Fingerspelling embedding + ClearML

train.py

@@ -15,7 +15,7 @@ from torchvision import transforms
 from torch.utils.data import DataLoader
 from pathlib import Path
 import copy
-
+import numpy as np
 from datasets import CzechSLRDataset, SLREmbeddingDataset, collate_fn_triplet_padd, collate_fn_padd
 from models import SPOTER, SPOTER_EMBEDDINGS, train_epoch, evaluate, train_epoch_embedding, \
     train_epoch_embedding_online, evaluate_embedding
@@ -32,7 +32,7 @@ except ImportError:
     pass
 
 
-PROJECT_NAME = "spoter"
+PROJECT_NAME = "SpoterEmbedding"
 CLEARML = "clearml"
 
 
@@ -75,12 +75,25 @@ def train(args, tracker: Tracker):
 
     # Construct the model
     if not args.classification_model:
-        slrt_model = SPOTER_EMBEDDINGS(
-            features=args.vector_length,
-            hidden_dim=args.hidden_dim,
-            norm_emb=args.normalize_embeddings,
-            dropout=args.dropout
-        )
+        # if finetune, load the weights from the classification model
+        if args.finetune:
+            checkpoint = torch.load(args.checkpoint_path, map_location=device)
+
+            slrt_model = SPOTER_EMBEDDINGS(
+                features=checkpoint["config_args"].vector_length,
+                hidden_dim=checkpoint["config_args"].hidden_dim,
+                norm_emb=checkpoint["config_args"].normalize_embeddings,
+                dropout=checkpoint["config_args"].dropout,
+            )
+        else:
+            slrt_model = SPOTER_EMBEDDINGS(
+                features=args.vector_length,
+                hidden_dim=args.hidden_dim,
+                norm_emb=args.normalize_embeddings,
+                dropout=args.dropout,
+            )
+            
+
         model_type = 'embed'
         if args.hard_triplet_mining == "None":
             cel_criterion = nn.TripletMarginLoss(margin=args.triplet_loss_margin, p=2)
@@ -233,6 +246,9 @@ def train(args, tracker: Tracker):
             val_accs.append(val_acc)
             tracker.log_scalar_metric("acc", "val", epoch, val_acc)
 
+        create_embedding_scatter_plots(tracker, slrt_model, train_loader, val_loader, device, id_to_label, epoch,
+                                       top_model_name)
+
         logger.info(f"Epoch time: {datetime.now() - start_time}")
         logger.info("[" + str(epoch) + "] TRAIN  loss: " + str(train_loss) + " acc: " + str(train_accs[-1]))
         logger.info("[" + str(epoch) + "] VALIDATION  acc: " + str(val_accs[-1]))