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) # In[13]: gtf.Model(); # In[14]: gtf.Set_Hyperparams(lr=0.0001, val_interval=1, es_min_delta=0.0, es_patience=0) # In[ ]:
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";
model.Train_Dataset(troot_dir, tcoco_dir, timg_dir, tset_dir, batch_size=8, image_size=352, use_gpu=True)
model.Val_Dataset(vroot_dir, vcoco_dir, vimg_dir, vset_dir)
model.Model(model_name="resnet34"); # resnet 50 brought cuda memory error.
model.Set_Hyperparams(lr=0.0001, val_interval=1, print_interval=20)
model.Train(num_epochs=300,output_model_name="karen_model.pt");
from infer_detector import Infer
gtf = Infer();
gtf.Model(model_path="/content/karen_model.pt");
#predictions are quite bad at the moment.
class_list=[]
with open("/content/Root_Dir/Coco_style/annotations/classes.txt") as file:
for line in file:
class_list.append(line.rstrip("\n"))
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)):
gpu_devices.append(int(tmp[i]))
gtf.Model(gpu_devices=gpu_devices)
gtf.Set_Hyperparams(lr=system["lr"],
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"],
total_iterations=system["iterations"],
val_interval=system["val_interval"])