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Merge pull request #69 from lucasdelimanogueira/tmp

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lucasdelimanogueira 2024-05-23 12:29:04 -03:00 • committed by GitHub
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16 changed files with 106 additions and 280 deletions
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2
README.md
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@ -74,7 +74,7 @@ import random
random.seed(1)
BATCH_SIZE = 32
device = "cpu"
device = "cuda" #cpu
epochs = 10
transform = transforms.Sequential(

2
examples/train.ipynb
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@ -99,7 +99,7 @@
"outputs": [],
"source": [
"BATCH_SIZE = 32\n",
"device = \"cpu\"\n",
"device = \"cuda\" #cpu\n",
"epochs = 10\n",
"\n",
"transform = transforms.Sequential(\n",

2
norch/__init__.py
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@ -4,6 +4,6 @@ from .optim import *
from .utils import *
from .norchvision import *
__version__ = "0.0.3"
__version__ = "0.0.4"
__author__ = 'Lucas de Lima Nogueira'
__credits__ = 'Lucas de Lima Nogueira'

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norch/__pycache__/tensor.cpython-38.pyc
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norch/autograd/__pycache__/functions.cpython-38.pyc
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10
norch/autograd/functions.py
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@ -136,7 +136,7 @@ class SumBackward:
input_shape = self.input[0].shape.copy()
if self.axis == -1:
# If axis is None, sum reduces the tensor to a scalar.
grad_output = float(gradient.tensor.contents.data[0]) * self.input[0].ones_like()
grad_output = float(gradient[[0] * len(gradient.shape)]) * self.input[0].ones_like()
else:
if self.keepdim:
@ -214,9 +214,9 @@ class MaxBackward:
max_value = self.input[0].max()
mask = self.input[0].equal(max_value)
grad_output = float(gradient.tensor.contents.data[0]) * self.input[0].ones_like()
grad_output = float(gradient[[0] * len(gradient.shape)]) * self.input[0].ones_like()
grad_output = (grad_output * mask) / mask.sum().tensor.contents.data[0]
grad_output = (grad_output * mask) / mask.sum()[0]
else:
@ -250,9 +250,9 @@ class MinBackward:
min_value = self.input[0].min()
mask = self.input[0].equal(min_value)
grad_output = float(gradient.tensor.contents.data[0]) * self.input[0].ones_like()
grad_output = float(gradient[[0] * len(gradient.shape)]) * self.input[0].ones_like()
grad_output = (grad_output * mask) / mask.sum().tensor.contents.data[0]
grad_output = (grad_output * mask) / mask.sum()[0]
else:
if self.keepdim:

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norch/nn/__pycache__/loss.cpython-38.pyc
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norch/nn/__pycache__/module.cpython-38.pyc
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4
norch/nn/functional.py
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@ -23,10 +23,10 @@ def softmax(x, dim=None):
def one_hot_encode(x, num_classes):
one_hot = [[0] * num_classes for _ in range(x.numel)]
# Set the appropriate elements to 1
for i in range(x.numel):
target_idx = int(x.tensor.contents.data[i])
target_idx = int(x[i])
one_hot[i][target_idx] = 1
return norch.Tensor(one_hot)

4
norch/nn/loss.py
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@ -44,7 +44,7 @@ class CrossEntropyLoss(Loss):
if input.ndim == 1:
if target.numel == 1:
num_classes = input.shape[0]
target = norch.one_hot_encode(target, num_classes)
target = norch.one_hot_encode(target, num_classes).to(target.device)
logits = norch.softmax(input, dim=0)
target = target.reshape(logits.shape)
@ -67,7 +67,7 @@ class CrossEntropyLoss(Loss):
# target -> Ground truth class indices:
num_classes = input.shape[1]
target = norch.one_hot_encode(target, num_classes)
target = norch.one_hot_encode(target, num_classes).to(target.device)
batch_size = input.shape[0]
logits = norch.softmax(input, dim=1)

6
norch/nn/module.py
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@ -51,8 +51,10 @@ class Module(ABC):
parameter.zero_grad()
def to(self, device):
for _, _, parameter in self.parameters():
parameter.to(device)
for module, name, _ in self.parameters():
parameter = getattr(module, name)
parameter = parameter.to(device)
setattr(module, name, parameter)
return self

2
norch/tensor.py
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@ -205,7 +205,7 @@ class Tensor:
if gradient is None:
if self.shape == [1]:
gradient = Tensor([1])
gradient = Tensor([1]).to(self.device)
else:
raise RuntimeError("Gradient argument must be specified for non-scalar tensors.")

2
setup.py
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@ -20,7 +20,7 @@ class CustomInstall(install):
setuptools.setup(
name = "norch",
version = "0.0.3",
version = "0.0.4",
author = "Lucas de Lima",
author_email = "nogueiralucasdelima@gmail.com",
description = "A deep learning framework",

7
test.py
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@ -1,7 +0,0 @@
import norch
from norch.utils import utils_unittests as utils
device = "cpu"
norch_tensor = norch.Tensor([[[[1, 2], [3, -4]], [[5, 6], [7, 8]]], [[[1, 2], [3, -4]], [[5, 6], [7, 8]]]]).to(device)
norch_result = norch_tensor.sum(axis=0)

280
tests/test_autograd.py
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@ -24,12 +24,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor2_grad = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[[1, 2.5], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor2 = torch.tensor([[[1, 1.], [1, 1.9]], [[1, 1], [1, 1]]], requires_grad=True)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[[1, 2.5], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_tensor2 = torch.tensor([[[1, 1.], [1, 1.9]], [[1, 1], [1, 1]]], requires_grad=True, device=self.device)
torch_result = (torch_tensor1 + torch_tensor2).sum()
torch_result.backward()
torch_tensor1_grad = torch_tensor1.grad
@ -50,11 +46,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor2_grad = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[[1, 2.5], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor2 = torch.tensor([[[1, 1.], [1, 1.9]], [[1, 1], [1, 1]]], requires_grad=True)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[[1, 2.5], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_tensor2 = torch.tensor([[[1, 1.], [1, 1.9]], [[1, 1], [1, 1]]], requires_grad=True, device=self.device)
torch_result = (torch_tensor1 + torch_tensor2).sum(axis=0).sum(axis=0).sum()
torch_result.backward()
@ -72,11 +65,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor2_grad = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[[1, 2.5], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor2 = torch.tensor([[[1, 1.], [1, 1.9]], [[1, 1], [1, 1]]], requires_grad=True)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[[1, 2.5], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_tensor2 = torch.tensor([[[1, 1.], [1, 1.9]], [[1, 1], [1, 1]]], requires_grad=True, device=self.device)
torch_result = (torch_tensor1 + torch_tensor2).sum(axis=1).sum()
torch_result.backward()
@ -97,9 +87,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result.backward()
norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True)
torch_tensor.to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True, device=self.device)
torch_result = torch_tensor.max()
torch_result.backward()
@ -118,9 +106,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result.backward()
norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True)
torch_tensor.to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True, device=self.device)
torch_max_axis, _ = torch_tensor.max(axis=1)
torch_result = torch_max_axis.sum()
@ -136,9 +122,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result.backward()
norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
torch_tensor = torch.tensor([[[10, 1], [-4, 0]], [[5., 50], [7, 8]]], requires_grad=True)
torch_tensor.to(self.device)
torch_tensor = torch.tensor([[[10, 1], [-4, 0]], [[5., 50], [7, 8]]], requires_grad=True, device=self.device)
torch_max_axis, _ = torch_tensor.max(axis=2)
torch_result = torch_max_axis.sum()
@ -202,9 +186,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result.backward()
norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True)
torch_tensor.to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True, device=self.device)
torch_result = torch_tensor.min()
torch_result.backward()
@ -223,9 +205,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result.backward()
norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True)
torch_tensor.to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True, device=self.device)
torch_min, _ = torch_tensor.min(axis=1)
torch_result = torch_min.sum()
@ -241,9 +221,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result.backward()
norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
torch_tensor = torch.tensor([[[10, 1], [-4, 0]], [[5., 50], [7, 8]]], requires_grad=True)
torch_tensor.to(self.device)
torch_tensor = torch.tensor([[[10, 1], [-4, 0]], [[5., 50], [7, 8]]], requires_grad=True, device=self.device)
torch_min, _ = torch_tensor.min(axis=2)
torch_result = torch_min.sum()
@ -264,12 +242,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor2_grad = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True) # Shape (3)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True, device=self.device) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True, device=self.device) # Shape (3)
torch_result = (torch_tensor1 + torch_tensor2).sum()
torch_result.backward()
torch_tensor1_grad = torch_tensor1.grad
@ -287,11 +261,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor2_grad = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True) # Shape (3)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True, device=self.device) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True, device=self.device) # Shape (3)
torch_result = (torch_tensor2 + torch_tensor1).sum()
torch_result.backward()
@ -312,12 +283,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad_sub = utils.to_torch(norch_tensor1_sub.grad).to(self.device)
norch_tensor2_grad_sub = utils.to_torch(norch_tensor2_sub.grad).to(self.device)
torch_tensor1_sub = torch.tensor([[[1, 2.5], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor2_sub = torch.tensor([[[1, 1.], [1, 1.9]], [[1, 1], [1, 1]]], requires_grad=True)
torch_tensor1_sub.to(self.device)
torch_tensor2_sub.to(self.device)
torch_tensor1_sub = torch.tensor([[[1, 2.5], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_tensor2_sub = torch.tensor([[[1, 1.], [1, 1.9]], [[1, 1], [1, 1]]], requires_grad=True, device=self.device)
torch_result_sub = (torch_tensor1_sub - torch_tensor2_sub).sum()
torch_result_sub.backward()
torch_tensor1_grad_sub = torch_tensor1_sub.grad
@ -337,12 +304,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor2_grad = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True) # Shape (3)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True, device=self.device) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True, device=self.device) # Shape (3)
torch_result = (torch_tensor1 - torch_tensor2).sum()
torch_result.backward()
torch_tensor1_grad = torch_tensor1.grad
@ -360,11 +323,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor2_grad = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True) # Shape (3)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True, device=self.device) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True, device=self.device) # Shape (3)
torch_result = (torch_tensor2 - torch_tensor1).sum()
torch_result.backward()
@ -386,12 +346,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad_div = utils.to_torch(norch_tensor1_div.grad).to(self.device)
norch_tensor2_grad_div = utils.to_torch(norch_tensor2_div.grad).to(self.device)
torch_tensor1_div = torch.tensor([[[2, 5.1], [6, -8]], [[10, 12], [14, 16]]], requires_grad=True)
torch_tensor2_div = torch.tensor([[[1, 1], [2, 2.2]], [[3, 3], [4, 4]]], requires_grad=True)
torch_tensor1_div.to(self.device)
torch_tensor2_div.to(self.device)
torch_tensor1_div = torch.tensor([[[2, 5.1], [6, -8]], [[10, 12], [14, 16]]], requires_grad=True, device=self.device)
torch_tensor2_div = torch.tensor([[[1, 1], [2, 2.2]], [[3, 3], [4, 4]]], requires_grad=True, device=self.device)
torch_result_div = (torch_tensor1_div / torch_tensor2_div).sum()
torch_result_div.backward()
torch_tensor1_grad_div = torch_tensor1_div.grad
@ -411,10 +367,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_div_scalar.backward()
norch_tensor_grad_div_scalar = utils.to_torch(norch_tensor_div_scalar.grad).to(self.device)
torch_tensor_div_scalar = torch.tensor([[[2, 4.7], [6, 8]], [[10, 12], [14, 16]]], requires_grad=True)
torch_tensor_div_scalar.to(self.device)
torch_tensor_div_scalar = torch.tensor([[[2, 4.7], [6, 8]], [[10, 12], [14, 16]]], requires_grad=True, device=self.device)
torch_result_div_scalar = (torch_tensor_div_scalar / scalar).sum()
torch_result_div_scalar.backward()
torch_tensor_grad_div_scalar = torch_tensor_div_scalar.grad
