317 lines
12 KiB
Python
317 lines
12 KiB
Python
import warnings
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from pathlib import Path
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from typing import Union
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import torch
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from torch import nn
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from torch.nn import functional as F
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from einops import rearrange
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import sys
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sys.path.append(str(Path(__file__).parent))
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from .util import AutoPad, Interpolate, ShapeMixin, F_x, Flatten
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DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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#
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# Sub - Modules
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###################
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class LinearModule(ShapeMixin, nn.Module):
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def __init__(self, in_shape, out_features, use_bias=True, activation=None,
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use_norm=False, dropout: Union[int, float] = 0, **kwargs):
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if list(kwargs.keys()):
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warnings.warn(f'The following arguments have been ignored: \n {list(kwargs.keys())}')
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super(LinearModule, self).__init__()
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self.in_shape = in_shape
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self.flat = Flatten(self.in_shape) if isinstance(self.in_shape, (tuple, list)) else F_x(in_shape)
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self.dropout = nn.Dropout(dropout) if dropout else F_x(self.flat.shape)
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self.norm = nn.LayerNorm(self.flat.shape) if use_norm else F_x(self.flat.shape)
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self.linear = nn.Linear(self.flat.shape, out_features, bias=use_bias)
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self.activation = activation() if activation else F_x(self.linear.out_features)
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def forward(self, x):
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tensor = self.flat(x)
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tensor = self.dropout(tensor)
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tensor = self.norm(tensor)
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tensor = self.linear(tensor)
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tensor = self.activation(tensor)
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return tensor
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class ConvModule(ShapeMixin, nn.Module):
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def __init__(self, in_shape, conv_filters, conv_kernel, activation: nn.Module = nn.ELU, pooling_size=None,
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bias=True, use_norm=False, dropout: Union[int, float] = 0, trainable: bool = True,
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conv_class=nn.Conv2d, conv_stride=1, conv_padding=0, **kwargs):
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super(ConvModule, self).__init__()
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assert isinstance(in_shape, (tuple, list)), f'"in_shape" should be a [list, tuple], but was {type(in_shape)}'
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assert len(in_shape) == 3, f'Length should be 3, but was {len(in_shape)}'
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if len(kwargs.keys()):
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warnings.warn(f'The following arguments have been ignored: \n {list(kwargs.keys())}')
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if use_norm and not trainable:
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warnings.warn('You set this module to be not trainable but the running norm is active.\n' +
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'We set it to "eval" mode.\n' +
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'Keep this in mind if you do a finetunning or retraining step.'
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)
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# Module Parameters
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self.in_shape = in_shape
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self.trainable = trainable
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in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
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# Convolution Parameters
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self.padding = conv_padding
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self.stride = conv_stride
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self.conv_filters = conv_filters
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self.conv_kernel = conv_kernel
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# Modules
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self.activation = activation() or nn.Identity()
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self.norm = nn.LayerNorm(self.in_shape, eps=1e-04) if use_norm else F_x(None)
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self.dropout = nn.Dropout2d(dropout) if dropout else F_x(None)
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self.pooling = nn.MaxPool2d(pooling_size) if pooling_size else F_x(None)
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self.conv = conv_class(in_channels, self.conv_filters, self.conv_kernel, bias=bias,
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padding=self.padding, stride=self.stride
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)
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if not self.trainable:
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for param in self.parameters():
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param.requires_grad = False
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self.norm = self.norm.eval()
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else:
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pass
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def forward(self, x):
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tensor = self.norm(x)
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tensor = self.conv(tensor)
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tensor = self.dropout(tensor)
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tensor = self.pooling(tensor)
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tensor = self.activation(tensor)
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return tensor
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class PreInitializedConvModule(ShapeMixin, nn.Module):
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def __init__(self, in_shape, weight_matrix):
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super(PreInitializedConvModule, self).__init__()
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self.in_shape = in_shape
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self.weight_matrix = weight_matrix
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raise NotImplementedError
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# ToDo Get the weight_matrix shape and init a conv_module of similar size,
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# override the weights then.
