98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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#
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# This source code is licensed under the BSD license found in the
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# LICENSE file in the root directory of this source tree.
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# CREDITS: Largely reusing the code from the reference VAN implementation
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# see https://github.com/Visual-Attention-Network
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import math
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from dataclasses import dataclass
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from typing import Optional
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import torch.nn as nn
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from xformers.components import Activation, build_activation
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from xformers.components.feedforward import Feedforward, FeedforwardConfig
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from . import register_feedforward
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@dataclass
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class ConvMlpConfig(FeedforwardConfig):
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hidden_layer_multiplier: int
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dim_model: int
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dim_model_out: Optional[int]
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act_layer: Activation
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dropout: float
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@register_feedforward("Conv2DFeedforward", ConvMlpConfig)
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class Conv2DFeedforward(Feedforward):
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"""
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A Convolutional feed-forward network, as proposed in VAN_ (Vision Attention Network, Guo et al.)
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.. _VAN: https://arxiv.org/pdf/2202.09741.pdf
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"""
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def __init__(
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self,
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dim_model: int,
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hidden_layer_multiplier: int = 1,
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dim_model_out: Optional[int] = None,
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activation: Activation = Activation.GeLU,
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dropout=0.0,
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*args,
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**kwargs,
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):
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super().__init__()
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out_features = dim_model_out or dim_model
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hidden_features = hidden_layer_multiplier * dim_model
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self.conv_mlp = nn.Sequential(
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nn.Conv2d(dim_model, hidden_features, 1),
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nn.Conv2d(
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hidden_features,
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hidden_features,
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3,
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1,
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1,
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bias=True,
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groups=hidden_features,
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),
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build_activation(activation),
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nn.Conv2d(hidden_features, out_features, 1),
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nn.Dropout(dropout),
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)
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# This feedforward requires a context length which is squared, often due to 2D pooling
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self.requires_squared_context = True
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def init_weights(self, **kwargs):
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# Follow the original init, but also make it possible to initialize from the outside
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def init_module(m: nn.Module):
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if isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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self.apply(init_module)
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def forward(self, x):
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# The conv layers expect NCHW, we have NLC by default
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B, L, C = x.shape
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HW = int(math.sqrt(x.shape[-2]))
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assert HW**2 == L, "Conv2DFeedforward requires squared context lengths"
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x = x.reshape((B, HW, HW, C)).swapdims(1, -1)
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# The actual FW, including the 2d convolutions
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x = self.conv_mlp(x)
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# back to NLC
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x = x.transpose(1, -1)
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return x.flatten(1, 2)
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