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General cleanup (#69)

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Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2022-12-08 08:28:13 -06:00 • committed by GitHub
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commit d63ee112d4
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13 changed files with 265 additions and 433 deletions
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37
ultralytics/yolo/v8/segment/train.py
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@ -4,27 +4,18 @@ import torch.nn as nn
import torch.nn.functional as F
from ultralytics.yolo import v8
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
from ultralytics.yolo.utils.metrics import FocalLoss, bbox_iou, smooth_BCE
from ultralytics.yolo.utils.modeling.tasks import SegmentationModel
from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy
from ultralytics.yolo.utils.plotting import plot_images_and_masks, plot_results_with_masks
from ultralytics.yolo.utils.plotting import plot_images, plot_results
from ultralytics.yolo.utils.torch_utils import de_parallel
from ..detect import DetectionTrainer
# BaseTrainer python usage
class SegmentationTrainer(BaseTrainer):
def get_dataloader(self, dataset_path, batch_size, mode="train", rank=0):
# TODO: manage splits differently
# calculate stride - check if model is initialized
gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
return build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode)[0]
def preprocess_batch(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255
return batch
class SegmentationTrainer(DetectionTrainer):
def load_model(self, model_cfg=None, weights=None):
model = SegmentationModel(model_cfg or weights["model"].yaml,
@ -37,16 +28,6 @@ class SegmentationTrainer(BaseTrainer):
v.requires_grad = True # train all layers
return model
def set_model_attributes(self):
nl = de_parallel(self.model).model[-1].nl # number of detection layers (to scale hyps)
self.args.box *= 3 / nl # scale to layers
self.args.cls *= self.data["nc"] / 80 * 3 / nl # scale to classes and layers
self.args.obj *= (self.args.img_size / 640) ** 2 * 3 / nl # scale to image size and layers
self.model.nc = self.data["nc"] # attach number of classes to model
self.model.args = self.args # attach hyperparameters to model
# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
self.model.names = self.data["names"]
def get_validator(self):
return v8.segment.SegmentationValidator(self.test_loader,
save_dir=self.save_dir,
@ -245,16 +226,10 @@ class SegmentationTrainer(BaseTrainer):
bboxes = batch["bboxes"]
paths = batch["im_file"]
batch_idx = batch["batch_idx"]
plot_images_and_masks(images,
batch_idx,
cls,
bboxes,
masks,
paths=paths,
fname=self.save_dir / f"train_batch{ni}.jpg")
plot_images(images, batch_idx, cls, bboxes, masks, paths=paths, fname=self.save_dir / f"train_batch{ni}.jpg")
def plot_metrics(self):
plot_results_with_masks(file=self.csv) # save results.png
plot_results(file=self.csv, segment=True) # save results.png
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)