110 lines
4.4 KiB
Python
110 lines
4.4 KiB
Python
from argparse import Namespace
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import warnings
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import torch
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from torch import nn
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from ml_lib.modules.blocks import TransformerModule
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from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x, SlidingWindow)
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from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
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BaseDataloadersMixin)
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MIN_NUM_PATCHES = 16
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class SequentialVisualTransformer(BinaryMaskDatasetMixin,
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BaseDataloadersMixin,
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BaseTrainMixin,
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BaseValMixin,
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BaseOptimizerMixin,
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LightningBaseModule
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):
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def __init__(self, hparams):
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super(SequentialVisualTransformer, self).__init__(hparams)
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# Dataset
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# =============================================================================
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self.dataset = self.build_dataset()
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self.in_shape = self.dataset.train_dataset.sample_shape
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assert len(self.in_shape) == 3, 'There need to be three Dimensions'
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channels, height, width = self.in_shape
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# Model Paramters
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# =============================================================================
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# Additional parameters
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self.embed_dim = self.params.embedding_size
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self.patch_size = self.params.patch_size
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self.height = height
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self.width = width
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self.channels = channels
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self.new_width = ((self.width - self.patch_size)//1) + 1
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num_patches = self.new_width - (self.patch_size // 2)
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patch_dim = channels * self.patch_size * self.height
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assert num_patches >= MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for ' + \
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f'attention. Try decreasing your patch size'
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# Correct the Embedding Dim
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if not self.embed_dim % self.params.heads == 0:
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self.embed_dim = (self.embed_dim // self.params.heads) * self.params.heads
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message = ('Embedding Dimension was fixed to be devideable by the number' +
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f' of attention heads, is now: {self.embed_dim}')
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for func in print, warnings.warn:
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func(message)
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# Utility Modules
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self.autopad = AutoPadToShape((self.height, self.new_width))
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self.dropout = nn.Dropout(self.params.dropout)
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self.slider = SlidingWindow((channels, *self.autopad.target_shape), (self.height, self.patch_size), keepdim=False)
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# Modules with Parameters
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self.transformer = TransformerModule(in_shape=self.embed_dim, hidden_size=self.params.lat_dim,
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n_heads=self.params.heads, num_layers=self.params.attn_depth,
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dropout=self.params.dropout, use_norm=self.params.use_norm,
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activation=self.params.activation_as_string
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)
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, self.embed_dim))
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self.patch_to_embedding = nn.Linear(patch_dim, self.embed_dim) if self.params.embedding_size \
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else F_x(self.embed_dim)
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self.cls_token = nn.Parameter(torch.randn(1, 1, self.embed_dim))
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self.to_cls_token = nn.Identity()
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self.mlp_head = nn.Sequential(
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nn.LayerNorm(self.embed_dim),
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nn.Linear(self.embed_dim, self.params.lat_dim),
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nn.GELU(),
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nn.Dropout(self.params.dropout),
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nn.Linear(self.params.lat_dim, 1),
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nn.Sigmoid()
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)
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def forward(self, x, mask=None):
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"""
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:param x: the sequence to the encoder (required).
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:param mask: the mask for the src sequence (optional).
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:return:
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"""
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tensor = self.autopad(x)
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tensor = self.slider(tensor)
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tensor = self.patch_to_embedding(tensor)
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b, n, _ = tensor.shape
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# cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
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cls_tokens = self.cls_token.repeat((b, 1, 1))
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tensor = torch.cat((cls_tokens, tensor), dim=1)
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tensor += self.pos_embedding[:, :(n + 1)]
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tensor = self.dropout(tensor)
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tensor = self.transformer(tensor, mask)
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tensor = self.to_cls_token(tensor[:, 0])
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tensor = self.mlp_head(tensor)
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return Namespace(main_out=tensor)
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