# Copyright 2021 Huawei Technologies Co., Ltd

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



from utils.google_utils import *

from utils.layers import *

from utils.parse_config import *

from utils import torch_utils



ONNX_EXPORT = False





def create_modules(module_defs, img_size, cfg):

    # Constructs module list of layer blocks from module configuration in module_defs



    img_size = [img_size] * 2 if isinstance(img_size, int) else img_size  # expand if necessary

    _ = module_defs.pop(0)  # cfg training hyperparams (unused)

    output_filters = [3]  # input channels

    module_list = nn.ModuleList()

    routs = []  # list of layers which rout to deeper layers

    yolo_index = -1



    for i, mdef in enumerate(module_defs):

        modules = nn.Sequential()



        if mdef['type'] == 'convolutional':

            bn = mdef['batch_normalize']

            filters = mdef['filters']

            k = mdef['size']  # kernel size

            stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])

            if isinstance(k, int):  # single-size conv

                modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1],

                                                       out_channels=filters,

                                                       kernel_size=k,

                                                       stride=stride,

                                                       padding=k // 2 if mdef['pad'] else 0,

                                                       groups=mdef['groups'] if 'groups' in mdef else 1,

                                                       bias=not bn))

            else:  # multiple-size conv

                modules.add_module('MixConv2d', MixConv2d(in_ch=output_filters[-1],

                                                          out_ch=filters,

                                                          k=k,

                                                          stride=stride,

                                                          bias=not bn))



            if bn:

                modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4))

            else:

                routs.append(i)  # detection output (goes into yolo layer)



            if mdef['activation'] == 'leaky':  # activation study https://github.com/ultralytics/yolov3/issues/441

                modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))

            elif mdef['activation'] == 'swish':

                modules.add_module('activation', Swish())

            elif mdef['activation'] == 'mish':

                modules.add_module('activation', Mish())

            elif mdef['activation'] == 'emb':

                modules.add_module('activation', F.normalize())

            elif mdef['activation'] == 'logistic':

                modules.add_module('activation', nn.Sigmoid())

            elif mdef['activation'] == 'silu':

                modules.add_module('activation', nn.SiLU())



        elif mdef['type'] == 'deformableconvolutional':

            bn = mdef['batch_normalize']

            filters = mdef['filters']

            k = mdef['size']  # kernel size

            stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])

            if isinstance(k, int):  # single-size conv

                modules.add_module('DeformConv2d', DeformConv2d(output_filters[-1],

                                                       filters,

                                                       kernel_size=k,

                                                       padding=k // 2 if mdef['pad'] else 0,

                                                       stride=stride,

                                                       bias=not bn,

                                                       modulation=True))

            else:  # multiple-size conv

                modules.add_module('MixConv2d', MixConv2d(in_ch=output_filters[-1],

                                                          out_ch=filters,

                                                          k=k,

                                                          stride=stride,

                                                          bias=not bn))



            if bn:

                modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4))

            else:

                routs.append(i)  # detection output (goes into yolo layer)



            if mdef['activation'] == 'leaky':  # activation study https://github.com/ultralytics/yolov3/issues/441

                modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))

            elif mdef['activation'] == 'swish':

                modules.add_module('activation', Swish())

            elif mdef['activation'] == 'mish':

                modules.add_module('activation', Mish())

            elif mdef['activation'] == 'silu':

                modules.add_module('activation', nn.SiLU())

                

        elif mdef['type'] == 'dropout':

            p = mdef['probability']

            modules = nn.Dropout(p)



        elif mdef['type'] == 'avgpool':

            modules = GAP()



        elif mdef['type'] == 'silence':

            filters = output_filters[-1]

            modules = Silence()



        elif mdef['type'] == 'scale_channels':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1]

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = ScaleChannel(layers=layers)



        elif mdef['type'] == 'shift_channels':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1]

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = ShiftChannel(layers=layers)



        elif mdef['type'] == 'shift_channels_2d':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1]

