Add PaddlePaddle export and inference (#9240)

* Add PaddlePaddle Model Export

Test on Yolov5 DockerEnviroment with paddlepaddle-gpu v2.2

Signed-off-by: Katteria <39751846+kisaragychihaya@users.noreply.github.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Cleanup Paddle Export

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

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* Update common.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update export.py

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* Update export.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update export.py

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* Update export.py

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* Use PyTorch2Paddle

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* Paddle no longer requires ONNX

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update export.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update export.py

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* Update benchmarks.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Add inference code of PaddlePaddle

Signed-off-by: Katteria <39751846+kisaragychihaya@users.noreply.github.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Update common.py

Signed-off-by: Katteria <39751846+kisaragychihaya@users.noreply.github.com>

* Update common.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Add paddlepaddle-gpu install if cuda

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Update common.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update common.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update common.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Signed-off-by: Katteria <39751846+kisaragychihaya@users.noreply.github.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

export.py

@@ -15,6 +15,7 @@ TensorFlow GraphDef         | `pb`                          | yolov5s.pb
 TensorFlow Lite             | `tflite`                      | yolov5s.tflite
 TensorFlow Edge TPU         | `edgetpu`                     | yolov5s_edgetpu.tflite
 TensorFlow.js               | `tfjs`                        | yolov5s_web_model/
+PaddlePaddle                | `paddle`                      | yolov5s_paddle_model/
 
 Requirements:
     $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU
@@ -54,7 +55,6 @@ from pathlib import Path
 
 import pandas as pd
 import torch
-import yaml
 from torch.utils.mobile_optimizer import optimize_for_mobile
 
 FILE = Path(__file__).resolve()
@@ -68,7 +68,7 @@ from models.experimental import attempt_load
 from models.yolo import ClassificationModel, Detect
 from utils.dataloaders import LoadImages
 from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
-                           check_yaml, colorstr, file_size, get_default_args, print_args, url2file)
+                           check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save)
 from utils.torch_utils import select_device, smart_inference_mode
 
 
@@ -85,7 +85,8 @@ def export_formats():
         ['TensorFlow GraphDef', 'pb', '.pb', True, True],
         ['TensorFlow Lite', 'tflite', '.tflite', True, False],
         ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
-        ['TensorFlow.js', 'tfjs', '_web_model', False, False],]
+        ['TensorFlow.js', 'tfjs', '_web_model', False, False],
+        ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
     return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
 
 
@@ -180,7 +181,7 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
 
 
 @try_export
-def export_openvino(model, file, half, prefix=colorstr('OpenVINO:')):
+def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
     # YOLOv5 OpenVINO export
     check_requirements('openvino-dev')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
     import openvino.inference_engine as ie
@@ -189,9 +190,23 @@ def export_openvino(model, file, half, prefix=colorstr('OpenVINO:')):
     f = str(file).replace('.pt', f'_openvino_model{os.sep}')
 
     cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
-    subprocess.check_output(cmd.split())  # export
+    subprocess.run(cmd.split(), check=True, env=os.environ)  # export
-    with open(Path(f) / file.with_suffix('.yaml').name, 'w') as g:
+    yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
-        yaml.dump({'stride': int(max(model.stride)), 'names': model.names}, g)  # add metadata.yaml
+    return f, None
+
+
+@try_export
+def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
+    # YOLOv5 Paddle export
+    check_requirements(('paddlepaddle', 'x2paddle'))
+    import x2paddle
+    from x2paddle.convert import pytorch2paddle
+
+    LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...')
+    f = str(file).replace('.pt', f'_paddle_model{os.sep}')
+
+    pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im])  # export
+    yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
     return f, None
 
 
@@ -464,7 +479,7 @@ def run(
     fmts = tuple(export_formats()['Argument'][1:])  # --include arguments
     flags = [x in include for x in fmts]
     assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
-    jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags  # export booleans
+    jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags  # export booleans
     file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)  # PyTorch weights
 
