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87 lines
2.8 KiB
87 lines
2.8 KiB
2 years ago
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# The code is based on:
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# https://github.com/dvlab-research/GridMask/blob/master/detection_grid/maskrcnn_benchmark/data/transforms/grid.py
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from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import division
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import numpy as np
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from PIL import Image
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class Gridmask(object):
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def __init__(self,
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use_h=True,
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use_w=True,
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rotate=1,
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offset=False,
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ratio=0.5,
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mode=1,
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prob=0.7,
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upper_iter=360000):
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super(Gridmask, self).__init__()
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self.use_h = use_h
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self.use_w = use_w
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self.rotate = rotate
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self.offset = offset
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self.ratio = ratio
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self.mode = mode
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self.prob = prob
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self.st_prob = prob
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self.upper_iter = upper_iter
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def __call__(self, x, curr_iter):
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self.prob = self.st_prob * min(1, 1.0 * curr_iter / self.upper_iter)
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if np.random.rand() > self.prob:
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return x
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h, w, _ = x.shape
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hh = int(1.5 * h)
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ww = int(1.5 * w)
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d = np.random.randint(2, h)
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self.l = min(max(int(d * self.ratio + 0.5), 1), d - 1)
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mask = np.ones((hh, ww), np.float32)
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st_h = np.random.randint(d)
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st_w = np.random.randint(d)
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if self.use_h:
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for i in range(hh // d):
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s = d * i + st_h
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t = min(s + self.l, hh)
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mask[s:t, :] *= 0
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if self.use_w:
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for i in range(ww // d):
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s = d * i + st_w
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t = min(s + self.l, ww)
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mask[:, s:t] *= 0
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r = np.random.randint(self.rotate)
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mask = Image.fromarray(np.uint8(mask))
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mask = mask.rotate(r)
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mask = np.asarray(mask)
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mask = mask[(hh - h) // 2:(hh - h) // 2 + h, (ww - w) // 2:(ww - w) // 2
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+ w].astype(np.float32)
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if self.mode == 1:
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mask = 1 - mask
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mask = np.expand_dims(mask, axis=-1)
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if self.offset:
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offset = (2 * (np.random.rand(h, w) - 0.5)).astype(np.float32)
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x = (x * mask + offset * (1 - mask)).astype(x.dtype)
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else:
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x = (x * mask).astype(x.dtype)
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return x
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