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = ShiftChannel2D(layers=layers)



        elif mdef['type'] == 'control_channels':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1]

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = ControlChannel(layers=layers)



        elif mdef['type'] == 'control_channels_2d':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1]

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = ControlChannel2D(layers=layers)



        elif mdef['type'] == 'alternate_channels':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1] * 2

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = AlternateChannel(layers=layers)



        elif mdef['type'] == 'alternate_channels_2d':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1] * 2

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = AlternateChannel2D(layers=layers)



        elif mdef['type'] == 'select_channels':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1]

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = SelectChannel(layers=layers)



        elif mdef['type'] == 'select_channels_2d':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1]

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = SelectChannel2D(layers=layers)



        elif mdef['type'] == 'sam':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1]

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = ScaleSpatial(layers=layers)



        elif mdef['type'] == 'BatchNorm2d':

            filters = output_filters[-1]

            modules = nn.BatchNorm2d(filters, momentum=0.03, eps=1E-4)

            if i == 0 and filters == 3:  # normalize RGB image

                # imagenet mean and var https://pytorch.org/docs/stable/torchvision/models.html#classification

                modules.running_mean = torch.tensor([0.485, 0.456, 0.406])

                modules.running_var = torch.tensor([0.0524, 0.0502, 0.0506])



        elif mdef['type'] == 'maxpool':

            k = mdef['size']  # kernel size

            stride = mdef['stride']

            maxpool = nn.MaxPool2d(kernel_size=k, stride=stride, padding=(k - 1) // 2)

            if k == 2 and stride == 1:  # yolov3-tiny

                modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1)))

                modules.add_module('MaxPool2d', maxpool)

            else:

                modules = maxpool



        elif mdef['type'] == 'local_avgpool':

            k = mdef['size']  # kernel size

            stride = mdef['stride']

            avgpool = nn.AvgPool2d(kernel_size=k, stride=stride, padding=(k - 1) // 2)

            if k == 2 and stride == 1:  # yolov3-tiny

                modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1)))

                modules.add_module('AvgPool2d', avgpool)

            else:

                modules = avgpool



        elif mdef['type'] == 'upsample':

            if ONNX_EXPORT:  # explicitly state size, avoid scale_factor

                g = (yolo_index + 1) * 2 / 32  # gain

                modules = nn.Upsample(size=tuple(int(x * g) for x in img_size))  # img_size = (320, 192)

            else:

                modules = nn.Upsample(scale_factor=mdef['stride'])



        elif mdef['type'] == 'route':  # nn.Sequential() placeholder for 'route' layer

            layers = mdef['layers']

            filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = FeatureConcat(layers=layers)



        elif mdef['type'] == 'route2':  # nn.Sequential() placeholder for 'route' layer

            layers = mdef['layers']

            filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = FeatureConcat2(layers=layers)



        elif mdef['type'] == 'route3':  # nn.Sequential() placeholder for 'route' layer

            layers = mdef['layers']

            filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = FeatureConcat3(layers=layers)



        elif mdef['type'] == 'route_lhalf':  # nn.Sequential() placeholder for 'route' layer

            layers = mdef['layers']

            filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])//2

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = FeatureConcat_l(layers=layers)



        elif mdef['type'] == 'shortcut':  # nn.Sequential() placeholder for 'shortcut' layer

            layers = mdef['from']

            filters = output_filters[-1]

            routs.extend([i + l if l < 0 else l for l in layers])

            modules = WeightedFeatureFusion(layers=layers, weight='weights_type' in mdef)



        elif mdef['type'] == 'reorg3d':  # yolov3-spp-pan-scale

            pass



        elif mdef['type'] == 'reorg':  # yolov3-spp-pan-scale

            filters = 4 * output_filters[-1]

            modules.add_module('Reorg', Reorg())



        elif mdef['type'] == 'dwt':  # yolov3-spp-pan-scale

            filters = 4 * output_filters[-1]

            modules.add_module('DWT', DWT())



