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focalmodulation.py
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import torch
import einops
import numpy as np
from torch.nn import Linear
from torch.nn import Conv1d
from torch.nn import ModuleList
from torch.nn import GELU
from torch.nn import Sigmoid
from torch.nn import Softmax
from torch.nn import Module
from attentionpooling import AttentionPooling1D
class FocalModulation1d(Module):
def __init__(self, dim, focal_levels, bias=True):
super().__init__()
self.dim = dim
self.focal_levels = np.sort(np.unique(focal_levels))
self.level_num = len(focal_levels)
self.toquery = Linear(in_features=dim, out_features=dim, bias=bias)
self.tovalue = Linear(in_features=dim, out_features=dim, bias=bias)
self.togates = Linear(in_features=dim, out_features=self.level_num+1, bias=bias)
self.outprojection = Linear(in_features=dim, out_features=dim, bias=True)
self.activation = GELU()
self.final_activation = Sigmoid()
self.mask_activation = Softmax(dim=-1)
self.focal = ModuleList()
for kl in focal_levels:
self.focal.append(Conv1d(in_channels=dim, out_channels=dim , kernel_size=kl, stride=1, groups=dim, padding=kl//2, bias=False))
self.mix_depth = Conv1d(in_channels=dim, out_channels=dim, kernel_size=1, stride=1, bias=bias)
def forward(self, x):
b, c, l = x.shape
focus = einops.einsum(x, self.tovalue.weight, "batch channel length, embedding channel -> batch embedding length") + self.tovalue.bias.view(1, -1, 1)
gates = einops.einsum(x, self.togates.weight, "batch channel length, gates channel -> batch gates length") + self.togates.bias.view(1, -1, 1)
x = einops.einsum(x, self.toquery.weight, "batch channel length, embedding channel -> batch embedding length") + self.toquery.bias.view(1, -1, 1)
focus = self.activation(self.focal[0](focus))
focus_sum = einops.einsum(focus, gates[:, 0, :].view(b,l), "batch embedding length, batch length -> batch embedding length")
for i, layer in enumerate(self.focal[1:], start=1):
focus = self.activation(layer(focus))
focus_sum = focus_sum + einops.einsum(focus, gates[:, i, :].view(b,l), "batch embedding length, batch length -> batch embedding length")
global_focus = self.activation(torch.mean(focus, axis=2, keepdim=True))
focus_sum = focus_sum + einops.einsum(global_focus, gates[:, self.level_num, :].view(b,l), "batch embedding length, batch length -> batch embedding length")
focus_sum = self.mix_depth(focus_sum)
mask = self.mask_activation(focus_sum)
x = self.final_activation(x) * mask
return x, mask#einops.einsum(x, self.outprojection.weight, "batch embedding length, channel embedding -> batch channel length") + self.outprojection.bias.view(1, -1, 1)
class FocalModulationviaPooling1d(Module):
def __init__(self, dim, focal_levels, bias=True):
super().__init__()
self.dim = dim
self.focal_levels = np.sort(np.unique(focal_levels))
self.level_num = len(focal_levels)
self.togates = Linear(in_features=dim, out_features=self.level_num+2, bias=bias)
self.activation = GELU()
self.mask_activation = Softmax(dim=-1)
self.channel_mask = Softmax(dim=-2)
self.focal = ModuleList()
for kl in focal_levels:
self.focal.append(AttentionPooling1D(kernel_size=kl, feature_size=dim, keep_shape=True))
self.mix_depth = Conv1d(in_channels=dim, out_channels=dim, kernel_size=1, stride=1, bias=bias)
def forward(self, x):
b, c, l = x.shape
gates = einops.einsum(torch.max(x, axis=-1)[0], self.togates.weight, "batch channel, gates channel -> batch gates") + self.togates.bias.view(1, -1)
gates = self.mask_activation(gates)
focus_sum = einops.einsum(x, gates[:, 0], "batch embedding length, batch -> batch embedding length")
for i, layer in enumerate(self.focal):
focus_sum = focus_sum + einops.einsum(layer(x)[0], gates[:, i+1], "batch embedding length, batch -> batch embedding length")
global_focus = torch.mean(x, axis=2, keepdim=True)
focus_sum = focus_sum + einops.einsum(global_focus, gates[:, self.level_num+1], "batch embedding length, batch -> batch embedding length")
focus_sum = self.mix_depth(focus_sum)
return self.channel_mask(focus_sum) #einops.einsum(x, self.outprojection.weight, "batch embedding length, channel embedding -> batch channel length") + self.outprojection.bias.view(1, -1, 1)
class FocalModulationMask1d(Module):
def __init__(self, dim, focal_levels, kernel_length, bias=True):
super().__init__()
self.dim = dim
self.focal_levels = np.sort(np.unique(focal_levels))
self.level_num = len(focal_levels)
self.kernel_length = kernel_length
#self.tovalue = Linear(in_features=dim, out_features=dim, bias=bias)
self.togates = Linear(in_features=dim, out_features=self.level_num+2, bias=bias)
self.outprojection = Linear(in_features=dim, out_features=dim, bias=True)
self.activation = GELU()
self.final_activation = Sigmoid()
#self.mask_activation = Softmax(dim=-1)
self.focal = ModuleList()
for kl in focal_levels:
self.focal.append(Conv1d(in_channels=dim, out_channels=dim , kernel_size=kl, stride=1, groups=dim, padding=kl//2, bias=False))
self.mix_depth = Conv1d(in_channels=dim, out_channels=dim, kernel_size=1, stride=1, bias=bias)
self.final_pooling = AttentionPooling1D(kernel_size=kernel_length, feature_size=dim)
def forward(self, x):
b, c, l = x.shape
gates = einops.einsum(torch.max(x, axis=-1)[0], self.togates.weight, "batch channel, gates channel -> batch gates") + self.togates.bias.view(1, -1)
#gates = self.mask_activation(gates)
focus = x
focus_sum = einops.einsum(x, gates[:, 0], "batch embedding length, batch -> batch embedding length")
for i, layer in enumerate(self.focal, start=1):
focus = self.activation(layer(focus))
focus_sum = focus_sum + einops.einsum(focus, gates[:, i], "batch embedding length, batch -> batch embedding length")
global_focus = self.activation(torch.mean(focus, axis=2, keepdim=True))
focus_sum = focus_sum + einops.einsum(global_focus, gates[:, self.level_num+1], "batch embedding length, batch -> batch embedding length")
focus_sum = self.final_pooling(self.mix_depth(focus_sum))[0].view(b,c)
return self.final_activation(self.outprojection(focus_sum))