I am trying to create a logistic model by using CIFAR10 data in PyTorch. After running the model for evaluation I run into an error :
RuntimeError: size mismatch, m1: [750 x 4096], m2: [1024 x 10] at C:\w\1\s\tmp_conda_3.7_100118\conda\conda-bld\pytorch_1579082551706\work\aten\src\TH/generic/THTensorMath.cpp:136
It seems like input_size is creating a problem, I dont know I am new to this. Please let me know what changes should I make in order to overcome this error.
These are the hyperparameters:
batch_size = 100
learning_rate = 0.001
# Other constants
input_size = 4*4*64
num_classes = 10
This is the cell that downloads and splits the dataset into train, validation and test.
transform = torchvision.transforms.Compose(
[torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])
testset = torchvision.datasets.CIFAR10(root='D:\PyTorch\cifar-10-python', train=False,download=False, transform=transform)
trainvalset = torchvision.datasets.CIFAR10(root='D:\PyTorch\cifar-10-python', train=True,download=False, transform=transform)
trainset, valset = torch.utils.data.random_split(trainvalset, [45000, 5000]) # 10% for validation
train_loader = torch.utils.data.DataLoader(trainset, batch_size=50, shuffle=True)
test_loader = torch.utils.data.DataLoader(testset, batch_size=1000, shuffle=False)
val_loader = torch.utils.data.DataLoader(valset, batch_size=1000, shuffle=False)
This is the architecture of my model.
class CifarModel(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(input_size, num_classes)
def forward(self, xb):
xb = xb.view(-1, 64*8*8)
#xb = xb.reshape(-1, 784)
print(xb.shape)
out = self.linear(xb)
return out
def training_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
return loss
def validation_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
acc = accuracy(out, labels) # Calculate accuracy
return {'val_loss': loss.detach(), 'val_acc': acc.detach()}
def validation_epoch_end(self, outputs):
batch_losses = [x['val_loss'] for x in outputs]
epoch_loss = torch.stack(batch_losses).mean() # Combine losses
batch_accs = [x['val_acc'] for x in outputs]
epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies
return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
def epoch_end(self, epoch, result):
print("Epoch [{}], val_loss: {:.4f}, val_acc: {:.4f}".format(epoch, result['val_loss'], result['val_acc']))
model = CifarModel()
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
def evaluate(model, val_loader):
outputs = [model.validation_step(batch) for batch in val_loader]
return model.validation_epoch_end(outputs)
def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
history = []
optimizer = opt_func(model.parameters(), lr)
for epoch in range(epochs):
# Training Phase
for batch in train_loader:
loss = model.training_step(batch)
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Validation phase
result = evaluate(model, val_loader)
model.epoch_end(epoch, result)
history.append(result)
return history
evaluate(model, val_loader)
Here you are specifying that the number of output classes should be 10:
num_classes = 10
Your forward function does not reflect this:
xb = xb.view(-1, 64*8*8) # you get 750x4096
out = self.linear(xb) # here an input of
# input_size to linear layer = 4*4*64 # 1024
# num_classes = 10
Modify it like this:
xb = xb.view(-1, 64*4*4) # you get 750x1024
out = self.linear(xb) # M1 750x1024 M2 1024x10:
# input_size = 4*4*64 # 1024
# num_classes = 10
Related
I am building a model to classify news (AG news dataset). The vocab size ~33k with custom embedding layer. I have run this for 20 epochs but the loss and accuracy (1.3 and 26% respec.) is almost constant even at the end of 20th epoch. Can someone please help me with this? Also, am I feeding the correct input to the fc layer? I am using CrossEntropyLoss as the loss function.