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def forward(self, x):
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x = torch.matmul(x, self.weight_matrix) # ToDo: This is an Placeholder
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return x
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class SobelFilter(ShapeMixin, nn.Module):
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def __init__(self, in_shape):
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super(SobelFilter, self).__init__()
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self.in_shape = in_shape
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self.sobel_x = torch.tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]).view(1, 1, 3, 3)
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self.sobel_y = torch.tensor([[1, 2, 1], [0, 0, 0], [-1, 2, -1]]).view(1, 1, 3, 3)
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def forward(self, x):
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# Apply Filters
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g_x = F.conv2d(x, self.sobel_x)
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g_y = F.conv2d(x, self.sobel_y)
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# Calculate the Edge
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g = torch.add(*[torch.pow(tensor, 2) for tensor in [g_x, g_y]])
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# Calculate the Gradient
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g_grad = torch.atan2(g_x, g_y)
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return g_x, g_y, g, g_grad
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class DeConvModule(ShapeMixin, nn.Module):
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def __init__(self, in_shape, conv_filters, conv_kernel, conv_stride=1, conv_padding=0,
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dropout: Union[int, float] = 0, autopad=0,
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activation: Union[None, nn.Module] = nn.ReLU, interpolation_scale=0,
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bias=True, use_norm=False, **kwargs):
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super(DeConvModule, self).__init__()
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warnings.warn(f'The following arguments have been ignored: \n {list(kwargs.keys())}')
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in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
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self.padding = conv_padding
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self.conv_kernel = conv_kernel
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self.stride = conv_stride
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self.in_shape = in_shape
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self.conv_filters = conv_filters
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self.autopad = AutoPad() if autopad else lambda x: x
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self.interpolation = Interpolate(scale_factor=interpolation_scale) if interpolation_scale else lambda x: x
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self.norm = nn.LayerNorm(in_channels, eps=1e-04) if use_norm else F_x(self.in_shape)
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self.dropout = nn.Dropout2d(dropout) if dropout else F_x(self.in_shape)
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self.de_conv = nn.ConvTranspose2d(in_channels, self.conv_filters, self.conv_kernel, bias=bias,
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padding=self.padding, stride=self.stride)
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self.activation = activation() if activation else lambda x: x
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def forward(self, x):
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x = self.norm(x)
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x = self.dropout(x)
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x = self.autopad(x)
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x = self.interpolation(x)
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tensor = self.de_conv(x)
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tensor = self.activation(tensor)
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return tensor
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class ResidualModule(ShapeMixin, nn.Module):
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def __init__(self, in_shape, module_class, n, use_norm=False, **module_parameters):
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assert n >= 1
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super(ResidualModule, self).__init__()
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self.in_shape = in_shape
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module_parameters.update(in_shape=in_shape)
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if use_norm:
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self.norm = nn.LayerNorm(self.in_shape if isinstance(self.in_shape, int) else self.in_shape[0])
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else:
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self.norm = F_x(self.in_shape)
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self.activation = module_parameters.get('activation', None)
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if self.activation is not None:
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self.activation = self.activation()
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else:
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self.activation = F_x(self.in_shape)
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self.residual_block = nn.ModuleList([module_class(**module_parameters) for _ in range(n)])
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assert self.in_shape == self.shape, f'The in_shape: {self.in_shape} - must match the out_shape: {self.shape}.'
