hybridnets/model.py

import torch.nn as nn
import torch
from torchvision.ops.boxes import nms as nms_torch
import torch.nn.functional as F
import math
from functools import partial


def nms(dets, thresh):
    return nms_torch(dets[:, :4], dets[:, 4], thresh)


class SeparableConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels=None, norm=True, activation=False, onnx_export=False):
        super(SeparableConvBlock, self).__init__()
        if out_channels is None:
            out_channels = in_channels

        # Q: whether separate conv
        #  share bias between depthwise_conv and pointwise_conv
        #  or just pointwise_conv apply bias.
        # A: Confirmed, just pointwise_conv applies bias, depthwise_conv has no bias.

        self.depthwise_conv = Conv2dStaticSamePadding(in_channels, in_channels,
                                                      kernel_size=3, stride=1, groups=in_channels, bias=False)
        self.pointwise_conv = Conv2dStaticSamePadding(in_channels, out_channels, kernel_size=1, stride=1)

        self.norm = norm
        if self.norm:
            # Warning: pytorch momentum is different from tensorflow's, momentum_pytorch = 1 - momentum_tensorflow
            self.bn = nn.BatchNorm2d(num_features=out_channels, momentum=0.01, eps=1e-3)

        self.activation = activation
        if self.activation:
            self.swish = MemoryEfficientSwish() if not onnx_export else Swish()

    def forward(self, x):
        x = self.depthwise_conv(x)
        x = self.pointwise_conv(x)

        if self.norm:
            x = self.bn(x)

        if self.activation:
            x = self.swish(x)

        return x


class BiFPN(nn.Module):
    def __init__(self, num_channels, conv_channels, first_time=False, epsilon=1e-4, onnx_export=False, attention=True,
                 use_p8=False):
        """

        Args:
            num_channels:
            conv_channels:
            first_time: whether the input comes directly from the efficientnet,
                        if True, downchannel it first, and downsample P5 to generate P6 then P7
            epsilon: epsilon of fast weighted attention sum of BiFPN, not the BN's epsilon
            onnx_export: if True, use Swish instead of MemoryEfficientSwish
        """
        super(BiFPN, self).__init__()
        self.epsilon = epsilon
        self.use_p8 = use_p8

        # Conv layers
        self.conv6_up = SeparableConvBlock(num_channels, onnx_export=onnx_export)
        self.conv5_up = SeparableConvBlock(num_channels, onnx_export=onnx_export)
        self.conv4_up = SeparableConvBlock(num_channels, onnx_export=onnx_export)
        self.conv3_up = SeparableConvBlock(num_channels, onnx_export=onnx_export)
        self.conv4_down = SeparableConvBlock(num_channels, onnx_export=onnx_export)
        self.conv5_down = SeparableConvBlock(num_channels, onnx_export=onnx_export)
        self.conv6_down = SeparableConvBlock(num_channels, onnx_export=onnx_export)
        self.conv7_down = SeparableConvBlock(num_channels, onnx_export=onnx_export)
        if use_p8:
            self.conv7_up = SeparableConvBlock(num_channels, onnx_export=onnx_export)
            self.conv8_down = SeparableConvBlock(num_channels, onnx_export=onnx_export)

        # Feature scaling layers
        self.p6_upsample = nn.Upsample(scale_factor=2, mode='nearest')
        self.p5_upsample = nn.Upsample(scale_factor=2, mode='nearest')
        self.p4_upsample = nn.Upsample(scale_factor=2, mode='nearest')
        self.p3_upsample = nn.Upsample(scale_factor=2, mode='nearest')

        self.p4_downsample = MaxPool2dStaticSamePadding(3, 2)
        self.p5_downsample = MaxPool2dStaticSamePadding(3, 2)
        self.p6_downsample = MaxPool2dStaticSamePadding(3, 2)
        self.p7_downsample = MaxPool2dStaticSamePadding(3, 2)
        if use_p8:
            self.p7_upsample = nn.Upsample(scale_factor=2, mode='nearest')
            self.p8_downsample = MaxPool2dStaticSamePadding(3, 2)

        self.swish = MemoryEfficientSwish() if not onnx_export else Swish()

