gemini-code-assist[bot] commented on code in PR #37186: URL: https://github.com/apache/beam/pull/37186#discussion_r3292810460
########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,637 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""This pipeline performs image captioning using a multi-model approach: +BLIP generates candidate captions, CLIP ranks them by image-text similarity. + +The pipeline reads image URIs from a GCS input file, decodes images, runs BLIP +caption generation in batches on GPU, then runs CLIP ranking in batches on GPU. +Results are written to BigQuery. +""" + +import argparse +import io +import json +import logging +import threading +import time +from typing import Any +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple + +import apache_beam as beam +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.runners.runner import PipelineResult +from apache_beam.transforms import window + +from google.api_core.exceptions import NotFound +from google.cloud import pubsub_v1 +import torch +import PIL.Image as PILImage + +# ============ Utility ============ + + +def now_millis() -> int: + return int(time.time() * 1000) + + +def decode_pil(image_bytes: bytes) -> PILImage.Image: + with PILImage.open(io.BytesIO(image_bytes)) as img: + img = img.convert("RGB") + img.load() + return img + + +# ============ DoFns ============ + + +class MakeKeyDoFn(beam.DoFn): + """Produce (uri, uri) so the URI is used as the stable key.""" + def process(self, element: str): + uri = element + yield uri, uri + + +class ReadImageBytesDoFn(beam.DoFn): + """Turn (uri, uri) -> (uri, dict(image_bytes)).""" + def process(self, kv: Tuple[str, str]): + uri, _ = kv + try: + with FileSystems.open(uri) as f: + image_bytes = f.read() + yield uri, {"image_bytes": image_bytes} + except Exception as e: + logging.warning("Failed to read image %s: %s", uri, e) + return + + +class DecodeImageDoFn(beam.DoFn): + """Turn (uri, dict(image_bytes)) -> (uri, dict(image)).""" + def process(self, kv: Tuple[str, Dict[str, Any]]): + uri, value = kv + image_bytes = value["image_bytes"] + + try: + image = decode_pil(image_bytes) + except Exception as e: + logging.warning("Failed to decode image %s: %s", uri, e) + image = PILImage.new("RGB", (224, 224), color=(0, 0, 0)) + + yield uri, {"image": image} + + +class PostProcessDoFn(beam.DoFn): + """Final PredictionResult -> row for BigQuery.""" + def __init__(self, blip_name: str, clip_name: str): + self.blip_name = blip_name + self.clip_name = clip_name + + def process(self, kv: Tuple[str, PredictionResult]): + uri, pred = kv + if hasattr(pred, "inference"): + inf = pred.inference or {} + else: + inf = pred + # Expected inference fields from CLIP handler: + # best_caption, best_score, candidates, scores, blip_ms, clip_ms, total_ms + best_caption = inf.get("best_caption", "") + best_score = inf.get("best_score", None) + candidates = inf.get("candidates", []) + scores = inf.get("scores", []) + blip_ms = inf.get("blip_ms", None) + clip_ms = inf.get("clip_ms", None) + total_ms = inf.get("total_ms", None) + + yield { + "image_id": uri, + "blip_model": self.blip_name, + "clip_model": self.clip_name, + "best_caption": best_caption, + "best_score": float(best_score) if best_score is not None else None, + "candidates": json.dumps(candidates), + "scores": json.dumps(scores), + "blip_ms": int(blip_ms) if blip_ms is not None else None, + "clip_ms": int(clip_ms) if clip_ms is not None else None, + "total_ms": int(total_ms) if total_ms is not None else None, + "infer_ms": now_millis(), + } + + +# ============ Model Handlers ============ + + +class BlipCaptionModelHandler(ModelHandler): + def __init__( + self, + model_name: str, + device: str, + batch_size: int, + num_captions: int, + max_new_tokens: int, + num_beams: int): + self.model_name = model_name + self.device = device + self.batch_size = batch_size + self.num_captions = num_captions + self.max_new_tokens = max_new_tokens + self.num_beams = num_beams + + def load_model(self): + from transformers import BlipForConditionalGeneration, BlipProcessor + processor = BlipProcessor.from_pretrained(self.model_name) + model = BlipForConditionalGeneration.from_pretrained(self.model_name) + return (model, processor) Review Comment:  Moving the model to the device and setting it to evaluation mode should be done once during model loading rather than in every inference call to improve performance. ```suggestion def load_model(self): from transformers import BlipForConditionalGeneration, BlipProcessor processor = BlipProcessor.from_pretrained(self.model_name) model = BlipForConditionalGeneration.from_pretrained(self.model_name) model.to(self.device) model.eval() return (model, processor) ``` ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,637 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""This pipeline performs image captioning using a multi-model approach: +BLIP generates candidate captions, CLIP ranks them by image-text similarity. + +The pipeline reads image URIs from a GCS input file, decodes images, runs BLIP +caption generation in batches on GPU, then runs CLIP ranking in batches on GPU. +Results are written to BigQuery. +""" + +import argparse +import io +import json +import logging +import threading +import time +from typing import Any +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple + +import apache_beam as beam +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.runners.runner import PipelineResult +from apache_beam.transforms import window + +from google.api_core.exceptions import NotFound +from google.cloud import pubsub_v1 +import torch +import PIL.Image as PILImage + +# ============ Utility ============ + + +def now_millis() -> int: + return int(time.time() * 1000) + + +def decode_pil(image_bytes: bytes) -> PILImage.Image: + with PILImage.open(io.BytesIO(image_bytes)) as img: + img = img.convert("RGB") + img.load() + return img + + +# ============ DoFns ============ + + +class MakeKeyDoFn(beam.DoFn): + """Produce (uri, uri) so the URI is used as the stable key.""" + def process(self, element: str): + uri = element + yield uri, uri + + +class ReadImageBytesDoFn(beam.DoFn): + """Turn (uri, uri) -> (uri, dict(image_bytes)).""" + def process(self, kv: Tuple[str, str]): + uri, _ = kv + try: + with FileSystems.open(uri) as f: + image_bytes = f.read() + yield uri, {"image_bytes": image_bytes} + except Exception as e: Review Comment:  Catching a broad `Exception` is discouraged as it can mask unexpected errors and make debugging difficult. Consider catching more specific exceptions (e.g., `IOError`, `GoogleCloudError`) or logging the exception details with the stack trace. ########## sdks/python/apache_beam/examples/inference/pytorch_imagenet_rightfit.py: ########## @@ -0,0 +1,522 @@ +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""This pipeline performs image classification using an open-source +PyTorch EfficientNet-B0 model optimized for T4 GPUs. +It reads image URIs from Pub/Sub, decodes and