@ -432,10 +385,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_scalar_div.backward()
norch_tensor_grad_scalar_div = utils.to_torch(norch_tensor_scalar_div.grad).to(self.device)
torch_tensor_scalar_div = torch.tensor([[[1, 2.23], [3, 4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor_scalar_div.to(self.device)
torch_tensor_scalar_div = torch.tensor([[[1, 2.23], [3, 4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_result_scalar_div = (scalar / torch_tensor_scalar_div).sum()
torch_result_scalar_div.backward()
torch_tensor_grad_scalar_div = torch_tensor_scalar_div.grad
@ -453,10 +403,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_power_st.backward()
norch_tensor_grad_power_st = utils.to_torch(norch_tensor_power_st.grad).to(self.device)
torch_tensor_power_st = torch.tensor([[[2, 3.21], [4, 2.1]], [[6, 7], [8, 9]]], requires_grad=True)
torch_tensor_power_st.to(self.device)
torch_tensor_power_st = torch.tensor([[[2, 3.21], [4, 2.1]], [[6, 7], [8, 9]]], requires_grad=True, device=self.device)
torch_result_power_st = (scalar ** torch_tensor_power_st).sum()
torch_result_power_st.backward()
torch_tensor_grad_power_st = torch_tensor_power_st.grad
@ -473,10 +420,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_power_ts.backward()
norch_tensor_grad_power_ts = utils.to_torch(norch_tensor_power_ts.grad).to(self.device)
torch_tensor_power_ts = torch.tensor([[[2, 3], [4, 2.1]], [[6, 7], [8, 9]]], requires_grad=True)
torch_tensor_power_ts.to(self.device)
torch_tensor_power_ts = torch.tensor([[[2, 3], [4, 2.1]], [[6, 7], [8, 9]]], requires_grad=True, device=self.device)
torch_result_power_ts = (torch_tensor_power_ts ** scalar).sum()
torch_result_power_ts.backward()
torch_tensor_grad_power_ts = torch_tensor_power_ts.grad
@ -494,12 +438,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad_matmul = utils.to_torch(norch_tensor1_matmul.grad).to(self.device)
norch_tensor2_grad_matmul = utils.to_torch(norch_tensor2_matmul.grad).to(self.device)
torch_tensor1_matmul = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor2_matmul = torch.tensor([[[1.1, 3], [4, 5]], [[6, 7], [8, 9]]], requires_grad=True)
torch_tensor1_matmul.to(self.device)
torch_tensor2_matmul.to(self.device)
torch_tensor1_matmul = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_tensor2_matmul = torch.tensor([[[1.1, 3], [4, 5]], [[6, 7], [8, 9]]], requires_grad=True, device=self.device)
torch_result_matmul = (torch_tensor1_matmul @ torch_tensor2_matmul).sum()
torch_result_matmul.backward()
torch_tensor1_grad_matmul = torch_tensor1_matmul.grad
@ -526,12 +466,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor2_grad_matmul = utils.to_torch(norch_tensor2_matmul.grad).to(self.device)
# Repeat the same process with torch tensors
torch_tensor1_matmul = torch.tensor([[[1., 2], [3, -4], [5, 6], [7, 8]] for _ in range(B)], requires_grad=True)
torch_tensor2_matmul = torch.tensor([[[2., 3, 1, 0, 4], [5, -1, 2, 3, 0]] for _ in range(B)], requires_grad=True)
torch_tensor1_matmul.to(self.device)
torch_tensor2_matmul.to(self.device)
torch_tensor1_matmul = torch.tensor([[[1., 2], [3, -4], [5, 6], [7, 8]] for _ in range(B)], requires_grad=True, device=self.device)
torch_tensor2_matmul = torch.tensor([[[2., 3, 1, 0, 4], [5, -1, 2, 3, 0]] for _ in range(B)], requires_grad=True, device=self.device)
torch_result_matmul = torch.matmul(torch_tensor1_matmul, torch_tensor2_matmul)
torch_result_matmul_sum = torch_result_matmul.sum()
torch_result_matmul_sum.backward()
@ -562,12 +498,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor2_grad_matmul = utils.to_torch(norch_tensor2_matmul.grad).to(self.device)
# Repeat the same process with torch tensors
torch_tensor1_matmul = torch.tensor([[1., 2], [3, -4], [5, 6], [7, 8]], requires_grad=True)
torch_tensor2_matmul = torch.tensor([[[2., 3, 1, 0, 4], [5, -1, 2, 3, 0]] for _ in range(B)], requires_grad=True)
torch_tensor1_matmul.to(self.device)
torch_tensor2_matmul.to(self.device)
torch_tensor1_matmul = torch.tensor([[1., 2], [3, -4], [5, 6], [7, 8]], requires_grad=True, device=self.device)
torch_tensor2_matmul = torch.tensor([[[2., 3, 1, 0, 4], [5, -1, 2, 3, 0]] for _ in range(B)], requires_grad=True, device=self.device)
torch_result_matmul = torch.matmul(torch_tensor1_matmul, torch_tensor2_matmul)
