Annolid on Detectron2 Tutorial#
Welcome to Annolid on detectron2! This is modified from the official colab tutorial of detectron2. Here, we will go through some basics usage of detectron2, including the following:
Run inference on images or videos, with an existing detectron2 model
Train a detectron2 model on a new dataset
You can make a copy of this tutorial by “File -> Open in playground mode” and play with it yourself. DO NOT request access to this tutorial.
Install detectron2#
# Is running in colab or in jupyter-notebook
try:
import google.colab
IN_COLAB = True
except:
IN_COLAB = False
# install dependencies:
!pip install pyyaml==5.3
import torch, torchvision
TORCH_VERSION = ".".join(torch.__version__.split(".")[:2])
CUDA_VERSION = torch.__version__.split("+")[-1]
print("torch: ", TORCH_VERSION, "; cuda: ", CUDA_VERSION)
# Install detectron2 that matches the above pytorch version
# See https://detectron2.readthedocs.io/tutorials/install.html for instructions
!pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/$CUDA_VERSION/torch$TORCH_VERSION/index.html
# If there is not yet a detectron2 release that matches the given torch + CUDA version, you need to install a different pytorch.
# exit(0) # After installation, you may need to "restart runtime" in Colab. This line can also restart runtime
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torch: 1.10 ; cuda: cu102
Looking in links: https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.10/index.html
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# import some common libraries
import json
import os
import cv2
import random
import glob
import numpy as np
if IN_COLAB:
from google.colab.patches import cv2_imshow
import matplotlib.pyplot as plt
%matplotlib inline
# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog
# is there a gpu
if torch.cuda.is_available():
GPU = True
print('gpu available')
else:
GPU = False
print('no gpu')
gpu available
Upload a labeled dataset.#
The following code is expecting the dataset in the COCO format to be in a .zip file. For example: sample_dataset.zip \
!pip install gdown
!gdown --id 1fUXCLnoJ5SwXg54mj0NBKGzidsV8ALVR
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Downloading...
From: https://drive.google.com/uc?id=1fUXCLnoJ5SwXg54mj0NBKGzidsV8ALVR
To: /home/jeremy/Documents/annolid/book/tutorials/novelctrlk6_8_coco_dataset.zip
100%|██████████████████████████████████████| 10.3M/10.3M [00:00<00:00, 93.6MB/s]
if IN_COLAB:
dataset = '/content/novelctrlk6_8_coco_dataset.zip'
else:
dataset = 'novelctrlk6_8_coco_dataset.zip'
Note1: If you want to use your own dataset instead of the demo one, please uncomment and edit the following code.#
Note2: please make sure there is no white space in your file path if you encounter file not found issues.#
# if IN_COLAB:
# from google.colab import files
# uploaded = files.upload()
# else:
# from ipywidgets import FileUpload
# from IPython.display import display
# !jupyter nbextension enable --py widgetsnbextension
# uploaded = FileUpload()
# display(uploaded)
# if IN_COLAB:
# dataset = list(uploaded.keys())[0]
# else:
# dataset = list(uploaded.value.keys())[0]
if IN_COLAB:
!unzip $dataset -d /content/
else:
!unzip -o $dataset -d .
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If your dataset has the same name as the file you uploaded, you do not need to manually input the name (just run the next cells). Otherwise, you need to replace DATASET_NAME and DATASET_DIR with your own strings like DATASET_NAME = "NameOfMyDataset" and DATASETDIR="NameOfMyDatasetDirectory". To do that, uncomment the commented out cell below and replace the strings with the appropriate names
DATASET_NAME = DATASET_DIR = f"{dataset.replace('.zip','')}"
# DATASET_NAME = 'NameOfMyDataset'
# DATASET_DIR = 'NameOfMyDatasetDirectory'
DATASET_NAME
'novelctrlk6_8_coco_dataset'
DATASET_DIR
'novelctrlk6_8_coco_dataset'
Run a pre-trained detectron2 model#
First, we check a random selected image from our training dataset:
# select and display one random image from the training set
img_file = random.choice(glob.glob(f"{DATASET_DIR}/train/JPEGImages/*.*"))
im = cv2.imread(img_file)
if IN_COLAB:
cv2_imshow(im)
else:
plt.imshow(im)
Then, we create a Detectron2 config and a detectron2 DefaultPredictor to run inference on this image.
cfg = get_cfg()
if GPU:
pass
else:
cfg.MODEL.DEVICE='cpu'
# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.1 # set threshold for this model
# Find a model from Detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
predictor = DefaultPredictor(cfg)
outputs = predictor(im)
/home/jeremy/anaconda3/envs/annolid-env/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
max_size = (max_size + (stride - 1)) // stride * stride
/home/jeremy/anaconda3/envs/annolid-env/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
# look at the outputs. See https://detectron2.readthedocs.io/tutorials/models.html#model-output-format for specification
print(outputs["instances"].pred_classes)
print(outputs["instances"].pred_boxes)
tensor([74, 74, 74, 65, 74, 6, 74, 56], device='cuda:0')
Boxes(tensor([[ 791.1396, 360.3369, 835.5459, 402.3984],
[ 903.7306, 528.1077, 940.4337, 564.2038],
[ 503.3293, 541.2484, 552.5011, 588.6934],
[ 18.4449, 136.3344, 165.7642, 986.1284],
[ 595.2624, 371.5288, 643.9555, 414.2992],
[ 75.0788, 0.0000, 1280.0000, 990.3613],
[ 892.3966, 519.0786, 948.0319, 575.4614],
[ 9.2645, 57.8977, 166.2878, 973.9976]], device='cuda:0'))
outputs['instances'].pred_masks
tensor([[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
...,
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]]], device='cuda:0')
MetadataCatalog.get(cfg.DATASETS.TRAIN[0])
namespace(name='coco_2017_train',
json_file='datasets/coco/annotations/instances_train2017.json',
image_root='datasets/coco/train2017',
evaluator_type='coco',
thing_dataset_id_to_contiguous_id={1: 0,
2: 1,
3: 2,
4: 3,
5: 4,
6: 5,
7: 6,
8: 7,
9: 8,
10: 9,
11: 10,
13: 11,
14: 12,
15: 13,
16: 14,
17: 15,
18: 16,
19: 17,
20: 18,
21: 19,
22: 20,
23: 21,
24: 22,
25: 23,
27: 24,
28: 25,
31: 26,
32: 27,
33: 28,
34: 29,
35: 30,
36: 31,
37: 32,
38: 33,
39: 34,
40: 35,
41: 36,
42: 37,
43: 38,
44: 39,
46: 40,
47: 41,
48: 42,
49: 43,
50: 44,
51: 45,
52: 46,
53: 47,
54: 48,
55: 49,
56: 50,
57: 51,
58: 52,
59: 53,
60: 54,
61: 55,
62: 56,
63: 57,
64: 58,
65: 59,
67: 60,
70: 61,
72: 62,
73: 63,
74: 64,
75: 65,
76: 66,
77: 67,
78: 68,
79: 69,
80: 70,
81: 71,
82: 72,
84: 73,
85: 74,
86: 75,
87: 76,
88: 77,
89: 78,
90: 79},
thing_classes=['person',
'bicycle',
'car',
'motorcycle',
'airplane',
'bus',
'train',
'truck',
'boat',
'traffic light',
'fire hydrant',
'stop sign',
'parking meter',
'bench',
