New Model, Many Changes

models/transformer_model_sequential.py

@@ -5,8 +5,6 @@ import warnings
 import torch
 from torch import nn
 
-from einops import repeat
-
 from ml_lib.modules.blocks import TransformerModule
 from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x, SlidingWindow)
 from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
@@ -39,16 +37,13 @@ class SequentialVisualTransformer(BinaryMaskDatasetMixin,
         self.embed_dim = self.params.embedding_size
         self.patch_size = self.params.patch_size
         self.height = height
+        self.width = width
+        self.channels = channels
 
-        # Automatic Image Shaping
-        image_size = (max(height, width) // self.patch_size) * self.patch_size
-        self.image_size = image_size + self.patch_size if image_size < max(height, width) else image_size
+        self.new_width = ((self.width - self.patch_size)//1) + 1
 
-        # This should be obsolete
-        assert self.image_size % self.patch_size == 0, 'image dimensions must be divisible by the patch size'
-
-        num_patches = (self.image_size // self.patch_size) ** 2
-        patch_dim = channels * self.patch_size * self.image_size
+        num_patches = self.new_width - (self.patch_size // 2)
+        patch_dim = channels * self.patch_size * self.height
         assert num_patches >= MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for ' + \
                                                f'attention. Try decreasing your patch size'
 
@@ -61,9 +56,9 @@ class SequentialVisualTransformer(BinaryMaskDatasetMixin,
                 func(message)
 
         # Utility Modules
-        self.autopad = AutoPadToShape((self.image_size, self.image_size))
+        self.autopad = AutoPadToShape((self.height, self.new_width))
         self.dropout = nn.Dropout(self.params.dropout)
-        self.slider = SlidingWindow((self.image_size, self.patch_size), keepdim=False)
+        self.slider = SlidingWindow((channels, *self.autopad.target_shape), (self.height, self.patch_size), keepdim=False)
 
         # Modules with Parameters
         self.transformer = TransformerModule(in_shape=self.embed_dim, hidden_size=self.params.lat_dim,