Here is my model class:
class NewsClassifier(nn.Module):
def __init__(self, vocab_weights = None, rnn_type = 'LSTM', vocab_size = len(vocab.vocab), n_classes = 4, embed_size = 300, rnn_units = 512, \
n_layers = 2, bi_dir = True, rnn_drop = 0.0, padding_index = vocab['<unk>']):
super().__init__()
self.rnn_units = rnn_units
self.n_classes = n_classes
self.rnn_type = rnn_type
if vocab_weights:
self.embedding = nn.Embedding.from_pretrained(torch.as_tensor(vocab_weights))
else:
self.embedding = nn.Embedding(vocab_size, embed_size, padding_idx = padding_index)
if rnn_type == 'LSTM':
self.rnn = nn.LSTM(embed_size, rnn_units, num_layers = n_layers, bidirectional = bi_dir, dropout = rnn_drop)
elif rnn_type == 'GRU':
self.rnn = nn.GRU(embed_size, rnn_units, num_layers = n_layers, bidirectional = bi_dir, dropout = rnn_drop)
else:
raise NotImplementError
self.fc = nn.Linear(2 * rnn_units if bi_dir else rnn_units, self.n_classes)
def forward(self, data, lens):
x_embed = self.embedding(data) # (padded_lens, batch_size, embed_dim)
x_packed = pack_padded_sequence(x_embed, lens.cpu(), enforce_sorted = False) #packing sequences and passing to RNN unit
if self.rnn_type == 'LSTM':
output_packed, (hidden,cell) = self.rnn(x_packed) #output is packed and cannot be fed to linear layers
else:
output_packed, hidden = self.rnn(x_packed) #For GRU there is only hidden state
#Though n number of layers are stacked the output is always 1
output_padded, _ = pad_packed_sequence(output_packed) #output is padded to be fed to linear layer (padded_lens, batch size, hidden_units)
#Picking only the last output --> equivalent to reutrn_sequences = False in Keras
out_reduced = torch.cat((output_padded[-1, :, : self.rnn_units], output_padded[-1, :, self.rnn_units :]), 1)
return self.fc(out_reduced)
model = NewsClassifier()
print(f'The total number of trainable parameters are : {sum(p.numel() for p in model.parameters() if p.requires_grad)}')
My training function is:
def train(model, iterator = trainDataloader, optimizer = optimizer, loss_fn = criterion):
e_loss = e_acc = i = 0
model.train()
for inputs, leng, labels in iterator:
inputs, leng, labels = inputs.to(device), leng.to(device), labels.to(device)
optimizer.zero_grad()
preds = model(inputs, leng).squeeze(1)
loss = loss_fn(preds, labels.long())
acc = accuracy(preds, labels)
loss.backward()
optimizer.step()
e_loss += loss.item()
e_acc += acc.item()
i += 1
return e_loss/i, e_acc/i
def predict(model, iterator = testDataloader, loss_fn = criterion):
e_loss = e_acc = i = 0
model.eval()
with torch.no_grad():
for inputs, leng, labels in iterator:
inputs, leng, labels = inputs.to(device), leng.to(device), labels.to(device)
preds = model(inputs, leng).squeeze(1)
loss = loss_fn(preds, labels.long())
acc = accuracy(preds, labels)
e_loss += loss.item()
e_acc += acc.item()
i += 1
return e_loss/i, e_acc/i
N_EPOCHS = 20
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
start_time = time.time()
train_loss, train_acc = train(model)
valid_loss, valid_acc = predict(model)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), 'tut1-model.pt')
print(f'Epoch: {epoch+1:02} / {N_EPOCHS} | Epoch Time: {epoch_mins}m {epoch_secs}s')
print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
print(f'\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')
I have been trying to train a model on vulnerability detection through source code. And, after a little bit of searching, I thought a very good starting point could be using a pre-trained transformer model from HuggingFace with PyTorch and pl.lightning torch. I chose DistilBert because it was the fastest one.
I have an imbalanced dataset, approximately 70% non-vulnerable and 30% vulnerable functions.
However, my results have been very bad. The model does not seem to learn and generalize. Specifically, during training the train loss is heavily oscillating, accuracy is around 70 percent and recall is extremely low (implying that the model always predicts one label).
