Add table-transformer-detection ONNXRT example (#1314)

yuwenzho · web-flow · commit 550cee2f1538 · 2023-10-17T14:07:06.000+08:00
Signed-off-by: yuwenzho &lt;yuwen.zhou@intel.com&gt;
diff --git a/examples/.config/model_params_onnxrt.json b/examples/.config/model_params_onnxrt.json
@@ -868,11 +868,18 @@
       "main_script": "main.py",
       "batch_size": 1
     },
-    "table_transformer": {
+    "table_transformer_structure_recognition": {
       "model_src_dir": "object_detection/table_transformer/quantization/ptq_static",
-      "dataset_location": "/tf_dataset/dataset/PubTables-1M-Structure",
-      "input_model": "/tf_dataset2/models/onnx/table-transformer/model.onnx",
-      "main_script": "table-transformer/src/main.py",
+      "dataset_location": "/tf_dataset/dataset/PubTables-1M",
+      "input_model": "/tf_dataset2/models/onnx/table-transformer/pubtables1m_structure_detr_r18.onnx",
+      "main_script": "patch",
+      "batch_size": 1
+    },
+    "table_transformer_detection": {
+      "model_src_dir": "object_detection/table_transformer/quantization/ptq_static",
+      "dataset_location": "/tf_dataset/dataset/PubTables-1M",
+      "input_model": "/tf_dataset2/models/onnx/table-transformer/pubtables1m_detection_detr_r18.onnx",
+      "main_script": "patch",
       "batch_size": 1
     },
     "hf_codebert": {
diff --git a/examples/README.md b/examples/README.md
@@ -1402,7 +1402,13 @@ Intel® Neural Compressor validated examples with multiple compression technique
     <td><a href="./onnxrt/object_detection/ssd_mobilenet_v2/quantization/ptq_static">qlinearops</a> / <a href="./onnxrt/object_detection/ssd_mobilenet_v2/quantization/ptq_static">qdq</a></td>
   </tr>
   <tr>
-    <td>Table Transformer</td>
+    <td>Table Transformer Structure Recognition</td>
+    <td>Object Detection</td>
+    <td>Post-Training Static Quantization</td>
+    <td><a href="./onnxrt/object_detection/table_transformer/quantization/ptq_static">qlinearops</a></td>
+  </tr>
+  <tr>
+    <td>Table Transformer Detection</td>
     <td>Object Detection</td>
     <td>Post-Training Static Quantization</td>
     <td><a href="./onnxrt/object_detection/table_transformer/quantization/ptq_static">qlinearops</a></td>
diff --git a/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/README.md b/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/README.md
@@ -1,7 +1,7 @@
 Step-by-Step
 ============
 
-This example show how to export, quantize and evaluate the DETR R18 model for table structure recognition task based on PubTables-1M dataset.
+This example show how to export, quantize and evaluate 2 [DETR](https://huggingface.co/docs/transformers/model_doc/detr) R18 models on [PubTables-1M](https://huggingface.co/datasets/bsmock/pubtables-1m) dataset, one for table detection and one for table structure recognition, dubbed Table Transformers.
 
 # Prerequisite
 
@@ -16,16 +16,20 @@ bash prepare.sh
 
 ## 2. Prepare Dataset
 
-Download dataset according to this [doc](https://github.com/microsoft/table-transformer/tree/main#training-and-evaluation-data).
+Download PubTables-1M dataset according to this [doc](https://github.com/microsoft/table-transformer/tree/main#training-and-evaluation-data).
+After downloading and extracting, PubTables-1M dataset folder should contain `PubTables-1M-Structure` and `PubTables-1M-Detection` folders.
 
 ## 3. Prepare Model
 
-```shell
-wget https://huggingface.co/bsmock/tatr-pubtables1m-v1.0/resolve/main/pubtables1m_structure_detr_r18.pth
+Prepare DETR R18 model for table structure recognition.
 
