def main():
data_transform = transforms.Compose([
ResizeSample(size=(256, 256)),
ToTensorSample(),
NormalizeSample(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Apply each of the above transforms on sample.
fig = plt.figure()
trial_meme_dataset = MemeDataset(
csv_file=os.path.join(os.getcwd(), 'data/data1.csv'),
image_dir=os.path.join(
os.path.expanduser('~'),
'Downloads/semeval-2020_trialdata/Meme_images/'))
for i in range(len(trial_meme_dataset)):
sample = trial_meme_dataset[i]
print(i, np.array(sample['image']).shape)
print('np.array(sample[\'image\'])[128,128,0]: {}'.format(
np.array(sample['image'])[128, 128, 0]))
transformed_sample = data_transform(sample)
print(i, np.array(transformed_sample['image']).shape)
# print(transformed_sample['image'].numpy().max(axis=1))
print('transformed_sample[\'image\'].numpy()[0,128,128]: {}'.format(
transformed_sample['image'].numpy()[0, 128, 128]))
ax = plt.subplot(1, 4, i + 1)
plt.tight_layout()
ax.set_title('Sample #{}'.format(i))
ax.axis('off')
plt.imshow(transformed_sample['image'].numpy().transpose((1, 2, 0)))
if i == 3:
plt.show()
break
def readData(datalabel, batch_size):
data_transform = transforms.Compose([
ResizeSample(size=(256, 256)),
ToTensorSample(),
NormalizeSample(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
if datalabel == 'trial':
dataset = MemeDataset(
csv_file=os.path.join(os.getcwd(), '../data/data1.csv'),
image_dir=os.path.join(
os.getcwd(), '../data/semeval-2020_trialdata/Meme_images/'),
transform=data_transform)
else:
dataset = MemeDataset(
csv_file=os.path.join(os.getcwd(), '../data/data_7000_new.csv'),
image_dir=os.path.join(
os.getcwd(),
'../data/memotion_analysis_training_data/data_7000/'),
transform=data_transform)
dataloader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
return list(dataloader), len(list(dataloader))
def main():
trial_meme_dataset_transformed = MemeDataset(
csv_file=os.path.join(os.getcwd(), '../data/data1.csv'),
image_dir=os.path.join(os.getcwd(),
'../data/semeval-2020_trialdata/Meme_images/'),
transform=transforms.Compose([
ResizeSample(size=(256, 256)),
ToTensorSample(),
NormalizeSample(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
for i in range(len(trial_meme_dataset_transformed)):
sample = trial_meme_dataset_transformed[i]
print(i, sample['image'].size())
if i == 3:
break
dataloader = DataLoader(dataset=trial_meme_dataset_transformed,
batch_size=4,
shuffle=True,
num_workers=4)
for i_batch, sample_batched in enumerate(dataloader):
print(i_batch, sample_batched['image'].size(),
sample_batched['image'].numpy().shape)
print('sample_batched[\'image_name\']:\n{}'.format(
sample_batched['image_name']))
print('sample_batched[\'humour_onehot\']:\n{}'.format(
sample_batched['humour_onehot']))
print('sample_batched[\'humour_int\']:\n{}'.format(
sample_batched['humour_int']))
print('sample_batched[\'offensive_onehot\']:\n{}'.format(
sample_batched['offensive_onehot']))
print('sample_batched[\'offensive_int\']:\n{}'.format(
sample_batched['offensive_int']))
print('sample_batched[\'ocr_extracted_text\']:\n{}'.format(
sample_batched['ocr_extracted_text']))
print('sample_batched[\'corrected_text\']:\n{}\n'.format(
sample_batched['corrected_text']))