        self.first_time = first_time
        if self.first_time:
            self.p5_down_channel = nn.Sequential(
                Conv2dStaticSamePadding(conv_channels[2], num_channels, 1),
                nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
            )
            self.p4_down_channel = nn.Sequential(
                Conv2dStaticSamePadding(conv_channels[1], num_channels, 1),
                nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
            )
            self.p3_down_channel = nn.Sequential(
                Conv2dStaticSamePadding(conv_channels[0], num_channels, 1),
                nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
            )

            self.p5_to_p6 = nn.Sequential(
                Conv2dStaticSamePadding(conv_channels[2], num_channels, 1),
                nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
                MaxPool2dStaticSamePadding(3, 2)
            )
            self.p6_to_p7 = nn.Sequential(
                MaxPool2dStaticSamePadding(3, 2)
            )
            if use_p8:
                self.p7_to_p8 = nn.Sequential(
                    MaxPool2dStaticSamePadding(3, 2)
                )

            self.p4_down_channel_2 = nn.Sequential(
                Conv2dStaticSamePadding(conv_channels[1], num_channels, 1),
                nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
            )
            self.p5_down_channel_2 = nn.Sequential(
                Conv2dStaticSamePadding(conv_channels[2], num_channels, 1),
                nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3),
            )

        # Weight
        self.p6_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
        self.p6_w1_relu = nn.ReLU()
        self.p5_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
        self.p5_w1_relu = nn.ReLU()
        self.p4_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
        self.p4_w1_relu = nn.ReLU()
        self.p3_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
        self.p3_w1_relu = nn.ReLU()

        self.p4_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True)
        self.p4_w2_relu = nn.ReLU()
        self.p5_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True)
        self.p5_w2_relu = nn.ReLU()
        self.p6_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True)
        self.p6_w2_relu = nn.ReLU()
        self.p7_w2 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
        self.p7_w2_relu = nn.ReLU()

        self.attention = attention

    def forward(self, inputs):
        """
        illustration of a minimal bifpn unit
            P7_0 -------------------------> P7_2 -------->
               |-------------|                ↑
                             ↓                |
            P6_0 ---------> P6_1 ---------> P6_2 -------->
               |-------------|--------------↑ ↑
                             ↓                |
            P5_0 ---------> P5_1 ---------> P5_2 -------->
               |-------------|--------------↑ ↑
                             ↓                |
            P4_0 ---------> P4_1 ---------> P4_2 -------->
               |-------------|--------------↑ ↑
                             |--------------↓ |
            P3_0 -------------------------> P3_2 -------->
        """

        # downsample channels using same-padding conv2d to target phase's if not the same
        # judge: same phase as target,
        # if same, pass;
        # elif earlier phase, downsample to target phase's by pooling
        # elif later phase, upsample to target phase's by nearest interpolation

        if self.attention:
            outs = self._forward_fast_attention(inputs)
        else:
            outs = self._forward(inputs)

        return outs

    def _forward_fast_attention(self, inputs):
        if self.first_time:
            p3, p4, p5 = inputs

            p6_in = self.p5_to_p6(p5)
            p7_in = self.p6_to_p7(p6_in)

            p3_in = self.p3_down_channel(p3)
            p4_in = self.p4_down_channel(p4)
            p5_in = self.p5_down_channel(p5)

        else:
            # P3_0, P4_0, P5_0, P6_0 and P7_0
            p3_in, p4_in, p5_in, p6_in, p7_in = inputs

        # P7_0 to P7_2

        # Weights for P6_0 and P7_0 to P6_1
        p6_w1 = self.p6_w1_relu(self.p6_w1)
        weight = p6_w1 / (torch.sum(p6_w1, dim=0) + self.epsilon)
        # Connections for P6_0 and P7_0 to P6_1 respectively
        p6_up = self.conv6_up(self.swish(weight[0] * p6_in + weight[1] * self.p6_upsample(p7_in)))
        # Weights for P5_0 and P6_1 to P5_1
        p5_w1 = self.p5_w1_relu(self.p5_w1)
        weight = p5_w1 / (torch.sum(p5_w1, dim=0) + self.epsilon)
        # Connections for P5_0 and P6_1 to P5_1 respectively
        p5_up = self.conv5_up(self.swish(weight[0] * p5_in + weight[1] * self.p5_upsample(p6_up)))

