Spaces:
Starting
on
A10G
Starting
on
A10G
import matplotlib.pyplot as plt | |
import annotator.uniformer.mmcv as mmcv | |
import torch | |
from annotator.uniformer.mmcv.parallel import collate, scatter | |
from annotator.uniformer.mmcv.runner import load_checkpoint | |
from annotator.uniformer.mmseg.datasets.pipelines import Compose | |
from annotator.uniformer.mmseg.models import build_segmentor | |
def init_segmentor(config, checkpoint=None, device='cuda:0'): | |
"""Initialize a segmentor from config file. | |
Args: | |
config (str or :obj:`mmcv.Config`): Config file path or the config | |
object. | |
checkpoint (str, optional): Checkpoint path. If left as None, the model | |
will not load any weights. | |
device (str, optional) CPU/CUDA device option. Default 'cuda:0'. | |
Use 'cpu' for loading model on CPU. | |
Returns: | |
nn.Module: The constructed segmentor. | |
""" | |
if isinstance(config, str): | |
config = mmcv.Config.fromfile(config) | |
elif not isinstance(config, mmcv.Config): | |
raise TypeError('config must be a filename or Config object, ' | |
'but got {}'.format(type(config))) | |
config.model.pretrained = None | |
config.model.train_cfg = None | |
model = build_segmentor(config.model, test_cfg=config.get('test_cfg')) | |
if checkpoint is not None: | |
checkpoint = load_checkpoint(model, checkpoint, map_location='cpu') | |
model.CLASSES = checkpoint['meta']['CLASSES'] | |
model.PALETTE = checkpoint['meta']['PALETTE'] | |
model.cfg = config # save the config in the model for convenience | |
model.to(device) | |
model.eval() | |
return model | |
class LoadImage: | |
"""A simple pipeline to load image.""" | |
def __call__(self, results): | |
"""Call function to load images into results. | |
Args: | |
results (dict): A result dict contains the file name | |
of the image to be read. | |
Returns: | |
dict: ``results`` will be returned containing loaded image. | |
""" | |
if isinstance(results['img'], str): | |
results['filename'] = results['img'] | |
results['ori_filename'] = results['img'] | |
else: | |
results['filename'] = None | |
results['ori_filename'] = None | |
img = mmcv.imread(results['img']) | |
results['img'] = img | |
results['img_shape'] = img.shape | |
results['ori_shape'] = img.shape | |
return results | |
def inference_segmentor(model, img): | |
"""Inference image(s) with the segmentor. | |
Args: | |
model (nn.Module): The loaded segmentor. | |
imgs (str/ndarray or list[str/ndarray]): Either image files or loaded | |
images. | |
Returns: | |
(list[Tensor]): The segmentation result. | |
""" | |
cfg = model.cfg | |
device = next(model.parameters()).device # model device | |
# build the data pipeline | |
test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:] | |
test_pipeline = Compose(test_pipeline) | |
# prepare data | |
data = dict(img=img) | |
data = test_pipeline(data) | |
data = collate([data], samples_per_gpu=1) | |
if next(model.parameters()).is_cuda: | |
# scatter to specified GPU | |
data = scatter(data, [device])[0] | |
else: | |
data['img_metas'] = [i.data[0] for i in data['img_metas']] | |
# forward the model | |
with torch.no_grad(): | |
result = model(return_loss=False, rescale=True, **data) | |
return result | |
def show_result_pyplot(model, | |
img, | |
result, | |
palette=None, | |
fig_size=(15, 10), | |
opacity=0.5, | |
title='', | |
block=True): | |
"""Visualize the segmentation results on the image. | |
Args: | |
model (nn.Module): The loaded segmentor. | |
img (str or np.ndarray): Image filename or loaded image. | |
result (list): The segmentation result. | |
palette (list[list[int]]] | None): The palette of segmentation | |
map. If None is given, random palette will be generated. | |
Default: None | |
fig_size (tuple): Figure size of the pyplot figure. | |
opacity(float): Opacity of painted segmentation map. | |
Default 0.5. | |
Must be in (0, 1] range. | |
title (str): The title of pyplot figure. | |
Default is ''. | |
block (bool): Whether to block the pyplot figure. | |
Default is True. | |
""" | |
if hasattr(model, 'module'): | |
model = model.module | |
img = model.show_result( | |
img, result, palette=palette, show=False, opacity=opacity) | |
# plt.figure(figsize=fig_size) | |
# plt.imshow(mmcv.bgr2rgb(img)) | |
# plt.title(title) | |
# plt.tight_layout() | |
# plt.show(block=block) | |
return mmcv.bgr2rgb(img) | |