xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

ultralytics 8.0.52 reduced TAL CUDA usage and AMP check fix (#1333)

Browse Source 
Co-authored-by: CNH5 <74132034+CNH5@users.noreply.github.com>
Co-authored-by: Huijae Lee <46982469+ZeroAct@users.noreply.github.com>
Co-authored-by: Lorenzo Mammana <lorenzom96@hotmail.it>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Hardik Dava <39372750+hardikdava@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
This commit is contained in:
Glenn Jocher
2023-03-10 03:27:06 +01:00
committed by GitHub
parent 790f9c067c
commit 177a68b39f
21 changed files with 132 additions and 147 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
ultralytics/nn/autobackend.py
View File

@ -184,7 +184,7 @@ class AutoBackend(nn.Module):
LOGGER.info(f'Loading {w} for CoreML inference...')
import coremltools as ct
model = ct.models.MLModel(w)
metadata = model.user_defined_metadata
metadata = dict(model.user_defined_metadata)
elif saved_model: # TF SavedModel
LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
import tensorflow as tf
@ -256,10 +256,10 @@ class AutoBackend(nn.Module):
nhwc = model.runtime.startswith("tensorflow")
'''
else:
from ultralytics.yolo.engine.exporter import EXPORT_FORMATS_TABLE
from ultralytics.yolo.engine.exporter import export_formats
raise TypeError(f"model='{w}' is not a supported model format. "
'See https://docs.ultralytics.com/tasks/detection/#export for help.'
f'\n\n{EXPORT_FORMATS_TABLE}')
f'\n\n{export_formats()}')
# Load external metadata YAML
if isinstance(metadata, (str, Path)) and Path(metadata).exists():

4
ultralytics/nn/autoshape.py
View File

@ -8,7 +8,6 @@ from pathlib import Path
import cv2
import numpy as np
import pandas as pd
import requests
import torch
import torch.nn as nn
@ -204,12 +203,13 @@ class Detections:
def pandas(self):
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
import pandas
new = copy(self) # return copy
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
setattr(new, k, [pandas.DataFrame(x, columns=c) for x in a])
return new
def tolist(self):

2
ultralytics/nn/tasks.py
View File

@ -122,7 +122,7 @@ class BaseModel(nn.Module):
bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
return sum(isinstance(v, bn) for v in self.modules()) < thresh # True if < 'thresh' BatchNorm layers in model
def info(self, verbose=False, imgsz=640):
def info(self, verbose=True, imgsz=640):
"""
Prints model information