PLT update, add Ocr model case

framework/e2e/PaddleLT_new/binary_search.py

@@ -13,8 +13,8 @@
 import numpy as np
 from layertest import LayerTest
 from strategy.compare import perf_compare
-from tools.logger import Logger
-from tools.res_save import save_pickle
+from pltools.logger import Logger
+from pltools.res_save import save_pickle
 
 
 def get_commits(start, end):

framework/e2e/PaddleLT_new/db/layer_db.py

@@ -15,8 +15,8 @@
 from db.snapshot import Snapshot
 
 # from strategy.compare import perf_compare
-from tools.logger import Logger
-from tools.res_save import xlsx_save
+from pltools.logger import Logger
+from pltools.res_save import xlsx_save
 
 
 class LayerBenchmarkDB(object):

framework/e2e/PaddleLT_new/debug/infer_debug_dropout.py

@@ -0,0 +1,145 @@
+import os
+import numpy as np
+import paddle
+import paddle.inference as paddle_infer
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.parameter_0 = self.create_parameter(
+            shape=[2048],
+            dtype=paddle.float32,
+        )
+        self.parameter_1 = self.create_parameter(
+            shape=[1024],
+            dtype=paddle.float32,
+        )
+        self.parameter_2 = self.create_parameter(
+            shape=[1024],
+            dtype=paddle.float32,
+        )
+        self.parameter_3 = self.create_parameter(
+            shape=[1024],
+            dtype=paddle.float32,
+        )
+        self.parameter_4 = self.create_parameter(
+            shape=[1024],
+            dtype=paddle.float32,
+        )
+        self.parameter_5 = self.create_parameter(
+            shape=[1024],
+            dtype=paddle.float32,
+        )
+        self.parameter_6 = self.create_parameter(
+            shape=[2048, 1024],
+            dtype=paddle.float32,
+        )
+        self.parameter_7 = self.create_parameter(
+            shape=[1024, 2048],
+            dtype=paddle.float32,
+        )
+
+    def forward(
+        self,
+        var_0,  # (shape: [1, 169, 1024], dtype: paddle.float32, stop_gradient: False)
+        var_1,  # (shape: [1, 169, 1024], dtype: paddle.float32, stop_gradient: False)
+    ):
+        var_2 = paddle.nn.functional.common.dropout(
+            var_0, p=0.1, axis=None, training=self.training, mode="upscale_in_train", name=None
+        )
+        var_3 = var_1.__add__(var_2)
+        var_4 = paddle.nn.functional.norm.layer_norm(
+            var_3, normalized_shape=[1024], weight=self.parameter_5, bias=self.parameter_3, epsilon=1e-05
+        )
+        var_5 = paddle.nn.functional.common.linear(x=var_4, weight=self.parameter_7, bias=self.parameter_0, name=None)
+        var_6 = paddle.nn.functional.activation.gelu(var_5)
+        var_7 = paddle.nn.functional.common.dropout(
+            var_6, p=0.1, axis=None, training=self.training, mode="upscale_in_train", name=None
+        )
+        var_8 = paddle.nn.functional.common.linear(x=var_7, weight=self.parameter_6, bias=self.parameter_1, name=None)
+        var_9 = paddle.nn.functional.common.dropout(
+            var_8, p=0.1, axis=None, training=self.training, mode="upscale_in_train", name=None
+        )
+        var_10 = var_4.__add__(var_9)
+        var_11 = paddle.nn.functional.norm.layer_norm(
+            var_10, normalized_shape=[1024], weight=self.parameter_2, bias=self.parameter_4, epsilon=1e-05
+        )
+        return var_11
+
+
+seed = 33
+
+
+def reset(seed):
+    """
+    重置模型图
+    :param seed: 随机种子
+    :return:
+    """
+    paddle.enable_static()
+    paddle.disable_static()
+    paddle.seed(seed)
+    np.random.seed(seed)
+    np.set_printoptions(threshold=5, edgeitems=3)
+
+
+# def _net_input():
+#     """get input"""
+#     reset(seed)
+#     data = (
+#         paddle.to_tensor(np.random.random(size=[1, 169, 1024]).astype('float32')),
+#         paddle.to_tensor(np.random.random(size=[1, 169, 1024]).astype('float32')),
+#     )
+#     return data
+
+data = [
+    np.random.random(size=[1, 169, 1024]).astype("float32"),
+    np.random.random(size=[1, 169, 1024]).astype("float32"),
+]
+
+tensor_data = [paddle.to_tensor(data[0]), paddle.to_tensor(data[1])]
+
+
+def _net_instant():
+    """get net"""
+    reset(seed)
+    net = LayerCase()
+    return net
+
+
+# 动态图结果
+reset(seed)
+net = _net_instant()
+net.eval()
+logit = net(*tensor_data)
+
+# export
+reset(seed)
+st_net = paddle.jit.to_static(_net_instant())
+st_net.eval()
+st_net(*tensor_data)
+paddle.jit.save(st_net, path=os.path.join("save_path", "jit_save"))
+
+# infer
+reset(seed)
+config = paddle_infer.Config("save_path/jit_save" + ".pdmodel", "save_path/jit_save" + ".pdiparams")
+config.enable_use_gpu(1000, 0)
+predictor = paddle_infer.create_predictor(config)
+input_names = predictor.get_input_names()
+for i, name in enumerate(input_names):
+    input_handle = predictor.get_input_handle(name)
+    input_tmp = data[i]
+    input_handle.copy_from_cpu(input_tmp)
+predictor.run()
+output_names = predictor.get_output_names()
+if len(output_names) > 1:
+    infer_res = []
+    for i, name in enumerate(output_names):
+        output_handle = predictor.get_output_handle(output_names[i])
+        infer_res.append(output_handle.copy_to_cpu())
+else:
+    output_handle = predictor.get_output_handle(output_names[0])
+    infer_res = output_handle.copy_to_cpu()
+
+print(logit - infer_res)

framework/e2e/PaddleLT_new/diy/data/single_img_dataset.py

@@ -9,7 +9,7 @@
 
 import paddle
 from paddle.io import Dataset, BatchSampler, DataLoader
-import tools.np_tool as tool
+import pltools.np_tool as tool
 
 
 class SingleImageWithoutLabelDataset(Dataset):

framework/e2e/PaddleLT_new/diy/data/struct_img_dataset.py

@@ -9,7 +9,7 @@
 
 import paddle
 from paddle.io import Dataset, BatchSampler, DataLoader
-import tools.np_tool as tool
+import pltools.np_tool as tool
 
 
 # class ListImageWithoutLabelDataset(Dataset):

framework/e2e/PaddleLT_new/engine/paddle_eval.py

@@ -13,9 +13,9 @@
 from generator.builder_layer import BuildLayer
 from generator.builder_data import BuildData
 
-from tools.logger import Logger
+from pltools.logger import Logger
 
-from tools.res_save import save_tensor
+from pltools.res_save import save_tensor
 
 
 class LayerEval(object):

framework/e2e/PaddleLT_new/engine/paddle_eval_bm.py

@@ -13,9 +13,9 @@
 from engine.paddle_xtools import reset
 from generator.builder_layer import BuildLayer
 from generator.builder_data import BuildData
-from tools.res_save import save_pickle
-from tools.statistics import trimmean, mean, best, best_top_k, perf_by_step
-from tools.logger import Logger
+from pltools.res_save import save_pickle
+from pltools.statistics import trimmean, mean, best, best_top_k, perf_by_step
+from pltools.logger import Logger
 
 
 class LayerEvalBM(object):

framework/e2e/PaddleLT_new/engine/paddle_export.py

@@ -12,7 +12,7 @@
 from generator.builder_layer import BuildLayer
 from generator.builder_data import BuildData
 
-from tools.logger import Logger
+from pltools.logger import Logger
 
 
 class LayerExport(object):

framework/e2e/PaddleLT_new/engine/paddle_infer.py

@@ -14,7 +14,7 @@
 from generator.builder_layer import BuildLayer
 from generator.builder_data import BuildData
 
-from tools.logger import Logger
+from pltools.logger import Logger
 
 
 class LayerInfer(object):

framework/e2e/PaddleLT_new/engine/paddle_train.py

@@ -15,7 +15,7 @@
 from generator.builder_optimizer import BuildOptimizer
 from generator.builder_loss import BuildLoss
 
-from tools.logger import Logger
+from pltools.logger import Logger
 
 
 class LayerTrain(object):

framework/e2e/PaddleLT_new/engine/paddle_train_bm.py

@@ -15,9 +15,9 @@
 from generator.builder_data import BuildData
 from generator.builder_optimizer import BuildOptimizer
 from generator.builder_loss import BuildLoss
-from tools.res_save import save_pickle
-from tools.statistics import trimmean, mean, best, best_top_k, perf_by_step
-from tools.logger import Logger
+from pltools.res_save import save_pickle
+from pltools.statistics import trimmean, mean, best, best_top_k, perf_by_step
+from pltools.logger import Logger
 
 
 class LayerTrainBM(object):

framework/e2e/PaddleLT_new/engine/torch_eval_bm.py

@@ -12,8 +12,8 @@
 from engine.torch_xtools import reset
 from generator.builder_layer import BuildLayer
 from generator.builder_data import BuildData
-from tools.res_save import save_pickle
-from tools.statistics import trimmean, mean, best, best_top_k, perf_by_step
+from pltools.res_save import save_pickle
+from pltools.statistics import trimmean, mean, best, best_top_k, perf_by_step
 
 
 class TorchLayerEvalBM(object):

framework/e2e/PaddleLT_new/generator/builder_data.py

@@ -14,11 +14,15 @@
     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
 
-import tools.np_tool as tool
+import pltools.np_tool as tool
 
 
 class BuildData(object):

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