mirror of
https://github.com/THU-MIG/yolov10.git
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ultralytics 8.0.169TQDM, INTERP_LINEAR and RTDETR load_image() updates (#4704)
Co-authored-by: Rustem Galiullin <rustemgal@gmail.com> Co-authored-by: Rustem Galiullin <rustem.galiullin@bayanat.ai> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
a4fabfdacf
commit
187b504d68
12
.github/workflows/ci.yaml
vendored
12
.github/workflows/ci.yaml
vendored
@ -128,18 +128,18 @@ jobs:
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python --version
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pip --version
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pip list
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#- name: Benchmark DetectionModel
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# shell: bash
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# run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.26
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# - name: Benchmark DetectionModel
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# shell: bash
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# run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.318
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- name: Benchmark SegmentationModel
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shell: bash
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.30
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.286
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- name: Benchmark ClassificationModel
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shell: bash
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.16
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.166
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- name: Benchmark PoseModel
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shell: bash
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.17
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run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.185
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- name: Merge Coverage Reports
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run: |
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coverage xml -o coverage-benchmarks.xml
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@ -35,5 +35,8 @@ RUN pip install --no-cache -e . thop
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# Run
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# t=ultralytics/ultralytics:latest-arm64 && sudo docker run -it --ipc=host $t
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# Pull and Run
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# t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host $t
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# Pull and Run with local volume mounted
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# t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
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@ -45,5 +45,8 @@ RUN rm -rf tmp
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# Run
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# t=ultralytics/ultralytics:latest-cpu && sudo docker run -it --ipc=host $t
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# Pull and Run
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# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host $t
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# Pull and Run with local volume mounted
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# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
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@ -42,5 +42,8 @@ ENV OMP_NUM_THREADS=1
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# Run
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# t=ultralytics/ultralytics:latest-jetson && sudo docker run -it --ipc=host $t
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# Pull and Run
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# t=ultralytics/ultralytics:latest-jetson && sudo docker pull $t && sudo docker run -it --ipc=host $t
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# Pull and Run with NVIDIA runtime
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# t=ultralytics/ultralytics:latest-jetson && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
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@ -45,5 +45,8 @@ RUN rm -rf tmp
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# Run
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# t=ultralytics/ultralytics:latest-python && sudo docker run -it --ipc=host $t
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# Pull and Run
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# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host $t
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# Pull and Run with local volume mounted
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# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
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@ -8,10 +8,11 @@ FROM ultralytics/ultralytics:latest
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# Set the working directory
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WORKDIR /actions-runner
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# Download and unpack the latest runner
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RUN curl -o actions-runner-linux-x64-2.308.0.tar.gz -L https://github.com/actions/runner/releases/download/v2.308.0/actions-runner-linux-x64-2.308.0.tar.gz && \
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tar xzf actions-runner-linux-x64-2.308.0.tar.gz && \
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rm actions-runner-linux-x64-2.308.0.tar.gz
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# Download and unpack the latest runner from https://github.com/actions/runner
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RUN FILENAME=actions-runner-linux-x64-2.308.0.tar.gz && \
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curl -o $FILENAME -L https://github.com/actions/runner/releases/download/v2.308.0/$FILENAME && \
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tar xzf $FILENAME && \
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rm $FILENAME
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# Install runner dependencies
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ENV RUNNER_ALLOW_RUNASROOT=1
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = '8.0.168'
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__version__ = '8.0.169'
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from ultralytics.models import RTDETR, SAM, YOLO
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from ultralytics.models.fastsam import FastSAM
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@ -1,19 +1,10 @@
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## Models
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Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration
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files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted
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and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image
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segmentation tasks.
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Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image segmentation tasks.
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These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like
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instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms,
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from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this
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directory provides a great starting point for your custom model development needs.
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These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms, from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this directory provides a great starting point for your custom model development needs.
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To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've
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selected a model, you can use the provided `*.yaml` file to train and deploy your custom YOLO model with ease. See full
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details at the Ultralytics [Docs](https://docs.ultralytics.com/models), and if you need help or have any questions, feel free
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to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!
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To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've selected a model, you can use the provided `*.yaml` file to train and deploy your custom YOLO model with ease. See full details at the Ultralytics [Docs](https://docs.ultralytics.com/models), and if you need help or have any questions, feel free to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!
