may be a good project to try tensortype

q_transformer/q_transformer.py

@@ -9,6 +9,8 @@
 from torch.nn import Module, ModuleList
 from torch.utils.data import Dataset, DataLoader
 
+from torchtyping import TensorType
+
 from einops import rearrange, repeat, pack, unpack
 from einops.layers.torch import Rearrange
 
@@ -232,14 +234,13 @@ def get_discount_matrix(self, timestep):
 
     def q_learn(
         self,
-        instructions: Tuple[str],
-        states: Tensor,
-        actions: Tensor,
-        next_states: Tensor,
-        reward: Tensor,
-        done: Tensor
+        instructions:   Tuple[str],
+        states:         TensorType['b', 'c', 'f', 'h', 'w', float],
+        actions:        TensorType['b', int],
+        next_states:    TensorType['b', 'c', 'f', 'h', 'w', float],
+        reward:         TensorType['b', float],
+        done:           TensorType['b', bool]
     ) -> Tuple[Tensor, QIntermediates]:
-
         # 'next' stands for the very next time step (whether state, q, actions etc)
 
         γ = self.discount_factor_gamma
@@ -270,12 +271,12 @@ def q_learn(
 
     def n_step_q_learn(
         self,
-        instructions: Tuple[str],
-        states: Tensor,
-        actions: Tensor,
-        next_states: Tensor,
-        rewards: Tensor,
-        dones: Tensor
+        instructions:   Tuple[str],
+        states:         TensorType['b', 't', 'c', 'f', 'h', 'w', float],
+        actions:        TensorType['b', 't', int],
+        next_states:    TensorType['b', 'c', 'f', 'h', 'w', float],
+        rewards:        TensorType['b', 't', float],
+        dones:          TensorType['b', 't', bool]
     ) -> Tuple[Tensor, QIntermediates]:
         """
         einops
@@ -285,8 +286,8 @@ def n_step_q_learn(
         f - frames
         h - height
         w - width
-        a - action bins
         t - timesteps
+        q - q values
         """
 
         num_timesteps, device = states.shape[1], states.device

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'q-transformer',
   packages = find_packages(exclude=[]),
-  version = '0.0.8',
+  version = '0.0.9',
   license='MIT',
   description = 'Q-Transformer',
   author = 'Phil Wang',
@@ -23,6 +23,7 @@
     'einops>=0.7.0',
     'ema-pytorch>=0.3.1',
     'classifier-free-guidance-pytorch>=0.1.4',
+    'torchtyping',
     'torch>=2.0'
   ],
   classifiers=[