auto-generating sphinx docs

pytorch · Feb 3, 2025 · 14dfdd5 · 14dfdd5
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diff --git a/main/_downloads/0935354946f64f2fcf766478aa4572c3/tensordict_module.zip b/main/_downloads/0935354946f64f2fcf766478aa4572c3/tensordict_module.zip
diff --git a/main/_downloads/150528e38f6816824f1e81ed67476a9f/export.zip b/main/_downloads/150528e38f6816824f1e81ed67476a9f/export.zip
diff --git a/main/_downloads/30cf47ff46cc4701c97ed46f4d91a0f2/tensordict_keys.zip b/main/_downloads/30cf47ff46cc4701c97ed46f4d91a0f2/tensordict_keys.zip
diff --git a/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip
diff --git a/main/_downloads/5b6beccb0b7dcf81c69b35cec5a445ee/tensorclass_imagenet.zip b/main/_downloads/5b6beccb0b7dcf81c69b35cec5a445ee/tensorclass_imagenet.zip
diff --git a/main/_downloads/71264da051c82d595130ca8e60350024/tensordict_shapes.zip b/main/_downloads/71264da051c82d595130ca8e60350024/tensordict_shapes.zip
diff --git a/main/_downloads/777fb49680f0121bef451f089b823d24/tensorclass_fashion.zip b/main/_downloads/777fb49680f0121bef451f089b823d24/tensorclass_fashion.zip
diff --git a/main/_downloads/89ddb148138028541dafab3ced0e0421/tensordict_memory.zip b/main/_downloads/89ddb148138028541dafab3ced0e0421/tensordict_memory.zip
diff --git a/main/_downloads/a3a53a08487286803e7e9713be78fb54/tensordict_preallocation.zip b/main/_downloads/a3a53a08487286803e7e9713be78fb54/tensordict_preallocation.zip
diff --git a/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip
diff --git a/main/_downloads/bddca9d9cab285e6fedc82c7769291ef/data_fashion.zip b/main/_downloads/bddca9d9cab285e6fedc82c7769291ef/data_fashion.zip
diff --git a/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip b/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip
diff --git a/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip b/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip
diff --git a/...ces/reference/generated/tensordict.nn.ProbabilisticTensorDictSequential.rst.txt b/...ces/reference/generated/tensordict.nn.ProbabilisticTensorDictSequential.rst.txt
@@ -0,0 +1,8 @@
+.. currentmodule:: tensordict.nn
+
+
+ProbabilisticTensorDictSequential
+=================================
+
+.. autoclass:: ProbabilisticTensorDictSequential
+    :members:
diff --git a/main/_sources/reference/generated/tensordict.nn.rand_one_hot.rst.txt b/main/_sources/reference/generated/tensordict.nn.rand_one_hot.rst.txt
@@ -0,0 +1,6 @@
+tensordict.nn.rand\_one\_hot
+============================
+
+.. currentmodule:: tensordict.nn
+
+.. autofunction:: rand_one_hot
diff --git a/main/_sources/reference/generated/tensordict.nn.set_interaction_type.rst.txt b/main/_sources/reference/generated/tensordict.nn.set_interaction_type.rst.txt
diff --git a/main/_sources/reference/nn.rst.txt b/main/_sources/reference/nn.rst.txt
@@ -194,6 +194,7 @@ to build distributions from network outputs and get summary statistics or sample
     TensorDictModuleBase
     TensorDictModule
     ProbabilisticTensorDictModule
+    ProbabilisticTensorDictSequential
     TensorDictSequential
     TensorDictModuleWrapper
     CudaGraphModule
@@ -257,6 +258,10 @@ Distributions
     NormalParamExtractor
     OneHotCategorical
     TruncatedNormal
+    InteractionType
+    set_interaction_type
+    add_custom_mapping
+    mappings
 
 
 Utils
@@ -270,8 +275,8 @@ Utils
 
     make_tensordict
     dispatch
-    set_interaction_type
     inv_softplus
     biased_softplus
     set_skip_existing
     skip_existing
+    rand_one_hot
diff --git a/main/_sources/sg_execution_times.rst.txt b/main/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:26.885** total execution time for 11 files **from all galleries**:
+**02:26.490** total execution time for 11 files **from all galleries**:
 
