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add plt torch case, test=model
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@Zeref996
Zeref996 committed Jan 21, 2025
1 parent c6caa80 commit 2aa0ad2
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59 changes: 59 additions & 0 deletions framework/e2e/PaddleLT_new/layerApicase/nn_zero_size/CTCLoss_zero_size_class.py
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import numpy as np
import paddle


class LayerCase(paddle.nn.Layer):
"""
case名称: CTCLoss_zero_size_class
"""

def __init__(self):
super(LayerCase, self).__init__()
self.func = paddle.nn.CTCLoss(blank=0, reduction='mean')

def forward(self, x, y, input_lengths, label_lengths ):
"""
forward
"""

paddle.seed(33)
np.random.seed(33)
out = self.func(x, y, input_lengths, label_lengths)
return out



def create_inputspec():
inputspec = (
paddle.static.InputSpec(shape=(-1, -1, -1), dtype=paddle.float32, stop_gradient=False),
paddle.static.InputSpec(shape=(-1, -1), dtype=paddle.int32, stop_gradient=False),
paddle.static.InputSpec(shape=(-1), dtype=paddle.int32, stop_gradient=False),
paddle.static.InputSpec(shape=(-1), dtype=paddle.int32, stop_gradient=False),
)
return inputspec

def create_tensor_inputs():
"""
paddle tensor
"""
inputs = (
paddle.to_tensor(-1 + (1 - -1) * np.random.random([0, 10, 10]).astype('float32'), dtype='float32', stop_gradient=False),
paddle.to_tensor(-1 + (1 - -1) * np.random.random([10, 10]).astype('int32'), dtype='int32', stop_gradient=True),
paddle.to_tensor(-1 + (1 - -1) * np.random.random([10]).astype('int64'), dtype='int64', stop_gradient=True),
paddle.to_tensor(-1 + (1 - -1) * np.random.random([10]).astype('int64'), dtype='int64', stop_gradient=True),
)
return inputs


def create_numpy_inputs():
"""
numpy array
"""
inputs = (
-1 + (1 - -1) * np.random.random([0, 10, 10]).astype('float32'),
-1 + (1 - -1) * np.random.random([10, 10]).astype('int32'),
-1 + (1 - -1) * np.random.random([10]).astype('int64'),
-1 + (1 - -1) * np.random.random([10]).astype('int64'),
)
return inputs

55 changes: 55 additions & 0 deletions framework/e2e/PaddleLT_new/layerApicase/nn_zero_size/Conv1DTranspose_zero_size_class.py
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import numpy as np
import paddle


class LayerCase(paddle.nn.Layer):
"""
case名称: Conv1DTranspose_zero_size_class
"""

def __init__(self):
super(LayerCase, self).__init__()
self.func = paddle.nn.Conv1DTranspose(
in_channels=0,
out_channels=1,
kernel_size=1,
stride=1,
padding=0,
dilation=1,
# groups=1,
padding_mode='zeros',
)

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, 0, -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([3, 0, 1]).astype('float32'), dtype='float32', stop_gradient=False), )
return inputs


def create_numpy_inputs():
"""
numpy array
"""
inputs = (-1 + (1 - -1) * np.random.random([3, 0, 1]).astype('float32'), )
return inputs

2 changes: 1 addition & 1 deletion framework/e2e/PaddleLT_new/layerApicase/nn_zero_size/Conv2DTranspose_zero_size_class.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ def __init__(self):
stride=1,
padding=0,
output_padding=0,
groups=1,
# groups=1,
dilation=1,
)

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55 changes: 55 additions & 0 deletions framework/e2e/PaddleLT_new/layerApicase/nn_zero_size/Conv3DTranspose_zero_size_class.py
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import numpy as np
import paddle


class LayerCase(paddle.nn.Layer):
"""
case名称: Conv3DTranspose_zero_size_class
"""

def __init__(self):
super(LayerCase, self).__init__()
self.func = paddle.nn.Conv3DTranspose(
in_channels=0,
out_channels=1,
kernel_size=1,
stride=1,
padding=0,
dilation=1,
# groups=1,
padding_mode='zeros',
)

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, 0, -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([3, 0, 1, 1, 1]).astype('float32'), dtype='float32', stop_gradient=False), )
return inputs


def create_numpy_inputs():
"""
numpy array
"""
inputs = (-1 + (1 - -1) * np.random.random([3, 0, 1, 1, 1]).astype('float32'), )
return inputs

