Updated todos in readme

2 files changed, 15 insertions, 33 deletions

diff --git a/README.md b/README.md
index dac7e98..ed93955 100644
--- a/README.md
+++ b/README.md
@@ -11,44 +11,26 @@ TBC
 
 Extract text from the iam dataset:
 ```
-poetry run extract-iam-text --use_words --save_text train.txt --save_tokens letters.txt
+poetry run python extract-iam-text --use_words --save_text train.txt --save_tokens letters.txt
 ```
 
 Create word pieces from the extracted training text:
 ```
-poetry run make-wordpieces --output_prefix iamdb_1kwp --text_file train.txt --num_pieces 100
+poetry run python make-wordpieces --output_prefix iamdb_1kwp --text_file train.txt --num_pieces 100
 ```
 
 Optionally, build a transition graph for word pieces:
 ```
-poetry run build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kwp_lex_1000.txt --blank optional --self_loops --save_path 1kwp_prune_0_10_optblank.bin --prune 0 10
+poetry run python build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kwp_lex_1000.txt --blank optional --self_loops --save_path 1kwp_prune_0_10_optblank.bin --prune 0 10
 ```
 (TODO: Not working atm, needed for GTN loss function)
 
 ## Todo
-- [x] create wordpieces
-  - [x] make_wordpieces.py
-  - [x] build_transitions.py
-  - [x] transform that encodes iam targets to wordpieces
-  - [x] transducer loss function
-- [ ] Train with word pieces
-  - [ ] Pad word pieces index to same length
-- [ ] Local attention in first layer of transformer
-- [ ] Halonet encoder
-- [ ] Implement CPC
-  - [ ] https://arxiv.org/pdf/1905.09272.pdf
-  - [ ] https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html?highlight=byol
-
-
-- [ ] Predictive coding
-  - https://arxiv.org/pdf/1807.03748.pdf
-  - https://arxiv.org/pdf/1904.05862.pdf
-  - https://arxiv.org/pdf/1910.05453.pdf
-  - https://blog.evjang.com/2016/11/tutorial-categorical-variational.html
-
-
-
-
+- [ ] Reimplement transformer from scratch
+- [ ] Implement Nyström attention (for efficient attention)
+- [ ] Dino 
+- [ ] Efficient-net b0 + transformer decoder
+- [ ] Test encoder pre-training ViT (CvT?) with Dino, then train decoder in a separate step
 
 
 ## Run Sweeps
diff --git a/notebooks/00-testing-stuff-out.ipynb b/notebooks/00-testing-stuff-out.ipynb
index 92faaf7..12c5145 100644
--- a/notebooks/00-testing-stuff-out.ipynb
+++ b/notebooks/00-testing-stuff-out.ipynb
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,26 +487,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "patch_size=4\n",
+    "patch_size=16\n",
     "p = rearrange(x, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([1, 1440, 16])"
+       "torch.Size([1, 1440, 256])"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }