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89 lines
2.9 KiB
89 lines
2.9 KiB
# 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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