The last hidden state contains the sequence of hidden-states at the output of the final layer, while the pooler_output provides a classification token after processing through a linear layer and tanh activation function for auxiliary pretraining tasks. Attentions weights are also available in case they’re needed.
To use this model, first load your dataset using Hugging Face Datasets or another library of choice. Then preprocess your images using AutoImageProcessor from transformers to obtain pixel values in the correct format for input into DPTModel. Finally, pass these inputs through the model and retrieve the desired output(s).
Here’s an example code snippet:
# Import necessary libraries
from transformers import AutoImageProcessor, DPTModel
import torch
from datasets import load_dataset
# Load dataset using Hugging Face Datasets
dataset = load_dataset("huggingface/cats-image", trust_remote_code=True)
# Get first image from test set
image = dataset["test"]["image"][0]
# Preprocess the image using AutoImageProcessor and return pixel values in PyTorch format
image_processor = AutoImageProcessor.from_pretrained("Intel/dpt-large")
inputs = image_processor(image, return_tensors="pt")
# Load DPTModel from pretrained weights
model = DPTModel.from_pretrained("Intel/dpt-large")
# Run the model on input and retrieve last hidden state
with torch.no_grad():
# Pass inputs through the model and retrieve outputs
outputs = model(**inputs)
# Retrieve last hidden state from outputs
last_hidden_states = outputs.last_hidden_state
In this example, we first load our dataset using Hugging Face Datasets and get the first image from the test set. We then preprocess the image using AutoImageProcessor to obtain pixel values in PyTorch format. Next, we load DPTModel from pretrained weights if available, otherwise initialize a new one with all layers defined in the constructor. Finally, we run the model on input using `**inputs` syntax and retrieve the last hidden state output by the model for further processing or analysis.
To train this DPTModel, you can follow these steps:
1. Load your dataset into PyTorch format (e.g., as a TensorDataset).
2. Define your training loop using `torch.optim` and `torch.nn.functional`.
3. Train the model for a specified number of epochs or until convergence is reached.
4. Evaluate the performance of the model on a validation set to ensure it’s not overfitting.
5. Save the trained weights using `model.save_pretrained()` and load them later if needed.
6. Use the loaded model for inference by passing input images through its forward method.
Here is an example training loop:
# Import necessary libraries
from transformers import DPTModel, AutoImageProcessor
import torch
from datasets import Dataset
from tqdm import tqdm
# Load dataset into PyTorch format
train_dataset = Dataset.load_from_disk("path/to/training/data")
val_dataset = Dataset.load_from_disk("path/to/validation/data")
# Define training loop
for epoch in range(num_epochs):
for batch, (inputs, labels) in enumerate(tqdm(train_dataloader)):
# Preprocess inputs using AutoImageProcessor and convert to PyTorch format
input_processor = AutoImageProcessor.from_pretrained("Intel/dpt-large")
inputs = input_processor(inputs, return_tensors="pt")
# Run the model on input and retrieve last hidden state
with torch.no_grad():
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
# Calculate loss using MSE or other appropriate metric
loss = ...
# Backpropagate the error and update weights using Adam optimizer
optimizer.step()
# Evaluate performance on validation set after each epoch
val_loss, val_acc = evaluate(val_dataset)
# Print results for each epoch
print("Epoch {}: Loss {:.4f}, Accuracy {:.2%}".format(epoch+1, loss, acc))
# Import necessary libraries
from transformers import DPTModel, AutoImageProcessor # Importing necessary libraries for using the DPTModel and AutoImageProcessor
import torch # Importing torch for using PyTorch framework
from datasets import Dataset # Importing Dataset for loading and processing data
from tqdm import tqdm # Importing tqdm for displaying progress bar during training
# Load dataset into PyTorch format
train_dataset = Dataset.load_from_disk("path/to/training/data") # Loading training dataset into PyTorch format
val_dataset = Dataset.load_from_disk("path/to/validation/data") # Loading validation dataset into PyTorch format
# Define training loop
for epoch in range(num_epochs): # Looping through the specified number of epochs
for batch, (inputs, labels) in enumerate(tqdm(train_dataloader)): # Looping through batches of data using tqdm to display progress
# Preprocess inputs using AutoImageProcessor and convert to PyTorch format
input_processor = AutoImageProcessor.from_pretrained("Intel/dpt-large") # Initializing AutoImageProcessor with the specified model
inputs = input_processor(inputs, return_tensors="pt") # Preprocessing inputs and converting them to PyTorch format
# Run the model on input and retrieve last hidden state
with torch.no_grad(): # Disabling gradient calculation for faster inference
outputs = model(**inputs) # Running the model on the preprocessed inputs
last_hidden_states = outputs.last_hidden_state # Retrieving the last hidden state from the model's output
# Calculate loss using MSE or other appropriate metric
loss = ... # Calculating the loss using the specified metric
# Backpropagate the error and update weights using Adam optimizer
optimizer.step() # Backpropagating the error and updating the weights using the specified optimizer
# Evaluate performance on validation set after each epoch
val_loss, val_acc = evaluate(val_dataset) # Evaluating the performance of the model on the validation set
# Print results for each epoch
print("Epoch {}: Loss {:.4f}, Accuracy {:.2%}".format(epoch+1, loss, acc)) # Printing the loss and accuracy for each epoch
In this example, we first load our training and validation datasets into PyTorch format using the `Dataset` class from transformers. We then define a training loop that iterates over batches of data and calculates the loss using MSE or another appropriate metric. After each epoch, we evaluate performance on the validation set and print results for each epoch.