        elif mdef['type'] == 'implicit_add':  # yolov3-spp-pan-scale

            filters = mdef['filters']

            modules = ImplicitA(channel=filters)



        elif mdef['type'] == 'implicit_mul':  # yolov3-spp-pan-scale

            filters = mdef['filters']

            modules = ImplicitM(channel=filters)



        elif mdef['type'] == 'implicit_cat':  # yolov3-spp-pan-scale

            filters = mdef['filters']

            modules = ImplicitC(channel=filters)



        elif mdef['type'] == 'implicit_add_2d':  # yolov3-spp-pan-scale

            channels = mdef['filters']

            filters = mdef['atoms']

            modules = Implicit2DA(atom=filters, channel=channels)



        elif mdef['type'] == 'implicit_mul_2d':  # yolov3-spp-pan-scale

            channels = mdef['filters']

            filters = mdef['atoms']

            modules = Implicit2DM(atom=filters, channel=channels)



        elif mdef['type'] == 'implicit_cat_2d':  # yolov3-spp-pan-scale

            channels = mdef['filters']

            filters = mdef['atoms']

            modules = Implicit2DC(atom=filters, channel=channels)



        elif mdef['type'] == 'yolo':

            yolo_index += 1

            stride = [8, 16, 32, 64, 128]  # P3, P4, P5, P6, P7 strides

            if any(x in cfg for x in ['yolov4-tiny', 'fpn', 'yolov3']):  # P5, P4, P3 strides

                stride = [32, 16, 8]

            layers = mdef['from'] if 'from' in mdef else []

            modules = YOLOLayer(anchors=mdef['anchors'][mdef['mask']],  # anchor list

                                nc=mdef['classes'],  # number of classes

                                img_size=img_size,  # (416, 416)

                                yolo_index=yolo_index,  # 0, 1, 2...

                                layers=layers,  # output layers

                                stride=stride[yolo_index])



            # Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)

            try:

                j = layers[yolo_index] if 'from' in mdef else -2

                bias_ = module_list[j][0].bias  # shape(255,)

                bias = bias_[:modules.no * modules.na].view(modules.na, -1)  # shape(3,85)

                #bias[:, 4] += -4.5  # obj

                bias.data[:, 4] += math.log(8 / (640 / stride[yolo_index]) ** 2)  # obj (8 objects per 640 image)

                bias.data[:, 5:] += math.log(0.6 / (modules.nc - 0.99))  # cls (sigmoid(p) = 1/nc)

                module_list[j][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad)

                

                #j = [-2, -5, -8]

                #for sj in j:

                #    bias_ = module_list[sj][0].bias

                #    bias = bias_[:modules.no * 1].view(1, -1)

                #    bias.data[:, 4] += math.log(8 / (640 / stride[yolo_index]) ** 2)

                #    bias.data[:, 5:] += math.log(0.6 / (modules.nc - 0.99))

                #    module_list[sj][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad)

            except:

                print('WARNING: smart bias initialization failure.')



        elif mdef['type'] == 'jde':

            yolo_index += 1

            stride = [8, 16, 32, 64, 128]  # P3, P4, P5, P6, P7 strides

            if any(x in cfg for x in ['yolov4-tiny', 'fpn', 'yolov3']):  # P5, P4, P3 strides

                stride = [32, 16, 8]

            layers = mdef['from'] if 'from' in mdef else []

            modules = JDELayer(anchors=mdef['anchors'][mdef['mask']],  # anchor list

                                nc=mdef['classes'],  # number of classes

                                img_size=img_size,  # (416, 416)

                                yolo_index=yolo_index,  # 0, 1, 2...

