# 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[ ]: gtf.Train(num_epochs=30, model_output_dir="trained/"); # In[ ]: # In[2]:
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]))
gtf.Model(model_name=system["model"], gpu_devices=gpu_devices)
gtf.Set_Hyperparams(lr=system["lr"],
val_interval=system["val_interval"],
print_interval=system["print_interval"])
gtf.Train(num_epochs=system["epochs"],
output_model_name=system["output_model_name"] + ".pt")
print("Completed")
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"],
val_interval=system["val_interval"],
es_min_delta=system["es_min_delta"],
es_patience=system["es_patience"])
gtf.Train(num_epochs=system["epochs"],
model_output_dir=system["output_model_dir"])
print("Completed")
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"))
class_list=class_list[:-1]
img_p="/content/Images_and_Labels/Images/0000002_00005_d_0000014_jpg.rf.555bf2106d899e56d45da0a48295f04c.jpg"
scores, labels, boxes = gtf.Predict(img_p, class_list, vis_threshold=0.4);
from IPython.display import Image