pytorch lightning Fail

Following code stop with

AttributeError: 'builtin_function_or_method' object has no attribute 'code'. Did you mean: 'call'?

(ns pytorchlightning.core
  (:gen-class)

  (:require 
   [libpython-clj2.python :as py
    :refer
    [ py. py.- 
     as-jvm
     set-attr!
     get-item
     ->py-tuple
     ->py-list
     ]]
   
   [libpython-clj2.require :refer [require-python]]
   ))

;;(py/initialize!)

(require-python
 '[builtins :as python]
 '[torch]
 '[torch.nn :as nn :refer [Linear]]
 '[torch.nn.functional :refer [mse_loss]]
 '[torch.utils.data :refer [DataLoader Dataset]]
 '[torch.optim]
 '[pytorch_lightning]
 )


(defonce model (atom nil))

(def LitModel
  (py/create-class
   "LitModel" [pytorch_lightning/LightningModule]
   {"__init__"
    (py/make-tuple-instance-fn
     (fn [self]
       (py. pytorch_lightning/LightningModule  __init__ self)
       (py/set-attr!  self "layer" (Linear 1 1))
       nil))
    
    "forward"
    (py/make-tuple-instance-fn
     (fn [self x] (py. self layer x))
     :arg-converter as-jvm
     :method-name "forward"
     )
    
    "training_step"
    (py/make-tuple-instance-fn
     (fn [self batch batch_idx]
       (mse_loss (py/get-item batch 1) (py. self forward (py/get-item batch 0)))
       ))
    "validation_step"
    (py/make-tuple-instance-fn
     (fn [self batch batch_idx]
       (mse_loss (py/get-item batch 1) (py. self forward (py/get-item batch 0)))
       ))
    "test_step"
    (py/make-tuple-instance-fn
     (fn [self batch batch_idx]
       (mse_loss (py/get-item batch 1) (py. self forward (py/get-item batch 0)))
       ))
    
    "configure_optimizers"
    (py/make-tuple-instance-fn
     (fn [self]
       (torch.optim/SGD
        (py. self parameters)
        :lr 0.02)
       ))
    }))

(def SimpleDataset
  (py/create-class
   "SimpleDataset" [Dataset]
   {"__init__"
    (py/make-tuple-instance-fn
     (fn [self data] (py/set-attr! self "data" data)  nil))
    "__len__"
    (py/make-tuple-instance-fn 
     (fn [self] (python/len (py.- self data))))
    "__getitem__"
    (py/make-tuple-instance-fn
     (fn [self idx]
       (py/->py-tuple
        [
         (torch/tensor [(py/get-item  (py/get-item (py.- self data) idx) 0)] :dtype torch/float32)
         (torch/tensor [(py/get-item  (py/get-item (py.- self data) idx) 1)] :dtype torch/float32)
         ]
        )
       ))
    }))


(def SimpleDataModule
  (py/create-class
   "SimpleDataModule" [pytorch_lightning/LightningDataModule]
   {"__init__"
    (py/make-tuple-instance-fn
     (fn [self data] (py/set-attr! self "data" data)  nil))
    
    "train_dataloader"
    (py/make-tuple-instance-fn
     (fn [self]
       (DataLoader (SimpleDataset (py.- self data )) :batch_size 2 :shuffle false)))
    "val_dataloader"
    (py/make-tuple-instance-fn
     (fn [self]
       (DataLoader (SimpleDataset (py.- self data )) :batch_size 2 :shuffle false)))
    "test_dataloader"
    (py/make-tuple-instance-fn
     (fn [self]
       (DataLoader (SimpleDataset (py.- self data )) :batch_size 2 :shuffle false)))
    }))




(defn -main [& args]

  (reset! model (LitModel))

  (def data [[1.0 3.0] [2.0 5.0] [3.0 7.0] [4.0 9.0] [5.0 11.0]] )
  (def data_pylist (py/->py-list data))

  ;; (println  (py/get-item  (py/get-item data_pylist 0) 0))

  (def train_dataset  (SimpleDataset data_pylist))
  (def train_loader (DataLoader train_dataset :batch_size 2 :shuffle false))

  ;; ;;for debug
  ;; (def train_pylist (python/list train_loader))
  ;; (def train_pylist_0 (py/get-item train_pylist 0))
  ;; (println (py. @model training_step train_pylist_0 0) );;;OK.  maybe this part work

  
  (def trainer (pytorch_lightning/Trainer :max_epochs 10 ))

  (def datamodu (SimpleDataModule data_pylist))
  
  (py. trainer fit @model train_loader)
  ;;(py. trainer fit @model datamodu)  ;;also fail

  )

Commencez à -main et comparez l’appel direct à training_step avec les appels Trainer fit qui échouent, en utilisant les définitions fournies de LitModel, SimpleDataset et SimpleDataModule. Reproduisez l’AttributeError et examinez le traceback pour identifier la frontière d’intégration qui échoue ; c’est terminé lorsque le flux d’entraînement montré fonctionne ou que l’incompatibilité est clairement documentée.