                                layers=layers,  # output layers

                                stride=stride[yolo_index])



            # Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)

            try:

                j = layers[yolo_index] if 'from' in mdef else -1

                bias_ = module_list[j][0].bias  # shape(255,)

                bias = bias_[:modules.no * modules.na].view(modules.na, -1)  # shape(3,85)

                #bias[:, 4] += -4.5  # obj

                bias.data[:, 4] += math.log(8 / (640 / stride[yolo_index]) ** 2)  # obj (8 objects per 640 image)

                bias.data[:, 5:] += math.log(0.6 / (modules.nc - 0.99))  # cls (sigmoid(p) = 1/nc)

                module_list[j][0].bias = torch.nn.Parameter(bias_, requires_grad=bias_.requires_grad)

            except:

                print('WARNING: smart bias initialization failure.')



        else:

            print('Warning: Unrecognized Layer Type: ' + mdef['type'])



        # Register module list and number of output filters

        module_list.append(modules)

        output_filters.append(filters)



    routs_binary = [False] * (i + 1)

    for i in routs:

        routs_binary[i] = True

    return module_list, routs_binary





class YOLOLayer(nn.Module):

    def __init__(self, anchors, nc, img_size, yolo_index, layers, stride):

        super(YOLOLayer, self).__init__()

        self.anchors = torch.Tensor(anchors)

        self.index = yolo_index  # index of this layer in layers

        self.layers = layers  # model output layer indices

        self.stride = stride  # layer stride

        self.nl = len(layers)  # number of output layers (3)

        self.na = len(anchors)  # number of anchors (3)

        self.nc = nc  # number of classes (80)

        self.no = nc + 5  # number of outputs (85)

        self.nx, self.ny, self.ng = 0, 0, 0  # initialize number of x, y gridpoints

        self.anchor_vec = self.anchors / self.stride

        self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2)



        if ONNX_EXPORT:

            self.training = False

            self.create_grids((img_size[1] // stride, img_size[0] // stride))  # number x, y grid points



    def create_grids(self, ng=(13, 13), device='cpu'):

        self.nx, self.ny = ng  # x and y grid size

        self.ng = torch.tensor(ng, dtype=torch.float)



        # build xy offsets

        if not self.training:

            yv, xv = torch.meshgrid([torch.arange(self.ny, device=device), torch.arange(self.nx, device=device)])

            self.grid = torch.stack((xv, yv), 2).view((1, 1, self.ny, self.nx, 2)).float()



        if self.anchor_vec.device != device:

            self.anchor_vec = self.anchor_vec.to(device)

            self.anchor_wh = self.anchor_wh.to(device)



    def forward(self, p, out):

        ASFF = False  # https://arxiv.org/abs/1911.09516

        if ASFF:

            i, n = self.index, self.nl  # index in layers, number of layers

            p = out[self.layers[i]]

            bs, _, ny, nx = p.shape  # bs, 255, 13, 13

            if (self.nx, self.ny) != (nx, ny):

                self.create_grids((nx, ny), p.device)



            # outputs and weights

            # w = F.softmax(p[:, -n:], 1)  # normalized weights

            w = torch.sigmoid(p[:, -n:]) * (2 / n)  # sigmoid weights (faster)

            # w = w / w.sum(1).unsqueeze(1)  # normalize across layer dimension



            # weighted ASFF sum

            p = out[self.layers[i]][:, :-n] * w[:, i:i + 1]

            for j in range(n):

                if j != i:

                    p += w[:, j:j + 1] * \

                         F.interpolate(out[self.layers[j]][:, :-n], size=[ny, nx], mode='bilinear', align_corners=False)



        elif ONNX_EXPORT:

            bs = 1  # batch size

        else:

            bs, _, ny, nx = p.shape  # bs, 255, 13, 13

            if (self.nx, self.ny) != (nx, ny):

                self.create_grids((nx, ny), p.device)



        # p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85)  # (bs, anchors, grid, grid, classes + xywh)

        p = p.view(bs, self.na, self.no, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous()  # prediction



        if self.training:

            return p



        elif ONNX_EXPORT:

