Image Segmentation
Transformers
PyTorch
English
garment-mask-generation
image-inpainting
fashion
garment-mask
densepose
human-parsing
Instructions to use Ekliipce/wearit-garment-mask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ekliipce/wearit-garment-mask with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Ekliipce/wearit-garment-mask")# Load model directly from transformers import GarmentMaskPipeline model = GarmentMaskPipeline.from_pretrained("Ekliipce/wearit-garment-mask", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 988 Bytes
436df5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | // Copyright (c) Facebook, Inc. and its affiliates.
#pragma once
#include <torch/types.h>
namespace detectron2 {
at::Tensor box_iou_rotated_cpu(
const at::Tensor& boxes1,
const at::Tensor& boxes2);
#if defined(WITH_CUDA) || defined(WITH_HIP)
at::Tensor box_iou_rotated_cuda(
const at::Tensor& boxes1,
const at::Tensor& boxes2);
#endif
// Interface for Python
// inline is needed to prevent multiple function definitions when this header is
// included by different cpps
inline at::Tensor box_iou_rotated(
const at::Tensor& boxes1,
const at::Tensor& boxes2) {
assert(boxes1.device().is_cuda() == boxes2.device().is_cuda());
if (boxes1.device().is_cuda()) {
#if defined(WITH_CUDA) || defined(WITH_HIP)
return box_iou_rotated_cuda(boxes1.contiguous(), boxes2.contiguous());
#else
AT_ERROR("Detectron2 is not compiled with GPU support!");
#endif
}
return box_iou_rotated_cpu(boxes1.contiguous(), boxes2.contiguous());
}
} // namespace detectron2
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