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# Copyright (c) 2021 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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# This code is based on https://github.com/akuxcw/GridMask
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import numpy as np
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from PIL import Image
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import pdb
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# curr
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CURR_EPOCH = 0
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# epoch for the prob to be the upper limit
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NUM_EPOCHS = 240
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class GridMask(object):
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def __init__(self, d1=96, d2=224, rotate=1, ratio=0.5, mode=0, prob=1.):
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self.d1 = d1
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self.d2 = d2
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self.rotate = rotate
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self.ratio = ratio
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self.mode = mode
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self.st_prob = prob
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self.prob = prob
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self.last_prob = -1
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def set_prob(self):
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global CURR_EPOCH
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global NUM_EPOCHS
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self.prob = self.st_prob * min(1, 1.0 * CURR_EPOCH / NUM_EPOCHS)
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def __call__(self, img):
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self.set_prob()
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if abs(self.last_prob - self.prob) > 1e-10:
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global CURR_EPOCH
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global NUM_EPOCHS
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print(
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"self.prob is updated, self.prob={}, CURR_EPOCH: {}, NUM_EPOCHS: {}".
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format(self.prob, CURR_EPOCH, NUM_EPOCHS))
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self.last_prob = self.prob
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# print("CURR_EPOCH: {}, NUM_EPOCHS: {}, self.prob is set as: {}".format(CURR_EPOCH, NUM_EPOCHS, self.prob) )
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if np.random.rand() > self.prob:
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return img
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_, h, w = img.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(self.d1, self.d2)
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#d = self.d
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self.l = int(d * self.ratio + 0.5)
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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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for i in range(-1, hh // d + 1):
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s = d * i + st_h
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t = s + self.l
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s = max(min(s, hh), 0)
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t = max(min(t, hh), 0)
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mask[s:t, :] *= 0
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for i in range(-1, ww // d + 1):
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s = d * i + st_w
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t = s + self.l
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s = max(min(s, ww), 0)
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t = max(min(t, ww), 0)
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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]
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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=0)
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img = (img * mask).astype(img.dtype)
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return img
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