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Add max_dim==2 argument to check_imgsz() (#789)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: andreaswimmer <53872150+andreaswimmer@users.noreply.github.com>
Co-authored-by: Mehran Ghandehari <mehran.maps@gmail.com>
This commit is contained in:
Glenn Jocher
2023-02-04 01:48:44 +04:00
committed by GitHub
parent 5a80ad98db
commit 0d182e80f1
11 changed files with 96 additions and 52 deletions
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8
ultralytics/yolo/data/datasets/ImageNet.yaml
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@ -1153,7 +1153,7 @@ map:
n02009229: little_blue_heron
n02009912: American_egret
n02011460: bittern
n02012849: crane
n02012849: crane_(bird)
n02013706: limpkin
n02017213: European_gallinule
n02018207: American_coot
@ -1536,7 +1536,7 @@ map:
n03124043: cowboy_boot
n03124170: cowboy_hat
n03125729: cradle
n03126707: crane
n03126707: crane_(machine)
n03127747: crash_helmet
n03127925: crate
n03131574: crib
@ -1657,8 +1657,8 @@ map:
n03706229: magnetic_compass
n03709823: mailbag
n03710193: mailbox
n03710637: maillot
n03710721: maillot
n03710637: maillot_(tights)
n03710721: maillot_(tank_suit)
n03717622: manhole_cover
n03720891: maraca
n03721384: marimba

37
ultralytics/yolo/engine/exporter.py
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@ -65,6 +65,7 @@ import pandas as pd
import torch
import ultralytics
from ultralytics.nn.autobackend import check_class_names
from ultralytics.nn.modules import Detect, Segment
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, guess_model_task
from ultralytics.yolo.cfg import get_cfg
@ -151,9 +152,12 @@ class Exporter:
assert not self.args.dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic'
# Checks
model.names = check_class_names(model.names)
# if self.args.batch == model.args['batch_size']: # user has not modified training batch_size
self.args.batch = 1
self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2) # check image size
if model.task == 'classify':
self.args.nms = self.args.agnostic_nms = False
if self.args.optimize:
assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
@ -194,8 +198,14 @@ class Exporter:
self.model = model
self.file = file
self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else (x.shape for x in y)
self.metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata
self.pretty_name = self.file.stem.replace('yolo', 'YOLO')
self.metadata = {
'description': f"Ultralytics {self.pretty_name} model trained on {self.model.args['data']}",
'author': 'Ultralytics',
'license': 'GPL-3.0 https://ultralytics.com/license',
'version': ultralytics.__version__,
'stride': int(max(model.stride)),
'names': model.names} # model metadata
# Exports
f = [''] * len(fmts) # exported filenames
@ -235,12 +245,11 @@ class Exporter:
# Finish
f = [str(x) for x in f if x] # filter out '' and None
if any(f):
task = guess_model_task(model)
s = "-WARNING ⚠️ not yet supported for YOLOv8 exported models"
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f"\nPredict: yolo task={task} mode=predict model={f[-1]} {s}"
f"\nValidate: yolo task={task} mode=val model={f[-1]} {s}"
f"\nPredict: yolo task={model.task} mode=predict model={f[-1]} {s}"
f"\nValidate: yolo task={model.task} mode=val model={f[-1]} {s}"
f"\nVisualize: https://netron.app")
self.run_callbacks("on_export_end")
@ -375,9 +384,13 @@ class Exporter:
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
f = self.file.with_suffix('.mlmodel')
task = self.model.task
model = iOSModel(self.model, self.im).eval() if self.args.nms else self.model
ts = torch.jit.trace(model, self.im, strict=False) # TorchScript model
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=self.im.shape, scale=1 / 255, bias=[0, 0, 0])])
classifier_config = ct.ClassifierConfig(list(model.names.values())) if task == 'classify' else None
ct_model = ct.convert(ts,
inputs=[ct.ImageType('image', shape=self.im.shape, scale=1 / 255, bias=[0, 0, 0])],
classifier_config=classifier_config)
bits, mode = (8, 'kmeans_lut') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
if bits < 32:
if MACOS: # quantization only supported on macOS
@ -387,6 +400,10 @@ class Exporter:
if self.args.nms:
ct_model = self._pipeline_coreml(ct_model)
ct_model.short_description = self.metadata['description']
ct_model.author = self.metadata['author']
ct_model.license = self.metadata['license']
ct_model.version = self.metadata['version']
ct_model.save(str(f))
return f, ct_model
@ -687,8 +704,8 @@ class Exporter:
out0_shape = out[out0.name].shape
out1_shape = out[out1.name].shape
else: # linux and windows can not run model.predict(), get sizes from pytorch output y
out0_shape = self.output_shape[1], self.output_shape[2] - 5 # (3780, 80)
out1_shape = self.output_shape[1], 4 # (3780, 4)
out0_shape = self.output_shape[2], self.output_shape[1] - 4 # (3780, 80)
out1_shape = self.output_shape[2], 4 # (3780, 4)
# Checks
names = self.metadata['names']
@ -714,7 +731,7 @@ class Exporter:
# flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r)
# Print
print(spec.description)
# print(spec.description)
# Model from spec
model = ct.models.MLModel(spec)
@ -771,10 +788,6 @@ class Exporter:
# Update metadata
pipeline.spec.specificationVersion = 5
pipeline.spec.description.metadata.versionString = f'Ultralytics YOLOv{ultralytics.__version__}'
pipeline.spec.description.metadata.shortDescription = f'Ultralytics {self.pretty_name} CoreML model'
pipeline.spec.description.metadata.author = 'Ultralytics (https://ultralytics.com)'
pipeline.spec.description.metadata.license = 'GPL-3.0 license (https://ultralytics.com/license)'
pipeline.spec.description.metadata.userDefined.update({
'IoU threshold': str(nms.iouThreshold),
'Confidence threshold': str(nms.confidenceThreshold)})