I was wondering if there is anything that might be obviously problematic with my code. This is the first time I am using a pre-trained model and pl lightning and I cannot really tell what might have gone wrong.
class Model(pl.LightningModule):
def __init__(self, n_classes, n_training_steps, n_warmup_steps, lr, fine_tune=False):
super().__init__()
self.save_hyperparameters()
self.bert = DistilBert.from_pretrained(BERT_MODEL_NAME, return_dict=True)
for name, param in self.bert.named_parameters():
param.requires_grad = False
self.classifier = nn.Linear(self.bert.config.hidden_size, self.hparams.n_classes)
self.criterion = nn.BCELoss()
def finetune(self):
self.fine_tune = True
for name, param in self.bert.named_parameters():
if 'layer.5' in name:
param.requires_grad = True
def forward(self, input_ids, attention_mask, labels=None):
x = self.bert(input_ids, attention_mask=attention_mask)
x = x.last_hidden_state[:,0,:]
x = self.classifier(x)
x = torch.sigmoid(x)
x = x.squeeze(dim=-1)
loss = 0
if labels is not None:
loss = self.criterion(x, labels.float())
return loss, x
def training_step(self, batch, batch_idx):
enc, labels = batch
input_ids, attention_mask = enc
loss, outputs = self.forward(input_ids, attention_mask, labels)
self.log("train_loss", loss, prog_bar=True, logger=True)
return {'loss': loss, 'predictions': outputs, 'labels': labels}
def validation_step(self, batch, batch_idx):
enc, labels = batch
input_ids, attention_mask = enc
loss, outputs = self.forward(input_ids, attention_mask, labels)
r = recall(outputs[:], labels[:])
self.log("val_loss", loss, prog_bar=True, logger=True)
self.log("val_recall", r, prog_bar=True, logger=True)
return {'loss': loss, 'predictions': outputs, 'labels': labels}
def test_step(self, batch, batch_idx):
enc, labels = batch
input_ids, attention_mask = enc
loss, outputs = self.forward(input_ids, attention_mask, labels)
self.log("test_loss", loss, prog_bar=True, logger=True)
return {'loss': loss, 'predictions': outputs, 'labels': labels}
def training_epoch_end(self, outputs):
labels = []
predictions = []
for o in outputs:
for o_labels in o['labels'].detach().cpu():
labels.append(o_labels)
for o_preds in o['predictions'].detach().cpu():
predictions.append(o_preds)
labels = torch.stack(labels).int()
predictions = torch.stack(predictions)
class_recall = recall(predictions[:], labels[:])
self.logger.experiment.add_scalar("recall/Train", class_recall, self.current_epoch)
def validation_epoch_end(self, outputs):
labels = []
predictions = []
for o in outputs:
for o_labels in o['labels'].detach().cpu():
labels.append(o_labels)
for o_preds in o['predictions'].detach().cpu():
predictions.append(o_preds)
labels = torch.stack(labels).int()
predictions = torch.stack(predictions)
class_recall = recall(predictions[:], labels[:])
self.logger.experiment.add_scalar("recall/Validation", class_recall, self.current_epoch)
def test_epoch_end(self, outputs):
labels = []
predictions = []
for o in outputs:
for o_labels in o['labels'].detach().cpu():
labels.append(o_labels)
for o_preds in o['predictions'].detach().cpu():
predictions.append(o_preds)
labels = torch.stack(labels).int()
predictions = torch.stack(predictions)
class_recall = recall(predictions[:], labels[:])
self.logger.experiment.add_scalar("recall/Test", class_recall, self.current_epoch)
def configure_optimizers(self):
optimizer = AdamW(self.parameters(), lr=self.hparams.lr if self.hparams.fine_tune == False else self.hparams.lr // 100)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=self.hparams.n_warmup_steps,
num_training_steps=self.hparams.n_training_steps
)
return dict(
optimizer=optimizer,
lr_scheduler=dict(
scheduler=scheduler,
interval='step'
)
)
if __name__ == "__main__":
data_module = SourceCodeDataModule(batch_size=BATCH_SIZE)
steps_per_epoch = len(train_loader) // BATCH_SIZE
total_training_steps = steps_per_epoch * N_EPOCHS
warmup_steps = total_training_steps // 5
model = Model(
n_classes=1,
n_warmup_steps = warmup_steps,
n_training_steps=total_training_steps,
lr=2e-5
)
logger = TensorBoardLogger("lightning_logs", name="bert_predictor")
early_stopping_callback = EarlyStopping(monitor='val_loss', patience=2)
trainer = pl.Trainer(
logger=logger,
checkpoint_callback=checkpoint_callback,
callbacks=[early_stopping_callback],
max_epochs=N_EPOCHS,
gpus=1 if str(device).startswith('cuda') else 0,
progress_bar_refresh_rate=30
)