torch_result_matmul_sum = torch_result_matmul.sum()
torch_result_matmul_sum.backward()
@ -591,10 +523,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_elemwise_mul_scalar.backward()
norch_tensor_grad_elemwise_mul_scalar = utils.to_torch(norch_tensor_elemwise_mul_scalar.grad).to(self.device)
torch_tensor_elemwise_mul_scalar = torch.tensor([[[1.1, 2], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor_elemwise_mul_scalar.to(self.device)
torch_tensor_elemwise_mul_scalar = torch.tensor([[[1.1, 2], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_result_elemwise_mul_scalar = (scalar * torch_tensor_elemwise_mul_scalar).sum()
torch_result_elemwise_mul_scalar.backward()
torch_tensor_grad_elemwise_mul_scalar = torch_tensor_elemwise_mul_scalar.grad
@ -613,12 +542,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor1_grad_elemwise_mul = utils.to_torch(norch_tensor1_elemwise_mul.grad).to(self.device)
norch_tensor2_grad_elemwise_mul = utils.to_torch(norch_tensor2_elemwise_mul.grad).to(self.device)
torch_tensor1_elemwise_mul = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True).to(self.device)
torch_tensor2_elemwise_mul = torch.tensor([[[1.1, 3], [4, 5]], [[6, 7], [8, 9]]], requires_grad=True).to(self.device)
torch_tensor1_elemwise_mul.to(self.device)
torch_tensor2_elemwise_mul.to(self.device)
torch_tensor1_elemwise_mul = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_tensor2_elemwise_mul = torch.tensor([[[1.1, 3], [4, 5]], [[6, 7], [8, 9]]], requires_grad=True, device=self.device)
torch_result_elemwise_mul = (torch_tensor1_elemwise_mul * torch_tensor2_elemwise_mul).sum()
torch_result_elemwise_mul.backward()
torch_tensor1_grad_elemwise_mul = torch_tensor1_elemwise_mul.grad
@ -636,10 +561,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_sin_tensor.backward()
torch_result_sin_tensor_grad = utils.to_torch(norch_sin_tensor.grad).to(self.device)
torch_sin_tensor = torch.tensor([[[2, 3.21], [4, 2.1]], [[6, 7], [8, 9]]], requires_grad=True)
torch_sin_tensor.to(self.device)
torch_sin_tensor = torch.tensor([[[2, 3.21], [4, 2.1]], [[6, 7], [8, 9]]], requires_grad=True, device=self.device)
torch_expected_sin_tensor = (torch.sin(torch_sin_tensor)).sum()
torch_expected_sin_tensor.backward()
torch_expected_sin_tensor_grad = torch_sin_tensor.grad
@ -655,10 +577,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_cos_tensor.backward()
torch_result_cos_tensor_grad = utils.to_torch(norch_cos_tensor.grad).to(self.device)
torch_cos_tensor = torch.tensor([[[2, 3.21], [4, 2.1]], [[6, 7], [8, 9]]], requires_grad=True)
torch_cos_tensor.to(self.device)
torch_cos_tensor = torch.tensor([[[2, 3.21], [4, 2.1]], [[6, 7], [8, 9]]], requires_grad=True, device=self.device)
torch_expected_cos_tensor = (torch.sin(torch_cos_tensor)).sum()
torch_expected_cos_tensor.backward()
torch_expected_cos_tensor_grad = torch_cos_tensor.grad
@ -676,10 +595,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result.backward()
norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True)
torch_tensor.to(self.device)
torch_tensor = torch.tensor([[[10, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True, device=self.device)
torch_sigmoid = torch.sigmoid(torch_tensor)
torch_result = torch_sigmoid.sum()
@ -701,12 +617,8 @@ class TestTensorAutograd(unittest.TestCase):
loss_norch.backward() # Backpropagate the loss
grad_norch = predictions_norch.grad
predictions_torch = torch.tensor([1.1, 2, 3, 4], requires_grad=True)
labels_torch = torch.tensor([4, 3, 2.1, 1])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([1.1, 2, 3, 4], requires_grad=True, device=self.device)
labels_torch = torch.tensor([4, 3, 2.1, 1], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
loss_torch_expected.backward() # Backpropagate the loss
grad_torch_expected = predictions_torch.grad
@ -731,12 +643,8 @@ class TestTensorAutograd(unittest.TestCase):
loss_norch.backward() # Backpropagate the loss
grad_norch = predictions_norch.grad
predictions_torch = torch.tensor([2.0, 1.0, 0.1], requires_grad=True)
labels_torch = torch.tensor(0)
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([2.0, 1.0, 0.1], requires_grad=True, device=self.device)
labels_torch = torch.tensor(0, device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
