lib/models/common2.py

import math
import numpy as np
import torch
import torch.nn as nn
from PIL import Image, ImageDraw


def autopad(k, p=None):  # kernel, padding
    # Pad to 'same'
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
    return p


class DepthSeperabelConv2d(nn.Module):
    """
    DepthSeperable Convolution 2d with residual connection
    """

    def __init__(self, inplanes, planes, kernel_size=3, stride=1, downsample=None, act=True):
        super(DepthSeperabelConv2d, self).__init__()
        self.depthwise = nn.Sequential(
            nn.Conv2d(inplanes, inplanes, kernel_size, stride=stride, groups=inplanes, padding=kernel_size // 2,
                      bias=False),
            nn.BatchNorm2d(inplanes)
        )
        # self.depthwise = nn.Conv2d(inplanes, inplanes, kernel_size,
        # stride=stride, groups=inplanes, padding=1, bias=False)
        # self.pointwise = nn.Conv2d(inplanes, planes, 1, bias=False)

        self.pointwise = nn.Sequential(
            nn.Conv2d(inplanes, planes, 1, bias=False),
            nn.BatchNorm2d(planes)
        )
        self.downsample = downsample
        self.stride = stride
        try:
            self.act = nn.Hardswish() if act else nn.Identity()
        except:
            self.act = nn.Identity()

    def forward(self, x):
        # residual = x

        out = self.depthwise(x)
        out = self.act(out)
        out = self.pointwise(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        out = self.act(out)

        return out


class SharpenConv(nn.Module):
    # SharpenConv convolution
    def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super(SharpenConv, self).__init__()
        sobel_kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype='float32')
        kenel_weight = np.vstack([sobel_kernel] * c2 * c1).reshape(c2, c1, 3, 3)
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.conv.weight.data = torch.from_numpy(kenel_weight)
        self.conv.weight.requires_grad = False
        self.bn = nn.BatchNorm2d(c2)
        try:
            self.act = nn.Hardswish() if act else nn.Identity()
        except:
            self.act = nn.Identity()

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def fuseforward(self, x):
        return self.act(self.conv(x))


class Conv(nn.Module):
    # Standard convolution
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        try:
            self.act = nn.Hardswish() if act else nn.Identity()
        except:
            self.act = nn.Identity()

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def fuseforward(self, x):
        return self.act(self.conv(x))


class Bottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
        super(Bottleneck, self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2

    def forward(self, x):
        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))


class BottleneckCSP(nn.Module):
    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super(BottleneckCSP, self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
        self.cv4 = Conv(2 * c_, c2, 1, 1)
        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
        self.act = nn.LeakyReLU(0.1, inplace=True)
        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])

    def forward(self, x):
        y1 = self.cv3(self.m(self.cv1(x)))
        y2 = self.cv2(x)
        return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))


class SPP(nn.Module):
    # Spatial pyramid pooling layer used in YOLOv3-SPP
    def __init__(self, c1, c2, k=(5, 9, 13)):
        super(SPP, self).__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])

    def forward(self, x):
        x = self.cv1(x)
        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))


class Contract(nn.Module):
    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
    def __init__(self, gain=2):
        super().__init__()
        self.gain = gain

    def forward(self, x):
        N, C, H, W = x.size()  # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
        s = self.gain
        x = x.view(N, C, H // s, s, W // s, s)  # x(1,64,40,2,40,2)
        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
        return x.view(N, C * s * s, H // s, W // s)  # x(1,256,40,40)


class Focus(nn.Module):
    # Focus wh information into c-space
    # slice concat conv
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super(Focus, self).__init__()
        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
        self.contract = Contract(gain=2)

    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
        # return self.conv(self.contract(x))


class Concat(nn.Module):
    # Concatenate a list of tensors along dimension
    def __init__(self, dimension=1):
        super(Concat, self).__init__()
        self.d = dimension

    def forward(self, x):
        """ print("***********************")
        for f in x:
            print(f.shape) """
        return torch.cat(x, self.d)


class Detect(nn.Module):
    stride = None  # strides computed during build

    def __init__(self, nc=13, anchors=(), ch=()):  # detection layer
        super(Detect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers 3
        self.na = len(anchors[0]) // 2  # number of anchors 3
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)  # (nl=3,na=3,2)
        self.register_buffer('anchors', a)  # shape(nl,na,2)
        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl=3,1,na=3,1,1,2)
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
        self.inplace = True  # use in-place ops (e.g. slice assignment)

    # def forward(self, x):
    #     z = []  # inference output
    #     for i in range(self.nl):
    #         x[i] = self.m[i](x[i])  # conv
    #         # print(str(i)+str(x[i].shape))
    #         bs, _, ny, nx = x[i].shape  # x(bs,255,w,w) to x(bs,3,w,w,85)
    #         x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
    #         # print(str(i)+str(x[i].shape))
    #
    #         if not self.training:  # inference
    #             if self.grid[i].shape[2:4] != x[i].shape[2:4]:
    #                 self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
    #             y = x[i].sigmoid()
    #             # print("**")
    #             # print(y.shape) #[1, 3, w, h, 85]
    #             # print(self.grid[i].shape) #[1, 3, w, h, 2]
    #             y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
    #             y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
    #             """print("**")
    #             print(y.shape)  #[1, 3, w, h, 85]
    #             print(y.view(bs, -1, self.no).shape) #[1, 3*w*h, 85]"""
    #             z.append(y.view(bs, -1, self.no))
    #     return x if self.training else (torch.cat(z, 1), x)

    def forward(self, x):
        z = []  # inference output
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv (bs,na*no,ny,nx)
            bs, _, ny, nx = x[i].shape

            # x(bs,255,20,20) to x(bs,3,20,20,nc+5) (bs,na,ny,nx,no=nc+5=4+1+nc)

            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            if not self.training:  # inference
                # if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
                #     self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                y = x[i].sigmoid()  # (bs,na,ny,nx,no=nc+5=4+1+nc)

                xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy (bs,na,ny,nx,2)
                wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2)  # wh (bs,na,ny,nx,2)
                y = torch.cat((xy, wh, y[..., 4:]), -1) # (bs,na,ny,nx,2+2+1+nc=xy+wh+conf+cls_prob)

                z.append(y.view(bs, -1, self.no))  # y (bs,na*ny*nx,no=2+2+1+nc=xy+wh+conf+cls_prob)

        return x if self.training else (torch.cat(z, 1), x)
        # torch.cat(z, 1) (bs,na*ny*nx*nl,no=2+2+1+nc=xy+wh+conf+cls_prob)

    @staticmethod
    def _make_grid(nx=20, ny=20):

        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()


"""class Detections:
    # detections class for YOLOv5 inference results
    def __init__(self, imgs, pred, names=None):
        super(Detections, self).__init__()
        d = pred[0].device  # device
        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
        self.imgs = imgs  # list of images as numpy arrays
        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
        self.names = names  # class names
        self.xyxy = pred  # xyxy pixels
        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
        self.n = len(self.pred)

    def display(self, pprint=False, show=False, save=False):
        colors = color_list()
        for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
            str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
            if pred is not None:
                for c in pred[:, -1].unique():
                    n = (pred[:, -1] == c).sum()  # detections per class
                    str += f'{n} {self.names[int(c)]}s, '  # add to string
                if show or save:
                    img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img  # from np
                    for *box, conf, cls in pred:  # xyxy, confidence, class
                        # str += '%s %.2f, ' % (names[int(cls)], conf)  # label
                        ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10])  # plot
            if save:
                f = f'results{i}.jpg'
                str += f"saved to '{f}'"
                img.save(f)  # save
            if show:
                img.show(f'Image {i}')  # show
            if pprint:
                print(str)

    def print(self):
        self.display(pprint=True)  # print results

    def show(self):
        self.display(show=True)  # show results

    def save(self):
        self.display(save=True)  # save results

    def __len__(self):
        return self.n

    def tolist(self):
        # return a list of Detections objects, i.e. 'for result in results.tolist():'
        x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
        for d in x:
            for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
                setattr(d, k, getattr(d, k)[0])  # pop out of list"""