demo saved model

Code/inference.py

+import json
 import torch
 import preprocess
 import models
 from transformers import BertTokenizer, RobertaTokenizer, BertModel, RobertaModel, RobertaPreTrainedModel, RobertaConfig,  BertConfig, BertPreTrainedModel, PreTrainedModel, AutoConfig, AutoModel, AutoTokenizer
 import re 
-import models
 import train
 from torch.utils.data import DataLoader, RandomSampler
 
@@ -14,11 +14,16 @@ sentence = sentence.split()
 
 print("Specify the target word position using square brackets (e.g. [0,2])")
 target_pos = input()
+target_json = json.loads(target_pos)
+print(type(target_json))
 
 print("Enter the label: 0 for literal, 1 for non-literal")
 label = int(input())
+#label_json=json.loads(label)
+
+print("Is this your target word: ", sentence[target_json[0]: target_json[1]])
 
-data_sample = {"sentence": sentence, "pos": target_pos, "label": label}
+data_sample = [{"sentence": sentence, "pos": target_json, "label": label}]
 print(data_sample)
 tokenizer=AutoTokenizer.from_pretrained("bert-base-uncased")
 
@@ -26,10 +31,10 @@ input_as_dataset=preprocess.tokenizer_new(tokenizer, data_sample, max_length=512
 # Load model
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-model=models.BertForWordClassification.from_pretrained("bert-base-uncased")
-model_path = "saved_models/bert_baseline.pth"
+model=models.WordClassificationModel.from_pretrained("bert-base-uncased")
+model_path = "saved_models/bert_.pth"
 model = torch.load(model_path, map_location=device)
-
+model.eval()
 
 
 train_sampler = RandomSampler(data_sample)
@@ -46,5 +51,10 @@ for batch in train_dataloader:
 	start_positions=batch[3]
 	end_positions=batch[4]
 	outputs=model(**inputs)
+	prediction=torch.argmax(outputs[0])
+	if prediciton == 1:
+		print("metonymy")
+	elif prediciton == 0:
+		print("literal")
 
-	print("Outputs: ", outputs)
+	#print("Outputs: ", 

Code/train.py

@@ -22,7 +22,7 @@ torch.cuda.empty_cache()
 
 #with torch.autocast("cuda"):
 
-def train(model, name, imdb, seed,mixup,lambda_value, mixepoch, tmix, mixlayer, train_dataset, test_dataset, num_epochs, learning_rate, batch_size, test_batch_size):
+def train(model, name, imdb, seed,mixup,lambda_value, mixepoch, tmix, mixlayer, train_dataset, test_dataset, num_epochs, learning_rate, batch_size, test_batch_size, model_save_path=None):
 	"""Train loop for models. Iterates over epochs and batches and gives inputs to model. After training, call evaluation.py for evaluation of finetuned model.
 	
 	Params:
@@ -107,7 +107,7 @@ def train(model, name, imdb, seed,mixup,lambda_value, mixepoch, tmix, mixlayer,
 			model.zero_grad()
 
 			if epoch==mixepoch:
-				print("mixepoch")
+				#print("mixepoch")
 				if mixup == True:
 					#calculate new last hidden states and predictions(logits)
 					new_matrix_batch, new_labels_batch = mixup_function(outputs[2], labels, lambda_value, threshold)
@@ -127,12 +127,12 @@ def train(model, name, imdb, seed,mixup,lambda_value, mixepoch, tmix, mixlayer,
 					lr_scheduler.step()
 					optimizer.zero_grad()
 					model.zero_grad()
-	
-	torch.save(model, "saved_models/bert_baseline.pth")
+	if model_save_path != None:
+		torch.save(model, model_save_path)
 
 	#evaluate trained model
-	evaluation_test = evaluation.evaluate_model(model, name,  test_dataset, test_batch_size, imdb)
-	evaluation_train = evaluation.evaluate_model(model, name, train_dataset, test_batch_size, imdb)
+	evaluation_test = Code.evaluation.evaluate_model(model, name,  test_dataset, test_batch_size, imdb)
+	evaluation_train = Code.evaluation.evaluate_model(model, name, train_dataset, test_batch_size, imdb)
  
 	print("TEST: ", evaluation_test)
 	print("TRAIN: ", evaluation_train)

main.py

@@ -26,16 +26,16 @@ def run(raw_args):
 	print("Loading tokenizers and initializing model...")
 	tokenizer=AutoTokenizer.from_pretrained(args.architecture)
 
-	models.set_seed(args.random_seed)
+	Code.models.set_seed(args.random_seed)
 	if args.model_type == "separate":
 		if args.architecture=="bert-base-uncased":
-			model=models.BertForWordClassification.from_pretrained(args.architecture).to("cuda")
+			model=Code.models.BertForWordClassification.from_pretrained(args.architecture).to("cuda")
 		elif args.architecture=="roberta-base":
-			model=models.RobertaForWordClassification.from_pretrained(args.architecture).to("cuda")
+			model=Code.models.RobertaForWordClassification.from_pretrained(args.architecture).to("cuda")
 		else:
 			print("non eligible model type selected")
 	elif args.model_type == "one":
-		model=models.WordClassificationModel(args.architecture, args.tmix, args.imdb).to("cuda")
+		model=Code.models.WordClassificationModel(args.architecture, args.tmix, args.imdb).to("cuda")
 	else:
 		print("non eligible model type selected")
 
@@ -43,22 +43,22 @@ def run(raw_args):
 	#preprocess...
 	print("preprocessing datasets...")
 	if args.imdb==True:
-		train_dataset=preprocess.tokenizer_imdb(tokenizer, data_train, args.max_length)
-		test_dataset=preprocess.tokenizer_imdb(tokenizer, data_test, args.max_length)
+		train_dataset=Code.preprocess.tokenizer_imdb(tokenizer, data_train, args.max_length)
+		test_dataset=Code.preprocess.tokenizer_imdb(tokenizer, data_test, args.max_length)
 
 	elif args.tokenizer=="salami":
-		train_dataset=preprocess.salami_tokenizer(tokenizer, data_train, args.max_length, masked=args.masking) #no context implemented
-		test_dataset=preprocess.salami_tokenizer(tokenizer, data_test, args.max_length, masked=args.masking)
+		train_dataset=Code.preprocess.salami_tokenizer(tokenizer, data_train, args.max_length, masked=args.masking) #no context implemented
+		test_dataset=Code.preprocess.salami_tokenizer(tokenizer, data_test, args.max_length, masked=args.masking)
 	
 	elif args.tokenizer=="swp":
 		print("train dataset preprocessing ")        
 		print(args.tcontext)
-		train_dataset=preprocess.tokenizer_new(tokenizer, data_train, args.max_length, masked=args.masking, old_dataset=args.tcontext)
-		test_dataset=preprocess.tokenizer_new(tokenizer, data_test, args.max_length, masked=args.masking, old_dataset=False) 
+		train_dataset=Code.preprocess.tokenizer_new(tokenizer, data_train, args.max_length, masked=args.masking, old_dataset=args.tcontext)
+		test_dataset=Code.preprocess.tokenizer_new(tokenizer, data_test, args.max_length, masked=args.masking, old_dataset=False) 
 	
 	elif args.tokenizer=="li":
-		train_dataset=preprocess.tokenizer_new(tokenizer, data_train, args.max_length, masked=args.masking) #no context implemented 
-		test_dataset=preprocess.tokenizer_new(tokenizer, data_test, args.max_length, masked=args.masking)
+		train_dataset=Code.preprocess.tokenizer_new(tokenizer, data_train, args.max_length, masked=args.masking) #no context implemented 
+		test_dataset=Code.preprocess.tokenizer_new(tokenizer, data_test, args.max_length, masked=args.masking)
 	
 	else:
 		print("non eligible tokenizer selected")
@@ -66,9 +66,9 @@ def run(raw_args):
 	#train&evaluate...
 	print("training..")
 	if args.train_loop=="swp":
-		evaluation_test, evaluation_train = train.train(model, args.architecture, args.imdb, args.random_seed, args.mix_up, args.lambda_value, args.mixepoch, args.tmix, args.mixlayer, train_dataset, test_dataset, args.epochs, args.learning_rate, args.batch_size, args.test_batch_size)
+		evaluation_test, evaluation_train = Code.train.train(model, args.architecture, args.imdb, args.random_seed, args.mix_up, args.lambda_value, args.mixepoch, args.tmix, args.mixlayer, train_dataset, test_dataset, args.epochs, args.learning_rate, args.batch_size, args.test_batch_size, args.model_save_path)
 	elif args.train_loop=="salami":
-		evaluation_test = train.train_salami(model,args.random_seed, train_dataset, test_dataset, args.batch_size, args.test_batch_size, args.learning_rate, args.epochs)
+		evaluation_test = Code.train.train_salami(model,args.random_seed, train_dataset, test_dataset, args.batch_size, args.test_batch_size, args.learning_rate, args.epochs)
 	else:
 		print("no eligible train loop selected")
 	
@@ -203,7 +203,7 @@ if __name__ == "__main__":
 		"--mixepoch", 
 		help="specify the epoch(s) in which to apply mixup",
 		type=int,
-		default=1) 
+		default=None) 
 
 
 	#Test arguments
@@ -219,6 +219,11 @@ if __name__ == "__main__":
 		"-sd",
 		"--save_directory",
 		help="Directory to save run")
+	
+	parser.add_argument(
+		"-msp",
+		"--model_save_path",
+		help="path to save model")
 
 	args = parser.parse_args()
 	run(args)