        # Weights for P4_0 and P5_1 to P4_1
        p4_w1 = self.p4_w1_relu(self.p4_w1)
        weight = p4_w1 / (torch.sum(p4_w1, dim=0) + self.epsilon)
        # Connections for P4_0 and P5_1 to P4_1 respectively
        p4_up = self.conv4_up(self.swish(weight[0] * p4_in + weight[1] * self.p4_upsample(p5_up)))

        # Weights for P3_0 and P4_1 to P3_2
        p3_w1 = self.p3_w1_relu(self.p3_w1)
        weight = p3_w1 / (torch.sum(p3_w1, dim=0) + self.epsilon)
        # Connections for P3_0 and P4_1 to P3_2 respectively
        p3_out = self.conv3_up(self.swish(weight[0] * p3_in + weight[1] * self.p3_upsample(p4_up)))

        if self.first_time:
            p4_in = self.p4_down_channel_2(p4)
            p5_in = self.p5_down_channel_2(p5)

        # Weights for P4_0, P4_1 and P3_2 to P4_2
        p4_w2 = self.p4_w2_relu(self.p4_w2)
        weight = p4_w2 / (torch.sum(p4_w2, dim=0) + self.epsilon)
        # Connections for P4_0, P4_1 and P3_2 to P4_2 respectively
        p4_out = self.conv4_down(
            self.swish(weight[0] * p4_in + weight[1] * p4_up + weight[2] * self.p4_downsample(p3_out)))

        # Weights for P5_0, P5_1 and P4_2 to P5_2
        p5_w2 = self.p5_w2_relu(self.p5_w2)
        weight = p5_w2 / (torch.sum(p5_w2, dim=0) + self.epsilon)
        # Connections for P5_0, P5_1 and P4_2 to P5_2 respectively
        p5_out = self.conv5_down(
            self.swish(weight[0] * p5_in + weight[1] * p5_up + weight[2] * self.p5_downsample(p4_out)))

        # Weights for P6_0, P6_1 and P5_2 to P6_2
        p6_w2 = self.p6_w2_relu(self.p6_w2)
        weight = p6_w2 / (torch.sum(p6_w2, dim=0) + self.epsilon)
        # Connections for P6_0, P6_1 and P5_2 to P6_2 respectively
        p6_out = self.conv6_down(
            self.swish(weight[0] * p6_in + weight[1] * p6_up + weight[2] * self.p6_downsample(p5_out)))

        # Weights for P7_0 and P6_2 to P7_2
        p7_w2 = self.p7_w2_relu(self.p7_w2)
        weight = p7_w2 / (torch.sum(p7_w2, dim=0) + self.epsilon)
        # Connections for P7_0 and P6_2 to P7_2
        p7_out = self.conv7_down(self.swish(weight[0] * p7_in + weight[1] * self.p7_downsample(p6_out)))

        return p3_out, p4_out, p5_out, p6_out, p7_out

    def _forward(self, inputs):
        if self.first_time:
            p3, p4, p5 = inputs

            p6_in = self.p5_to_p6(p5)
            p7_in = self.p6_to_p7(p6_in)
            if self.use_p8:
                p8_in = self.p7_to_p8(p7_in)

            p3_in = self.p3_down_channel(p3)
            p4_in = self.p4_down_channel(p4)
            p5_in = self.p5_down_channel(p5)

        else:
            if self.use_p8:
                # P3_0, P4_0, P5_0, P6_0, P7_0 and P8_0
                p3_in, p4_in, p5_in, p6_in, p7_in, p8_in = inputs
            else:
                # P3_0, P4_0, P5_0, P6_0 and P7_0
                p3_in, p4_in, p5_in, p6_in, p7_in = inputs

        if self.use_p8:
            # P8_0 to P8_2

            # Connections for P7_0 and P8_0 to P7_1 respectively
            p7_up = self.conv7_up(self.swish(p7_in + self.p7_upsample(p8_in)))

            # Connections for P6_0 and P7_0 to P6_1 respectively
            p6_up = self.conv6_up(self.swish(p6_in + self.p6_upsample(p7_up)))
        else:
            # P7_0 to P7_2

            # Connections for P6_0 and P7_0 to P6_1 respectively
            p6_up = self.conv6_up(self.swish(p6_in + self.p6_upsample(p7_in)))