'bird',
'cat',
'dog',
'horse',
'sheep',
'cow',
'elephant',
'bear',
'zebra',
'giraffe',
'backpack',
'umbrella',
'handbag',
'tie',
'suitcase',
'frisbee',
'skis',
'snowboard',
'sports ball',
'kite',
'baseball bat',
'baseball glove',
'skateboard',
'surfboard',
'tennis racket',
'bottle',
'wine glass',
'cup',
'fork',
'knife',
'spoon',
'bowl',
'banana',
'apple',
'sandwich',
'orange',
'broccoli',
'carrot',
'hot dog',
'pizza',
'donut',
'cake',
'chair',
'couch',
'potted plant',
'bed',
'dining table',
'toilet',
'tv',
'laptop',
'mouse',
'remote',
'keyboard',
'cell phone',
'microwave',
'oven',
'toaster',
'sink',
'refrigerator',
'book',
'clock',
'vase',
'scissors',
'teddy bear',
'hair drier',
'toothbrush'],
thing_colors=[[220, 20, 60],
[119, 11, 32],
[0, 0, 142],
[0, 0, 230],
[106, 0, 228],
[0, 60, 100],
[0, 80, 100],
[0, 0, 70],
[0, 0, 192],
[250, 170, 30],
[100, 170, 30],
[220, 220, 0],
[175, 116, 175],
[250, 0, 30],
[165, 42, 42],
[255, 77, 255],
[0, 226, 252],
[182, 182, 255],
[0, 82, 0],
[120, 166, 157],
[110, 76, 0],
[174, 57, 255],
[199, 100, 0],
[72, 0, 118],
[255, 179, 240],
[0, 125, 92],
[209, 0, 151],
[188, 208, 182],
[0, 220, 176],
[255, 99, 164],
[92, 0, 73],
[133, 129, 255],
[78, 180, 255],
[0, 228, 0],
[174, 255, 243],
[45, 89, 255],
[134, 134, 103],
[145, 148, 174],
[255, 208, 186],
[197, 226, 255],
[171, 134, 1],
[109, 63, 54],
[207, 138, 255],
[151, 0, 95],
[9, 80, 61],
[84, 105, 51],
[74, 65, 105],
[166, 196, 102],
[208, 195, 210],
[255, 109, 65],
[0, 143, 149],
[179, 0, 194],
[209, 99, 106],
[5, 121, 0],
[227, 255, 205],
[147, 186, 208],
[153, 69, 1],
[3, 95, 161],
[163, 255, 0],
[119, 0, 170],
[0, 182, 199],
[0, 165, 120],
[183, 130, 88],
[95, 32, 0],
[130, 114, 135],
[110, 129, 133],
[166, 74, 118],
[219, 142, 185],
[79, 210, 114],
[178, 90, 62],
[65, 70, 15],
[127, 167, 115],
[59, 105, 106],
[142, 108, 45],
[196, 172, 0],
[95, 54, 80],
[128, 76, 255],
[201, 57, 1],
[246, 0, 122],
[191, 162, 208]])
# We can use `Visualizer` to draw the predictions on the image.
v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
if IN_COLAB:
cv2_imshow(out.get_image()[:, :, ::-1])
else:
plt.imshow(out.get_image()[:, :, ::-1])
As we can see, the network doesn’t detect what we want. That is expected as we have not fine-tuned the network with our custom dataset. We are going to do that in the next steps.
Train on a custom dataset#
In this section, we show how to train an existing detectron2 model on a custom dataset in COCO format.
Prepare the dataset#
Register the custom dataset to Detectron2, following the detectron2 custom dataset tutorial. Here, the dataset is in COCO format, therefore we register into Detectron2’s standard format. User should write such a function when using a dataset in custom format. See the tutorial for more details.
from detectron2.data.datasets import register_coco_instances
from detectron2.data import get_detection_dataset_dicts
from detectron2.data.datasets import builtin_meta
register_coco_instances(f"{DATASET_NAME}_train", {}, f"{DATASET_DIR}/train/annotations.json", f"{DATASET_DIR}/train/")
register_coco_instances(f"{DATASET_NAME}_valid", {}, f"{DATASET_DIR}/valid/annotations.json", f"{DATASET_DIR}/valid/")
dataset_dicts = get_detection_dataset_dicts([f"{DATASET_NAME}_train"])
[01/24 15:50:21 d2.data.datasets.coco]: Loaded 118 images in COCO format from novelctrlk6_8_coco_dataset/train/annotations.json
[01/24 15:50:21 d2.data.build]: Removed 0 images with no usable annotations. 118 images left.
[01/24 15:50:21 d2.data.build]: Distribution of instances among all 7 categories:
| category | #instances | category | #instances | category | #instances |
|:------------:|:-------------|:----------:|:-------------|:----------:|:-------------|
| _background_ | 0 | nose | 118 | left_ear | 118 |
| right_ear | 117 | tail_base | 119 | mouse | 118 |
| centroid | 1 | | | | |
| total | 591 | | | | |
_dataset_metadata = MetadataCatalog.get(f"{DATASET_NAME}_train")
_dataset_metadata.thing_colors = [cc['color'] for cc in builtin_meta.COCO_CATEGORIES]
_dataset_metadata
namespace(name='novelctrlk6_8_coco_dataset_train',
json_file='novelctrlk6_8_coco_dataset/train/annotations.json',
image_root='novelctrlk6_8_coco_dataset/train/',
evaluator_type='coco',
thing_classes=['_background_',
'nose',
'left_ear',
'right_ear',
'tail_base',
'mouse',
'centroid'],
thing_dataset_id_to_contiguous_id={0: 0,
1: 1,
2: 2,
3: 3,
4: 4,
5: 5,
6: 6},
thing_colors=[[220, 20, 60],
[119, 11, 32],
[0, 0, 142],
[0, 0, 230],
[106, 0, 228],
[0, 60, 100],
[0, 80, 100],
[0, 0, 70],
[0, 0, 192],
[250, 170, 30],
[100, 170, 30],
[220, 220, 0],
[175, 116, 175],
[250, 0, 30],
[165, 42, 42],
[255, 77, 255],
[0, 226, 252],
[182, 182, 255],
[0, 82, 0],
[120, 166, 157],
[110, 76, 0],
[174, 57, 255],
[199, 100, 0],
[72, 0, 118],
[255, 179, 240],
[0, 125, 92],
[209, 0, 151],
[188, 208, 182],
[0, 220, 176],
[255, 99, 164],
[92, 0, 73],
[133, 129, 255],
[78, 180, 255],
[0, 228, 0],
[174, 255, 243],
[45, 89, 255],
[134, 134, 103],
[145, 148, 174],
[255, 208, 186],
[197, 226, 255],
[171, 134, 1],
[109, 63, 54],
[207, 138, 255],
[151, 0, 95],
[9, 80, 61],
[84, 105, 51],
[74, 65, 105],
[166, 196, 102],
[208, 195, 210],
[255, 109, 65],
[0, 143, 149],
[179, 0, 194],
[209, 99, 106],
[5, 121, 0],
[227, 255, 205],
[147, 186, 208],
[153, 69, 1],
[3, 95, 161],
[163, 255, 0],
[119, 0, 170],
[0, 182, 199],
[0, 165, 120],
[183, 130, 88],
[95, 32, 0],
[130, 114, 135],
[110, 129, 133],
[166, 74, 118],
[219, 142, 185],
[79, 210, 114],
[178, 90, 62],
[65, 70, 15],
[127, 167, 115],
[59, 105, 106],
[142, 108, 45],
[196, 172, 0],
[95, 54, 80],
[128, 76, 255],
[201, 57, 1],
[246, 0, 122],
[191, 162, 208],
[255, 255, 128],
[147, 211, 203],
[150, 100, 100],
[168, 171, 172],
[146, 112, 198],
[210, 170, 100],
[92, 136, 89],
[218, 88, 184],
[241, 129, 0],
[217, 17, 255],
[124, 74, 181],
[70, 70, 70],
[255, 228, 255],
[154, 208, 0],
[193, 0, 92],
[76, 91, 113],
[255, 180, 195],
[106, 154, 176],
[230, 150, 140],
[60, 143, 255],
[128, 64, 128],
[92, 82, 55],
[254, 212, 124],
[73, 77, 174],
[255, 160, 98],
[255, 255, 255],
[104, 84, 109],
[169, 164, 131],
[225, 199, 255],
[137, 54, 74],
[135, 158, 223],
[7, 246, 231],
[107, 255, 200],
[58, 41, 149],
[183, 121, 142],
[255, 73, 97],
[107, 142, 35],
[190, 153, 153],
[146, 139, 141],
[70, 130, 180],
[134, 199, 156],
[209, 226, 140],
[96, 36, 108],
[96, 96, 96],
[64, 170, 64],
[152, 251, 152],
[208, 229, 228],
[206, 186, 171],
[152, 161, 64],
[116, 112, 0],
[0, 114, 143],
[102, 102, 156],
[250, 141, 255]])
NUM_CLASSES = len(_dataset_metadata.thing_classes)
print(f"{NUM_CLASSES} Number of classes in the dataset")
7 Number of classes in the dataset