# observe 4th batch and stop.
if i_batch == 3:
plt.figure()
show_batch(sample_batched)
plt.axis('off')
plt.ioff()
plt.show()
break
def readData(self, datalabel):
data_transform = transforms.Compose([
ResizeSample(size=(256, 256)),
ToTensorSample(),
NormalizeSample(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
if datalabel == 'trial':
dataset = MemeDataset(
csv_file=os.path.join(os.getcwd(), '../data/data1.csv'),
image_dir = os.path.join(os.getcwd(), '../data/semeval-2020_trialdata/Meme_images/'),
transform= data_transform)
else:
dataset = MemeDataset(
csv_file=os.path.join(os.getcwd(), '../data/data_7000_new.csv'),
image_dir = os.path.join(os.getcwd(), '../data/memotion_analysis_training_data/data_7000/'),
transform=data_transform)
return dataset
def get_transformed_dataset(textEmb_path, data_path, img_path):
'''
Get the embedding for the text, which is used as the text feature, and the dataset.
'''
imgname_textEmbs = MyDataLoader.read_text_embeddings_Idx(textEmb_path)
data_transform = transforms.Compose([
ResizeSample(size=(256, 256)),
ToTensorSample(),
NormalizeSample(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
meme_dataset_transformed = MemeDataset(
csv_file=os.path.join(os.getcwd(), data_path),
image_dir=os.path.join(os.getcwd(), img_path),
transform=data_transform)
return imgname_textEmbs, meme_dataset_transformed
def get_dataloaders(data_path, img_path, batch_size, split_seq):
# split_seq: [0.8, 0.2], 80% data for training, 10% for validation, the rest of data for testing
data_transform = transforms.Compose([
ResizeSample(size=(299, 299)),
# ResizeSample(size=(256,256)),
ToTensorSample(),
NormalizeSample((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
meme_dataset_transformed = MemeDataset(
csv_file=os.path.join(os.getcwd(), data_path),
image_dir=os.path.join(os.getcwd(), img_path),
transform=data_transform)
# Split the dataset
train_len = int(len(meme_dataset_transformed) * split_seq[0])
# val_len = int(len(meme_dataset_transformed) * split_seq[1])
test_len = len(meme_dataset_transformed) - train_len
# meme_train, meme_val, meme_test = random_split(meme_dataset_transformed, [train_len, val_len, test_len])
meme_train, meme_val = random_split(meme_dataset_transformed,
[train_len, test_len])
# The dataloader for training, validation and testing dataset
train_dataloader = DataLoader(dataset=meme_train,
batch_size=batch_size,
shuffle=True,
num_workers=4)
val_dataloader = DataLoader(dataset=meme_val,
batch_size=batch_size,
shuffle=True,
num_workers=4)
# test_dataloader = DataLoader(dataset=meme_test, batch_size=4,
# shuffle=True, num_workers=4)
# dataloaders_dict = {'train': train_dataloader, 'val': val_dataloader, 'test': test_dataloader}
dataloaders_dict = {'train': train_dataloader, 'val': val_dataloader}
return dataloaders_dict
def main():
data_transform = transforms.Compose([
ResizeSample(size=(256, 256)),
ToTensorSample(),
NormalizeSample(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
trial_meme_dataset_transformed = MemeDataset(
csv_file=os.path.join(os.getcwd(), '../data/data1.csv'),
image_dir=os.path.join(os.getcwd(),
'../data/semeval-2020_trialdata/Meme_images/'),
transform=data_transform)
train_meme_dataset_transformed = MemeDataset(
csv_file=os.path.join(os.getcwd(), '../data/data_7000_new.csv'),
image_dir=os.path.join(
os.getcwd(), '../data/memotion_analysis_training_data/data_7000/'),
transform=data_transform)
evaluate_classification(
meme_dataset_transformed=trial_meme_dataset_transformed)
evaluate_classification(
meme_dataset_transformed=train_meme_dataset_transformed)
# image_dir=os.path.join(os.getcwd(),
# '../data/semeval-2020_trialdata/Meme_images/'),
# transform=transforms.Compose(
# [ResizeSample(size=(224, 224)),
# ToTensorSample(),
# NormalizeSample(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])]))
trial_meme_dataset_transformed = MemeDataset(
csv_file=os.path.join(os.getcwd(), '../data/data_7000_new.csv'),
image_dir=os.path.join(
os.getcwd(), '../data/memotion_analysis_training_data/data_7000/'),
transform=transforms.Compose([
ResizeSample(size=(224, 224)),
ToTensorSample(),
NormalizeSample(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]))
trial_meme_train, trial_meme_val, _ = random_split(
dataset=trial_meme_dataset_transformed, lengths=[5988, 1000, 3])
# Create training and validation dataloaders
sample_weights_train = make_weights_for_balanced_classes(trial_meme_train,
num_classes=3)
weighted_sampler_train = WeightedRandomSampler(sample_weights_train,
len(sample_weights_train))
train_dataloader = DataLoader(dataset=trial_meme_train,
batch_size=4,
sampler=weighted_sampler_train,
num_workers=4)
def main():
# Create training and validation datasets
print("Initializing Datasets and Dataloaders...")