            # Avoid broadcasting for ANE operations

            m = self.na * self.nx * self.ny

            ng = 1. / self.ng.repeat(m, 1)

            grid = self.grid.repeat(1, self.na, 1, 1, 1).view(m, 2)

            anchor_wh = self.anchor_wh.repeat(1, 1, self.nx, self.ny, 1).view(m, 2) * ng



            p = p.view(m, self.no)

            xy = torch.sigmoid(p[:, 0:2]) + grid  # x, y

            wh = torch.exp(p[:, 2:4]) * anchor_wh  # width, height

            p_cls = torch.sigmoid(p[:, 4:5]) if self.nc == 1 else \

                torch.sigmoid(p[:, 5:self.no]) * torch.sigmoid(p[:, 4:5])  # conf

            return p_cls, xy * ng, wh



        else:  # inference

            io = p.sigmoid()

            io[..., :2] = (io[..., :2] * 2. - 0.5 + self.grid)

            io[..., 2:4] = (io[..., 2:4] * 2) ** 2 * self.anchor_wh

            io[..., :4] *= self.stride

            #io = p.clone()  # inference output

            #io[..., :2] = torch.sigmoid(io[..., :2]) + self.grid  # xy

            #io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh  # wh yolo method

            #io[..., :4] *= self.stride

            #torch.sigmoid_(io[..., 4:])

            return io.view(bs, -1, self.no), p  # view [1, 3, 13, 13, 85] as [1, 507, 85]





class JDELayer(nn.Module):

    def __init__(self, anchors, nc, img_size, yolo_index, layers, stride):

        super(JDELayer, self).__init__()

        self.anchors = torch.Tensor(anchors)

        self.index = yolo_index  # index of this layer in layers

        self.layers = layers  # model output layer indices

        self.stride = stride  # layer stride

        self.nl = len(layers)  # number of output layers (3)

        self.na = len(anchors)  # number of anchors (3)

        self.nc = nc  # number of classes (80)

        self.no = nc + 5  # number of outputs (85)

        self.nx, self.ny, self.ng = 0, 0, 0  # initialize number of x, y gridpoints

        self.anchor_vec = self.anchors / self.stride

        self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2)



        if ONNX_EXPORT:

            self.training = False

            self.create_grids((img_size[1] // stride, img_size[0] // stride))  # number x, y grid points



    def create_grids(self, ng=(13, 13), device='cpu'):

        self.nx, self.ny = ng  # x and y grid size

        self.ng = torch.tensor(ng, dtype=torch.float)



        # build xy offsets

        if not self.training:

            yv, xv = torch.meshgrid([torch.arange(self.ny, device=device), torch.arange(self.nx, device=device)])

            self.grid = torch.stack((xv, yv), 2).view((1, 1, self.ny, self.nx, 2)).float()



        if self.anchor_vec.device != device:

            self.anchor_vec = self.anchor_vec.to(device)

            self.anchor_wh = self.anchor_wh.to(device)



    def forward(self, p, out):

        ASFF = False  # https://arxiv.org/abs/1911.09516

        if ASFF:

            i, n = self.index, self.nl  # index in layers, number of layers

            p = out[self.layers[i]]

            bs, _, ny, nx = p.shape  # bs, 255, 13, 13

            if (self.nx, self.ny) != (nx, ny):

                self.create_grids((nx, ny), p.device)



            # outputs and weights

            # w = F.softmax(p[:, -n:], 1)  # normalized weights

            w = torch.sigmoid(p[:, -n:]) * (2 / n)  # sigmoid weights (faster)

            # w = w / w.sum(1).unsqueeze(1)  # normalize across layer dimension



            # weighted ASFF sum

            p = out[self.layers[i]][:, :-n] * w[:, i:i + 1]

            for j in range(n):

                if j != i:

                    p += w[:, j:j + 1] * \

                         F.interpolate(out[self.layers[j]][:, :-n], size=[ny, nx], mode='bilinear', align_corners=False)



        elif ONNX_EXPORT:

            bs = 1  # batch size

        else:

            bs, _, ny, nx = p.shape  # bs, 255, 13, 13

            if (self.nx, self.ny) != (nx, ny):

                self.create_grids((nx, ny), p.device)