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### Usage
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@ -37,8 +28,7 @@ model.train(data="coco128.yaml", epochs=100) # train the model
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## Pre-trained Model Architectures
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Ultralytics supports many model architectures. Visit https://docs.ultralytics.com/models to view detailed information
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and usage. Any of these models can be used by loading their configs or pretrained checkpoints if available.
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Ultralytics supports many model architectures. Visit https://docs.ultralytics.com/models to view detailed information and usage. Any of these models can be used by loading their configs or pretrained checkpoints if available.
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## Contributing New Models
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@ -13,9 +13,8 @@ import cv2
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import numpy as np
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import psutil
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from ultralytics.utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT
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from ultralytics.utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM
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from .utils import HELP_URL, IMG_FORMATS
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@ -141,7 +140,7 @@ class BaseDataset(Dataset):
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if self.single_cls:
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self.labels[i]['cls'][:, 0] = 0
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def load_image(self, i):
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def load_image(self, i, rect_mode=True):
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"""Loads 1 image from dataset index 'i', returns (im, resized hw)."""
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im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
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if im is None: # not cached in RAM
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@ -152,11 +151,13 @@ class BaseDataset(Dataset):
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if im is None:
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raise FileNotFoundError(f'Image Not Found {f}')
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h0, w0 = im.shape[:2] # orig hw
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r = self.imgsz / max(h0, w0) # ratio
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if r != 1: # if sizes are not equal
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interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
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im = cv2.resize(im, (min(math.ceil(w0 * r), self.imgsz), min(math.ceil(h0 * r), self.imgsz)),
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interpolation=interp)
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if rect_mode: # resize long side to imgsz while maintaining aspect ratio
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r = self.imgsz / max(h0, w0) # ratio
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if r != 1: # if sizes are not equal
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w, h = (min(math.ceil(w0 * r), self.imgsz), min(math.ceil(h0 * r), self.imgsz))
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im = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
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elif not (h0 == w0 == self.imgsz): # resize by stretching image to square imgsz
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im = cv2.resize(im, (self.imgsz, self.imgsz), interpolation=cv2.INTER_LINEAR)
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# Add to buffer if training with augmentations
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if self.augment:
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@ -176,7 +177,7 @@ class BaseDataset(Dataset):
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fcn = self.cache_images_to_disk if cache == 'disk' else self.load_image
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with ThreadPool(NUM_THREADS) as pool:
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results = pool.imap(fcn, range(self.ni))
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pbar = tqdm(enumerate(results), total=self.ni, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
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pbar = TQDM(enumerate(results), total=self.ni, disable=LOCAL_RANK > 0)
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for i, x in pbar:
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if cache == 'disk':
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b += self.npy_files[i].stat().st_size
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@ -190,7 +191,7 @@ class BaseDataset(Dataset):
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"""Saves an image as an *.npy file for faster loading."""
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f = self.npy_files[i]
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if not f.exists():
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np.save(f.as_posix(), cv2.imread(self.im_files[i]))
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np.save(f.as_posix(), cv2.imread(self.im_files[i]), allow_pickle=False)
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def check_cache_ram(self, safety_margin=0.5):
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"""Check image caching requirements vs available memory."""