trial_meme_dataset_transformed = MemeDataset(
csv_file=os.path.join(os.getcwd(), '../data/data1.csv'),
image_dir=os.path.join(os.getcwd(),
'../data/semeval-2020_trialdata/Meme_images/'),
transform=transforms.Compose([
ResizeSample(size=(299, 299)), # For Inception
# ResizeSample(size=(224, 224)), # For other pretrained models
ToTensorSample(),
NormalizeSample(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
trial_meme_train, trial_meme_val = random_split(
dataset=trial_meme_dataset_transformed, lengths=[800, 200])
# Create training and validation dataloaders
# Balanced class============================================================
# sample_weights_train = make_weights_for_balanced_classes(
# trial_meme_train, num_classes=3)
# weighted_sampler_train = WeightedRandomSampler(
# sample_weights_train, len(sample_weights_train))
# train_dataloader = DataLoader(dataset=trial_meme_train, batch_size=4,
# sampler=weighted_sampler_train, num_workers=4)
# sample_weights_val = make_weights_for_balanced_classes(
# trial_meme_val, num_classes=3)
# weighted_sampler_val = WeightedRandomSampler(
# sample_weights_val, len(sample_weights_val))
# val_dataloader = DataLoader(dataset=trial_meme_val, batch_size=4,
# sampler=weighted_sampler_val, num_workers=4)
# ==========================================================================
# Imbalanced class==========================================================
train_dataloader = DataLoader(dataset=trial_meme_train,
batch_size=4,
shuffle=True,
num_workers=4)
val_dataloader = DataLoader(dataset=trial_meme_val,
batch_size=4,
shuffle=True,
num_workers=4)
# ==========================================================================
dataloaders_dict = {'train': train_dataloader, 'val': val_dataloader}
# Detect if we have a GPU available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
deepsent_config = {
'num_classes': 3, # negative, positive, neutral
'batch_size': 4,
'vocab_size': 400000,
'embedding_dim': 300
}
deepsent = DeepSentimentModel(**deepsent_config)
# deepsent = DeepSentimentVanillaModel(**deepsent_config)
# deepsent = ShallownetGloveModel(**deepsent_config)
# Send the model to GPU
deepsent = deepsent.to(device)
# Gather the parameters to be optimized/updated in this run. If we are
# finetuning we will be updating all parameters. However, if we are
# doing feature extract method, we will only update the parameters
# that we have just initialized, i.e. the parameters with requires_grad
# is True.
feature_extract = True
params_to_update = deepsent.parameters()
print("Params to learn:")
if feature_extract:
params_to_update = []
for name, param in deepsent.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
print("\t", name)
else:
for name, param in deepsent.named_parameters():
if param.requires_grad == True:
print("\t", name)
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(params_to_update, lr=0.001, momentum=0.9)
# Setup the loss fxn
criterion = nn.CrossEntropyLoss()
# Train and evaluate
deepsent, hist = train_model(model=deepsent,
dataloaders=dataloaders_dict,
criterion=criterion,
optimizer=optimizer_ft,
num_epochs=10,
is_inception=True,
target_label='overall_sentiment_ternary_int')