# BSD 3-Clause License
#
# Copyright (c) 2017 xxxx
# All rights reserved.
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ============================================================================
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
'''
option = {}
# option["ACL_OP_DEBUG_LEVEL"] = 3 # 算子debug功能，暂不开启
# option["ACL_DEBUG_DIR"] = "debug_file" # 算子debug功能对应文件夹，暂不开启
option["ACL_OP_COMPILER_CACHE_MODE"] = "enable" # cache功能启用
option["ACL_OP_COMPILER_CACHE_DIR"] = "./kernel_meta" # cache所在文件夹
print("option:",option)
torch.npu.set_option(option)
'''
def conv(in_channels, out_channels, kernel_size=3, padding=1, bn=True, dilation=1, stride=1, relu=True, bias=True):
modules = [nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)]
if bn:
modules.append(nn.BatchNorm2d(out_channels))
if relu:
modules.append(nn.ReLU(inplace=True))
return nn.Sequential(*modules)
class Decoder(nn.Module):
def __init__(self, in_channels, squeeze_channels):
super().__init__()
self.squeeze = conv(in_channels, squeeze_channels)
def forward(self, x, encoder_features):
x = self.squeeze(x)
x = F.interpolate(x, size=(encoder_features.shape[2], encoder_features.shape[3]),
mode='bilinear', align_corners=True)
up = torch.cat([encoder_features, x], 1)
return up
class FOTSModel(nn.Module):
def __init__(self, crop_height=640):
super().__init__()
self.crop_height = crop_height
self.resnet = torchvision.models.resnet34(pretrained=True)
self.conv1 = nn.Sequential(
self.resnet.conv1,
self.resnet.bn1,
self.resnet.relu,
) # 64
self.encoder1 = self.resnet.layer1 # 64
self.encoder2 = self.resnet.layer2 # 128
self.encoder3 = self.resnet.layer3 # 256
self.encoder4 = self.resnet.layer4 # 512
self.center = nn.Sequential(
conv(512, 512, stride=2),
conv(512, 1024)
)
self.decoder4 = Decoder(1024, 512)
self.decoder3 = Decoder(1024, 256)
self.decoder2 = Decoder(512, 128)
self.decoder1 = Decoder(256, 64)
self.remove_artifacts = conv(128, 64)
self.confidence = conv(64, 1, kernel_size=1, padding=0, bn=False, relu=False)
self.distances = conv(64, 4, kernel_size=1, padding=0, bn=False, relu=False)
self.angle = conv(64, 1, kernel_size=1, padding=0, bn=False, relu=False)
def forward(self, x):
x = self.conv1(x)
x = F.max_pool2d(x, kernel_size=2, stride=2)
e1 = self.encoder1(x)
e2 = self.encoder2(e1)
e3 = self.encoder3(e2)
e4 = self.encoder4(e3)
f = self.center(e4)
d4 = self.decoder4(f, e4)
d3 = self.decoder3(d4, e3)
d2 = self.decoder2(d3, e2)
d1 = self.decoder1(d2, e1)
final = self.remove_artifacts(d1)
confidence = self.confidence(final)
distances = self.distances(final)
distances = torch.sigmoid(distances) * self.crop_height
angle = self.angle(final)
angle = torch.sigmoid(angle) * np.pi / 2
return confidence, distances, angle
# class FOTSModel(nn.Module):
# """This model is described in the paper, but it trains slower and gives slightly worse results"""
# def __init__(self, crop_height=640):
# super().__init__()
# self.crop_height = crop_height
# self.resnet = torchvision.models.resnet50(pretrained=True)
# self.conv1 = nn.Sequential(
# self.resnet.conv1,
# self.resnet.bn1,
# self.resnet.relu,
# ) # 64 * 4
# self.encoder1 = self.resnet.layer1 # 64 * 4
# self.encoder2 = self.resnet.layer2 # 128 * 4
# self.encoder3 = self.resnet.layer3 # 256 * 4
# self.encoder4 = self.resnet.layer4 # 512 * 4
# self.decoder3 = Decoder(512 * 4, 256 * 4)
# self.decoder2 = Decoder(256 * 4 * 2, 128 * 4)
# self.decoder1 = Decoder(128 * 4 * 2, 64 * 4)
# self.confidence = conv(64 * 4 * 2, 1, kernel_size=1, padding=0, bn=False, relu=False)
# self.distances = conv(64 * 4 * 2, 4, kernel_size=1, padding=0, bn=False, relu=False)
# self.angle = conv(64 * 4 * 2, 1, kernel_size=1, padding=0, bn=False, relu=False)
# def forward(self, x):
# x = self.conv1(x)
# x = F.max_pool2d(x, kernel_size=2, stride=2)
# e1 = self.encoder1(x)
# e2 = self.encoder2(e1)
# e3 = self.encoder3(e2)
# e4 = self.encoder4(e3)
# d3 = self.decoder3(e4, e3)
# d2 = self.decoder2(d3, e2)
# d1 = self.decoder1(d2, e1)
# confidence = self.confidence(d1)
# distances = self.distances(d1)
# distances = torch.sigmoid(distances) * self.crop_height
# angle = self.angle(d1)
# angle = torch.sigmoid(angle) * np.pi / 2
# return confidence, distances, angle