output.{}.conv.weights".format(i)),
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)
name="yolo_output.{}.conv.bias".format(i)))
self.outputs.append(block_out)
route2 = conv_bn_layer(
input=route2,
ch_out=128,
if i < len(blocks) - 1:
route = conv_bn_layer(
input=route,
ch_out=256//(2**i),
filter_size=1,
stride=1,
padding=0,
i=96)
is_test=(not self.is_train),
name="yolo_transition.{}".format(i))
# upsample
route2 = upsample(route2)
# concat
route2 = fluid.layers.concat(
input=[route2,scale3],
axis=1)
route = upsample(route)
# 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]
......@@ -183,9 +126,7 @@ class YOLOv3(object):
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)
......@@ -193,13 +134,13 @@ class YOLOv3(object):
x=out,
gtbox=self.gtbox,
gtlabel=self.gtlabel,
# gtscore=self.gtscore,
gtscore=self.gtscore,
anchors=anchors,
anchor_mask=mask,
class_num=class_num,
ignore_thresh=ignore_thresh,
downsample_ratio=self.downsample,
# use_label_smooth=False,
use_label_smooth=cfg.label_smooth,
name="yolo_loss"+str(i))
self.losses.append(fluid.layers.reduce_mean(loss))
else:
......@@ -221,7 +162,6 @@ class YOLOv3(object):
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(
......@@ -234,12 +174,6 @@ class YOLOv3(object):
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:
......@@ -273,7 +207,3 @@ class YOLOv3(object):
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 @ b2389e38
......@@ -255,8 +255,8 @@ def train(size=416,
random_sizes=[],
interval=10,
pyreader_num=1,
num_workers=16,
max_queue=32,
num_workers=2,
max_queue=4,
use_multiprocessing=True):
generator = dsr.get_reader('train', size, batch_size, shuffle, int(mixup_iter/pyreader_num), random_sizes)
......
fluid/PaddleCV/yolov3/train.py
浏览文件 @ b2389e38
......@@ -26,7 +26,7 @@ from utility import parse_args, print_arguments, SmoothedValue
import paddle
import paddle.fluid as fluid
import reader
import models.yolov3 as models
from models.yolov3 import YOLOv3
from learning_rate import exponential_with_warmup_decay
from config import cfg
......@@ -42,26 +42,20 @@ def train():
if not os.path.exists(cfg.model_save_dir):
os.makedirs(cfg.model_save_dir)
model = models.YOLOv3(cfg.model_cfg_path, use_pyreader=cfg.use_pyreader)
model = YOLOv3(cfg.model_cfg_path, use_pyreader=cfg.use_pyreader)
model.build_model()
input_size = model.get_input_size()
input_size = cfg.input_size
loss = model.loss()
loss.persistable = True
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(cfg.learning_rate)
learning_rate = cfg.learning_rate
boundaries = cfg.lr_steps
gamma = cfg.lr_gamma
step_num = len(cfg.lr_steps)
if isinstance(gamma, list):
values = [learning_rate * g for g in gamma]
else:
values = [learning_rate * (gamma**i) for i in range(step_num + 1)]
optimizer = fluid.optimizer.Momentum(
......@@ -70,10 +64,9 @@ def train():
boundaries=boundaries,
values=values,
warmup_iter=cfg.warm_up_iter,
warmup_factor=cfg.warm_up_factor,
start_step=cfg.start_iter),
regularization=fluid.regularizer.L2Decay(float(cfg.decay)),
momentum=float(cfg.momentum))
warmup_factor=cfg.warm_up_factor),
regularization=fluid.regularizer.L2Decay(cfg.weight_decay),
momentum=cfg.momentum)
optimizer.minimize(loss)
fluid.memory_optimize(fluid.default_main_program())
......@@ -98,11 +91,11 @@ 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(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)
train_reader = reader.train(input_size, batch_size=cfg.batch_size/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(cfg.batch), shuffle=True, mixup_iter=mixup_iter, random_sizes=random_sizes, use_multiprocessing=cfg.use_multiprocess)
train_reader = reader.train(input_size, batch_size=cfg.batch_size, 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 @ b2389e38
......@@ -108,14 +108,15 @@ def parse_args():
add_arg('start_iter', int, 0, "Start iteration.")
add_arg('use_multiprocess', bool, True, "add multiprocess.")
#SOLVER
add_arg('batch_size', int, 64, "Learning rate.")
add_arg('learning_rate', float, 0.001, "Learning rate.")
add_arg('max_iter', int, 500200, "Iter number.")
add_arg('snapshot_iter', int, 2000, "Save model every snapshot stride.")
add_arg('label_smooth', bool, True, "Use label smooth in class label.")
add_arg('no_mixup_iter', int, 40000, "Disable mixup in last N iter.")
# TRAIN TEST INFER
add_arg('input_size', int, 608, "Image input size of YOLOv3.")
add_arg('random_shape', bool, True, "Resize to random shape for train reader.")
add_arg('label_smooth', bool, True, "Use label smooth in class label.")
add_arg('no_mixup_iter', int, 40000, "Disable mixup in last N iter.")
add_arg('valid_thresh', float, 0.005, "Valid confidence score for NMS.")
add_arg('nms_thresh', float, 0.45, "NMS threshold.")
add_arg('nms_topk', int, 400, "The number of boxes to perform NMS.")
......
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