damccorm commented on code in PR #37186: URL: https://github.com/apache/beam/pull/37186#discussion_r3208861843
########## .github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_2_Pytorch_Image_Classification_Rightfit.txt: ########## @@ -0,0 +1,41 @@ +# Licensed to the Apache Software Foundation (ASF) under one Review Comment: For large PRs like this, please avoid rebasing in between reviews. It breaks GitHub's "Changes since your last review" feature, and forces me to mostly start reviewing from scratch. ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,651 @@ +# +# 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 load_image_from_uri(uri: str) -> bytes: Review Comment: I think we can just inline this, I think separating it out makes things less readable (should be same number of lines, and its unclear what kind of exception we're protecting against) ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,651 @@ +# +# 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 load_image_from_uri(uri: str) -> bytes: + with FileSystems.open(uri) as f: + return f.read() + + +def sha1_hex(s: str) -> str: + import hashlib Review Comment: Can this be a top level import? Then we also don't need a separate function since its a one-liner ########## sdks/python/apache_beam/examples/inference/pytorch_imagenet_rightfit.py: ########## @@ -0,0 +1,540 @@ +# 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 ensures exactly-once semantics via stateful deduplication and +idempotent BigQuery writes, allowing 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.coders import BytesCoder +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 userstate +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 DedupDoFn(beam.DoFn): Review Comment: Why do we need to dedupe here? Also, can we just use https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.deduplicate.html ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,651 @@ +# +# 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 load_image_from_uri(uri: str) -> bytes: + with FileSystems.open(uri) as f: + return f.read() + + +def sha1_hex(s: str) -> str: + import hashlib + return hashlib.sha1(s.encode("utf-8")).hexdigest() + + +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 (image_id, uri) where image_id is stable for dedup and keys.""" + def process(self, element: str): + uri = element + image_id = sha1_hex(uri) + yield image_id, uri + + +class ReadImageBytesDoFn(beam.DoFn): + """Turn (image_id, uri) -> (image_id, dict(image_bytes, uri)).""" + def process(self, kv: Tuple[str, str]): + image_id, uri = kv + try: + b = load_image_from_uri(uri) + yield image_id, {"image_bytes": b, "uri": uri} + except Exception as e: + logging.warning("Failed to read image %s (%s): %s", image_id, uri, e) + return + + +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]): + image_id, 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": image_id, + "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 = [] + uris = [] + bytes_list = [] + for x in batch: + b = x["image_bytes"] + bytes_list.append(b) + uris.append(x.get("uri", "")) + try: + images.append(decode_pil(b)) + except Exception as e: + # fallback: a blank image (so pipeline keeps going) + logging.warning("Failed to decode image %s: %s", uris[-1], e) + images.append(PILImage.new("RGB", (224, 224), color=(0, 0, 0))) Review Comment: Generating this image seems more natural as a preprocessing map function (not part of the ModelHandler) ########## sdks/python/apache_beam/examples/inference/pytorch_imagenet_rightfit.py: ########## @@ -0,0 +1,540 @@ +# 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 ensures exactly-once semantics via stateful deduplication and +idempotent BigQuery writes, allowing 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.coders import BytesCoder +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 userstate +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 DedupDoFn(beam.DoFn): + seen = userstate.ReadModifyWriteStateSpec('seen', BytesCoder()) + + def process(self, element, seen=beam.DoFn.StateParam(seen)): + if seen.read() == b'1': + return + seen.write(b'1') + yield element + + +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) + + # Dedup + parser.add_argument( + '--enable_dedup', default='false', choices=['true', 'false']) + + 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' Review Comment: Isn't the point of this pipeline to use right fitting with resource hints? https://docs.cloud.google.com/dataflow/docs/guides/right-fitting ########## .github/workflows/beam_Inference_Python_Benchmarks_Dataflow.yml: ########## @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -name: Inference Python Benchmarks Dataflow +name: Inference Python Benchmarks Dataflow (1 part) Review Comment: > for example there is a quota for GPU machines Could we solve this problem by just having things run sequentially? (e.g. job2 depends on job1) Actually just running the steps sequentially would work just as well > Also it is hard to read one large .yml file, easy to make a mistake. I'm not sure splitting it really helps; it now means that if something is going wrong in benchmarks (the primary reason you'd need to read these files), there are 2 places to look. ########## .github/workflows/beam_Inference_Python_Benchmarks_Dataflow.yml: ########## @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -name: Inference Python Benchmarks Dataflow +name: Inference Python Benchmarks Dataflow (1 part) Review Comment: I think we should consolidate back to a single file ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,651 @@ +# +# 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 load_image_from_uri(uri: str) -> bytes: + with FileSystems.open(uri) as f: + return f.read() + + +def sha1_hex(s: str) -> str: + import hashlib + return hashlib.sha1(s.encode("utf-8")).hexdigest() + + +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 (image_id, uri) where image_id is stable for dedup and keys.""" + def process(self, element: str): + uri = element + image_id = sha1_hex(uri) + yield image_id, uri + + +class ReadImageBytesDoFn(beam.DoFn): + """Turn (image_id, uri) -> (image_id, dict(image_bytes, uri)).""" + def process(self, kv: Tuple[str, str]): + image_id, uri = kv + try: + b = load_image_from_uri(uri) + yield image_id, {"image_bytes": b, "uri": uri} + except Exception as e: + logging.warning("Failed to read image %s (%s): %s", image_id, uri, e) + return + + +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]): + image_id, 