        # Connections for P5_0 and P6_1 to P5_1 respectively
        p5_up = self.conv5_up(self.swish(p5_in + self.p5_upsample(p6_up)))

        # Connections for P4_0 and P5_1 to P4_1 respectively
        p4_up = self.conv4_up(self.swish(p4_in + self.p4_upsample(p5_up)))

        # Connections for P3_0 and P4_1 to P3_2 respectively
        p3_out = self.conv3_up(self.swish(p3_in + self.p3_upsample(p4_up)))

        if self.first_time:
            p4_in = self.p4_down_channel_2(p4)
            p5_in = self.p5_down_channel_2(p5)

        # Connections for P4_0, P4_1 and P3_2 to P4_2 respectively
        p4_out = self.conv4_down(
            self.swish(p4_in + p4_up + self.p4_downsample(p3_out)))

        # Connections for P5_0, P5_1 and P4_2 to P5_2 respectively
        p5_out = self.conv5_down(
            self.swish(p5_in + p5_up + self.p5_downsample(p4_out)))

        # Connections for P6_0, P6_1 and P5_2 to P6_2 respectively
        p6_out = self.conv6_down(
            self.swish(p6_in + p6_up + self.p6_downsample(p5_out)))

        if self.use_p8:
            # Connections for P7_0, P7_1 and P6_2 to P7_2 respectively
            p7_out = self.conv7_down(
                self.swish(p7_in + p7_up + self.p7_downsample(p6_out)))

            # Connections for P8_0 and P7_2 to P8_2
            p8_out = self.conv8_down(self.swish(p8_in + self.p8_downsample(p7_out)))

            return p3_out, p4_out, p5_out, p6_out, p7_out, p8_out
        else:
            # Connections for P7_0 and P6_2 to P7_2
            p7_out = self.conv7_down(self.swish(p7_in + self.p7_downsample(p6_out)))

            return p3_out, p4_out, p5_out, p6_out, p7_out


class Regressor(nn.Module):
    def __init__(self, in_channels, num_anchors, num_layers, pyramid_levels=5, onnx_export=False):
        super(Regressor, self).__init__()
        self.num_layers = num_layers

        self.conv_list = nn.ModuleList(
            [SeparableConvBlock(in_channels, in_channels, norm=False, activation=False) for i in range(num_layers)])
        self.bn_list = nn.ModuleList(
            [nn.ModuleList([nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3) for i in range(num_layers)]) for j in
             range(pyramid_levels)])
        self.header = SeparableConvBlock(in_channels, num_anchors * 4, norm=False, activation=False)
        self.swish = MemoryEfficientSwish() if not onnx_export else Swish()

    def forward(self, inputs):
        feats = []
        for feat, bn_list in zip(inputs, self.bn_list):
            for i, bn, conv in zip(range(self.num_layers), bn_list, self.conv_list):
                feat = conv(feat)
                feat = bn(feat)
                feat = self.swish(feat)
            feat = self.header(feat)

            feat = feat.permute(0, 2, 3, 1)
            feat = feat.contiguous().view(feat.shape[0], -1, 4)

            feats.append(feat)

        feats = torch.cat(feats, dim=1)

        return feats


class Conv3x3BNSwish(nn.Module):
    def __init__(self, in_channels, out_channels, upsample=False):
        super().__init__()

        self.swish = Swish()

        self.upsample = upsample

        self.block = nn.Sequential(
            Conv2dStaticSamePadding(in_channels, out_channels, kernel_size=(3, 3), stride=1, padding=1, bias=False),
            nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3),
        )

        self.conv_sp = SeparableConvBlock(out_channels, onnx_export=False)

