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https://github.com/alibaba/EasyCV.git
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9aaa600f79
* add attention layer and more loss function * add attention layer and various loss functions * add siou loss * add tah,various attention layers, and different loss functions * add asff sim, gsconv * blade utils fit faster * blade optimize for yolox static & fp16 * decode output for yolox control by cfg * add reparameterize_models for export * e2e trt_nms plugin export support and numeric test * split preprocess from end2end+blade, speedup from 17ms->7.2ms Co-authored-by: zouxinyi0625 <zouxinyi.zxy@alibaba-inc.com>
55 lines
2.6 KiB
Python
55 lines
2.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# Adapt from: https://github.com/open-mmlab/mmpose/blob/master/mmpose/models/utils/ops.py
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import math
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import warnings
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import torch
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import torch.nn.functional as F
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def resize_tensor(input,
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size=None,
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scale_factor=None,
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mode='nearest',
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align_corners=None,
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warning=True):
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"""Resize tensor with F.interpolate.
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Args:
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input (Tensor): the input tensor.
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size (Tuple[int, int]): output spatial size.
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scale_factor (float or Tuple[float]): multiplier for spatial size.
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If scale_factor is a tuple, its length has to match input.dim().
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mode (str): algorithm used for upsampling:
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'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area'. Default: 'nearest'
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align_corners (bool): Geometrically, we consider the pixels of the input and output as squares rather than points.
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If set to True, the input and output tensors are aligned by the center points of their corner pixels,
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preserving the values at the corner pixels.
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If set to False, the input and output tensors are aligned by the corner points of their corner pixels,
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and the interpolation uses edge value padding for out-of-boundary values,
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making this operation independent of input size when scale_factor is kept the same.
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This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'.
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"""
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if warning:
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if size is not None and align_corners:
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input_h, input_w = tuple(int(x) for x in input.shape[2:])
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output_h, output_w = tuple(int(x) for x in size)
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if output_h > input_h or output_w > output_h:
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if ((output_h > 1 and output_w > 1 and input_h > 1
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and input_w > 1) and (output_h - 1) % (input_h - 1)
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and (output_w - 1) % (input_w - 1)):
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warnings.warn(
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f'When align_corners={align_corners}, '
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'the output would more aligned if '
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f'input size {(input_h, input_w)} is `x+1` and '
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f'out size {(output_h, output_w)} is `nx+1`')
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if isinstance(size, torch.Size):
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size = tuple(int(x) for x in size)
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return F.interpolate(input, size, scale_factor, mode, align_corners)
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def make_divisible(x, divisor):
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# Upward revision the value x to make it evenly divisible by the divisor.
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return math.ceil(x / divisor) * divisor
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