-bash export.sh --input_model=/path/to/pubtables1m_structure_detr_r18.pth \
-               --output_model=/path/to/export \ # model path as *.onnx
-               --dataset_location=/path/to/dataset_folder # dataset_folder should contains 'words' sub-folder
+```
+python prepare_model.py  --input_model=structure_detr  --output_model=pubtables1m_structure_detr_r18.onnx --dataset_location=/path/to/pubtables-1m
+```
+
+Prepare DETR R18 model for table detection.
+```
+python prepare_model.py  --input_model=detection_detr  --output_model=pubtables1m_detection_detr_r18.onnx --dataset_location=/path/to/pubtables-1m
 ```
 
 # Run
@@ -35,15 +39,15 @@ bash export.sh --input_model=/path/to/pubtables1m_structure_detr_r18.pth \
 Static quantization with QOperator format:
 
 ```bash
-bash run_tuning.sh --input_model=path/to/model  \ # model path as *.onnx
+bash run_quant.sh --input_model=path/to/model  \ # model path as *.onnx
                    --output_model=path/to/save \ # model path as *.onnx
-                   --dataset_location=/path/to/dataset_folder # dataset_folder should contains 'words' sub-folder
+                   --dataset_location=/path/to/pubtables-1m # dataset_folder should contains `PubTables-1M-Structure` and/or `PubTables-1M-Detection` folders
 ```
 
 ## 2. Benchmark
 
