General refactoring and improvements (#373)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -32,21 +32,21 @@ class AutoBackend(nn.Module):
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fp16 (bool): If True, use half precision. Default: False
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fuse (bool): Whether to fuse the model or not. Default: True
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Supported formats and their usage:
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Platform | Weights Format
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-----------------------|------------------
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PyTorch | *.pt
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TorchScript | *.torchscript
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ONNX Runtime | *.onnx
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ONNX OpenCV DNN | *.onnx --dnn
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OpenVINO | *.xml
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CoreML | *.mlmodel
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TensorRT | *.engine
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TensorFlow SavedModel | *_saved_model
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TensorFlow GraphDef | *.pb
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TensorFlow Lite | *.tflite
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TensorFlow Edge TPU | *_edgetpu.tflite
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PaddlePaddle | *_paddle_model
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Supported formats and their naming conventions:
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| Format | Suffix |
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|-----------------------|------------------|
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| PyTorch | *.pt |
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| TorchScript | *.torchscript |
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| ONNX Runtime | *.onnx |
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| ONNX OpenCV DNN | *.onnx --dnn |
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| OpenVINO | *.xml |
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| CoreML | *.mlmodel |
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| TensorRT | *.engine |
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| TensorFlow SavedModel | *_saved_model |
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| TensorFlow GraphDef | *.pb |
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| TensorFlow Lite | *.tflite |
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| TensorFlow Edge TPU | *_edgetpu.tflite |
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| PaddlePaddle | *_paddle_model |
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"""
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super().__init__()
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w = str(weights[0] if isinstance(weights, list) else weights)
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@ -357,7 +357,7 @@ class AutoBackend(nn.Module):
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This function takes a path to a model file and returns the model type
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Args:
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p: path to the model file. Defaults to path/to/model.pt
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p: path to the model file. Defaults to path/to/model.pt
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"""
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# Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
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# types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
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@ -374,12 +374,11 @@ class AutoBackend(nn.Module):
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@staticmethod
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def _load_metadata(f=Path('path/to/meta.yaml')):
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"""
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> Loads the metadata from a yaml file
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Loads the metadata from a yaml file
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Args:
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f: The path to the metadata file.
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f: The path to the metadata file.
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"""
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from ultralytics.yolo.utils.files import yaml_load
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# Load metadata from meta.yaml if it exists
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if f.exists():
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@ -5,28 +5,11 @@ Common modules
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import math
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import warnings
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from copy import copy
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from pathlib import Path
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import cv2
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import numpy as np
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import pandas as pd
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import requests
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import torch
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import torch.nn as nn
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from PIL import Image, ImageOps
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from torch.cuda import amp
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from ultralytics.nn.autobackend import AutoBackend
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from ultralytics.yolo.data.augment import LetterBox
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from ultralytics.yolo.utils import LOGGER, colorstr
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from ultralytics.yolo.utils.files import increment_path
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from ultralytics.yolo.utils.ops import Profile, make_divisible, non_max_suppression, scale_boxes, xyxy2xywh
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from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
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from ultralytics.yolo.utils.tal import dist2bbox, make_anchors
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from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode
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# from utils.plots import feature_visualization TODO
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def autopad(k, p=None, d=1): # kernel, padding, dilation
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@ -365,216 +348,6 @@ class Concat(nn.Module):
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return torch.cat(x, self.d)
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class AutoShape(nn.Module):
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# YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
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conf = 0.25 # NMS confidence threshold
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iou = 0.45 # NMS IoU threshold
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agnostic = False # NMS class-agnostic