        # p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85)  # (bs, anchors, grid, grid, classes + xywh)

        p = p.view(bs, self.na, self.no, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous()  # prediction



        if self.training:

            return p



        elif ONNX_EXPORT:

            # Avoid broadcasting for ANE operations

            m = self.na * self.nx * self.ny

            ng = 1. / self.ng.repeat(m, 1)

            grid = self.grid.repeat(1, self.na, 1, 1, 1).view(m, 2)

            anchor_wh = self.anchor_wh.repeat(1, 1, self.nx, self.ny, 1).view(m, 2) * ng



            p = p.view(m, self.no)

            xy = torch.sigmoid(p[:, 0:2]) + grid  # x, y

            wh = torch.exp(p[:, 2:4]) * anchor_wh  # width, height

            p_cls = torch.sigmoid(p[:, 4:5]) if self.nc == 1 else \

                torch.sigmoid(p[:, 5:self.no]) * torch.sigmoid(p[:, 4:5])  # conf

            return p_cls, xy * ng, wh



        else:  # inference

            #io = p.sigmoid()

            #io[..., :2] = (io[..., :2] * 2. - 0.5 + self.grid)

            #io[..., 2:4] = (io[..., 2:4] * 2) ** 2 * self.anchor_wh

            #io[..., :4] *= self.stride

            io = p.clone()  # inference output

            io[..., :2] = torch.sigmoid(io[..., :2]) * 2. - 0.5 + self.grid  # xy

            io[..., 2:4] = (torch.sigmoid(io[..., 2:4]) * 2) ** 2 * self.anchor_wh  # wh yolo method

            io[..., :4] *= self.stride

            io[..., 4:] = F.softmax(io[..., 4:])

            return io.view(bs, -1, self.no), p  # view [1, 3, 13, 13, 85] as [1, 507, 85]



class Darknet(nn.Module):

    # YOLOv3 object detection model



    def __init__(self, cfg, img_size=(416, 416), verbose=False):

        super(Darknet, self).__init__()



        self.module_defs = parse_model_cfg(cfg)

        self.module_list, self.routs = create_modules(self.module_defs, img_size, cfg)

        self.yolo_layers = get_yolo_layers(self)

        # torch_utils.initialize_weights(self)



        # Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346

        self.version = np.array([0, 2, 5], dtype=np.int32)  # (int32) version info: major, minor, revision

        self.seen = np.array([0], dtype=np.int64)  # (int64) number of images seen during training

        self.info(verbose) if not ONNX_EXPORT else None  # print model description



    def forward(self, x, augment=False, verbose=False):



        if not augment:

            return self.forward_once(x)

        else:  # Augment images (inference and test only) https://github.com/ultralytics/yolov3/issues/931

            img_size = x.shape[-2:]  # height, width

            s = [0.83, 0.67]  # scales

            y = []

            for i, xi in enumerate((x,

                                    torch_utils.scale_img(x.flip(3), s[0], same_shape=False),  # flip-lr and scale

                                    torch_utils.scale_img(x, s[1], same_shape=False),  # scale

                                    )):

                # cv2.imwrite('img%g.jpg' % i, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])

                y.append(self.forward_once(xi)[0])



            y[1][..., :4] /= s[0]  # scale

            y[1][..., 0] = img_size[1] - y[1][..., 0]  # flip lr

            y[2][..., :4] /= s[1]  # scale



            # for i, yi in enumerate(y):  # coco small, medium, large = < 32**2 < 96**2 <

            #     area = yi[..., 2:4].prod(2)[:, :, None]

            #     if i == 1:

            #         yi *= (area < 96. ** 2).float()

            #     elif i == 2:

            #         yi *= (area > 32. ** 2).float()

            #     y[i] = yi



            y = torch.cat(y, 1)

            return y, None



    def forward_once(self, x, augment=False, verbose=False):

        img_size = x.shape[-2:]  # height, width

        yolo_out, out = [], []

        if verbose:

            print('0', x.shape)

            str = ''