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def forward(self, x):
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tensor = self.norm(x)
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for module in self.residual_block:
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tensor = module(tensor)
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# noinspection PyUnboundLocalVariable
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tensor = tensor + x
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tensor = self.activation(tensor)
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return tensor
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class RecurrentModule(ShapeMixin, nn.Module):
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def __init__(self, in_shape, hidden_size, num_layers=1, cell_type=nn.GRU, bias=True, dropout=0):
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super(RecurrentModule, self).__init__()
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self.bias = bias
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self.num_layers = num_layers
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self.in_shape = in_shape
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self.hidden_size = hidden_size
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self.dropout = dropout
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self.rnn = cell_type(self.in_shape[-1] * self.in_shape[-2], hidden_size,
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num_layers=num_layers,
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bias=self.bias,
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batch_first=True,
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dropout=self.dropout)
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def forward(self, x):
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tensor = self.rnn(x)
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return tensor
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class FeedForward(nn.Module):
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def __init__(self, dim, hidden_dim, dropout=0., activation=nn.GELU):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(dim, hidden_dim),
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activation() or F_x(None),
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nn.Dropout(dropout),
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nn.Linear(hidden_dim, dim),
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activation() or F_x(None),
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nn.Dropout(dropout)
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)
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def forward(self, x):
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return self.net(x)
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class Attention(nn.Module):
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def __init__(self, dim, heads=8, head_dim=64, dropout=0.):
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super().__init__()
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inner_dim = head_dim * heads
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project_out = not (heads == 1 and head_dim == dim)
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self.heads = heads
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self.scale = head_dim ** -0.5
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self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, dim),
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nn.Dropout(dropout)
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) if project_out else nn.Identity()
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def forward(self, x, mask=None, return_attn_weights=False):
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# noinspection PyTupleAssignmentBalance
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b, n, _, h = *x.shape, self.heads
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qkv = self.to_qkv(x).chunk(3, dim=-1)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), qkv)
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dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
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if mask is not None:
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mask_value = -torch.finfo(dots.dtype).max
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mask = F.pad(mask.flatten(1), (1, 0), value=True)
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assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'
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mask = mask[:, None, :] * mask[:, :, None]
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dots.masked_fill_(~mask, mask_value)
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del mask
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attn = dots.softmax(dim=-1)
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out = torch.einsum('bhij,bhjd->bhid', attn, v)
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out = rearrange(out, 'b h n d -> b n (h d)')
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out = self.to_out(out)
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if return_attn_weights:
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return out, attn
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else:
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return out
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class TransformerModule(ShapeMixin, nn.Module):
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def __init__(self, in_shape, depth, heads, mlp_dim, head_dim=32, dropout=None, use_norm=False,
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activation=nn.GELU, use_residual=True):
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super(TransformerModule, self).__init__()
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self.in_shape = in_shape
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self.use_residual = use_residual
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self.flat = Flatten(self.in_shape) if isinstance(self.in_shape, (tuple, list)) else F_x(in_shape)
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self.embedding_dim = self.flat.flat_shape
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self.norm = nn.LayerNorm(self.embedding_dim) if use_norm else F_x(None)
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self.attns = nn.ModuleList([Attention(self.embedding_dim, heads=heads, dropout=dropout, head_dim=head_dim)
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for _ in range(depth)])
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self.mlps = nn.ModuleList([FeedForward(self.embedding_dim, mlp_dim, dropout=dropout, activation=activation)
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for _ in range(depth)])
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def forward(self, x, mask=None, return_attn_weights=False, **_):
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tensor = self.flat(x)
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attn_weights = list()
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for attn, mlp in zip(self.attns, self.mlps):
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# Attention
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attn_tensor = self.norm(tensor)
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if return_attn_weights:
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attn_tensor, attn_weight = attn(attn_tensor, mask=mask, return_attn_weights=return_attn_weights)
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attn_weights.append(attn_weight)
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else:
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attn_tensor = attn(attn_tensor, mask=mask)
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tensor = tensor + attn_tensor if self.use_residual else attn_tensor
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# MLP
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mlp_tensor = self.norm(tensor)
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mlp_tensor = mlp(mlp_tensor)
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tensor = tensor + mlp_tensor if self.use_residual else mlp_tensor
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return (tensor, attn_weights) if return_attn_weights else tensor
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