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multi_label = False # NMS multiple labels per box
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classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
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max_det = 1000 # maximum number of detections per image
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amp = False # Automatic Mixed Precision (AMP) inference
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def __init__(self, model, verbose=True):
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super().__init__()
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if verbose:
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LOGGER.info('Adding AutoShape... ')
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copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes
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self.dmb = isinstance(model, AutoBackend) # DetectMultiBackend() instance
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self.pt = not self.dmb or model.pt # PyTorch model
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self.model = model.eval()
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if self.pt:
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m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
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m.inplace = False # Detect.inplace=False for safe multithread inference
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m.export = True # do not output loss values
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def _apply(self, fn):
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# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
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self = super()._apply(fn)
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if self.pt:
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m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
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m.stride = fn(m.stride)
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m.grid = list(map(fn, m.grid))
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if isinstance(m.anchor_grid, list):
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m.anchor_grid = list(map(fn, m.anchor_grid))
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return self
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@smart_inference_mode()
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def forward(self, ims, size=640, augment=False, profile=False):
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# Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
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# file: ims = 'data/images/zidane.jpg' # str or PosixPath
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# URI: = 'https://ultralytics.com/images/zidane.jpg'
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# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
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# PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
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# numpy: = np.zeros((640,1280,3)) # HWC
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# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
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# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
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dt = (Profile(), Profile(), Profile())
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with dt[0]:
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if isinstance(size, int): # expand
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size = (size, size)
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p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param
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autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference
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if isinstance(ims, torch.Tensor): # torch
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with amp.autocast(autocast):
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return self.model(ims.to(p.device).type_as(p), augment=augment) # inference
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# Pre-process
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n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images
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shape0, shape1, files = [], [], [] # image and inference shapes, filenames
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for i, im in enumerate(ims):
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f = f'image{i}' # filename
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if isinstance(im, (str, Path)): # filename or uri
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im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
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im = np.asarray(ImageOps.exif_transpose(im))
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elif isinstance(im, Image.Image): # PIL Image
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im, f = np.asarray(ImageOps.exif_transpose(im)), getattr(im, 'filename', f) or f
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files.append(Path(f).with_suffix('.jpg').name)
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if im.shape[0] < 5: # image in CHW
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im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
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im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input
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s = im.shape[:2] # HWC
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shape0.append(s) # image shape
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g = max(size) / max(s) # gain
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shape1.append([y * g for y in s])
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ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
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shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size # inf shape
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x = [LetterBox(shape1, auto=False)(image=im)["img"] for im in ims] # pad
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x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW
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x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
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with amp.autocast(autocast):
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# Inference
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with dt[1]:
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y = self.model(x, augment=augment) # forward
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# Post-process
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with dt[2]:
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y = non_max_suppression(y if self.dmb else y[0],
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self.conf,
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self.iou,
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self.classes,
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self.agnostic,
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self.multi_label,
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max_det=self.max_det) # NMS
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for i in range(n):