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": image_id, + "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 Review Comment: Should this just be a top-level import? ########## sdks/python/apache_beam/examples/inference/pytorch_image_object_detection.py: ########## @@ -0,0 +1,546 @@ +# +# 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 object detection using an open-source PyTorch +TorchVision detection model (e.g., Faster R-CNN ResNet50 FPN) on GPU. + +It reads image URIs from a GCS input file, decodes and preprocesses images, +runs batched GPU inference via RunInference, post-processes detection outputs, +and writes results to BigQuery. + +The pipeline targets stable and reproducible performance measurements for +GPU inference workloads (no right-fitting; fixed batch size). +""" + +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 Sequence +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 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 torch +import PIL.Image as PILImage + +# ============ Utility & Preprocessing ============ + + +def now_millis() -> int: + return int(time.time() * 1000) + + +def load_image_from_uri(uri: str) -> bytes: Review Comment: Same as above - does this need to be a separate function? ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,651 @@ +# +# 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 load_image_from_uri(uri: str) -> bytes: + with FileSystems.open(uri) as f: + return f.read() + + +def sha1_hex(s: str) -> str: + import hashlib + return hashlib.sha1(s.encode("utf-8")).hexdigest() + + +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 (image_id, uri) where image_id is stable for dedup and keys.""" + def process(self, element: str): + uri = element + image_id = sha1_hex(uri) + yield image_id, uri Review Comment: ```suggestion def process(self, element: str): uri = element yield uri, uri ``` Any reason not to just do this? ########## sdks/python/apache_beam/examples/inference/pytorch_image_object_detection.py: ########## @@ -0,0 +1,546 @@ +# +# 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 object detection using an open-source PyTorch +TorchVision detection model (e.g., Faster R-CNN ResNet50 FPN) on GPU. + +It reads image URIs from a GCS input file, decodes and preprocesses images, +runs batched GPU inference via RunInference, post-processes detection outputs, +and writes results to BigQuery. + +The pipeline targets stable and reproducible performance measurements for +GPU inference workloads (no right-fitting; fixed batch size). +""" + +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 Sequence +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 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 torch +import PIL.Image as PILImage + +# ============ Utility & Preprocessing ============ + + +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_to_tens( + image_bytes: bytes, + resize_shorter_side: Optional[int] = None) -> torch.Tensor: + """Decode bytes -> RGB PIL -> optional resize -> float tensor [0..1], CHW. + + Note: TorchVision detection models apply their own normalization internally. + """ + with PILImage.open(io.BytesIO(image_bytes)) as img: + img = img.convert("RGB") + + if resize_shorter_side and resize_shorter_side > 0: + w, h = img.size + # Resize so that shorter side == resize_shorter_side, keep aspect ratio. + if w < h: + new_w = resize_shorter_side + new_h = int(h * (resize_shorter_side / float(w))) + else: + new_h = resize_shorter_side + new_w = int(w * (resize_shorter_side / float(h))) + img = img.resize((new_w, new_h)) + + import numpy as np + arr = np.asarray(img).astype("float32") / 255.0 # H,W,3 in [0..1] + arr = np.transpose(arr, (2, 0, 1)) # CHW + return torch.from_numpy(arr) + + +def sha1_hex(s: str) -> str: + import hashlib + return hashlib.sha1(s.encode("utf-8")).hexdigest() + + +# ============ DoFns ============ + + +class MakeKeyDoFn(beam.DoFn): + """Produce (image_id, uri) where image_id is stable for dedup and keys.""" + def process(self, element: str): + uri = element + image_id = sha1_hex(uri) Review Comment: Same as above, can we just use the uri as a key? ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,651 @@ +# +# 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 load_image_from_uri(uri: str) -> bytes: + with FileSystems.open(uri) as f: + return f.read() + + +def sha1_hex(s: str) -> str: + import hashlib + return hashlib.sha1(s.encode("utf-8")).hexdigest() + + +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 (image_id, uri) where image_id is stable for dedup and keys.""" + def process(self, element: str): + uri = element + image_id = sha1_hex(uri) + yield image_id, uri Review Comment: That could also simplify downstream processing (we wouldn't need to pass around uri in the value) ########## sdks/python/apache_beam/examples/inference/pytorch_image_captioning.py: ########## @@ -0,0 +1,651 @@ +# +# 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 load_image_from_uri(uri: str) -> bytes: + with FileSystems.open(uri) as f: + return f.read() + + +def sha1_hex(s: str) -> str: + import hashlib + return hashlib.sha1(s.encode("utf-8")).hexdigest() + + +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 (image_id, uri) where image_id is stable for dedup and keys.""" + def process(self, element: str): + uri = element + image_id = sha1_hex(uri) + yield image_id, uri + + +class ReadImageBytesDoFn(beam.DoFn): + """Turn (image_id, uri) -> (image_id, dict(image_bytes, uri)).""" + def process(self, kv: Tuple[str, str]): + image_id, uri = kv + try: + b = load_image_from_uri(uri) + yield image_id, {"image_bytes": b, "uri": uri} + except Exception as e: + logging.warning("Failed to read image %s (%s): %s", image_id, uri, e) + return + + +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]): + image_id, 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": image_id, + "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 = [] + uris = [] + bytes_list = [] + for x in batch: + b = x["image_bytes"] + bytes_list.append(b) + uris.append(x.get("uri", "")) + try: + images.append(decode_pil(b)) + except Exception as e: + # fallback: a blank image (so pipeline keeps going) + logging.warning("Failed to decode image %s: %s", uris[-1], e) + images.append(PILImage.new("RGB", (224, 224), color=(0, 0, 0))) Review Comment: Then we wouldn't need to repeat it in both model handlers as well -- This is an automated message from the Apache Git Service. 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