     # Load PyTorch model
@@ -497,29 +512,28 @@ def run(
     if half and not coreml:
         im, model = im.half(), model.half()  # to FP16
     shape = tuple((y[0] if isinstance(y, tuple) else y).shape)  # model output shape
+    metadata = {'stride': int(max(model.stride)), 'names': model.names}  # model metadata
     LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
 
     # Exports
-    f = [''] * 10  # exported filenames
+    f = [''] * len(fmts)  # exported filenames
     warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning)  # suppress TracerWarning
-    if jit:
+    if jit:  # TorchScript
         f[0], _ = export_torchscript(model, im, file, optimize)
     if engine:  # TensorRT required before ONNX
         f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
     if onnx or xml:  # OpenVINO requires ONNX
         f[2], _ = export_onnx(model, im, file, opset, train, dynamic, simplify)
     if xml:  # OpenVINO
-        f[3], _ = export_openvino(model, file, half)
+        f[3], _ = export_openvino(file, metadata, half)
-    if coreml:
+    if coreml:  # CoreML
         f[4], _ = export_coreml(model, im, file, int8, half)
-
+    if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
-    # TensorFlow Exports
-    if any((saved_model, pb, tflite, edgetpu, tfjs)):
         if int8 or edgetpu:  # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
             check_requirements('flatbuffers==1.12')  # required before `import tensorflow`
         assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
         assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
-        f[5], model = export_saved_model(model.cpu(),
+        f[5], s_model = export_saved_model(model.cpu(),
                                            im,
                                            file,
                                            dynamic,
@@ -531,13 +545,15 @@ def run(
                                            conf_thres=conf_thres,
                                            keras=keras)
         if pb or tfjs:  # pb prerequisite to tfjs
-            f[6], _ = export_pb(model, file)
+            f[6], _ = export_pb(s_model, file)
         if tflite or edgetpu:
-            f[7], _ = export_tflite(model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
+            f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
         if edgetpu:
             f[8], _ = export_edgetpu(file)
         if tfjs:
             f[9], _ = export_tfjs(file)
+    if paddle:  # PaddlePaddle
+        f[10], _ = export_paddle(model, im, file, metadata)
 
     # Finish
     f = [str(x) for x in f if x]  # filter out '' and None

models/common.py

@@ -320,14 +320,16 @@ class DetectMultiBackend(nn.Module):
         #   TensorFlow GraphDef:            *.pb
         #   TensorFlow Lite:                *.tflite
         #   TensorFlow Edge TPU:            *_edgetpu.tflite
+        #   PaddlePaddle:                   *_paddle_model
         from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import
 
         super().__init__()
         w = str(weights[0] if isinstance(weights, list) else weights)
-        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self._model_type(w)  # get backend
+        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = self._model_type(w)  # type
         w = attempt_download(w)  # download if not local
         fp16 &= pt or jit or onnx or engine  # FP16
         stride = 32  # default stride
+        cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
 
         if pt:  # PyTorch
             model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
@@ -351,7 +353,6 @@ class DetectMultiBackend(nn.Module):
             net = cv2.dnn.readNetFromONNX(w)
         elif onnx:  # ONNX Runtime
             LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
-            cuda = torch.cuda.is_available() and device.type != 'cpu'
             check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
             import onnxruntime
             providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
@@ -408,8 +409,7 @@ class DetectMultiBackend(nn.Module):
             LOGGER.info(f'Loading {w} for CoreML inference...')
             import coremltools as ct
             model = ct.models.MLModel(w)
-        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
+        elif saved_model:  # TF SavedModel
-            if saved_model:  # SavedModel
             LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
             import tensorflow as tf
             keras = False  # assume TF1 saved_model
@@ -423,7 +423,7 @@ class DetectMultiBackend(nn.Module):
                 ge = x.graph.as_graph_element
                 return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
 
-                gd = tf.Graph().as_graph_def()  # graph_def
+            gd = tf.Graph().as_graph_def()  # TF GraphDef
             with open(w, 'rb') as f:
                 gd.ParseFromString(f.read())
             frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs="Identity:0")
@@ -433,21 +433,34 @@ class DetectMultiBackend(nn.Module):
             except ImportError:
                 import tensorflow as tf
                 Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
-                if edgetpu:  # Edge TPU https://coral.ai/software/#edgetpu-runtime
+            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
                 LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
                 delegate = {
                     'Linux': 'libedgetpu.so.1',
                     'Darwin': 'libedgetpu.1.dylib',
                     'Windows': 'edgetpu.dll'}[platform.system()]
                 interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
-                else:  # Lite
+            else:  # TFLite
                 LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
                 interpreter = Interpreter(model_path=w)  # load TFLite model
             interpreter.allocate_tensors()  # allocate
             input_details = interpreter.get_input_details()  # inputs
             output_details = interpreter.get_output_details()  # outputs
-            elif tfjs:
+        elif tfjs:  # TF.js
             raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
+        elif paddle:  # PaddlePaddle
+            LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
+            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
+            import paddle.inference as pdi
+            if not Path(w).is_file():  # if not *.pdmodel
+                w = next(Path(w).rglob('*.pdmodel'))  # get *.xml file from *_openvino_model dir
+            weights = Path(w).with_suffix('.pdiparams')
+            config = pdi.Config(str(w), str(weights))
+            if cuda:
+                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
+            predictor = pdi.create_predictor(config)
+            input_names = predictor.get_input_names()
+            input_handle = predictor.get_input_handle(input_names[0])
         else:
             raise NotImplementedError(f'ERROR: {w} is not a supported format')
 
@@ -502,6 +515,13 @@ class DetectMultiBackend(nn.Module):
             else:
                 k = 'var_' + str(sorted(int(k.replace('var_', '')) for k in y)[-1])  # output key
                 y = y[k]  # output
+        elif self.paddle:  # PaddlePaddle
+            im = im.cpu().numpy().astype("float32")
+            self.input_handle.copy_from_cpu(im)
+            self.predictor.run()
+            output_names = self.predictor.get_output_names()
+            output_handle = self.predictor.get_output_handle(output_names[0])
+            y = output_handle.copy_to_cpu()
         else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
             im = im.permute(0, 2, 3, 1).cpu().numpy()  # torch BCHW to numpy BHWC shape(1,320,192,3)
             if self.saved_model:  # SavedModel
@@ -542,13 +562,13 @@ class DetectMultiBackend(nn.Module):
     def _model_type(p='path/to/model.pt'):
         # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
         from export import export_formats
-        suffixes = list(export_formats().Suffix) + ['.xml']  # export suffixes
+        sf = list(export_formats().Suffix) + ['.xml']  # export suffixes
-        check_suffix(p, suffixes)  # checks
+        check_suffix(p, sf)  # checks
         p = Path(p).name  # eliminate trailing separators
-        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, xml2 = (s in p for s in suffixes)
+        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, xml2 = (s in p for s in sf)
         xml |= xml2  # *_openvino_model or *.xml
         tflite &= not edgetpu  # *.tflite
-        return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs
+        return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle
 
     @staticmethod
     def _load_metadata(f=Path('path/to/meta.yaml')):

utils/benchmarks.py

@@ -61,7 +61,7 @@ def run(
     device = select_device(device)
     for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, CPU, GPU)
         try:
-            assert i not in (9, 10), 'inference not supported'  # Edge TPU and TF.js are unsupported
+            assert i not in (9, 10, 11), 'inference not supported'  # Edge TPU, TF.js and Paddle are unsupported
             assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13'  # CoreML
             if 'cpu' in device.type:
                 assert cpu, 'inference not supported on CPU'