loss_torch_expected.backward() # Backpropagate the loss
grad_torch_expected = predictions_torch.grad
@ -753,12 +661,8 @@ class TestTensorAutograd(unittest.TestCase):
loss_norch.backward() # Backpropagate the loss
grad_norch = predictions_norch.grad
predictions_torch = torch.tensor([[0.5, 1.5, 2.5], [1.0, 2.0, 3.0]], requires_grad=True)
labels_torch = torch.tensor([2, 1])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[0.5, 1.5, 2.5], [1.0, 2.0, 3.0]], requires_grad=True, device=self.device)
labels_torch = torch.tensor([2, 1], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
loss_torch_expected.backward() # Backpropagate the loss
grad_torch_expected = predictions_torch.grad
@ -775,12 +679,8 @@ class TestTensorAutograd(unittest.TestCase):
loss_norch.backward() # Backpropagate the loss
grad_norch = predictions_norch.grad
predictions_torch = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], requires_grad=True)
labels_torch = torch.tensor([1, 2])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], requires_grad=True, device=self.device)
labels_torch = torch.tensor([1, 2], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
loss_torch_expected.backward() # Backpropagate the loss
grad_torch_expected = predictions_torch.grad
@ -797,12 +697,8 @@ class TestTensorAutograd(unittest.TestCase):
loss_norch.backward() # Backpropagate the loss
grad_norch = predictions_norch.grad
predictions_torch = torch.tensor([[0.5, 0.2, 0.1], [0.1, 0.5, 0.7]], requires_grad=True)
labels_torch = torch.tensor([[1., 0, 0], [0, 1, 0]])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[0.5, 0.2, 0.1], [0.1, 0.5, 0.7]], requires_grad=True, device=self.device)
labels_torch = torch.tensor([[1., 0, 0], [0, 1, 0]], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
loss_torch_expected.backward() # Backpropagate the loss
grad_torch_expected = predictions_torch.grad
@ -861,10 +757,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_reshape.backward()
norch_tensor_grad_reshape = utils.to_torch(norch_tensor_reshape.grad).to(self.device)
torch_tensor_reshape = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor_reshape.to(self.device)
torch_tensor_reshape = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_result_reshape = torch_tensor_reshape.reshape(new_shape).sum()
torch_result_reshape.backward()
torch_tensor_grad_reshape = torch_tensor_reshape.grad
@ -882,10 +775,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_transpose.backward()
norch_tensor_grad_transpose = utils.to_torch(norch_tensor_transpose.grad).to(self.device)
torch_tensor_transpose = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor_transpose.to(self.device)
torch_tensor_transpose = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_result_transpose = torch_tensor_transpose.transpose(axis1, axis2).sum()
torch_result_transpose.backward()
torch_tensor_grad_transpose = torch_tensor_transpose.grad
@ -902,10 +792,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_T.backward()
norch_tensor_grad_T = utils.to_torch(norch_tensor_T.grad).to(self.device)
torch_tensor_T = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_tensor_T.to(self.device)
torch_tensor_T = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True, device=self.device)
torch_result_T = torch_tensor_T.mT.sum()
torch_result_T.backward()
torch_tensor_grad_T = torch_tensor_T.grad
@ -926,11 +813,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor_grad_reshape_matmul1 = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor_grad_reshape_matmul2 = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True)
torch_tensor2 = torch.tensor([[1, 5.1], [0.1, -4], [0, 6], [7, 8]], dtype=torch.float32, requires_grad=True)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_tensor2 = torch.tensor([[1, 5.1], [0.1, -4], [0, 6], [7, 8]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_result_reshape_matmul = (torch_tensor1.reshape(new_shape) @ torch_tensor2).sum()
torch_result_reshape_matmul.backward()
@ -951,10 +835,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_unsqueeze_0.backward()
norch_tensor_grad_unsqueeze_0 = utils.to_torch(norch_tensor_unsqueeze.grad).to(self.device)
torch_tensor_unsqueeze = torch.tensor([[1., 2], [3, 4]], requires_grad=True)