# First just train the final layer.
trainer.fit(model, datamodule=data_module)
result = trainer.test(model, datamodule=data_module)
print(f"Result when training classifier only: {result}")
# Then train the whole model
model = Model.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)
model.finetune()
trainer.fit(model, datamodule=data_module)
result = trainer.test(model, datamodule=data_module)
print(f"Result when fine tuning: {result}")
Here,
def finetune(self):
self.fine_tune = True
for name, param in self.bert.named_parameters():
if 'layer.5' in name:
param.requires_grad = True
try to unfreeze more layers at the end of the neural net, maybe the weights are saturated and not learning enough. Also, pay attention to the loss you are using, as well as the activation function at the output.
This is my model:
class BiLSTM(nn.Module):
def __init__(self):
super(BiLSTM, self).__init__()
self.hidden_size = 128
drp = 0.2
n_classes = len(le.classes_)
self.embedding = nn.Embedding(max_features, embed_size)
self.embedding.weight = nn.Parameter(torch.tensor(embedding_matrix, dtype=torch.float32))
self.embedding.weight.requires_grad = False
self.lstm = nn.LSTM(embed_size, self.hidden_size, bidirectional=True, batch_first=True)
self.linear = nn.Linear(self.hidden_size*4 , 128)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(drp)
self.out = nn.Linear(128, n_classes)
def forward(self, x):
#rint(x.size())
h_embedding = self.embedding(x)
_embedding = torch.squeeze(torch.unsqueeze(h_embedding, 0))
h_lstm, _ = self.lstm(h_embedding)
avg_pool = torch.mean(h_lstm, 1)
max_pool, _ = torch.max(h_lstm, 1)
conc = torch.cat(( avg_pool, max_pool), 1)
conc = self.relu(self.linear(conc))
conc = self.dropout(conc)
out = self.out(conc)
return out
n_epochs = 87
model = BiLSTM()
loss_fn = nn.CrossEntropyLoss(reduction='mean',)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=0.002)
model.cuda()
# Load train and test in CUDA Memory
x_train = torch.tensor(train_X, dtype=torch.long).cuda()
y_train = torch.tensor(train_y, dtype=torch.long).cuda()
x_cv = torch.tensor(test_X, dtype=torch.long).cuda()
y_cv = torch.tensor(test_y, dtype=torch.long).cuda()
# Create Torch datasets
train = torch.utils.data.TensorDataset(x_train, y_train)
valid = torch.utils.data.TensorDataset(x_cv, y_cv)
# Create Data Loaders
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True)
valid_loader = torch.utils.data.DataLoader(valid, batch_size=batch_size, shuffle=True)
train_loss = []
valid_loss = []
for epoch in range(n_epochs):
start_time = time.time()
# Set model to train configuration
model.train()
avg_loss = 0.
for i, (x_batch, y_batch) in enumerate(train_loader):
# Predict/Forward Pass
y_pred = model(x_batch)
# Compute loss
loss = loss_fn(y_pred, y_batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_loss += loss.item() / len(train_loader)
#acc =n-avg_loss
# Set model to validation configuration
model.eval()
avg_val_loss = 0.
