mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
8880f696b6
* Add eca_nfnet_l2 weights, 84.7 @ 384x384 * All 'non-std' (ie transformer / mlp) models have classifier / default_cfg test added * Fix #694 reset_classifer / num_features / forward_features / num_classes=0 consistency for transformer / mlp models * Add direct loading of npz to vision transformer (pure transformer so far, hybrid to come) * Rename vit_deit* to deit_* * Remove some deprecated vit hybrid model defs * Clean up classifier flatten for conv classifiers and unusual cases (mobilenetv3/ghostnet) * Remove explicit model fns for levit conv, just pass in arg
109 lines
3.7 KiB
Python
109 lines
3.7 KiB
Python
""" MLP module w/ dropout and configurable activation layer
|
|
|
|
Hacked together by / Copyright 2020 Ross Wightman
|
|
"""
|
|
from torch import nn as nn
|
|
|
|
|
|
class Mlp(nn.Module):
|
|
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
|
|
"""
|
|
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
self.fc1 = nn.Linear(in_features, hidden_features)
|
|
self.act = act_layer()
|
|
self.fc2 = nn.Linear(hidden_features, out_features)
|
|
self.drop = nn.Dropout(drop)
|
|
|
|
def forward(self, x):
|
|
x = self.fc1(x)
|
|
x = self.act(x)
|
|
x = self.drop(x)
|
|
x = self.fc2(x)
|
|
x = self.drop(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
|
|
"""
|
|
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.Sigmoid, drop=0.):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
assert hidden_features % 2 == 0
|
|
self.fc1 = nn.Linear(in_features, hidden_features)
|
|
self.act = act_layer()
|
|
self.fc2 = nn.Linear(hidden_features // 2, out_features)
|
|
self.drop = nn.Dropout(drop)
|
|
|
|
def init_weights(self):
|
|
# override init of fc1 w/ gate portion set to weight near zero, bias=1
|
|
fc1_mid = self.fc1.bias.shape[0] // 2
|
|
nn.init.ones_(self.fc1.bias[fc1_mid:])
|
|
nn.init.normal_(self.fc1.weight[fc1_mid:], std=1e-6)
|
|
|
|
def forward(self, x):
|
|
x = self.fc1(x)
|
|
x, gates = x.chunk(2, dim=-1)
|
|
x = x * self.act(gates)
|
|
x = self.drop(x)
|
|
x = self.fc2(x)
|
|
x = self.drop(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,
|
|
gate_layer=None, drop=0.):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
self.fc1 = nn.Linear(in_features, hidden_features)
|
|
self.act = act_layer()
|
|
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.fc2 = nn.Linear(hidden_features, out_features)
|
|
self.drop = nn.Dropout(drop)
|
|
|
|
def forward(self, x):
|
|
x = self.fc1(x)
|
|
x = self.act(x)
|
|
x = self.drop(x)
|
|
x = self.gate(x)
|
|
x = self.fc2(x)
|
|
x = self.drop(x)
|
|
return x
|
|
|
|
|
|
class ConvMlp(nn.Module):
|
|
""" MLP using 1x1 convs that keeps spatial dims
|
|
"""
|
|
def __init__(
|
|
self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, norm_layer=None, drop=0.):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=True)
|
|
self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
|
|
self.act = act_layer()
|
|
self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=True)
|
|
self.drop = nn.Dropout(drop)
|
|
|
|
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
|