Defined V1

.gitignore

@@ -119,3 +119,4 @@ dmypy.json
 dataset/*
 .saved/*
 *.Identifier
+*.zip

.vscode/launch.json

@@ -0,0 +1,15 @@
+{
+    // Usare IntelliSense per informazioni sui possibili attributi.
+    // Al passaggio del mouse vengono visualizzate le descrizioni degli attributi esistenti.
+    // Per altre informazioni, visitare: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: File corrente",
+            "type": "python",
+            "request": "launch",
+            "program": "/home/christian/Documenti/GitHub/Image-Captioning/v1/NeuralNet.py",
+            "console": "integratedTerminal"
+        }
+    ]
+}

light_version/Dataset.py → bck_old/Dataset.py

@@ -117,6 +117,7 @@ def pack_minibatch_evaluation(self, data):
         images = torch.stack(images, 0)
 
         caption_lengths = [len(caption) for caption in captions]
+        captions
         captions = nn.utils.rnn.pad_sequence(captions, padding_value=0, batch_first=True)
         return images,captions.type(torch.LongTensor),caption_lengths 
 

light_version/NeuralNet.py → bck_old/NeuralNet.py

@@ -9,46 +9,37 @@
 
 device = "cuda:0"
 class EncoderCNN(nn.Module):
-    def __init__(self, embed_size):
+    def __init__(self, embedding_size):
         super(EncoderCNN, self).__init__()
         resnet = models.resnet50(pretrained=True)
         for param in resnet.parameters():
             param.requires_grad_(False)
 
         modules = list(resnet.children())[:-1]   # remove last fc layer
         self.resnet = nn.Sequential(*modules)
-        self.linear = nn.Linear(resnet.fc.in_features, 50) 
+        self.linear = nn.Linear(resnet.fc.in_features, embedding_size) 
 
     def forward(self, images):
-
         features = self.resnet(images) 
-        features = features.reshape(features.size(0), -1)
+        features = features.reshape(features.size(0), -1) # (Batch Size, Embedding Dim.)
         features = self.linear(features)
         return features
 
 class DecoderRNN(nn.Module):
-    def __init__(self, hidden_size, padding_index, vocab_size, embeddings ):
+    def __init__(self, hidden_size, padding_index, vocab_size, embeddings, embedding_size):
         """Set the hyper-parameters and build the layers."""
         super(DecoderRNN, self).__init__()
-        # Keep track of hidden_size for initialization of hidden state
-        self.hidden_size = hidden_size
 
         # Embedding layer that turns words into a vector of a specified size
-        self.word_embeddings = nn.Embedding.from_pretrained(embeddings, freeze=True, padding_idx = 0)
+        self.word_embeddings = nn.Embedding(vocab_size, embedding_size, padding_idx=padding_index)
 
         # The LSTM takes embedded word vectors (of a specified size) as input
         # and outputs hidden states of size hidden_dim
-        self.lstm = nn.LSTM(input_size=50, \
-                            hidden_size=1024, # LSTM hidden units 
-                            num_layers=1, # number of LSTM layer
-                            batch_first=True,  # input & output will have batch size as 1st dimension
-                            dropout=0, # Not applying dropout 
-                            bidirectional=False, # unidirectional LSTM
-                           )
+        self.lstm_unit = torch.nn.LSTMCell(embedding_size, hidden_size)
 
         # The linear layer that maps the hidden state output dimension
         # to the number of words we want as output, vocab_size
-        self.linear_1 = nn.Linear(1024, vocab_size)
+        self.linear_1 = nn.Linear(hidden_size, vocab_size)
 
     def init_hidden_state(self, encoder_out):
         """