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ultralytics 8.1.43 40% faster ultralytics imports (#9547)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-04-05 15:29:09 +02:00 • committed by GitHub
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commit a2628657a1
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21 changed files with 240 additions and 225 deletions
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21
ultralytics/data/augment.py
View file

@ -8,7 +8,7 @@ from typing import Tuple, Union
import cv2
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from ultralytics.utils import LOGGER, colorstr
from ultralytics.utils.checks import check_version
@ -167,8 +167,8 @@ class BaseMixTransform:
text2id = {text: i for i, text in enumerate(mix_texts)}
for label in [labels] + labels["mix_labels"]:
for i, l in enumerate(label["cls"].squeeze(-1).tolist()):
text = label["texts"][int(l)]
for i, cls in enumerate(label["cls"].squeeze(-1).tolist()):
text = label["texts"][int(cls)]
label["cls"][i] = text2id[tuple(text)]
label["texts"] = mix_texts
return labels
@ -1133,7 +1133,7 @@ def classify_transforms(
size=224,
mean=DEFAULT_MEAN,
std=DEFAULT_STD,
interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,
interpolation=Image.BILINEAR,
crop_fraction: float = DEFAULT_CROP_FTACTION,
):
"""
@ -1149,6 +1149,7 @@ def classify_transforms(
Returns:
(T.Compose): torchvision transforms
"""
import torchvision.transforms as T # scope for faster 'import ultralytics'
if isinstance(size, (tuple, list)):
assert len(size) == 2
@ -1157,12 +1158,12 @@ def classify_transforms(
scale_size = math.floor(size / crop_fraction)
scale_size = (scale_size, scale_size)
# aspect ratio is preserved, crops center within image, no borders are added, image is lost
# Aspect ratio is preserved, crops center within image, no borders are added, image is lost
if scale_size[0] == scale_size[1]:
# simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg)
# Simple case, use torchvision built-in Resize with the shortest edge mode (scalar size arg)
tfl = [T.Resize(scale_size[0], interpolation=interpolation)]
else:
# resize shortest edge to matching target dim for non-square target
# Resize the shortest edge to matching target dim for non-square target
tfl = [T.Resize(scale_size)]
tfl += [T.CenterCrop(size)]
@ -1192,7 +1193,7 @@ def classify_augmentations(
hsv_v=0.4, # image HSV-Value augmentation (fraction)
force_color_jitter=False,
erasing=0.0,
interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,
interpolation=Image.BILINEAR,
):
"""
Classification transforms with augmentation for training. Inspired by timm/data/transforms_factory.py.
@ -1216,7 +1217,9 @@ def classify_augmentations(
Returns:
(T.Compose): torchvision transforms
"""
# Transforms to apply if albumentations not installed
# Transforms to apply if Albumentations not installed
import torchvision.transforms as T # scope for faster 'import ultralytics'
if not isinstance(size, int):
raise TypeError(f"classify_transforms() size {size} must be integer, not (list, tuple)")
scale = tuple(scale or (0.08, 1.0)) # default imagenet scale range