preprocesses them in parallel, +and runs inference with adaptive batch sizing for optimal GPU utilization. +The pipeline targets stable and reproducible performance measurements under +continuous load. +Resources like Pub/Sub topic/subscription cleanup is handled programmatically. +""" + +import argparse +import io +import json +import logging +import threading +import time +from typing import Optional +from typing import Tuple + +import torch +import torch.nn.functional as F + +import apache_beam as beam +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.runners.runner import PipelineResult +from apache_beam.transforms import window + +from google.api_core.exceptions import NotFound +from google.cloud import pubsub_v1 +import PIL.Image as PILImage + +# ============ Utility & Preprocessing ============ + +IMAGENET_MEAN = [0.485, 0.456, 0.406] +IMAGENET_STD = [0.229, 0.224, 0.225] + + +def now_millis() -> int: + return int(time.time() * 1000) + + +def load_image_from_uri(uri: str) -> bytes: + with FileSystems.open(uri) as f: + return f.read() + + +def decode_and_preprocess(image_bytes: bytes, size: int = 224) -> torch.Tensor: + """Decode bytes->RGB PIL->resize/crop->tensor->normalize.""" + with PILImage.open(io.BytesIO(image_bytes)) as img: + img = img.convert("RGB") + img.thumbnail((256, 256)) + w, h = img.size + left = (w - size) // 2 + top = (h - size) // 2 + img = img.crop( + (max(0, left), max(0, top), min(w, left + size), min(h, top + size))) + + # To tensor [0..1] + import numpy as np + mean = np.array(IMAGENET_MEAN, dtype=np.float32) + std = np.array(IMAGENET_STD, dtype=np.float32) + arr = np.asarray(img).astype("float32") / 255.0 # H,W,3 + # Normalize + arr = (arr - mean) / std + # HWC -> CHW + arr = np.transpose(arr, (2, 0, 1)).astype("float32") + return torch.from_numpy(arr).float() # float32, shape (3,224,224) + + +class MakeKeyDoFn(beam.DoFn): + """Produce (image_id, payload) stable for dedup & BQ insertId.""" + def __init__(self, input_mode: str): + self.input_mode = input_mode + + def process(self, element: str | bytes): + # Input can be raw bytes from Pub/Sub or a GCS URI string, depends on mode + if self.input_mode == "bytes": + # element is bytes message, assume it includes + # {"image_id": "...", "bytes": base64?} or just raw bytes. + import hashlib + b = element if isinstance(element, + (bytes, + bytearray)) else element.encode('utf-8') + image_id = hashlib.sha1(b).hexdigest() + yield image_id, b + else: + # gcs_uris: element is uri string; image_id = sha1(uri) + import hashlib + uri = element.decode("utf-8") if isinstance( + element, (bytes, bytearray)) else str(element) + image_id = hashlib.sha1(uri.encode("utf-8")).hexdigest() + yield image_id, uri + + +class DecodePreprocessDoFn(beam.DoFn): + """Turn (image_id, bytes|uri) -> (image_id, torch.Tensor)""" + def __init__( + self, input_mode: str, image_size: int = 224, decode_threads: int = 4): + self.input_mode = input_mode + self.image_size = image_size + self.decode_threads = decode_threads + + def process(self, kv: Tuple[str, object]): + image_id, payload = kv + start = now_millis() + + try: + if self.input_mode == "bytes": + b = payload if isinstance(payload, + (bytes, bytearray)) else bytes(payload) + else: + uri = payload if isinstance(payload, str) else payload.decode("utf-8") + b = load_image_from_uri(uri) + + tensor = decode_and_preprocess(b, self.image_size) + preprocess_ms = now_millis() - start + yield image_id, {"tensor": tensor, "preprocess_ms": preprocess_ms} + except Exception as e: + logging.warning("Decode failed for %s: %s", image_id, e) + return + + +class PostProcessDoFn(beam.DoFn): + """PredictionResult -> dict row for BQ.""" + def __init__(self, top_k: int, model_name: str): + self.top_k = top_k + self.model_name = model_name + + def process(self, kv: Tuple[str, PredictionResult]): + image_id, pred = kv + + # pred can be PredictionResult OR raw inference object. + inference_obj = pred.inference if hasattr(pred, "inference") else pred + + # inference_obj can be dict {'logits': tensor} OR tensor directly. + if isinstance(inference_obj, dict): + logits = inference_obj.get("logits", None) + if logits is None: + # fallback: try first value if dict shape differs + try: + logits = next(iter(inference_obj.values())) Review Comment:  Falling back to the first value in the model output dictionary is fragile. If the model returns multiple outputs (e.g., auxiliary logits or metadata), this might select the wrong tensor. It is safer to require an explicit key or handle known output structures for the supported models. ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,637 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""This pipeline performs image captioning using a multi-model approach: +BLIP generates candidate captions, CLIP ranks them by image-text similarity. + +The pipeline reads image URIs from a GCS input file, decodes images, runs BLIP +caption generation in batches on GPU, then runs CLIP ranking in batches on GPU. +Results are written to BigQuery. +""" + +import argparse +import io +import json +import logging +import threading +import time +from typing import Any +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple + +import apache_beam as beam +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.runners.runner import PipelineResult +from apache_beam.transforms import window + +from google.api_core.exceptions import NotFound +from google.cloud import pubsub_v1 +import torch +import PIL.Image as PILImage + +# ============ Utility ============ + + +def now_millis() -> int: + return int(time.time() * 1000) + + +def decode_pil(image_bytes: bytes) -> PILImage.Image: + with PILImage.open(io.BytesIO(image_bytes)) as img: + img = img.convert("RGB") + img.load() + return img + + +# ============ DoFns ============ + + +class MakeKeyDoFn(beam.DoFn): + """Produce (uri, uri) so the URI is used as the stable key.""" + def process(self, element: str): + uri = element + yield uri, uri + + +class ReadImageBytesDoFn(beam.DoFn): + """Turn (uri, uri) -> (uri, dict(image_bytes)).""" + def process(self, kv: Tuple[str, str]): + uri, _ = kv + try: + with FileSystems.open(uri) as f: + image_bytes = f.read() + yield uri, {"image_bytes": image_bytes} + except Exception as e: + logging.warning("Failed to read image %s: %s", uri, e) + return + + +class DecodeImageDoFn(beam.DoFn): + """Turn (uri, dict(image_bytes)) -> (uri, dict(image)).""" + def process(self, kv: Tuple[str, Dict[str, Any]]): + uri, value = kv + image_bytes = value["image_bytes"] + + try: + image = decode_pil(image_bytes) + except Exception as e: + logging.warning("Failed to decode image %s: %s", uri, e) + image = PILImage.new("RGB", (224, 224), color=(0, 0, 0)) + + yield uri, {"image": image} + + +class PostProcessDoFn(beam.DoFn): + """Final PredictionResult -> row for BigQuery.""" + def __init__(self, blip_name: str, clip_name: str): + self.blip_name = blip_name + self.clip_name = clip_name + + def process(self, kv: Tuple[str, PredictionResult]): + uri, pred = kv + if hasattr(pred, "inference"): + inf = pred.inference or {} + else: + inf = pred + # Expected inference fields from CLIP handler: + # best_caption, best_score, candidates, scores, blip_ms, clip_ms, total_ms + best_caption = inf.get("best_caption", "") + best_score = inf.get("best_score", None) + candidates = inf.get("candidates", []) + scores = inf.get("scores", []) + blip_ms = inf.get("blip_ms", None) + clip_ms = inf.get("clip_ms", None) + total_ms = inf.get("total_ms", None) + + yield { + "image_id": uri, + "blip_model": self.blip_name, + "clip_model": self.clip_name, + "best_caption": best_caption, + "best_score": float(best_score) if best_score is not None else None, + "candidates": json.dumps(candidates), + "scores": json.dumps(scores), + "blip_ms": int(blip_ms) if blip_ms is not None else None, + "clip_ms": int(clip_ms) if clip_ms is not None else None, + "total_ms": int(total_ms) if total_ms is not None else None, + "infer_ms": now_millis(), + } + + +# ============ Model Handlers ============ + + +class BlipCaptionModelHandler(ModelHandler): + def __init__( + self, + model_name: str, + device: str, + batch_size: int, + num_captions: int, + max_new_tokens: int, + num_beams: int): + self.model_name = model_name + self.device = device + self.batch_size = batch_size + self.num_captions = num_captions + self.max_new_tokens = max_new_tokens + self.num_beams = num_beams + + def load_model(self): + from transformers import BlipForConditionalGeneration, BlipProcessor + processor = BlipProcessor.from_pretrained(self.model_name) + model = BlipForConditionalGeneration.from_pretrained(self.model_name) + return (model, processor) + + def batch_elements_kwargs(self): + return {"max_batch_size": self.batch_size} + + def run_inference( + self, batch: List[Dict[str, Any]], model_bundle, inference_args=None): + + model, processor = model_bundle + model.to(self.device) + model.eval() + start = now_millis() + + images = [x["image"] for x in batch] + + # Processor makes pixel_values + inputs = processor(images=images, return_tensors="pt") + pixel_values = inputs["pixel_values"].to(self.device) + + # Generate captions + # We use num_return_sequences to generate multiple candidates per image. + # Note: this will produce (B * num_captions) sequences. + with torch.no_grad(): + generated_ids = model.generate( + pixel_values=pixel_values, + max_new_tokens=self.max_new_tokens, + num_beams=max(self.num_beams, self.num_captions), + num_return_sequences=self.num_captions, + do_sample=False, + ) + + captions_all = processor.batch_decode( + generated_ids, skip_special_tokens=True) + + # Group candidates per image + candidates_per_image = [] + idx = 0 + for _ in range(len(batch)): + candidates_per_image.append(captions_all[idx:idx + self.num_captions]) + idx += self.num_captions + + blip_ms = now_millis() - start + + results = [] + for i in range(len(batch)): + results.append({ + "image": images[i], + "candidates": candidates_per_image[i], + "blip_ms": blip_ms, + }) + return results + + def get_metrics_namespace(self) -> str: + return "blip_captioning" + + +class ClipRankModelHandler(ModelHandler): + def __init__( + self, + model_name: str, + device: str, + batch_size: int, + score_normalize: bool): + self.model_name = model_name + self.device = device + self.batch_size = batch_size + self.score_normalize = score_normalize + + def load_model(self): + from transformers import CLIPModel, CLIPProcessor + processor = CLIPProcessor.from_pretrained(self.model_name) + model = CLIPModel.from_pretrained(self.model_name) + return (model, processor) Review Comment:  Moving the model to the device and setting it to evaluation mode should be done once during model loading to avoid redundant operations during inference. ```suggestion def load_model(self): from transformers import CLIPModel, CLIPProcessor processor = CLIPProcessor.from_pretrained(self.model_name) model = CLIPModel.from_pretrained(self.model_name) model.to(self.device) model.eval() return (model, processor) ``` ########## sdks/python/apache_beam/examples/inference/pytorch_imagenet_rightfit.py: ########## @@ -0,0 +1,522 @@ +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""This pipeline performs image classification using an open-source +PyTorch EfficientNet-B0 model optimized for T4 GPUs. +It reads image URIs from Pub/Sub, decodes and preprocesses them in parallel, +and runs inference with adaptive batch sizing for optimal GPU utilization. +The pipeline targets stable and reproducible performance measurements under +continuous load. +Resources like Pub/Sub topic/subscription cleanup is handled programmatically. +""" + +import argparse +import io +import json +import logging +import threading +import time +from typing import Optional +from typing import Tuple + +import torch +import torch.nn.functional as F + +import apache_beam as beam +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.runners.runner import PipelineResult +from apache_beam.transforms import window + +from google.api_core.exceptions import NotFound +from google.cloud import pubsub_v1 +import PIL.Image as PILImage + +# ============ Utility & Preprocessing ============ + +IMAGENET_MEAN = [0.485, 0.456, 0.406] +IMAGENET_STD = [0.229, 0.224, 0.225] + + +def now_millis() -> int: + return int(time.time() * 1000) + + +def load_image_from_uri(uri: str) -> bytes: + with FileSystems.open(uri) as f: + return f.read() + + +def decode_and_preprocess(image_bytes: bytes, size: int = 224) -> torch.Tensor: + """Decode bytes->RGB PIL->resize/crop->tensor->normalize.""" + with PILImage.open(io.BytesIO(image_bytes)) as img: + img = img.convert("RGB") + img.thumbnail((256, 256)) + w, h = img.size + left = (w - size) // 2 + top = (h - size) // 2 + img = img.crop( + (max(0, left), max(0, top), min(w, left + size), min(h, top + size))) + + # To tensor [0..1] + import numpy as np + mean = np.array(IMAGENET_MEAN, dtype=np.float32) + std = np.array(IMAGENET_STD, dtype=np.float32) + arr = np.asarray(img).astype("float32") / 255.0 # H,W,3 + # Normalize + arr = (arr - mean) / std + # HWC -> CHW + arr = np.transpose(arr, (2, 0, 1)).astype("float32") + return torch.from_numpy(arr).float() # float32, shape (3,224,224) + + +class MakeKeyDoFn(beam.DoFn): + """Produce (image_id, payload) stable for dedup & BQ insertId.""" + def __init__(self, input_mode: str): + self.input_mode = input_mode + + def process(self, element: str | bytes): + # Input can be raw bytes from Pub/Sub or a GCS URI string, depends on mode + if self.input_mode == "bytes": + # element is bytes message, assume it includes + # {"image_id": "...", "bytes": base64?