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"""File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80)
"""
from pathlib import Path
import torch
from models.yolo import Model
from utils.general import set_logging
from utils.google_utils import attempt_download
dependencies = ['torch', 'yaml']
set_logging()
def create(name, pretrained, channels, classes, autoshape):
"""Creates a specified YOLOv5 model
Arguments:
name (str): name of model, i.e. 'yolov5s'
pretrained (bool): load pretrained weights into the model
channels (int): number of input channels
classes (int): number of model classes
Returns:
pytorch model
"""
config = Path(__file__).parent / 'models' / f'{name}.yaml' # model.yaml path
try:
model = Model(config, channels, classes)
if pretrained:
fname = f'{name}.pt' # checkpoint filename
attempt_download(fname) # download if not found locally
ckpt = torch.load(fname, map_location=torch.device('cpu')) # load
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter
model.load_state_dict(state_dict, strict=False) # load
if len(ckpt['model'].names) == classes:
model.names = ckpt['model'].names # set class names attribute
if autoshape:
model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
return model
except Exception as e:
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url
raise Exception(s) from e
def yolov5s(pretrained=False, channels=3, classes=80, autoshape=True):
"""YOLOv5-small model from https://github.com/ultralytics/yolov5
Arguments:
pretrained (bool): load pretrained weights into the model, default=False
channels (int): number of input channels, default=3
classes (int): number of model classes, default=80
Returns:
pytorch model
"""
return create('yolov5s', pretrained, channels, classes, autoshape)
def yolov5m(pretrained=False, channels=3, classes=80, autoshape=True):
"""YOLOv5-medium model from https://github.com/ultralytics/yolov5
Arguments:
pretrained (bool): load pretrained weights into the model, default=False
channels (int): number of input channels, default=3
classes (int): number of model classes, default=80
Returns:
pytorch model
"""
return create('yolov5m', pretrained, channels, classes, autoshape)
def yolov5l(pretrained=False, channels=3, classes=80, autoshape=True):
"""YOLOv5-large model from https://github.com/ultralytics/yolov5
Arguments:
pretrained (bool): load pretrained weights into the model, default=False
channels (int): number of input channels, default=3
classes (int): number of model classes, default=80
Returns:
pytorch model
"""
return create('yolov5l', pretrained, channels, classes, autoshape)
def yolov5x(pretrained=False, channels=3, classes=80, autoshape=True):
"""YOLOv5-xlarge model from https://github.com/ultralytics/yolov5
Arguments:
pretrained (bool): load pretrained weights into the model, default=False
channels (int): number of input channels, default=3
classes (int): number of model classes, default=80
Returns:
pytorch model
"""
return create('yolov5x', pretrained, channels, classes, autoshape)
def custom(path_or_model='path/to/model.pt', autoshape=True):
"""YOLOv5-custom model from https://github.com/ultralytics/yolov5
Arguments (3 options):
path_or_model (str): 'path/to/model.pt'
path_or_model (dict): torch.load('path/to/model.pt')
path_or_model (nn.Module): torch.load('path/to/model.pt')['model']
Returns:
pytorch model
"""
model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint
if isinstance(model, dict):
model = model['model'] # load model
hub_model = Model(model.yaml).to(next(model.parameters()).device) # create
hub_model.load_state_dict(model.float().state_dict()) # load state_dict
hub_model.names = model.names # class names
return hub_model.autoshape() if autoshape else hub_model
if __name__ == '__main__':
model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True) # pretrained example
# model = custom(path_or_model='path/to/model.pt') # custom example
# Verify inference
import numpy as np
from PIL import Image
imgs = [Image.open('data/images/bus.jpg'), # PIL
'data/images/zidane.jpg', # filename
'https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', # URI
np.zeros((640, 480, 3))] # numpy
results = model(imgs) # batched inference
results.print()
results.save()
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