To verify the data loading is correct, let’s visualize the annotations of a randomly selected sample in the training set:
for d in random.sample(dataset_dicts, 2):
if '\\' in d['file_name']:
d['file_name'] = d['file_name'].replace('\\','/')
img = cv2.imread(d["file_name"])
visualizer = Visualizer(img[:, :, ::-1], metadata=_dataset_metadata, scale=0.5)
out = visualizer.draw_dataset_dict(d)
if IN_COLAB:
cv2_imshow(out.get_image()[:, :, ::-1])
else:
plt.imshow(out.get_image()[:, :, ::-1])
Train!#
Now, let’s fine-tune the COCO-pretrained R50-FPN Mask R-CNN model with our custom dataset. It takes ~2 hours to train 3000 iterations on Colab’s K80 GPU, or ~1.5 hours on a P100 GPU.
if GPU:
!nvidia-smi
Mon Jan 24 15:50:22 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.86 Driver Version: 470.86 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:C1:00.0 Off | N/A |
| 38% 47C P2 57W / 250W | 2224MiB / 7979MiB | 7% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1405 G /usr/lib/xorg/Xorg 132MiB |
| 0 N/A N/A 64929 G /usr/lib/xorg/Xorg 261MiB |
| 0 N/A N/A 65055 G /usr/bin/gnome-shell 10MiB |
| 0 N/A N/A 65407 G ...AAAAAAAAA= --shared-files 8MiB |
| 0 N/A N/A 216386 C ...vs/annolid-env/bin/python 1795MiB |
+-----------------------------------------------------------------------------+
from detectron2.engine import DefaultTrainer
cfg = get_cfg()
if GPU:
pass
else:
cfg.MODEL.DEVICE='cpu'
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.DATASETS.TRAIN = (f"{DATASET_NAME}_train",)
cfg.DATASETS.TEST = ()
cfg.DATALOADER.NUM_WORKERS = 2 #@param
cfg.DATALOADER.SAMPLER_TRAIN = "RepeatFactorTrainingSampler"
cfg.DATALOADER.REPEAT_THRESHOLD = 0.3
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") # Let training initialize from model zoo
cfg.SOLVER.IMS_PER_BATCH = 4 #@param
cfg.SOLVER.BASE_LR = 0.0025 #@param # pick a good LR
cfg.SOLVER.MAX_ITER = 3000 #@param # 300 iterations seems good enough for 100 frames dataset; you will need to train longer for a practical dataset
cfg.SOLVER.CHECKPOINT_PERIOD = 1000 #@param
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 32 #@param # faster, and good enough for this toy dataset (default: 512)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = NUM_CLASSES # (see https://detectron2.readthedocs.io/tutorials/datasets.html#update-the-config-for-new-datasets)
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=False)
[01/24 15:50:22 d2.engine.defaults]: Model:
GeneralizedRCNN(
(backbone): FPN(
(fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(top_block): LastLevelMaxPool()
(bottom_up): ResNet(
(stem): BasicStem(
(conv1): Conv2d(
3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
)
(res2): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv1): Conv2d(
64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
)
)
(res3): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv1): Conv2d(
256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
(3): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
)
(res4): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
(conv1): Conv2d(
512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(3): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(4): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(5): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
)
(res5): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
(conv1): Conv2d(
1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
)
)
)
)
(proposal_generator): RPN(
(rpn_head): StandardRPNHead(
(conv): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
(anchor_generator): DefaultAnchorGenerator(
(cell_anchors): BufferList()
)
)
(roi_heads): StandardROIHeads(
(box_pooler): ROIPooler(
(level_poolers): ModuleList(
(0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)
(1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
(2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
(3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
)
)
(box_head): FastRCNNConvFCHead(
(flatten): Flatten(start_dim=1, end_dim=-1)
(fc1): Linear(in_features=12544, out_features=1024, bias=True)
(fc_relu1): ReLU()
(fc2): Linear(in_features=1024, out_features=1024, bias=True)
(fc_relu2): ReLU()
)
(box_predictor): FastRCNNOutputLayers(
(cls_score): Linear(in_features=1024, out_features=8, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=28, bias=True)
)
(mask_pooler): ROIPooler(
(level_poolers): ModuleList(
(0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)
(1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)
(2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
(3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
)
)
(mask_head): MaskRCNNConvUpsampleHead(
(mask_fcn1): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(mask_fcn2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(mask_fcn3): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(mask_fcn4): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))
(deconv_relu): ReLU()
(predictor): Conv2d(256, 7, kernel_size=(1, 1), stride=(1, 1))
)
)
)
[01/24 15:50:22 d2.data.datasets.coco]: Loaded 118 images in COCO format from novelctrlk6_8_coco_dataset/train/annotations.json
[01/24 15:50:22 d2.data.build]: Removed 0 images with no usable annotations. 118 images left.
[01/24 15:50:22 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[01/24 15:50:22 d2.data.build]: Using training sampler RepeatFactorTrainingSampler
[01/24 15:50:22 d2.data.common]: Serializing 118 elements to byte tensors and concatenating them all ...
[01/24 15:50:22 d2.data.common]: Serialized dataset takes 0.31 MiB
WARNING [01/24 15:50:22 d2.solver.build]: SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. These values will be ignored.
Skip loading parameter 'roi_heads.box_predictor.cls_score.weight' to the model due to incompatible shapes: (81, 1024) in the checkpoint but (8, 1024) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.cls_score.bias' to the model due to incompatible shapes: (81,) in the checkpoint but (8,) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.bbox_pred.weight' to the model due to incompatible shapes: (320, 1024) in the checkpoint but (28, 1024) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.bbox_pred.bias' to the model due to incompatible shapes: (320,) in the checkpoint but (28,) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.mask_head.predictor.weight' to the model due to incompatible shapes: (80, 256, 1, 1) in the checkpoint but (7, 256, 1, 1) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.mask_head.predictor.bias' to the model due to incompatible shapes: (80,) in the checkpoint but (7,) in the model! You might want to double check if this is expected.