289
ultralytics/data/dataset.py
View file

@ -1,18 +1,17 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
from itertools import repeat
import json
from collections import defaultdict
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
import cv2
import json
import numpy as np
import torch
import torchvision
from PIL import Image
from torch.utils.data import ConcatDataset
from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr
from ultralytics.utils.ops import resample_segments
from .augment import (
@ -103,16 +102,16 @@ class YOLODataset(BaseDataset):
nc += nc_f
if im_file:
x["labels"].append(
dict(
im_file=im_file,
shape=shape,
cls=lb[:, 0:1], # n, 1
bboxes=lb[:, 1:], # n, 4
segments=segments,
keypoints=keypoint,
normalized=True,
bbox_format="xywh",
)
{
"im_file": im_file,
"shape": shape,
"cls": lb[:, 0:1], # n, 1
"bboxes": lb[:, 1:], # n, 4
"segments": segments,
"keypoints": keypoint,
"normalized": True,
"bbox_format": "xywh",
}
)
if msg:
msgs.append(msg)
@ -245,125 +244,6 @@ class YOLODataset(BaseDataset):
return new_batch
# Classification dataloaders -------------------------------------------------------------------------------------------
class ClassificationDataset(torchvision.datasets.ImageFolder):
"""
Extends torchvision ImageFolder to support YOLO classification tasks, offering functionalities like image
augmentation, caching, and verification. It's designed to efficiently handle large datasets for training deep
learning models, with optional image transformations and caching mechanisms to speed up training.
This class allows for augmentations using both torchvision and Albumentations libraries, and supports caching images
in RAM or on disk to reduce IO overhead during training. Additionally, it implements a robust verification process
to ensure data integrity and consistency.
Attributes:
cache_ram (bool): Indicates if caching in RAM is enabled.
cache_disk (bool): Indicates if caching on disk is enabled.
samples (list): A list of tuples, each containing the path to an image, its class index, path to its .npy cache
file (if caching on disk), and optionally the loaded image array (if caching in RAM).
torch_transforms (callable): PyTorch transforms to be applied to the images.
"""
def __init__(self, root, args, augment=False, prefix=""):
"""
Initialize YOLO object with root, image size, augmentations, and cache settings.
Args:
root (str): Path to the dataset directory where images are stored in a class-specific folder structure.
args (Namespace): Configuration containing dataset-related settings such as image size, augmentation
parameters, and cache settings. It includes attributes like `imgsz` (image size), `fraction` (fraction
of data to use), `scale`, `fliplr`, `flipud`, `cache` (disk or RAM caching for faster training),
`auto_augment`, `hsv_h`, `hsv_s`, `hsv_v`, and `crop_fraction`.
augment (bool, optional): Whether to apply augmentations to the dataset. Default is False.
prefix (str, optional): Prefix for logging and cache filenames, aiding in dataset identification and
debugging. Default is an empty string.
"""
super().__init__(root=root)
if augment and args.fraction < 1.0: # reduce training fraction
self.samples = self.samples[: round(len(self.samples) * args.fraction)]
self.prefix = colorstr(f"{prefix}: ") if prefix else ""
self.cache_ram = args.cache is True or str(args.cache).lower() == "ram" # cache images into RAM
self.cache_disk = str(args.cache).lower() == "disk" # cache images on hard drive as uncompressed *.npy files
self.samples = self.verify_images() # filter out bad images
self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im
scale = (1.0 - args.scale, 1.0) # (0.08, 1.0)
self.torch_transforms = (
classify_augmentations(
size=args.imgsz,
scale=scale,
hflip=args.fliplr,
vflip=args.flipud,
erasing=args.erasing,
auto_augment=args.auto_augment,
hsv_h=args.hsv_h,
hsv_s=args.hsv_s,
hsv_v=args.hsv_v,
)
if augment
else classify_transforms(size=args.imgsz, crop_fraction=args.crop_fraction)
)
def __getitem__(self, i):
"""Returns subset of data and targets corresponding to given indices."""
f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image
if self.cache_ram:
if im is None: # Warning: two separate if statements required here, do not combine this with previous line
im = self.samples[i][3] = cv2.imread(f)
elif self.cache_disk:
if not fn.exists(): # load npy
np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False)
im = np.load(fn)
else: # read image
im = cv2.imread(f) # BGR
# Convert NumPy array to PIL image
im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
sample = self.torch_transforms(im)
return {"img": sample, "cls": j}
def __len__(self) -> int:
"""Return the total number of samples in the dataset."""
return len(self.samples)
def verify_images(self):
"""Verify all images in dataset."""
desc = f"{self.prefix}Scanning {self.root}..."
path = Path(self.root).with_suffix(".cache") # *.cache file path
with contextlib.suppress(FileNotFoundError, AssertionError, AttributeError):
cache = load_dataset_cache_file(path) # attempt to load a *.cache file
assert cache["version"] == DATASET_CACHE_VERSION # matches current version
assert cache["hash"] == get_hash([x[0] for x in self.samples]) # identical hash
nf, nc, n, samples = cache.pop("results") # found, missing, empty, corrupt, total
if LOCAL_RANK in {-1, 0}:
d = f"{desc} {nf} images, {nc} corrupt"
TQDM(None, desc=d, total=n, initial=n)
if cache["msgs"]:
LOGGER.info("\n".join(cache["msgs"])) # display warnings
return samples
# Run scan if *.cache retrieval failed
nf, nc, msgs, samples, x = 0, 0, [], [], {}
with ThreadPool(NUM_THREADS) as pool:
results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
pbar = TQDM(results, desc=desc, total=len(self.samples))
for sample, nf_f, nc_f, msg in pbar:
if nf_f:
samples.append(sample)
if msg:
msgs.append(msg)
nf += nf_f
nc += nc_f
pbar.desc = f"{desc} {nf} images, {nc} corrupt"
pbar.close()
if msgs:
LOGGER.info("\n".join(msgs))
x["hash"] = get_hash([x[0] for x in self.samples])
x["results"] = nf, nc, len(samples), samples
x["msgs"] = msgs # warnings
save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION)
return samples
class YOLOMultiModalDataset(YOLODataset):
"""
Dataset class for loading object detection and/or segmentation labels in YOLO format.
@ -447,15 +327,15 @@ class GroundingDataset(YOLODataset):
bboxes.append(box)
lb = np.array(bboxes, dtype=np.float32) if len(bboxes) else np.zeros((0, 5), dtype=np.float32)
labels.append(
dict(
im_file=im_file,
shape=(h, w),
cls=lb[:, 0:1], # n, 1
bboxes=lb[:, 1:], # n, 4
normalized=True,
bbox_format="xywh",
texts=texts,
)
{
"im_file": im_file,
"shape": (h, w),
"cls": lb[:, 0:1], # n, 1
"bboxes": lb[:, 1:], # n, 4
"normalized": True,
"bbox_format": "xywh",
"texts": texts,
}
)
return labels
@ -497,3 +377,128 @@ class SemanticDataset(BaseDataset):
def __init__(self):
"""Initialize a SemanticDataset object."""
super().__init__()
class ClassificationDataset:
"""
Extends torchvision ImageFolder to support YOLO classification tasks, offering functionalities like image
augmentation, caching, and verification. It's designed to efficiently handle large datasets for training deep
learning models, with optional image transformations and caching mechanisms to speed up training.
This class allows for augmentations using both torchvision and Albumentations libraries, and supports caching images
in RAM or on disk to reduce IO overhead during training. Additionally, it implements a robust verification process
to ensure data integrity and consistency.
Attributes:
cache_ram (bool): Indicates if caching in RAM is enabled.
cache_disk (bool): Indicates if caching on disk is enabled.
samples (list): A list of tuples, each containing the path to an image, its class index, path to its .npy cache
file (if caching on disk), and optionally the loaded image array (if caching in RAM).
torch_transforms (callable): PyTorch transforms to be applied to the images.
"""
def __init__(self, root, args, augment=False, prefix=""):
"""
Initialize YOLO object with root, image size, augmentations, and cache settings.
Args:
root (str): Path to the dataset directory where images are stored in a class-specific folder structure.
args (Namespace): Configuration containing dataset-related settings such as image size, augmentation
parameters, and cache settings. It includes attributes like `imgsz` (image size), `fraction` (fraction
of data to use), `scale`, `fliplr`, `flipud`, `cache` (disk or RAM caching for faster training),
`auto_augment`, `hsv_h`, `hsv_s`, `hsv_v`, and `crop_fraction`.
augment (bool, optional): Whether to apply augmentations to the dataset. Default is False.
prefix (str, optional): Prefix for logging and cache filenames, aiding in dataset identification and
debugging. Default is an empty string.
"""
import torchvision # scope for faster 'import ultralytics'
# Base class assigned as attribute rather than used as base class to allow for scoping slow torchvision import
self.base = torchvision.datasets.ImageFolder(root=root)
self.samples = self.base.samples
self.root = self.base.root
# Initialize attributes
if augment and args.fraction < 1.0: # reduce training fraction
self.samples = self.samples[: round(len(self.samples) * args.fraction)]
self.prefix = colorstr(f"{prefix}: ") if prefix else ""
self.cache_ram = args.cache is True or str(args.cache).lower() == "ram" # cache images into RAM
self.cache_disk = str(args.cache).lower() == "disk" # cache images on hard drive as uncompressed *.npy files
self.samples = self.verify_images() # filter out bad images
self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im
scale = (1.0 - args.scale, 1.0) # (0.08, 1.0)
self.torch_transforms = (
classify_augmentations(
size=args.imgsz,
scale=scale,
hflip=args.fliplr,
vflip=args.flipud,
erasing=args.erasing,
auto_augment=args.auto_augment,
hsv_h=args.hsv_h,
hsv_s=args.hsv_s,
hsv_v=args.hsv_v,
)
if augment
else classify_transforms(size=args.imgsz, crop_fraction=args.crop_fraction)
)
def __getitem__(self, i):
"""Returns subset of data and targets corresponding to given indices."""
f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image
if self.cache_ram:
if im is None: # Warning: two separate if statements required here, do not combine this with previous line
im = self.samples[i][3] = cv2.imread(f)
elif self.cache_disk:
if not fn.exists(): # load npy
np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False)
im = np.load(fn)
else: # read image
im = cv2.imread(f) # BGR
# Convert NumPy array to PIL image
im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
sample = self.torch_transforms(im)
return {"img": sample, "cls": j}
def __len__(self) -> int:
"""Return the total number of samples in the dataset."""
return len(self.samples)
def verify_images(self):
"""Verify all images in dataset."""
desc = f"{self.prefix}Scanning {self.root}..."
path = Path(self.root).with_suffix(".cache") # *.cache file path
with contextlib.suppress(FileNotFoundError, AssertionError, AttributeError):
cache = load_dataset_cache_file(path) # attempt to load a *.cache file
assert cache["version"] == DATASET_CACHE_VERSION # matches current version
assert cache["hash"] == get_hash([x[0] for x in self.samples]) # identical hash
nf, nc, n, samples = cache.pop("results") # found, missing, empty, corrupt, total
if LOCAL_RANK in {-1, 0}:
d = f"{desc} {nf} images, {nc} corrupt"
TQDM(None, desc=d, total=n, initial=n)
if cache["msgs"]:
LOGGER.info("\n".join(cache["msgs"])) # display warnings
return samples
# Run scan if *.cache retrieval failed
nf, nc, msgs, samples, x = 0, 0, [], [], {}
with ThreadPool(NUM_THREADS) as pool:
results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
pbar = TQDM(results, desc=desc, total=len(self.samples))
for sample, nf_f, nc_f, msg in pbar:
if nf_f:
samples.append(sample)
if msg:
msgs.append(msg)
nf += nf_f
nc += nc_f
pbar.desc = f"{desc} {nf} images, {nc} corrupt"
pbar.close()
if msgs:
LOGGER.info("\n".join(msgs))
x["hash"] = get_hash([x[0] for x in self.samples])
x["results"] = nf, nc, len(samples), samples
x["msgs"] = msgs # warnings
save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION)
return samples

