Add examples for image-to-text task (#2754)

* add blip2 examples

* fix formatting

* address pr comments

* .

* fix formatting

* use blip model

* use blip for cli examples

* format

cli/foundation-models/system/inference/image-to-text/deploy-batch.yaml

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+$schema: https://azuremlschemas.azureedge.net/latest/batchDeployment.schema.json
+name: demo
+description: "Batch endpoint for image-to-text task"
+type: model
+resources:
+    instance_count: 1
+settings:
+    mini_batch_size: 1
+

cli/foundation-models/system/inference/image-to-text/deploy-online.yaml

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+$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
+name: image-to-text-demo
+instance_type: Standard_DS5_v2
+instance_count: 1
+liveness_probe:
+  initial_delay: 180
+  period: 180
+  failure_threshold: 49
+  timeout: 299
+request_settings:
+  request_timeout_ms: 90000
+

cli/foundation-models/system/inference/image-to-text/image-to-text-batch-endpoint.sh

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+set -x
+# The commands in this file map to steps in this notebook: https://aka.ms/azureml-infer-batch-sdk-blip-image-to-text
+# The sample scoring file available in the same folder as the above notebook.
+
+# script inputs
+registry_name="azureml"
+subscription_id="<SUBSCRIPTION_ID>"
+resource_group_name="<RESOURCE_GROUP>"
+workspace_name="<WORKSPACE_NAME>"
+
+# This is the model from system registry that needs to be deployed
+model_name="Salesforce-BLIP-image-captioning-base"
+model_label="latest"
+
+deployment_compute="cpu-cluster"
+# todo: fetch deployment_sku from the min_inference_sku tag of the model
+deployment_sku="Standard_DS5_v2"
+
+
+version=$(date +%s)
+endpoint_name="image-to-text-$version"
+deployment_name="demo-$version"
+
+# Prepare data for deployment
+data_path="data_batch"
+python ./prepare_data.py --mode "batch" --data_path $data_path
+# sample request data in folder of csv files with image and text columns
+sample_request_csv_folder="./data_batch/odfridgeObjects/batch"
+
+# 1. Setup pre-requisites
+if [ "$subscription_id" = "<SUBSCRIPTION_ID>" ] || \
+   ["$resource_group_name" = "<RESOURCE_GROUP>" ] || \
+   [ "$workspace_name" = "<WORKSPACE_NAME>" ]; then 
+    echo "Please update the script with the subscription_id, resource_group_name and workspace_name"
+    exit 1
+fi
+
+az account set -s $subscription_id
+workspace_info="--resource-group $resource_group_name --workspace-name $workspace_name"
+
+# 2. Check if the model exists in the registry
+# Need to confirm model show command works for registries outside the tenant (aka system registry)
+if ! az ml model show --name $model_name --label $model_label --registry-name $registry_name 
+then
+    echo "Model $model_name:$model_label does not exist in registry $registry_name"
+    exit 1
+fi
+
+# Get the latest model version
+model_version=$(az ml model show --name $model_name --label $model_label --registry-name $registry_name --query version --output tsv)
+
+# 3. Check if compute $deployment_compute exists, else create it
+if az ml compute show --name $deployment_compute $workspace_info
+then
+    echo "Compute cluster $deployment_compute already exists"
+else
+    echo "Creating compute cluster $deployment_compute"
+    az ml compute create --name $deployment_compute --type amlcompute --min-instances 0 --max-instances 2 --size $deployment_sku $workspace_info || {
+        echo "Failed to create compute cluster $deployment_compute"
+        exit 1
+    }
+fi
+
+# 4. Deploy the model to an endpoint
+# Create batch endpoint
+az ml batch-endpoint create --name $endpoint_name $workspace_info  || {
+    echo "endpoint create failed"; exit 1;
+}
+
+# Deploy model from registry to endpoint in workspace
+az ml batch-deployment create --file ./deploy-batch.yaml $workspace_info --set \
+  endpoint_name=$endpoint_name model=azureml://registries/$registry_name/models/$model_name/versions/$model_version \
+  compute=$deployment_compute \
+  name=$deployment_name || {
+    echo "deployment create failed"; exit 1;
+}
+
+# 5 Try a scoring request with csv file
+
+# Check if scoring data file exists
+if [ -d $sample_request_csv_folder ]; then
+    echo "Invoking endpoint $endpoint_name with following input:\n\n"
+    echo "\n\n"
+else
+    echo "Scoring file $sample_request_csv_folder does not exist"
+    exit 1
+fi
+
+# Invoke the endpoint
+# Note: If job failed with Out of Memory Error then 
+# please try splitting your input into smaller csv files or
+# decrease the mini_batch_size for the deployment (see deploy-batch.yaml).
+csv_inference_job=$(az ml batch-endpoint invoke --name $endpoint_name \
+ --deployment-name $deployment_name --input $sample_request_csv_folder --input-type \
+  uri_folder $workspace_info --query name --output tsv) || {
+    echo "endpoint invoke failed"; exit 1;
+}
+
+# wait for the job to complete
+az ml job stream --name $csv_inference_job $workspace_info || {
+    echo "job stream failed"; exit 1;
+}
+
+# 6. Delete the endpoint
+# Batch endpoints use compute resources only when jobs are submitted. You can keep the 
+# batch endpoint for your reference without worrying about compute bills, or choose to delete the endpoint. 
+# If you created your compute cluster to have zero minimum instances and scale down soon after being idle, 
+# you won't be charged for an unused compute.
+az ml batch-endpoint delete --name $endpoint_name $workspace_info --yes || {
+    echo "endpoint delete failed"; exit 1;
+}