Some model parameters or buffers are not found in the checkpoint:
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.box_predictor.cls_score.{bias, weight}
roi_heads.mask_head.predictor.{bias, weight}
# Look at training curves in tensorboard:
%load_ext tensorboard
%tensorboard --logdir output
Reusing TensorBoard on port 6007 (pid 153647), started 3 days, 3:53:51 ago. (Use '!kill 153647' to kill it.)
trainer.train()
[01/24 15:50:23 d2.engine.train_loop]: Starting training from iteration 0
/home/jeremy/anaconda3/envs/annolid-env/lib/python3.7/site-packages/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
max_size = (max_size + (stride - 1)) // stride * stride
[01/24 15:50:30 d2.utils.events]: eta: 0:19:15 iter: 19 total_loss: 5.866 loss_cls: 2.116 loss_box_reg: 0.7876 loss_mask: 0.6877 loss_rpn_cls: 1.838 loss_rpn_loc: 0.3639 time: 0.3819 data_time: 0.0178 lr: 4.9952e-05 max_mem: 4110M
[01/24 15:50:38 d2.utils.events]: eta: 0:19:57 iter: 39 total_loss: 3.228 loss_cls: 1.397 loss_box_reg: 0.8517 loss_mask: 0.6707 loss_rpn_cls: 0.06869 loss_rpn_loc: 0.2262 time: 0.3873 data_time: 0.0076 lr: 9.9902e-05 max_mem: 4110M
[01/24 15:50:46 d2.utils.events]: eta: 0:19:50 iter: 59 total_loss: 2.656 loss_cls: 0.9456 loss_box_reg: 0.8464 loss_mask: 0.6467 loss_rpn_cls: 0.04745 loss_rpn_loc: 0.1731 time: 0.3905 data_time: 0.0076 lr: 0.00014985 max_mem: 4110M
[01/24 15:50:54 d2.utils.events]: eta: 0:19:43 iter: 79 total_loss: 2.518 loss_cls: 0.8525 loss_box_reg: 0.8349 loss_mask: 0.6366 loss_rpn_cls: 0.03442 loss_rpn_loc: 0.1621 time: 0.3912 data_time: 0.0071 lr: 0.0001998 max_mem: 4110M
[01/24 15:51:02 d2.utils.events]: eta: 0:19:36 iter: 99 total_loss: 2.353 loss_cls: 0.7515 loss_box_reg: 0.8358 loss_mask: 0.5841 loss_rpn_cls: 0.02699 loss_rpn_loc: 0.1345 time: 0.3910 data_time: 0.0071 lr: 0.00024975 max_mem: 4110M
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[01/24 16:01:21 d2.utils.events]: eta: 0:09:02 iter: 1679 total_loss: 0.8811 loss_cls: 0.08528 loss_box_reg: 0.3442 loss_mask: 0.352 loss_rpn_cls: 0.005305 loss_rpn_loc: 0.1551 time: 0.3908 data_time: 0.0074 lr: 0.0025 max_mem: 4110M
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[01/24 16:01:37 d2.utils.events]: eta: 0:08:46 iter: 1719 total_loss: 0.8401 loss_cls: 0.0855 loss_box_reg: 0.3491 loss_mask: 0.3362 loss_rpn_cls: 0.00481 loss_rpn_loc: 0.07337 time: 0.3909 data_time: 0.0074 lr: 0.0025 max_mem: 4110M
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[01/24 16:01:52 d2.utils.events]: eta: 0:08:29 iter: 1759 total_loss: 0.8205 loss_cls: 0.09494 loss_box_reg: 0.3206 loss_mask: 0.3274 loss_rpn_cls: 0.005982 loss_rpn_loc: 0.08127 time: 0.3909 data_time: 0.0074 lr: 0.0025 max_mem: 4110M
[01/24 16:02:00 d2.utils.events]: eta: 0:08:21 iter: 1779 total_loss: 0.8455 loss_cls: 0.08995 loss_box_reg: 0.327 loss_mask: 0.335 loss_rpn_cls: 0.004902 loss_rpn_loc: 0.082 time: 0.3909 data_time: 0.0080 lr: 0.0025 max_mem: 4110M
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[01/24 16:02:16 d2.utils.events]: eta: 0:08:05 iter: 1819 total_loss: 0.8513 loss_cls: 0.08107 loss_box_reg: 0.3235 loss_mask: 0.3433 loss_rpn_cls: 0.004668 loss_rpn_loc: 0.07178 time: 0.3910 data_time: 0.0071 lr: 0.0025 max_mem: 4110M
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[01/24 16:03:03 d2.utils.events]: eta: 0:07:15 iter: 1939 total_loss: 0.8285 loss_cls: 0.08617 loss_box_reg: 0.3315 loss_mask: 0.3323 loss_rpn_cls: 0.005909 loss_rpn_loc: 0.0664 time: 0.3908 data_time: 0.0075 lr: 0.0025 max_mem: 4110M
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[01/24 16:03:18 d2.utils.events]: eta: 0:06:59 iter: 1979 total_loss: 0.8141 loss_cls: 0.07377 loss_box_reg: 0.3162 loss_mask: 0.3031 loss_rpn_cls: 0.007409 loss_rpn_loc: 0.08263 time: 0.3907 data_time: 0.0075 lr: 0.0025 max_mem: 4110M
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[01/24 16:03:34 d2.utils.events]: eta: 0:06:42 iter: 2019 total_loss: 0.7544 loss_cls: 0.06828 loss_box_reg: 0.2922 loss_mask: 0.3069 loss_rpn_cls: 0.007459 loss_rpn_loc: 0.0721 time: 0.3907 data_time: 0.0076 lr: 0.0025 max_mem: 4110M
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[01/24 16:03:50 d2.utils.events]: eta: 0:06:26 iter: 2059 total_loss: 0.7799 loss_cls: 0.06554 loss_box_reg: 0.3012 loss_mask: 0.3215 loss_rpn_cls: 0.002805 loss_rpn_loc: 0.07614 time: 0.3907 data_time: 0.0072 lr: 0.0025 max_mem: 4110M
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[01/24 16:04:05 d2.utils.events]: eta: 0:06:10 iter: 2099 total_loss: 0.8162 loss_cls: 0.08762 loss_box_reg: 0.3046 loss_mask: 0.332 loss_rpn_cls: 0.005186 loss_rpn_loc: 0.09014 time: 0.3907 data_time: 0.0076 lr: 0.0025 max_mem: 4110M
[01/24 16:04:13 d2.utils.events]: eta: 0:06:01 iter: 2119 total_loss: 0.8482 loss_cls: 0.09176 loss_box_reg: 0.3172 loss_mask: 0.3343 loss_rpn_cls: 0.004934 loss_rpn_loc: 0.06162 time: 0.3907 data_time: 0.0076 lr: 0.0025 max_mem: 4110M
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[01/24 16:04:52 d2.utils.events]: eta: 0:05:20 iter: 2219 total_loss: 0.8075 loss_cls: 0.07753 loss_box_reg: 0.2903 loss_mask: 0.3152 loss_rpn_cls: 0.004838 loss_rpn_loc: 0.09314 time: 0.3906 data_time: 0.0072 lr: 0.0025 max_mem: 4110M