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scale_boxes(shape1, y[i][:, :4], shape0[i])
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return Detections(ims, y, files, dt, self.names, x.shape)
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class Detections:
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# YOLOv8 detections class for inference results
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def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
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super().__init__()
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d = pred[0].device # device
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gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations
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self.ims = ims # list of images as numpy arrays
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self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
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self.names = names # class names
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self.files = files # image filenames
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self.times = times # profiling times
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self.xyxy = pred # xyxy pixels
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self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
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self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
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self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
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self.n = len(self.pred) # number of images (batch size)
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self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms)
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self.s = tuple(shape) # inference BCHW shape
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def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
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s, crops = '', []
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for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
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s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
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if pred.shape[0]:
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for c in pred[:, -1].unique():
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n = (pred[:, -1] == c).sum() # detections per class
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s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
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s = s.rstrip(', ')
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if show or save or render or crop:
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annotator = Annotator(im, example=str(self.names))
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for *box, conf, cls in reversed(pred): # xyxy, confidence, class
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label = f'{self.names[int(cls)]} {conf:.2f}'
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if crop:
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file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
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crops.append({
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'box': box,
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'conf': conf,
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'cls': cls,
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'label': label,
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'im': save_one_box(box, im, file=file, save=save)})
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else: # all others
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annotator.box_label(box, label if labels else '', color=colors(cls))
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im = annotator.im
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else:
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s += '(no detections)'
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im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
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if show:
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im.show(self.files[i]) # show
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if save:
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f = self.files[i]
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im.save(save_dir / f) # save
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if i == self.n - 1:
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LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
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if render:
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self.ims[i] = np.asarray(im)
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if pprint:
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s = s.lstrip('\n')
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return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
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if crop:
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if save:
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LOGGER.info(f'Saved results to {save_dir}\n')
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return crops
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def show(self, labels=True):
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self._run(show=True, labels=labels) # show results
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def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
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save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir
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self._run(save=True, labels=labels, save_dir=save_dir) # save results
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def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
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save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
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return self._run(crop=True, save=save, save_dir=save_dir) # crop results
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def render(self, labels=True):
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self._run(render=True, labels=labels) # render results
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return self.ims
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def pandas(self):
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# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
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new = copy(self) # return copy
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ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
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cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
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for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
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a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
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setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
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return new
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def tolist(self):
