<gh_stars>1-10
from math import floor, ceil
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


def getPoolingKernel(kernel_size=25):
    half_size = float(kernel_size) / 2.0
    xc2 = []
    for i in range(kernel_size):
        xc2.append(half_size - abs(float(i) + 0.5 - half_size))
    xc2 = np.array(xc2)
    kernel = np.outer(xc2.T, xc2)
    kernel = kernel / (half_size ** 2)
    return kernel

def get_bin_weight_kernel_size_and_stride(patch_size, num_spatial_bins):
    ks = 2 * int(patch_size / (num_spatial_bins + 1))
    stride = patch_size // num_spatial_bins
    pad = ks // 4
    return ks, stride, pad


class SIFTNet(nn.Module):
    def CircularGaussKernel(self, kernlen=21, circ=True, sigma_type='vlfeat'):
        halfSize = float(kernlen) / 2.
        r2 = float(halfSize ** 2)
        if sigma_type == 'hesamp':
            sigma_mul_2 = 0.9 * r2
        elif sigma_type == 'vlfeat':
            sigma_mul_2 = kernlen ** 2
        else:
            raise ValueError('Unknown sigma_type', sigma_type, 'try hesamp or vlfeat')
        disq = 0
        kernel = np.zeros((kernlen, kernlen))
        for y in range(kernlen):
            for x in range(kernlen):
                disq = (y - halfSize + 0.5) ** 2 + (x - halfSize + 0.5) ** 2
                kernel[y, x] = math.exp(-disq / sigma_mul_2)
                if circ and (disq >= r2):
                    kernel[y, x] = 0.
        return kernel

    def __repr__(self):
        return self.__class__.__name__ + '(' + 'num_ang_bins=' + str(self.num_ang_bins) + \
               ', ' + 'num_spatial_bins=' + str(self.num_spatial_bins) + \
               ', ' + 'patch_size=' + str(self.patch_size) + \
               ', ' + 'rootsift=' + str(self.rootsift) + \
               ', ' + 'sigma_type=' + str(self.sigma_type) + \
               ', ' + 'mask_type=' + str(self.mask_type) + \
               ', ' + 'clipval=' + str(self.clipval) + ')'

    def __init__(self,
                 patch_size=65,
                 num_ang_bins=8,
                 num_spatial_bins=4,
                 clipval=0.2,
                 rootsift=False,
                 mask_type='CircularGauss',
                 sigma_type='hesamp'):
        super(SIFTNet, self).__init__()
        self.eps = 1e-10
        self.num_ang_bins = num_ang_bins
        self.num_spatial_bins = num_spatial_bins
        self.clipval = clipval
        self.rootsift = rootsift
        self.mask_type = mask_type
        self.patch_size = patch_size
        self.sigma_type = sigma_type

        if self.mask_type == 'CircularGauss':
            self.gk = torch.from_numpy(
                self.CircularGaussKernel(kernlen=patch_size, circ=True, sigma_type=sigma_type).astype(np.float32))
        elif self.mask_type == 'Gauss':
            self.gk = torch.from_numpy(
                self.CircularGaussKernel(kernlen=patch_size, circ=False, sigma_type=sigma_type).astype(np.float32))
        elif self.mask_type == 'Uniform':
            self.gk = torch.ones(patch_size, patch_size).float() / float(patch_size * patch_size)
        else:
            raise ValueError(mask_type, 'is unknown mask type')

        self.bin_weight_kernel_size, self.bin_weight_stride, self.pad = get_bin_weight_kernel_size_and_stride(
            patch_size, num_spatial_bins)
        self.gx = nn.Conv2d(1, 1, kernel_size=(1, 3), bias=False)
        self.gx.weight.data = torch.tensor(np.array([[[[-1, 0, 1]]]], dtype=np.float32))

        self.gy = nn.Conv2d(1, 1, kernel_size=(3, 1), bias=False)
        self.gy.weight.data = torch.from_numpy(np.array([[[[-1], [0], [1]]]], dtype=np.float32))
        nw = getPoolingKernel(kernel_size=self.bin_weight_kernel_size)

        self.pk = nn.Conv2d(1, 1, kernel_size=(nw.shape[0], nw.shape[1]),
                            stride=(self.bin_weight_stride, self.bin_weight_stride),
                            padding=(self.pad, self.pad),
                            bias=False)
        new_weights = np.array(nw.reshape((1, 1, nw.shape[0], nw.shape[1])))
        self.pk.weight.data = torch.from_numpy(new_weights.astype(np.float32))
        return

    def forward(self, x):
        gx = self.gx(F.pad(x, (1, 1, 0, 0), 'replicate'))
        gy = self.gy(F.pad(x, (0, 0, 1, 1), 'replicate'))
        mag = torch.sqrt(gx * gx + gy * gy + self.eps)
        ori = torch.atan2(gy, gx + self.eps)
        mag = mag * self.gk.expand_as(mag).to(mag.device)
        o_big = (ori + 2.0 * math.pi) / (2.0 * math.pi) * float(self.num_ang_bins)
        bo0_big_ = torch.floor(o_big)
        wo1_big_ = o_big - bo0_big_
        bo0_big = bo0_big_ % self.num_ang_bins
        bo1_big = (bo0_big + 1) % self.num_ang_bins
        wo0_big = (1.0 - wo1_big_) * mag
        wo1_big = wo1_big_ * mag
        ang_bins = []
        for i in range(0, self.num_ang_bins):
            out = self.pk((bo0_big == i).float() * wo0_big + (bo1_big == i).float() * wo1_big)
            ang_bins.append(out)
        ang_bins = torch.cat(ang_bins, 1)
        ang_bins = ang_bins.view(ang_bins.size(0), -1)
        ang_bins = F.normalize(ang_bins, p=2)
        ang_bins = torch.clamp(ang_bins, 0., float(self.clipval))
        ang_bins = F.normalize(ang_bins, p=2)
        if self.rootsift:
            ang_bins = torch.sqrt(F.normalize(ang_bins, p=1) + 1e-10)
        return ang_bins


class SIFTFC(nn.Module):
    def __init__(self, num_class):
        super(SIFTFC, self).__init__()
        self.sift_net = SIFTNet(patch_size=150)
        self.fc1 = torch.nn.Linear(128, 512)
        self.fc2 = torch.nn.Linear(512, 256)
        self.fc3 = torch.nn.Linear(256, num_class)
    def forward(self, x):
        x = self.sift_net(x)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x


class SpatialPyramidPooling2d(nn.Module):
    r"""apply spatial pyramid pooling over a 4d input(a mini-batch of 2d inputs
    with additional channel dimension) as described in the paper
    'Spatial Pyramid Pooling in deep convolutional Networks for visual recognition'
    Args:
        num_level:
        pool_type: max_pool, avg_pool, Default:max_pool
    By the way, the target output size is num_grid:
        num_grid = 0
        for i in range num_level:
            num_grid += (i + 1) * (i + 1)
        num_grid = num_grid * channels # channels is the channel dimension of input data
    examples:
        >>> input = torch.randn((1,3,32,32), dtype=torch.float32)
        >>> net = torch.nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size=3,stride=1),\
                                      nn.ReLU(),\
                                      SpatialPyramidPooling2d(num_level=2,pool_type='avg_pool'),\
                                      nn.Linear(32 * (1*1 + 2*2), 10))
        >>> output = net(input)
    """

    def __init__(self, num_level, pool_type='max_pool'):
        super(SpatialPyramidPooling2d, self).__init__()
        self.num_level = num_level
        self.pool_type = pool_type

    def forward(self, x):
        N, C, H, W = x.size()
        for i in range(self.num_level):
            level = i + 1
            kernel_size = (ceil(H / level), ceil(W / level))
            stride = (ceil(H / level), ceil(W / level))
            padding = (floor((kernel_size[0] * level - H + 1) / 2), floor((kernel_size[1] * level - W + 1) / 2))

            if self.pool_type == 'max_pool':
                tensor = (F.max_pool2d(x, kernel_size=kernel_size, stride=stride, padding=padding)).view(N, -1)
            else:
                tensor = (F.avg_pool2d(x, kernel_size=kernel_size, stride=stride, padding=padding)).view(N, -1)

            if i == 0:
                res = tensor
            else:
                res = torch.cat((res, tensor), 1)
        return res

    def __repr__(self):
        return self.__class__.__name__ + '(' \
               + 'num_level = ' + str(self.num_level) \
               + ', pool_type = ' + str(self.pool_type) + ')'