        # self.block = nn.Sequential(
        #     nn.Conv2d(
        #         in_channels, out_channels, (3, 3), stride=1, padding=1, bias=False
        #     ),
        #     nn.GroupNorm(32, out_channels),
        #     nn.ReLU(inplace=True),
        # )

    def forward(self, x):
        x = self.conv_sp(self.swish(self.block(x)))
        if self.upsample:
            x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
        return x


class SegmentationBlock(nn.Module):
    def __init__(self, in_channels, out_channels, n_upsamples=0):
        super().__init__()

        blocks = [Conv3x3BNSwish(in_channels, out_channels, upsample=bool(n_upsamples))]

        if n_upsamples > 1:
            for _ in range(1, n_upsamples):
                blocks.append(Conv3x3BNSwish(out_channels, out_channels, upsample=True))

        self.block = nn.Sequential(*blocks)

    def forward(self, x):
        return self.block(x)


class MergeBlock(nn.Module):
    def __init__(self, policy):
        super().__init__()
        if policy not in ["add", "cat"]:
            raise ValueError(
                "`merge_policy` must be one of: ['add', 'cat'], got {}".format(
                    policy
                )
            )
        self.policy = policy

    def forward(self, x):
        if self.policy == 'add':
            return sum(x)
        elif self.policy == 'cat':
            return torch.cat(x, dim=1)
        else:
            raise ValueError(
                "`merge_policy` must be one of: ['add', 'cat'], got {}".format(self.policy)
            )


class BiFPNDecoder(nn.Module):
    def __init__(
            self,
            encoder_depth=5,
            pyramid_channels=64,
            segmentation_channels=64,
            dropout=0.2,
            merge_policy="add", ):
        super().__init__()

        self.seg_blocks = nn.ModuleList([
            SegmentationBlock(pyramid_channels, segmentation_channels, n_upsamples=n_upsamples)
            for n_upsamples in [5,4, 3, 2, 1]
        ])
        
        self.seg_p2 = SegmentationBlock(32, 64, n_upsamples=0)

        self.merge = MergeBlock(merge_policy)

        self.dropout = nn.Dropout2d(p=dropout, inplace=True)

    def forward(self, inputs):
        p2, p3, p4, p5, p6, p7 = inputs

        feature_pyramid = [seg_block(p) for seg_block, p in zip(self.seg_blocks, [p7, p6, p5, p4, p3])]
        
        p2 = self.seg_p2(p2)
            
        p3,p4,p5,p6,p7 = feature_pyramid

        x = self.merge((p2,p3,p4,p5,p6,p7))

        x = self.dropout(x)

        return x


class Classifier(nn.Module):
    def __init__(self, in_channels, num_anchors, num_classes, num_layers, pyramid_levels=5, onnx_export=False):
        super(Classifier, self).__init__()
        self.num_anchors = num_anchors
        self.num_classes = num_classes
        self.num_layers = num_layers
        self.conv_list = nn.ModuleList(
            [SeparableConvBlock(in_channels, in_channels, norm=False, activation=False) for i in range(num_layers)])
        self.bn_list = nn.ModuleList(
            [nn.ModuleList([nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3) for i in range(num_layers)]) for j in
             range(pyramid_levels)])
        self.header = SeparableConvBlock(in_channels, num_anchors * num_classes, norm=False, activation=False)
        self.swish = MemoryEfficientSwish() if not onnx_export else Swish()

    def forward(self, inputs):
        feats = []
        for feat, bn_list in zip(inputs, self.bn_list):
            for i, bn, conv in zip(range(self.num_layers), bn_list, self.conv_list):
                feat = conv(feat)
                feat = bn(feat)
                feat = self.swish(feat)
            feat = self.header(feat)

            feat = feat.permute(0, 2, 3, 1)
            feat = feat.contiguous().view(feat.shape[0], feat.shape[1], feat.shape[2], self.num_anchors,
                                          self.num_classes)
            feat = feat.contiguous().view(feat.shape[0], -1, self.num_classes)

            feats.append(feat)

        feats = torch.cat(feats, dim=1)
        feats = feats.sigmoid()

        return feats


class SwishImplementation(torch.autograd.Function):
    @staticmethod
    def forward(ctx, i):
        result = i * torch.sigmoid(i)
        ctx.save_for_backward(i)
        return result

    @staticmethod
    def backward(ctx, grad_output):
        i = ctx.saved_variables[0]
        sigmoid_i = torch.sigmoid(i)
        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))


class MemoryEfficientSwish(nn.Module):
    def forward(self, x):
        return SwishImplementation.apply(x)


class Swish(nn.Module):
    def forward(self, x):
        return x * torch.sigmoid(x)


def drop_connect(inputs, p, training):
    """ Drop connect. """
    if not training: return inputs
    batch_size = inputs.shape[0]
    keep_prob = 1 - p
    random_tensor = keep_prob
    random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)
    binary_tensor = torch.floor(random_tensor)
    output = inputs / keep_prob * binary_tensor
    return output


def get_same_padding_conv2d(image_size=None):
    """ Chooses static padding if you have specified an image size, and dynamic padding otherwise.
        Static padding is necessary for ONNX exporting of models. """
    if image_size is None:
        return Conv2dDynamicSamePadding
    else:
        return partial(Conv2dStaticSamePadding, image_size=image_size)


class Conv2dDynamicSamePadding(nn.Conv2d):
    """ 2D Convolutions like TensorFlow, for a dynamic image size """

    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):
        super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
        self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2

    def forward(self, x):
        ih, iw = x.size()[-2:]
        kh, kw = self.weight.size()[-2:]
        sh, sw = self.stride
        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
        pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
        pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
        if pad_h > 0 or pad_w > 0:
            x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
        return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)


class MBConvBlock(nn.Module):
    """
    Mobile Inverted Residual Bottleneck Block

    Args:
        block_args (namedtuple): BlockArgs, see above
        global_params (namedtuple): GlobalParam, see above

    Attributes:
        has_se (bool): Whether the block contains a Squeeze and Excitation layer.
    """

    def __init__(self, block_args, global_params):
        super().__init__()
        self._block_args = block_args
        self._bn_mom = 1 - global_params.batch_norm_momentum
        self._bn_eps = global_params.batch_norm_epsilon
        self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1)
        self.id_skip = block_args.id_skip  # skip connection and drop connect

        # Get static or dynamic convolution depending on image size
        Conv2d = get_same_padding_conv2d(image_size=global_params.image_size)

        # Expansion phase
        inp = self._block_args.input_filters  # number of input channels
        oup = self._block_args.input_filters * self._block_args.expand_ratio  # number of output channels
        if self._block_args.expand_ratio != 1:
            self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False)
            self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)

        # Depthwise convolution phase
        k = self._block_args.kernel_size
        s = self._block_args.stride
        self._depthwise_conv = Conv2d(
            in_channels=oup, out_channels=oup, groups=oup,  # groups makes it depthwise
            kernel_size=k, stride=s, bias=False)
        self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)

        # Squeeze and Excitation layer, if desired
        if self.has_se:
            num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio))
            self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1)
            self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1)

        # Output phase
        final_oup = self._block_args.output_filters
        self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False)
        self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps)
        self._swish = MemoryEfficientSwish()

    def forward(self, inputs, drop_connect_rate=None):
        """
        :param inputs: input tensor
        :param drop_connect_rate: drop connect rate (float, between 0 and 1)
        :return: output of block
        """

        # Expansion and Depthwise Convolution
        x = inputs
        if self._block_args.expand_ratio != 1:
            x = self._expand_conv(inputs)
            x = self._bn0(x)
            x = self._swish(x)

        x = self._depthwise_conv(x)
        x = self._bn1(x)
        x = self._swish(x)

        # Squeeze and Excitation
        if self.has_se:
            x_squeezed = F.adaptive_avg_pool2d(x, 1)
            x_squeezed = self._se_reduce(x_squeezed)
            x_squeezed = self._swish(x_squeezed)
            x_squeezed = self._se_expand(x_squeezed)
            x = torch.sigmoid(x_squeezed) * x

        x = self._project_conv(x)
        x = self._bn2(x)

        # Skip connection and drop connect
        input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters
        if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters:
            if drop_connect_rate:
                x = drop_connect(x, p=drop_connect_rate, training=self.training)
            x = x + inputs  # skip connection
        return x

    def set_swish(self, memory_efficient=True):
        """Sets swish function as memory efficient (for training) or standard (for export)"""
        self._swish = MemoryEfficientSwish() if memory_efficient else Swish()


class Conv2dStaticSamePadding(nn.Module):
    """
    The real keras/tensorflow conv2d with same padding
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True, groups=1, dilation=1, **kwargs):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride,
                              bias=bias, groups=groups)
        self.stride = self.conv.stride
        self.kernel_size = self.conv.kernel_size
        self.dilation = self.conv.dilation

        if isinstance(self.stride, int):
            self.stride = [self.stride] * 2
        elif len(self.stride) == 1:
            self.stride = [self.stride[0]] * 2

