logging via wandb

.gitignore

@@ -182,3 +182,4 @@ scripts/
 logger/*
 explore_data.ipynb
 run.sh
+wandb

gfun/generalizedFunnelling.py

@@ -47,7 +47,7 @@ class GeneralizedFunnelling:
         self.posteriors_vgf = posterior
         self.wce_vgf = wce
         self.multilingual_vgf = multilingual
-        self.trasformer_vgf = textual_transformer
+        self.textual_trasformer_vgf = textual_transformer
         self.visual_transformer_vgf = visual_transformer
         self.probabilistic = probabilistic
         self.num_labels = num_labels
@@ -142,7 +142,7 @@ class GeneralizedFunnelling:
             wce_vgf = WceGen(n_jobs=self.n_jobs)
             self.first_tier_learners.append(wce_vgf)
 
-        if self.trasformer_vgf:
+        if self.textual_trasformer_vgf:
             transformer_vgf = TextualTransformerGen(
                 dataset_name=self.dataset_name,
                 model_name=self.textaul_transformer_name,
@@ -198,7 +198,8 @@ class GeneralizedFunnelling:
             self.posteriors_vgf,
             self.multilingual_vgf,
             self.wce_vgf,
-            self.trasformer_vgf,
+            self.textual_trasformer_vgf,
+            self.visual_transformer_vgf,
             self.aggfunc,
         )
         print(f"- model id: {self._model_id}")
@@ -372,7 +373,7 @@ class GeneralizedFunnelling:
                 "rb",
             ) as vgf:
                 first_tier_learners.append(pickle.load(vgf))
-        if self.trasformer_vgf:
+        if self.textual_trasformer_vgf:
             with open(
                 os.path.join(
                     "models", "vgfs", "transformer", f"transformerGen_{model_id}.pkl"
@@ -427,7 +428,15 @@ def get_params(optimc=False):
     return [{"kernel": [kernel], "C": c_range, "gamma": ["auto"]}]
 
 
-def get_unique_id(dataset_name, posterior, multilingual, wce, transformer, aggfunc):
+def get_unique_id(
+    dataset_name,
+    posterior,
+    multilingual,
+    wce,
+    textual_transformer,
+    visual_transformer,
+    aggfunc,
+):
     from datetime import datetime
 
     now = datetime.now().strftime("%y%m%d")
@@ -435,6 +444,7 @@ def get_unique_id(dataset_name, posterior, multilingual, wce, transformer, aggfu
     model_id += "p" if posterior else ""
     model_id += "m" if multilingual else ""
     model_id += "w" if wce else ""
-    model_id += "t" if transformer else ""
+    model_id += "t" if textual_transformer else ""
+    model_id += "v" if visual_transformer else ""
     model_id += f"_{aggfunc}"
     return f"{model_id}_{now}"

gfun/vgfs/commons.py

@@ -12,6 +12,7 @@ from torch.optim import AdamW
 from torch.utils.data import DataLoader, Dataset
 from transformers.modeling_outputs import ModelOutput
 
+import wandb
 from evaluation.evaluate import evaluate, log_eval
 
 PRINT_ON_EPOCH = 1
@@ -114,6 +115,7 @@ class Trainer:
         patience,
         experiment_name,
         checkpoint_path,
+        vgf_name,
     ):
         self.device = device
         self.model = model.to(device)
@@ -130,6 +132,7 @@ class Trainer:
             verbose=False,
             experiment_name=experiment_name,
         )
+        self.vgf_name = vgf_name
 
     def init_optimizer(self, optimizer_name, lr):
         if optimizer_name.lower() == "adamw":
@@ -138,6 +141,25 @@ class Trainer:
             raise ValueError(f"Optimizer {optimizer_name} not supported")
 
