示例#1
0
        val_coco_dir = ""
        val_img_dir = ""
        val_set_dir = system["val_monk_img_dir"]

    else:

        val_root_dir = system["val_coco_root_dir"]
        val_coco_dir = system["val_coco_coco_dir"]
        val_img_dir = system["val_coco_img_dir"]
        val_set_dir = system["val_coco_set_dir"]

system["epochs"] = int(system["epochs"])
system["val_interval"] = int(system["val_interval"])
system["lr"] = float(system["lr"])

gtf = Detector()

gtf.Train_Dataset(root_dir,
                  coco_dir,
                  img_dir,
                  set_dir,
                  batch_size=system["batch_size"],
                  image_size=system["image_size"],
                  use_gpu=system["use_gpu"])

if (system["val_data"] == "yes"):
    gtf.Val_Dataset(val_root_dir, val_coco_dir, val_img_dir, val_set_dir)

tmp = system["devices"].split(",")
gpu_devices = []
for i in range(len(tmp)):
示例#2
0
# In[8]:


from train_detector import Detector


# In[9]:


# pwd


# In[10]:


gtf = Detector();


# In[11]:


root_dir = ".";
coco_dir = "coco_dataset_3class";
img_dir = ".";
set_dir = "Images";


# In[12]:


gtf.Train_Dataset(root_dir, coco_dir, img_dir, set_dir, batch_size=8, image_size=512, use_gpu=True)
        val_coco_dir = ""
        val_img_dir = ""
        val_set_dir = system["val_monk_img_dir"]

    else:

        val_root_dir = system["val_coco_root_dir"]
        val_coco_dir = system["val_coco_coco_dir"]
        val_img_dir = system["val_coco_img_dir"]
        val_set_dir = system["val_coco_set_dir"]

system["epochs"] = int(system["epochs"])
system["val_interval"] = int(system["val_interval"])
system["lr"] = float(system["lr"])

gtf = Detector()

gtf.Train_Dataset(root_dir,
                  coco_dir,
                  img_dir,
                  set_dir,
                  batch_size=system["batch_size"],
                  use_gpu=system["use_gpu"])

if (system["val_data"] == "yes"):
    gtf.Val_Dataset(val_root_dir, val_coco_dir, val_img_dir, val_set_dir)

tmp = system["devices"].split(",")
gpu_devices = []
for i in range(len(tmp)):
    gpu_devices.append(int(tmp[i]))
# Check TF version
import tensorflow as tf

print(tf.__version__)

import os
import sys

sys.path.append("MONK/Monk_Object_Detection/13_tf_obj_2/lib/")

from train_detector import Detector

gtf = Detector()

print(gtf.list_models())

train_img_dir = "COCO_CREATION/results/Train/images"
train_anno_dir = "COCO_CREATION/results/Train/annotations"
class_list_file = "COCO_CREATION/pascal-voc-classes.txt"

gtf.set_train_dataset(train_img_dir,
                      train_anno_dir,
                      class_list_file,
                      batch_size=24,
                      trainval_split=0.8)

## Output dir
output_dir = os.path.join("data_tfrecord")

gtf.create_tfrecord(data_output_dir=output_dir)
示例#5
0
copy_to_root()

train_num_img=sum([1 for i in os.listdir("/content/Root_Dir/Coco_style/images/Train")])
valid_num_img=sum([1 for i in os.listdir("/content/Root_Dir/Coco_style/images/Val")])
print("There are {} train images and {} valid images.".format(train_num_img,valid_num_img))

#!pip install efficientnet_pytorch
#!pip install tensorboardx

#https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/example_notebooks/5_pytorch_retinanet/Train%20Resnet18%20-%20With%20validation%20Dataset.ipynb

import os
os.chdir("/content/Computer-vision-and-Drones-Thesis/models/Modified Monk Retinanet/lib")
from train_detector import Detector
model = Detector();

os.chdir("/content/")

troot_dir = "Root_Dir";
tcoco_dir = "Coco_style";
timg_dir = "images";
tset_dir = "Train";



vroot_dir = "Root_Dir";
vcoco_dir = "Coco_style";
vimg_dir = "images";
vset_dir = "Val";
        val_img_dir = ""; 
        val_set_dir = system["val_monk_img_dir"];



    else:

        val_root_dir = system["val_coco_root_dir"];
        val_coco_dir = system["val_coco_coco_dir"];
        val_img_dir = system["val_coco_img_dir"];
        val_set_dir = system["val_coco_set_dir"];




gtf = Detector();


gtf.Train_Dataset(root_dir, coco_dir, img_dir, set_dir, 
                    batch_size=system["batch_size"], 
                    num_workers=4)

if(system["val_data"] == "yes"):
    gtf.Val_Dataset(val_root_dir, val_coco_dir, val_img_dir, val_set_dir)



gtf.Model(model_name=system["model"]);


gtf.Hyper_Params(lr=system["lr"], 
示例#7
0
        val_root_dir = system["val_coco_root_dir"] + "/" + system[
            "val_coco_coco_dir"]
        val_img_dir = system["val_coco_img_dir"] + "/" + system[
            "val_coco_set_dir"]
        val_anno_dir = labels_dir

    else:

        val_root_dir = system["val_yolo_root_dir"]
        val_img_dir = system["val_yolo_img_dir"]
        val_anno_dir = system["val_yolo_anno_dir"]
        val_classes_file = system["val_yolo_classes_file"]

from train_detector import Detector

gtf = Detector()

gtf.set_train_dataset(root_dir + "/" + img_dir,
                      root_dir + "/" + anno_dir,
                      root_dir + "/" + classes_file,
                      batch_size=system["batch_size"],
                      img_size=system["img_size"],
                      cache_images=system["cache_images"])

if (system["val_data"] == "yes"):
    gtf.set_val_dataset(val_root_dir + "/" + val_img_dir,
                        val_root_dir + "/" + val_anno_dir)

gtf.set_model(model_name=system["model"])

gtf.set_hyperparams(optimizer=system["optimizer"],