cli/foundation-models/system/inference/image-to-text/image-to-text-online-endpoint.sh

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+set -x
+# The commands in this file map to steps in this notebook: https://aka.ms/azureml-infer-online-sdk-blip-image-to-text
+# The sample scoring file available in the same folder as the above notebook
+
+# script inputs
+registry_name="azureml"
+subscription_id="<SUBSCRIPTION_ID>"
+resource_group_name="<RESOURCE_GROUP>"
+workspace_name="<WORKSPACE_NAME>"
+
+# This is the model from system registry that needs to be deployed
+model_name="Salesforce-BLIP-image-captioning-base"
+model_label="latest"
+
+version=$(date +%s)
+endpoint_name="image-to-text-$version"
+
+# Todo: fetch deployment_sku from the min_inference_sku tag of the model
+deployment_sku="Standard_DS5_v2"
+
+# Prepare data for deployment
+data_path="./data_online"
+python ./prepare_data.py --data_path $data_path --mode "online"
+# sample_request_data
+sample_request_data="$data_path/odfridgeObjects/sample_request_data.json"
+# 1. Setup pre-requisites
+if [ "$subscription_id" = "<SUBSCRIPTION_ID>" ] || \
+   ["$resource_group_name" = "<RESOURCE_GROUP>" ] || \
+   [ "$workspace_name" = "<WORKSPACE_NAME>" ]; then 
+    echo "Please update the script with the subscription_id, resource_group_name and workspace_name"
+    exit 1
+fi
+
+az account set -s $subscription_id
+workspace_info="--resource-group $resource_group_name --workspace-name $workspace_name"
+
+# 2. Check if the model exists in the registry
+# Need to confirm model show command works for registries outside the tenant (aka system registry)
+if ! az ml model show --name $model_name --label $model_label --registry-name $registry_name 
+then
+    echo "Model $model_name:$model_label does not exist in registry $registry_name"
+    exit 1
+fi
+
+# Get the latest model version
+model_version=$(az ml model show --name $model_name --label $model_label --registry-name $registry_name --query version --output tsv)
+
+# 3. Deploy the model to an endpoint
+# Create online endpoint 
+az ml online-endpoint create --name $endpoint_name $workspace_info  || {
+    echo "endpoint create failed"; exit 1;
+}
+
+# Deploy model from registry to endpoint in workspace
+az ml online-deployment create --file deploy-online.yaml $workspace_info --all-traffic --set \
+  endpoint_name=$endpoint_name model=azureml://registries/$registry_name/models/$model_name/versions/$model_version \
+  instance_type=$deployment_sku || {
+    echo "deployment create failed"; exit 1;
+}
+
+# 4. Try a sample scoring request
+
+# Check if scoring data file exists
+if [ -f $sample_request_data ]; then
+    echo "Invoking endpoint $endpoint_name with $sample_request_data\n\n"
+else
+    echo "Scoring file $sample_request_data does not exist"
+    exit 1
+fi
+
+az ml online-endpoint invoke --name $endpoint_name --request-file $sample_request_data $workspace_info || {
+    echo "endpoint invoke failed"; exit 1;
+}
+
+# 6. Delete the endpoint and sample_request_data.json
+az ml online-endpoint delete --name $endpoint_name $workspace_info --yes || {
+    echo "endpoint delete failed"; exit 1;
+}
+
+rm $sample_request_data