6
ultralytics/yolo/engine/model.py
View File

@ -1,6 +1,7 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
from pathlib import Path
from typing import List
import sys
from ultralytics import yolo # noqa
@ -9,7 +10,7 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, Segmentat
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, callbacks, yaml_load
from ultralytics.yolo.utils.checks import check_yaml
from ultralytics.yolo.utils.checks import check_yaml, check_imgsz
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
# Map head to model, trainer, validator, and predictor classes
@ -131,7 +132,7 @@ class YOLO:
Check the 'configuration' section in the documentation for all available options.
Returns:
(dict): The prediction results.
(List[ultralytics.yolo.engine.results.Results]): The prediction results.
"""
overrides = self.overrides.copy()
overrides["conf"] = 0.25
@ -161,6 +162,7 @@ class YOLO:
args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
args.data = data or args.data
args.task = self.task
args.imgsz = check_imgsz(args.imgsz, max_dim=1)
validator = self.ValidatorClass(args=args)
validator(model=self.model)

2
ultralytics/yolo/engine/trainer.py
View File

@ -202,7 +202,7 @@ class BaseTrainer:
self.model = DDP(self.model, device_ids=[rank])
# Check imgsz
gs = max(int(self.model.stride.max() if hasattr(self.model, 'stride') else 32), 32) # grid size (max stride)
self.args.imgsz = check_imgsz(self.args.imgsz, stride=gs, floor=gs)
self.args.imgsz = check_imgsz(self.args.imgsz, stride=gs, floor=gs, max_dim=1)
# Batch size
if self.batch_size == -1:
if RANK == -1: # single-GPU only, estimate best batch size

10
ultralytics/yolo/utils/__init__.py
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@ -467,6 +467,13 @@ def set_sentry():
"""
def before_send(event, hint):
if 'exc_info' in hint:
exc_type, exc_value, tb = hint['exc_info']
if exc_type in (KeyboardInterrupt, FileNotFoundError) \
or 'out of memory' in str(exc_value) \
or not sys.argv[0].endswith('yolo'):
return None # do not send event
env = 'Colab' if is_colab() else 'Kaggle' if is_kaggle() else 'Jupyter' if is_jupyter() else \
'Docker' if is_docker() else platform.system()
event['tags'] = {
@ -477,6 +484,7 @@ def set_sentry():
return event
if SETTINGS['sync'] and \
RANK in {-1, 0} and \
not is_pytest_running() and \
not is_github_actions_ci() and \
((is_pip_package() and not is_git_dir()) or
@ -491,7 +499,7 @@ def set_sentry():
release=ultralytics.__version__,
environment='production', # 'dev' or 'production'
before_send=before_send,
ignore_errors=[KeyboardInterrupt])
ignore_errors=[KeyboardInterrupt, FileNotFoundError])
# Disable all sentry logging
for logger in "sentry_sdk", "sentry_sdk.errors":

9
ultralytics/yolo/utils/checks.py
View File

@ -40,7 +40,7 @@ def is_ascii(s) -> bool:
return all(ord(c) < 128 for c in s)
def check_imgsz(imgsz, stride=32, min_dim=1, floor=0):
def check_imgsz(imgsz, stride=32, min_dim=1, max_dim=2, floor=0):
"""
Verify image size is a multiple of the given stride in each dimension. If the image size is not a multiple of the
stride, update it to the nearest multiple of the stride that is greater than or equal to the given floor value.
@ -66,6 +66,13 @@ def check_imgsz(imgsz, stride=32, min_dim=1, floor=0):
raise TypeError(f"'imgsz={imgsz}' is of invalid type {type(imgsz).__name__}. "
f"Valid imgsz types are int i.e. 'imgsz=640' or list i.e. 'imgsz=[640,640]'")
# Apply max_dim
if max_dim == 1:
LOGGER.warning(f"WARNING ⚠️ 'train' and 'val' imgsz types must be integer, updating to 'imgsz={max(imgsz)}'. "
f"'predict' and 'export' imgsz may be list or integer, "
f"i.e. 'yolo export imgsz=640,480' or 'yolo export imgsz=640'")
imgsz = [max(imgsz)]
# Make image size a multiple of the stride
sz = [max(math.ceil(x / stride) * stride, floor) for x in imgsz]