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@ -7,7 +7,8 @@ from pathlib import Path
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import cv2
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import numpy as np
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from tqdm import tqdm
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from ultralytics.utils import TQDM
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def coco91_to_coco80_class():
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@ -90,7 +91,7 @@ def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keyp
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imgToAnns[ann['image_id']].append(ann)
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# Write labels file
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for img_id, anns in tqdm(imgToAnns.items(), desc=f'Annotations {json_file}'):
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for img_id, anns in TQDM(imgToAnns.items(), desc=f'Annotations {json_file}'):
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img = images[f'{img_id:d}']
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h, w, f = img['height'], img['width'], img['file_name']
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@ -222,7 +223,7 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
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save_dir.mkdir(parents=True, exist_ok=True)
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image_paths = list(image_dir.iterdir())
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for image_path in tqdm(image_paths, desc=f'Processing {phase} images'):
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for image_path in TQDM(image_paths, desc=f'Processing {phase} images'):
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if image_path.suffix != '.png':
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continue
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image_name_without_ext = image_path.stem
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@ -8,9 +8,8 @@ import cv2
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import numpy as np
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import torch
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import torchvision
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from tqdm import tqdm
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from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT, colorstr, is_dir_writeable
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from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr, is_dir_writeable
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from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms
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from .base import BaseDataset
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@ -60,7 +59,7 @@ class YOLODataset(BaseDataset):
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iterable=zip(self.im_files, self.label_files, repeat(self.prefix),
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repeat(self.use_keypoints), repeat(len(self.data['names'])), repeat(nkpt),
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repeat(ndim)))
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pbar = tqdm(results, desc=desc, total=total, bar_format=TQDM_BAR_FORMAT)
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pbar = TQDM(results, desc=desc, total=total)
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for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar:
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nm += nm_f
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nf += nf_f
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@ -107,7 +106,7 @@ class YOLODataset(BaseDataset):
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nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total
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if exists and LOCAL_RANK in (-1, 0):
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d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt'
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tqdm(None, desc=self.prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display results
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TQDM(None, desc=self.prefix + d, total=n, initial=n) # display results
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if cache['msgs']:
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LOGGER.info('\n'.join(cache['msgs'])) # display warnings
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@ -244,7 +243,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
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im = self.samples[i][3] = cv2.imread(f)
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elif self.cache_disk:
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if not fn.exists(): # load npy
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np.save(fn.as_posix(), cv2.imread(f))
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np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False)
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im = np.load(fn)
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else: # read image
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im = cv2.imread(f) # BGR
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@ -269,7 +268,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
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nf, nc, n, samples = cache.pop('results') # found, missing, empty, corrupt, total
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if LOCAL_RANK in (-1, 0):
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d = f'{desc} {nf} images, {nc} corrupt'
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tqdm(None, desc=d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT)
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TQDM(None, desc=d, total=n, initial=n)
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if cache['msgs']:
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LOGGER.info('\n'.join(cache['msgs'])) # display warnings
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return samples
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@ -278,7 +277,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
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nf, nc, msgs, samples, x = 0, 0, [], [], {}
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with ThreadPool(NUM_THREADS) as pool:
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results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
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pbar = tqdm(results, desc=desc, total=len(self.samples), bar_format=TQDM_BAR_FORMAT)
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pbar = TQDM(results, desc=desc, total=len(self.samples))
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for sample, nf_f, nc_f, msg in pbar:
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if nf_f:
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samples.append(sample)
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@ -15,11 +15,10 @@ from tarfile import is_tarfile
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import cv2
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import numpy as np
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from PIL import Image, ImageOps
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from tqdm import tqdm
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from ultralytics.nn.autobackend import check_class_names
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from ultralytics.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, SETTINGS_YAML, clean_url, colorstr, emojis,
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yaml_load)
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from ultralytics.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, SETTINGS_YAML, TQDM, clean_url, colorstr,
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emojis, yaml_load)
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from ultralytics.utils.checks import check_file, check_font, is_ascii
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from ultralytics.utils.downloads import download, safe_download, unzip_file
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from ultralytics.utils.ops import segments2boxes
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@ -510,7 +509,7 @@ class HUBDatasetStats:
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use_keypoints=self.task == 'pose')
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x = np.array([
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np.bincount(label['cls'].astype(int).flatten(), minlength=self.data['nc'])
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for label in tqdm(dataset.labels, total=len(dataset), desc='Statistics')]) # shape(128x80)
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for label in TQDM(dataset.labels, total=len(dataset), desc='Statistics')]) # shape(128x80)
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self.stats[split] = {
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'instance_stats': {
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'total': int(x.sum()),
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@ -541,7 +540,7 @@ class HUBDatasetStats:
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continue
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dataset = YOLODataset(img_path=self.data[split], data=self.data)
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with ThreadPool(NUM_THREADS) as pool:
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for _ in tqdm(pool.imap(self._hub_ops, dataset.im_files), total=len(dataset), desc=f'{split} images'):
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for _ in TQDM(pool.imap(self._hub_ops, dataset.im_files), total=len(dataset), desc=f'{split} images'):
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pass
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LOGGER.info(f'Done. All images saved to {self.im_dir}')
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return self.im_dir
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@ -614,7 +613,7 @@ def autosplit(path=DATASETS_DIR / 'coco8/images', weights=(0.9, 0.1, 0.0), annot
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(path.parent / x).unlink() # remove existing
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LOGGER.info(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
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for i, img in tqdm(zip(indices, files), total=n):
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for i, img in TQDM(zip(indices, files), total=n):
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if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label
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with open(path.parent / txt[i], 'a') as f:
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f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n') # add image to txt file
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@ -21,13 +21,12 @@ from torch import distributed as dist
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from torch import nn, optim
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from torch.cuda import amp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from tqdm import tqdm
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from ultralytics.cfg import get_cfg, get_save_dir
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from ultralytics.data.utils import check_cls_dataset, check_det_dataset
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from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
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from ultralytics.utils import (DEFAULT_CFG, LOGGER, RANK, TQDM_BAR_FORMAT, __version__, callbacks, clean_url, colorstr,
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emojis, yaml_save)
|
||||
from ultralytics.utils import (DEFAULT_CFG, LOGGER, RANK, TQDM, __version__, callbacks, clean_url, colorstr, emojis,
|
||||
yaml_save)
|
||||
from ultralytics.utils.autobatch import check_train_batch_size
|
||||
from ultralytics.utils.checks import check_amp, check_file, check_imgsz, print_args
|
||||
from ultralytics.utils.dist import ddp_cleanup, generate_ddp_command
|
||||
@ -326,7 +325,7 @@ class BaseTrainer:
|
||||
|
||||
if RANK in (-1, 0):
|
||||
LOGGER.info(self.progress_string())
|
||||
pbar = tqdm(enumerate(self.train_loader), total=nb, bar_format=TQDM_BAR_FORMAT)
|
||||
pbar = TQDM(enumerate(self.train_loader), total=nb)
|
||||
self.tloss = None
|
||||
self.optimizer.zero_grad()
|
||||
for i, batch in pbar:
|
||||
|
||||
@ -24,12 +24,11 @@ from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.cfg import get_cfg, get_save_dir
|
||||
from ultralytics.data.utils import check_cls_dataset, check_det_dataset
|
||||
from ultralytics.nn.autobackend import AutoBackend
|
||||
from ultralytics.utils import LOGGER, TQDM_BAR_FORMAT, callbacks, colorstr, emojis
|
||||
from ultralytics.utils import LOGGER, TQDM, callbacks, colorstr, emojis
|
||||
from ultralytics.utils.checks import check_imgsz
|
||||
from ultralytics.utils.ops import Profile
|
||||
from ultralytics.utils.torch_utils import de_parallel, select_device, smart_inference_mode
|
||||
@ -154,12 +153,7 @@ class BaseValidator:
|
||||
model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz)) # warmup
|
||||
|
||||
dt = Profile(), Profile(), Profile(), Profile()
|
||||
n_batches = len(self.dataloader)
|
||||
desc = self.get_desc()
|
||||
# NOTE: keeping `not self.training` in tqdm will eliminate pbar after segmentation evaluation during training,
|
||||
# which may affect classification task since this arg is in yolov5/classify/val.py.
|
||||
# bar = tqdm(self.dataloader, desc, n_batches, not self.training, bar_format=TQDM_BAR_FORMAT)
|
||||
bar = tqdm(self.dataloader, desc, n_batches, bar_format=TQDM_BAR_FORMAT)
|
||||
bar = TQDM(self.dataloader, desc=self.get_desc(), total=len(self.dataloader))
|
||||
self.init_metrics(de_parallel(model))
|
||||
self.jdict = [] # empty before each val
|
||||
for batch_i, batch in enumerate(bar):
|
||||
|
||||
@ -9,10 +9,9 @@ import time
|
||||
from pathlib import Path
|
||||
|
||||
import requests
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.utils import (ENVIRONMENT, LOGGER, ONLINE, RANK, SETTINGS, TESTS_RUNNING, TQDM_BAR_FORMAT, TryExcept,
|
||||
__version__, colorstr, get_git_origin_url, is_colab, is_git_dir, is_pip_package)
|
||||
from ultralytics.utils import (ENVIRONMENT, LOGGER, ONLINE, RANK, SETTINGS, TESTS_RUNNING, TQDM, TryExcept, __version__,
|
||||
colorstr, get_git_origin_url, is_colab, is_git_dir, is_pip_package)
|
||||
from ultralytics.utils.downloads import GITHUB_ASSETS_NAMES
|
||||
|
||||
PREFIX = colorstr('Ultralytics HUB: ')
|
||||
@ -80,7 +79,7 @@ def requests_with_progress(method, url, **kwargs):
|
||||
response = requests.request(method, url, stream=True, **kwargs)
|
||||
total = int(response.headers.get('content-length', 0)) # total size
|
||||
try:
|
||||
pbar = tqdm(total=total, unit='B', unit_scale=True, unit_divisor=1024, bar_format=TQDM_BAR_FORMAT)
|
||||
pbar = TQDM(total=total, unit='B', unit_scale=True, unit_divisor=1024)
|
||||
for data in response.iter_content(chunk_size=1024):
|
||||
pbar.update(len(data))
|
||||
pbar.close()
|
||||
|
||||
@ -8,9 +8,8 @@ import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.utils import TQDM_BAR_FORMAT
|
||||
from ultralytics.utils import TQDM
|
||||
|
||||
|
||||
class FastSAMPrompt:
|
||||
@ -87,7 +86,7 @@ class FastSAMPrompt:
|
||||
retina=False,
|
||||
withContours=True):
|
||||
n = len(annotations)
|
||||
pbar = tqdm(annotations, total=n, bar_format=TQDM_BAR_FORMAT)
|
||||
pbar = TQDM(annotations, total=n)
|
||||
for ann in pbar:
|
||||
result_name = os.path.basename(ann.path)
|
||||
image = ann.orig_img
|
||||
@ -156,7 +155,7 @@ class FastSAMPrompt:
|
||||
save_path.parent.mkdir(exist_ok=True, parents=True)
|
||||
cv2.imwrite(str(save_path), img_array)
|
||||
plt.close()
|
||||
pbar.set_description('Saving {} to {}'.format(result_name, save_path))
|
||||
pbar.set_description(f'Saving {result_name} to {save_path}')
|
||||
|
||||
@staticmethod
|
||||
def fast_show_mask(
|
||||
|
||||
@ -2,8 +2,6 @@
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from ultralytics.data import YOLODataset
|
||||
@ -21,30 +19,9 @@ class RTDETRDataset(YOLODataset):
|
||||
super().__init__(*args, data=data, use_segments=False, use_keypoints=False, **kwargs)
|
||||
|
||||
# NOTE: add stretch version load_image for rtdetr mosaic
|
||||
def load_image(self, i):
|
||||
def load_image(self, i, rect_mode=False):
|
||||
"""Loads 1 image from dataset index 'i', returns (im, resized hw)."""
|
||||
im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
|
||||
if im is None: # not cached in RAM
|
||||
if fn.exists(): # load npy
|
||||
im = np.load(fn)
|
||||
else: # read image
|
||||
im = cv2.imread(f) # BGR
|
||||
if im is None:
|
||||
raise FileNotFoundError(f'Image Not Found {f}')
|
||||
h0, w0 = im.shape[:2] # orig hw
|
||||
im = cv2.resize(im, (self.imgsz, self.imgsz), interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
# Add to buffer if training with augmentations
|
||||
if self.augment:
|
||||
self.ims[i], self.im_hw0[i], self.im_hw[i] = im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized
|
||||
self.buffer.append(i)
|
||||
if len(self.buffer) >= self.max_buffer_length:
|
||||
j = self.buffer.pop(0)
|
||||
self.ims[j], self.im_hw0[j], self.im_hw[j] = None, None, None
|
||||
|
||||
return im, (h0, w0), im.shape[:2]
|
||||
|
||||
return self.ims[i], self.im_hw0[i], self.im_hw[i]
|
||||
return super().load_image(i=i, rect_mode=rect_mode)
|
||||
|
||||
def build_transforms(self, hyp=None):
|
||||
"""Temporary, only for evaluation."""
|
||||
|
||||
@ -69,7 +69,7 @@ while True:
|
||||
|
||||
## Change tracker parameters
|
||||
|
||||
You can change the tracker parameters by eding the `tracker.yaml` file which is located in the ultralytics/cfg/trackers folder.
|
||||
You can change the tracker parameters by editing the `tracker.yaml` file which is located in the ultralytics/cfg/trackers folder.
|
||||
|
||||
## Command Line Interface (CLI)
|
||||
|
||||
@ -81,6 +81,8 @@ yolo segment track source=... tracker=...
|
||||
yolo pose track source=... tracker=...
|
||||
```
|
||||
|
||||
By default, trackers will use the configuration in `ultralytics/cfg/trackers`.
|
||||
We also support using a modified tracker config file. Please refer to the tracker config files
|
||||
in `ultralytics/cfg/trackers`.<br>
|
||||
By default, trackers will use the configuration in `ultralytics/cfg/trackers`. We also support using a modified tracker config file. Please refer to the tracker config files in `ultralytics/cfg/trackers`.
|
||||
|
||||
## Contributing New Trackers
|
||||
|
||||
If you've developed a new tracker architecture or have improvements for existing trackers that you'd like to contribute to the Ultralytics community, please submit your contribution in a new Pull Request. For more details, visit our [Contributing Guide](https://docs.ultralytics.com/help/contributing).
|
||||
|
||||
@ -20,6 +20,7 @@ import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
import yaml
|
||||
from tqdm import tqdm as tqdm_original
|
||||
|
||||
from ultralytics import __version__
|
||||
|
||||
@ -35,7 +36,7 @@ DEFAULT_CFG_PATH = ROOT / 'cfg/default.yaml'
|
||||
NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
|
||||
AUTOINSTALL = str(os.getenv('YOLO_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
|
||||
VERBOSE = str(os.getenv('YOLO_VERBOSE', True)).lower() == 'true' # global verbose mode
|
||||
TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format
|
||||
TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' if VERBOSE else None # tqdm bar format
|
||||
LOGGING_NAME = 'ultralytics'
|
||||
MACOS, LINUX, WINDOWS = (platform.system() == x for x in ['Darwin', 'Linux', 'Windows']) # environment booleans
|
||||
ARM64 = platform.machine() in ('arm64', 'aarch64') # ARM64 booleans
|
||||
@ -106,6 +107,22 @@ os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' # for deterministic training
|
||||
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # suppress verbose TF compiler warnings in Colab
|
||||
|
||||
|
||||
class TQDM(tqdm_original):
|
||||
"""
|
||||
Custom Ultralytics tqdm class with different default arguments.
|
||||
|
||||
Args:
|
||||
(*args): Positional arguments passed to original tqdm.
|
||||
(**kwargs): Keyword arguments, with custom defaults applied.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
# Set new default values (these can still be overridden when calling TQDM)
|
||||
kwargs['disable'] = not VERBOSE or kwargs.get('disable', False) # logical 'and' with default value if passed
|
||||
kwargs.setdefault('bar_format', TQDM_BAR_FORMAT) # override default value if passed
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
|
||||
class SimpleClass:
|
||||
"""
|
||||
Ultralytics SimpleClass is a base class providing helpful string representation, error reporting, and attribute
|
||||
|
||||
@ -32,12 +32,11 @@ from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch.cuda
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics import YOLO
|
||||
from ultralytics.cfg import TASK2DATA, TASK2METRIC
|
||||
from ultralytics.engine.exporter import export_formats
|
||||
from ultralytics.utils import ASSETS, LINUX, LOGGER, MACOS, SETTINGS
|
||||
from ultralytics.utils import ASSETS, LINUX, LOGGER, MACOS, SETTINGS, TQDM
|
||||
from ultralytics.utils.checks import check_requirements, check_yolo
|
||||
from ultralytics.utils.files import file_size
|
||||
from ultralytics.utils.torch_utils import select_device
|
||||
@ -285,7 +284,7 @@ class ProfileModels:
|
||||
|
||||
# Timed runs
|
