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final_training.py
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final_training.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import torch
print(torch.__version__)
# In[2]:
get_ipython().run_line_magic('cd', 'detectron2')
# In[3]:
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common libraries
import numpy as np
import os, json, cv2, random,pickle
# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor,DefaultTrainer
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.data import MetadataCatalog, DatasetCatalog,build_detection_test_loader, build_detection_train_loader
from detectron2.data.datasets import register_coco_instances
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.structures import BoxMode
import detectron2.data.transforms as T
from detectron2.data import detection_utils as utils
from detectron2.data import datasets
from detectron2 import model_zoo
# In[4]:
torch.cuda.is_available()
# In[5]:
def cv2_imshow(im):
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
plt.figure(), plt.imshow(im), plt.axis('off');
# In[6]:
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from matplotlib.pyplot import imshow
from PIL import Image
import IPython
def cv2_imshow(img):
img = img[:,:,[2,1,0]]
img = Image.fromarray(img)
plt.figure(figsize=(20, 20))
plt.imshow(img)
plt.axis('off')
plt.show()
# In[7]:
dataset_name = "/storage/Fylkesveg/Sign_training_material/training_sahi"
# In[8]:
train_dataset_name = "trafficsigns_train"
train_image_path = "/storage/Fylkesveg/Sign_training_material/training_sahi/train/coco_images_800_02"
train_json_annot_path = "/storage/Fylkesveg/Sign_training_material/training_sahi/train/coco-with-negatives.json"
f = open(train_json_annot_path, 'r')
train_annotations = json.load(f)
f.close()
num_classes = len(train_annotations['categories'])
# In[9]:
cfg_save_path = 'IS_cfg.pickle'
training_dict = datasets.load_coco_json(train_json_annot_path, train_image_path,
dataset_name=train_dataset_name)
# In[10]:
test_dataset_name = 'trafficsigns_valid'
test_image_path = "/storage/Fylkesveg/Sign_training_material/training_sahi/valid/coco_images_800_02"
test_json_annot_path = "/storage/Fylkesveg/Sign_training_material/training_sahi/valid/coco-with-negatives.json"
f = open(test_json_annot_path, 'r')
test_annotations = json.load(f)
f.close()
num_classes = len(test_annotations['categories'])
# In[11]:
from detectron2.data.datasets import register_coco_instances
register_coco_instances(train_dataset_name, {}, train_json_annot_path, train_image_path)
register_coco_instances(test_dataset_name, {}, test_json_annot_path, test_image_path)
# In[12]:
trafficsigns_metadata = MetadataCatalog.get("trafficsigns_train")
dataset_dicts = DatasetCatalog.get("trafficsigns_train")
# In[13]:
print(type(trafficsigns_metadata))
MetadataCatalog.get("trafficsigns_train")
# In[14]:
cfg = get_cfg()
device = 'cuda'
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml"))
cfg.DATASETS.TRAIN = ("trafficsigns_train",)
cfg.DATASETS.TEST = ()
cfg.DATALOADER.NUM_WORKERS = 4
cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS = False
#cfg.MODEL.WEIGHTS = "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" # Let training initialize from model zoo
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml")
cfg.SOLVER.IMS_PER_BATCH = 1
cfg.SOLVER.BASE_LR = 0.00025
cfg.SOLVER.MAX_ITER = 270000
cfg.SOLVER.STEPS = (210000, 250000)
#cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 20
cfg.INPUT.MIN_SIZE_TRAIN = [640,672,704,736,768,800]
cfg.OUTPUT_DIR = 'output/trafficsigns_train_X101-FPN/'
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
# In[15]:
yaml_cfg_path = 'output/trafficsigns_X101-FPN/cfg.yaml'
with open(yaml_cfg_path, 'w') as yaml_cfg_file:
yaml_cfg_file.write(cfg.dump())
# In[50]:
outputs
# In[16]:
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=False)
trainer.train()
# In[23]:
get_ipython().system('kill 27747')
# In[24]:
# Look at training curves in tensorboard:
get_ipython().run_line_magic('reload_ext', 'tensorboard')
get_ipython().run_line_magic('tensorboard', '--logdir output')
# In[25]:
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set the testing threshold for this model
