HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Python refactorings and simplifications (#7549)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Hassaan Farooq <103611273+hassaanfarooq01@users.noreply.github.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-01-12 19:34:03 +01:00 • committed by GitHub
parent 0da13831cf
commit f6309b8e70
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
40 changed files with 127 additions and 189 deletions
Download patch file Download diff file Expand all files Collapse all files

1
ultralytics/models/fastsam/model.py
View file

@ -3,7 +3,6 @@
from pathlib import Path
from ultralytics.engine.model import Model
from .predict import FastSAMPredictor
from .val import FastSAMValidator

1
ultralytics/models/nas/model.py
View file

@ -17,7 +17,6 @@ import torch
from ultralytics.engine.model import Model
from ultralytics.utils.torch_utils import model_info, smart_inference_mode
from .predict import NASPredictor
from .val import NASValidator

1
ultralytics/models/rtdetr/train.py
View file

@ -7,7 +7,6 @@ import torch
from ultralytics.models.yolo.detect import DetectionTrainer
from ultralytics.nn.tasks import RTDETRDetectionModel
from ultralytics.utils import RANK, colorstr
from .val import RTDETRDataset, RTDETRValidator

3
ultralytics/models/rtdetr/val.py
View file

@ -122,8 +122,7 @@ class RTDETRValidator(DetectionValidator):
bbox = ops.xywh2xyxy(bbox) # target boxes
bbox[..., [0, 2]] *= ori_shape[1] # native-space pred
bbox[..., [1, 3]] *= ori_shape[0] # native-space pred
prepared_batch = dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
return prepared_batch
return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
def _prepare_pred(self, pred, pbatch):
"""Prepares and returns a batch with transformed bounding boxes and class labels."""

1
ultralytics/models/sam/build.py
View file

@ -11,7 +11,6 @@ from functools import partial
import torch
from ultralytics.utils.downloads import attempt_download_asset
from .modules.decoders import MaskDecoder
from .modules.encoders import ImageEncoderViT, PromptEncoder
from .modules.sam import Sam

1
ultralytics/models/sam/model.py
View file

@ -18,7 +18,6 @@ from pathlib import Path
from ultralytics.engine.model import Model
from ultralytics.utils.torch_utils import model_info
from .build import build_sam
from .predict import Predictor

1
ultralytics/models/sam/predict.py
View file

@ -18,7 +18,6 @@ from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results import Results
from ultralytics.utils import DEFAULT_CFG, ops
from ultralytics.utils.torch_utils import select_device
from .amg import (
batch_iterator,
batched_mask_to_box,

1
ultralytics/models/utils/loss.py
View file

@ -6,7 +6,6 @@ import torch.nn.functional as F
from ultralytics.utils.loss import FocalLoss, VarifocalLoss
from ultralytics.utils.metrics import bbox_iou
from .ops import HungarianMatcher

3
ultralytics/models/yolo/detect/val.py
View file

@ -104,8 +104,7 @@ class DetectionValidator(BaseValidator):
if len(cls):
bbox = ops.xywh2xyxy(bbox) * torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]] # target boxes
ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad) # native-space labels
prepared_batch = dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
return prepared_batch
return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
def _prepare_pred(self, pred, pbatch):
"""Prepares a batch of images and annotations for validation."""

42
ultralytics/models/yolo/obb/val.py
View file

@ -77,8 +77,7 @@ class OBBValidator(DetectionValidator):
if len(cls):
bbox[..., :4].mul_(torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]]) # target boxes
ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad, xywh=True) # native-space labels
prepared_batch = dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
return prepared_batch
return dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
def _prepare_pred(self, pred, pbatch):
"""Prepares and returns a batch for OBB validation with scaled and padded bounding boxes."""
@ -139,32 +138,21 @@ class OBBValidator(DetectionValidator):
pred_txt.mkdir(parents=True, exist_ok=True)
data = json.load(open(pred_json))
# Save split results
LOGGER.info(f"Saving predictions with DOTA format to {str(pred_txt)}...")
LOGGER.info(f"Saving predictions with DOTA format to {pred_txt}...")
for d in data:
image_id = d["image_id"]
score = d["score"]
classname = self.names[d["category_id"]].replace(" ", "-")
p = d["poly"]
lines = "{} {} {} {} {} {} {} {} {} {}\n".format(
image_id,
score,
d["poly"][0],
d["poly"][1],
d["poly"][2],
d["poly"][3],
d["poly"][4],
d["poly"][5],
d["poly"][6],
d["poly"][7],
)
with open(str(pred_txt / f"Task1_{classname}") + ".txt", "a") as f:
f.writelines(lines)
with open(f'{pred_txt / f"Task1_{classname}"}.txt', "a") as f:
f.writelines(f"{image_id} {score} {p[0]} {p[1]} {p[2]} {p[3]} {p[4]} {p[5]} {p[6]} {p[7]}\n")
# Save merged results, this could result slightly lower map than using official merging script,
# because of the probiou calculation.
pred_merged_txt = self.save_dir / "predictions_merged_txt" # predictions
pred_merged_txt.mkdir(parents=True, exist_ok=True)
merged_results = defaultdict(list)
LOGGER.info(f"Saving merged predictions with DOTA format to {str(pred_merged_txt)}...")
LOGGER.info(f"Saving merged predictions with DOTA format to {pred_merged_txt}...")
for d in data:
image_id = d["image_id"].split("__")[0]
pattern = re.compile(r"\d+___\d+")
@ -188,22 +176,10 @@ class OBBValidator(DetectionValidator):
b = ops.xywhr2xyxyxyxy(bbox[:, :5]).view(-1, 8)
for x in torch.cat([b, bbox[:, 5:7]], dim=-1).tolist():
classname = self.names[int(x[-1])].replace(" ", "-")
poly = [round(i, 3) for i in x[:-2]]
p = [round(i, 3) for i in x[:-2]] # poly
score = round(x[-2], 3)
lines = "{} {} {} {} {} {} {} {} {} {}\n".format(
image_id,
score,
poly[0],
poly[1],
poly[2],
poly[3],
poly[4],
poly[5],
poly[6],
poly[7],
)
with open(str(pred_merged_txt / f"Task1_{classname}") + ".txt", "a") as f:
f.writelines(lines)
with open(f'{pred_merged_txt / f"Task1_{classname}"}.txt', "a") as f:
f.writelines(f"{image_id} {score} {p[0]} {p[1]} {p[2]} {p[3]} {p[4]} {p[5]} {p[6]} {p[7]}\n")
return stats