} or just raw bytes. + import hashlib + b = element if isinstance(element, + (bytes, + bytearray)) else element.encode('utf-8') + image_id = hashlib.sha1(b).hexdigest() + yield image_id, b + else: + # gcs_uris: element is uri string; image_id = sha1(uri) + import hashlib + uri = element.decode("utf-8") if isinstance( + element, (bytes, bytearray)) else str(element) + image_id = hashlib.sha1(uri.encode("utf-8")).hexdigest() + yield image_id, uri + + +class DecodePreprocessDoFn(beam.DoFn): + """Turn (image_id, bytes|uri) -> (image_id, torch.Tensor)""" + def __init__( + self, input_mode: str, image_size: int = 224, decode_threads: int = 4): + self.input_mode = input_mode + self.image_size = image_size + self.decode_threads = decode_threads + + def process(self, kv: Tuple[str, object]): + image_id, payload = kv + start = now_millis() + + try: + if self.input_mode == "bytes": + b = payload if isinstance(payload, + (bytes, bytearray)) else bytes(payload) + else: + uri = payload if isinstance(payload, str) else payload.decode("utf-8") + b = load_image_from_uri(uri) + + tensor = decode_and_preprocess(b, self.image_size) + preprocess_ms = now_millis() - start + yield image_id, {"tensor": tensor, "preprocess_ms": preprocess_ms} + except Exception as e: + logging.warning("Decode failed for %s: %s", image_id, e) + return + + +class PostProcessDoFn(beam.DoFn): + """PredictionResult -> dict row for BQ.""" + def __init__(self, top_k: int, model_name: str): + self.top_k = top_k + self.model_name = model_name + + def process(self, kv: Tuple[str, PredictionResult]): + image_id, pred = kv + + # pred can be PredictionResult OR raw inference object. + inference_obj = pred.inference if hasattr(pred, "inference") else pred + + # inference_obj can be dict {'logits': tensor} OR tensor directly. + if isinstance(inference_obj, dict): + logits = inference_obj.get("logits", None) + if logits is None: + # fallback: try first value if dict shape differs + try: + logits = next(iter(inference_obj.values())) + logging.warning( + 'Could not find <logits> key in model output.' + 'Falling back to first value in dict.') + except Exception: + logging.warning('Could not find <logits> key in dict.') + else: + logits = inference_obj + + if not isinstance(logits, torch.Tensor): + logging.warning( + "Unexpected logits type for %s: %s", image_id, type(logits)) + return + + # Ensure shape [1, C] + if logits.ndim == 1: + logits = logits.unsqueeze(0) + + probs = F.softmax(logits, dim=-1) # [B, C] + values, indices = torch.topk( + probs, k=min(self.top_k, probs.shape[-1]), dim=-1 + ) + + topk = [{ + "class_id": int(idx.item()), "score": float(val.item()) + } for idx, val in zip(indices[0], values[0])] + + yield { + "image_id": image_id, + "model_name": self.model_name, + "topk": json.dumps(topk), + "infer_ms": now_millis(), + } + + +# ============ Args & Helpers ============ + + +def parse_known_args(argv): + parser = argparse.ArgumentParser() + # I/O & runtime + parser.add_argument( + '--project', default='apache-beam-testing', help='GCP project ID') + parser.add_argument( + '--mode', default='streaming', choices=['streaming', 'batch']) + parser.add_argument( + '--output_table', + required=True, + help='BigQuery output table: dataset.table') + parser.add_argument( + '--publish_to_big_query', default='true', choices=['true', 'false']) + parser.add_argument( + '--input_mode', default='gcs_uris', choices=['gcs_uris', 'bytes']) + parser.add_argument( + '--input', + required=True, + help='GCS path to file with URIs (for load) OR unused for bytes') + parser.add_argument( + '--pubsub_topic', + default='projects/apache-beam-testing/topics/images_topic') + parser.add_argument( + '--pubsub_subscription', + default='projects/apache-beam-testing/subscriptions/images_subscription') + parser.add_argument( + '--feeder_start_delay_sec', + type=int, + default=900, + help=( + 'Delay before starting the feeder pipeline that reads URIs from GCS ' + 'and publishes them to Pub/Sub. This delay allows the main streaming ' + 'pipeline workers to start and scale before data ingestion begins.'), + ) + + # Model & inference + parser.add_argument( + '--pretrained_model_name', + default='efficientnet_b0', + help='OSS model name (e.g., efficientnet_b0|mobilenetv3_large_100)') + parser.add_argument( + '--model_state_dict_path', + default=None, + help='Optional state_dict to load') + parser.add_argument('--device', default='GPU', choices=['CPU', 'GPU']) + parser.add_argument('--image_size', type=int, default=224) + parser.add_argument('--top_k', type=int, default=5) + parser.add_argument( + '--inference_batch_size', + default='auto', + help='int or "auto"; auto tries 64→32→16') + + # Windows + parser.add_argument('--window_sec', type=int, default=60) + parser.add_argument('--trigger_proc_time_sec', type=int, default=30) + + known_args, pipeline_args = parser.parse_known_args(argv) + return known_args, pipeline_args + + +def ensure_pubsub_resources( + project: str, topic_path: str, subscription_path: str): + publisher = pubsub_v1.PublisherClient() + subscriber = pubsub_v1.SubscriberClient() + + topic_name = topic_path.split("/")[-1] + subscription_name = subscription_path.split("/")[-1] + + full_topic_path = publisher.topic_path(project, topic_name) + full_subscription_path = subscriber.subscription_path( + project, subscription_name) + + try: + publisher.get_topic(request={"topic": full_topic_path}) + except NotFound: + publisher.create_topic(name=full_topic_path) + + try: + subscriber.get_subscription( + request={"subscription": full_subscription_path}) + except NotFound: + subscriber.create_subscription( + name=full_subscription_path, topic=full_topic_path) + + +def cleanup_pubsub_resources( + project: str, topic_path: str, subscription_path: str): + publisher = pubsub_v1.PublisherClient() + subscriber = pubsub_v1.SubscriberClient() + + topic_name = topic_path.split("/")[-1] + subscription_name = subscription_path.split("/")[-1] + + full_topic_path = publisher.topic_path(project, topic_name) + full_subscription_path = subscriber.subscription_path( + project, subscription_name) + + try: + subscriber.delete_subscription( + request={"subscription": full_subscription_path}) + print(f"Deleted subscription: {subscription_name}") + except NotFound: + print(f"Subscription already deleted: {subscription_name}") + + try: + publisher.delete_topic(request={"topic": full_topic_path}) + print(f"Deleted topic: {topic_name}") + except NotFound: + print(f"Topic already deleted: {topic_name}") + + +def override_or_add(args, flag, value): + if flag in args: + idx = args.index(flag) + args[idx + 1] = str(value) + else: + args.extend([flag, str(value)]) + + +# ============ Model factory (timm) ============ + + +def create_timm_m(model_name: str, num_classes: int = 1000): + import timm + model = timm.create_model( + model_name, pretrained=True, num_classes=num_classes) + model.eval() + return model + + +def pick_batch_size(arg: str) -> Optional[int]: + if isinstance(arg, str) and arg.lower() == 'auto': + return None + try: + return int(arg) + except Exception: + return None + + +# ============ Load pipeline ============ + + +def run_load_pipeline(known_args, pipeline_args): + """Reads GCS file with URIs and publishes them to Pub/Sub (for streaming).""" + # enforce smaller/CPU-only defaults for feeder + override_or_add(pipeline_args, '--device', 