fixed not being able to use other resnets as a base

image_classifier/__main__.py

@@ -35,6 +35,7 @@ def main():
 		parser.add_argument("--base64", type=str)
 		parser.add_argument("--write", type=Path)
 		parser.add_argument("--temp", type=float, default=1.0)
+		parser.add_argument("--limit", type=int, default=0)
 		args, unknown = parser.parse_known_args()			
 
 		images = []
@@ -43,9 +44,13 @@ def main():
 				for p in args.path.rglob("./*.jpg"):
 					image = Image.open(p).convert('RGB')
 					images.append(image)
+					if args.limit and len(images) >= args.limit:
+						break
 				for p in args.path.rglob("./*.png"):
 					image = Image.open(p).convert('RGB')
 					images.append(image)
+					if args.limit and len(images) >= args.limit:
+						break
 			else:
 				image = Image.open(args.path).convert('RGB')
 				images.append(image)

image_classifier/data.py

@@ -158,13 +158,13 @@ class Dataset(_Dataset):
 
 	def __getitem__(self, index):
 		path = self.paths[index]
-		tokens = tokenize( path.stem.upper() )
+		text = path.stem.upper()
-		text = torch.tensor( tokens ).to(dtype=torch.uint8)
 
 		image = Image.open(path).convert('RGB')
 		width, height = image.size
 		
-		image = self.transform(image).to(dtype=self.image_dtype) # resnet has to be RGB
+		text = torch.tensor( tokenize( text ) ).to(dtype=torch.uint8)
+		image = self.transform(image).to(dtype=self.image_dtype)
 
 		return dict(
 			index=index,
@@ -215,36 +215,24 @@ def _create_dataloader(dataset, training):
 	)
 
 def _load_train_val_paths( val_ratio=0.1 ):
-	paths = []
 	train_paths = []
 	val_paths = []
 
 	for data_dir in cfg.dataset.training:
-		paths.extend(data_dir.rglob("*.jpg"))
+		train_paths.extend(data_dir.rglob("*.jpg"))
-		paths.extend(data_dir.rglob("*.png"))
+		train_paths.extend(data_dir.rglob("*.png"))
 
-	if len(paths) > 0:
+	if len(train_paths) > 0:
 		random.seed(0)
-		random.shuffle(paths)
+		random.shuffle(train_paths)
-		train_paths.extend(paths)
 
-	if len(cfg.dataset.validation) == 0:
+	for data_dir in cfg.dataset.validation:
-		val_len = math.floor(len(train_paths) * val_ratio)
+		val_paths.extend(data_dir.rglob("*.jpg"))
-		train_len = math.floor(len(train_paths) * (1 - val_ratio))
+		val_paths.extend(data_dir.rglob("*.png"))
 
-		val_paths = train_paths[:-val_len]
+	if len(val_paths) > 0:
-		train_paths = train_paths[:train_len]
+		random.seed(0)
-	else:
+		random.shuffle(val_paths)
-		paths = []
-
-		for data_dir in cfg.dataset.validation:
-			paths.extend(data_dir.rglob("*.jpg"))
-			paths.extend(data_dir.rglob("*.png"))
-
-		if len(paths) > 0:
-			random.seed(0)
-			random.shuffle(paths)
-			val_paths.extend(paths)
 
 	train_paths, val_paths = map(sorted, [train_paths, val_paths])
 
@@ -596,3 +584,6 @@ if __name__ == "__main__":
 			decode_to_file( resps, f"./data/{task}.resps.wav", device="cpu" )
 			break
 """
+
+if __name__ == "__main__":
+	...

image_classifier/models/base.py

@@ -35,7 +35,7 @@ class Model(nn.Module):
 
 		self.n_tokens = n_tokens
 		self.n_len = n_len + 2 # start/stop tokens
-		self.d_model = d_model
+		# self.d_model = d_model
 		self.d_resnet = d_resnet
 
 		ResNet = resnet18
@@ -56,7 +56,7 @@ class Model(nn.Module):
 			ResNet = resnet152
 
 		self.resnet = ResNet(pretrained=False)
-		self.resnet.fc = nn.Linear( self.d_model, self.n_tokens * self.n_len )
+		self.resnet.fc = nn.Linear( self.resnet.fc.in_features, self.n_tokens * self.n_len )
 
 		self.accuracy_metric = MulticlassAccuracy(
 			n_tokens,
@@ -96,10 +96,13 @@ class Model(nn.Module):
 				nll = sum( loss ) / len( loss ),
 			)
 
-			self.stats = dict(
+			try:
-				acc = self.accuracy_metric( pred, labels ),
+				self.stats = dict(
-				precision = self.precision_metric( pred, labels ),
+					acc = self.accuracy_metric( pred, labels ),
-			)
+					precision = self.precision_metric( pred, labels ),
+				)
+			except Exception as e:
+				pass
 		
 
 		answer = [ "".join([ self.symmap[f'{x.item()}'] for x in t ]) for t in pred ]