91f563c2a2baa5a120a81e91da19f29e9d37b42f,models.py,,create_modules,#Any#Any#,8

Before Change

        // if i == 0:
        //     modules.add_module("BatchNorm2d_0", nn.BatchNorm2d(output_filters[-1], momentum=0.1))

        if mdef["type"] == "convolutional":
            bn = mdef["batch_normalize"]
            filters = mdef["filters"]
            size = mdef["size"]
            stride = mdef["stride"] if "stride" in mdef else (mdef["stride_y"], mdef["stride_x"])
            modules.add_module("Conv2d", nn.Conv2d(in_channels=output_filters[-1],
                                                   out_channels=filters,
                                                   kernel_size=size,
                                                   stride=stride,
                                                   padding=(size - 1) // 2 if mdef["pad"] else 0,
                                                   groups=mdef["groups"] if "groups" in mdef else 1,
                                                   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))
                // modules.add_module("activation", nn.PReLU(num_parameters=1, init=0.10))
            elif mdef["activation"] == "swish":
                modules.add_module("activation", Swish())

        elif mdef["type"] == "maxpool":
            size = mdef["size"]
            stride = mdef["stride"]
            maxpool = nn.MaxPool2d(kernel_size=size, stride=stride, padding=(size - 1) // 2)
            if size == 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"] == "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])

        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"] == "yolo":
            yolo_index += 1
            l = 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=l)  // output layers

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

After Change

        elif mdef["type"] == "yolo":
            yolo_index += 1
            stride = [32, 16, 8, 4, 2][yolo_index]  // P3-P7 stride
            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=mdef["from"] if "from" in mdef else [],  // output layers
                                stride=stride)

            // Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)
            try:
                bo = -4.5  //  obj bias

Instances