PLT update, add Ocr model case (#2937)

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* add plt export, test=model

* fix plt start, test=model

* fix plt start, test=model

* fix plt start, test=model

* fix plt start, test=model

* fix plt perf, test=model

* add plt bm ignore, test=model

* update plt start.sh, test=model

* update plt start.sh, test=model

* add inputspec plt ci, test=model

* add inputspec plt ci, test=model

* update plt ci, test=model

* update plt ci, test=model

* update plt ci, test=model

* update plt ci, test=model

* update plt ci, test=model

* update plt gsb, test=model

* fix plt statistic, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* plt update net instance, test=model

* fix plt tools, test=model

* update ply add model case, test=model

* update ply add model case, test=model

* update ply add model case, test=model

Author: Zeref996

framework/e2e/PaddleLT_new/generator/builder_data.py

@@ -14,6 +14,10 @@
     import diy
     import layerApicase
     import layercase
+
+    if os.environ.get("USE_PADDLE_MODEL", "None") == "PaddleOCR":
+        import layerModelcase
+        import PaddleOCR
 elif os.environ.get("FRAMEWORK") == "torch":
     import torch
     import layerTorchcase

framework/e2e/PaddleLT_new/generator/builder_layer.py

@@ -12,6 +12,10 @@
     import diy
     import layerApicase
     import layercase
+
+    if os.environ.get("USE_PADDLE_MODEL", "None") == "PaddleOCR":
+        import layerModelcase
+        import PaddleOCR
 elif os.environ.get("FRAMEWORK") == "torch":
     import torch
     import layerTorchcase

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/__init__.py

@@ -0,0 +1,18 @@
+import os
+import glob
+
+# 获取当前文件所在目录
+current_dir = os.path.dirname(__file__)
+
+# 获取当前目录下所有的文件夹路径（注意：这里不需要尾随的斜杠）  
+folders = glob.glob(os.path.join(current_dir, '*'))  
+  
+# 过滤出文件夹（排除文件）  
+folders = [folder for folder in folders if os.path.isdir(folder) and not os.path.basename(folder) == '__pycache__']
+
+# 动态导入所有 .py 文件
+for folder in folders:
+    # 获取文件名（不含扩展名）
+    module_name = os.path.basename(folder)
+    # 导入模块
+    __import__('layerModelcase.Ocr_cases.' + module_name, globals(), locals(), [])

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/__init__.py

@@ -0,0 +1,15 @@
+import os
+import glob
+
+# 获取当前文件所在目录
+current_dir = os.path.dirname(__file__)
+
+# 获取当前目录下所有的 .py 文件路径
+py_files = glob.glob(os.path.join(current_dir, "*.py"))
+
+# 动态导入所有 .py 文件
+for py_file in py_files:
+    # 获取文件名（不含扩展名）
+    module_name = os.path.basename(py_file)[:-3]
+    # 导入模块
+    __import__('layerModelcase.Ocr_cases.backbones.' + module_name, globals(), locals(), [])

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_DenseNet.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "DenseNet"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_EfficientNetb3_PREN.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "EfficientNetb3_PREN"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_HybridTransformer.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "HybridTransformer"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_MTB.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "MTB"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_MicroNet.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "MicroNet"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_MobileNetV1Enhance.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "MobileNetV3"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_MobileNetV3.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "MobileNetV3"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_PPHGNet_small.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "PPHGNet_small"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_PPLCNetV3.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "PPLCNetV3"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_RepSVTR.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "RepSVTR"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs

framework/e2e/PaddleLT_new/layerModelcase/Ocr_cases/backbones/cls_ResNet.py

@@ -0,0 +1,30 @@
+import paddle
+import numpy as np
+import PaddleOCR.ppocr.modeling.backbones as backbones
+
+
+def LayerCase():
+    """模型库中间态"""
+    model = backbones.build_backbone(config={"name": "ResNet"}, model_type="cls")
+    return model
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    inputs = (
+        paddle.rand(shape=[1, 3, 224, 224], dtype=paddle.float32),
+    )
+    return inputs
+
+
+def create_numpy_inputs():
+    inputs = (
+        np.random.random(size=[1, 3, 224, 224]).astype('float32'),
+    )
+    return inputs