（未完成，待補(bǔ)充）
獲取Faster RCNN源碼
（開源的很多，論文里也有，在這里不多贅述）
替換自己的數(shù)據(jù)集（圖片+標(biāo)簽文件）
（需要使用labeling生成標(biāo)簽文件）
打開終端，進(jìn)入gpupytorch環(huán)境
運(yùn)行voc_annotation.py文件生成與訓(xùn)練文件
E:\DeepLearningModel\Model01>activate gpupytorch
(gpupytorch) E:\DeepLearningModel\Model01>python voc_annotation.py
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll
warnings.warn("loaded more than 1 DLL from .libs:\n%s" %
Generate txt in ImageSets.
train and val size 777
train size 699
Generate txt in ImageSets done.
Generate 2007_train.txt and 2007_val.txt for train.?結(jié)果所示：
(gpupytorch) E:\DeepLearningModel\Model01>python voc_annotation.py
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll
warnings.warn("loaded more than 1 DLL from .libs:\n%s" %
Generate txt in ImageSets.
train and val size 777
train size 699
Generate txt in ImageSets done.
Generate 2007_train.txt and 2007_val.txt for train.
Generate 2007_train.txt and 2007_val.txt for train done.
| leopard | 174 |
| boar | 491 |
| roe_deer | 352 |
(gpupytorch) E:\DeepLearningModel\Model01>
運(yùn)行：train.py文件
import colorsys
import os
import time
import numpy as np
import torch
import torch.nn as nn
from PIL import Image, ImageDraw, ImageFont
from nets.frcnn import FasterRCNN
from utils.utils import (cvtColor, get_classes, get_new_img_size, resize_image,
preprocess_input, show_config)
from utils.utils_bbox import DecodeBox
class FRCNN(object):
_defaults = {
"model_path" : 'logs/loss_2024_03_05_22_26_24.pth',
"classes_path" : 'model_data/voc_classes.txt',
"backbone" : "resnet50",
"confidence" : 0.5,
"nms_iou" : 0.3,
'anchors_size' : [8, 16, 32],
"cuda" : True,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
self._defaults[name] = value
self.class_names, self.num_classes = get_classes(self.classes_path)
self.std = torch.Tensor([0.1, 0.1, 0.2, 0.2]).repeat(self.num_classes + 1)[None]
if self.cuda:
self.std = self.std.cuda()
self.bbox_util = DecodeBox(self.std, self.num_classes)
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
self.generate()
show_config(**self._defaults)
#---------------------------------------------------#
# 載入模型
#---------------------------------------------------#
def generate(self):
self.net = FasterRCNN(self.num_classes, "predict", anchor_scales = self.anchors_size, backbone = self.backbone)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.net.load_state_dict(torch.load(self.model_path, map_location=device))
self.net = self.net.eval()
print('{} model, anchors, and classes loaded.'.format(self.model_path))
if self.cuda:
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
#---------------------------------------------------#
# 檢測圖片
#---------------------------------------------------#
def detect_image(self, image, crop = False, count = False):
#---------------------------------------------------#
# 計(jì)算輸入圖片的高和寬
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------#
# 計(jì)算resize后的圖片的大小，resize后的圖片短邊為600
#---------------------------------------------------#
input_shape = get_new_img_size(image_shape[0], image_shape[1])
#---------------------------------------------------------#
# 在這里將圖像轉(zhuǎn)換成RGB圖像，防止灰度圖在預(yù)測時(shí)報(bào)錯(cuò)。
# 代碼僅僅支持RGB圖像的預(yù)測，所有其它類型的圖像都會(huì)轉(zhuǎn)化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 給原圖像進(jìn)行resize，resize到短邊為600的大小上
#---------------------------------------------------------#
image_data = resize_image(image, [input_shape[1], input_shape[0]])
#---------------------------------------------------------#
# 添加上batch_size維度
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#-------------------------------------------------------------#
# roi_cls_locs 建議框的調(diào)整參數(shù)
# roi_scores 建議框的種類得分
# rois 建議框的坐標(biāo)
#-------------------------------------------------------------#
roi_cls_locs, roi_scores, rois, _ = self.net(images)
#-------------------------------------------------------------#
# 利用classifier的預(yù)測結(jié)果對(duì)建議框進(jìn)行解碼，獲得預(yù)測框
#-------------------------------------------------------------#
results = self.bbox_util.forward(roi_cls_locs, roi_scores, rois, image_shape, input_shape,
nms_iou = self.nms_iou, confidence = self.confidence)
#---------------------------------------------------------#
# 如果沒有檢測出物體，返回原圖
#---------------------------------------------------------#
if len(results[0]) <= 0:
return image
top_label = np.array(results[0][:, 5], dtype = 'int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
#---------------------------------------------------------#
# 設(shè)置字體與邊框厚度
#---------------------------------------------------------#
font = ImageFont.truetype(font='model_data/simhei.ttf', size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = int(max((image.size[0] + image.size[1]) // np.mean(input_shape), 1))
#---------------------------------------------------------#
# 計(jì)數(shù)
#---------------------------------------------------------#
if count:
print("top_label:", top_label)
classes_nums = np.zeros([self.num_classes])
for i in range(self.num_classes):
num = np.sum(top_label == i)
if num > 0:
print(self.class_names[i], " : ", num)
classes_nums[i] = num
print("classes_nums:", classes_nums)
#---------------------------------------------------------#
# 是否進(jìn)行目標(biāo)的裁剪
#---------------------------------------------------------#
if crop:
for i, c in list(enumerate(top_label)):
top, left, bottom, right = top_boxes[i]
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
dir_save_path = "img_crop"
if not os.path.exists(dir_save_path):
os.makedirs(dir_save_path)
crop_image = image.crop([left, top, right, bottom])
crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0)
print("save crop_" + str(i) + ".png to " + dir_save_path)
#---------------------------------------------------------#
# 圖像繪制
#---------------------------------------------------------#
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
# print(label, top, left, bottom, right)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
return image
def get_FPS(self, image, test_interval):
#---------------------------------------------------#
# 計(jì)算輸入圖片的高和寬
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
input_shape = get_new_img_size(image_shape[0], image_shape[1])
#---------------------------------------------------------#
# 在這里將圖像轉(zhuǎn)換成RGB圖像，防止灰度圖在預(yù)測時(shí)報(bào)錯(cuò)。
# 代碼僅僅支持RGB圖像的預(yù)測，所有其它類型的圖像都會(huì)轉(zhuǎn)化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 給原圖像進(jìn)行resize，resize到短邊為600的大小上
#---------------------------------------------------------#
image_data = resize_image(image, [input_shape[1], input_shape[0]])
#---------------------------------------------------------#
# 添加上batch_size維度
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
roi_cls_locs, roi_scores, rois, _ = self.net(images)
#-------------------------------------------------------------#
# 利用classifier的預(yù)測結(jié)果對(duì)建議框進(jìn)行解碼，獲得預(yù)測框
#-------------------------------------------------------------#
results = self.bbox_util.forward(roi_cls_locs, roi_scores, rois, image_shape, input_shape,
nms_iou = self.nms_iou, confidence = self.confidence)
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
roi_cls_locs, roi_scores, rois, _ = self.net(images)
#-------------------------------------------------------------#
# 利用classifier的預(yù)測結(jié)果對(duì)建議框進(jìn)行解碼，獲得預(yù)測框
#-------------------------------------------------------------#
results = self.bbox_util.forward(roi_cls_locs, roi_scores, rois, image_shape, input_shape,
nms_iou = self.nms_iou, confidence = self.confidence)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
#---------------------------------------------------#
# 檢測圖片
#---------------------------------------------------#
def get_map_txt(self, image_id, image, class_names, map_out_path):
f = open(os.path.join(map_out_path, "detection-results/"+image_id+".txt"),"w")
#---------------------------------------------------#