 .. container::
 
@@ -33,22 +33,22 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_tutorials_tensorclass_fashion.py` (``reference/generated/tutorials/tensorclass_fashion.py``)
-     - 01:00.697
+     - 01:00.417
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:55.185
+     - 00:55.141
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:16.816
+     - 00:16.763
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
      - 00:11.021
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.663
+     - 00:01.648
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.474
+     - 00:01.471
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``reference/generated/tutorials/tensordict_keys.py``)
      - 00:00.009

diff --git a/main/_sources/tutorials/data_fashion.rst.txt b/main/_sources/tutorials/data_fashion.rst.txt
@@ -423,164 +423,164 @@ adjust how we unpack the data to the more explicit key-based retrieval offered b
         is_shared=False)
     Epoch 1
     -------------------------
-    loss: 2.316633 [    0/60000]
-    loss: 2.307225 [ 6400/60000]
-    loss: 2.285488 [12800/60000]
-    loss: 2.274556 [19200/60000]
-    loss: 2.267080 [25600/60000]
-    loss: 2.228321 [32000/60000]
-    loss: 2.242993 [38400/60000]
-    loss: 2.208107 [44800/60000]
-    loss: 2.199419 [51200/60000]
-    loss: 2.172009 [57600/60000]
+    loss: 2.292876 [    0/60000]
+    loss: 2.283355 [ 6400/60000]
+    loss: 2.263936 [12800/60000]
+    loss: 2.273287 [19200/60000]
+    loss: 2.236377 [25600/60000]
+    loss: 2.214329 [32000/60000]
+    loss: 2.223937 [38400/60000]
+    loss: 2.185483 [44800/60000]
+    loss: 2.176149 [51200/60000]
+    loss: 2.157408 [57600/60000]
     Test Error: 
-     Accuracy: 27.5%, Avg loss: 2.168821 
+     Accuracy: 47.8%, Avg loss: 2.149705 
 
     Epoch 2
     -------------------------
-    loss: 2.185052 [    0/60000]
-    loss: 2.180913 [ 6400/60000]
-    loss: 2.122231 [12800/60000]
-    loss: 2.135427 [19200/60000]
-    loss: 2.095721 [25600/60000]
-    loss: 2.026379 [32000/60000]
-    loss: 2.065620 [38400/60000]
-    loss: 1.988877 [44800/60000]
-    loss: 1.983771 [51200/60000]
-    loss: 1.921114 [57600/60000]
+    loss: 2.151449 [    0/60000]
+    loss: 2.149319 [ 6400/60000]
+    loss: 2.089481 [12800/60000]
+    loss: 2.119855 [19200/60000]
+    loss: 2.046867 [25600/60000]
+    loss: 1.995784 [32000/60000]
+    loss: 2.019445 [38400/60000]
+    loss: 1.938274 [44800/60000]
+    loss: 1.927196 [51200/60000]
+    loss: 1.870499 [57600/60000]
     Test Error: 
-     Accuracy: 57.6%, Avg loss: 1.919113 
+     Accuracy: 60.4%, Avg loss: 1.868008 
 
     Epoch 3
     -------------------------
-    loss: 1.955521 [    0/60000]
-    loss: 1.929771 [ 6400/60000]
-    loss: 1.815067 [12800/60000]
-    loss: 1.856079 [19200/60000]
-    loss: 1.743889 [25600/60000]
-    loss: 1.686423 [32000/60000]
-    loss: 1.723525 [38400/60000]
-    loss: 1.620473 [44800/60000]
-    loss: 1.631592 [51200/60000]
-    loss: 1.530928 [57600/60000]
+    loss: 1.893928 [    0/60000]
+    loss: 1.873756 [ 6400/60000]
+    loss: 1.753070 [12800/60000]
+    loss: 1.804454 [19200/60000]
+    loss: 1.670851 [25600/60000]
+    loss: 1.636076 [32000/60000]
+    loss: 1.651654 [38400/60000]
+    loss: 1.554687 [44800/60000]
+    loss: 1.565008 [51200/60000]
+    loss: 1.473971 [57600/60000]
     Test Error: 
-     Accuracy: 61.8%, Avg loss: 1.546323 
+     Accuracy: 61.9%, Avg loss: 1.491119 
 