torch_tensor_unsqueeze.to(self.device)
torch_tensor_unsqueeze = torch.tensor([[1., 2], [3, 4]], requires_grad=True, device=self.device)
torch_result_unsqueeze_0 = torch_tensor_unsqueeze.unsqueeze(0).sum()
torch_result_unsqueeze_0.backward()
torch_tensor_grad_unsqueeze_0 = torch_tensor_unsqueeze.grad
@ -967,10 +848,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_unsqueeze_1.backward()
norch_tensor_grad_unsqueeze_1 = utils.to_torch(norch_tensor_unsqueeze.grad).to(self.device)
torch_tensor_unsqueeze = torch.tensor([[1., 2.], [3, 4]], requires_grad=True)
torch_tensor_unsqueeze.to(self.device)
torch_tensor_unsqueeze = torch.tensor([[1., 2.], [3, 4]], requires_grad=True, device=self.device)
torch_result_unsqueeze_1 = torch_tensor_unsqueeze.unsqueeze(1).sum()
torch_result_unsqueeze_1.backward()
torch_tensor_grad_unsqueeze_1 = torch_tensor_unsqueeze.grad
@ -983,10 +861,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_unsqueeze_2.backward()
norch_tensor_grad_unsqueeze_2 = utils.to_torch(norch_tensor_unsqueeze.grad).to(self.device)
torch_tensor_unsqueeze = torch.tensor([[1., 2], [3, 4]], requires_grad=True)
torch_tensor_unsqueeze.to(self.device)
torch_tensor_unsqueeze = torch.tensor([[1., 2], [3, 4]], requires_grad=True, device=self.device)
torch_result_unsqueeze_2 = torch_tensor_unsqueeze.unsqueeze(2).sum()
torch_result_unsqueeze_2.backward()
torch_tensor_grad_unsqueeze_2 = torch_tensor_unsqueeze.grad
@ -1006,11 +881,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor_grad_unsqueeze_matmul1 = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor_grad_unsqueeze_matmul2 = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32, requires_grad=True)
torch_tensor2 = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32, requires_grad=True)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_tensor2 = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_result_unsqueeze_matmul = (torch_tensor1 @ torch_tensor2.unsqueeze(0)).sum()
torch_result_unsqueeze_matmul.backward()
@ -1030,10 +902,7 @@ class TestTensorAutograd(unittest.TestCase):
norch_result_squeeze_0.backward()
norch_tensor_grad_squeeze_0 = utils.to_torch(norch_tensor_squeeze.grad).to(self.device)
torch_tensor_squeeze = torch.tensor([[[1., 2], [3, 4]]], requires_grad=True)
torch_tensor_squeeze.to(self.device)
torch_tensor_squeeze = torch.tensor([[[1., 2], [3, 4]]], requires_grad=True, device=self.device)
torch_result_squeeze_0 = torch_tensor_squeeze.squeeze(0).sum()
torch_result_squeeze_0.backward()
torch_tensor_grad_squeeze_0 = torch_tensor_squeeze.grad
@ -1050,14 +919,11 @@ class TestTensorAutograd(unittest.TestCase):
# Squeeze at dim=0 then matmul
norch_result_squeeze_matmul = (norch_tensor1.squeeze(0) @ norch_tensor2).sum()
norch_result_squeeze_matmul.backward()
norch_tensor_grad_squeeze_matmul1 = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor_grad_squeeze_matmul1 = utils.to_torch(norch_tensor1.grad,).to(self.device)
norch_tensor_grad_squeeze_matmul2 = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[[1., 2], [3, 4]]], dtype=torch.float32, requires_grad=True)
torch_tensor2 = torch.tensor([[[1., 2], [3, 4]]], dtype=torch.float32, requires_grad=True)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[[1., 2], [3, 4]]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_tensor2 = torch.tensor([[[1., 2], [3, 4]]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_result_squeeze_matmul = (torch_tensor1.squeeze(0) @ torch_tensor2).sum()
torch_result_squeeze_matmul.backward()
@ -1080,11 +946,8 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor_grad_T_matmul1 = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor_grad_T_matmult2 = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True)
torch_tensor2 = torch.tensor([[1, 5.1], [0.1, -4], [0, 6], [7, 8]], dtype=torch.float32, requires_grad=True)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_tensor2 = torch.tensor([[1, 5.1], [0.1, -4], [0, 6], [7, 8]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_result_T_matmul = (torch_tensor1.T @ torch_tensor2).sum()
torch_result_T_matmul.backward()
@ -1099,7 +962,7 @@ class TestTensorAutograd(unittest.TestCase):
The code has a problem on the following operation
tensor1.reshape(..) @ tensor1
print(tensor1.grad)