val_preds = np.zeros((len(x_cv),len(le.classes_)))
for i, (x_batch, y_batch) in enumerate(valid_loader):
y_pred = model(x_batch).detach()
avg_val_loss += loss_fn(y_pred, y_batch).item() / len(valid_loader)
#val_accuracy = n- avg_val_loss
# keep/store predictions
val_preds[i * batch_size:(i+1) * batch_size] =F.softmax(y_pred).cpu().numpy()
val_preds=val_preds*5000
# Check Accuracy
val_accuracy = sum(val_preds.argmax(axis=1)==test_y)/len(test_y)
train_loss.append(avg_loss)
valid_loss.append(avg_val_loss)
elapsed_time = time.time() - start_time
print('Epoch {}/{} \t Train_loss={:.4f} \t val_loss={:.4f} \t val_acc={:.4f} \t time={:.2f}s'.format(
epoch + 1, n_epochs , avg_loss, avg_val_loss, val_accuracy, elapsed_time))
This is the output I am receiving.I have used the BiLSTM model. I tried changing the loss techniques and dropout value but it didn't work. The issue is I guess the model is overfitting how can I increase the accuracy this is the output I am receiving.I have used the BiLSTM model. I tried changing the loss techniques and dropout value but it didn't work. The issue is I guess the model is overfitting how can I increase the accuracy
This is the output I am receiving.I have used the BiLSTM model. I tried changing the loss techniques and dropout value but it didn't work. The issue is I guess the model is overfitting how can I increase the accuracy, The size of the dataset is 3000
[1]: https://i.stack.imgur.com/NbK92.png [output looks like this][1]
[1]: https://i.stack.imgur.com/ll12J.png [Data Looks Like this][1]
As a Pytorch newbie (coming from tensorflow), I am unsure of how to implement Early Stopping. My research has led me discover that pytorch does not have a native way to so this. I have also discovered torchsample, but am unable to install it in my conda environment for whatever reason. Is there a simple way to go about applying early stopping without it? Here is my current setup:
class RegressionDataset(Dataset):
def __init__(self, X_data, y_data):
self.X_data = X_data
self.y_data = y_data
def __getitem__(self, index):
return self.X_data[index], self.y_data[index]
def __len__(self):
return len(self.X_data)
train_dataset = RegressionDataset(torch.from_numpy(X_train).float(), torch.from_numpy(y_train).float())
val_dataset = RegressionDataset(torch.from_numpy(X_val).float(), torch.from_numpy(y_val).float())
test_dataset = RegressionDataset(torch.from_numpy(X_test).float(), torch.from_numpy(y_test).float())
# Model Params
EPOCHS = 100
BATCH_SIZE = 1000
LEARNING_RATE = 0.001
NUM_FEATURES = np.shape(X_test)[1]
# Initialize Dataloader
train_loader = DataLoader(dataset = train_dataset, batch_size=BATCH_SIZE, shuffle = True)
val_loader = DataLoader(dataset = val_dataset, batch_size=BATCH_SIZE)
test_loader = DataLoader(dataset = test_dataset, batch_size=BATCH_SIZE)
# Define Neural Network Architecture
class MultipleRegression(nn.Module):
def __init__(self, num_features):
super(MultipleRegression, self).__init__()
# Define architecture
self.layer_1 = nn.Linear(num_features, 16)
self.layer_2 = nn.Linear(16, 32)
self.layer_3 = nn.Linear(32, 25)
self.layer_4 = nn.Linear(25, 20)
self.layer_5 = nn.Linear(20, 16)
self.layer_out = nn.Linear(16, 1)
self.relu = nn.ReLU() # ReLU applied to all layers
# Initialize weights and biases
nn.init.xavier_uniform_(self.layer_1.weight)
nn.init.zeros_(self.layer_1.bias)
nn.init.xavier_uniform_(self.layer_2.weight)
nn.init.zeros_(self.layer_2.bias)
nn.init.xavier_uniform_(self.layer_3.weight)
nn.init.zeros_(self.layer_3.bias)
nn.init.xavier_uniform_(self.layer_4.weight)
nn.init.zeros_(self.layer_4.bias)
nn.init.xavier_uniform_(self.layer_5.weight)
nn.init.zeros_(self.layer_5.bias)
nn.init.xavier_uniform_(self.layer_out.weight)