'CPU') + override_or_add(pipeline_args, '--num_workers', '5') + override_or_add(pipeline_args, '--max_num_workers', '10') + override_or_add( + pipeline_args, '--job_name', f"images-load-pubsub-{int(time.time())}") + override_or_add(pipeline_args, '--project', known_args.project) + pipeline_args = [ + arg for arg in pipeline_args if not arg.startswith("--experiments") + ] + + pipeline_options = PipelineOptions(pipeline_args) + pipeline = beam.Pipeline(options=pipeline_options) + + _ = ( + pipeline + | 'ReadGCSFile' >> beam.io.ReadFromText(known_args.input) + | 'FilterEmpty' >> beam.Filter(lambda line: line.strip()) + | 'ToBytes' >> beam.Map(lambda line: line.encode('utf-8')) + | 'ToPubSub' >> beam.io.WriteToPubSub(topic=known_args.pubsub_topic)) + return pipeline.run() + + +# ============ Main pipeline ============ + + +def run( + argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult: + known_args, pipeline_args = parse_known_args(argv) + + if known_args.mode == 'streaming': + ensure_pubsub_resources( + project=known_args.project, + topic_path=known_args.pubsub_topic, + subscription_path=known_args.pubsub_subscription) + + # Start feeder thread that reads URIs from GCS and fills Pub/Sub. + # Delay is used to allow the main streaming pipeline workers to start + # and autoscale before the feeder pipeline begins publishing messages. + threading.Thread( + target=lambda: ( + time.sleep(known_args.feeder_start_delay_sec), run_load_pipeline( + known_args, pipeline_args)), + daemon=True).start() + + # StandardOptions + pipeline_options = PipelineOptions(pipeline_args) + pipeline_options.view_as(SetupOptions).save_main_session = save_main_session + pipeline_options.view_as(StandardOptions).streaming = ( + known_args.mode == 'streaming') + + # Build model handler with right-fitting batch size + desired_batch = pick_batch_size(known_args.inference_batch_size) + tried = [64, 32, 16] if desired_batch is None else [desired_batch] + + # Device + device = 'GPU' if known_args.device.upper() == 'GPU' else 'CPU' + + bs_ok = None + last_err = None + for bs in tried: + try: + model_handler = PytorchModelHandlerTensor( + model_class=lambda: create_timm_m(known_args.pretrained_model_name), + model_params={}, + state_dict_path=known_args.model_state_dict_path, + device=device, + inference_batch_size=bs + if bs is not None else 64, # start guess for warmup + ) + # quick warmup to validate memory (single dummy tensor) + dummy = torch.zeros((3, known_args.image_size, known_args.image_size), + dtype=torch.float32) + _ = model_handler.load_model() # ensures weights on device + with torch.no_grad(): + mdl = model_handler._model + mdl(torch.unsqueeze(dummy, 0)) + bs_ok = bs if bs is not None else 64 + break + except RuntimeError as e: + last_err = e + logging.warning("Batch size %s failed during warmup: %s", bs, e) + continue + + if bs_ok is None: + logging.warning( + "Falling back to batch_size=8 due to previous errors: %s", last_err) + bs_ok = 8 + model_handler = PytorchModelHandlerTensor( + model_class=lambda: create_timm_m(known_args.pretrained_model_name), + model_params={}, + state_dict_path=known_args.model_state_dict_path, + device=device, + inference_batch_size=bs_ok, + ) Review Comment:  The "right-fitting" logic to determine the optimal batch size is executed on the pipeline submission machine (the launcher). This is problematic because the launcher's environment (e.g., GPU availability, VRAM) often differs significantly from the actual Beam workers (e.g., Dataflow workers with T4 GPUs). This can lead to incorrect batch size selection or submission failures if the launcher lacks the necessary hardware or libraries. Furthermore, calling `load_model()` on the launcher can trigger unnecessary downloads and side effects. This logic should be removed or moved to a component that executes within the worker context. ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,637 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""This pipeline performs image captioning using a multi-model approach: +BLIP generates candidate captions, CLIP ranks them by image-text similarity. + +The pipeline reads image URIs from a GCS input file, decodes images, runs BLIP +caption generation in batches on GPU, then runs CLIP ranking in batches on GPU. +Results are written to BigQuery. +""" + +import argparse +import io +import json +import logging +import threading +import time +from typing import Any +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple + +import apache_beam as beam +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.runners.runner import PipelineResult +from apache_beam.transforms import window + +from google.api_core.exceptions import NotFound +from google.cloud import pubsub_v1 +import torch +import PIL.Image as PILImage + +# ============ Utility ============ + + +def now_millis() -> int: + return int(time.time() * 1000) + + +def decode_pil(image_bytes: bytes) -> PILImage.Image: + with PILImage.open(io.BytesIO(image_bytes)) as img: + img = img.convert("RGB") + img.load() + return img + + +# ============ DoFns ============ + + +class MakeKeyDoFn(beam.DoFn): + """Produce (uri, uri) so the URI is used as the stable key.""" + def process(self, element: str): + uri = element + yield uri, uri + + +class ReadImageBytesDoFn(beam.DoFn): + """Turn (uri, uri) -> (uri, dict(image_bytes)).""" + def process(self, kv: Tuple[str, str]): + uri, _ = kv + try: + with FileSystems.open(uri) as f: + image_bytes = f.read() + yield uri, {"image_bytes": image_bytes} + except Exception as e: + logging.warning("Failed to read image %s: %s", uri, e) + return + + +class DecodeImageDoFn(beam.DoFn): + """Turn (uri, dict(image_bytes)) -> (uri, dict(image)).""" + def process(self, kv: Tuple[str, Dict[str, Any]]): + uri, value = kv + image_bytes = value["image_bytes"] + + try: + image = decode_pil(image_bytes) + except Exception as e: + logging.warning("Failed to decode image %s: %s", uri, e) + image = PILImage.new("RGB", (224, 224), color=(0, 0, 0)) + + yield uri, {"image": image} + + +class PostProcessDoFn(beam.DoFn): + """Final PredictionResult -> row for BigQuery.""" + def __init__(self, blip_name: str, clip_name: str): + self.blip_name = blip_name + self.clip_name = clip_name + + def process(self, kv: Tuple[str, PredictionResult]): + uri, pred = kv + if hasattr(pred, "inference"): + inf = pred.inference or {} + else: + inf = pred + # Expected inference fields from CLIP handler: + # best_caption, best_score, candidates, scores, blip_ms, clip_ms, total_ms + best_caption = inf.get("best_caption", "") + best_score = inf.get("best_score", None) + candidates = inf.get("candidates", []) + scores = inf.get("scores", []) + blip_ms = inf.get("blip_ms", None) + clip_ms = inf.get("clip_ms", None) + total_ms = inf.get("total_ms", None) + + yield { + "image_id": uri, + "blip_model": self.blip_name, + "clip_model": self.clip_name, + "best_caption": best_caption, + "best_score": float(best_score) if best_score is not None else