59 changes: 59 additions & 0 deletions framework/e2e/PaddleLT_new/layerApicase/nn_zero_size/CosineEmbeddingLoss_zero_size_class.py
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import numpy as np
import paddle


class LayerCase(paddle.nn.Layer):
"""
case名称: CosineEmbeddingLoss_zero_size_class
"""

def __init__(self):
super(LayerCase, self).__init__()
self.func = paddle.nn.CosineEmbeddingLoss(
margin=0,
reduction='mean',
)

def forward(self, x, y, label ):
"""
forward
"""

paddle.seed(33)
np.random.seed(33)
out = self.func(x, y, label)
return out



def create_inputspec():
inputspec = (
paddle.static.InputSpec(shape=(-1, -1, ), dtype=paddle.float32, stop_gradient=False),
paddle.static.InputSpec(shape=(-1, -1, ), dtype=paddle.float32, stop_gradient=False),
paddle.static.InputSpec(shape=(-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([10, 0]).astype('float32'), dtype='float32', stop_gradient=False),
paddle.to_tensor(-1 + (1 - -1) * np.random.random([10, 0]).astype('float32'), dtype='float32', stop_gradient=False),
paddle.to_tensor(-1 + (1 - -1) * np.random.random([10]).astype('float32'), dtype='float32', stop_gradient=False),
)
return inputs


def create_numpy_inputs():
"""
numpy array
"""
inputs = (
-1 + (1 - -1) * np.random.random([10, 0]).astype('float32'),
-1 + (1 - -1) * np.random.random([10, 0]).astype('float32'),
-1 + (1 - -1) * np.random.random([10]).astype('float32'),
)
return inputs

53 changes: 53 additions & 0 deletions framework/e2e/PaddleLT_new/layerApicase/nn_zero_size/CosineSimilarity_zero_size_class.py
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import numpy as np
import paddle


class LayerCase(paddle.nn.Layer):
"""
case名称: CosineSimilarity_zero_size_class
"""

def __init__(self):
super(LayerCase, self).__init__()
self.func = paddle.nn.CosineSimilarity(axis=1, eps=0.00000001)

def forward(self, x, y ):
"""
forward
"""

paddle.seed(33)
np.random.seed(33)
out = self.func(x, y)
return out



def create_inputspec():
inputspec = (
paddle.static.InputSpec(shape=(-1, -1, -1, -1), dtype=paddle.float32, stop_gradient=False),
paddle.static.InputSpec(shape=(-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([12, 0, 10, 10]).astype('float32'), dtype='float32', stop_gradient=False),
paddle.to_tensor(-1 + (1 - -1) * np.random.random([12, 0, 10, 10]).astype('float32'), dtype='float32', stop_gradient=False),
)
return inputs


def create_numpy_inputs():
"""
numpy array
"""
inputs = (
-1 + (1 - -1) * np.random.random([12, 0, 10, 10]).astype('float32'),
-1 + (1 - -1) * np.random.random([12, 0, 10, 10]).astype('float32'),
)
return inputs

4 changes: 2 additions & 2 deletions framework/e2e/PaddleLT_new/layerApicase/nn_zero_size/Dropout2D_zero_size_class.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,14 +33,14 @@ def create_tensor_inputs():
"""
paddle tensor
"""
inputs = (paddle.to_tensor(-1 + (1 - -1) * np.random.random([3, 0, 1, 1]).astype('float32'), dtype='float32', stop_gradient=False), )
inputs = (paddle.to_tensor(-1 + (1 - -1) * np.random.random([12, 0, 10, 10]).astype('float32'), dtype='float32', stop_gradient=False), )
return inputs


def create_numpy_inputs():
"""
numpy array
"""
inputs = (-1 + (1 - -1) * np.random.random([3, 0, 1, 1]).astype('float32'), )
inputs = (-1 + (1 - -1) * np.random.random([12, 0, 10, 10]).astype('float32'), )
return inputs

46 changes: 46 additions & 0 deletions framework/e2e/PaddleLT_new/layerApicase/nn_zero_size/Dropout3D_zero_size_class.py
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@@ -0,0 +1,46 @@
import numpy as np
import paddle


class LayerCase(paddle.nn.Layer):
"""
case名称: Dropout3D_zero_size_class
"""

def __init__(self):
super(LayerCase, self).__init__()
self.func = paddle.nn.Dropout3D(p=0.5)

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, 0, -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([12, 0, 10, 10, 10]).astype('float32'), dtype='float32', stop_gradient=False), )
return inputs


def create_numpy_inputs():
"""
numpy array
"""
inputs = (-1 + (1 - -1) * np.random.random([12, 0, 10, 10, 10]).astype('float32'), )
return inputs

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