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[01/24 16:05:08 d2.utils.events]: eta: 0:05:04 iter: 2259 total_loss: 0.8045 loss_cls: 0.07612 loss_box_reg: 0.2909 loss_mask: 0.3226 loss_rpn_cls: 0.004271 loss_rpn_loc: 0.08464 time: 0.3905 data_time: 0.0070 lr: 0.0025 max_mem: 4110M
[01/24 16:05:15 d2.utils.events]: eta: 0:04:56 iter: 2279 total_loss: 0.7421 loss_cls: 0.06963 loss_box_reg: 0.2848 loss_mask: 0.2973 loss_rpn_cls: 0.005594 loss_rpn_loc: 0.074 time: 0.3905 data_time: 0.0074 lr: 0.0025 max_mem: 4110M
[01/24 16:05:23 d2.utils.events]: eta: 0:04:47 iter: 2299 total_loss: 0.7325 loss_cls: 0.06316 loss_box_reg: 0.2725 loss_mask: 0.2934 loss_rpn_cls: 0.004791 loss_rpn_loc: 0.06442 time: 0.3905 data_time: 0.0075 lr: 0.0025 max_mem: 4110M
[01/24 16:05:31 d2.utils.events]: eta: 0:04:39 iter: 2319 total_loss: 0.7725 loss_cls: 0.06565 loss_box_reg: 0.2898 loss_mask: 0.3171 loss_rpn_cls: 0.006268 loss_rpn_loc: 0.06388 time: 0.3905 data_time: 0.0079 lr: 0.0025 max_mem: 4110M
[01/24 16:05:39 d2.utils.events]: eta: 0:04:31 iter: 2339 total_loss: 0.7485 loss_cls: 0.06332 loss_box_reg: 0.2828 loss_mask: 0.3039 loss_rpn_cls: 0.00415 loss_rpn_loc: 0.05863 time: 0.3906 data_time: 0.0078 lr: 0.0025 max_mem: 4110M
[01/24 16:05:47 d2.utils.events]: eta: 0:04:23 iter: 2359 total_loss: 0.7846 loss_cls: 0.07007 loss_box_reg: 0.2858 loss_mask: 0.3245 loss_rpn_cls: 0.006562 loss_rpn_loc: 0.09424 time: 0.3906 data_time: 0.0071 lr: 0.0025 max_mem: 4110M
[01/24 16:05:55 d2.utils.events]: eta: 0:04:14 iter: 2379 total_loss: 0.7779 loss_cls: 0.0613 loss_box_reg: 0.2871 loss_mask: 0.3272 loss_rpn_cls: 0.006423 loss_rpn_loc: 0.09873 time: 0.3906 data_time: 0.0074 lr: 0.0025 max_mem: 4110M
[01/24 16:06:03 d2.utils.events]: eta: 0:04:06 iter: 2399 total_loss: 0.7636 loss_cls: 0.07906 loss_box_reg: 0.2831 loss_mask: 0.2981 loss_rpn_cls: 0.006569 loss_rpn_loc: 0.06379 time: 0.3907 data_time: 0.0079 lr: 0.0025 max_mem: 4110M
[01/24 16:06:11 d2.utils.events]: eta: 0:03:58 iter: 2419 total_loss: 0.7757 loss_cls: 0.06201 loss_box_reg: 0.3014 loss_mask: 0.3154 loss_rpn_cls: 0.005191 loss_rpn_loc: 0.09087 time: 0.3907 data_time: 0.0076 lr: 0.0025 max_mem: 4110M
[01/24 16:06:18 d2.utils.events]: eta: 0:03:50 iter: 2439 total_loss: 0.7629 loss_cls: 0.0604 loss_box_reg: 0.2975 loss_mask: 0.3187 loss_rpn_cls: 0.005063 loss_rpn_loc: 0.07084 time: 0.3907 data_time: 0.0074 lr: 0.0025 max_mem: 4110M
[01/24 16:06:26 d2.utils.events]: eta: 0:03:42 iter: 2459 total_loss: 0.7612 loss_cls: 0.06892 loss_box_reg: 0.2804 loss_mask: 0.3093 loss_rpn_cls: 0.005773 loss_rpn_loc: 0.07157 time: 0.3907 data_time: 0.0081 lr: 0.0025 max_mem: 4110M
[01/24 16:06:34 d2.utils.events]: eta: 0:03:33 iter: 2479 total_loss: 0.7679 loss_cls: 0.06639 loss_box_reg: 0.2874 loss_mask: 0.3192 loss_rpn_cls: 0.006395 loss_rpn_loc: 0.07307 time: 0.3907 data_time: 0.0075 lr: 0.0025 max_mem: 4110M
[01/24 16:06:42 d2.utils.events]: eta: 0:03:25 iter: 2499 total_loss: 0.8202 loss_cls: 0.0897 loss_box_reg: 0.2987 loss_mask: 0.3328 loss_rpn_cls: 0.004282 loss_rpn_loc: 0.0591 time: 0.3907 data_time: 0.0079 lr: 0.0025 max_mem: 4110M
[01/24 16:06:50 d2.utils.events]: eta: 0:03:17 iter: 2519 total_loss: 0.722 loss_cls: 0.06425 loss_box_reg: 0.2707 loss_mask: 0.3244 loss_rpn_cls: 0.005136 loss_rpn_loc: 0.05746 time: 0.3908 data_time: 0.0077 lr: 0.0025 max_mem: 4110M
[01/24 16:06:58 d2.utils.events]: eta: 0:03:09 iter: 2539 total_loss: 0.7225 loss_cls: 0.07053 loss_box_reg: 0.2691 loss_mask: 0.3041 loss_rpn_cls: 0.005394 loss_rpn_loc: 0.06182 time: 0.3907 data_time: 0.0069 lr: 0.0025 max_mem: 4110M
[01/24 16:07:06 d2.utils.events]: eta: 0:03:01 iter: 2559 total_loss: 0.7422 loss_cls: 0.05897 loss_box_reg: 0.2694 loss_mask: 0.3197 loss_rpn_cls: 0.006067 loss_rpn_loc: 0.06645 time: 0.3908 data_time: 0.0074 lr: 0.0025 max_mem: 4110M
[01/24 16:07:13 d2.utils.events]: eta: 0:02:52 iter: 2579 total_loss: 0.7224 loss_cls: 0.06885 loss_box_reg: 0.2601 loss_mask: 0.3084 loss_rpn_cls: 0.006304 loss_rpn_loc: 0.05965 time: 0.3907 data_time: 0.0075 lr: 0.0025 max_mem: 4110M
[01/24 16:07:21 d2.utils.events]: eta: 0:02:44 iter: 2599 total_loss: 0.738 loss_cls: 0.06593 loss_box_reg: 0.2724 loss_mask: 0.3113 loss_rpn_cls: 0.005491 loss_rpn_loc: 0.06485 time: 0.3907 data_time: 0.0078 lr: 0.0025 max_mem: 4110M
[01/24 16:07:29 d2.utils.events]: eta: 0:02:36 iter: 2619 total_loss: 0.7355 loss_cls: 0.06217 loss_box_reg: 0.2646 loss_mask: 0.3103 loss_rpn_cls: 0.005245 loss_rpn_loc: 0.0795 time: 0.3907 data_time: 0.0077 lr: 0.0025 max_mem: 4110M
[01/24 16:07:37 d2.utils.events]: eta: 0:02:28 iter: 2639 total_loss: 0.7292 loss_cls: 0.06508 loss_box_reg: 0.2748 loss_mask: 0.3125 loss_rpn_cls: 0.004652 loss_rpn_loc: 0.06013 time: 0.3908 data_time: 0.0069 lr: 0.0025 max_mem: 4110M
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[01/24 16:08:01 d2.utils.events]: eta: 0:02:03 iter: 2699 total_loss: 0.7362 loss_cls: 0.05955 loss_box_reg: 0.2715 loss_mask: 0.3122 loss_rpn_cls: 0.004264 loss_rpn_loc: 0.07365 time: 0.3908 data_time: 0.0075 lr: 0.0025 max_mem: 4110M