     Epoch 4
     -------------------------
-    loss: 1.619225 [    0/60000]
-    loss: 1.582403 [ 6400/60000]
-    loss: 1.432203 [12800/60000]
-    loss: 1.501907 [19200/60000]
-    loss: 1.371325 [25600/60000]
-    loss: 1.368950 [32000/60000]
-    loss: 1.392523 [38400/60000]
-    loss: 1.314770 [44800/60000]
-    loss: 1.336692 [51200/60000]
-    loss: 1.240931 [57600/60000]
+    loss: 1.556790 [    0/60000]
+    loss: 1.530334 [ 6400/60000]
+    loss: 1.377608 [12800/60000]
+    loss: 1.457017 [19200/60000]
+    loss: 1.323445 [25600/60000]
+    loss: 1.330096 [32000/60000]
+    loss: 1.343076 [38400/60000]
+    loss: 1.268062 [44800/60000]
+    loss: 1.295799 [51200/60000]
+    loss: 1.211058 [57600/60000]
     Test Error: 
-     Accuracy: 62.7%, Avg loss: 1.263385 
+     Accuracy: 63.9%, Avg loss: 1.231329 
 
     Epoch 5
     -------------------------
-    loss: 1.352172 [    0/60000]
-    loss: 1.330544 [ 6400/60000]
-    loss: 1.164969 [12800/60000]
-    loss: 1.265779 [19200/60000]
-    loss: 1.133228 [25600/60000]
-    loss: 1.163435 [32000/60000]
-    loss: 1.189454 [38400/60000]
-    loss: 1.129025 [44800/60000]
-    loss: 1.154566 [51200/60000]
-    loss: 1.074657 [57600/60000]
+    loss: 1.308637 [    0/60000]
+    loss: 1.296877 [ 6400/60000]
+    loss: 1.127608 [12800/60000]
+    loss: 1.239060 [19200/60000]
+    loss: 1.105856 [25600/60000]
+    loss: 1.137060 [32000/60000]
+    loss: 1.160426 [38400/60000]
+    loss: 1.095086 [44800/60000]
+    loss: 1.129022 [51200/60000]
+    loss: 1.059775 [57600/60000]
     Test Error: 
-     Accuracy: 64.0%, Avg loss: 1.091707 
+     Accuracy: 65.1%, Avg loss: 1.072701 
 
-    TensorDict training done! time:  8.6239 s
+    TensorDict training done! time:  8.5516 s
     Epoch 1
     -------------------------
-    loss: 2.303011 [    0/60000]
-    loss: 2.287436 [ 6400/60000]
-    loss: 2.274800 [12800/60000]
-    loss: 2.269296 [19200/60000]
-    loss: 2.256683 [25600/60000]
-    loss: 2.223480 [32000/60000]
-    loss: 2.236421 [38400/60000]
-    loss: 2.202974 [44800/60000]
-    loss: 2.200173 [51200/60000]
-    loss: 2.161506 [57600/60000]
+    loss: 2.309895 [    0/60000]
+    loss: 2.290487 [ 6400/60000]
+    loss: 2.263228 [12800/60000]
+    loss: 2.248584 [19200/60000]
+    loss: 2.248923 [25600/60000]
+    loss: 2.215442 [32000/60000]
+    loss: 2.225330 [38400/60000]
+    loss: 2.191122 [44800/60000]
+    loss: 2.185660 [51200/60000]
+    loss: 2.156927 [57600/60000]
     Test Error: 
-     Accuracy: 46.0%, Avg loss: 2.161499 
+     Accuracy: 35.7%, Avg loss: 2.146915 
 
     Epoch 2
     -------------------------
-    loss: 2.178989 [    0/60000]
-    loss: 2.161377 [ 6400/60000]
-    loss: 2.110821 [12800/60000]
-    loss: 2.120728 [19200/60000]
-    loss: 2.080936 [25600/60000]
-    loss: 2.013035 [32000/60000]
-    loss: 2.048943 [38400/60000]
-    loss: 1.974167 [44800/60000]
-    loss: 1.977030 [51200/60000]
-    loss: 1.885004 [57600/60000]
+    loss: 2.162492 [    0/60000]
+    loss: 2.153710 [ 6400/60000]
+    loss: 2.087680 [12800/60000]
+    loss: 2.101584 [19200/60000]
+    loss: 2.071230 [25600/60000]
+    loss: 2.003889 [32000/60000]
+    loss: 2.038064 [38400/60000]
+    loss: 1.957708 [44800/60000]
+    loss: 1.964049 [51200/60000]
+    loss: 1.902878 [57600/60000]
     Test Error: 
-     Accuracy: 57.7%, Avg loss: 1.898815 
+     Accuracy: 57.2%, Avg loss: 1.891697 
 