        # Augment images (inference and test only)

        if augment:  # https://github.com/ultralytics/yolov3/issues/931

            nb = x.shape[0]  # batch size

            s = [0.83, 0.67]  # scales

            x = torch.cat((x,

                           torch_utils.scale_img(x.flip(3), s[0]),  # flip-lr and scale

                           torch_utils.scale_img(x, s[1]),  # scale

                           ), 0)



        for i, module in enumerate(self.module_list):

            name = module.__class__.__name__

            #print(name)

            if name in ['WeightedFeatureFusion', 'FeatureConcat', 'FeatureConcat2', 'FeatureConcat3', 'FeatureConcat_l', 'ScaleChannel', 'ShiftChannel', 'ShiftChannel2D', 'ControlChannel', 'ControlChannel2D', 'AlternateChannel', 'AlternateChannel2D', 'SelectChannel', 'SelectChannel2D', 'ScaleSpatial']:  # sum, concat

                if verbose:

                    l = [i - 1] + module.layers  # layers

                    sh = [list(x.shape)] + [list(out[i].shape) for i in module.layers]  # shapes

                    str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, sh)])

                x = module(x, out)  # WeightedFeatureFusion(), FeatureConcat()

            elif name in ['ImplicitA', 'ImplicitM', 'ImplicitC', 'Implicit2DA', 'Implicit2DM', 'Implicit2DC']:

                x = module()

            elif name == 'YOLOLayer':

                yolo_out.append(module(x, out))

            elif name == 'JDELayer':

                yolo_out.append(module(x, out))

            else:  # run module directly, i.e. mtype = 'convolutional', 'upsample', 'maxpool', 'batchnorm2d' etc.

                #print(module)

                #print(x.shape)

                x = module(x)



            out.append(x if self.routs[i] else [])

            if verbose:

                print('%g/%g %s -' % (i, len(self.module_list), name), list(x.shape), str)

                str = ''



        if self.training:  # train

            return yolo_out

        elif ONNX_EXPORT:  # export

            x = [torch.cat(x, 0) for x in zip(*yolo_out)]

            return x[0], torch.cat(x[1:3], 1)  # scores, boxes: 3780x80, 3780x4

        else:  # inference or test

            x, p = zip(*yolo_out)  # inference output, training output

            x = torch.cat(x, 1)  # cat yolo outputs

            if augment:  # de-augment results

                x = torch.split(x, nb, dim=0)

                x[1][..., :4] /= s[0]  # scale

                x[1][..., 0] = img_size[1] - x[1][..., 0]  # flip lr

                x[2][..., :4] /= s[1]  # scale

                x = torch.cat(x, 1)

            return x, p



    def fuse(self):

        # Fuse Conv2d + BatchNorm2d layers throughout model

        print('Fusing layers...')

        fused_list = nn.ModuleList()

        for a in list(self.children())[0]:

            if isinstance(a, nn.Sequential):

                for i, b in enumerate(a):

                    if isinstance(b, nn.modules.batchnorm.BatchNorm2d):

                        # fuse this bn layer with the previous conv2d layer

                        conv = a[i - 1]

                        fused = torch_utils.fuse_conv_and_bn(conv, b)

                        a = nn.Sequential(fused, *list(a.children())[i + 1:])

                        break

            fused_list.append(a)

        self.module_list = fused_list

        self.info() if not ONNX_EXPORT else None  # yolov3-spp reduced from 225 to 152 layers



    def info(self, verbose=False):

        torch_utils.model_info(self, verbose)





def get_yolo_layers(model):

    return [i for i, m in enumerate(model.module_list) if m.__class__.__name__ in ['YOLOLayer', 'JDELayer']]  # [89, 101, 113]





def load_darknet_weights(self, weights, cutoff=-1):

    # Parses and loads the weights stored in 'weights'



    # Establish cutoffs (load layers between 0 and cutoff. if cutoff = -1 all are loaded)

    file = Path(weights).name

    if file == 'darknet53.conv.74':

        cutoff = 75

    elif file == 'yolov3-tiny.conv.15':

        cutoff = 15



    # Read weights file

    with open(weights, 'rb') as f:

        # Read Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346

        self.version = np.fromfile(f, dtype=np.int32, count=3)  # (int32) version info: major, minor, revision

        self.seen = np.fromfile(f, dtype=np.int64, count=1)  # (int64) number of images seen during training



        weights = np.fromfile(f, dtype=np.float32)  # the rest are weights



    ptr = 0

    for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):

        if mdef['type'] == 'convolutional':

            conv = module[0]

            if mdef['batch_normalize']:

                # Load BN bias, weights, running mean and running variance

                bn = module[1]

                nb = bn.bias.numel()  # number of biases

                # Bias

                bn.bias.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.bias))

                ptr += nb

                # Weight

                bn.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.weight))

                ptr += nb

                # Running Mean

                bn.running_mean.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_mean))

                ptr += nb

                # Running Var

                bn.running_var.data.copy_(torch.from_numpy(weights[ptr:ptr + nb]).view_as(bn.running_var))

                ptr += nb

            else:

                # Load conv. bias

                nb = conv.bias.numel()

                conv_b = torch.from_numpy(weights[ptr:ptr + nb]).view_as(conv.bias)

                conv.bias.data.copy_(conv_b)

                ptr += nb

            # Load conv. weights

            nw = conv.weight.numel()  # number of weights

            conv.weight.data.copy_(torch.from_numpy(weights[ptr:ptr + nw]).view_as(conv.weight))

            ptr += nw





def save_weights(self, path='model.weights', cutoff=-1):

    # Converts a PyTorch model to Darket format (*.pt to *.weights)

    # Note: Does not work if model.fuse() is applied

    with open(path, 'wb') as f:

        # Write Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346

        self.version.tofile(f)  # (int32) version info: major, minor, revision

        self.seen.tofile(f)  # (int64) number of images seen during training



        # Iterate through layers

        for i, (mdef, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):

            if mdef['type'] == 'convolutional':

                conv_layer = module[0]

                # If batch norm, load bn first

                if mdef['batch_normalize']:

                    bn_layer = module[1]

                    bn_layer.bias.data.cpu().numpy().tofile(f)

                    bn_layer.weight.data.cpu().numpy().tofile(f)

                    bn_layer.running_mean.data.cpu().numpy().tofile(f)

                    bn_layer.running_var.data.cpu().numpy().tofile(f)

                # Load conv bias

                else:

                    conv_layer.bias.data.cpu().numpy().tofile(f)

                # Load conv weights

                conv_layer.weight.data.cpu().numpy().tofile(f)





def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights', saveto='converted.weights'):

    # Converts between PyTorch and Darknet format per extension (i.e. *.weights convert to *.pt and vice versa)

    # from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')



    # Initialize model

    model = Darknet(cfg)

    ckpt = torch.load(weights)  # load checkpoint

    try:

        ckpt['model'] = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}

        model.load_state_dict(ckpt['model'], strict=False)

        save_weights(model, path=saveto, cutoff=-1)

    except KeyError as e:

        print(e)



def attempt_download(weights):

    # Attempt to download pretrained weights if not found locally

    weights = weights.strip()

    msg = weights + ' missing, try downloading from https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0'



    if len(weights) > 0 and not os.path.isfile(weights):

        d = {''}



        file = Path(weights).name

        if file in d:

            r = gdrive_download(id=d[file], name=weights)

        else:  # download from pjreddie.com

            url = 'https://pjreddie.com/media/files/' + file

            print('Downloading ' + url)

            r = os.system('curl -f ' + url + ' -o ' + weights)



        # Error check

        if not (r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6):  # weights exist and > 1MB

            os.system('rm ' + weights)  # remove partial downloads

            raise Exception(msg)