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ultralytics 8.0.29 DDP-cls and default arg fixes (#813)

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Glenn Jocher 2023-02-06 02:30:03 +04:00 committed by GitHub
parent 21ae321bc2
commit 7a7c8dc7b7
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9 changed files with 38 additions and 38 deletions
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2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
__version__ = "8.0.28"
__version__ = "8.0.29"
from ultralytics.yolo.engine.model import YOLO
from ultralytics.yolo.utils import ops

4
ultralytics/yolo/cfg/__init__.py
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@ -262,8 +262,8 @@ def entrypoint(debug=''):
LOGGER.warning(f"WARNING ⚠️ 'format=' is missing. Using default 'format={overrides['format']}'.")
# Run command in python
cfg = get_cfg(overrides=overrides)
getattr(model, mode)(**vars(cfg))
# getattr(model, mode)(**vars(get_cfg(overrides=overrides))) # default args using default.yaml
getattr(model, mode)(**overrides) # default args from model
# Special modes --------------------------------------------------------------------------------------------------------

15
ultralytics/yolo/engine/exporter.py
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@ -184,9 +184,6 @@ class Exporter:
y = model(im) # dry runs
if self.args.half and not coreml and not xml:
im, model = im.half(), model.half() # to FP16
shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} and "
f"output shape {shape} ({file_size(file):.1f} MB)")
# Warnings
warnings.filterwarnings('ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
@ -207,6 +204,9 @@ class Exporter:
'stride': int(max(model.stride)),
'names': model.names} # model metadata
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} and "
f"output shape {self.output_shape} ({file_size(file):.1f} MB)")
# Exports
f = [''] * len(fmts) # exported filenames
if jit: # TorchScript
@ -220,9 +220,8 @@ class Exporter:
if coreml: # CoreML
f[4], _ = self._export_coreml()
if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
raise NotImplementedError('YOLOv8 TensorFlow export support is still under development. '
LOGGER.warning('WARNING ⚠️ YOLOv8 TensorFlow export support is still under development. '
'Please consider contributing to the effort if you have TF expertise. Thank you!')
assert not isinstance(model, ClassificationModel), 'ClassificationModel TF exports not yet supported.'
nms = False
f[5], s_model = self._export_saved_model(nms=nms or self.args.agnostic_nms or tfjs,
agnostic_nms=self.args.agnostic_nms or tfjs)
@ -236,7 +235,7 @@ class Exporter:
agnostic_nms=self.args.agnostic_nms)
if edgetpu:
f[8], _ = self._export_edgetpu()
self._add_tflite_metadata(f[8] or f[7], num_outputs=len(s_model.outputs))
self._add_tflite_metadata(f[8] or f[7], num_outputs=len(self.output_shape))
if tfjs:
f[9], _ = self._export_tfjs()
if paddle: # PaddlePaddle
@ -552,13 +551,13 @@ class Exporter:
return f, keras_model
@try_export
def _export_pb(self, keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
def _export_pb(self, keras_model, prefix=colorstr('TensorFlow GraphDef:')):
# YOLOv8 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
import tensorflow as tf # noqa
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 # noqa
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
f = file.with_suffix('.pb')
f = self.file.with_suffix('.pb')
m = tf.function(lambda x: keras_model(x)) # full model
m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))

7
ultralytics/yolo/engine/model.py
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@ -119,7 +119,6 @@ class YOLO:
def fuse(self):
self.model.fuse()
@smart_inference_mode()
def predict(self, source=None, stream=False, **kwargs):
"""
Perform prediction using the YOLO model.
@ -258,8 +257,6 @@ class YOLO:
@staticmethod
def _reset_ckpt_args(args):
for arg in 'verbose', 'project', 'name', 'exist_ok', 'resume', 'batch', 'epochs', 'cache', 'save_json', \
'half', 'v5loader':
for arg in 'augment', 'verbose', 'project', 'name', 'exist_ok', 'resume', 'batch', 'epochs', 'cache', \
'save_json', 'half', 'v5loader', 'device', 'cfg', 'save', 'rect', 'plots':
args.pop(arg, None)
args["device"] = '' # set device to '' to prevent auto-DDP usage

2
ultralytics/yolo/engine/trainer.py
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@ -457,7 +457,7 @@ class BaseTrainer:
def get_validator(self):
raise NotImplementedError("get_validator function not implemented in trainer")
def get_dataloader(self, dataset_path, batch_size=16, rank=0):
def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode="train"):
"""
Returns dataloader derived from torch.data.Dataloader.
"""

10
ultralytics/yolo/utils/__init__.py
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@ -485,18 +485,20 @@ def set_sentry():
if SETTINGS['sync'] and \
RANK in {-1, 0} and \
sys.argv[0].endswith('yolo') and \
not is_pytest_running() and \
not is_github_actions_ci() and \
((is_pip_package() and not is_git_dir()) or
(get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git" and get_git_branch() == "main")):
import sentry_sdk # noqa
import ultralytics
import sentry_sdk # noqa
from ultralytics import __version__
sentry_sdk.init(
dsn="https://1f331c322109416595df20a91f4005d3@o4504521589325824.ingest.sentry.io/4504521592406016",
dsn="https://f805855f03bb4363bc1e16cb7d87b654@o4504521589325824.ingest.sentry.io/4504521592406016",
debug=False,
traces_sample_rate=1.0,
release=ultralytics.__version__,
release=__version__,
environment='production', # 'dev' or 'production'
before_send=before_send,
ignore_errors=[KeyboardInterrupt, FileNotFoundError])

19
ultralytics/yolo/utils/checks.py
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@ -1,5 +1,5 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import contextlib
import glob
import inspect
import math
@ -7,9 +7,9 @@ import os
import platform
import re
import shutil
import subprocess
import urllib
from pathlib import Path
from subprocess import check_output
from typing import Optional
import cv2
@ -155,11 +155,10 @@ def check_online() -> bool:
bool: True if connection is successful, False otherwise.
"""
import socket
try:
# Check host accessibility by attempting to establish a connection
socket.create_connection(("1.1.1.1", 443), timeout=5)
with contextlib.suppress(subprocess.CalledProcessError):
host = socket.gethostbyname("www.github.com")
socket.create_connection((host, 80), timeout=2)
return True
except OSError:
return False
@ -181,6 +180,7 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
# Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str)
prefix = colorstr('red', 'bold', 'requirements:')
check_python() # check python version
file = None
if isinstance(requirements, Path): # requirements.txt file
file = requirements.resolve()
assert file.exists(), f"{prefix} {file} not found, check failed."
@ -202,9 +202,8 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
LOGGER.info(f"{prefix} YOLOv8 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...")
try:
assert check_online(), "AutoUpdate skipped (offline)"
LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode())
source = file if 'file' in locals() else requirements
s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
LOGGER.info(subprocess.check_output(f'pip install {s} {cmds}', shell=True).decode())
s = f"{prefix} {n} package{'s' * (n > 1)} updated per {file or requirements}\n" \
f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
LOGGER.info(s)
except Exception as e:
@ -306,7 +305,7 @@ def git_describe(path=ROOT): # path must be a directory
# Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
try:
assert (Path(path) / '.git').is_dir()
return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
return subprocess.check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
except AssertionError:
return ''

2
ultralytics/yolo/utils/torch_utils.py
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@ -246,7 +246,7 @@ def intersect_dicts(da, db, exclude=()):
def is_parallel(model):
# Returns True if model is of type DP or DDP
return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
return isinstance(model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel))
def de_parallel(model):

11
ultralytics/yolo/v8/classify/train.py
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@ -1,5 +1,4 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import sys
import torch
import torchvision
@ -9,7 +8,7 @@ from ultralytics.yolo import v8
from ultralytics.yolo.data import build_classification_dataloader
from ultralytics.yolo.engine.trainer import BaseTrainer
from ultralytics.yolo.utils import DEFAULT_CFG
from ultralytics.yolo.utils.torch_utils import strip_optimizer
from ultralytics.yolo.utils.torch_utils import strip_optimizer, is_parallel
class ClassificationTrainer(BaseTrainer):
@ -56,7 +55,7 @@ class ClassificationTrainer(BaseTrainer):
# Load a YOLO model locally, from torchvision, or from Ultralytics assets
if model.endswith(".pt"):
self.model, _ = attempt_load_one_weight(model, device='cpu')
for p in model.parameters():
for p in self.model.parameters():
p.requires_grad = True # for training
elif model.endswith(".yaml"):
self.model = self.get_model(cfg=model)
@ -75,8 +74,12 @@ class ClassificationTrainer(BaseTrainer):
augment=mode == "train",
rank=rank,
workers=self.args.workers)
# Attach inference transforms
if mode != "train":
self.model.transforms = loader.dataset.torch_transforms # attach inference transforms
if is_parallel(self.model):
self.model.module.transforms = loader.dataset.torch_transforms
else:
self.model.transforms = loader.dataset.torch_transforms
return loader
def preprocess_batch(self, batch):