modify the model

tutorials/09 - Image Captioning/model.py

@@ -10,9 +10,15 @@ class EncoderCNN(nn.Module):
         """Load pretrained ResNet-152 and replace top fc layer."""
         super(EncoderCNN, self).__init__()
         self.resnet = models.resnet152(pretrained=True)
-        self.resnet.fc = nn.Linear(self.resnet.fc.in_features, embed_size)
+        # For efficient memory usage.
         for param in self.resnet.parameters():
             param.requires_grad = False
+        self.resnet.fc = nn.Linear(self.resnet.fc.in_features, embed_size)
+        self.init_weights()
+    
+    def init_weights(self):
+        self.resnet.fc.weight.data.uniform_(-0.1, 0.1)
+        self.resnet.fc.bias.data.fill_(0)
         
     def forward(self, images):
         """Extract image feature vectors."""
@@ -30,6 +36,11 @@ class DecoderRNN(nn.Module):
         self.embed = nn.Embedding(vocab_size, embed_size)
         self.lstm = nn.LSTM(embed_size, hidden_size, num_layers)
         self.linear = nn.Linear(hidden_size, vocab_size)
+    
+    def init_weights(self):
+        self.embed.weight.data.uniform_(-0.1, 0.1)
+        self.linear.weigth.data.uniform_(-0.1, 0.1)
+        self.linear.bias.data.fill_(0)
         
     def forward(self, features, captions, lengths):
         """Decode image feature vectors and generate caption."""

tutorials/09 - Image Captioning/sample.py

@@ -1,6 +1,7 @@
 import os
 import numpy as np
 import torch
+import torchvision.transforms as T
 import pickle
 import matplotlib.pyplot as plt
 from PIL import Image
@@ -8,6 +9,12 @@ from model import EncoderCNN, DecoderRNN
 from vocab import Vocabulary
 from torch.autograd import Variable
 
+# Image processing 
+transform = T.Compose([
+    T.CenterCrop(224),
+    T.ToTensor(),
+    T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
+
 # Hyper Parameters
 embed_size = 128
 hidden_size = 512
@@ -18,11 +25,10 @@ with open('./data/vocab.pkl', 'rb') as f:
     vocab = pickle.load(f)
     
 # Load an image array
-images = os.listdir('./data/val2014resized/')
+images = os.listdir('./data/train2014resized/')
-image_path = './data/val2014resized/' + images[12]  
+image_path = './data/train2014resized/' + images[12] 
-with open(image_path, 'r+b') as f:
+img = Image.open(image_path)
-    img = np.asarray(Image.open(f))
+image = transform(img).unsqueeze(0) 
-image = torch.from_numpy(img.transpose(2, 0, 1)).float().unsqueeze(0) / 255 - 0.5
 
 # Load the trained models
 encoder = torch.load('./encoder.pkl')

tutorials/09 - Image Captioning/train.py

@@ -1,6 +1,6 @@
 from data import get_loader 
 from vocab import Vocabulary
-from models import EncoderCNN, DecoderRNN 
+from model import EncoderCNN, DecoderRNN 
 from torch.autograd import Variable 
 from torch.nn.utils.rnn import pack_padded_sequence
 import torch
@@ -10,10 +10,11 @@ import torchvision.transforms as T
 import pickle 
 
 # Hyper Parameters
-num_epochs = 5
+num_epochs = 1
-batch_size = 100
+batch_size = 32
-embed_size = 128
+embed_size = 256
 hidden_size = 512
+crop_size = 224
 num_layers = 1
 learning_rate = 0.001
 train_image_path = './data/train2014resized/'
@@ -21,6 +22,7 @@ train_json_path = './data/annotations/captions_train2014.json'
 
 # Image Preprocessing
 transform = T.Compose([
+    T.RandomCrop(crop_size),
     T.RandomHorizontalFlip(), 
     T.ToTensor(), 
     T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
@@ -42,7 +44,8 @@ decoder.cuda()
         
 # Loss and Optimizer
 criterion = nn.CrossEntropyLoss()
-optimizer = torch.optim.Adam(decoder.parameters(), lr=learning_rate)
+params = list(decoder.parameters()) + list(encoder.resnet.fc.parameters())
+optimizer = torch.optim.Adam(params, lr=learning_rate)
 
 # Train the Decoder
 for epoch in range(num_epochs):
@@ -63,7 +66,7 @@ for epoch in range(num_epochs):
         if i % 100 == 0:
             print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f'
                   %(epoch, num_epochs, i, total_step, loss.data[0], np.exp(loss.data[0])))    
-            
+
 # Save the Model
 torch.save(decoder, 'decoder.pkl')
 torch.save(encoder, 'encoder.pkl')