Plt Support torch test, test=model

framework/e2e/PaddleLT_new/layerApicase/__init__.py

@@ -1,3 +1,5 @@
 import layerApicase.perf_monitor
 import layerApicase.nn_sublayer
+import layerApicase.nn_extreme_size
 import layerApicase.math_sublayer
+import layerApicase.math_extreme_size

framework/e2e/PaddleLT_new/layerApicase/math_extreme_size/__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__('layerApicase.math_extreme_size.' + module_name, globals(), locals(), [])

framework/e2e/PaddleLT_new/layerApicase/math_extreme_size/abs_giant_size_func.py

@@ -0,0 +1,46 @@
+import numpy as np
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    """
+    case名称: abs_base
+    api简介: 求绝对值
+    """
+
+    def __init__(self):
+        super(LayerCase, self).__init__()
+
+    def forward(self, x, ):
+        """
+        forward
+        """
+
+        paddle.seed(33)
+        np.random.seed(33)
+        out = paddle.abs(x,  )
+        return out
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, -1, -1, -1, -1), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    """
+    paddle tensor
+    """
+    inputs = (paddle.to_tensor(-1 + (1 - -1) * np.random.random([1024, 256, 128, 100, 2]).astype('float32'), dtype='float32', stop_gradient=False), )
+    return inputs
+
+
+def create_numpy_inputs():
+    """
+    numpy array
+    """
+    inputs = (-1 + (1 - -1) * np.random.random([1024, 256, 128, 100, 2]).astype('float32'), )
+    return inputs
+

framework/e2e/PaddleLT_new/layerApicase/nn_extreme_size/Conv2D_giant_size_class.py

@@ -0,0 +1,47 @@
+import numpy as np
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    """
+    case名称: Conv2D_giant_size_class
+    api简介: 2维卷积
+    """
+
+    def __init__(self):
+        super(LayerCase, self).__init__()
+        self.func = paddle.nn.Conv2D(kernel_size=[3, 3], in_channels=256, out_channels=1, )
+
+    def forward(self, data, ):
+        """
+        forward
+        """
+
+        paddle.seed(33)
+        np.random.seed(33)
+        out = self.func(data, )
+        return out
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 256, -1, -1), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    """
+    paddle tensor
+    """
+    inputs = (paddle.to_tensor(-1 + (1 - -1) * np.random.random([1024, 256, 128, 200]).astype('float32'), dtype='float32', stop_gradient=False), )
+    return inputs
+
+
+def create_numpy_inputs():
+    """
+    numpy array
+    """
+    inputs = (-1 + (1 - -1) * np.random.random([1024, 256, 128, 200]).astype('float32'), )
+    return inputs
+

framework/e2e/PaddleLT_new/layerApicase/nn_extreme_size/__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__('layerApicase.nn_extreme_size.' + module_name, globals(), locals(), [])

framework/e2e/PaddleLT_new/torch_case/layerApicase/math_extreme_size/__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__('torch_case.layerApicase.math_extreme_size.' + module_name, globals(), locals(), [])

framework/e2e/PaddleLT_new/torch_case/layerApicase/math_extreme_size/abs_giant_size_func.py

@@ -0,0 +1,39 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+class LayerCase(nn.Module):
+    """
+    case名称: abs_base
+    api简介: 求绝对值
+    """
+
+    def __init__(self):
+        super(LayerCase, self).__init__()
+
+    def forward(self, x):
+        """
+        forward
+        """
+        torch.manual_seed(33)
+        np.random.seed(33)
+        out = torch.abs(x)
+        return out
+
+
+def create_tensor_inputs():
+    """
+    PyTorch tensor
+    """
+    inputs = (torch.tensor((-1 + 2 * np.random.random([1024, 256, 128, 100, 2])).astype(np.float32), dtype=torch.float32, requires_grad=True), )
+    return inputs
+
+
+def create_numpy_inputs():
+    """
+    numpy array
+    """
+    # 生成一个形状为[1024, 256, 128, 100, 2]的随机numpy数组，数据范围在[-1, 1)
+    inputs = ((-1 + 2 * np.random.random([1024, 256, 128, 100, 2])).astype('float32'),)
+    return inputs

framework/e2e/PaddleLT_new/torch_case/layerApicase/nn_extreme_size/Conv2D_giant_size_class.py

@@ -0,0 +1,39 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+class LayerCase(nn.Module):
+    """
+    case名称: Conv2D_giant_size_class
+    api简介: 2维卷积
+    """
+
+    def __init__(self):
+        super(LayerCase, self).__init__()
+        self.func = nn.Conv2d(in_channels=256, out_channels=1, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, data):
+        """
+        forward
+        """
+        torch.manual_seed(33)
+        np.random.seed(33)
+        out = self.func(data)
+        return out
+
+
+def create_tensor_inputs():
+    """
+    PyTorch tensor
+    """
+    inputs = (torch.tensor((-1 + 2 * np.random.random([1024, 256, 128, 200])).astype(np.float32), dtype=torch.float32, requires_grad=True), )
+    return inputs
+
+
+def create_numpy_inputs():
+    """
+    numpy array
+    """
+    inputs = ((-1 + 2 * np.random.random([1024, 256, 128, 200])).astype('float32'),)
+    return inputs

framework/e2e/PaddleLT_new/torch_case/layerApicase/nn_extreme_size/__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__('torch_case.layerApicase.nn_extreme_size.' + module_name, globals(), locals(), [])

framework/e2e/PaddleLT_new/torch_case/layerApicase/nn_sublayer/AdaptiveAvgPool3D_8_class.py

@@ -0,0 +1,39 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+class LayerCase(nn.Module):
+    """
+    case名称: AdaptiveAvgPool3D_8
+    api简介: 3维自适应池化
+    """
+
+    def __init__(self):
+        super(LayerCase, self).__init__()
+        self.func = nn.AdaptiveAvgPool3d(output_size=(1, 1, 1))
+
+    def forward(self, data):
+        """
+        forward
+        """
+        torch.manual_seed(33)
+        np.random.seed(33)
+        out = self.func(data)
+        return out
+
+
+def create_tensor_inputs():
+    """
+    PyTorch tensor
+    """
+    inputs = (torch.tensor(-10 + (10 - -10) * np.random.random([2, 3, 8, 32, 32]).astype('float32'), dtype=torch.float32, requires_grad=True), )
+    return inputs
+
+
+def create_numpy_inputs():
+    """
+    numpy array
+    """
+    inputs = (-10 + (10 - -10) * np.random.random([2, 3, 8, 32, 32]).astype('float32'), )
+    return inputs