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add postprocessing_masks gpu version #276

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merged 4 commits into from
Apr 6, 2022
Merged

add postprocessing_masks gpu version #276

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42f61ae
add postprocessing_masks gpu version
Mar 25, 2022
f9587b1
default device cpu
Mar 30, 2022
0458f32
pre-commit fix
Mar 31, 2022
6f05691
Merge branch 'master' of github.com:open-mmlab/mmdeploy
Apr 6, 2022
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35 changes: 21 additions & 14 deletions mmdeploy/codebase/mmdet/deploy/object_detection_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,6 +60,7 @@ def __init__(self, backend: Backend, backend_files: Sequence[str],
super().__init__(deploy_cfg=deploy_cfg)
self.CLASSES = class_names
self.deploy_cfg = deploy_cfg
self.device = device
self._init_wrapper(
backend=backend, backend_files=backend_files, device=device)

Expand Down Expand Up @@ -114,6 +115,7 @@ def postprocessing_masks(det_bboxes: np.ndarray,
det_masks: np.ndarray,
img_w: int,
img_h: int,
device: str = 'cpu',
mask_thr_binary: float = 0.5) -> np.ndarray:
"""Additional processing of masks. Resizes masks from [num_det, 28, 28]
to [num_det, img_w, img_h]. Analog of the 'mmdeploy.codebase.mmdet.
Expand All @@ -138,17 +140,25 @@ def postprocessing_masks(det_bboxes: np.ndarray,
return np.zeros((0, img_h, img_w))

if isinstance(masks, np.ndarray):
masks = torch.tensor(masks)
bboxes = torch.tensor(bboxes)
masks = torch.tensor(masks, device=torch.device(device))
bboxes = torch.tensor(bboxes, device=torch.device(device))

result_masks = []
for bbox, mask in zip(bboxes, masks):

x0_int, y0_int = 0, 0
x1_int, y1_int = img_w, img_h

img_y = torch.arange(y0_int, y1_int, dtype=torch.float32) + 0.5
img_x = torch.arange(x0_int, x1_int, dtype=torch.float32) + 0.5
img_y = torch.arange(
y0_int,
y1_int,
dtype=torch.float32,
device=torch.device(device)) + 0.5
img_x = torch.arange(
x0_int,
x1_int,
dtype=torch.float32,
device=torch.device(device)) + 0.5
x0, y0, x1, y1 = bbox

img_y = (img_y - y0) / (y1 - y0) * 2 - 1
Expand All @@ -169,10 +179,8 @@ def postprocessing_masks(det_bboxes: np.ndarray,
grid[None, :, :, :],
align_corners=False)

mask = img_masks
mask = (mask >= mask_thr_binary).to(dtype=torch.bool)
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result_masks.append(mask.numpy())
result_masks = np.concatenate(result_masks, axis=1)
result_masks.append(img_masks)
result_masks = torch.cat(result_masks, 1)
return result_masks.squeeze(0)

def forward(self, img: Sequence[torch.Tensor], img_metas: Sequence[dict],
Expand Down Expand Up @@ -206,6 +214,8 @@ def forward(self, img: Sequence[torch.Tensor], img_metas: Sequence[dict],
if isinstance(scale_factor, (list, tuple, np.ndarray)):
assert len(scale_factor) == 4
scale_factor = np.array(scale_factor)[None, :] # [1,4]
scale_factor = torch.from_numpy(scale_factor).to(
device=torch.device(self.device))
dets[:, :4] /= scale_factor

if 'border' in img_metas[i]:
Expand All @@ -216,7 +226,7 @@ def forward(self, img: Sequence[torch.Tensor], img_metas: Sequence[dict],
y_off = img_metas[i]['border'][0]
dets[:, [0, 2]] -= x_off
dets[:, [1, 3]] -= y_off
dets[:, :4] *= (dets[:, :4] > 0).astype(dets.dtype)
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Why do you remove astype?

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dets的数据类型是torch.tensor，dtype是float32，不需要astype

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(dets[:, :4] > 0) will give you a bool tensor.

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torch.tensor dtype float32与bool 相乘会自动转换为float32

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@grimoire grimoire Apr 1, 2022 •
edited
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I see. A better way is using torch.nn.functional.threshold, which might be faster.

import torch
from torch.profiler import profile, ProfilerActivity


def func_test(func,
              data,
              threshold,
              num_test=10,
              activate=ProfilerActivity.CPU):

    # warmup
    for _ in range(num_test):
        out = func(data, threshold)

    with profile(activities=[activate], record_shapes=True) as prof:
        for _ in range(num_test):
            out = func(data, threshold)

    print(prof.key_averages().table(
        sort_by="cpu_time_total", top_level_events_only=True, row_limit=30))

    return out


def main():
    data = torch.rand(1, 3, 224, 224)
    threshold = 0.5
    activate = ProfilerActivity.CPU

    out0 = func_test(
        lambda data, threshold: data * (data > threshold),
        data,
        threshold,
        activate=activate)
    out1 = func_test(
        lambda data, threshold: torch.nn.functional.threshold(
            data, threshold, 0),
        data,
        threshold,
        activate=activate)
    torch.testing.assert_allclose(out0, out1)


if __name__ == '__main__':
    main()

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牛皮。

dets[:, :4] *= (dets[:, :4] > 0)

dets_results = bbox2result(dets, labels, len(self.CLASSES))

Expand All @@ -234,16 +244,14 @@ def forward(self, img: Sequence[torch.Tensor], img_metas: Sequence[dict],
'export_postprocess_mask', True)
if not export_postprocess_mask:
masks = End2EndModel.postprocessing_masks(
dets[:, :4], masks, ori_w, ori_h)
dets[:, :4], masks, ori_w, ori_h, self.device)
else:
masks = masks[:, :img_h, :img_w]
# avoid to resize masks with zero dim
if rescale and masks.shape[0] != 0:
masks = masks.astype(np.float32)
masks = torch.from_numpy(masks)
masks = torch.nn.functional.interpolate(
masks.unsqueeze(0), size=(ori_h, ori_w))
masks = masks.squeeze(0).detach().numpy()
masks = masks.squeeze(0)
if masks.dtype != bool:
masks = masks >= 0.5
segms_results = [[] for _ in range(len(self.CLASSES))]
Expand All @@ -267,7 +275,6 @@ def forward_test(self, imgs: torch.Tensor, *args, **kwargs) -> \
"""
outputs = self.wrapper({self.input_name: imgs})
outputs = self.wrapper.output_to_list(outputs)
outputs = [out.detach().cpu().numpy() for out in outputs]
return outputs

def show_result(self,
Expand Down