     Epoch 3
     -------------------------
-    loss: 1.948882 [    0/60000]
-    loss: 1.905324 [ 6400/60000]
-    loss: 1.794196 [12800/60000]
-    loss: 1.819826 [19200/60000]
-    loss: 1.724633 [25600/60000]
-    loss: 1.666429 [32000/60000]
-    loss: 1.690138 [38400/60000]
-    loss: 1.601758 [44800/60000]
-    loss: 1.617406 [51200/60000]
-    loss: 1.492366 [57600/60000]
+    loss: 1.927803 [    0/60000]
+    loss: 1.901445 [ 6400/60000]
+    loss: 1.777262 [12800/60000]
+    loss: 1.814932 [19200/60000]
+    loss: 1.728945 [25600/60000]
+    loss: 1.678206 [32000/60000]
+    loss: 1.701154 [38400/60000]
+    loss: 1.600237 [44800/60000]
+    loss: 1.627035 [51200/60000]
+    loss: 1.526987 [57600/60000]
     Test Error: 
-     Accuracy: 61.0%, Avg loss: 1.526873 
+     Accuracy: 60.2%, Avg loss: 1.535037 
 
     Epoch 4
     -------------------------
-    loss: 1.613432 [    0/60000]
-    loss: 1.562069 [ 6400/60000]
-    loss: 1.418200 [12800/60000]
-    loss: 1.472821 [19200/60000]
-    loss: 1.363791 [25600/60000]
-    loss: 1.349146 [32000/60000]
-    loss: 1.369352 [38400/60000]
-    loss: 1.302855 [44800/60000]
-    loss: 1.325800 [51200/60000]
-    loss: 1.219427 [57600/60000]
+    loss: 1.604841 [    0/60000]
+    loss: 1.572237 [ 6400/60000]
+    loss: 1.413552 [12800/60000]
+    loss: 1.477304 [19200/60000]
+    loss: 1.375301 [25600/60000]
+    loss: 1.372313 [32000/60000]
+    loss: 1.381226 [38400/60000]
+    loss: 1.304628 [44800/60000]
+    loss: 1.339577 [51200/60000]
+    loss: 1.242289 [57600/60000]
     Test Error: 
-     Accuracy: 63.9%, Avg loss: 1.253359 
+     Accuracy: 62.6%, Avg loss: 1.262859 
 
     Epoch 5
     -------------------------
-    loss: 1.345231 [    0/60000]
-    loss: 1.315563 [ 6400/60000]
-    loss: 1.153394 [12800/60000]
-    loss: 1.247195 [19200/60000]
-    loss: 1.128013 [25600/60000]
-    loss: 1.140230 [32000/60000]
-    loss: 1.172961 [38400/60000]
-    loss: 1.116331 [44800/60000]
-    loss: 1.143755 [51200/60000]
-    loss: 1.060270 [57600/60000]
+    loss: 1.340297 [    0/60000]
+    loss: 1.329028 [ 6400/60000]
+    loss: 1.153737 [12800/60000]
+    loss: 1.252457 [19200/60000]
+    loss: 1.134847 [25600/60000]
+    loss: 1.167557 [32000/60000]
+    loss: 1.183226 [38400/60000]
+    loss: 1.120279 [44800/60000]
+    loss: 1.156876 [51200/60000]
+    loss: 1.079154 [57600/60000]
     Test Error: 
-     Accuracy: 65.0%, Avg loss: 1.085642 
+     Accuracy: 64.4%, Avg loss: 1.093753 
 
-    Training done! time:  34.3338 s
+    Training done! time:  33.9425 s
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 55.185 seconds)
+   **Total running time of the script:** (0 minutes 55.141 seconds)
 
 
 .. _sphx_glr_download_tutorials_data_fashion.py: