fix: more imports + loading files

app.py

@@ -22,10 +22,10 @@ map_loc = None if torch.cuda.is_available() and use_gpu else 'cpu'
 
 # Inverse Cooking
 ingrs_vocab = pickle.load(
-    hf_hub_download(REPO_ID, 'data/ingr_vocab.pkl', HF_TOKEN), 'rb'
+    open(hf_hub_download(REPO_ID, 'data/ingr_vocab.pkl', token=HF_TOKEN), 'rb')
 )
 vocab = pickle.load(
-    hf_hub_download(REPO_ID, 'data/instr_vocab.pkl', HF_TOKEN), 'rb'
+    open(hf_hub_download(REPO_ID, 'data/instr_vocab.pkl', token=HF_TOKEN), 'rb')
 )
 
 ingr_vocab_size = len(ingrs_vocab)
@@ -56,7 +56,7 @@ args.ingrs_only = False
 # Load the trained model parameters
 model = get_model(args, ingr_vocab_size, instrs_vocab_size)
 model.load_state_dict(torch.load(
-    hf_hub_download(REPO_ID, 'data/modelbest.ckpt', HF_TOKEN), map_location=map_loc)
+    hf_hub_download(REPO_ID, 'data/modelbest.ckpt', token=HF_TOKEN), map_location=map_loc)
 )
 model = model.to(device)
 model.eval()

src/model.py

@@ -4,10 +4,10 @@ import torch
 import torch.nn as nn
 import random
 import numpy as np
-from modules.encoder import EncoderCNN, EncoderLabels
-from modules.transformer_decoder import DecoderTransformer
-from modules.multihead_attention import MultiheadAttention
-from utils.metrics import softIoU, MaskedCrossEntropyCriterion
+from src.modules.encoder import EncoderCNN, EncoderLabels
+from src.modules.transformer_decoder import DecoderTransformer
+from src.modules.multihead_attention import MultiheadAttention
+from src.utils.metrics import softIoU, MaskedCrossEntropyCriterion
 import pickle
 import os
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

src/modules/multihead_attention.py

@@ -11,7 +11,7 @@ from torch import nn
 from torch.nn import Parameter
 import torch.nn.functional as F
 
-from modules.utils import fill_with_neg_inf, get_incremental_state, set_incremental_state
+from src.modules.utils import fill_with_neg_inf, get_incremental_state, set_incremental_state
 
 
 class MultiheadAttention(nn.Module):

src/modules/transformer_decoder.py

@@ -13,8 +13,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn.modules.utils import _single
-import modules.utils as utils
-from modules.multihead_attention import MultiheadAttention
+import src.modules.utils as utils
+from src.modules.multihead_attention import MultiheadAttention
 import numpy as np
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 import copy