# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.

# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


import torch

# First
torch.manual_seed(0)
torch.set_printoptions(precision=8)
m1 = torch.nn.Conv1d(2, 1, 2, stride=1, padding=0, dilation=2, bias=False)
input = torch.randn(1, 2, 5, requires_grad=True)
print("Input: ", input)
result = m1(input)
print("Result: ", result)
result.sum().backward()
print("Gradient for input: ", input.grad)
print("Gradient for weights: ", m1.weight.grad)

# Second
m1 = torch.nn.Conv1d(4, 3, 3, stride=2, padding=1, dilation=3, bias=True)
input = torch.randn(2, 4, 5, requires_grad=True)
print("Input: ", input)
result = m1(input)
print("Result: ", result)
result.sum().backward()
print("Gradient for input: ", input.grad)
print("Gradient for weights: ", m1.weight.grad)
print("Gradient for bias: ", m1.bias.grad)

# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.

# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


import torch

# First
torch.manual_seed(0)
torch.set_printoptions(precision=8)
m1 = torch.nn.Conv1d(2, 1, 2, stride=1, padding=0, dilation=2, bias=False)
input = torch.randn(1, 2, 5, requires_grad=True)
print("Input: ", input)
result = m1(input)
print("Result: ", result)
result.sum().backward()
print("Gradient for input: ", input.grad)
print("Gradient for weights: ", m1.weight.grad)

# Second
m1 = torch.nn.Conv1d(4, 3, 3, stride=2, padding=1, dilation=3, bias=True)
input = torch.randn(2, 4, 5, requires_grad=True)
print("Input: ", input)
result = m1(input)
print("Result: ", result)
result.sum().backward()
print("Gradient for input: ", input.grad)
print("Gradient for weights: ", m1.weight.grad)
print("Gradient for bias: ", m1.bias.grad)

# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # First # Second