class SPPNet(nn.Module):
    def __init__(self, num_level=3, pool_type='max_pool'):
        super(SPPNet, self).__init__()
        self.num_level = num_level
        self.pool_type = pool_type
        self.feature = nn.Sequential(nn.Conv2d(3, 32, 3),
                                     nn.ReLU(),
                                     nn.MaxPool2d(2),
                                     nn.Conv2d(32, 64, 3),
                                     nn.ReLU(),
                                     nn.Conv2d(64, 64, 3),
                                     nn.ReLU())
        self.num_grid = self._cal_num_grids(num_level)
        self.spp_layer = SpatialPyramidPooling2d(num_level)
        self.linear = nn.Sequential(nn.Linear(self.num_grid * 64, 512),
                                    nn.Linear(512, 9))

    def _cal_num_grids(self, level):
        count = 0
        for i in range(level):
            count += (i + 1) * (i + 1)
        return count

    def forward(self, x):
        x = self.feature(x)
        x = self.spp_layer(x)
        x = self.linear(x)
        return torch.nn.functional.log_softmax(x, dim=1)


class Model(torch.nn.Module):
    '''
    A DIY model
    '''
    def __init__(self):
        super(Model, self).__init__()
        self.conv = torch.nn.Sequential(torch.nn.Conv2d(1, 32, 3, padding=1),
                                         torch.nn.ReLU(),
                                         torch.nn.Conv2d(32, 64, 3, padding=1),
                                         torch.nn.ReLU())
        self.fc = torch.nn.Sequential(torch.nn.Linear(28*28*64, 10))

    def forward(self, x):
        x = self.conv(x)
        x = x.view(-1, 28*28*64)
        x = self.fc(x)
from math import floor, ceil
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


def getPoolingKernel(kernel_size=25):
    half_size = float(kernel_size) / 2.0
    xc2 = []
    for i in range(kernel_size):
        xc2.append(half_size - abs(float(i) + 0.5 - half_size))
    xc2 = np.array(xc2)
    kernel = np.outer(xc2.T, xc2)
    kernel = kernel / (half_size ** 2)
    return kernel

def get_bin_weight_kernel_size_and_stride(patch_size, num_spatial_bins):
    ks = 2 * int(patch_size / (num_spatial_bins + 1))
    stride = patch_size // num_spatial_bins
    pad = ks // 4
    return ks, stride, pad


class SIFTNet(nn.Module):
    def CircularGaussKernel(self, kernlen=21, circ=True, sigma_type='vlfeat'):
        halfSize = float(kernlen) / 2.
        r2 = float(halfSize ** 2)
        if sigma_type == 'hesamp':
            sigma_mul_2 = 0.9 * r2
        elif sigma_type == 'vlfeat':
            sigma_mul_2 = kernlen ** 2
        else:
            raise ValueError('Unknown sigma_type', sigma_type, 'try hesamp or vlfeat')
        disq = 0
        kernel = np.zeros((kernlen, kernlen))
        for y in range(kernlen):
            for x in range(kernlen):
                disq = (y - halfSize + 0.5) ** 2 + (x - halfSize + 0.5) ** 2
                kernel[y, x] = math.exp(-disq / sigma_mul_2)
                if circ and (disq >= r2):
                    kernel[y, x] = 0.
        return kernel

    def __repr__(self):
        return self.__class__.__name__ + '(' + 'num_ang_bins=' + str(self.num_ang_bins) + \
               ', ' + 'num_spatial_bins=' + str(self.num_spatial_bins) + \
               ', ' + 'patch_size=' + str(self.patch_size) + \
               ', ' + 'rootsift=' + str(self.rootsift) + \
               ', ' + 'sigma_type=' + str(self.sigma_type) + \
               ', ' + 'mask_type=' + str(self.mask_type) + \
               ', ' + 'clipval=' + str(self.clipval) + ')'

    def __init__(self,
                 patch_size=65,
                 num_ang_bins=8,
                 num_spatial_bins=4,
                 clipval=0.2,
                 rootsift=False,
                 mask_type='CircularGauss',
                 sigma_type='hesamp'):
        super(SIFTNet, self).__init__()
        self.eps = 1e-10
        self.num_ang_bins = num_ang_bins
        self.num_spatial_bins = num_spatial_bins
        self.clipval = clipval
        self.rootsift = rootsift
        self.mask_type = mask_type
        self.patch_size = patch_size
        self.sigma_type = sigma_type