 ```bash
 bash run_benchmark.sh --input_model=path/to/model  \ # model path as *.onnx
-                      --dataset_location=/path/to/dataset_folder # dataset_folder should contains 'words' sub-folder
+                      --dataset_location=/path/to/pubtables-1m # dataset_folder should contains `PubTables-1M-Structure` and/or `PubTables-1M-Detection` folders
                       --mode=performance # or accuracy
 ```
diff --git a/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/export.sh b/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/export.sh
diff --git a/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/patch b/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/patch
@@ -110,7 +110,7 @@ index 73ae39e..2049449 100644
          i = torch.arange(w, device=x.device)
          j = torch.arange(h, device=x.device)
 diff --git a/src/eval.py b/src/eval.py
-index e3a0565..5514db5 100644
+index e3a0565..d66b318 100644
 --- a/src/eval.py
 +++ b/src/eval.py
 @@ -4,6 +4,7 @@ Copyright (C) 2021 Microsoft Corporation
@@ -152,8 +152,14 @@ index e3a0565..5514db5 100644
  
          if args.debug:
              for target, pred_logits, pred_boxes in zip(targets, outputs['pred_logits'], outputs['pred_boxes']):
+@@ -696,3 +703,4 @@ def eval_coco(args, model, criterion, postprocessors, data_loader_test, dataset_
+     print("COCO metrics summary: AP50: {:.3f}, AP75: {:.3f}, AP: {:.3f}, AR: {:.3f}".format(
+         pubmed_stats['coco_eval_bbox'][1], pubmed_stats['coco_eval_bbox'][2],
+         pubmed_stats['coco_eval_bbox'][0], pubmed_stats['coco_eval_bbox'][8]))
++    return pubmed_stats['coco_eval_bbox'][0]
+\ No newline at end of file
 diff --git a/src/main.py b/src/main.py
-index 74cd13c..1e5e5e9 100644
+index 74cd13c..c30377d 100644
 --- a/src/main.py
 +++ b/src/main.py
 @@ -41,6 +41,7 @@ def get_args():
@@ -209,7 +215,7 @@ index 74cd13c..1e5e5e9 100644
  
          dataset_test = PDFTablesDataset(os.path.join(args.data_root_dir,
                                                       "test"),
-@@ -169,6 +180,29 @@ def get_data(args):
+@@ -169,6 +180,28 @@ def get_data(args):
                                        num_workers=args.num_workers)
          return data_loader_test, dataset_test
  
@@ -234,12 +240,11 @@ index 74cd13c..1e5e5e9 100644
 +                                      collate_fn=utils.collate_fn,
 +                                      num_workers=args.num_workers)
 +        return OXDataloader(data_loader_test, args.batch_size), dataset_test
-+
 +
      elif args.mode == "grits" or args.mode == "grits-all":
          dataset_test = PDFTablesDataset(os.path.join(args.data_root_dir,
                                                       "test"),
-@@ -337,6 +371,20 @@ def train(args, model, criterion, postprocessors, device):
+@@ -337,6 +370,20 @@ def train(args, model, criterion, postprocessors, device):
  
      print('Total training time: ', datetime.now() - start_time)
  
@@ -260,7 +265,7 @@ index 74cd13c..1e5e5e9 100644
  
  def main():
      cmd_args = get_args().__dict__
-@@ -350,7 +398,7 @@ def main():
+@@ -350,7 +397,7 @@ def main():
      print('-' * 100)
  
      # Check for debug mode
@@ -269,7 +274,7 @@ index 74cd13c..1e5e5e9 100644
          print("Running evaluation/inference in DEBUG mode, processing will take longer. Saving output to: {}.".format(args.debug_save_dir))
          os.makedirs(args.debug_save_dir, exist_ok=True)
  
-@@ -366,10 +414,33 @@ def main():
+@@ -366,10 +413,35 @@ def main():
  
      if args.mode == "train":
          train(args, model, criterion, postprocessors, device)
@@ -278,7 +283,9 @@ index 74cd13c..1e5e5e9 100644
          data_loader_test, dataset_test = get_data(args)
 -        eval_coco(args, model, criterion, postprocessors, data_loader_test, dataset_test, device)
 -
-+        eval_coco(args, args.input_onnx_model, criterion, postprocessors, data_loader_test, dataset_test, device)
++        ap_result = eval_coco(args, args.input_onnx_model, criterion, postprocessors, data_loader_test, dataset_test, device)
++        print("Batch size = %d" % args.batch_size)
++        print("Accuracy: %.5f" % ap_result)
 +    elif args.mode == "export":
 +        data_loader_test, dataset_test = get_data(args)
 +        export(args, model, data_loader_test, device)
@@ -303,6 +310,6 @@ index 74cd13c..1e5e5e9 100644
 +        from neural_compressor.config import BenchmarkConfig
 +        config = BenchmarkConfig(warmup=10, iteration=100, cores_per_instance=4, num_of_instance=1)
 +        fit(args.input_onnx_model, config, b_dataloader=data_loader_test)
-
+ 
  if __name__ == "__main__":
      main()
diff --git a/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/prepare_model.py b/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/prepare_model.py
@@ -0,0 +1,100 @@
+import argparse
+import os
+import subprocess
+import sys
+from urllib import request
+
+MODEL_URLS = {"structure_detr": "https://huggingface.co/bsmock/tatr-pubtables1m-v1.0/resolve/main/pubtables1m_structure_detr_r18.pth",
+              "detection_detr": "https://huggingface.co/bsmock/tatr-pubtables1m-v1.0/resolve/main/pubtables1m_detection_detr_r18.pth"}
+MAX_TIMES_RETRY_DOWNLOAD = 5
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_model",
+                        type=str,
+                        required=False,
+                        choices=["structure_detr", "detection_detr"],
+                        default="structure_detr")
+    parser.add_argument("--output_model", type=str, required=True)