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# return a list of Detections objects, i.e. 'for result in results.tolist():'
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r = range(self.n) # iterable
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x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
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# for d in x:
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# for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
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# setattr(d, k, getattr(d, k)[0]) # pop out of list
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return x
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def print(self):
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LOGGER.info(self.__str__())
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def __len__(self): # override len(results)
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return self.n
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def __str__(self): # override print(results)
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return self._run(pprint=True) # print results
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def __repr__(self):
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return f'YOLOv8 {self.__class__} instance\n' + self.__str__()
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class Proto(nn.Module):
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# YOLOv8 mask Proto module for segmentation models
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def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks
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237
ultralytics/nn/results.py
Normal file
237
ultralytics/nn/results.py
Normal file
@ -0,0 +1,237 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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"""
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Common modules
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"""
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from copy import copy
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from pathlib import Path
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import cv2
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import numpy as np
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import pandas as pd
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import requests
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import torch
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import torch.nn as nn
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from PIL import Image, ImageOps
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from torch.cuda import amp
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from ultralytics.nn.autobackend import AutoBackend
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from ultralytics.yolo.data.augment import LetterBox
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from ultralytics.yolo.utils import LOGGER, colorstr
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from ultralytics.yolo.utils.files import increment_path
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from ultralytics.yolo.utils.ops import Profile, make_divisible, non_max_suppression, scale_boxes, xyxy2xywh
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from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
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from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode
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class AutoShape(nn.Module):
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# YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
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conf = 0.25 # NMS confidence threshold
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iou = 0.45 # NMS IoU threshold
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agnostic = False # NMS class-agnostic
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multi_label = False # NMS multiple labels per box
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classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
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max_det = 1000 # maximum number of detections per image
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amp = False # Automatic Mixed Precision (AMP) inference
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def __init__(self, model, verbose=True):
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super().__init__()
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if verbose:
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LOGGER.info('Adding AutoShape... ')
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copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes
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self.dmb = isinstance(model, AutoBackend) # DetectMultiBackend() instance
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self.pt = not self.dmb or model.pt # PyTorch model
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self.model = model.eval()
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if self.pt:
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m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
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m.inplace = False # Detect.inplace=False for safe multithread inference
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m.export = True # do not output loss values
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def _apply(self, fn):
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# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
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self = super()._apply(fn)
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if self.pt:
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m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
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m.stride = fn(m.stride)
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m.grid = list(map(fn, m.grid))
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if isinstance(m.anchor_grid, list):
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m.anchor_grid = list(map(fn, m.anchor_grid))
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return self
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@smart_inference_mode()
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def forward(self, ims, size=640, augment=False, profile=False):
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# Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
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# file: ims = 'data/images/zidane.jpg' # str or PosixPath
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# URI: = 'https://ultralytics.com/images/zidane.jpg'
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# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
|
||||
# PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
|
||||
# numpy: = np.zeros((640,1280,3)) # HWC
|
||||
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
|
||||
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
|
||||
|
||||
dt = (Profile(), Profile(), Profile())
|
||||
with dt[0]:
|
||||
if isinstance(size, int): # expand
|
||||
size = (size, size)
|
||||
p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param
|
||||
autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference
|
||||
if isinstance(ims, torch.Tensor): # torch
|
||||
with amp.autocast(autocast):
|
||||
return self.model(ims.to(p.device).type_as(p), augment=augment) # inference
|
||||
|
||||
# Pre-process
|
||||
n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images
|
||||
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
|
||||
for i, im in enumerate(ims):
|
||||
f = f'image{i}' # filename
|
||||
if isinstance(im, (str, Path)): # filename or uri
|
||||
im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
|
||||
im = np.asarray(ImageOps.exif_transpose(im))
|
||||
elif isinstance(im, Image.Image): # PIL Image
|
||||
im, f = np.asarray(ImageOps.exif_transpose(im)), getattr(im, 'filename', f) or f
|
||||
files.append(Path(f).with_suffix('.jpg').name)
|
||||
if im.shape[0] < 5: # image in CHW
|
||||
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
|
||||
im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input
|
||||
s = im.shape[:2] # HWC
|
||||
shape0.append(s) # image shape
|
||||
g = max(size) / max(s) # gain
|
||||
shape1.append([y * g for y in s])
|
||||
ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
|
||||
shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size # inf shape
|
||||
x = [LetterBox(shape1, auto=False)(image=im)["img"] for im in ims] # pad
|
||||
x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW
|
||||
x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
|
||||
|
||||
with amp.autocast(autocast):
|
||||
# Inference
|
||||
with dt[1]:
|
||||
y = self.model(x, augment=augment) # forward
|
||||
|
||||
# Post-process
|
||||
with dt[2]:
|
||||
y = non_max_suppression(y if self.dmb else y[0],
|
||||
self.conf,
|
||||
self.iou,
|
||||
self.classes,
|
||||
self.agnostic,
|
||||
self.multi_label,
|
||||
max_det=self.max_det) # NMS
|
||||
for i in range(n):
|
||||
scale_boxes(shape1, y[i][:, :4], shape0[i])
|
||||
|
||||
return Detections(ims, y, files, dt, self.names, x.shape)
|
||||
|
||||
|
||||
class Detections:
|
||||
# YOLOv8 detections class for inference results
|
||||
def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
|
||||
super().__init__()
|
||||
d = pred[0].device # device
|
||||
gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations
|
||||
self.ims = ims # list of images as numpy arrays
|
||||
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
|
||||
self.names = names # class names
|
||||
self.files = files # image filenames
|
||||
self.times = times # profiling times
|
||||
self.xyxy = pred # xyxy pixels
|
||||
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
|
||||
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
|
||||
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
|
||||
self.n = len(self.pred) # number of images (batch size)
|
||||
self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms)
|
||||
self.s = tuple(shape) # inference BCHW shape
|
||||
|
||||
def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
|
||||
s, crops = '', []
|
||||
for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
|
||||
s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
|
||||
if pred.shape[0]:
|
||||
for c in pred[:, -1].unique():
|
||||
n = (pred[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
s = s.rstrip(', ')
|
||||
if show or save or render or crop:
|
||||
annotator = Annotator(im, example=str(self.names))
|
||||
for *box, conf, cls in reversed(pred): # xyxy, confidence, class
|
||||
label = f'{self.names[int(cls)]} {conf:.2f}'
|
||||
if crop:
|
||||
file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
|
||||
crops.append({
|
||||
'box': box,
|
||||
'conf': conf,
|
||||
'cls': cls,
|
||||
'label': label,
|
||||
'im': save_one_box(box, im, file=file, save=save)})
|
||||
else: # all others
|
||||
annotator.box_label(box, label if labels else '', color=colors(cls))
|
||||
im = annotator.im
|
||||
else:
|
||||
s += '(no detections)'
|
||||
|
||||
im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
|
||||
if show:
|
||||
im.show(self.files[i]) # show
|
||||
if save:
|
||||
f = self.files[i]
|
||||
im.save(save_dir / f) # save
|
||||
if i == self.n - 1:
|
||||
LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
|
||||
if render:
|
||||
self.ims[i] = np.asarray(im)
|
||||
if pprint:
|
||||
s = s.lstrip('\n')
|
||||
return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
|
||||
if crop:
|
||||
if save:
|
||||
LOGGER.info(f'Saved results to {save_dir}\n')
|
||||
return crops
|
||||
|
||||
def show(self, labels=True):
|
||||
self._run(show=True, labels=labels) # show results
|
||||
|
||||
def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
|
||||
save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir
|
||||
self._run(save=True, labels=labels, save_dir=save_dir) # save results
|
||||
|
||||
def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
|
||||
save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
|
||||
return self._run(crop=True, save=save, save_dir=save_dir) # crop results
|
||||
|
||||
def render(self, labels=True):
|
||||
self._run(render=True, labels=labels) # render results
|
||||
return self.ims
|
||||
|
||||
def pandas(self):
|
||||
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
|
||||
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])
|
||||
return new
|
||||
|
||||
def tolist(self):
|
||||
# return a list of Detections objects, i.e. 'for result in results.tolist():'
|
||||
r = range(self.n) # iterable
|
||||
x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
|
||||
# for d in x:
|
||||
# for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
||||
# setattr(d, k, getattr(d, k)[0]) # pop out of list
|
||||
return x
|
||||
|
||||
def print(self):
|
||||
LOGGER.info(self.__str__())
|
||||
|
||||
def __len__(self): # override len(results)
|
||||
return self.n
|
||||
|
||||
def __str__(self): # override print(results)
|
||||
return self._run(pprint=True) # print results
|
||||
|
||||
def __repr__(self):
|
||||
return f'YOLOv8 {self.__class__} instance\n' + self.__str__()
|
||||
|
||||
|
||||
print('works')
|
||||
@ -57,7 +57,7 @@ class BaseModel(nn.Module):
|
||||
x = m(x) # run
|
||||
y.append(x if m.i in self.save else None) # save output
|
||||
if visualize:
|
||||
pass
|
||||
LOGGER.info('visualize feature not yet supported')
|
||||
# TODO: feature_visualization(x, m.type, m.i, save_dir=visualize)
|
||||
return x
|
||||
|
||||
@ -106,8 +106,8 @@ class BaseModel(nn.Module):
|
||||
Prints model information
|
||||
|
||||
Args:
|
||||
verbose (bool): if True, prints out the model information. Defaults to False
|
||||
imgsz (int): the size of the image that the model will be trained on. Defaults to 640
|
||||
verbose (bool): if True, prints out the model information. Defaults to False
|
||||
imgsz (int): the size of the image that the model will be trained on. Defaults to 640
|
||||
"""
|
||||
model_info(self, verbose, imgsz)
|
||||
|
||||
@ -117,10 +117,10 @@ class BaseModel(nn.Module):
|
||||
parameters or registered buffers
|
||||
|
||||
Args:
|
||||
fn: the function to apply to the model
|
||||
fn: the function to apply to the model
|
||||
|
||||
Returns:
|
||||
A model that is a Detect() object.
|
||||
A model that is a Detect() object.
|
||||
"""
|
||||
self = super()._apply(fn)
|
||||
m = self.model[-1] # Detect()
|
||||
@ -135,7 +135,7 @@ class BaseModel(nn.Module):
|
||||
This function loads the weights of the model from a file
|
||||
|
||||
Args:
|
||||
weights (str): The weights to load into the model.
|
||||
weights (str): The weights to load into the model.
|
||||
"""
|
||||
# Force all tasks to implement this function
|
||||
raise NotImplementedError("This function needs to be implemented by derived classes!")
|
||||
|
||||
Reference in New Issue
Block a user