cli/foundation-models/system/inference/image-to-text/prepare_data.py

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+import argparse
+import base64
+import json
+import os
+import shutil
+import urllib.request
+import pandas as pd
+from zipfile import ZipFile
+
+
+def download_and_unzip(dataset_parent_dir: str) -> None:
+    """Download image dataset and unzip it.
+
+    :param dataset_parent_dir: dataset parent directory to which dataset will be downloaded
+    :type dataset_parent_dir: str
+    """
+    # Create directory, if it does not exist
+    os.makedirs(dataset_parent_dir, exist_ok=True)
+
+    # download data
+    download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip"
+    print(f"Downloading data from {download_url}")
+
+    # Extract current dataset name from dataset url
+    dataset_name = os.path.basename(download_url).split(".")[0]
+    # Get dataset path for later use
+    dataset_dir = os.path.join(dataset_parent_dir, dataset_name)
+
+    if os.path.exists(dataset_dir):
+        shutil.rmtree(dataset_dir)
+
+    # Get the name of zip file
+    data_file = os.path.join(dataset_parent_dir, f"{dataset_name}.zip")
+
+    # Download data from public url
+    urllib.request.urlretrieve(download_url, filename=data_file)
+
+    # extract files
+    with ZipFile(data_file, "r") as zip:
+        print("extracting files...")
+        zip.extractall(path=dataset_parent_dir)
+        print("done")
+    # delete zip file
+    os.remove(data_file)
+    return dataset_dir
+
+
+def read_image(image_path: str) -> bytes:
+    """Read image from path.
+
+    :param image_path: image path
+    :type image_path: str
+    :return: image in bytes format
+    :rtype: bytes
+    """
+    with open(image_path, "rb") as f:
+        return f.read()
+
+
+def prepare_data_for_online_inference(dataset_dir: str) -> None:
+    """Prepare request json for online inference.
+
+    :param dataset_dir: dataset directory
+    :type dataset_dir: str
+    """
+    sample_image_1 = os.path.join(dataset_dir, "images", "99.jpg")
+    sample_image_2 = os.path.join(dataset_dir, "images", "1.jpg")
+
+    request_json = {
+        "input_data": {
+            "columns": ["image"],
+            "index": [0, 1],
+            "data": [
+                [base64.encodebytes(read_image(sample_image_1)).decode("utf-8")],
+                [base64.encodebytes(read_image(sample_image_2)).decode("utf-8")],
+            ],
+        }
+    }
+
+    request_file_name = os.path.join(dataset_dir, "sample_request_data.json")
+
+    with open(request_file_name, "w") as request_file:
+        json.dump(request_json, request_file)
+
+
+def prepare_data_for_batch_inference(dataset_dir: str) -> None:
+    """Prepare image folder and csv file for batch inference.
+
+    This function will create a folder of csv files with images in base64 format.
+    :param dataset_dir: dataset directory
+    :type dataset_dir: str
+    """
+
+    csv_folder_path = os.path.join(dataset_dir, "batch")
+    os.makedirs(csv_folder_path, exist_ok=True)
+    batch_input_file = "batch_input.csv"
+    dataset_dir = os.path.join(dataset_dir, "images")
+
+    image_list = []
+    for image_name in os.listdir(dataset_dir):
+        image = read_image(os.path.join(dataset_dir, image_name))
+        data = base64.encodebytes(image).decode("utf-8")
+        image_list.append(data)
+
+    # Divide the image list into files of 10 rows each
+    batch_size_per_predict = 10
+    divided_list = [
+        image_list[i * batch_size_per_predict : (i + 1) * batch_size_per_predict]
+        for i in range(
+            (len(image_list) + batch_size_per_predict - 1) // batch_size_per_predict
+        )
+    ]
+
+    list_num = 0
+    for l in divided_list:
+        batch_df = pd.DataFrame(l, columns=["image"])
+        filepath = os.path.join(csv_folder_path, str(list_num) + batch_input_file)
+        list_num = list_num + 1
+        batch_df.to_csv(filepath)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Prepare data for image-to-text task")
+    parser.add_argument(
+        "--data_path", type=str, default="data", help="Dataset location"
+    )
+    parser.add_argument(
+        "--mode",
+        type=str,
+        default="online",
+        help="prepare data for online or batch inference",
+    )
+
+    args, unknown = parser.parse_known_args()
+    args_dict = vars(args)
+
+    dataset_dir = download_and_unzip(
+        dataset_parent_dir=os.path.join(
+            os.path.dirname(os.path.realpath(__file__)), args.data_path
+        ),
+    )
+
+    if args.mode == "online":
+        prepare_data_for_online_inference(dataset_dir=dataset_dir)
+    else:
+        prepare_data_for_batch_inference(dataset_dir=dataset_dir)