     def train(self, train_dataloader, eval_dataloader, epochs=10):
+        wandb.init(
+            project="gfun",
+            name="allhere",
+            # reinit=True,
+            config={
+                "vgf": self.vgf_name,
+                "architecture": self.model.name_or_path,
+                "learning_rate": self.optimizer.defaults["lr"],
+                "epochs": epochs,
+                "train batch size": train_dataloader.batch_size,
+                "eval batch size": eval_dataloader.batch_size,
+                "max len": train_dataloader.dataset.X.shape[-1],
+                "patience": self.earlystopping.patience,
+                "evaluate every": self.evaluate_steps,
+                "print eval every": self.print_eval,
+                "print train steps": self.print_steps,
+            },
+        )
+
         print(
             f"""- Training params for {self.experiment_name}:
                     - epochs: {epochs}
@@ -150,11 +172,14 @@ class Trainer:
                     - print eval every: {self.print_eval}
                     - print train steps: {self.print_steps}\n"""
         )
+
         for epoch in range(epochs):
             self.train_epoch(train_dataloader, epoch)
             if (epoch + 1) % self.evaluate_steps == 0:
                 print_eval = (epoch + 1) % self.print_eval == 0
-                metric_watcher = self.evaluate(eval_dataloader, print_eval=print_eval)
+                metric_watcher = self.evaluate(
+                    eval_dataloader, epoch, print_eval=print_eval
+                )
                 stop = self.earlystopping(metric_watcher, self.model, epoch + 1)
                 if stop:
                     print(
@@ -183,9 +208,16 @@ class Trainer:
             if (epoch + 1) % PRINT_ON_EPOCH == 0:
                 if ((b_idx + 1) % self.print_steps == 0) or b_idx == 0:
                     print(f"Epoch: {epoch+1} Step: {b_idx+1} Loss: {loss:.4f}")
+            wandb.log(
+                {
+                    f"{wandb.config['vgf']}_training_loss": loss,
+                    # "epoch": epoch,
+                    # f"{wandb.config['vgf']}_epoch": epoch,
+                }
+            )
         return self
 
-    def evaluate(self, dataloader, print_eval=True):
+    def evaluate(self, dataloader, epoch, print_eval=True):
         self.model.eval()
 
         lY = defaultdict(list)
@@ -210,6 +242,14 @@ class Trainer:
 
         l_eval = evaluate(lY, lY_hat)
         average_metrics = log_eval(l_eval, phase="validation", verbose=print_eval)
+        wandb.log(
+            {
+                f"{wandb.config['vgf']}_eval_metric": average_metrics[0],
+                f"{wandb.config['vgf']}_eval_loss": loss,
+                # "epoch": epoch,
+                # f"{wandb.config['vgf']}_epoch": epoch,
+            }
+        )
         return average_metrics[0]  # macro-F1
 
 

gfun/vgfs/textualTransformerGen.py

@@ -130,6 +130,7 @@ class TextualTransformerGen(ViewGen, TransformerGen):
         experiment_name = (
             f"{self.model_name}-{self.epochs}-{self.batch_size}-{self.dataset_name}"
         )
+
         trainer = Trainer(
             model=self.model,
             optimizer_name="adamW",
@@ -141,6 +142,7 @@ class TextualTransformerGen(ViewGen, TransformerGen):
             patience=self.patience,
             experiment_name=experiment_name,
             checkpoint_path="models/vgfs/transformer",
+            vgf_name="textual_trf",
         )
         trainer.train(
             train_dataloader=tra_dataloader,

gfun/vgfs/visualTransformerGen.py

@@ -97,7 +97,10 @@ class VisualTransformerGen(ViewGen, TransformerGen):
             shuffle=False,
         )
 
-        experiment_name = f"{self.model_name}-{self.epochs}-{self.batch_size}"
+        experiment_name = (
+            f"{self.model_name}-{self.epochs}-{self.batch_size}-{self.dataset_name}"
+        )
+
         trainer = Trainer(
             model=self.model,
             optimizer_name="adamW",
@@ -109,6 +112,7 @@ class VisualTransformerGen(ViewGen, TransformerGen):
             patience=self.patience,
             experiment_name=experiment_name,
             checkpoint_path="models/vgfs/transformer",
+            vgf_name="visual_trf",
         )
 
         trainer.train(