        if isinstance(self.kernel_size, int):
            self.kernel_size = [self.kernel_size] * 2
        elif len(self.kernel_size) == 1:
            self.kernel_size = [self.kernel_size[0]] * 2

    def forward(self, x):
        h, w = x.shape[-2:]
        
        extra_h = (math.ceil(w / self.stride[1]) - 1) * self.stride[1] - w + self.kernel_size[1]
        extra_v = (math.ceil(h / self.stride[0]) - 1) * self.stride[0] - h + self.kernel_size[0]
        
        left = extra_h // 2
        right = extra_h - left
        top = extra_v // 2
        bottom = extra_v - top

        x = F.pad(x, [left, right, top, bottom])

        x = self.conv(x)
        return x


class MaxPool2dStaticSamePadding(nn.Module):
    """
    The real keras/tensorflow MaxPool2d with same padding
    """

    def __init__(self, *args, **kwargs):
        super().__init__()
        self.pool = nn.MaxPool2d(*args, **kwargs)
        self.stride = self.pool.stride
        self.kernel_size = self.pool.kernel_size

        if isinstance(self.stride, int):
            self.stride = [self.stride] * 2
        elif len(self.stride) == 1:
            self.stride = [self.stride[0]] * 2

        if isinstance(self.kernel_size, int):
            self.kernel_size = [self.kernel_size] * 2
        elif len(self.kernel_size) == 1:
            self.kernel_size = [self.kernel_size[0]] * 2

    def forward(self, x):
        h, w = x.shape[-2:]
        
        extra_h = (math.ceil(w / self.stride[1]) - 1) * self.stride[1] - w + self.kernel_size[1]
        extra_v = (math.ceil(h / self.stride[0]) - 1) * self.stride[0] - h + self.kernel_size[0]

        left = extra_h // 2
        right = extra_h - left
        top = extra_v // 2
        bottom = extra_v - top

        x = F.pad(x, [left, right, top, bottom])

        x = self.pool(x)
        return x


class Activation(nn.Module):

    def __init__(self, name, **params):

        super().__init__()

        if name is None or name == 'identity':
            self.activation = nn.Identity(**params)
        elif name == 'sigmoid':
            self.activation = nn.Sigmoid()
        elif name == 'softmax2d':
            self.activation = nn.Softmax(dim=1, **params)
        elif name == 'softmax':
            self.activation = nn.Softmax(**params)
        elif name == 'logsoftmax':
            self.activation = nn.LogSoftmax(**params)
        elif name == 'tanh':
            self.activation = nn.Tanh()
        # elif name == 'argmax':
        #     self.activation = ArgMax(**params)
        # elif name == 'argmax2d':
        #     self.activation = ArgMax(dim=1, **params)
        # elif name == 'clamp':
        #     self.activation = Clamp(**params)
        elif callable(name):
            self.activation = name(**params)
        else:
            raise ValueError('Activation should be callable/sigmoid/softmax/logsoftmax/tanh/None; got {}'.format(name))
    def forward(self, x):
        return self.activation(x)


class SegmentationHead(nn.Sequential):

    def __init__(self, in_channels, out_channels, kernel_size=3, activation=None, upsampling=1):
        conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2)
        upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampling) if upsampling > 1 else nn.Identity()
        activation = Activation(activation)
        super().__init__(conv2d, upsampling, activation)


class ClassificationHead(nn.Sequential):

    def __init__(self, in_channels, classes, pooling="avg", dropout=0.2, activation=None):
        if pooling not in ("max", "avg"):
            raise ValueError("Pooling should be one of ('max', 'avg'), got {}.".format(pooling))
        pool = nn.AdaptiveAvgPool2d(1) if pooling == 'avg' else nn.AdaptiveMaxPool2d(1)
        flatten = nn.Flatten()
        dropout = nn.Dropout(p=dropout, inplace=True) if dropout else nn.Identity()
        linear = nn.Linear(in_channels, classes, bias=True)
        activation = Activation(activation)
        super().__init__(pool, flatten, dropout, linear, activation)


if __name__ == '__main__':
    from tensorboardX import SummaryWriter


    def count_parameters(model):
        return sum(p.numel() for p in model.parameters() if p.requires_grad)