+    parser.add_argument("--dataset_location", type=str, required=True)
+    return parser.parse_args()
+
+
+def progressbar(cur, total=100):
+    percent = '{:.2%}'.format(cur / total)
+    sys.stdout.write("\r[%-100s] %s" % ('#' * int(cur), percent))
+    sys.stdout.flush()
+
+
+def schedule(blocknum, blocksize, totalsize):
+    if totalsize == 0:
+        percent = 0
+    else:
+        percent = min(1.0, blocknum * blocksize / totalsize) * 100
+    progressbar(percent)
+
+
+def download_model(url, retry_times=5):
+    model_name = url.split("/")[-1]
+    if os.path.isfile(model_name):
+        print(f"{model_name} exists, skip download")
+        return True
+
+    print("download model...")
+    retries = 0
+    while retries < retry_times:
+        try:
+            request.urlretrieve(url, model_name, schedule)
+            break
+        except KeyboardInterrupt:
+            return False
+        except:
+            retries += 1
+            print(f"Download failed{', Retry downloading...' if retries < retry_times else '!'}")
+    return retries < retry_times
+
+
+def export_model(input_model, output_model, dataset_location):
+    print("\nexport model...")
+
+    if not os.path.exists("./table-transformer"):
+        subprocess.run("bash prepare.sh", shell=True)
+    
+    model_load_path = os.path.abspath(MODEL_URLS[input_model].split("/")[-1])
+    output_model = os.path.join(os.path.dirname(model_load_path), output_model)
+    if input_model == "detection_detr":
+        data_root_dir = os.path.join(dataset_location, "PubTables-1M-Detection")
+        data_type = "detection"
+        config_file = "detection_config.json"
+    elif input_model == "structure_detr":
+        data_root_dir = os.path.join(dataset_location, "PubTables-1M-Structure")
+        data_type = "structure"
+        config_file = "structure_config.json"
+    table_words_dir = os.path.join(data_root_dir, "words")
+
+    os.chdir("table-transformer/src")
+
+    command = f"python main.py \
+                --model_load_path {model_load_path} \
+                --output_model {output_model} \
+                --data_root_dir {data_root_dir} \
+                --table_words_dir {table_words_dir} \
+                --mode export \
+                --data_type {data_type} \
+                --device cpu \
+                --config_file {config_file}"
+
+    subprocess.run(command, shell=True)
+    assert os.path.exists(output_model), f"Export failed! {output_model} doesn't exist!"
+
+
+def prepare_model(input_model, output_model, dataset_location):
+    is_download_successful = download_model(MODEL_URLS[args.input_model], MAX_TIMES_RETRY_DOWNLOAD)
+    if is_download_successful:
+        export_model(input_model, output_model, dataset_location)
+
+
+if __name__ == "__main__":
+    args = parse_arguments()
+    prepare_model(args.input_model, args.output_model, args.dataset_location)
diff --git a/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/run_benchmark.sh b/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/run_benchmark.sh
@@ -35,15 +35,28 @@ function run_benchmark {
 	      bash prepare.sh
     fi
 
+    if [[ "${input_model}" =~ "structure" ]]; then
+        task_data_dir="PubTables-1M-Structure"
+        data_type="structure"
+        config_file="structure_config.json"
+    fi
+    if [[ "${input_model}" =~ "detection" ]]; then
+        task_data_dir="PubTables-1M-Detection"
+        data_type="detection"
+        config_file="detection_config.json"
+    fi
+
+    input_model=$(realpath "$input_model")
+
     cd table-transformer/src
     python main.py \
             --input_onnx_model ${input_model} \
-            --data_root_dir ${dataset_location} \
-            --table_words_dir ${dataset_location}/words \
+            --data_root_dir "${dataset_location}/${task_data_dir}" \
+            --table_words_dir "${dataset_location}/${task_data_dir}/words" \
             --mode ${mode} \
-            --data_type structure \
+            --data_type ${data_type} \
             --device cpu \
-            --config_file structure_config.json
+            --config_file ${config_file}
 }
 
 main "$@"
diff --git a/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/run_quant.sh b/examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/run_quant.sh
@@ -35,16 +35,30 @@ function run_tuning {
 	      bash prepare.sh
     fi
 
+    if [[ "${input_model}" =~ "structure" ]]; then
+        task_data_dir="PubTables-1M-Structure"
+        data_type="structure"
+        config_file="structure_config.json"
+    fi
+    if [[ "${input_model}" =~ "detection" ]]; then
+        task_data_dir="PubTables-1M-Detection"
+        data_type="detection"
+        config_file="detection_config.json"
+    fi
+
+    input_model=$(realpath "$input_model")
+    output_model=$(realpath "$output_model")
+
     cd table-transformer/src
     python main.py \
             --input_onnx_model ${input_model} \
             --output_model ${output_model} \
-            --data_root_dir ${dataset_location} \
-            --table_words_dir ${dataset_location}/words \
+            --data_root_dir "${dataset_location}/${task_data_dir}" \
+            --table_words_dir "${dataset_location}/${task_data_dir}/words" \
             --mode quantize \
-            --data_type structure \
+            --data_type ${data_type} \
             --device cpu \
-            --config_file structure_config.json
+            --config_file ${config_file}
 }
 
 main "$@"