[01/24 16:08:08 d2.utils.events]: eta: 0:01:55 iter: 2719 total_loss: 0.7716 loss_cls: 0.07078 loss_box_reg: 0.2659 loss_mask: 0.3117 loss_rpn_cls: 0.004439 loss_rpn_loc: 0.05428 time: 0.3907 data_time: 0.0075 lr: 0.0025 max_mem: 4110M
[01/24 16:08:16 d2.utils.events]: eta: 0:01:46 iter: 2739 total_loss: 0.7406 loss_cls: 0.06837 loss_box_reg: 0.2739 loss_mask: 0.302 loss_rpn_cls: 0.004251 loss_rpn_loc: 0.05812 time: 0.3908 data_time: 0.0072 lr: 0.0025 max_mem: 4110M
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[01/24 16:08:39 d2.utils.events]: eta: 0:01:22 iter: 2799 total_loss: 0.7228 loss_cls: 0.06149 loss_box_reg: 0.2572 loss_mask: 0.3058 loss_rpn_cls: 0.004003 loss_rpn_loc: 0.08938 time: 0.3907 data_time: 0.0072 lr: 0.0025 max_mem: 4110M
[01/24 16:08:47 d2.utils.events]: eta: 0:01:14 iter: 2819 total_loss: 0.6929 loss_cls: 0.06439 loss_box_reg: 0.2431 loss_mask: 0.3143 loss_rpn_cls: 0.004293 loss_rpn_loc: 0.08209 time: 0.3907 data_time: 0.0080 lr: 0.0025 max_mem: 4110M
[01/24 16:08:55 d2.utils.events]: eta: 0:01:05 iter: 2839 total_loss: 0.7354 loss_cls: 0.06543 loss_box_reg: 0.2581 loss_mask: 0.2997 loss_rpn_cls: 0.003436 loss_rpn_loc: 0.07749 time: 0.3907 data_time: 0.0075 lr: 0.0025 max_mem: 4110M
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[01/24 16:09:11 d2.utils.events]: eta: 0:00:49 iter: 2879 total_loss: 0.7002 loss_cls: 0.06534 loss_box_reg: 0.2586 loss_mask: 0.3017 loss_rpn_cls: 0.003973 loss_rpn_loc: 0.06161 time: 0.3907 data_time: 0.0074 lr: 0.0025 max_mem: 4110M
[01/24 16:09:18 d2.utils.events]: eta: 0:00:41 iter: 2899 total_loss: 0.7334 loss_cls: 0.0603 loss_box_reg: 0.266 loss_mask: 0.308 loss_rpn_cls: 0.005058 loss_rpn_loc: 0.0801 time: 0.3906 data_time: 0.0073 lr: 0.0025 max_mem: 4110M
[01/24 16:09:26 d2.utils.events]: eta: 0:00:32 iter: 2919 total_loss: 0.7113 loss_cls: 0.06522 loss_box_reg: 0.2543 loss_mask: 0.31 loss_rpn_cls: 0.005714 loss_rpn_loc: 0.06439 time: 0.3906 data_time: 0.0083 lr: 0.0025 max_mem: 4110M
[01/24 16:09:34 d2.utils.events]: eta: 0:00:24 iter: 2939 total_loss: 0.6856 loss_cls: 0.05759 loss_box_reg: 0.247 loss_mask: 0.2956 loss_rpn_cls: 0.004916 loss_rpn_loc: 0.09112 time: 0.3906 data_time: 0.0079 lr: 0.0025 max_mem: 4110M
[01/24 16:09:42 d2.utils.events]: eta: 0:00:16 iter: 2959 total_loss: 0.6846 loss_cls: 0.06041 loss_box_reg: 0.2483 loss_mask: 0.3047 loss_rpn_cls: 0.004346 loss_rpn_loc: 0.04959 time: 0.3906 data_time: 0.0079 lr: 0.0025 max_mem: 4110M
[01/24 16:09:50 d2.utils.events]: eta: 0:00:08 iter: 2979 total_loss: 0.7064 loss_cls: 0.04828 loss_box_reg: 0.2511 loss_mask: 0.3122 loss_rpn_cls: 0.005772 loss_rpn_loc: 0.05256 time: 0.3906 data_time: 0.0079 lr: 0.0025 max_mem: 4110M
[01/24 16:09:58 d2.utils.events]: eta: 0:00:00 iter: 2999 total_loss: 0.6975 loss_cls: 0.05745 loss_box_reg: 0.2554 loss_mask: 0.3072 loss_rpn_cls: 0.007148 loss_rpn_loc: 0.04798 time: 0.3906 data_time: 0.0080 lr: 0.0025 max_mem: 4110M
[01/24 16:09:58 d2.engine.hooks]: Overall training speed: 2998 iterations in 0:19:31 (0.3906 s / it)
[01/24 16:09:58 d2.engine.hooks]: Total training time: 0:19:34 (0:00:03 on hooks)
Inference & evaluation using the trained model#
Now, let’s run inference with the trained model on the validation dataset. First, let’s create a predictor using the model we just trained:
# Inference should use the config with parameters that are used in training
# cfg now already contains everything we've set previously.
# We simply update the weights with the newly trained ones to perform inference:
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth") # path to the model we just trained
# set a custom testing threshold
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.15 #@param {type: "slider", min:0.0, max:1.0, step: 0.01}
predictor = DefaultPredictor(cfg)
Then, we randomly select several samples to visualize the prediction results.
from detectron2.utils.visualizer import ColorMode
dataset_dicts = get_detection_dataset_dicts([f"{DATASET_NAME}_valid"])
for d in random.sample(dataset_dicts, 4):
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=_dataset_metadata,
scale=0.5,
instance_mode=ColorMode.SEGMENTATION # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
if IN_COLAB:
cv2_imshow(out.get_image()[:, :, ::-1])
else:
plt.imshow(out.get_image()[:, :, ::-1])
plt.show()
[01/24 16:09:59 d2.data.datasets.coco]: Loaded 60 images in COCO format from novelctrlk6_8_coco_dataset/valid/annotations.json
[01/24 16:09:59 d2.data.build]: Removed 0 images with no usable annotations. 60 images left.
[01/24 16:09:59 d2.data.build]: Distribution of instances among all 7 categories:
| category | #instances | category | #instances | category | #instances |
|:------------:|:-------------|:----------:|:-------------|:----------:|:-------------|
| _background_ | 0 | nose | 60 | left_ear | 60 |
| right_ear | 60 | tail_base | 60 | mouse | 60 |
| centroid | 0 | | | | |
| total | 300 | | | | |
A more robust way to evaluate the model is to use a metric called Average Precision (AP) already implemented in the detectron2 package. If you want more precision on what the AP is, you can take a look here and here.
#TODO: expand on how to interpret AP#
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.data import build_detection_test_loader
if IN_COLAB:
evaluator = COCOEvaluator(f"{DATASET_NAME}_valid", cfg, False, output_dir="/content/eval_output/")
else:
evaluator = COCOEvaluator(f"{DATASET_NAME}_valid", cfg, False, output_dir="eval_output/")
val_loader = build_detection_test_loader(cfg, f"{DATASET_NAME}_valid")
print(inference_on_dataset(predictor.model, val_loader, evaluator))
# another equivalent way to evaluate the model is to use `trainer.test`
WARNING [01/24 16:10:00 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.
[01/24 16:10:00 d2.data.datasets.coco]: Loaded 60 images in COCO format from novelctrlk6_8_coco_dataset/valid/annotations.json
[01/24 16:10:00 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[01/24 16:10:00 d2.data.common]: Serializing 60 elements to byte tensors and concatenating them all ...
[01/24 16:10:00 d2.data.common]: Serialized dataset takes 0.15 MiB
[01/24 16:10:00 d2.evaluation.evaluator]: Start inference on 60 batches
[01/24 16:10:01 d2.evaluation.evaluator]: Inference done 11/60. Dataloading: 0.0009 s/iter. Inference: 0.0536 s/iter. Eval: 0.0208 s/iter. Total: 0.0754 s/iter. ETA=0:00:03
[01/24 16:10:05 d2.evaluation.evaluator]: Total inference time: 0:00:03.942296 (0.071678 s / iter per device, on 1 devices)
[01/24 16:10:05 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:02 (0.053275 s / iter per device, on 1 devices)
[01/24 16:10:05 d2.evaluation.coco_evaluation]: Preparing results for COCO format ...
[01/24 16:10:05 d2.evaluation.coco_evaluation]: Saving results to eval_output/coco_instances_results.json
[01/24 16:10:05 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[01/24 16:10:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *bbox*
[01/24 16:10:05 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.01 seconds.
[01/24 16:10:05 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
[01/24 16:10:05 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.833
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.358
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.778
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.500
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.531
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.531
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.469
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.778
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[01/24 16:10:05 d2.evaluation.coco_evaluation]: Evaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:------:|:-----:|
| 42.753 | 83.293 | 35.842 | 35.066 | 77.809 | nan |
[01/24 16:10:05 d2.evaluation.coco_evaluation]: Some metrics cannot be computed and is shown as NaN.
[01/24 16:10:05 d2.evaluation.coco_evaluation]: Per-category bbox AP:
| category | AP | category | AP | category | AP |
|:-------------|:-------|:-----------|:-------|:-----------|:-------|
| _background_ | nan | nose | 41.524 | left_ear | 29.425 |
| right_ear | 27.213 | tail_base | 42.101 | mouse | 73.502 |
| centroid | nan | | | | |
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[01/24 16:10:05 d2.evaluation.fast_eval_api]: Evaluate annotation type *segm*
[01/24 16:10:05 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds.
[01/24 16:10:05 d2.evaluation.fast_eval_api]: Accumulating evaluation results...
[01/24 16:10:05 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.03 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.138
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.209
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.191
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.684
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.148
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.148
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.148
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.722
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[01/24 16:10:05 d2.evaluation.coco_evaluation]: Evaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:-----:|:------:|:-----:|
| 13.767 | 20.873 | 19.094 | 0.109 | 68.399 | nan |
[01/24 16:10:05 d2.evaluation.coco_evaluation]: Some metrics cannot be computed and is shown as NaN.
[01/24 16:10:05 d2.evaluation.coco_evaluation]: Per-category segm AP:
| category | AP | category | AP | category | AP |
|:-------------|:------|:-----------|:------|:-----------|:-------|
| _background_ | nan | nose | 0.008 | left_ear | 0.022 |
| right_ear | 0.000 | tail_base | 0.406 | mouse | 68.399 |
| centroid | nan | | | | |
OrderedDict([('bbox', {'AP': 42.753210587802776, 'AP50': 83.29297961427778, 'AP75': 35.842403348894564, 'APs': 35.065901020146, 'APm': 77.80886784330606, 'APl': nan, 'AP-_background_': nan, 'AP-nose': 41.524377143563854, 'AP-left_ear': 29.425107373005453, 'AP-right_ear': 27.213367406152706, 'AP-tail_base': 42.100752157862, 'AP-mouse': 73.50244885842984, 'AP-centroid': nan}), ('segm', {'AP': 13.767241314834507, 'AP50': 20.873474730404745, 'AP75': 19.09422779012595, 'APs': 0.10918434130059328, 'APm': 68.39946920897016, 'APl': nan, 'AP-_background_': nan, 'AP-nose': 0.00825082508250825, 'AP-left_ear': 0.02241154347992939, 'AP-right_ear': 0.0, 'AP-tail_base': 0.40607499663993546, 'AP-mouse': 68.39946920897016, 'AP-centroid': nan})])
Let’s test our newly trained model on a new video#
!gdown --id 1aMCeFWng0JkRbw9LOytXXrv4I2qCiH0h
Downloading...