10
ultralytics/data/explorer/explorer.py
View file

@ -9,7 +9,6 @@ import numpy as np
import torch
from PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment import Format
@ -172,7 +171,7 @@ class Explorer:
def sql_query(
self, query: str, return_type: str = "pandas"
) -> Union[DataFrame, Any, None]: # pandas.dataframe or pyarrow.Table
) -> Union[Any, None]: # pandas.DataFrame or pyarrow.Table
"""
Run a SQL-Like query on the table. Utilizes LanceDB predicate pushdown.
@ -247,7 +246,7 @@ class Explorer:
idx: Union[int, List[int]] = None,
limit: int = 25,
return_type: str = "pandas",
) -> Union[DataFrame, Any]: # pandas.dataframe or pyarrow.Table
) -> Any: # pandas.DataFrame or pyarrow.Table
"""
Query the table for similar images. Accepts a single image or a list of images.
@ -312,7 +311,7 @@ class Explorer:
img = plot_query_result(similar, plot_labels=labels)
return Image.fromarray(img)
def similarity_index(self, max_dist: float = 0.2, top_k: float = None, force: bool = False) -> DataFrame:
def similarity_index(self, max_dist: float = 0.2, top_k: float = None, force: bool = False) -> Any: # pd.DataFrame
"""
Calculate the similarity index of all the images in the table. Here, the index will contain the data points that
are max_dist or closer to the image in the embedding space at a given index.
@ -447,12 +446,11 @@ class Explorer:
"""
result = prompt_sql_query(query)
try:
df = self.sql_query(result)
return self.sql_query(result)
except Exception as e:
LOGGER.error("AI generated query is not valid. Please try again with a different prompt")
LOGGER.error(e)
return None
return df
def visualize(self, result):
"""

6
ultralytics/data/explorer/gui/dash.py
View file

@ -3,8 +3,6 @@
import time
from threading import Thread
import pandas as pd
from ultralytics import Explorer
from ultralytics.utils import ROOT, SETTINGS
from ultralytics.utils.checks import check_requirements
@ -148,12 +146,14 @@ def run_ai_query():
'OpenAI API key not found in settings. Please run yolo settings openai_api_key="..."'
)
return
import pandas # scope for faster 'import ultralytics'
st.session_state["error"] = None
query = st.session_state.get("ai_query")
if query.rstrip().lstrip():
exp = st.session_state["explorer"]
res = exp.ask_ai(query)
if not isinstance(res, pd.DataFrame) or res.empty:
if not isinstance(res, pandas.DataFrame) or res.empty:
st.session_state["error"] = "No results found using AI generated query. Try another query or rerun it."
return
st.session_state["imgs"] = res["im_file"].to_list()

5
ultralytics/data/explorer/utils.py
View file

@ -5,7 +5,6 @@ from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
@ -64,8 +63,10 @@ def plot_query_result(similar_set, plot_labels=True):
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
import pandas # scope for faster 'import ultralytics'
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
similar_set.to_dict(orient="list") if isinstance(similar_set, pandas.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]