diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..bb71c89 --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +# Ignore all files in the evaluation directory +training + +# Optional: Un-ignore specific files or directories within evaluation +# !evaluation/some-important-file.txt +# !evaluation/some-important-directory/ diff --git a/detection-team/evaluation/.gitignore b/detection-team/evaluation/.gitignore new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/detection-team/evaluation/.gitignore @@ -0,0 +1 @@ + diff --git a/detection-team/evaluation/__pycache__/detectron2-evaluation.cpython-39.pyc b/detection-team/evaluation/__pycache__/detectron2-evaluation.cpython-39.pyc new file mode 100644 index 0000000..e015543 Binary files /dev/null and b/detection-team/evaluation/__pycache__/detectron2-evaluation.cpython-39.pyc differ diff --git a/detection-team/evaluation/__pycache__/detectron2-gpu-utilization-evaluation.cpython-39.pyc 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+image_000000024_jpg.rf.6a4bf4926a9ffe6f714f52909f148563,1,193,326,199,330 +image_000000024_jpg.rf.6a4bf4926a9ffe6f714f52909f148563,1,202,317,205,326 +image_000000024_jpg.rf.6a4bf4926a9ffe6f714f52909f148563,1,202,333,209,343 diff --git a/detection-team/evaluation/compare_gpu_utilization_csvs.ipynb b/detection-team/evaluation/compare_gpu_utilization_csvs.ipynb new file mode 100644 index 0000000..b76eba6 --- /dev/null +++ b/detection-team/evaluation/compare_gpu_utilization_csvs.ipynb @@ -0,0 +1,73 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# List of CSV file paths\n", + "file_paths = [\n", + " 'gpu_utilization_detectron2.csv',\n", + " 'gpu_utilization_ResNet.csv',\n", + " 'gpu_utilization_GroundingDINO.csv',\n", + " 'gpu_utilization_YOLO.csv'\n", + "]\n", + "\n", + "# Read the CSV files into DataFrames\n", + "dfs = [pd.read_csv(file_path) for file_path in file_paths]\n", + "\n", + "# Merge DataFrames on the 'timestamp' column\n", + "merged_df = pd.concat(dfs, keys=['Detectron2', 'ResNet', 'GroundingDINO', 'YOLO'], names=['File', 'Index'])\n", + "\n", + "# Reset index to have a flat DataFrame\n", + "merged_df = merged_df.reset_index(level=0)\n", + "\n", + "# Perform comparisons\n", + "# Example: Calculate the mean utilization for each file and compare\n", + "mean_utilization = merged_df.groupby('File').agg({\n", + " 'gpu_utilization': 'mean',\n", + " 'memory_utilization': 'mean',\n", + " 'encoder_utilization': 'mean',\n", + " 'decoder_utilization': 'mean'\n", + "})\n", + "\n", + "print(\"Mean Utilization Comparison:\")\n", + "print(mean_utilization)\n", + "\n", + "# Calculate the sum of utilization for each file and compare\n", + "sum_utilization = merged_df.groupby('File').agg({\n", + " 'gpu_utilization': 'sum',\n", + " 'memory_utilization': 'sum',\n", + " 'encoder_utilization': 'sum',\n", + " 'decoder_utilization': 'sum'\n", + "})\n", + "\n", + "print(\"\\nSum Utilization Comparison:\")\n", + "print(sum_utilization)\n", + "\n", + "# Example: Find differences in GPU utilization between files\n", + "comparison_df = merged_df.pivot(index='timestamp', columns='File', values='gpu_utilization')\n", + "comparison_df['Difference_Detectron2_ResNet'] = comparison_df['Detectron2'] - comparison_df['ResNet']\n", + "comparison_df['Difference_Detectron2_GroundingDINO'] = comparison_df['Detectron2'] - comparison_df['GroundingDINO']\n", + "comparison_df['Difference_Detectron2_YOLO'] = comparison_df['Detectron2'] - comparison_df['YOLO']\n", + "\n", + "print(\"\\nGPU Utilization Differences:\")\n", + "print(comparison_df)\n", + "\n", + "# Save the comparison results to CSV\n", + "mean_utilization.to_csv('mean_utilization_comparison.csv')\n", + "comparison_df.to_csv('gpu_utilization_differences.csv')\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/detection-team/evaluation/create_data_csv.py b/detection-team/evaluation/create_data_csv.py new file mode 100644 index 0000000..74e34ce --- /dev/null +++ b/detection-team/evaluation/create_data_csv.py @@ -0,0 +1,58 @@ +# Import necessary libraries +import os +import cv2 +import pandas as pd +import warnings + +# Suppress warnings +warnings.filterwarnings("ignore") + +# Setup logger for Detectron2 + +# Global variables +Selected_Fold = 2 # 0..2 + +# Function to get image dimensions +def get_image_dimensions(image_dir, image_name): + image_path = os.path.join(image_dir, image_name + ".jpg") # Assuming images are .jpg files + image = cv2.imread(image_path) + if image is None: + raise FileNotFoundError(f"Image {image_path} not found.") + height, width = image.shape[:2] + return width, height + +# Function to extract bounding boxes from labels +def extract_bounding_boxes(labels_dir, image_dir): + data = [] + for filename in os.listdir(labels_dir): + if filename.endswith(".txt"): # Assuming the bounding box files have .txt extension + image_name = os.path.splitext(filename)[0] + width, height = get_image_dimensions(image_dir, image_name) + with open(os.path.join(labels_dir, filename), 'r') as file: + for line in file: + parts = line.strip().split() + if len(parts) == 5: # Assuming the format includes class label + class_label, x_center, y_center, bbox_width, bbox_height = map(float, parts) + xmin = int((x_center - bbox_width / 2) * width) + ymin = int((y_center - bbox_height / 2) * height) + xmax = int((x_center + bbox_width / 2) * width) + ymax = int((y_center + bbox_height / 2) * height) + data.append([image_name, int(class_label), xmin, ymin, xmax, ymax]) + else: + print(f"Unexpected format in file: {filename}") + return data + +# Function to create CSV file from labels +def create_csv(labels_dir, image_dir, output_csv): + data = extract_bounding_boxes(labels_dir, image_dir) + df = pd.DataFrame(data, columns=["image_name", "class_label", "xmin", "ymin", "xmax", "ymax"]) + df.to_csv(output_csv, index=False) + print(f"CSV file '{output_csv}' created successfully.") + +#generate CSV file for the dataset +if __name__=='__main__': + path_to_data="data_for_evaluation" + labels_directory = path_to_data + "/labels" # Path to labels directory + image_directory = path_to_data + "/images" # Path to images directory + output_csv_file = "bounding_boxes_with_labels.csv" # Output CSV file name + create_csv(labels_directory, image_directory, output_csv_file) \ No newline at end of file diff --git a/detection-team/evaluation/my-data/images/image_000000001_jpg.rf.02ab6664294833e5f0e89130ecded0b8.jpg b/detection-team/evaluation/data_for_evaluation/images/image_000000001_jpg.rf.02ab6664294833e5f0e89130ecded0b8.jpg similarity index 100% rename from detection-team/evaluation/my-data/images/image_000000001_jpg.rf.02ab6664294833e5f0e89130ecded0b8.jpg rename to 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a/detection-team/evaluation/detectron2-gpu-utilization-evaluation.py b/detection-team/evaluation/detectron2-gpu-utilization-evaluation.py new file mode 100644 index 0000000..8c418fb --- /dev/null +++ b/detection-team/evaluation/detectron2-gpu-utilization-evaluation.py @@ -0,0 +1,95 @@ +import os +import cv2 +import torch +import pandas as pd +import time + +from detectron2_for_evaluation.config import get_cfg +from detectron2_for_evaluation.engine.defaults import DefaultPredictor +from detectron2_for_evaluation import model_zoo + +def setup_predictor(): + cfg = get_cfg() + cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")) + cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml") + cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 + + # Check if CUDA (GPU) is available, else use CPU + if torch.cuda.is_available(): + cfg.MODEL.DEVICE = 'cuda' + else: + cfg.MODEL.DEVICE = 'cpu' + + return DefaultPredictor(cfg) + +def get_predictions(predictor, image_path): + im = cv2.imread(image_path) + outputs = predictor(im) + return outputs["instances"].to("cpu") # Ensure this is set to CPU + +def load_ground_truth(csv_file): + df = pd.read_csv(csv_file) + ground_truth = {} + for _, row in df.iterrows(): + image_name = row["image_name"] + xmin, ymin, xmax, ymax = int(row["xmin"]), int(row["ymin"]), int(row["xmax"]), int(row["ymax"]) + if image_name not in ground_truth: + ground_truth[image_name] = [] + ground_truth[image_name].append((xmin, ymin, xmax, ymax)) + return ground_truth + +def check_overlap(box1, box2): + x1, y1, x2, y2 = box1 + x1_p, y1_p, x2_p, y2_p = box2 + + # Check if there is any overlap + if x1 < x2_p and x2 > x1_p and y1 < y2_p and y2 > y1_p: + return True + return False + +def compute_accuracy(ground_truth, predictor, image_dir): + start_time = time.time() # Start timing + + total_true_positive = 0 + total_ground_truth = 0 + + for image_name, gt_boxes in ground_truth.items(): + image_path = os.path.join(image_dir, image_name + ".jpg") + predictions = get_predictions(predictor, image_path) + + pred_boxes = predictions.pred_boxes.tensor.numpy() + + # Check if any predictions overlap with ground truth + for gt_box in gt_boxes: + gt_xmin, gt_ymin, gt_xmax, gt_ymax = gt_box + match_found = False + for pred_box in pred_boxes: + if check_overlap((gt_xmin, gt_ymin, gt_xmax, gt_ymax), tuple(pred_box)): + match_found = True + break + if match_found: + total_true_positive += 1 + total_ground_truth += 1 + + accuracy = total_true_positive / total_ground_truth if total_ground_truth > 0 else 0 + + end_time = time.time() # End timing + elapsed_time = end_time - start_time + return accuracy + #print(f"Accuracy: {accuracy:.2f}") + #print(f"Time taken: {elapsed_time:.2f} seconds") + +def get_accuracy_option1(path_to_data): + csv_file_path = "bounding_boxes_with_labels.csv" # path to your CSV file + image_directory = path_to_data+"/images" # path to your images directory + + predictor = setup_predictor() + + # Load ground truth and compute accuracy + ground_truth = load_ground_truth(csv_file_path) + return compute_accuracy(ground_truth, predictor, image_directory) + + +def detectron2_evaluation(path_to_data): + # make_coco_dataset(path_to_data) + return (get_accuracy_option1(path_to_data))#,get_accuracy_option2(path_to_data)) \ No newline at end of file diff --git a/detection-team/evaluation/detectron2_for_evaluation/__pycache__/config.cpython-39.pyc b/detection-team/evaluation/detectron2_for_evaluation/__pycache__/config.cpython-39.pyc new file mode 100644 index 0000000..01a0927 Binary files /dev/null and b/detection-team/evaluation/detectron2_for_evaluation/__pycache__/config.cpython-39.pyc differ diff --git a/detection-team/evaluation/detectron2_for_evaluation/__pycache__/defaults.cpython-39.pyc b/detection-team/evaluation/detectron2_for_evaluation/__pycache__/defaults.cpython-39.pyc new file mode 100644 index 0000000..e5a84da Binary files /dev/null and b/detection-team/evaluation/detectron2_for_evaluation/__pycache__/defaults.cpython-39.pyc differ diff --git a/detection-team/evaluation/detectron2_for_evaluation/__pycache__/model_zoo.cpython-39.pyc b/detection-team/evaluation/detectron2_for_evaluation/__pycache__/model_zoo.cpython-39.pyc new file mode 100644 index 0000000..06ec1fd Binary files /dev/null and b/detection-team/evaluation/detectron2_for_evaluation/__pycache__/model_zoo.cpython-39.pyc differ diff --git a/detection-team/evaluation/detectron2_for_evaluation/config.py b/detection-team/evaluation/detectron2_for_evaluation/config.py new file mode 100644 index 0000000..49a55b1 --- /dev/null +++ b/detection-team/evaluation/detectron2_for_evaluation/config.py @@ -0,0 +1,265 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import functools +import inspect +import logging +from fvcore.common.config import CfgNode as _CfgNode + +from detectron2.utils.file_io import PathManager + + +class CfgNode(_CfgNode): + """ + The same as `fvcore.common.config.CfgNode`, but different in: + + 1. Use unsafe yaml loading by default. + Note that this may lead to arbitrary code execution: you must not + load a config file from untrusted sources before manually inspecting + the content of the file. + 2. Support config versioning. + When attempting to merge an old config, it will convert the old config automatically. + + .. automethod:: clone + .. automethod:: freeze + .. automethod:: defrost + .. automethod:: is_frozen + .. automethod:: load_yaml_with_base + .. automethod:: merge_from_list + .. automethod:: merge_from_other_cfg + """ + + @classmethod + def _open_cfg(cls, filename): + return PathManager.open(filename, "r") + + # Note that the default value of allow_unsafe is changed to True + def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = True) -> None: + """ + Load content from the given config file and merge it into self. + + Args: + cfg_filename: config filename + allow_unsafe: allow unsafe yaml syntax + """ + assert PathManager.isfile(cfg_filename), f"Config file '{cfg_filename}' does not exist!" + loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe) + loaded_cfg = type(self)(loaded_cfg) + + # defaults.py needs to import CfgNode + from .defaults import _C + + latest_ver = _C.VERSION + assert ( + latest_ver == self.VERSION + ), "CfgNode.merge_from_file is only allowed on a config object of latest version!" + + logger = logging.getLogger(__name__) + + loaded_ver = loaded_cfg.get("VERSION", None) + if loaded_ver is None: + from .compat import guess_version + + loaded_ver = guess_version(loaded_cfg, cfg_filename) + assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format( + loaded_ver, self.VERSION + ) + + if loaded_ver == self.VERSION: + self.merge_from_other_cfg(loaded_cfg) + else: + # compat.py needs to import CfgNode + from .compat import upgrade_config, downgrade_config + + logger.warning( + "Loading an old v{} config file '{}' by automatically upgrading to v{}. " + "See docs/CHANGELOG.md for instructions to update your files.".format( + loaded_ver, cfg_filename, self.VERSION + ) + ) + # To convert, first obtain a full config at an old version + old_self = downgrade_config(self, to_version=loaded_ver) + old_self.merge_from_other_cfg(loaded_cfg) + new_config = upgrade_config(old_self) + self.clear() + self.update(new_config) + + def dump(self, *args, **kwargs): + """ + Returns: + str: a yaml string representation of the config + """ + # to make it show up in docs + return super().dump(*args, **kwargs) + + +global_cfg = CfgNode() + + +def get_cfg() -> CfgNode: + """ + Get a copy of the default config. + + Returns: + a detectron2 CfgNode instance. + """ + from .defaults import _C + + return _C.clone() + + +def set_global_cfg(cfg: CfgNode) -> None: + """ + Let the global config point to the given cfg. + + Assume that the given "cfg" has the key "KEY", after calling + `set_global_cfg(cfg)`, the key can be accessed by: + :: + from detectron2.config import global_cfg + print(global_cfg.KEY) + + By using a hacky global config, you can access these configs anywhere, + without having to pass the config object or the values deep into the code. + This is a hacky feature introduced for quick prototyping / research exploration. + """ + global global_cfg + global_cfg.clear() + global_cfg.update(cfg) + + +def configurable(init_func=None, *, from_config=None): + """ + Decorate a function or a class's __init__ method so that it can be called + with a :class:`CfgNode` object using a :func:`from_config` function that translates + :class:`CfgNode` to arguments. + + Examples: + :: + # Usage 1: Decorator on __init__: + class A: + @configurable + def __init__(self, a, b=2, c=3): + pass + + @classmethod + def from_config(cls, cfg): # 'cfg' must be the first argument + # Returns kwargs to be passed to __init__ + return {"a": cfg.A, "b": cfg.B} + + a1 = A(a=1, b=2) # regular construction + a2 = A(cfg) # construct with a cfg + a3 = A(cfg, b=3, c=4) # construct with extra overwrite + + # Usage 2: Decorator on any function. Needs an extra from_config argument: + @configurable(from_config=lambda cfg: {"a: cfg.A, "b": cfg.B}) + def a_func(a, b=2, c=3): + pass + + a1 = a_func(a=1, b=2) # regular call + a2 = a_func(cfg) # call with a cfg + a3 = a_func(cfg, b=3, c=4) # call with extra overwrite + + Args: + init_func (callable): a class's ``__init__`` method in usage 1. The + class must have a ``from_config`` classmethod which takes `cfg` as + the first argument. + from_config (callable): the from_config function in usage 2. It must take `cfg` + as its first argument. + """ + + if init_func is not None: + assert ( + inspect.isfunction(init_func) + and from_config is None + and init_func.__name__ == "__init__" + ), "Incorrect use of @configurable. Check API documentation for examples." + + @functools.wraps(init_func) + def wrapped(self, *args, **kwargs): + try: + from_config_func = type(self).from_config + except AttributeError as e: + raise AttributeError( + "Class with @configurable must have a 'from_config' classmethod." + ) from e + if not inspect.ismethod(from_config_func): + raise TypeError("Class with @configurable must have a 'from_config' classmethod.") + + if _called_with_cfg(*args, **kwargs): + explicit_args = _get_args_from_config(from_config_func, *args, **kwargs) + init_func(self, **explicit_args) + else: + init_func(self, *args, **kwargs) + + return wrapped + + else: + if from_config is None: + return configurable # @configurable() is made equivalent to @configurable + assert inspect.isfunction( + from_config + ), "from_config argument of configurable must be a function!" + + def wrapper(orig_func): + @functools.wraps(orig_func) + def wrapped(*args, **kwargs): + if _called_with_cfg(*args, **kwargs): + explicit_args = _get_args_from_config(from_config, *args, **kwargs) + return orig_func(**explicit_args) + else: + return orig_func(*args, **kwargs) + + wrapped.from_config = from_config + return wrapped + + return wrapper + + +def _get_args_from_config(from_config_func, *args, **kwargs): + """ + Use `from_config` to obtain explicit arguments. + + Returns: + dict: arguments to be used for cls.__init__ + """ + signature = inspect.signature(from_config_func) + if list(signature.parameters.keys())[0] != "cfg": + if inspect.isfunction(from_config_func): + name = from_config_func.__name__ + else: + name = f"{from_config_func.__self__}.from_config" + raise TypeError(f"{name} must take 'cfg' as the first argument!") + support_var_arg = any( + param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD] + for param in signature.parameters.values() + ) + if support_var_arg: # forward all arguments to from_config, if from_config accepts them + ret = from_config_func(*args, **kwargs) + else: + # forward supported arguments to from_config + supported_arg_names = set(signature.parameters.keys()) + extra_kwargs = {} + for name in list(kwargs.keys()): + if name not in supported_arg_names: + extra_kwargs[name] = kwargs.pop(name) + ret = from_config_func(*args, **kwargs) + # forward the other arguments to __init__ + ret.update(extra_kwargs) + return ret + + +def _called_with_cfg(*args, **kwargs): + """ + Returns: + bool: whether the arguments contain CfgNode and should be considered + forwarded to from_config. + """ + from omegaconf import DictConfig + + if len(args) and isinstance(args[0], (_CfgNode, DictConfig)): + return True + if isinstance(kwargs.pop("cfg", None), (_CfgNode, DictConfig)): + return True + # `from_config`'s first argument is forced to be "cfg". + # So the above check covers all cases. + return False diff --git a/detection-team/evaluation/detectron2_for_evaluation/defaults.py b/detection-team/evaluation/detectron2_for_evaluation/defaults.py new file mode 100644 index 0000000..5d97ec9 --- /dev/null +++ b/detection-team/evaluation/detectron2_for_evaluation/defaults.py @@ -0,0 +1,652 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .config import CfgNode as CN + +# NOTE: given the new config system +# (https://detectron2.readthedocs.io/en/latest/tutorials/lazyconfigs.html), +# we will stop adding new functionalities to default CfgNode. + +# ----------------------------------------------------------------------------- +# Convention about Training / Test specific parameters +# ----------------------------------------------------------------------------- +# Whenever an argument can be either used for training or for testing, the +# corresponding name will be post-fixed by a _TRAIN for a training parameter, +# or _TEST for a test-specific parameter. +# For example, the number of images during training will be +# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be +# IMAGES_PER_BATCH_TEST + +# ----------------------------------------------------------------------------- +# Config definition +# ----------------------------------------------------------------------------- + +_C = CN() + +# The version number, to upgrade from old configs to new ones if any +# changes happen. It's recommended to keep a VERSION in your config file. +_C.VERSION = 2 + +_C.MODEL = CN() +_C.MODEL.LOAD_PROPOSALS = False +_C.MODEL.MASK_ON = False +_C.MODEL.KEYPOINT_ON = False +_C.MODEL.DEVICE = "cuda" +_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" + +# Path (a file path, or URL like detectron2://.., https://..) to a checkpoint file +# to be loaded to the model. You can find available models in the model zoo. +_C.MODEL.WEIGHTS = "" + +# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR). +# To train on images of different number of channels, just set different mean & std. +# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675] +_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675] +# When using pre-trained models in Detectron1 or any MSRA models, +# std has been absorbed into its conv1 weights, so the std needs to be set 1. +# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std) +_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0] + + +# ----------------------------------------------------------------------------- +# INPUT +# ----------------------------------------------------------------------------- +_C.INPUT = CN() +# By default, {MIN,MAX}_SIZE options are used in transforms.ResizeShortestEdge. +# Please refer to ResizeShortestEdge for detailed definition. +# Size of the smallest side of the image during training +_C.INPUT.MIN_SIZE_TRAIN = (800,) +# Sample size of smallest side by choice or random selection from range give by +# INPUT.MIN_SIZE_TRAIN +_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice" +# Maximum size of the side of the image during training +_C.INPUT.MAX_SIZE_TRAIN = 1333 +# Size of the smallest side of the image during testing. Set to zero to disable resize in testing. +_C.INPUT.MIN_SIZE_TEST = 800 +# Maximum size of the side of the image during testing +_C.INPUT.MAX_SIZE_TEST = 1333 +# Mode for flipping images used in data augmentation during training +# choose one of ["horizontal, "vertical", "none"] +_C.INPUT.RANDOM_FLIP = "horizontal" + +# `True` if cropping is used for data augmentation during training +_C.INPUT.CROP = CN({"ENABLED": False}) +# Cropping type. See documentation of `detectron2.data.transforms.RandomCrop` for explanation. +_C.INPUT.CROP.TYPE = "relative_range" +# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of +# pixels if CROP.TYPE is "absolute" +_C.INPUT.CROP.SIZE = [0.9, 0.9] + + +# Whether the model needs RGB, YUV, HSV etc. +# Should be one of the modes defined here, as we use PIL to read the image: +# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes +# with BGR being the one exception. One can set image format to BGR, we will +# internally use RGB for conversion and flip the channels over +_C.INPUT.FORMAT = "BGR" +# The ground truth mask format that the model will use. +# Mask R-CNN supports either "polygon" or "bitmask" as ground truth. +_C.INPUT.MASK_FORMAT = "polygon" # alternative: "bitmask" + + +# ----------------------------------------------------------------------------- +# Dataset +# ----------------------------------------------------------------------------- +_C.DATASETS = CN() +# List of the dataset names for training. Must be registered in DatasetCatalog +# Samples from these datasets will be merged and used as one dataset. +_C.DATASETS.TRAIN = () +# List of the pre-computed proposal files for training, which must be consistent +# with datasets listed in DATASETS.TRAIN. +_C.DATASETS.PROPOSAL_FILES_TRAIN = () +# Number of top scoring precomputed proposals to keep for training +_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000 +# List of the dataset names for testing. Must be registered in DatasetCatalog +_C.DATASETS.TEST = () +# List of the pre-computed proposal files for test, which must be consistent +# with datasets listed in DATASETS.TEST. +_C.DATASETS.PROPOSAL_FILES_TEST = () +# Number of top scoring precomputed proposals to keep for test +_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000 + +# ----------------------------------------------------------------------------- +# DataLoader +# ----------------------------------------------------------------------------- +_C.DATALOADER = CN() +# Number of data loading threads +_C.DATALOADER.NUM_WORKERS = 4 +# If True, each batch should contain only images for which the aspect ratio +# is compatible. This groups portrait images together, and landscape images +# are not batched with portrait images. +_C.DATALOADER.ASPECT_RATIO_GROUPING = True +# Options: TrainingSampler, RepeatFactorTrainingSampler +_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler" +# Repeat threshold for RepeatFactorTrainingSampler +_C.DATALOADER.REPEAT_THRESHOLD = 0.0 +# if True, take square root when computing repeating factor +_C.DATALOADER.REPEAT_SQRT = True +# Tf True, when working on datasets that have instance annotations, the +# training dataloader will filter out images without associated annotations +_C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True + +# ---------------------------------------------------------------------------- # +# Backbone options +# ---------------------------------------------------------------------------- # +_C.MODEL.BACKBONE = CN() + +_C.MODEL.BACKBONE.NAME = "build_resnet_backbone" +# Freeze the first several stages so they are not trained. +# There are 5 stages in ResNet. The first is a convolution, and the following +# stages are each group of residual blocks. +_C.MODEL.BACKBONE.FREEZE_AT = 2 + + +# ---------------------------------------------------------------------------- # +# FPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.FPN = CN() +# Names of the input feature maps to be used by FPN +# They must have contiguous power of 2 strides +# e.g., ["res2", "res3", "res4", "res5"] +_C.MODEL.FPN.IN_FEATURES = [] +_C.MODEL.FPN.OUT_CHANNELS = 256 + +# Options: "" (no norm), "GN" +_C.MODEL.FPN.NORM = "" + +# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg" +_C.MODEL.FPN.FUSE_TYPE = "sum" + + +# ---------------------------------------------------------------------------- # +# Proposal generator options +# ---------------------------------------------------------------------------- # +_C.MODEL.PROPOSAL_GENERATOR = CN() +# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals" +_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN" +# Proposal height and width both need to be greater than MIN_SIZE +# (a the scale used during training or inference) +_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0 + + +# ---------------------------------------------------------------------------- # +# Anchor generator options +# ---------------------------------------------------------------------------- # +_C.MODEL.ANCHOR_GENERATOR = CN() +# The generator can be any name in the ANCHOR_GENERATOR registry +_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator" +# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input. +# Format: list[list[float]]. SIZES[i] specifies the list of sizes to use for +# IN_FEATURES[i]; len(SIZES) must be equal to len(IN_FEATURES) or 1. +# When len(SIZES) == 1, SIZES[0] is used for all IN_FEATURES. +_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]] +# Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect +# ratios are generated by an anchor generator. +# Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W) +# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true, +# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used +# for all IN_FEATURES. +_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]] +# Anchor angles. +# list[list[float]], the angle in degrees, for each input feature map. +# ANGLES[i] specifies the list of angles for IN_FEATURES[i]. +_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]] +# Relative offset between the center of the first anchor and the top-left corner of the image +# Value has to be in [0, 1). Recommend to use 0.5, which means half stride. +# The value is not expected to affect model accuracy. +_C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0 + +# ---------------------------------------------------------------------------- # +# RPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.RPN = CN() +_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead" # used by RPN_HEAD_REGISTRY + +# Names of the input feature maps to be used by RPN +# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN +_C.MODEL.RPN.IN_FEATURES = ["res4"] +# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels +# Set to -1 or a large value, e.g. 100000, to disable pruning anchors +_C.MODEL.RPN.BOUNDARY_THRESH = -1 +# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD] +# Minimum overlap required between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD +# ==> positive RPN example: 1) +# Maximum overlap allowed between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD +# ==> negative RPN example: 0) +# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD) +# are ignored (-1) +_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7] +_C.MODEL.RPN.IOU_LABELS = [0, -1, 1] +# Number of regions per image used to train RPN +_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256 +# Target fraction of foreground (positive) examples per RPN minibatch +_C.MODEL.RPN.POSITIVE_FRACTION = 0.5 +# Options are: "smooth_l1", "giou", "diou", "ciou" +_C.MODEL.RPN.BBOX_REG_LOSS_TYPE = "smooth_l1" +_C.MODEL.RPN.BBOX_REG_LOSS_WEIGHT = 1.0 +# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets +_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) +# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0 +_C.MODEL.RPN.LOSS_WEIGHT = 1.0 +# Number of top scoring RPN proposals to keep before applying NMS +# When FPN is used, this is *per FPN level* (not total) +_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000 +_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000 +# Number of top scoring RPN proposals to keep after applying NMS +# When FPN is used, this limit is applied per level and then again to the union +# of proposals from all levels +# NOTE: When FPN is used, the meaning of this config is different from Detectron1. +# It means per-batch topk in Detectron1, but per-image topk here. +# See the "find_top_rpn_proposals" function for details. +_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000 +_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000 +# NMS threshold used on RPN proposals +_C.MODEL.RPN.NMS_THRESH = 0.7 +# Set this to -1 to use the same number of output channels as input channels. +_C.MODEL.RPN.CONV_DIMS = [-1] + +# ---------------------------------------------------------------------------- # +# ROI HEADS options +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_HEADS = CN() +_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads" +# Number of foreground classes +_C.MODEL.ROI_HEADS.NUM_CLASSES = 80 +# Names of the input feature maps to be used by ROI heads +# Currently all heads (box, mask, ...) use the same input feature map list +# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN +_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"] +# IOU overlap ratios [IOU_THRESHOLD] +# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD) +# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD) +_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5] +_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1] +# RoI minibatch size *per image* (number of regions of interest [ROIs]) during training +# Total number of RoIs per training minibatch = +# ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH +# E.g., a common configuration is: 512 * 16 = 8192 +_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512 +# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0) +_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25 + +# Only used on test mode + +# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to +# balance obtaining high recall with not having too many low precision +# detections that will slow down inference post processing steps (like NMS) +# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down +# inference. +_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05 +# Overlap threshold used for non-maximum suppression (suppress boxes with +# IoU >= this threshold) +_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5 +# If True, augment proposals with ground-truth boxes before sampling proposals to +# train ROI heads. +_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True + +# ---------------------------------------------------------------------------- # +# Box Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_BOX_HEAD = CN() +# C4 don't use head name option +# Options for non-C4 models: FastRCNNConvFCHead, +_C.MODEL.ROI_BOX_HEAD.NAME = "" +# Options are: "smooth_l1", "giou", "diou", "ciou" +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE = "smooth_l1" +# The final scaling coefficient on the box regression loss, used to balance the magnitude of its +# gradients with other losses in the model. See also `MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT`. +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT = 1.0 +# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets +# These are empirically chosen to approximately lead to unit variance targets +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0) +# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0 +_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0 +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2" + +_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0 +# Hidden layer dimension for FC layers in the RoI box head +_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024 +_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0 +# Channel dimension for Conv layers in the RoI box head +_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256 +# Normalization method for the convolution layers. +# Options: "" (no norm), "GN", "SyncBN". +_C.MODEL.ROI_BOX_HEAD.NORM = "" +# Whether to use class agnostic for bbox regression +_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False +# If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes. +_C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False + +# Federated loss can be used to improve the training of LVIS +_C.MODEL.ROI_BOX_HEAD.USE_FED_LOSS = False +# Sigmoid cross entrophy is used with federated loss +_C.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE = False +# The power value applied to image_count when calcualting frequency weight +_C.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT_POWER = 0.5 +# Number of classes to keep in total +_C.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CLASSES = 50 + +# ---------------------------------------------------------------------------- # +# Cascaded Box Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_BOX_CASCADE_HEAD = CN() +# The number of cascade stages is implicitly defined by the length of the following two configs. +_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = ( + (10.0, 10.0, 5.0, 5.0), + (20.0, 20.0, 10.0, 10.0), + (30.0, 30.0, 15.0, 15.0), +) +_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7) + + +# ---------------------------------------------------------------------------- # +# Mask Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_MASK_HEAD = CN() +_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead" +_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0 # The number of convs in the mask head +_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256 +# Normalization method for the convolution layers. +# Options: "" (no norm), "GN", "SyncBN". +_C.MODEL.ROI_MASK_HEAD.NORM = "" +# Whether to use class agnostic for mask prediction +_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2" + + +# ---------------------------------------------------------------------------- # +# Keypoint Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_KEYPOINT_HEAD = CN() +_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead" +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8)) +_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17 # 17 is the number of keypoints in COCO. + +# Images with too few (or no) keypoints are excluded from training. +_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1 +# Normalize by the total number of visible keypoints in the minibatch if True. +# Otherwise, normalize by the total number of keypoints that could ever exist +# in the minibatch. +# The keypoint softmax loss is only calculated on visible keypoints. +# Since the number of visible keypoints can vary significantly between +# minibatches, this has the effect of up-weighting the importance of +# minibatches with few visible keypoints. (Imagine the extreme case of +# only one visible keypoint versus N: in the case of N, each one +# contributes 1/N to the gradient compared to the single keypoint +# determining the gradient direction). Instead, we can normalize the +# loss by the total number of keypoints, if it were the case that all +# keypoints were visible in a full minibatch. (Returning to the example, +# this means that the one visible keypoint contributes as much as each +# of the N keypoints.) +_C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True +# Multi-task loss weight to use for keypoints +# Recommended values: +# - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True +# - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False +_C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0 +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2" + +# ---------------------------------------------------------------------------- # +# Semantic Segmentation Head +# ---------------------------------------------------------------------------- # +_C.MODEL.SEM_SEG_HEAD = CN() +_C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead" +_C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"] +# Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for +# the correposnding pixel. +_C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255 +# Number of classes in the semantic segmentation head +_C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54 +# Number of channels in the 3x3 convs inside semantic-FPN heads. +_C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128 +# Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride. +_C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4 +# Normalization method for the convolution layers. Options: "" (no norm), "GN". +_C.MODEL.SEM_SEG_HEAD.NORM = "GN" +_C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0 + +_C.MODEL.PANOPTIC_FPN = CN() +# Scaling of all losses from instance detection / segmentation head. +_C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0 + +# options when combining instance & semantic segmentation outputs +_C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True}) # "COMBINE.ENABLED" is deprecated & not used +_C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5 +_C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096 +_C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5 + + +# ---------------------------------------------------------------------------- # +# RetinaNet Head +# ---------------------------------------------------------------------------- # +_C.MODEL.RETINANET = CN() + +# This is the number of foreground classes. +_C.MODEL.RETINANET.NUM_CLASSES = 80 + +_C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"] + +# Convolutions to use in the cls and bbox tower +# NOTE: this doesn't include the last conv for logits +_C.MODEL.RETINANET.NUM_CONVS = 4 + +# IoU overlap ratio [bg, fg] for labeling anchors. +# Anchors with < bg are labeled negative (0) +# Anchors with >= bg and < fg are ignored (-1) +# Anchors with >= fg are labeled positive (1) +_C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5] +_C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1] + +# Prior prob for rare case (i.e. foreground) at the beginning of training. +# This is used to set the bias for the logits layer of the classifier subnet. +# This improves training stability in the case of heavy class imbalance. +_C.MODEL.RETINANET.PRIOR_PROB = 0.01 + +# Inference cls score threshold, only anchors with score > INFERENCE_TH are +# considered for inference (to improve speed) +_C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05 +# Select topk candidates before NMS +_C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000 +_C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5 + +# Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets +_C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) + +# Loss parameters +_C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0 +_C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25 +_C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1 +# Options are: "smooth_l1", "giou", "diou", "ciou" +_C.MODEL.RETINANET.BBOX_REG_LOSS_TYPE = "smooth_l1" + +# One of BN, SyncBN, FrozenBN, GN +# Only supports GN until unshared norm is implemented +_C.MODEL.RETINANET.NORM = "" + + +# ---------------------------------------------------------------------------- # +# ResNe[X]t options (ResNets = {ResNet, ResNeXt} +# Note that parts of a resnet may be used for both the backbone and the head +# These options apply to both +# ---------------------------------------------------------------------------- # +_C.MODEL.RESNETS = CN() + +_C.MODEL.RESNETS.DEPTH = 50 +_C.MODEL.RESNETS.OUT_FEATURES = ["res4"] # res4 for C4 backbone, res2..5 for FPN backbone + +# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt +_C.MODEL.RESNETS.NUM_GROUPS = 1 + +# Options: FrozenBN, GN, "SyncBN", "BN" +_C.MODEL.RESNETS.NORM = "FrozenBN" + +# Baseline width of each group. +# Scaling this parameters will scale the width of all bottleneck layers. +_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64 + +# Place the stride 2 conv on the 1x1 filter +# Use True only for the original MSRA ResNet; use False for C2 and Torch models +_C.MODEL.RESNETS.STRIDE_IN_1X1 = True + +# Apply dilation in stage "res5" +_C.MODEL.RESNETS.RES5_DILATION = 1 + +# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet +# For R18 and R34, this needs to be set to 64 +_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256 +_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64 + +# Apply Deformable Convolution in stages +# Specify if apply deform_conv on Res2, Res3, Res4, Res5 +_C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False] +# Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168); +# Use False for DeformableV1. +_C.MODEL.RESNETS.DEFORM_MODULATED = False +# Number of groups in deformable conv. +_C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1 + + +# ---------------------------------------------------------------------------- # +# Solver +# ---------------------------------------------------------------------------- # +_C.SOLVER = CN() + +# Options: WarmupMultiStepLR, WarmupCosineLR. +# See detectron2/solver/build.py for definition. +_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR" + +_C.SOLVER.MAX_ITER = 40000 + +_C.SOLVER.BASE_LR = 0.001 +# The end lr, only used by WarmupCosineLR +_C.SOLVER.BASE_LR_END = 0.0 + +_C.SOLVER.MOMENTUM = 0.9 + +_C.SOLVER.NESTEROV = False + +_C.SOLVER.WEIGHT_DECAY = 0.0001 +# The weight decay that's applied to parameters of normalization layers +# (typically the affine transformation) +_C.SOLVER.WEIGHT_DECAY_NORM = 0.0 + +_C.SOLVER.GAMMA = 0.1 +# The iteration number to decrease learning rate by GAMMA. +_C.SOLVER.STEPS = (30000,) +# Number of decays in WarmupStepWithFixedGammaLR schedule +_C.SOLVER.NUM_DECAYS = 3 + +_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000 +_C.SOLVER.WARMUP_ITERS = 1000 +_C.SOLVER.WARMUP_METHOD = "linear" +# Whether to rescale the interval for the learning schedule after warmup +_C.SOLVER.RESCALE_INTERVAL = False + +# Save a checkpoint after every this number of iterations +_C.SOLVER.CHECKPOINT_PERIOD = 5000 + +# Number of images per batch across all machines. This is also the number +# of training images per step (i.e. per iteration). If we use 16 GPUs +# and IMS_PER_BATCH = 32, each GPU will see 2 images per batch. +# May be adjusted automatically if REFERENCE_WORLD_SIZE is set. +_C.SOLVER.IMS_PER_BATCH = 16 + +# The reference number of workers (GPUs) this config is meant to train with. +# It takes no effect when set to 0. +# With a non-zero value, it will be used by DefaultTrainer to compute a desired +# per-worker batch size, and then scale the other related configs (total batch size, +# learning rate, etc) to match the per-worker batch size. +# See documentation of `DefaultTrainer.auto_scale_workers` for details: +_C.SOLVER.REFERENCE_WORLD_SIZE = 0 + +# Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for +# biases. This is not useful (at least for recent models). You should avoid +# changing these and they exist only to reproduce Detectron v1 training if +# desired. +_C.SOLVER.BIAS_LR_FACTOR = 1.0 +_C.SOLVER.WEIGHT_DECAY_BIAS = None # None means following WEIGHT_DECAY + +# Gradient clipping +_C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False}) +# Type of gradient clipping, currently 2 values are supported: +# - "value": the absolute values of elements of each gradients are clipped +# - "norm": the norm of the gradient for each parameter is clipped thus +# affecting all elements in the parameter +_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value" +# Maximum absolute value used for clipping gradients +_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0 +# Floating point number p for L-p norm to be used with the "norm" +# gradient clipping type; for L-inf, please specify .inf +_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0 + +# Enable automatic mixed precision for training +# Note that this does not change model's inference behavior. +# To use AMP in inference, run inference under autocast() +_C.SOLVER.AMP = CN({"ENABLED": False}) + +# ---------------------------------------------------------------------------- # +# Specific test options +# ---------------------------------------------------------------------------- # +_C.TEST = CN() +# For end-to-end tests to verify the expected accuracy. +# Each item is [task, metric, value, tolerance] +# e.g.: [['bbox', 'AP', 38.5, 0.2]] +_C.TEST.EXPECTED_RESULTS = [] +# The period (in terms of steps) to evaluate the model during training. +# Set to 0 to disable. +_C.TEST.EVAL_PERIOD = 0 +# The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval +# When empty, it will use the defaults in COCO. +# Otherwise it should be a list[float] with the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. +_C.TEST.KEYPOINT_OKS_SIGMAS = [] +# Maximum number of detections to return per image during inference (100 is +# based on the limit established for the COCO dataset). +_C.TEST.DETECTIONS_PER_IMAGE = 100 + +_C.TEST.AUG = CN({"ENABLED": False}) +_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200) +_C.TEST.AUG.MAX_SIZE = 4000 +_C.TEST.AUG.FLIP = True + +_C.TEST.PRECISE_BN = CN({"ENABLED": False}) +_C.TEST.PRECISE_BN.NUM_ITER = 200 + +# ---------------------------------------------------------------------------- # +# Misc options +# ---------------------------------------------------------------------------- # +# Directory where output files are written +_C.OUTPUT_DIR = "./output" +# Set seed to negative to fully randomize everything. +# Set seed to positive to use a fixed seed. Note that a fixed seed increases +# reproducibility but does not guarantee fully deterministic behavior. +# Disabling all parallelism further increases reproducibility. +_C.SEED = -1 +# Benchmark different cudnn algorithms. +# If input images have very different sizes, this option will have large overhead +# for about 10k iterations. It usually hurts total time, but can benefit for certain models. +# If input images have the same or similar sizes, benchmark is often helpful. +_C.CUDNN_BENCHMARK = False +# The period (in terms of steps) for minibatch visualization at train time. +# Set to 0 to disable. +_C.VIS_PERIOD = 0 + +# global config is for quick hack purposes. +# You can set them in command line or config files, +# and access it with: +# +# from detectron2.config import global_cfg +# print(global_cfg.HACK) +# +# Do not commit any configs into it. +_C.GLOBAL = CN() +_C.GLOBAL.HACK = 1.0 diff --git a/detection-team/evaluation/detectron2_for_evaluation/engine/__pycache__/defaults.cpython-39.pyc b/detection-team/evaluation/detectron2_for_evaluation/engine/__pycache__/defaults.cpython-39.pyc new file mode 100644 index 0000000..214074a Binary files /dev/null and b/detection-team/evaluation/detectron2_for_evaluation/engine/__pycache__/defaults.cpython-39.pyc differ diff --git a/detection-team/evaluation/detectron2_for_evaluation/engine/__pycache__/hooks.cpython-39.pyc b/detection-team/evaluation/detectron2_for_evaluation/engine/__pycache__/hooks.cpython-39.pyc new file mode 100644 index 0000000..ecf3417 Binary files /dev/null and b/detection-team/evaluation/detectron2_for_evaluation/engine/__pycache__/hooks.cpython-39.pyc differ diff --git a/detection-team/evaluation/detectron2_for_evaluation/engine/__pycache__/train_loop.cpython-39.pyc b/detection-team/evaluation/detectron2_for_evaluation/engine/__pycache__/train_loop.cpython-39.pyc new file mode 100644 index 0000000..b195a82 Binary files /dev/null and b/detection-team/evaluation/detectron2_for_evaluation/engine/__pycache__/train_loop.cpython-39.pyc differ diff --git a/detection-team/evaluation/detectron2_for_evaluation/engine/defaults.py b/detection-team/evaluation/detectron2_for_evaluation/engine/defaults.py new file mode 100644 index 0000000..1f8bbe1 --- /dev/null +++ b/detection-team/evaluation/detectron2_for_evaluation/engine/defaults.py @@ -0,0 +1,322 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +This file contains components with some default boilerplate logic user may need +in training / testing. They will not work for everyone, but many users may find them useful. + +The behavior of functions/classes in this file is subject to change, +since they are meant to represent the "common default behavior" people need in their projects. +""" + +import argparse +import logging +import os +import sys +import weakref +from collections import OrderedDict +from typing import Optional +import torch +from fvcore.nn.precise_bn import get_bn_modules +from omegaconf import OmegaConf +from torch.nn.parallel import DistributedDataParallel + +import detectron2.data.transforms as T +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig +from detectron2.data import ( + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.evaluation import ( + DatasetEvaluator, + inference_on_dataset, + print_csv_format, + verify_results, +) +from detectron2.modeling import build_model +from detectron2.solver import build_lr_scheduler, build_optimizer +from detectron2.utils import comm +from detectron2.utils.collect_env import collect_env_info +from detectron2.utils.env import seed_all_rng +from detectron2.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import setup_logger + +# from . import hooks +# from .train_loop import AMPTrainer, SimpleTrainer, TrainerBase + +__all__ = [ + "create_ddp_model", + "default_argument_parser", + "default_setup", + "default_writers", + "DefaultPredictor", + "DefaultTrainer", +] + + +def create_ddp_model(model, *, fp16_compression=False, **kwargs): + """ + Create a DistributedDataParallel model if there are >1 processes. + + Args: + model: a torch.nn.Module + fp16_compression: add fp16 compression hooks to the ddp object. + See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook + kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`. + """ # noqa + if comm.get_world_size() == 1: + return model + if "device_ids" not in kwargs: + kwargs["device_ids"] = [comm.get_local_rank()] + ddp = DistributedDataParallel(model, **kwargs) + if fp16_compression: + from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks + + ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook) + return ddp + + +def default_argument_parser(epilog=None): + """ + Create a parser with some common arguments used by detectron2 users. + + Args: + epilog (str): epilog passed to ArgumentParser describing the usage. + + Returns: + argparse.ArgumentParser: + """ + parser = argparse.ArgumentParser( + epilog=epilog + or f""" +Examples: + +Run on single machine: + $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml + +Change some config options: + $ {sys.argv[0]} --config-file cfg.yaml MODEL.WEIGHTS /path/to/weight.pth SOLVER.BASE_LR 0.001 + +Run on multiple machines: + (machine0)$ {sys.argv[0]} --machine-rank 0 --num-machines 2 --dist-url [--other-flags] + (machine1)$ {sys.argv[0]} --machine-rank 1 --num-machines 2 --dist-url [--other-flags] +""", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") + parser.add_argument( + "--resume", + action="store_true", + help="Whether to attempt to resume from the checkpoint directory. " + "See documentation of `DefaultTrainer.resume_or_load()` for what it means.", + ) + parser.add_argument("--eval-only", action="store_true", help="perform evaluation only") + parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*") + parser.add_argument("--num-machines", type=int, default=1, help="total number of machines") + parser.add_argument( + "--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)" + ) + + # PyTorch still may leave orphan processes in multi-gpu training. + # Therefore we use a deterministic way to obtain port, + # so that users are aware of orphan processes by seeing the port occupied. + port = 2**15 + 2**14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2**14 + parser.add_argument( + "--dist-url", + default="tcp://127.0.0.1:{}".format(port), + help="initialization URL for pytorch distributed backend. See " + "https://pytorch.org/docs/stable/distributed.html for details.", + ) + parser.add_argument( + "opts", + help=""" +Modify config options at the end of the command. For Yacs configs, use +space-separated "PATH.KEY VALUE" pairs. +For python-based LazyConfig, use "path.key=value". + """.strip(), + default=None, + nargs=argparse.REMAINDER, + ) + return parser + + +def _try_get_key(cfg, *keys, default=None): + """ + Try select keys from cfg until the first key that exists. Otherwise return default. + """ + if isinstance(cfg, CfgNode): + cfg = OmegaConf.create(cfg.dump()) + for k in keys: + none = object() + p = OmegaConf.select(cfg, k, default=none) + if p is not none: + return p + return default + + +def _highlight(code, filename): + try: + import pygments + except ImportError: + return code + + from pygments.lexers import Python3Lexer, YamlLexer + from pygments.formatters import Terminal256Formatter + + lexer = Python3Lexer() if filename.endswith(".py") else YamlLexer() + code = pygments.highlight(code, lexer, Terminal256Formatter(style="monokai")) + return code + + +def default_setup(cfg, args): + """ + Perform some basic common setups at the beginning of a job, including: + + 1. Set up the detectron2 logger + 2. Log basic information about environment, cmdline arguments, and config + 3. Backup the config to the output directory + + Args: + cfg (CfgNode or omegaconf.DictConfig): the full config to be used + args (argparse.NameSpace): the command line arguments to be logged + """ + output_dir = _try_get_key(cfg, "OUTPUT_DIR", "output_dir", "train.output_dir") + if comm.is_main_process() and output_dir: + PathManager.mkdirs(output_dir) + + rank = comm.get_rank() + setup_logger(output_dir, distributed_rank=rank, name="fvcore") + logger = setup_logger(output_dir, distributed_rank=rank) + + logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size())) + logger.info("Environment info:\n" + collect_env_info()) + + logger.info("Command line arguments: " + str(args)) + if hasattr(args, "config_file") and args.config_file != "": + logger.info( + "Contents of args.config_file={}:\n{}".format( + args.config_file, + _highlight(PathManager.open(args.config_file, "r").read(), args.config_file), + ) + ) + + if comm.is_main_process() and output_dir: + # Note: some of our scripts may expect the existence of + # config.yaml in output directory + path = os.path.join(output_dir, "config.yaml") + if isinstance(cfg, CfgNode): + logger.info("Running with full config:\n{}".format(_highlight(cfg.dump(), ".yaml"))) + with PathManager.open(path, "w") as f: + f.write(cfg.dump()) + else: + LazyConfig.save(cfg, path) + logger.info("Full config saved to {}".format(path)) + + # make sure each worker has a different, yet deterministic seed if specified + seed = _try_get_key(cfg, "SEED", "train.seed", default=-1) + seed_all_rng(None if seed < 0 else seed + rank) + + # cudnn benchmark has large overhead. It shouldn't be used considering the small size of + # typical validation set. + if not (hasattr(args, "eval_only") and args.eval_only): + torch.backends.cudnn.benchmark = _try_get_key( + cfg, "CUDNN_BENCHMARK", "train.cudnn_benchmark", default=False + ) + + +def default_writers(output_dir: str, max_iter: Optional[int] = None): + """ + Build a list of :class:`EventWriter` to be used. + It now consists of a :class:`CommonMetricPrinter`, + :class:`TensorboardXWriter` and :class:`JSONWriter`. + + Args: + output_dir: directory to store JSON metrics and tensorboard events + max_iter: the total number of iterations + + Returns: + list[EventWriter]: a list of :class:`EventWriter` objects. + """ + PathManager.mkdirs(output_dir) + return [ + # It may not always print what you want to see, since it prints "common" metrics only. + CommonMetricPrinter(max_iter), + JSONWriter(os.path.join(output_dir, "metrics.json")), + TensorboardXWriter(output_dir), + ] + + +class DefaultPredictor: + """ + Create a simple end-to-end predictor with the given config that runs on + single device for a single input image. + + Compared to using the model directly, this class does the following additions: + + 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. + 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. + 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. + 4. Take one input image and produce a single output, instead of a batch. + + This is meant for simple demo purposes, so it does the above steps automatically. + This is not meant for benchmarks or running complicated inference logic. + If you'd like to do anything more complicated, please refer to its source code as + examples to build and use the model manually. + + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + + Examples: + :: + pred = DefaultPredictor(cfg) + inputs = cv2.imread("input.jpg") + outputs = pred(inputs) + """ + + def __init__(self, cfg): + self.cfg = cfg.clone() # cfg can be modified by model + self.model = build_model(self.cfg) + self.model.eval() + if len(cfg.DATASETS.TEST): + self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.MODEL.WEIGHTS) + + self.aug = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + + self.input_format = cfg.INPUT.FORMAT + assert self.input_format in ["RGB", "BGR"], self.input_format + + def __call__(self, original_image): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + image.to(self.cfg.MODEL.DEVICE) + + inputs = {"image": image, "height": height, "width": width} + + predictions = self.model([inputs])[0] + return predictions + + diff --git a/detection-team/evaluation/detectron2_for_evaluation/faster_rcnn_R_101_FPN_3x.yaml b/detection-team/evaluation/detectron2_for_evaluation/faster_rcnn_R_101_FPN_3x.yaml new file mode 100644 index 0000000..57a098f --- /dev/null +++ b/detection-team/evaluation/detectron2_for_evaluation/faster_rcnn_R_101_FPN_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: False + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/detection-team/evaluation/detectron2_for_evaluation/model_zoo.py b/detection-team/evaluation/detectron2_for_evaluation/model_zoo.py new file mode 100644 index 0000000..4e80ffd --- /dev/null +++ b/detection-team/evaluation/detectron2_for_evaluation/model_zoo.py @@ -0,0 +1,213 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os +from typing import Optional +import pkg_resources +import torch + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate +from detectron2.modeling import build_model + + +class _ModelZooUrls: + """ + Mapping from names to officially released Detectron2 pre-trained models. + """ + + S3_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" + + # format: {config_path.yaml} -> model_id/model_final_{commit}.pkl + CONFIG_PATH_TO_URL_SUFFIX = { + # COCO Detection with Faster R-CNN + "COCO-Detection/faster_rcnn_R_50_C4_1x": "137257644/model_final_721ade.pkl", + "COCO-Detection/faster_rcnn_R_50_DC5_1x": "137847829/model_final_51d356.pkl", + "COCO-Detection/faster_rcnn_R_50_FPN_1x": "137257794/model_final_b275ba.pkl", + "COCO-Detection/faster_rcnn_R_50_C4_3x": "137849393/model_final_f97cb7.pkl", + "COCO-Detection/faster_rcnn_R_50_DC5_3x": "137849425/model_final_68d202.pkl", + "COCO-Detection/faster_rcnn_R_50_FPN_3x": "137849458/model_final_280758.pkl", + "COCO-Detection/faster_rcnn_R_101_C4_3x": "138204752/model_final_298dad.pkl", + "COCO-Detection/faster_rcnn_R_101_DC5_3x": "138204841/model_final_3e0943.pkl", + "COCO-Detection/faster_rcnn_R_101_FPN_3x": "137851257/model_final_f6e8b1.pkl", + "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x": "139173657/model_final_68b088.pkl", + # COCO Detection with RetinaNet + "COCO-Detection/retinanet_R_50_FPN_1x": "190397773/model_final_bfca0b.pkl", + "COCO-Detection/retinanet_R_50_FPN_3x": "190397829/model_final_5bd44e.pkl", + "COCO-Detection/retinanet_R_101_FPN_3x": "190397697/model_final_971ab9.pkl", + # COCO Detection with RPN and Fast R-CNN + "COCO-Detection/rpn_R_50_C4_1x": "137258005/model_final_450694.pkl", + "COCO-Detection/rpn_R_50_FPN_1x": "137258492/model_final_02ce48.pkl", + "COCO-Detection/fast_rcnn_R_50_FPN_1x": "137635226/model_final_e5f7ce.pkl", + # COCO Instance Segmentation Baselines with Mask R-CNN + "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x": "137259246/model_final_9243eb.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x": "137260150/model_final_4f86c3.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "137260431/model_final_a54504.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x": "137849525/model_final_4ce675.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x": "137849551/model_final_84107b.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x": "137849600/model_final_f10217.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x": "138363239/model_final_a2914c.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x": "138363294/model_final_0464b7.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x": "138205316/model_final_a3ec72.pkl", + "COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x": "139653917/model_final_2d9806.pkl", # noqa + # New baselines using Large-Scale Jitter and Longer Training Schedule + "new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ": "42047764/model_final_bb69de.pkl", + "new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ": "42047638/model_final_89a8d3.pkl", + "new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ": "42019571/model_final_14d201.pkl", + "new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ": "42025812/model_final_4f7b58.pkl", + "new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ": "42131867/model_final_0bb7ae.pkl", + "new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ": "42073830/model_final_f96b26.pkl", + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ": "42047771/model_final_b7fbab.pkl", # noqa + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ": "42132721/model_final_5d87c1.pkl", # noqa + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ": "42025447/model_final_f1362d.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ": "42047784/model_final_6ba57e.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ": "42047642/model_final_27b9c1.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ": "42045954/model_final_ef3a80.pkl", # noqa + # COCO Person Keypoint Detection Baselines with Keypoint R-CNN + "COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x": "137261548/model_final_04e291.pkl", + "COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x": "137849621/model_final_a6e10b.pkl", + "COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x": "138363331/model_final_997cc7.pkl", + "COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x": "139686956/model_final_5ad38f.pkl", + # COCO Panoptic Segmentation Baselines with Panoptic FPN + "COCO-PanopticSegmentation/panoptic_fpn_R_50_1x": "139514544/model_final_dbfeb4.pkl", + "COCO-PanopticSegmentation/panoptic_fpn_R_50_3x": "139514569/model_final_c10459.pkl", + "COCO-PanopticSegmentation/panoptic_fpn_R_101_3x": "139514519/model_final_cafdb1.pkl", + # LVIS Instance Segmentation Baselines with Mask R-CNN + "LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "144219072/model_final_571f7c.pkl", # noqa + "LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x": "144219035/model_final_824ab5.pkl", # noqa + "LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x": "144219108/model_final_5e3439.pkl", # noqa + # Cityscapes & Pascal VOC Baselines + "Cityscapes/mask_rcnn_R_50_FPN": "142423278/model_final_af9cf5.pkl", + "PascalVOC-Detection/faster_rcnn_R_50_C4": "142202221/model_final_b1acc2.pkl", + # Other Settings + "Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5": "138602867/model_final_65c703.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5": "144998336/model_final_821d0b.pkl", + "Misc/cascade_mask_rcnn_R_50_FPN_1x": "138602847/model_final_e9d89b.pkl", + "Misc/cascade_mask_rcnn_R_50_FPN_3x": "144998488/model_final_480dd8.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_syncbn": "169527823/model_final_3b3c51.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_gn": "138602888/model_final_dc5d9e.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_3x_gn": "138602908/model_final_01ca85.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_9x_gn": "183808979/model_final_da7b4c.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn": "184226666/model_final_5ce33e.pkl", + "Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x": "139797668/model_final_be35db.pkl", + "Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv": "18131413/model_0039999_e76410.pkl", # noqa + # D1 Comparisons + "Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x": "137781054/model_final_7ab50c.pkl", # noqa + "Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x": "137781281/model_final_62ca52.pkl", # noqa + "Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x": "137781195/model_final_cce136.pkl", + } + + @staticmethod + def query(config_path: str) -> Optional[str]: + """ + Args: + config_path: relative config filename + """ + name = config_path.replace(".yaml", "").replace(".py", "") + if name in _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX: + suffix = _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX[name] + return _ModelZooUrls.S3_PREFIX + name + "/" + suffix + return None + + +def get_checkpoint_url(config_path): + """ + Returns the URL to the model trained using the given config + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + + Returns: + str: a URL to the model + """ + url = _ModelZooUrls.query(config_path) + if url is None: + raise RuntimeError("Pretrained model for {} is not available!".format(config_path)) + return url + + +def get_config_file(config_path): + """ + Returns path to a builtin config file. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + + Returns: + str: the real path to the config file. + """ + cfg_file = pkg_resources.resource_filename( + "detectron2.model_zoo", os.path.join("configs", config_path) + ) + if not os.path.exists(cfg_file): + raise RuntimeError("{} not available in Model Zoo!".format(config_path)) + return cfg_file + + +def get_config(config_path, trained: bool = False): + """ + Returns a config object for a model in model zoo. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + trained (bool): If True, will set ``MODEL.WEIGHTS`` to trained model zoo weights. + If False, the checkpoint specified in the config file's ``MODEL.WEIGHTS`` is used + instead; this will typically (though not always) initialize a subset of weights using + an ImageNet pre-trained model, while randomly initializing the other weights. + + Returns: + CfgNode or omegaconf.DictConfig: a config object + """ + cfg_file = get_config_file(config_path) + if cfg_file.endswith(".yaml"): + cfg = get_cfg() + cfg.merge_from_file(cfg_file) + if trained: + cfg.MODEL.WEIGHTS = get_checkpoint_url(config_path) + return cfg + elif cfg_file.endswith(".py"): + cfg = LazyConfig.load(cfg_file) + if trained: + url = get_checkpoint_url(config_path) + if "train" in cfg and "init_checkpoint" in cfg.train: + cfg.train.init_checkpoint = url + else: + raise NotImplementedError + return cfg + + +def get(config_path, trained: bool = False, device: Optional[str] = None): + """ + Get a model specified by relative path under Detectron2's official ``configs/`` directory. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + trained (bool): see :func:`get_config`. + device (str or None): overwrite the device in config, if given. + + Returns: + nn.Module: a detectron2 model. Will be in training mode. + + Example: + :: + from detectron2 import model_zoo + model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True) + """ + cfg = get_config(config_path, trained) + if device is None and not torch.cuda.is_available(): + device = "cpu" + if device is not None and isinstance(cfg, CfgNode): + cfg.MODEL.DEVICE = device + + if isinstance(cfg, CfgNode): + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + model = instantiate(cfg.model) + if device is not None: + model = model.to(device) + if "train" in cfg and "init_checkpoint" in cfg.train: + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + return model diff --git a/detection-team/evaluation/detectron2_gpu_utilization_evaluation_main.py b/detection-team/evaluation/detectron2_gpu_utilization_evaluation_main.py new file mode 100644 index 0000000..998733d --- /dev/null +++ b/detection-team/evaluation/detectron2_gpu_utilization_evaluation_main.py @@ -0,0 +1,16 @@ +import importlib +import os +# Import the module dynamically +detectron2_evaluation_module = importlib.import_module("detectron2-gpu-utilization-evaluation") + +# Access the function from the module +detectron2_evaluation = getattr(detectron2_evaluation_module, "detectron2_evaluation") + + +PATH_TO_DATA="data_for_evaluation" + +if __name__=='__main__': + # print the accuracy + # for example: print(groundingDINO_evaluation(path_to_data)) + print(detectron2_evaluation(PATH_TO_DATA)) + #(0.9608865710560626, 0.5566465256797583) \ No newline at end of file diff --git a/detection-team/evaluation/gpu_utilization_GroundingDINO.csv b/detection-team/evaluation/gpu_utilization_GroundingDINO.csv new file mode 100644 index 0000000..99208ca --- /dev/null +++ b/detection-team/evaluation/gpu_utilization_GroundingDINO.csv @@ -0,0 +1,575 @@ 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2024,0,0,0,0 +Thu Aug 1 12:26:17 2024,0,0,0,0 +Thu Aug 1 12:26:18 2024,0,0,0,0 +Thu Aug 1 12:26:19 2024,0,0,0,0 +Thu Aug 1 12:26:20 2024,0,0,0,0 +Thu Aug 1 12:26:21 2024,0,0,0,0 +Thu Aug 1 12:26:22 2024,0,0,0,0 diff --git a/detection-team/evaluation/gpu_utilization_ResNet.csv b/detection-team/evaluation/gpu_utilization_ResNet.csv new file mode 100644 index 0000000..1a88d61 --- /dev/null +++ b/detection-team/evaluation/gpu_utilization_ResNet.csv @@ -0,0 +1,19 @@ +timestamp,gpu_utilization,memory_utilization,encoder_utilization,decoder_utilization +Wed Aug 7 12:46:03 2024,0,0,0,0 +Wed Aug 7 12:46:04 2024,0,0,0,0 +Wed Aug 7 12:46:05 2024,0,0,0,0 +Wed Aug 7 12:46:06 2024,0,0,0,0 +Wed Aug 7 12:46:07 2024,0,0,0,0 +Wed Aug 7 12:46:08 2024,0,0,0,0 +Wed Aug 7 12:46:09 2024,0,0,0,0 +Wed Aug 7 12:46:10 2024,0,0,0,0 +Wed Aug 7 12:46:11 2024,0,0,0,0 +Wed Aug 7 12:46:12 2024,0,0,0,0 +Wed Aug 7 12:46:13 2024,0,0,0,0 +Wed Aug 7 12:46:14 2024,0,0,0,0 +Wed Aug 7 12:46:15 2024,2,1,0,0 +Wed Aug 7 12:46:16 2024,0,0,0,0 +Wed Aug 7 12:46:17 2024,0,0,0,0 +Wed Aug 7 12:46:19 2024,0,0,0,0 +Wed Aug 7 12:46:20 2024,0,0,0,0 +Wed Aug 7 12:46:21 2024,0,0,0,0 diff --git a/detection-team/evaluation/gpu_utilization_YOLO.csv b/detection-team/evaluation/gpu_utilization_YOLO.csv new file mode 100644 index 0000000..2d10855 --- /dev/null +++ b/detection-team/evaluation/gpu_utilization_YOLO.csv @@ -0,0 +1,180 @@ +timestamp,gpu_utilization,memory_utilization,encoder_utilization,decoder_utilization +2024-08-04 22:11:07,4,1,0,0 +2024-08-04 22:11:12,3,1,0,0 +2024-08-04 22:11:17,0,0,0,0 +2024-08-04 22:11:22,0,0,0,0 +2024-08-04 22:11:27,4,1,0,0 +2024-08-04 22:11:32,0,0,0,0 +2024-08-04 22:11:37,0,0,0,0 +2024-08-04 22:11:43,4,1,0,0 +2024-08-04 22:11:48,0,0,0,0 +2024-08-04 22:11:53,0,0,0,0 +2024-08-04 22:11:58,4,1,0,0 +2024-08-04 22:12:03,0,0,0,0 +2024-08-04 22:12:08,0,0,0,0 +2024-08-04 22:12:13,4,1,0,0 +2024-08-04 22:12:18,0,0,0,0 +2024-08-04 22:12:23,0,0,0,0 +2024-08-04 22:12:28,0,0,0,0 +2024-08-04 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04:33:57,0,0,0,0 +2024-08-05 04:33:58,0,0,0,0 +2024-08-05 04:33:59,0,0,0,0 +2024-08-05 04:34:00,0,0,0,0 +2024-08-05 04:34:01,0,0,0,0 +2024-08-05 04:34:02,0,0,0,0 +2024-08-05 04:34:03,0,0,0,0 +2024-08-05 04:34:04,0,0,0,0 +2024-08-05 04:34:06,0,0,0,0 +2024-08-05 04:34:07,0,0,0,0 +2024-08-05 04:34:08,0,0,0,0 +2024-08-05 04:34:09,0,0,0,0 +2024-08-05 04:34:10,3,1,0,0 +2024-08-05 04:34:11,0,0,0,0 +2024-08-05 04:34:12,0,0,0,0 +2024-08-05 04:34:13,4,1,0,0 +2024-08-05 04:34:14,0,0,0,0 +2024-08-05 04:34:15,0,0,0,0 +2024-08-05 04:34:16,2,1,0,0 +2024-08-05 04:34:17,0,0,0,0 +2024-08-05 04:34:18,0,0,0,0 +2024-08-05 04:34:19,1,0,0,0 +2024-08-05 04:34:20,0,0,0,0 +2024-08-05 04:34:21,0,0,0,0 +2024-08-05 04:34:22,3,1,0,0 +2024-08-05 04:34:24,0,0,0,0 +2024-08-05 04:34:25,0,0,0,0 +2024-08-05 04:34:26,3,1,0,0 +2024-08-05 04:34:27,0,0,0,0 +2024-08-05 04:34:28,0,0,0,0 +2024-08-05 04:34:29,3,1,0,0 +2024-08-05 04:34:30,3,1,0,0 +2024-08-05 04:34:31,0,0,0,0 +2024-08-05 04:34:32,0,0,0,0 +2024-08-05 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+1,39 @@ +timestamp,gpu_utilization,memory_utilization,encoder_utilization,decoder_utilization +Sun Aug 4 05:15:37 2024,0,0,0,0 +Sun Aug 4 05:15:38 2024,0,0,0,0 +Sun Aug 4 05:15:39 2024,0,0,0,0 +Sun Aug 4 05:15:40 2024,0,0,0,0 +Sun Aug 4 05:15:41 2024,0,0,0,0 +Sun Aug 4 05:15:42 2024,0,0,0,0 +Sun Aug 4 05:15:43 2024,0,0,0,0 +Sun Aug 4 05:15:44 2024,0,0,0,0 +Sun Aug 4 05:15:45 2024,0,0,0,0 +Sun Aug 4 05:15:46 2024,0,0,0,0 +Sun Aug 4 05:15:47 2024,0,0,0,0 +Sun Aug 4 05:15:48 2024,0,0,0,0 +Sun Aug 4 05:15:49 2024,0,0,0,0 +Sun Aug 4 05:15:50 2024,0,0,0,0 +Sun Aug 4 05:15:51 2024,0,0,0,0 +Sun Aug 4 05:15:52 2024,4,0,0,0 +Sun Aug 4 05:15:53 2024,5,0,0,0 +Sun Aug 4 05:15:54 2024,0,0,0,0 +Sun Aug 4 05:15:55 2024,0,0,0,0 +Sun Aug 4 05:15:56 2024,1,0,0,0 +Sun Aug 4 05:15:57 2024,23,16,0,0 +Sun Aug 4 05:15:58 2024,80,56,0,0 +Sun Aug 4 05:15:59 2024,69,41,0,0 +Sun Aug 4 05:16:00 2024,80,55,0,0 +Sun Aug 4 05:16:01 2024,79,51,0,0 +Sun Aug 4 05:16:02 2024,79,56,0,0 +Sun Aug 4 05:16:03 2024,70,43,0,0 +Sun Aug 4 05:16:04 2024,70,41,0,0 +Sun Aug 4 05:16:05 2024,66,44,0,0 +Sun Aug 4 05:16:07 2024,63,42,0,0 +Sun Aug 4 05:16:08 2024,81,51,0,0 +Sun Aug 4 05:16:09 2024,84,58,0,0 +Sun Aug 4 05:16:10 2024,0,0,0,0 +Sun Aug 4 05:16:11 2024,0,0,0,0 +Sun Aug 4 05:16:12 2024,0,0,0,0 +Sun Aug 4 05:16:13 2024,0,0,0,0 +Sun Aug 4 05:16:14 2024,0,0,0,0 +Sun Aug 4 05:16:15 2024,0,0,0,0 diff --git a/detection-team/evaluation/main.py b/detection-team/evaluation/main.py deleted file mode 100644 index 2885ddb..0000000 --- a/detection-team/evaluation/main.py +++ /dev/null @@ -1,6 +0,0 @@ -PATH_TO_DATA="my_data" - -if __name__=='__main__': - # print the accuracy - # for example: print(groundingDINO_evaluation(path_to_data)) - pass \ No newline at end of file diff --git a/detection-team/evaluation/my-data/images/image_000000025_jpg.rf.a4679f93eddd4aa151e58f2771def032.jpg b/detection-team/evaluation/my-data/images/image_000000025_jpg.rf.a4679f93eddd4aa151e58f2771def032.jpg deleted file mode 100644 index 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a/detection-team/evaluation/my-data/labels/image_000000302_jpg.rf.002c0bbc63d42ca41a470a45c8799b3f.txt b/detection-team/evaluation/my-data/labels/image_000000302_jpg.rf.002c0bbc63d42ca41a470a45c8799b3f.txt deleted file mode 100644 index 23bd829..0000000 --- a/detection-team/evaluation/my-data/labels/image_000000302_jpg.rf.002c0bbc63d42ca41a470a45c8799b3f.txt +++ /dev/null @@ -1,13 +0,0 @@ -0 0.06129807692307692 0.4326923076923077 0.1201923076923077 0.5637019230769231 -0 0.16826923076923078 0.33533653846153844 0.0889423076923077 0.328125 -0 0.2620192307692308 0.3112980769230769 0.10336538461538461 0.30649038461538464 -0 0.7367788461538461 0.296875 0.021634615384615384 0.06009615384615385 -0 0.7692307692307693 0.3449519230769231 0.016826923076923076 0.06610576923076923 -0 0.7836538461538461 0.31971153846153844 0.019230769230769232 0.09615384615384616 -0 0.8064903846153846 0.3401442307692308 0.016826923076923076 0.07932692307692307 -0 0.8401442307692307 0.30288461538461536 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at end of file diff --git a/detection-team/evaluation/my-data/labels/image_000000360_jpg.rf.710ec224ff9d2a00e638aac586b4ff65.txt b/detection-team/evaluation/my-data/labels/image_000000360_jpg.rf.710ec224ff9d2a00e638aac586b4ff65.txt deleted file mode 100644 index dd7b7fb..0000000 --- a/detection-team/evaluation/my-data/labels/image_000000360_jpg.rf.710ec224ff9d2a00e638aac586b4ff65.txt +++ /dev/null @@ -1,7 +0,0 @@ -0 0.3076923076923077 0.6201923076923077 0.0985576923076923 0.31971153846153844 -0 0.4098557692307692 0.5805288461538461 0.06490384615384616 0.36538461538461536 -0 0.6394230769230769 0.6069711538461539 0.09615384615384616 0.36899038461538464 -0 0.7608173076923077 0.5180288461538461 0.07692307692307693 0.1778846153846154 -1 0.171875 0.5576923076923077 0.11899038461538461 0.13701923076923078 -1 0.34375 0.5420673076923077 0.29447115384615385 0.21153846153846154 -1 0.5709134615384616 0.5841346153846154 0.36899038461538464 0.2403846153846154 \ No newline at end of file diff --git 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0.9278846153846154 0.21153846153846154 0.13581730769230768 0.15625 \ No newline at end of file diff --git a/detection-team/evaluation/my-data/labels/image_000000369_jpg.rf.2b1ef672610f21c460c27bbddce76e80.txt b/detection-team/evaluation/my-data/labels/image_000000369_jpg.rf.2b1ef672610f21c460c27bbddce76e80.txt deleted file mode 100644 index 508cbcb..0000000 --- a/detection-team/evaluation/my-data/labels/image_000000369_jpg.rf.2b1ef672610f21c460c27bbddce76e80.txt +++ /dev/null @@ -1,3 +0,0 @@ -0 0.5420673076923077 0.5396634615384616 0.0733173076923077 0.13221153846153846 -0 0.8930288461538461 0.6213942307692307 0.21153846153846154 0.7536057692307693 -1 0.5216346153846154 0.6466346153846154 0.6694711538461539 0.41586538461538464 \ No newline at end of file diff --git a/detection-team/evaluation/my-data/labels/image_000000379_jpg.rf.610ed0b4a38c3f335495ce843e2b3c3e.txt b/detection-team/evaluation/my-data/labels/image_000000379_jpg.rf.610ed0b4a38c3f335495ce843e2b3c3e.txt deleted file mode 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a/detection-team/evaluation/my-data/labels/image_000000423_jpg.rf.dcf75c134e1aa53317b3fc02098e35cc.txt b/detection-team/evaluation/my-data/labels/image_000000423_jpg.rf.dcf75c134e1aa53317b3fc02098e35cc.txt deleted file mode 100644 index df20b14..0000000 --- a/detection-team/evaluation/my-data/labels/image_000000423_jpg.rf.dcf75c134e1aa53317b3fc02098e35cc.txt +++ /dev/null @@ -1,4 +0,0 @@ -0 0.44471153846153844 0.5288461538461539 0.46033653846153844 0.9110576923076923 -0 0.765625 0.390625 0.039663461538461536 0.1814903846153846 -0 0.875 0.35336538461538464 0.03245192307692308 0.11658653846153846 -1 0.7103365384615384 0.38100961538461536 0.08653846153846154 0.09495192307692307 \ No newline at end of file diff --git a/detection-team/evaluation/my-data/labels/image_000000432_jpg.rf.720b1baed9304fe04ffbbc223055cd9f.txt b/detection-team/evaluation/my-data/labels/image_000000432_jpg.rf.720b1baed9304fe04ffbbc223055cd9f.txt deleted file mode 100644 index cbcae7e..0000000 --- a/detection-team/evaluation/my-data/labels/image_000000432_jpg.rf.720b1baed9304fe04ffbbc223055cd9f.txt +++ /dev/null @@ -1,21 +0,0 @@ -0 0.2247596153846154 0.5600961538461539 0.014423076923076924 0.03125 -0 0.2439903846153846 0.6201923076923077 0.03245192307692308 0.11778846153846154 -0 0.2536057692307692 0.5661057692307693 0.01201923076923077 0.021634615384615384 -0 0.27403846153846156 0.6165865384615384 0.030048076923076924 0.12139423076923077 -0 0.3125 0.6490384615384616 0.04326923076923077 0.1875 -0 0.3173076923076923 0.5540865384615384 0.018028846153846152 0.02403846153846154 -0 0.3329326923076923 0.6418269230769231 0.030048076923076924 0.15865384615384615 -0 0.34375 0.5697115384615384 0.019230769230769232 0.055288461538461536 -0 0.35216346153846156 0.6310096153846154 0.015625 0.14302884615384615 -0 0.36658653846153844 0.6478365384615384 0.018028846153846152 0.15745192307692307 -0 0.3894230769230769 0.640625 0.04927884615384615 0.18028846153846154 -0 0.375 0.5576923076923077 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b/detection-team/evaluation/my-data/labels/image_000000435_jpg.rf.7dedd5d9aa53159ad5841bcabe40f4e6.txt deleted file mode 100644 index a8a9788..0000000 --- a/detection-team/evaluation/my-data/labels/image_000000435_jpg.rf.7dedd5d9aa53159ad5841bcabe40f4e6.txt +++ /dev/null @@ -1,11 +0,0 @@ -0 0.23677884615384615 0.4362980769230769 0.03485576923076923 0.1346153846153846 -0 0.3004807692307692 0.37740384615384615 0.02403846153846154 0.0625 -0 0.65625 0.5060096153846154 0.054086538461538464 0.1875 -1 0.03365384615384615 0.4110576923076923 0.0673076923076923 0.052884615384615384 -1 0.06129807692307692 0.44711538461538464 0.12259615384615384 0.10817307692307693 -1 0.06129807692307692 0.5685096153846154 0.12259615384615384 0.20793269230769232 -1 0.38221153846153844 0.3918269230769231 0.06009615384615385 0.06610576923076923 -1 0.7283653846153846 0.43149038461538464 0.20552884615384615 0.12259615384615384 -1 0.9134615384615384 0.36658653846153844 0.06610576923076923 0.028846153846153848 -1 0.9447115384615384 0.4074519230769231 0.08653846153846154 0.06610576923076923 -1 0.9627403846153846 0.36658653846153844 0.0733173076923077 0.04807692307692308 \ No newline at end of file diff --git a/detection-team/evaluation/my-data/labels/image_000000438_jpg.rf.c98f03d99a01411cabadc928a66f60f3.txt b/detection-team/evaluation/my-data/labels/image_000000438_jpg.rf.c98f03d99a01411cabadc928a66f60f3.txt deleted file mode 100644 index 04cf2e2..0000000 --- a/detection-team/evaluation/my-data/labels/image_000000438_jpg.rf.c98f03d99a01411cabadc928a66f60f3.txt +++ /dev/null @@ -1,3 +0,0 @@ -0 0.5072115384615384 0.6682692307692307 0.14903846153846154 0.36177884615384615 -1 0.4651442307692308 0.5649038461538461 0.3737980769230769 0.10697115384615384 -1 0.828125 0.5480769230769231 0.3425480769230769 0.13822115384615385 \ No newline at end of file diff --git a/detection-team/evaluation/write_gpu_utilization_to_csv.py b/detection-team/evaluation/write_gpu_utilization_to_csv.py new file mode 100644 index 0000000..4048058 --- /dev/null +++ b/detection-team/evaluation/write_gpu_utilization_to_csv.py @@ -0,0 +1,83 @@ +import subprocess +import time +import csv +import re + +# File to write the CSV data +csv_file = 'gpu_utilization.csv' + +# Function to parse the nvidia-smi output and extract relevant data +def parse_nvidia_smi_output(output): + data = { + 'timestamp': '', + 'gpu_utilization': '', + 'memory_utilization': '', + 'encoder_utilization': '', + 'decoder_utilization': '' + } + + # Regular expressions for extracting data + timestamp_pattern = re.compile(r"Timestamp\s+:\s+(.*)") + gpu_pattern = re.compile(r"Gpu\s+:\s+(\d+)\s*%") + memory_pattern = re.compile(r"Memory\s+:\s+(\d+)\s*%") + encoder_pattern = re.compile(r"Encoder\s+:\s+(\d+)\s*%") + decoder_pattern = re.compile(r"Decoder\s+:\s+(\d+)\s*%") + + # Extract the timestamp + timestamp_match = timestamp_pattern.search(output) + if timestamp_match: + data['timestamp'] = timestamp_match.group(1) + + # Extract utilization data + gpu_match = gpu_pattern.search(output) + if gpu_match: + data['gpu_utilization'] = gpu_match.group(1) + + memory_match = memory_pattern.search(output) + if memory_match: + data['memory_utilization'] = memory_match.group(1) + + encoder_match = encoder_pattern.search(output) + if encoder_match: + data['encoder_utilization'] = encoder_match.group(1) + + decoder_match = decoder_pattern.search(output) + if decoder_match: + data['decoder_utilization'] = decoder_match.group(1) + + return data + +# Initialize CSV file and write header if it does not exist +def initialize_csv(): + try: + with open(csv_file, 'x', newline='') as file: + writer = csv.DictWriter(file, fieldnames=['timestamp', 'gpu_utilization', 'memory_utilization', 'encoder_utilization', 'decoder_utilization']) + writer.writeheader() + except FileExistsError: + pass + +# Append data to the CSV file +def append_to_csv(data): + with open(csv_file, 'a', newline='') as file: + writer = csv.DictWriter(file, fieldnames=['timestamp', 'gpu_utilization', 'memory_utilization', 'encoder_utilization', 'decoder_utilization']) + writer.writerow(data) + +# Main function to run the script +def main(): + initialize_csv() + while True: + # Run the nvidia-smi command + result = subprocess.run(['nvidia-smi', '-q', '-i', '0', '-d', 'UTILIZATION'], capture_output=True, text=True) + output = result.stdout + + # Parse the output + data = parse_nvidia_smi_output(output) + + # Append the data to CSV + append_to_csv(data) + + # Wait before the next read + time.sleep(1) + +if __name__ == "__main__": + main()