nn.init.zeros_(self.layer_out.bias)
def forward(self, inputs):
x = self.relu(self.layer_1(inputs))
x = self.relu(self.layer_2(x))
x = self.relu(self.layer_3(x))
x = self.relu(self.layer_4(x))
x = self.relu(self.layer_5(x))
x = self.layer_out(x)
return(x)
def predict(self, test_inputs):
x = self.relu(self.layer_1(test_inputs))
x = self.relu(self.layer_2(x))
x = self.relu(self.layer_3(x))
x = self.relu(self.layer_4(x))
x = self.relu(self.layer_5(x))
x = self.layer_out(x)
return(x)
# Check for GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
model = MultipleRegression(NUM_FEATURES)
model.to(device)
print(model)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr = LEARNING_RATE)
# define dictionary to store loss/epochs for training and validation
loss_stats = {
"train": [],
"val": []
}
# begin training
print("Begin Training")
for e in tqdm(range(1, EPOCHS+1)):
# Training
train_epoch_loss = 0
model.train()
for X_train_batch, y_train_batch in train_loader:
X_train_batch, y_train_batch = X_train_batch.to(device), y_train_batch.to(device)
optimizer.zero_grad()
y_train_pred = model(X_train_batch)
train_loss = criterion(y_train_pred, y_train_batch.unsqueeze(1))
train_loss.backward()
optimizer.step()
train_epoch_loss += train_loss.item()
# validation
with torch.no_grad():
val_epoch_loss = 0
model.eval()
for X_val_batch, y_val_batch in val_loader:
X_val_batch, y_val_batch = X_val_batch.to(device), y_val_batch.to(device)
y_val_pred = model(X_val_batch)
val_loss = criterion(y_val_pred, y_val_batch.unsqueeze(1))
val_epoch_loss += val_loss.item()
loss_stats["train"].append(train_epoch_loss/len(train_loader))
loss_stats["val"].append(val_epoch_loss/len(val_loader))
print(f"Epoch {e}: \ Train loss: {train_epoch_loss/len(train_loader):.5f} \ Val loss: {val_epoch_loss/len(val_loader):.5f}")
# Visualize loss and accuracy
train_val_loss_df = pd.DataFrame.from_dict(loss_stats).reset_index().melt(id_vars=["index"]).rename(columns = {"index":"epochs"})
plt.figure()
sns.lineplot(data = train_val_loss_df, x = "epochs", y = "value", hue = "variable").set_title("Train-Val Loss/Epoch")
# Test model
y_pred_list = []
with torch.no_grad():
model.eval()
for X_batch, _ in test_loader:
X_batch = X_batch.to(device)
y_test_pred = model(X_batch)
y_pred_list.append(y_test_pred.cpu().numpy())
y_pred_list = [a.squeeze().tolist() for a in y_pred_list]
y_pred_list = [item for sublist in y_pred_list for item in sublist]
y_pred_list = np.array(y_pred_list)
mse = mean_squared_error(y_test, y_pred_list)
r_square = r2_score(y_test, y_pred_list)
print("Mean Squared Error :", mse)
print("R^2 :", r_square)
A basic way to do this is to keep track of the best validation loss obtained so far.
You can have a variable best_loss = 0 initialized before your loop over epochs (or you could do other things like best loss per epoch, etc.).
After each validation pass then do:
if val_loss > best_loss:
best_loss = val_loss
# At this point also save a snapshot of the current model
torch.save(model, 'my_model_best_loss.pth')
Then, if the best_loss does not improve significantly after some number of training steps, or by the end of the epoch, or if it val_loss gets worse, break out of the loop and terminate the training there.
For implementing algorithms like early stopping (and your training loop in general) you may find it easier to give PyTorch Lightning a try (no affiliation, but it's much easier than trying to roll everything by hand).
I made a CNN model for emotion recognition on 5 emotions. I wanted to test it on an single image to get the individual class predictions for each emotion.
Evaluating the model works, but I can't seem to find how to make a prediction with a single image. How can I do that?