cfg.DATASETS.TEST = ("trafficsigns_train", )
predictor = DefaultPredictor(cfg)
# In[27]:
from detectron2.utils.visualizer import ColorMode
dataset_dicts = DatasetCatalog.get("trafficsigns_train")
for d in random.sample(dataset_dicts, 10):
im = cv2.imread(d["file_name"])
outputs = predictor(im) # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format
v = Visualizer(im[:, :, ::-1],
metadata=trafficsigns_metadata,
scale=0.5,
instance_mode=ColorMode.IMAGE # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
cv2_imshow(out.get_image()[:, :, ::-1])
# In[28]:
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.data import build_detection_test_loader
evaluator = COCOEvaluator("trafficsigns_train", output_dir="./output")
val_loader = build_detection_test_loader(cfg, "trafficsigns_train")
print(inference_on_dataset(predictor.model, val_loader, evaluator))
# In[29]:
#Validation Sample
# In[ ]:
test_dataset_name = 'trafficsigns_valid'
test_image_path = "/storage/Fylkesveg/Sign_training_material/training_sahi/valid/coco_images_800_02"
test_json_annot_path = "/storage/Fylkesveg/Sign_training_material/training_sahi/valid/coco-with-negatives.json"
f = open(test_json_annot_path, 'r')
test_annotations = json.load(f)
f.close()
num_classes = len(test_annotations['categories'])
# In[30]:
trafficsigns_metadata = MetadataCatalog.get("trafficsigns_valid")
dataset_dicts = DatasetCatalog.get("trafficsigns_valid")
# In[31]:
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set the testing threshold for this model
cfg.DATASETS.TEST = ("trafficsigns_valid", )
predictor = DefaultPredictor(cfg)
# In[32]:
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.data import build_detection_test_loader
evaluator = COCOEvaluator("trafficsigns_valid", output_dir="./output")
val_loader = build_detection_test_loader(cfg, "trafficsigns_valid")
print(inference_on_dataset(predictor.model, val_loader, evaluator))
# In[34]:
from detectron2.utils.visualizer import ColorMode
dataset_dicts = DatasetCatalog.get("trafficsigns_valid")
for d in random.sample(dataset_dicts, 10):
im = cv2.imread(d["file_name"])
outputs = predictor(im) # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format
v = Visualizer(im[:, :, ::-1],
metadata=trafficsigns_metadata,
scale=0.5,
instance_mode=ColorMode.IMAGE # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
cv2_imshow(out.get_image()[:, :, ::-1])
# ### Confusion Matrix from scratch
# In[54]:
get_ipython().run_line_magic('cd', '"/srv/notebooks/detectron2/object_detection_confusion_matrix"')
# In[91]:
from confusion_matrix import ConfusionMatrix
cm = ConfusionMatrix(20, CONF_THRESHOLD=0.3, IOU_THRESHOLD=0.5)
for d in dataset_dicts_validation:
if len(d["annotations"]) < 1:
continue
img = cv2.imread(d["file_name"])
outputs = predictor(img)
labels = list()
detections = list()
for ann in d["annotations"]:
bbox = [
ann["bbox"][0], # X1
ann["bbox"][1], # Y1
ann["bbox"][0] + ann["bbox"][2], #X2
ann["bbox"][1] + ann["bbox"][3], #Y2
]
labels.append([ann["category_id"]] + bbox)
for coord, conf, cls in zip(
outputs["instances"].get("pred_boxes").tensor.cpu().numpy(),
outputs["instances"].get("scores").cpu().numpy(),
outputs["instances"].get("pred_classes").cpu().numpy()
):
detections.append(list(coord) + [conf] + [cls])
cm.process_batch(np.array(detections), np.array(labels))
# In[109]:
categories = MetadataCatalog.get("trafficsigns_train").thing_classes
row_labels = categories + ['extra row']
col_labels = categories + ['extra col']
row_labels
# In[100]:
d = dataset_dicts_validation[0]
img = cv2.imread(d["file_name"])
outputs = predictor(img)
labels = list()
detections = list()
for ann in d["annotations"]:
bbox = [
ann["bbox"][0], # X1
ann["bbox"][1], # Y1
ann["bbox"][0] + ann["bbox"][2], #X2
ann["bbox"][1] + ann["bbox"][3], #Y2
]
labels.append([ann["category_id"]] + bbox)
for coord, conf, cls in zip(
outputs["instances"].get("pred_boxes").tensor.cpu().numpy(),
outputs["instances"].get("scores").cpu().numpy(),
outputs["instances"].get("pred_classes").cpu().numpy()
):
detections.append(list(coord) + [conf] + [cls])
cm.process_batch(np.array(detections), np.array(labels))
# In[110]:
import pandas as pd
df = pd.DataFrame(cm.matrix, columns=col_labels, index=row_labels)
df
# In[ ]: