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- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # This code is based on https://github.com/akuxcw/GridMask
- import numpy as np
- from PIL import Image
- import pdb
- # curr
- CURR_EPOCH = 0
- # epoch for the prob to be the upper limit
- NUM_EPOCHS = 240
- class GridMask(object):
- def __init__(self, d1=96, d2=224, rotate=1, ratio=0.5, mode=0, prob=1.):
- self.d1 = d1
- self.d2 = d2
- self.rotate = rotate
- self.ratio = ratio
- self.mode = mode
- self.st_prob = prob
- self.prob = prob
- self.last_prob = -1
- def set_prob(self):
- global CURR_EPOCH
- global NUM_EPOCHS
- self.prob = self.st_prob * min(1, 1.0 * CURR_EPOCH / NUM_EPOCHS)
- def __call__(self, img):
- self.set_prob()
- if abs(self.last_prob - self.prob) > 1e-10:
- global CURR_EPOCH
- global NUM_EPOCHS
- print(
- "self.prob is updated, self.prob={}, CURR_EPOCH: {}, NUM_EPOCHS: {}".
- format(self.prob, CURR_EPOCH, NUM_EPOCHS))
- self.last_prob = self.prob
- # print("CURR_EPOCH: {}, NUM_EPOCHS: {}, self.prob is set as: {}".format(CURR_EPOCH, NUM_EPOCHS, self.prob) )
- if np.random.rand() > self.prob:
- return img
- _, h, w = img.shape
- hh = int(1.5 * h)
- ww = int(1.5 * w)
- d = np.random.randint(self.d1, self.d2)
- #d = self.d
- self.l = int(d * self.ratio + 0.5)
- mask = np.ones((hh, ww), np.float32)
- st_h = np.random.randint(d)
- st_w = np.random.randint(d)
- for i in range(-1, hh // d + 1):
- s = d * i + st_h
- t = s + self.l
- s = max(min(s, hh), 0)
- t = max(min(t, hh), 0)
- mask[s:t, :] *= 0
- for i in range(-1, ww // d + 1):
- s = d * i + st_w
- t = s + self.l
- s = max(min(s, ww), 0)
- t = max(min(t, ww), 0)
- mask[:, s:t] *= 0
- r = np.random.randint(self.rotate)
- mask = Image.fromarray(np.uint8(mask))
- mask = mask.rotate(r)
- mask = np.asarray(mask)
- mask = mask[(hh - h) // 2:(hh - h) // 2 + h, (ww - w) // 2:(ww - w) //
- 2 + w]
- if self.mode == 1:
- mask = 1 - mask
- mask = np.expand_dims(mask, axis=0)
- img = (img * mask).astype(img.dtype)
- return img
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