pytorch-image-models/timm/layers/mlp.py

""" MLP module w/ dropout and configurable activation layer

Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partial

from torch import nn as nn

from .grn import GlobalResponseNorm
from .helpers import to_2tuple


class Mlp(nn.Module):
    """ MLP as used in Vision Transformer, MLP-Mixer and related networks

    NOTE: When use_conv=True, expects 2D NCHW tensors, otherwise N*C expected.
    """
    def __init__(
            self,
            in_features,
            hidden_features=None,
            out_features=None,
            act_layer=nn.GELU,
            norm_layer=None,
            bias=True,
            drop=0.,
            use_conv=False,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)
        drop_probs = to_2tuple(drop)
        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear

        self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
        self.act = act_layer()
        self.drop1 = nn.Dropout(drop_probs[0])
        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
        self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
        self.drop2 = nn.Dropout(drop_probs[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop1(x)
        x = self.norm(x)
        x = self.fc2(x)
        x = self.drop2(x)
        return x


class GluMlp(nn.Module):
    """ MLP w/ GLU style gating
    See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202

    NOTE: When use_conv=True, expects 2D NCHW tensors, otherwise N*C expected.
    """
    def __init__(
            self,
            in_features,
            hidden_features=None,
            out_features=None,
            act_layer=nn.Sigmoid,
            norm_layer=None,
            bias=True,
            drop=0.,
            use_conv=False,
            gate_last=True,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        assert hidden_features % 2 == 0
        bias = to_2tuple(bias)
        drop_probs = to_2tuple(drop)
        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
        self.chunk_dim = 1 if use_conv else -1
        self.gate_last = gate_last  # use second half of width for gate

        self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
        self.act = act_layer()
        self.drop1 = nn.Dropout(drop_probs[0])
        self.norm = norm_layer(hidden_features // 2) if norm_layer is not None else nn.Identity()
        self.fc2 = linear_layer(hidden_features // 2, out_features, bias=bias[1])
        self.drop2 = nn.Dropout(drop_probs[1])

    def init_weights(self):
        # override init of fc1 w/ gate portion set to weight near zero, bias=1
        if self.fc1.bias is not None:
            nn.init.ones_(self.fc1.bias[self.fc1.bias.shape[0] // 2:])
        nn.init.normal_(self.fc1.weight[self.fc1.weight.shape[0] // 2:], std=1e-6)

    def forward(self, x):
        x = self.fc1(x)
        x1, x2 = x.chunk(2, dim=self.chunk_dim)
        x = x1 * self.act(x2) if self.gate_last else self.act(x1) * x2
        x = self.drop1(x)
        x = self.norm(x)
        x = self.fc2(x)
        x = self.drop2(x)
        return x


SwiGLUPacked = partial(GluMlp, act_layer=nn.SiLU, gate_last=False)


class SwiGLU(nn.Module):
    """ SwiGLU
    NOTE: GluMLP above can implement SwiGLU, but this impl has split fc1 and
    better matches some other common impl which makes mapping checkpoints simpler.
    """
    def __init__(
            self,
            in_features,
            hidden_features=None,
            out_features=None,
            act_layer=nn.SiLU,
            norm_layer=None,
            bias=True,
            drop=0.,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)
        drop_probs = to_2tuple(drop)

        self.fc1_g = nn.Linear(in_features, hidden_features, bias=bias[0])
        self.fc1_x = nn.Linear(in_features, hidden_features, bias=bias[0])
        self.act = act_layer()
        self.drop1 = nn.Dropout(drop_probs[0])
        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
        self.drop2 = nn.Dropout(drop_probs[1])

    def init_weights(self):
        # override init of fc1 w/ gate portion set to weight near zero, bias=1
        if self.fc1_g.bias is not None:
            nn.init.ones_(self.fc1_g.bias)
        nn.init.normal_(self.fc1_g.weight, std=1e-6)

    def forward(self, x):
        x_gate = self.fc1_g(x)
        x = self.fc1_x(x)
        x = self.act(x_gate) * x
        x = self.drop1(x)
        x = self.norm(x)
        x = self.fc2(x)
        x = self.drop2(x)
        return x


class GatedMlp(nn.Module):
    """ MLP as used in gMLP
    """
    def __init__(
            self,
            in_features,
            hidden_features=None,
            out_features=None,
            act_layer=nn.GELU,
            norm_layer=None,
            gate_layer=None,
            bias=True,
            drop=0.,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)
        drop_probs = to_2tuple(drop)

        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
        self.act = act_layer()
        self.drop1 = nn.Dropout(drop_probs[0])
        if gate_layer is not None:
            assert hidden_features % 2 == 0
            self.gate = gate_layer(hidden_features)
            hidden_features = hidden_features // 2  # FIXME base reduction on gate property?
        else:
            self.gate = nn.Identity()
        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
        self.drop2 = nn.Dropout(drop_probs[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop1(x)
        x = self.gate(x)
        x = self.norm(x)
        x = self.fc2(x)
        x = self.drop2(x)
        return x


class ConvMlp(nn.Module):
    """ MLP using 1x1 convs that keeps spatial dims (for 2D NCHW tensors)
    """
    def __init__(
            self,
            in_features,
            hidden_features=None,
            out_features=None,
            act_layer=nn.ReLU,
            norm_layer=None,
            bias=True,
            drop=0.,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)

        self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
        self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
        self.act = act_layer()
        self.drop = nn.Dropout(drop)
        self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.norm(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        return x


class GlobalResponseNormMlp(nn.Module):
    """ MLP w/ Global Response Norm (see grn.py), nn.Linear or 1x1 Conv2d

    NOTE: Intended for '2D' NCHW (use_conv=True) or NHWC (use_conv=False, channels-last) tensor layouts
    """
    def __init__(
            self,
            in_features,
            hidden_features=None,
            out_features=None,
            act_layer=nn.GELU,
            bias=True,
            drop=0.,
            use_conv=False,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)
        drop_probs = to_2tuple(drop)
        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear

        self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
        self.act = act_layer()
        self.drop1 = nn.Dropout(drop_probs[0])
        self.grn = GlobalResponseNorm(hidden_features, channels_last=not use_conv)
        self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
        self.drop2 = nn.Dropout(drop_probs[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop1(x)
        x = self.grn(x)
        x = self.fc2(x)
        x = self.drop2(x)
        return x