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169 lines
5.8 KiB
169 lines
5.8 KiB
2 years ago
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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import numpy as np
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from paddle.io import Dataset, DataLoader
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import paddle
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import paddle.nn as nn
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import math
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import functools
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from paddle.nn import Conv1DTranspose, Conv2DTranspose, Conv3DTranspose, Linear
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# 处理图片数据:裁切、水平翻转、调整图片数据形状、归一化数据
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def data_transform(img,
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resize_w,
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resize_h,
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load_size=286,
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pos=[0, 0, 256, 256],
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flip=True,
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is_image=True):
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if is_image:
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resized = img.resize((resize_w, resize_h), Image.BICUBIC)
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else:
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resized = img.resize((resize_w, resize_h), Image.NEAREST)
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croped = resized.crop((pos[0], pos[1], pos[2], pos[3]))
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fliped = ImageOps.mirror(croped) if flip else croped
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fliped = np.array(fliped) # transform to numpy array
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expanded = np.expand_dims(fliped, 2) if len(fliped.shape) < 3 else fliped
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transposed = np.transpose(expanded, (2, 0, 1)).astype('float32')
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if is_image:
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normalized = transposed / 255. * 2. - 1.
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else:
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normalized = transposed
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return normalized
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# 定义CoCo数据集对象
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class COCODateset(Dataset):
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def __init__(self, opt):
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super(COCODateset, self).__init__()
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inst_dir = opt.dataroot + 'train_inst/'
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_, _, inst_list = next(os.walk(inst_dir))
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self.inst_list = np.sort(inst_list)
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self.opt = opt
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def __getitem__(self, idx):
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ins = Image.open(self.opt.dataroot + 'train_inst/' + self.inst_list[
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idx])
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img = Image.open(self.opt.dataroot + 'train_img/' + self.inst_list[idx]
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.replace(".png", ".jpg"))
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img = img.convert('RGB')
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w, h = img.size
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resize_w, resize_h = 0, 0
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if w < h:
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resize_w, resize_h = self.opt.load_size, int(h *
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self.opt.load_size / w)
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else:
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resize_w, resize_h = int(w * self.opt.load_size /
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h), self.opt.load_size
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left = random.randint(0, resize_w - self.opt.crop_size)
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top = random.randint(0, resize_h - self.opt.crop_size)
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flip = False
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img = data_transform(
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img,
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resize_w,
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resize_h,
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load_size=opt.load_size,
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pos=[
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left, top, left + self.opt.crop_size, top + self.opt.crop_size
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],
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flip=flip,
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is_image=True)
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ins = data_transform(
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ins,
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resize_w,
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resize_h,
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load_size=opt.load_size,
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pos=[
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left, top, left + self.opt.crop_size, top + self.opt.crop_size
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],
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flip=flip,
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is_image=False)
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return img, ins, self.inst_list[idx]
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def __len__(self):
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return len(self.inst_list)
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def data_onehot_pro(instance, opt):
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shape = instance.shape
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nc = opt.label_nc + 1 if opt.contain_dontcare_label \
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else opt.label_nc
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shape[1] = nc
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semantics = paddle.nn.functional.one_hot(instance.astype('int64'). \
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reshape([opt.batchSize, opt.crop_size, opt.crop_size]), nc). \
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transpose((0, 3, 1, 2))
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# edge
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edge = np.zeros(instance.shape, 'int64')
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t = instance.numpy()
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edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1])
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edge[:, :, :, :-1] = edge[:, :, :, :-1] | (
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t[:, :, :, 1:] != t[:, :, :, :-1])
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edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
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edge[:, :, :-1, :] = edge[:, :, :-1, :] | (
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t[:, :, 1:, :] != t[:, :, :-1, :])
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edge = paddle.to_tensor(edge).astype('float32')
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semantics = paddle.concat([semantics, edge], 1)
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return semantics
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# 设置除 spade 以外的归一化层
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def build_norm_layer(norm_type='instance'):
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"""Return a normalization layer
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Args:
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norm_type (str) -- the name of the normalization layer: batch | instance | none
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For BatchNorm, we do not use learnable affine parameters and track running statistics (mean/stddev).
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For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
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"""
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if norm_type == 'batch':
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norm_layer = functools.partial(
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nn.BatchNorm2D, weight_attr=False, bias_attr=False)
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elif norm_type == 'syncbatch':
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norm_layer = functools.partial(
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nn.SyncBatchNorm, weight_attr=False, bias_attr=False)
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elif norm_type == 'instance':
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norm_layer = functools.partial(nn.InstanceNorm2D, )
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elif norm_type == 'spectral':
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norm_layer = functools.partial(Spectralnorm)
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elif norm_type == 'none':
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def norm_layer(x):
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return Identity()
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else:
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raise NotImplementedError('normalization layer [%s] is not found' %
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norm_type)
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return norm_layer
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def simam(x, e_lambda=1e-4):
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b, c, h, w = x.shape
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n = w * h - 1
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x_minus_mu_square = (x - x.mean(axis=[2, 3], keepdim=True))**2
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y = x_minus_mu_square / (4 * (x_minus_mu_square.sum(
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axis=[2, 3], keepdim=True) / n + e_lambda)) + 0.5
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return x * nn.functional.sigmoid(y)
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class Dict(dict):
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__setattr__ = dict.__setitem__
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__getattr__ = dict.__getitem__
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