Add an example of object segmentation (ClipSeg)

Makefile

@@ -78,6 +78,9 @@ slow_tests_trl: test_installs
  python -m pip install peft==0.7.0
  python -m pytest tests/test_trl.py -v -s -k "test_calculate_loss"
 
+slow_tests_object_segmentation: test_installs
+ python -m pytest tests/test_object_segmentation.py
+
 # Check if examples are up to date with the Transformers library
 example_diff_tests: test_installs
  python -m pytest tests/test_examples_match_transformers.py

README.md

@@ -198,6 +198,7 @@ The following model architectures, tasks and device distributions have been vali
 | ESMFold | | <div style="text-align:left"><li>Single card</li></div> | <li>[protein folding](https://github.com/huggingface/optimum-habana/tree/main/examples/protein-folding)</li> |
 | Blip | | <div style="text-align:left"><li>Single card</li></div> | <li>[visual question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/visual-question-answering)</li><li>[image to text](https://github.com/huggingface/optimum-habana/tree/main/examples/image-to-text)</li> |
 | OWLViT | | <div style="text-align:left"><li>Single card</li></div> | <li>[zero shot object detection](https://github.com/huggingface/optimum-habana/tree/main/examples/zero-shot-object-detection)</li> |
+| ClipSeg | | <div style="text-align:left"><li>Single card</li></div> | <li>[object segmentation](https://github.com/huggingface/optimum-habana/tree/main/examples/object-segementation)</li> |
 
 </div>
 
@@ -241,4 +242,4 @@ Please refer to Habana Gaudi's official [installation guide](https://docs.habana
 
 ## Development
 
-Check the [contributor guide](https://github.com/huggingface/optimum/blob/main/CONTRIBUTING.md) for instructions.
+Check the [contributor guide](https://github.com/huggingface/optimum/blob/main/CONTRIBUTING.md) for instructions.

docs/source/index.mdx

@@ -70,6 +70,8 @@ In the tables below, ✅ means single-card, multi-card and DeepSpeed have all be
 | ESMFold | | <div style="text-align:left"><li>Single card</li></div> | <li>[protein folding](https://github.com/huggingface/optimum-habana/tree/main/examples/protein-folding)</li> |
 | Blip | | <div style="text-align:left"><li>Single card</li></div> | <li>[visual question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/visual-question-answering)</li><li>[image to text](https://github.com/huggingface/optimum-habana/tree/main/examples/image-to-text)</li> |
 | OWLViT | | <div style="text-align:left"><li>Single card</li></div> | <li>[zero shot object detection](https://github.com/huggingface/optimum-habana/tree/main/examples/zero-shot-object-detection)</li> |
+| ClipSeg | | <div style="text-align:left"><li>Single card</li></div> | <li>[object segmentation](https://github.com/huggingface/optimum-habana/tree/main/examples/object-segementation)</li> |
+
 
 - Diffusers
 
@@ -113,4 +115,4 @@ Besides, [this page](https://github.com/huggingface/optimum-habana/tree/main/exa
  <p class="text-gray-700">Technical descriptions of how the Habana classes and methods of 🤗 Optimum Habana work.</p>
  </a>
  </div>
-</div>
+</div>

examples/object-segementation/README.md

@@ -0,0 +1,32 @@
+<!---
+Copyright 2024 The HuggingFace Team. All rights reserved.
+Licensed 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.
+-->
+
+# Object Segmentation Examples
+
+This directory contains an example script that demonstrates how to perform object segmentation on Gaudi with graph mode.
+
+## Single-HPU inference
+
+```bash
+python3 run_example.py \
+ --model_name_or_path "CIDAS/clipseg-rd64-refined" \
+ --image_path "http://images.cocodataset.org/val2017/000000039769.jpg" \
+ --prompt "cat, remote, blanket" \
+ --warmup 3 \
+ --n_iterations 20 \
+ --use_hpu_graphs \
+ --bf16 \
+ --print_result
+```
+Models that have been validated:
+ - [clipseg-rd64-refined ](https://huggingface.co/CIDAS/clipseg-rd64-refined)

