110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
# 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 adapted from https://github.com/zhunzhong07/Random-Erasing, and refer to Timm(https://github.com/rwightman/pytorch-image-models).
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# reference: https://arxiv.org/abs/1708.04896
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from functools import partial
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import math
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import random
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import numpy as np
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class Pixels(object):
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def __init__(self, mode="const", mean=[0., 0., 0.]):
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self._mode = mode
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self._mean = np.array(mean)
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def __call__(self, h=224, w=224, c=3, channel_first=False):
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if self._mode == "rand":
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return np.random.normal(size=(
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1, 1, 3)) if not channel_first else np.random.normal(size=(
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3, 1, 1))
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elif self._mode == "pixel":
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return np.random.normal(size=(
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h, w, c)) if not channel_first else np.random.normal(size=(
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c, h, w))
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elif self._mode == "const":
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return np.reshape(self._mean, (
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1, 1, c)) if not channel_first else np.reshape(self._mean,
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(c, 1, 1))
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else:
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raise Exception(
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"Invalid mode in RandomErasing, only support \"const\", \"rand\", \"pixel\""
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)
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class RandomErasing(object):
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"""RandomErasing.
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"""
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def __init__(self,
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EPSILON=0.5,
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sl=0.02,
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sh=0.4,
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r1=0.3,
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mean=[0., 0., 0.],
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attempt=100,
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use_log_aspect=False,
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mode='const'):
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self.EPSILON = eval(EPSILON) if isinstance(EPSILON, str) else EPSILON
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self.sl = eval(sl) if isinstance(sl, str) else sl
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self.sh = eval(sh) if isinstance(sh, str) else sh
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r1 = eval(r1) if isinstance(r1, str) else r1
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self.r1 = (math.log(r1), math.log(1 / r1)) if use_log_aspect else (
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r1, 1 / r1)
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self.use_log_aspect = use_log_aspect
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self.attempt = attempt
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self.get_pixels = Pixels(mode, mean)
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def __call__(self, img):
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if random.random() > self.EPSILON:
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return img
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for _ in range(self.attempt):
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if isinstance(img, np.ndarray):
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img_h, img_w, img_c = img.shape
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channel_first = False
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else:
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img_c, img_h, img_w = img.shape
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channel_first = True
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area = img_h * img_w
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target_area = random.uniform(self.sl, self.sh) * area
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aspect_ratio = random.uniform(*self.r1)
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if self.use_log_aspect:
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aspect_ratio = math.exp(aspect_ratio)
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h = int(round(math.sqrt(target_area * aspect_ratio)))
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w = int(round(math.sqrt(target_area / aspect_ratio)))
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if w < img_w and h < img_h:
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pixels = self.get_pixels(h, w, img_c, channel_first)
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x1 = random.randint(0, img_h - h)
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y1 = random.randint(0, img_w - w)
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if img_c == 3:
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if channel_first:
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img[:, x1:x1 + h, y1:y1 + w] = pixels
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else:
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img[x1:x1 + h, y1:y1 + w, :] = pixels
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else:
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if channel_first:
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img[0, x1:x1 + h, y1:y1 + w] = pixels[0]
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else:
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img[x1:x1 + h, y1:y1 + w, 0] = pixels[:, :, 0]
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return img
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return img
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