From: https://drive.google.com/uc?id=1aMCeFWng0JkRbw9LOytXXrv4I2qCiH0h
To: /home/jeremy/Documents/annolid/book/tutorials/novelctrl.mkv
100%|█████████████████████████████████████████| 122M/122M [00:00<00:00, 194MB/s]
You can also download your own videos#
#!wget https://hosting-website.com/your-video.mp4
if IN_COLAB:
VIDEO_INPUT="/content/novelctrl.mkv"
OUTPUT_DIR = "/content/eval_output"
else:
VIDEO_INPUT="novelctrl.mkv"
OUTPUT_DIR = "eval_output"
If you use your own video / dataset you need to update the VIDEO_INPUT name#
# VIDEO_INPUT="YOUR_VIDEO_NAME"
import cv2
video = cv2.VideoCapture(VIDEO_INPUT)
width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
frames_per_second = video.get(cv2.CAP_PROP_FPS)
num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
basename = os.path.basename(VIDEO_INPUT)
import os
os.makedirs(OUTPUT_DIR,exist_ok=True)
def _frame_from_video(video):
attempt = 0
for i in range(num_frames):
success, frame = video.read()
if success:
yield frame
else:
attempt += 1
if attempt >= 2000:
break
else:
video.set(cv2.CAP_PROP_POS_FRAMES, i+1)
print('Cannot read this frame:', i)
continue
import pandas as pd
import pycocotools.mask as mask_util
class_names = _dataset_metadata.thing_classes
print(class_names)
['_background_', 'nose', 'left_ear', 'right_ear', 'tail_base', 'mouse', 'centroid']
frame_number = 0
tracking_results = []
VIS = True
for frame in _frame_from_video(video):
im = frame
outputs = predictor(im)
out_dict = {}
instances = outputs["instances"].to("cpu")
num_instance = len(instances)
if num_instance == 0:
out_dict['frame_number'] = frame_number
out_dict['x1'] = None
out_dict['y1'] = None
out_dict['x2'] = None
out_dict['y2'] = None
out_dict['instance_name'] = None
out_dict['class_score'] = None
out_dict['segmentation'] = None
tracking_results.append(out_dict)
out_dict = {}
else:
boxes = instances.pred_boxes.tensor.numpy()
boxes = boxes.tolist()
scores = instances.scores.tolist()
classes = instances.pred_classes.tolist()
has_mask = instances.has("pred_masks")
if has_mask:
rles =[
mask_util.encode(np.array(mask[:,:,None], order="F", dtype="uint8"))[0]
for mask in instances.pred_masks
]
for rle in rles:
rle["counts"] = rle["counts"].decode("utf-8")
assert len(rles) == len(boxes)
for k in range(num_instance):
box = boxes[k]
out_dict['frame_number'] = frame_number
out_dict['x1'] = box[0]
out_dict['y1'] = box[1]
out_dict['x2'] = box[2]
out_dict['y2'] = box[3]
out_dict['instance_name'] = class_names[classes[k]]
out_dict['class_score'] = scores[k]
out_dict['segmentation'] = rles[k]
if frame_number % 1000 == 0:
print(f"Frame number {frame_number}: {out_dict}")
tracking_results.append(out_dict)
out_dict = {}
# format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format
if VIS:
v = Visualizer(im[:, :, ::-1],
metadata=_dataset_metadata,
scale=0.5,
instance_mode=ColorMode.IMAGE_BW # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
out_image = out.get_image()[:, :, ::-1]
if frame_number % 1000 == 0:
if IN_COLAB:
cv2_imshow(out_image)
else:
plt.imshow(out_image)
plt.show()
#Trun off the visulization to save time after the first frame
VIS = False
frame_number += 1
print(f"Processing frame number {frame_number}")
video.release()
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Frame number 2000: {'frame_number': 2000, 'x1': 485.2485046386719, 'y1': 288.8238525390625, 'x2': 590.8497314453125, 'y2': 376.17730712890625, 'instance_name': 'mouse', 'class_score': 0.46464571356773376, 'segmentation': {'size': [1024, 1280], 'counts': 'W[U?6fo08L1O1000000000O100O1O1O001O1O00001O00000000O1000000O100O10000O100O001O010N10010N1O1O100O1N20000O1O10000O001000O001001N0010O01L4N2O1O1M3O1O2O0N20010O10O01O1O00100O101O00000000001O00000000001O0000000000000001O4Jlf`e0'}}
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All the tracking results will be saved to this Pandas dataframe.#
df = pd.DataFrame(tracking_results).dropna()
df.head()
| frame_number | x1 | y1 | x2 | y2 | instance_name | class_score | segmentation | |
|---|---|---|---|---|---|---|---|---|
| 411 | 411 | 245.362183 | 758.588318 | 569.893555 | 1023.786316 | mouse | 0.977099 | {'size': [1024, 1280], 'counts': 'mki7k0Ro09G6... |
| 412 | 411 | 224.934937 | 760.690002 | 425.875854 | 961.253723 | mouse | 0.150213 | {'size': [1024, 1280], 'counts': '[l`71jo09G8J... |
| 413 | 412 | 280.250763 | 667.388245 | 645.244751 | 1018.583252 | mouse | 0.925735 | {'size': [1024, 1280], 'counts': 'ToU95bo0:L5K... |
| 414 | 412 | 476.011932 | 734.419922 | 498.180939 | 762.044128 | tail_base | 0.387823 | {'size': [1024, 1280], 'counts': 'UgQ?8go04M2M... |
| 415 | 412 | 471.099609 | 733.858887 | 500.164062 | 760.749329 | nose | 0.250220 | {'size': [1024, 1280], 'counts': 'Vgl>6ho04M4M... |
Calculate the bbox center point x, y locations#
cx = (df.x1 + df.x2)/2
cy = (df.y1 + df.y2)/2
df['cx'] = cx
df['cy'] = cy
df.head()
| frame_number | x1 | y1 | x2 | y2 | instance_name | class_score | segmentation | cx | cy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 411 | 411 | 245.362183 | 758.588318 | 569.893555 | 1023.786316 | mouse | 0.977099 | {'size': [1024, 1280], 'counts': 'mki7k0Ro09G6... | 407.627869 | 891.187317 |
| 412 | 411 | 224.934937 | 760.690002 | 425.875854 | 961.253723 | mouse | 0.150213 | {'size': [1024, 1280], 'counts': '[l`71jo09G8J... | 325.405396 | 860.971863 |
| 413 | 412 | 280.250763 | 667.388245 | 645.244751 | 1018.583252 | mouse | 0.925735 | {'size': [1024, 1280], 'counts': 'ToU95bo0:L5K... | 462.747757 | 842.985748 |
| 414 | 412 | 476.011932 | 734.419922 | 498.180939 | 762.044128 | tail_base | 0.387823 | {'size': [1024, 1280], 'counts': 'UgQ?8go04M2M... | 487.096436 | 748.232025 |
| 415 | 412 | 471.099609 | 733.858887 | 500.164062 | 760.749329 | nose | 0.250220 | {'size': [1024, 1280], 'counts': 'Vgl>6ho04M4M... | 485.631836 | 747.304108 |
Only save the top 1 prediction for each frame for each class#
Note: You can change the number to save top n predictions for each frame and an instance name. head(2), head(5), or head(n)
To save all the predictions, please use df.to_csv('my_tracking_results.csv').
df_top = df.groupby(['frame_number','instance_name'],sort=False).head(1)
df_top.head()
| frame_number | x1 | y1 | x2 | y2 | instance_name | class_score | segmentation | cx | cy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 411 | 411 | 245.362183 | 758.588318 | 569.893555 | 1023.786316 | mouse | 0.977099 | {'size': [1024, 1280], 'counts': 'mki7k0Ro09G6... | 407.627869 | 891.187317 |
| 413 | 412 | 280.250763 | 667.388245 | 645.244751 | 1018.583252 | mouse | 0.925735 | {'size': [1024, 1280], 'counts': 'ToU95bo0:L5K... | 462.747757 | 842.985748 |
| 414 | 412 | 476.011932 | 734.419922 | 498.180939 | 762.044128 | tail_base | 0.387823 | {'size': [1024, 1280], 'counts': 'UgQ?8go04M2M... | 487.096436 | 748.232025 |
| 415 | 412 | 471.099609 | 733.858887 | 500.164062 | 760.749329 | nose | 0.250220 | {'size': [1024, 1280], 'counts': 'Vgl>6ho04M4M... | 485.631836 | 747.304108 |
| 417 | 413 | 326.388855 | 591.585510 | 694.334595 | 1006.914246 | mouse | 0.985295 | {'size': [1024, 1280], 'counts': '_kg:6ho0:E7J... | 510.361725 | 799.249878 |
Visualize the center points with plotly scatter plot#
df_vis = df_top[df_top.instance_name != 'Text'][['frame_number','cx','cy','instance_name']]
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
fig = px.scatter(df_vis,
x="cx",
y="cy",
color="instance_name",
hover_data=['frame_number','cx','cy'])
fig.show()
from pathlib import Path
tracking_results_csv = f"{Path(dataset).stem}_{Path(VIDEO_INPUT).stem}_{cfg.SOLVER.MAX_ITER}_iters_mask_rcnn_tracking_results_with_segmenation.csv"
df_top.to_csv(tracking_results_csv)
Download the tracking result CSV file to your local device#
if IN_COLAB:
from google.colab import files
files.download(tracking_results_csv)
Save and download the trained model weights#
final_model_file = os.path.join(cfg.OUTPUT_DIR,'model_final.pth')
files.download(final_model_file)