examples/object-segementation/run_example.py

@@ -0,0 +1,118 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed 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
+
+# Copied from https://huggingface.co/docs/transformers/main/en/model_doc/clipseg
+
+import argparse
+import time
+
+import habana_frameworks.torch as ht
+import requests
+import torch
+from PIL import Image
+from torchvision.utils import save_image
+from transformers import AutoProcessor, CLIPSegForImageSegmentation
+
+from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+
+ parser.add_argument(
+ "--model_name_or_path",
+ default="CIDAS/clipseg-rd64-refined",
+ type=str,
+ help="Path of the pre-trained model",
+ )
+ parser.add_argument(
+ "--image_path",
+ default="http://images.cocodataset.org/val2017/000000039769.jpg",
+ type=str,
+ help='Path of the input image. Should be a single string (eg: --image_path "URL")',
+ )
+ parser.add_argument(
+ "--prompt",
+ default="a cat,a remote,a blanket",
+ type=str,
+ help='Prompt for classification. It should be a string seperated by comma. (eg: --prompt "a photo of a cat, a photo of a dog")',
+ )
+ parser.add_argument(
+ "--use_hpu_graphs",
+ action="store_true",
+ help="Whether to use HPU graphs or not. Using HPU graphs should give better latencies.",
+ )
+ parser.add_argument(
+ "--bf16",
+ action="store_true",
+ help="Whether to use bf16 precision for classification.",
+ )
+ parser.add_argument(
+ "--print_result",
+ action="store_true",
+ help="Whether to print the classification results.",
+ )
+ parser.add_argument("--warmup", type=int, default=3, help="Number of warmup iterations for benchmarking.")
+ parser.add_argument("--n_iterations", type=int, default=5, help="Number of inference iterations for benchmarking.")
+
+ args = parser.parse_args()
+
+ adapt_transformers_to_gaudi()
+
+ processor = AutoProcessor.from_pretrained(args.model_name_or_path)
+ model = CLIPSegForImageSegmentation.from_pretrained(
+ args.model_name_or_path
+ ) # Use CLIPSegForImageSegmentation instead of automodel.
+ # The output will contains the logits which are required to generated segmented images
+
+ image = Image.open(requests.get(args.image_path, stream=True).raw)
+ texts = []
+ for text in args.prompt.split(","):
+ texts.append(text)
+
+ if args.use_hpu_graphs:
+ model = ht.hpu.wrap_in_hpu_graph(model)
+
+ autocast = torch.autocast(device_type="hpu", dtype=torch.bfloat16, enabled=args.bf16)
+ model.to("hpu")
+
+ with torch.no_grad(), autocast:
+ for i in range(args.warmup):
+ inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to("hpu")
+ outputs = model(**inputs)
+ torch.hpu.synchronize()
+
+ total_model_time = 0
+ for i in range(args.n_iterations):
+ inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to("hpu")
+ model_start_time = time.time()
+ outputs = model(**inputs)
+ torch.hpu.synchronize()
+ model_end_time = time.time()
+ total_model_time = total_model_time + (model_end_time - model_start_time)
+
+ if args.print_result:
+ if i == 0: # generate/output once only
+ logits = outputs.logits
+ for j in range(logits.shape[0]):
+ threshold = 0.5
+ segmented_image = ((torch.sigmoid(logits[j]) > threshold) * 255).unsqueeze(0)
+ segmented_image = segmented_image.to(torch.float32)
+ save_image(segmented_image, "segmented_" + texts[j].strip() + ".png")
+ print("Segmented images are generated.")
+
+ print("n_iterations: " + str(args.n_iterations))
+ print("Total latency (ms): " + str(total_model_time * 1000))
+ print("Average latency (ms): " + str(total_model_time * 1000 / args.n_iterations))