None, + "candidates": json.dumps(candidates), + "scores": json.dumps(scores), + "blip_ms": int(blip_ms) if blip_ms is not None else None, + "clip_ms": int(clip_ms) if clip_ms is not None else None, + "total_ms": int(total_ms) if total_ms is not None else None, + "infer_ms": now_millis(), + } + + +# ============ Model Handlers ============ + + +class BlipCaptionModelHandler(ModelHandler): + def __init__( + self, + model_name: str, + device: str, + batch_size: int, + num_captions: int, + max_new_tokens: int, + num_beams: int): + self.model_name = model_name + self.device = device + self.batch_size = batch_size + self.num_captions = num_captions + self.max_new_tokens = max_new_tokens + self.num_beams = num_beams + + def load_model(self): + from transformers import BlipForConditionalGeneration, BlipProcessor + processor = BlipProcessor.from_pretrained(self.model_name) + model = BlipForConditionalGeneration.from_pretrained(self.model_name) + return (model, processor) + + def batch_elements_kwargs(self): + return {"max_batch_size": self.batch_size} + + def run_inference( + self, batch: List[Dict[str, Any]], model_bundle, inference_args=None): + + model, processor = model_bundle + model.to(self.device) + model.eval() + start = now_millis() + + images = [x["image"] for x in batch] + + # Processor makes pixel_values + inputs = processor(images=images, return_tensors="pt") + pixel_values = inputs["pixel_values"].to(self.device) + + # Generate captions + # We use num_return_sequences to generate multiple candidates per image. + # Note: this will produce (B * num_captions) sequences. + with torch.no_grad(): + generated_ids = model.generate( + pixel_values=pixel_values, + max_new_tokens=self.max_new_tokens, + num_beams=max(self.num_beams, self.num_captions), + num_return_sequences=self.num_captions, + do_sample=False, + ) + + captions_all = processor.batch_decode( + generated_ids, skip_special_tokens=True) + + # Group candidates per image + candidates_per_image = [] + idx = 0 + for _ in range(len(batch)): + candidates_per_image.append(captions_all[idx:idx + self.num_captions]) + idx += self.num_captions + + blip_ms = now_millis() - start + + results = [] + for i in range(len(batch)): + results.append({ + "image": images[i], + "candidates": candidates_per_image[i], + "blip_ms": blip_ms, + }) + return results + + def get_metrics_namespace(self) -> str: + return "blip_captioning" + + +class ClipRankModelHandler(ModelHandler): + def __init__( + self, + model_name: str, + device: str, + batch_size: int, + score_normalize: bool): + self.model_name = model_name + self.device = device + self.batch_size = batch_size + self.score_normalize = score_normalize + + def load_model(self): + from transformers import CLIPModel, CLIPProcessor + processor = CLIPProcessor.from_pretrained(self.model_name) + model = CLIPModel.from_pretrained(self.model_name) + return (model, processor) + + def batch_elements_kwargs(self): + return {"max_batch_size": self.batch_size} + + def run_inference( + self, batch: List[Dict[str, Any]], model_bundle, inference_args=None): + + model, processor = model_bundle + model.to(self.device) + model.eval() Review Comment:  Redundant model device placement and evaluation mode setting. These operations should be handled in `load_model`. ```suggestion model, processor = model_bundle ``` ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,637 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""This pipeline performs image captioning using a multi-model approach: +BLIP generates candidate captions, CLIP ranks them by image-text similarity. + +The pipeline reads image URIs from a GCS input file, decodes images, runs BLIP +caption generation in batches on GPU, then runs CLIP ranking in batches on GPU. +Results are written to BigQuery. +""" + +import argparse +import io +import json +import logging +import threading +import time +from typing import Any +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple + +import apache_beam as beam +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.runners.runner import PipelineResult +from apache_beam.transforms import window + +from google.api_core.exceptions import NotFound +from google.cloud import pubsub_v1 +import torch +import PIL.Image as PILImage + +# ============ Utility ============ + + +def now_millis() -> int: + return int(time.time() * 1000) + + +def decode_pil(image_bytes: bytes) -> PILImage.Image: + with PILImage.open(io.BytesIO(image_bytes)) as img: + img = img.convert("RGB") + img.load() + return img + + +# ============ DoFns ============ + + +class MakeKeyDoFn(beam.DoFn): + """Produce (uri, uri) so the URI is used as the stable key.""" + def process(self, element: str): + uri = element + yield uri, uri + + +class ReadImageBytesDoFn(beam.DoFn): + """Turn (uri, uri) -> (uri, dict(image_bytes)).""" + def process(self, kv: Tuple[str, str]): + uri, _ = kv + try: + with FileSystems.open(uri) as f: + image_bytes = f.read() + yield uri, {"image_bytes": image_bytes} + except Exception as e: + logging.warning("Failed to read image %s: %s", uri, e) + return + + +class DecodeImageDoFn(beam.DoFn): + """Turn (uri, dict(image_bytes)) -> (uri, dict(image)).""" + def process(self, kv: Tuple[str, Dict[str, Any]]): + uri, value = kv + image_bytes = value["image_bytes"] + + try: + image = decode_pil(image_bytes) + except Exception as e: + logging.warning("Failed to decode image %s: %s", uri, e) + image = PILImage.new("RGB", (224, 224), color=(0, 0, 0)) + + yield uri, {"image": image} + + +class PostProcessDoFn(beam.DoFn): + """Final PredictionResult -> row for BigQuery.""" + def __init__(self, blip_name: str, clip_name: str): + self.blip_name = blip_name + self.clip_name = clip_name + + def process(self, kv: Tuple[str, PredictionResult]): + uri, pred = kv + if hasattr(pred, "inference"): + inf = pred.inference or {} + else: + inf = pred + # Expected inference fields from CLIP handler: + # best_caption, best_score, candidates, scores, blip_ms, clip_ms, total_ms + best_caption = inf.get("best_caption", "") + best_score = inf.get("best_score", None) + candidates = inf.get("candidates", []) + scores = inf.get("scores", []) + blip_ms = inf.get("blip_ms", None) + clip_ms = inf.get("clip_ms", None) + total_ms = inf.get("total_ms", None) + + yield { + "image_id": uri, + "blip_model": self.blip_name, + "clip_model": self.clip_name, + "best_caption": best_caption, + "best_score": float(best_score) if best_score is not None else None, + "candidates": json.dumps(candidates), + "scores": json.dumps(scores), + "blip_ms": int(blip_ms) if blip_ms is not None else None, + "clip_ms": int(clip_ms) if clip_ms is not None else None, + "total_ms": int(total_ms) if total_ms is not None else None, + "infer_ms": now_millis(), + } + + +# ============ Model Handlers ============ + + +class BlipCaptionModelHandler(ModelHandler): + def __init__( + self, + model_name: str, + device: str, + batch_size: int, + num_captions: int, + max_new_tokens: int, + num_beams: int): + self.model_name = model_name + self.device = device + self.batch_size = batch_size + self.num_captions = num_captions + self.max_new_tokens = max_new_tokens + self.num_beams = num_beams + + def load_model(self): + from transformers import BlipForConditionalGeneration, BlipProcessor + processor = BlipProcessor.from_pretrained(self.model_name) + model = BlipForConditionalGeneration.from_pretrained(self.model_name) + return (model, processor) + + def batch_elements_kwargs(self): + return {"max_batch_size": self.batch_size} + + def run_inference( + self, batch: List[Dict[str, Any]], model_bundle, inference_args=None): + + model, processor = model_bundle + model.to(self.device) + model.eval() Review Comment:  Redundant model device placement and evaluation mode setting. These operations should be handled in `load_model`. ```suggestion model, processor = model_bundle ``` ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,637 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""This pipeline performs image captioning using a multi-model approach: +BLIP generates candidate captions, CLIP ranks them by image-text similarity. + +The pipeline reads image URIs from a GCS input file, decodes images, runs BLIP +caption generation in batches on GPU, then runs CLIP ranking in batches on GPU. +Results are written to BigQuery. +""" + +import argparse +import io +import json +import logging +import threading +import time +from typing import Any +from typing import Dict +from typing import List +from typing import Optional +from typing import Tuple + +import apache_beam as beam +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.options.pipeline_options import StandardOptions +from apache_beam.runners.runner import PipelineResult +from apache_beam.transforms import window + +from google.api_core.exceptions import NotFound +from google.cloud import pubsub_v1 +import torch +import PIL.Image as PILImage + +# ============ Utility ============ + + +def now_millis() -> int: + return int(time.time() * 1000) + + +def decode_pil(image_bytes: bytes) -> PILImage.Image: + with PILImage.open(io.BytesIO(image_bytes)) as img: + img = img.convert("RGB") + img.load() + return img + + +# ============ DoFns ============ + + +class MakeKeyDoFn(beam.DoFn): + """Produce (uri, uri) so the URI is used as the stable key.""" + def process(self, element: str): + uri = element + yield uri, uri + + +class ReadImageBytesDoFn(beam.DoFn): + """Turn (uri, uri) -> (uri, dict(image_bytes)).""" + def process(self, kv: Tuple[str, str]): + uri, _ = kv + try: + with FileSystems.open(uri) as f: + image_bytes = f.read() + yield uri, {"image_bytes": image_bytes} + except Exception as e: + logging.warning("Failed to read image %s: %s", uri, e) + return + + +class DecodeImageDoFn(beam.DoFn): + """Turn (uri, dict(image_bytes)) -> (uri, dict(image)).""" + def process(self, kv: Tuple[str, Dict[str, Any]]): + uri, value = kv + image_bytes = value["image_bytes"] + + try: + image = decode_pil(image_bytes) + except Exception as e: + logging.warning("Failed to decode image %s: %s", uri, e) + image = PILImage.new("RGB", (224, 224), color=(0, 0, 0)) + + yield uri, {"image": image} + + +class PostProcessDoFn(beam.DoFn): + """Final PredictionResult -> row for BigQuery.""" + def __init__(self, blip_name: str, clip_name: str): + self.blip_name = blip_name + self.clip_name = clip_name + + def process(self, kv: Tuple[str, PredictionResult]): + uri, pred = kv + if hasattr(pred, "inference"): + inf = pred.inference or {} + else: + inf = pred + # Expected inference fields from CLIP handler: + # best_caption, best_score, candidates, scores, blip_ms, clip_ms, total_ms + best_caption = inf.get("best_caption", "") + best_score = inf.get("best_score", None) + candidates = inf.get("candidates", []) + scores = inf.get("scores", []) + blip_ms = inf.get("blip_ms", None) + clip_ms = inf.get("clip_ms", None) + total_ms = inf.get("total_ms", None) + + yield { + "image_id": uri, + "blip_model": self.blip_name, + "clip_model": self.clip_name, + "best_caption": best_caption, + "best_score": float(best_score) if best_score is not None else None, + "candidates": json.dumps(candidates), + "scores": json.dumps(scores), + "blip_ms": int(blip_ms) if blip_ms is not None else None, + "clip_ms": int(clip_ms) if clip_ms is not None else None, + "total_ms": int(total_ms) if total_ms is not None else None, + "infer_ms": now_millis(), + } + + +# ============ Model Handlers ============ + + +class BlipCaptionModelHandler(ModelHandler): + def __init__( + self, + model_name: str, + device: str, + batch_size: int, + num_captions: int, + max_new_tokens: int, + num_beams: int): + self.model_name = model_name + self.device = device + self.batch_size = batch_size + self.num_captions = num_captions + self.max_new_tokens = max_new_tokens + self.num_beams = num_beams + + def load_model(self): + from transformers import BlipForConditionalGeneration, BlipProcessor + processor = BlipProcessor.from_pretrained(self.model_name) + model = BlipForConditionalGeneration.from_pretrained(self.model_name) + return (model, processor) + + def batch_elements_kwargs(self): + return {"max_batch_size": self.batch_size} + + def run_inference( + self, batch: List[Dict[str, Any]], model_bundle, inference_args=None): + + model, processor = model_bundle + model.to(self.device) + model.eval() + start = now_millis() + + images = [x["image"] for x in batch] + + # Processor makes pixel_values + inputs = processor(images=images, return_tensors="pt") + pixel_values = inputs["pixel_values"].to(self.device) + + # Generate captions + # We use num_return_sequences to generate multiple candidates per image. + # Note: this will produce (B * num_captions) sequences. + with torch.no_grad(): + generated_ids = model.generate( + pixel_values=pixel_values, + max_new_tokens=self.max_new_tokens, + num_beams=max(self.num_beams, self.num_captions), + num_return_sequences=self.num_captions, + do_sample=False, + ) + + captions_all = processor.batch_decode( + generated_ids, skip_special_tokens=True) + + # Group candidates per image + candidates_per_image = [] + idx = 0 + for _ in range(len(batch)): + candidates_per_image.append(captions_all[idx:idx + self.num_captions]) + idx += self.num_captions + + blip_ms = now_millis() - start + + results = [] + for i in range(len(batch)): + results.append({ + "image": images[i], + "candidates": candidates_per_image[i], + "blip_ms": blip_ms, + }) + return results + + def get_metrics_namespace(self) -> str: + return "blip_captioning" + + +class ClipRankModelHandler(ModelHandler): + def __init__( + self, + model_name: str, + device: str, + batch_size: int, + score_normalize: bool): + self.model_name = model_name + self.device = device + self.batch_size = batch_size + self.score_normalize = score_normalize + + def load_model(self): + from transformers import CLIPModel, CLIPProcessor + processor = CLIPProcessor.from_pretrained(self.model_name) + model = CLIPModel.from_pretrained(self.model_name) + return (model, processor) + + def batch_elements_kwargs(self): + return {"max_batch_size": self.batch_size} + + def run_inference( + self, batch: List[Dict[str, Any]], model_bundle, inference_args=None): + + model, processor = model_bundle + model.to(self.device) + model.eval() + start_batch = now_millis() + + # Flat lists for a single batched CLIP forward pass + images: List[PILImage.Image] = [] + texts: List[str] = [] + offsets: List[Tuple[int, int]] = [] + # per element -> [start, end) in flat arrays + candidates_list: List[List[str]] = [] + blip_ms_list: List[Optional[int]] = [] + + for x in batch: + img = x["image"] + candidates = [str(c) for c in (x.get("candidates", []) or [])] + candidates_list.append(candidates) + blip_ms_list.append(x.get("blip_ms", None)) + + start_i = len(texts) + for c in candidates: + images.append(img) + texts.append(c) + end_i = len(texts) + offsets.append((start_i, end_i)) + + results: List[Dict[str, Any]] = [] + + # Fast path: no candidates at all + if not texts: + for blip_ms in blip_ms_list: + total_ms = int(blip_ms) if blip_ms is not None else None + results.append({ + "best_caption": "", + "best_score": None, + "candidates": [], + "scores": [], + "blip_ms": blip_ms, + "clip_ms": 0, + "total_ms": total_ms, + }) + return results + + with torch.no_grad(): + inputs = processor( + text=texts, + images=images, + return_tensors="pt", + padding=True, + truncation=True, + ) + inputs = { + k: (v.to(self.device) if torch.is_tensor(v) else v) + for k, v in inputs.items() + } + + # avoid NxN logits inside CLIPModel.forward() + img = model.get_image_features( + pixel_values=inputs["pixel_values"]) # [N, D] + txt = model.get_text_features( + input_ids=inputs["input_ids"], + attention_mask=inputs.get("attention_mask"), + ) # [N, D] + + img = img / img.norm(dim=-1, keepdim=True) + txt = txt / txt.norm(dim=-1, keepdim=True) + + logit_scale = model.logit_scale.exp() # scalar tensor + pair_scores = (img * txt).sum(dim=-1) * logit_scale # [N] + pair_scores_cpu = pair_scores.detach().cpu().tolist() + + batch_ms = now_millis() - start_batch + total_pairs = len(texts) + + items = zip(offsets, candidates_list, blip_ms_list) + for (start_i, end_i), candidates, blip_ms in items: + if start_i == end_i: + total_ms = int(blip_ms) if blip_ms is not None else None + results.append({ + "best_caption": "", + "best_score": None, + "candidates": [], + "scores": [], + "blip_ms": blip_ms, + "clip_ms": 0, + "total_ms": total_ms, + }) + continue + + scores = [float(pair_scores_cpu[j]) for j in range(start_i, end_i)] + + if self.score_normalize: + scores_t = torch.tensor(scores, dtype=torch.float32) + scores = torch.softmax(scores_t, dim=0).tolist() + + best_idx = max(range(len(scores)), key=lambda i, s=scores: s[i]) + + pairs = end_i - start_i + clip_ms_elem = int(batch_ms * (pairs / max(1, total_pairs))) + if pairs > 0: + clip_ms_elem = max(1, clip_ms_elem) + + total_ms = int(blip_ms) + clip_ms_elem if blip_ms is not None else None + results.append({ + "best_caption": candidates[best_idx], + "best_score": float(scores[best_idx]), + "candidates": candidates, + "scores": scores, + "blip_ms": blip_ms, + "clip_ms": clip_ms_elem, + "total_ms": total_ms, + }) + + return results + + def get_metrics_namespace(self) -> str: + return "clip_ranking" + + +# ============ Args & Helpers ============ + + +def parse_known_args(argv): + parser = argparse.ArgumentParser() + + # I/O & runtime + parser.add_argument( + '--mode', default='streaming', choices=['streaming', 'batch']) + parser.add_argument( + '--project', default='apache-beam-testing', help='GCP project ID') + parser.add_argument( + '--input', required=True, help='GCS path to file with image URIs') + parser.add_argument( + '--pubsub_topic', + default='projects/apache-beam-testing/topics/images_topic') + parser.add_argument( + '--pubsub_subscription', + default='projects/apache-beam-testing/subscriptions/images_subscription') + parser.add_argument( + '--output_table', + required=True, + help='BigQuery output table: dataset.table') + parser.add_argument( + '--publish_to_big_query', default='true', choices=['true', 'false']) + parser.add_argument( + '--feeder_start_delay_sec', + type=int, + default=900, + help=( + 'Delay before starting the feeder pipeline that reads URIs from GCS ' + 'and publishes them to Pub/Sub. This delay allows the main streaming ' + 'pipeline workers to start and scale before data ingestion begins.'), + ) + + # Device + parser.add_argument('--device', default='GPU', choices=['CPU', 'GPU']) + + # BLIP + parser.add_argument( + '--blip_model_name', default='Salesforce/blip-image-captioning-base') + parser.add_argument('--blip_batch_size', type=int, default=4) + parser.add_argument('--num_captions', type=int, default=5) + parser.add_argument('--max_new_tokens', type=int, default=30) + parser.add_argument('--num_beams', type=int, default=5) + + # CLIP + parser.add_argument( + '--clip_model_name', default='openai/clip-vit-base-patch32') + parser.add_argument('--clip_batch_size', type=int, default=8) + parser.add_argument( + '--clip_score_normalize', default='false', choices=['true', 'false']) + + # Windows + parser.add_argument('--window_sec', type=int, default=60) + parser.add_argument('--trigger_proc_time_sec', type=int, default=30) + + known_args, pipeline_args = parser.parse_known_args(argv) + return known_args, pipeline_args + + +def ensure_pubsub_resources( + project: str, topic_path: str, subscription_path: str): + publisher = pubsub_v1.PublisherClient() + subscriber = pubsub_v1.SubscriberClient() + + topic_name = topic_path.split("/")[-1] + subscription_name = subscription_path.split("/")[-1] + + full_topic_path = publisher.topic_path(project, topic_name) + full_subscription_path = subscriber.subscription_path( + project, subscription_name) + + try: + publisher.get_topic(request={"topic": full_topic_path}) + except NotFound: + publisher.create_topic(name=full_topic_path) + + try: + subscriber.get_subscription( + request={"subscription": full_subscription_path}) + except NotFound: + subscriber.create_subscription( + name=full_subscription_path, topic=full_topic_path) + + +def cleanup_pubsub_resources( + project: str, topic_path: str, subscription_path: str): + publisher = pubsub_v1.PublisherClient() + subscriber = pubsub_v1.SubscriberClient() + + topic_name = topic_path.split("/")[-1] + subscription_name = subscription_path.split("/")[-1] + + full_topic_path = publisher.topic_path(project, topic_name) + full_subscription_path = subscriber.subscription_path( + project, subscription_name) + + try: + subscriber.delete_subscription( + request={"subscription": full_subscription_path}) + print(f"Deleted subscription: {subscription_name}") Review Comment:  Using `print` for logging in a Beam pipeline is discouraged as it may not be captured correctly by the runner's logging system. Use the `logging` module instead. ```suggestion logging.info(f"Deleted subscription: {subscription_name}") ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
