demo/MegEngine/python/models/network_blocks.py

#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright (c) Megvii Inc. All rights reserved.

import megengine.functional as F
import megengine.module as M


class UpSample(M.Module):

    def __init__(self, scale_factor=2, mode="bilinear"):
        super().__init__()
        self.scale_factor = scale_factor
        self.mode = mode

    def forward(self, x):
        return F.vision.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)


class SiLU(M.Module):
    """export-friendly version of M.SiLU()"""

    @staticmethod
    def forward(x):
        return x * F.sigmoid(x)


def get_activation(name="silu"):
    if name == "silu":
        module = SiLU()
    elif name == "relu":
        module = M.ReLU()
    elif name == "lrelu":
        module = M.LeakyReLU(0.1)
    else:
        raise AttributeError("Unsupported act type: {}".format(name))
    return module


class BaseConv(M.Module):
    """A Conv2d -> Batchnorm -> silu/leaky relu block"""

    def __init__(self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu"):
        super().__init__()
        # same padding
        pad = (ksize - 1) // 2
        self.conv = M.Conv2d(
            in_channels,
            out_channels,
            kernel_size=ksize,
            stride=stride,
            padding=pad,
            groups=groups,
            bias=bias,
        )
        self.bn = M.BatchNorm2d(out_channels)
        self.act = get_activation(act)

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

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


class DWConv(M.Module):
    """Depthwise Conv + Conv"""
    def __init__(self, in_channels, out_channels, ksize, stride=1, act="silu"):
        super().__init__()
        self.dconv = BaseConv(
            in_channels, in_channels, ksize=ksize,
            stride=stride, groups=in_channels, act=act
        )
        self.pconv = BaseConv(
            in_channels, out_channels, ksize=1,
            stride=1, groups=1, act=act
        )

    def forward(self, x):
        x = self.dconv(x)
        return self.pconv(x)


class Bottleneck(M.Module):
    # Standard bottleneck
    def __init__(
        self, in_channels, out_channels, shortcut=True,
        expansion=0.5, depthwise=False, act="silu"
    ):
        super().__init__()
        hidden_channels = int(out_channels * expansion)
        Conv = DWConv if depthwise else BaseConv
        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
        self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act)
        self.use_add = shortcut and in_channels == out_channels

    def forward(self, x):
        y = self.conv2(self.conv1(x))
        if self.use_add:
            y = y + x
        return y


class ResLayer(M.Module):
    "Residual layer with `in_channels` inputs."
    def __init__(self, in_channels: int):
        super().__init__()
        mid_channels = in_channels // 2
        self.layer1 = BaseConv(in_channels, mid_channels, ksize=1, stride=1, act="lrelu")
        self.layer2 = BaseConv(mid_channels, in_channels, ksize=3, stride=1, act="lrelu")

    def forward(self, x):
        out = self.layer2(self.layer1(x))
        return x + out


class SPPBottleneck(M.Module):
    """Spatial pyramid pooling layer used in YOLOv3-SPP"""
    def __init__(self, in_channels, out_channels, kernel_sizes=(5, 9, 13), activation="silu"):
        super().__init__()
        hidden_channels = in_channels // 2
        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=activation)
        self.m = [M.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2) for ks in kernel_sizes]
        conv2_channels = hidden_channels * (len(kernel_sizes) + 1)
        self.conv2 = BaseConv(conv2_channels, out_channels, 1, stride=1, act=activation)

    def forward(self, x):
        x = self.conv1(x)
        x = F.concat([x] + [m(x) for m in self.m], axis=1)
        x = self.conv2(x)
        return x


class CSPLayer(M.Module):
    """C3 in yolov5, CSP Bottleneck with 3 convolutions"""

    def __init__(
        self, in_channels, out_channels, n=1,
        shortcut=True, expansion=0.5, depthwise=False, act="silu"
    ):
        """
        Args:
            in_channels (int): input channels.
            out_channels (int): output channels.
            n (int): number of Bottlenecks. Default value: 1.
        """
        # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        hidden_channels = int(out_channels * expansion)  # hidden channels
        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
        self.conv2 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
        self.conv3 = BaseConv(2 * hidden_channels, out_channels, 1, stride=1, act=act)
        module_list = [
            Bottleneck(hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act)
            for _ in range(n)
        ]
        self.m = M.Sequential(*module_list)

    def forward(self, x):
        x_1 = self.conv1(x)
        x_2 = self.conv2(x)
        x_1 = self.m(x_1)
        x = F.concat((x_1, x_2), axis=1)
        return self.conv3(x)


class Focus(M.Module):
    """Focus width and height information into channel space."""

    def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="silu"):
        super().__init__()
        self.conv = BaseConv(in_channels * 4, out_channels, ksize, stride, act=act)

    def forward(self, x):
        # shape of x (b,c,w,h) -> y(b,4c,w/2,h/2)
        patch_top_left = x[..., ::2, ::2]
        patch_top_right = x[..., ::2, 1::2]
        patch_bot_left = x[..., 1::2, ::2]
        patch_bot_right = x[..., 1::2, 1::2]
        x = F.concat(
            (patch_top_left, patch_bot_left, patch_top_right, patch_bot_right,), axis=1,
        )
        return self.conv(x)