tests/test_object_segmentation.py

@@ -0,0 +1,114 @@
+#
+# Licensed 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.
+
+import time
+from unittest import TestCase
+
+import habana_frameworks.torch as ht
+import numpy as np
+import requests
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoProcessor
+
+from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
+
+
+adapt_transformers_to_gaudi()
+
+# For Gaudi 2
+LATENCY_ClipSeg_BF16_GRAPH_BASELINE = 5.3107380867004395
+
+
+class GaudiClipSegTester(TestCase):
+ """
+ Tests for ClipSeg model
+ """
+
+ def prepare_model_and_processor(self):
+ model = AutoModel.from_pretrained("CIDAS/clipseg-rd64-refined").to("hpu")
+ processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
+ model = model.eval()
+ return model, processor
+
+ def prepare_data(self):
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ image = Image.open(requests.get(url, stream=True).raw)
+ texts = ["a cat", "a remote", "a blanket"]
+ return texts, image
+
+ def test_inference_default(self):
+ model, processor = self.prepare_model_and_processor()
+ texts, image = self.prepare_data()
+ inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to("hpu")
+ outputs = model(**inputs)
+ probs = outputs.logits_per_image.softmax(dim=-1).detach().cpu().numpy()[0]
+ expected_scores = np.array([0.02889409, 0.87959206, 0.09151383]) # from CPU
+ self.assertEqual(len(probs), 3)
+ self.assertLess(np.abs(probs - expected_scores).max(), 0.01)
+
+ def test_inference_autocast(self):
+ model, processor = self.prepare_model_and_processor()
+ texts, image = self.prepare_data()
+ inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to("hpu")
+
+ with torch.autocast(device_type="hpu", dtype=torch.bfloat16): # Autocast BF16
+ outputs = model(**inputs)
+ probs = outputs.logits_per_image.softmax(dim=-1).to(torch.float32).detach().cpu().numpy()[0]
+ expected_scores = np.array([0.02889409, 0.87959206, 0.09151383]) # from CPU
+ self.assertEqual(len(probs), 3)
+ self.assertEqual(probs.argmax(), expected_scores.argmax())
+
+ def test_inference_hpu_graphs(self):
+ model, processor = self.prepare_model_and_processor()
+ texts, image = self.prepare_data()
+ inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to("hpu")
+
+ model = ht.hpu.wrap_in_hpu_graph(model) # Apply graph
+
+ outputs = model(**inputs)
+ probs = outputs.logits_per_image.softmax(dim=-1).to(torch.float32).detach().cpu().numpy()[0]
+ expected_scores = np.array([0.02889409, 0.87959206, 0.09151383]) # from CPU
+ self.assertEqual(len(probs), 3)
+ self.assertEqual(probs.argmax(), expected_scores.argmax())
+
+ def test_no_latency_regression_autocast(self):
+ warmup = 3
+ iterations = 20
+
+ model, processor = self.prepare_model_and_processor()
+ texts, image = self.prepare_data()
+
+ model = ht.hpu.wrap_in_hpu_graph(model)
+
+ with torch.no_grad(), torch.autocast(device_type="hpu", dtype=torch.bfloat16, enabled=True):
+ for i in range(warmup):
+ inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to(
+ "hpu"
+ )
+ _ = model(**inputs)
+ torch.hpu.synchronize()
+
+ total_model_time = 0
+ for i in range(iterations):
+ inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to(
+ "hpu"
+ )
+ model_start_time = time.time()
+ _ = model(**inputs)
+ torch.hpu.synchronize()
+ model_end_time = time.time()
+ total_model_time = total_model_time + (model_end_time - model_start_time)
+
+ latency = total_model_time * 1000 / iterations # in terms of ms
+ self.assertLessEqual(latency, 1.05 * LATENCY_ClipSeg_BF16_GRAPH_BASELINE)