        if self.mask_type == 'CircularGauss':
            self.gk = torch.from_numpy(
                self.CircularGaussKernel(kernlen=patch_size, circ=True, sigma_type=sigma_type).astype(np.float32))
        elif self.mask_type == 'Gauss':
            self.gk = torch.from_numpy(
                self.CircularGaussKernel(kernlen=patch_size, circ=False, sigma_type=sigma_type).astype(np.float32))
        elif self.mask_type == 'Uniform':
            self.gk = torch.ones(patch_size, patch_size).float() / float(patch_size * patch_size)
        else:
            raise ValueError(mask_type, 'is unknown mask type')

        self.bin_weight_kernel_size, self.bin_weight_stride, self.pad = get_bin_weight_kernel_size_and_stride(
            patch_size, num_spatial_bins)
        self.gx = nn.Conv2d(1, 1, kernel_size=(1, 3), bias=False)
        self.gx.weight.data = torch.tensor(np.array([[[[-1, 0, 1]]]], dtype=np.float32))

        self.gy = nn.Conv2d(1, 1, kernel_size=(3, 1), bias=False)
        self.gy.weight.data = torch.from_numpy(np.array([[[[-1], [0], [1]]]], dtype=np.float32))
        nw = getPoolingKernel(kernel_size=self.bin_weight_kernel_size)

        self.pk = nn.Conv2d(1, 1, kernel_size=(nw.shape[0], nw.shape[1]),
                            stride=(self.bin_weight_stride, self.bin_weight_stride),
                            padding=(self.pad, self.pad),
                            bias=False)
        new_weights = np.array(nw.reshape((1, 1, nw.shape[0], nw.shape[1])))
        self.pk.weight.data = torch.from_numpy(new_weights.astype(np.float32))
        return

    def forward(self, x):
        gx = self.gx(F.pad(x, (1, 1, 0, 0), 'replicate'))
        gy = self.gy(F.pad(x, (0, 0, 1, 1), 'replicate'))
        mag = torch.sqrt(gx * gx + gy * gy + self.eps)
        ori = torch.atan2(gy, gx + self.eps)
        mag = mag * self.gk.expand_as(mag).to(mag.device)
        o_big = (ori + 2.0 * math.pi) / (2.0 * math.pi) * float(self.num_ang_bins)
        bo0_big_ = torch.floor(o_big)
        wo1_big_ = o_big - bo0_big_
        bo0_big = bo0_big_ % self.num_ang_bins
        bo1_big = (bo0_big + 1) % self.num_ang_bins
        wo0_big = (1.0 - wo1_big_) * mag
        wo1_big = wo1_big_ * mag
        ang_bins = []
        for i in range(0, self.num_ang_bins):
            out = self.pk((bo0_big == i).float() * wo0_big + (bo1_big == i).float() * wo1_big)
            ang_bins.append(out)
        ang_bins = torch.cat(ang_bins, 1)
        ang_bins = ang_bins.view(ang_bins.size(0), -1)
        ang_bins = F.normalize(ang_bins, p=2)
        ang_bins = torch.clamp(ang_bins, 0., float(self.clipval))
        ang_bins = F.normalize(ang_bins, p=2)
        if self.rootsift:
            ang_bins = torch.sqrt(F.normalize(ang_bins, p=1) + 1e-10)
        return ang_bins


class SIFTFC(nn.Module):
    def __init__(self, num_class):
        super(SIFTFC, self).__init__()
        self.sift_net = SIFTNet(patch_size=150)
        self.fc1 = torch.nn.Linear(128, 512)
        self.fc2 = torch.nn.Linear(512, 256)
        self.fc3 = torch.nn.Linear(256, num_class)
    def forward(self, x):
        x = self.sift_net(x)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x