The Model
def conv_block(in_channels, out_channels, pool=False):
layers = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ELU(inplace=True)]
if pool: layers.append(nn.MaxPool2d(2))
return nn.Sequential(*layers)
class ResNet(ImageClassificationBase):
def __init__(self, in_channels, num_classes):
super().__init__()
self.conv1 = conv_block(in_channels, 128)
self.conv2 = conv_block(128, 128, pool=True)
self.res1 = nn.Sequential(conv_block(128, 128), conv_block(128, 128))
self.drop1 = nn.Dropout(0.5)
self.conv3 = conv_block(128, 256)
self.conv4 = conv_block(256, 256, pool=True)
self.res2 = nn.Sequential(conv_block(256, 256), conv_block(256, 256))
self.drop2 = nn.Dropout(0.5)
self.conv5 = conv_block(256, 512)
self.conv6 = conv_block(512, 512, pool=True)
self.res3 = nn.Sequential(conv_block(512, 512), conv_block(512, 512))
self.drop3 = nn.Dropout(0.5)
self.classifier = nn.Sequential(nn.MaxPool2d(6),
nn.Flatten(),
nn.Linear(512, num_classes))
def forward(self, xb):
out = self.conv1(xb)
out = self.conv2(out)
out = self.res1(out) + out
out = self.drop1(out)
out = self.conv3(out)
out = self.conv4(out)
out = self.res2(out) + out
out = self.drop2(out)
out = self.conv5(out)
out = self.conv6(out)
out = self.res3(out) + out
out = self.drop3(out)
out = self.classifier(out)
return out
The fit_one_cycle function is called to train the model
#torch.no_grad()
def evaluate(model, val_loader):
model.eval()
outputs = [model.validation_step(batch) for batch in val_loader]
return model.validation_epoch_end(outputs)
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
def fit_one_cycle(epochs, max_lr, model, train_loader, val_loader,
weight_decay=0, grad_clip=None, opt_func=torch.optim.SGD):
torch.cuda.empty_cache()
history = []
# Set up custom optimizer with weight decay
optimizer = opt_func(model.parameters(), max_lr, weight_decay=weight_decay)
# Set up one-cycle learning rate scheduler
sched = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr, epochs=epochs,
steps_per_epoch=len(train_loader))
for epoch in range(epochs):
# Training Phase
model.train()
train_losses = []
lrs = []
for batch in train_loader:
loss = model.training_step(batch)
train_losses.append(loss)
loss.backward()
# Gradient clipping
if grad_clip:
nn.utils.clip_grad_value_(model.parameters(), grad_clip)
optimizer.step()
optimizer.zero_grad()
# Record & update learning rate
lrs.append(get_lr(optimizer))
sched.step()
# Validation phase
result = evaluate(model, val_loader)
result['train_loss'] = torch.stack(train_losses).mean().item()
result['lrs'] = lrs
model.epoch_end(epoch, result)
history.append(result)
return history
This returns the accuracy and loss, i want to change this so it returns prediction percentages for each class.
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
class ImageClassificationBase(nn.Module):
def training_step(self, batch):
images, labels = batch
out = self(images)
loss = F.cross_entropy(out, labels)
return loss
def validation_step(self, batch):
images, labels = batch
out = self(images)
loss = F.cross_entropy(out, labels)
acc = accuracy(out, labels)
return {'val_loss': loss, 'val_acc': acc}
def validation_epoch_end(self, outputs):
batch_losses = [x['val_loss'] for x in outputs]
epoch_loss = torch.stack(batch_losses).mean()
batch_accs = [x['val_acc'] for x in outputs]
epoch_acc = torch.stack(batch_accs).mean()
return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
def epoch_end(self, epoch, result):
print("Epoch [{}], last_lr: {:.5f}, train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}".format(
epoch, result['lrs'][-1], result['train_loss'], result['val_loss'], result['val_acc']))
Evaluating the model works, but I can't seem to find how to make a
prediction with a single image. How can I do that?
Simply, if you have a single image make sure to:
use additional 1 dimension at the beginning
make sure to use CHW format instead of HWC (or specify that within pytorch, check out how to do that here)
For example:
my_model = CNN(...)
random_image = torch.randn(1, 3, 100, 100) # 3 channels, 100x100 img
BTW. Your accuracy could be written a little simpler like this:
def accuracy(outputs, labels):
preds = torch.argmax(outputs, dim=1)
return torch.sum(preds == labels) / len(preds)
Getting class probability
Similar to argmax you can use softmax which transforms logits (unnormalized probability outputted by your network) into probabilities:
def probability(outputs):
return torch.nn.functional.softmax(outputs, dim=1)