# 計(jì)算輸入圖片的高和寬
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
input_shape = get_new_img_size(image_shape[0], image_shape[1])
#---------------------------------------------------------#
# 在這里將圖像轉(zhuǎn)換成RGB圖像，防止灰度圖在預(yù)測時(shí)報(bào)錯(cuò)。
# 代碼僅僅支持RGB圖像的預(yù)測，所有其它類型的圖像都會(huì)轉(zhuǎn)化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 給原圖像進(jìn)行resize，resize到短邊為600的大小上
#---------------------------------------------------------#
image_data = resize_image(image, [input_shape[1], input_shape[0]])
#---------------------------------------------------------#
# 添加上batch_size維度
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
roi_cls_locs, roi_scores, rois, _ = self.net(images)
#-------------------------------------------------------------#
# 利用classifier的預(yù)測結(jié)果對(duì)建議框進(jìn)行解碼，獲得預(yù)測框
#-------------------------------------------------------------#
results = self.bbox_util.forward(roi_cls_locs, roi_scores, rois, image_shape, input_shape,
nms_iou = self.nms_iou, confidence = self.confidence)
#--------------------------------------#
# 如果沒有檢測到物體，則返回原圖
#--------------------------------------#
if len(results[0]) <= 0:
return
top_label = np.array(results[0][:, 5], dtype = 'int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = str(top_conf[i])
top, left, bottom, right = box
if predicted_class not in class_names:
continue
f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))
f.close()
return
?終端/編碼器運(yùn)行：
E:\DeepLearningModel\Model01>activate gpupytorch
(gpupytorch) E:\DeepLearningModel\Model01>python train.py
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll
warnings.warn("loaded more than 1 DLL from .libs:\n%s" %
Number of devices: 1
initialize network with normal type
Load weights model_data/voc_weights_resnet.pth.
Successful Load Key: ['extractor.0.weight', 'extractor.1.weight', 'extractor.1.bias', 'extractor.1.running_mean', 'extractor.1.running_var', 'extractor.1.num_batches_tracked', 'extractor.4.0.conv1.weight', 'extractor.4.0.bn1.weight', 'extractor.4.0.bn1.bias', 'extractor.4.0.bn1.running_mean', 'extractor.4.0.bn1.running_var', 'extractor.4.0.bn1.num_batches_tracked', 'extractor.4.0.conv2.weight', 'extractor.4.0.bn2.weight', 'extractor.4.0.bn2.bias', 'extractor.4.0.bn2.running_mean', 'extractor.4.0.bn2.running_var', 'e ……
Successful Load Key Num: 324
Fail To Load Key: ['head.cls_loc.weight', 'head.cls_loc.bias', 'head.score.weight', 'head.score.bias'] ……
Fail To Load Key num: 4
溫馨提示，head部分沒有載入是正?，F(xiàn)象，Backbone部分沒有載入是錯(cuò)誤的。
Configurations:
----------------------------------------------------------------------
| keys | values|
----------------------------------------------------------------------
| classes_path | model_data/voc_classes.txt|
| model_path | model_data/voc_weights_resnet.pth|
| input_shape | [600, 600]|
| Init_Epoch | 0|
| Freeze_Epoch | 50|
| UnFreeze_Epoch | 100|
| Freeze_batch_size | 4|
| Unfreeze_batch_size | 2|
| Freeze_Train | True|
| Init_lr | 0.0001|
| Min_lr | 1.0000000000000002e-06|
| optimizer_type | adam|
| momentum | 0.9|
| lr_decay_type | cos|
| save_period | 5|
| save_dir | logs|
| num_workers | 4|
| num_train | 699|
| num_val | 78|
----------------------------------------------------------------------
Start Train
Epoch 1/100: 0%| | 0/174 [00:00<?, ?it/s<class 'dict'>]D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll
查看結(jié)果：文章來源：http://www.zghlxwxcb.cn/news/detail-839159.html
Calculate Map.
96.35% = boar AP || score_threhold=0.5 : F1=0.81 ; Recall=97.92% ; Precision=69.12%
94.74% = leopard AP || score_threhold=0.5 : F1=0.90 ; Recall=94.74% ; Precision=85.71%
94.97% = roe_deer AP || score_threhold=0.5 : F1=0.86 ; Recall=96.88% ; Precision=77.50%
mAP = 95.35%
Get map done.
Epoch:100/100
Total Loss: 0.505 || Val Loss: 0.621
Save best model to best_epoch_weights.pth
文章來源地址http://www.zghlxwxcb.cn/news/detail-839159.html
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