class SpatialPyramidPooling2d(nn.Module):
    r"""apply spatial pyramid pooling over a 4d input(a mini-batch of 2d inputs
    with additional channel dimension) as described in the paper
    'Spatial Pyramid Pooling in deep convolutional Networks for visual recognition'
    Args:
        num_level:
        pool_type: max_pool, avg_pool, Default:max_pool
    By the way, the target output size is num_grid:
        num_grid = 0
        for i in range num_level:
            num_grid += (i + 1) * (i + 1)
        num_grid = num_grid * channels # channels is the channel dimension of input data
    examples:
        >>> input = torch.randn((1,3,32,32), dtype=torch.float32)
        >>> net = torch.nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size=3,stride=1),\
                                      nn.ReLU(),\
                                      SpatialPyramidPooling2d(num_level=2,pool_type='avg_pool'),\
                                      nn.Linear(32 * (1*1 + 2*2), 10))
        >>> output = net(input)
    """

    def __init__(self, num_level, pool_type='max_pool'):
        super(SpatialPyramidPooling2d, self).__init__()
        self.num_level = num_level
        self.pool_type = pool_type

    def forward(self, x):
        N, C, H, W = x.size()
        for i in range(self.num_level):
            level = i + 1
            kernel_size = (ceil(H / level), ceil(W / level))
            stride = (ceil(H / level), ceil(W / level))
            padding = (floor((kernel_size[0] * level - H + 1) / 2), floor((kernel_size[1] * level - W + 1) / 2))

            if self.pool_type == 'max_pool':
                tensor = (F.max_pool2d(x, kernel_size=kernel_size, stride=stride, padding=padding)).view(N, -1)
            else:
                tensor = (F.avg_pool2d(x, kernel_size=kernel_size, stride=stride, padding=padding)).view(N, -1)

            if i == 0:
                res = tensor
            else:
                res = torch.cat((res, tensor), 1)
        return res

    def __repr__(self):
        return self.__class__.__name__ + '(' \
               + 'num_level = ' + str(self.num_level) \
               + ', pool_type = ' + str(self.pool_type) + ')'


class SPPNet(nn.Module):
    def __init__(self, num_level=3, pool_type='max_pool'):
        super(SPPNet, self).__init__()
        self.num_level = num_level
        self.pool_type = pool_type
        self.feature = nn.Sequential(nn.Conv2d(3, 32, 3),
                                     nn.ReLU(),
                                     nn.MaxPool2d(2),
                                     nn.Conv2d(32, 64, 3),
                                     nn.ReLU(),
                                     nn.Conv2d(64, 64, 3),
                                     nn.ReLU())
        self.num_grid = self._cal_num_grids(num_level)
        self.spp_layer = SpatialPyramidPooling2d(num_level)
        self.linear = nn.Sequential(nn.Linear(self.num_grid * 64, 512),
                                    nn.Linear(512, 9))

    def _cal_num_grids(self, level):
        count = 0
        for i in range(level):
            count += (i + 1) * (i + 1)
        return count

    def forward(self, x):
        x = self.feature(x)
        x = self.spp_layer(x)
        x = self.linear(x)
        return torch.nn.functional.log_softmax(x, dim=1)


class Model(torch.nn.Module):
    '''
    A DIY model
    '''
    def __init__(self):
        super(Model, self).__init__()
        self.conv = torch.nn.Sequential(torch.nn.Conv2d(1, 32, 3, padding=1),
                                         torch.nn.ReLU(),
                                         torch.nn.Conv2d(32, 64, 3, padding=1),
                                         torch.nn.ReLU())
        self.fc = torch.nn.Sequential(torch.nn.Linear(28*28*64, 10))

    def forward(self, x):
        x = self.conv(x)
        x = x.view(-1, 28*28*64)
        x = self.fc(x)
apply spatial pyramid pooling over a 4d input(a mini-batch of 2d inputs
     with additional channel dimension) as described in the paper
     'Spatial Pyramid Pooling in deep convolutional Networks for visual recognition'
     Args:
         num_level:
         pool_type: max_pool, avg_pool, Default:max_pool
     By the way, the target output size is num_grid:
         num_grid = 0
         for i in range num_level:
             num_grid += (i + 1) * (i + 1)
         num_grid = num_grid * channels # channels is the channel dimension of input data
     examples:
         >>> input = torch.randn((1,3,32,32), dtype=torch.float32)
         >>> net = torch.nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size=3,stride=1),\
                                       nn.ReLU(),\
                                       SpatialPyramidPooling2d(num_level=2,pool_type='avg_pool'),\
                                       nn.Linear(32 * (1*1 + 2*2), 10))
         >>> output = net(input) A DIY model