This script is designed to load a pretrained PyTorch model for MNIST digit classification from a tar.gz file, extract it, and use the model to perform inference on a given input image.
%%writefile inference.py
import torch
import torchvision.transforms as transforms
from PIL import Image
from torch.autograd import Variable
import argparse
import tarfile
class CustomModel(torch.nn.Module):
def __init__(self):
super(CustomModel, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 10, 5)
self.conv2 = torch.nn.Conv2d(10, 20, 5)
self.fc1 = torch.nn.Linear(320, 50)
self.fc2 = torch.nn.Linear(50, 10)
def forward(self, x):
x = torch.relu(self.conv1(x))
x = torch.max_pool2d(x, 2)
x = torch.relu(self.conv2(x))
x = torch.max_pool2d(x, 2)
x = torch.flatten(x, 1)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
output = torch.log_softmax(x, dim=1)
return output
def extract_tar_gz(file_path, output_dir):
with tarfile.open(file_path, 'r:gz') as tar:
tar.extractall(path=output_dir)
# Parse command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--tar_gz_file_path', type=str, required=True, help='Path to the tar.gz file')
parser.add_argument('--output_directory', type=str, required=True, help='Output directory to extract the tar.gz file')
parser.add_argument('--image_path', type=str, required=True, help='Path to the input image file')
args = parser.parse_args()
# Extract the tar.gz file
tar_gz_file_path = args.tar_gz_file_path
output_directory = args.output_directory
extract_tar_gz(tar_gz_file_path, output_directory)
# Load the model
model_path = f"{output_directory}/model.pth"
model = CustomModel()
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
model.eval()
# Transformations for the MNIST dataset
transform = transforms.Compose([
transforms.Resize((28, 28)),
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
])
# Function to run inference on an image
def run_inference(image, model):
image_tensor = transform(image).unsqueeze(0) # Apply transformations and add batch dimension
input = Variable(image_tensor)
# Perform inference
output = model(input)
_, predicted = torch.max(output.data, 1)
return predicted.item()
# Example usage
image_path = args.image_path
image = Image.open(image_path)
predicted_class = run_inference(image, model)
print(f"Predicted class: {predicted_class}")
Running the inference script
To use this script, you need to provide the paths to the tar.gz file containing the pre-trained model, the output directory where the model will be extracted, and the input image file for which you want to perform inference. The script will output the predicted digit (class) for the given input image.
-w /inputs Setting the current working directory at /inputs in the container
-i src=s3://sagemaker-sample-files/datasets/image/MNIST/model/pytorch-training-2020-11-21-22-02-56-203/model.tar.gz,dst=/model/,opt=region=us-east-1: Mounting the s3 bucket at the destination path provided /model/ and specifying the region where the bucket is located opt=region=us-east-1
-i git://github.com/js-ts/mnist-test.git: Flag to mount the source code repo from GitHub. It would mount the repo at /inputs/js-ts/mnist-test in this case it also contains the test image.
pytorch/pytorch: The name of the Docker image.
-- python /inputs/js-ts/mnist-test/inference.py --tar_gz_file_path /model/model.tar.gz --output_directory /model-pth --image_path /inputs/js-ts/mnist-test/image.png: The command to run inference on the model.
/model/model.tar.gz is the path to the model file.
/model-pth is the output directory for the model.
/inputs/js-ts/mnist-test/image.png is the path to the input image.
The job has been submitted and Bacalhau has printed out the related job id. We store that in an environment variable so that we can reuse it later on.