||||
run_times = []
|
||||
for _ in tqdm(range(num_runs), desc=engine_file):
|
||||
for _ in TQDM(range(num_runs), desc=engine_file):
|
||||
results = model(input_data, imgsz=self.imgsz, verbose=False)
|
||||
run_times.append(results[0].speed['inference']) # Convert to milliseconds
|
||||
|
||||
@ -336,7 +335,7 @@ class ProfileModels:
|
||||
|
||||
# Timed runs
|
||||
run_times = []
|
||||
for _ in tqdm(range(num_runs), desc=onnx_file):
|
||||
for _ in TQDM(range(num_runs), desc=onnx_file):
|
||||
start_time = time.time()
|
||||
sess.run([output_name], {input_name: input_data})
|
||||
run_times.append((time.time() - start_time) * 1000) # Convert to milliseconds
|
||||
|
||||
@ -9,6 +9,7 @@ try:
|
||||
from clearml import Task
|
||||
from clearml.binding.frameworks.pytorch_bind import PatchPyTorchModelIO
|
||||
from clearml.binding.matplotlib_bind import PatchedMatplotlib
|
||||
|
||||
assert hasattr(clearml, '__version__') # verify package is not directory
|
||||
|
||||
except (ImportError, AssertionError):
|
||||
|
||||
@ -6,6 +6,7 @@ try:
|
||||
assert not TESTS_RUNNING # do not log pytest
|
||||
assert SETTINGS['mlflow'] is True # verify integration is enabled
|
||||
import mlflow
|
||||
|
||||
assert hasattr(mlflow, '__version__') # verify package is not directory
|
||||
|
||||
import os
|
||||
|
||||
@ -11,9 +11,8 @@ from urllib import parse, request
|
||||
|
||||
import requests
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.utils import LOGGER, TQDM_BAR_FORMAT, checks, clean_url, emojis, is_online, url2file
|
||||
from ultralytics.utils import LOGGER, TQDM, checks, clean_url, emojis, is_online, url2file
|
||||
|
||||
# Define Ultralytics GitHub assets maintained at https://github.com/ultralytics/assets
|
||||
GITHUB_ASSETS_REPO = 'ultralytics/assets'
|
||||
@ -101,11 +100,7 @@ def zip_directory(directory, compress=True, exclude=('.DS_Store', '__MACOSX'), p
|
||||
zip_file = directory.with_suffix('.zip')
|
||||
compression = ZIP_DEFLATED if compress else ZIP_STORED
|
||||
with ZipFile(zip_file, 'w', compression) as f:
|
||||
for file in tqdm(files_to_zip,
|
||||
desc=f'Zipping {directory} to {zip_file}...',
|
||||
unit='file',
|
||||
disable=not progress,
|
||||
bar_format=TQDM_BAR_FORMAT):
|
||||
for file in TQDM(files_to_zip, desc=f'Zipping {directory} to {zip_file}...', unit='file', disable=not progress):
|
||||
f.write(file, file.relative_to(directory))
|
||||
|
||||
return zip_file # return path to zip file
|
||||
@ -163,11 +158,7 @@ def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX'), exist_ok=Fals
|
||||
LOGGER.warning(f'WARNING ⚠️ Skipping {file} unzip as destination directory {path} is not empty.')
|
||||
return path
|
||||
|
||||
for f in tqdm(files,
|
||||
desc=f'Unzipping {file} to {Path(path).resolve()}...',
|
||||
unit='file',
|
||||
disable=not progress,
|
||||
bar_format=TQDM_BAR_FORMAT):
|
||||
for f in TQDM(files, desc=f'Unzipping {file} to {Path(path).resolve()}...', unit='file', disable=not progress):
|
||||
zipObj.extract(f, path=extract_path)
|
||||
|
||||
return path # return unzip dir
|
||||
@ -297,13 +288,12 @@ def safe_download(url,
|
||||
if method == 'torch':
|
||||
torch.hub.download_url_to_file(url, f, progress=progress)
|
||||
else:
|
||||
with request.urlopen(url) as response, tqdm(total=int(response.getheader('Content-Length', 0)),
|
||||
with request.urlopen(url) as response, TQDM(total=int(response.getheader('Content-Length', 0)),
|
||||
desc=desc,
|
||||
disable=not progress,
|
||||
unit='B',
|
||||
unit_scale=True,
|
||||
unit_divisor=1024,
|
||||
bar_format=TQDM_BAR_FORMAT) as pbar:
|
||||
unit_divisor=1024) as pbar:
|
||||
with open(f, 'wb') as f_opened:
|
||||
for data in response:
|
||||
f_opened.write(data)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user