(also transpose and .T)
(also transpsoe and .T)
"""
pass
@ -1115,12 +978,9 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor_grad_transpose_matmul1 = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor_grad_transpose_matmult2 = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True)
torch_tensor2 = torch.tensor([[1, 5.1], [0.1, -4], [0, 6], [7, 8]], dtype=torch.float32, requires_grad=True)
torch_tensor1.to(self.device)
torch_tensor2.to(self.device)
torch_tensor1 = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_tensor2 = torch.tensor([[1, 5.1], [0.1, -4], [0, 6], [7, 8]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_result_transpose_matmul = (torch_tensor1.T @ torch_tensor2).sum()
torch_result_transpose_matmul.backward()
torch_tensor_grad_transpose_matmul1 = torch_tensor1.grad

65
tests/test_nn.py
View file

@ -26,11 +26,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([[1.1, 2, 3, 4], [1.1, 2, 3, 4]])
labels_torch = torch.tensor([[1.1, 2, 3, 4], [1.1, 2, 3, 3]])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[1.1, 2, 3, 4], [1.1, 2, 3, 4]], device=self.device)
labels_torch = torch.tensor([[1.1, 2, 3, 4], [1.1, 2, 3, 3]], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -42,11 +39,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([1.1, 2, 3, 4])
labels_torch = torch.tensor([4, 3, 2.1, 1])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([1.1, 2, 3, 4], device=self.device)
labels_torch = torch.tensor([4, 3, 2.1, 1], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -66,11 +60,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([2.0, 1.0, 0.1])
labels_torch = torch.tensor(0)
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([2.0, 1.0, 0.1], device=self.device)
labels_torch = torch.tensor(0, device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -83,11 +74,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([[0.5, 1.5, 2.5], [1.0, 2.0, 3.0]])
labels_torch = torch.tensor([2, 1])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[0.5, 1.5, 2.5], [1.0, 2.0, 3.0]], device=self.device)
labels_torch = torch.tensor([2, 1], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -99,11 +87,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
labels_torch = torch.tensor([1, 2])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], device=self.device)
labels_torch = torch.tensor([1, 2], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -131,11 +116,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([[0.5, 0.2, 0.1], [0.1, 0.5, 0.7]])
labels_torch = torch.tensor([[1., 0, 0], [0, 1, 0]])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[0.5, 0.2, 0.1], [0.1, 0.5, 0.7]], device=self.device)
labels_torch = torch.tensor([[1., 0, 0], [0, 1, 0]], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -161,9 +143,7 @@ class TestNNModuleActivationFn(unittest.TestCase):
sigmoid_norch = sigmoid_fn_norch.forward(x)
sigmoid_torch_result = utils.to_torch(sigmoid_norch).to(self.device)
x = torch.tensor([[1, 2, 3]])
x.to(self.device)
x = torch.tensor([[1, 2, 3]], device=self.device)
sigmoid_torch_expected = sigmoid_fn_torch.forward(x)
@ -174,9 +154,7 @@ class TestNNModuleActivationFn(unittest.TestCase):
sigmoid_norch = sigmoid_fn_norch.forward(x)
sigmoid_torch_result = utils.to_torch(sigmoid_norch).to(self.device)
x = torch.tensor([-1, 2, -3])
x.to(self.device)
x = torch.tensor([-1, 2, -3], device=self.device)
sigmoid_torch_expected = sigmoid_fn_torch.forward(x)
@ -187,9 +165,7 @@ class TestNNModuleActivationFn(unittest.TestCase):
sigmoid_norch = sigmoid_fn_norch.forward(x)
sigmoid_torch_result = utils.to_torch(sigmoid_norch).to(self.device)
x = torch.tensor([0, 0, 0])
x.to(self.device)
x = torch.tensor([0, 0, 0], device=self.device)
sigmoid_torch_expected = sigmoid_fn_torch.forward(x)
@ -205,9 +181,9 @@ class TestNNModuleActivationFn(unittest.TestCase):
# Define the input tensors for different test cases
test_cases = [
(norch.Tensor([[[1., 2, 3], [4, 5, 6]]]), torch.tensor([[[1., 2, 3], [4, 5, 6]]])),
(norch.Tensor([[[1., -1, 0], [2, -2, 0]]]), torch.tensor([[[1., -1, 0], [2, -2, 0]]])),
(norch.Tensor([[[0., 0, 0], [0, 0, 0]]]), torch.tensor([[[0., 0, 0], [0, 0, 0]]]))
(norch.Tensor([[[1., 2, 3], [4, 5, 6]]]).to(self.device), torch.tensor([[[1., 2, 3], [4, 5, 6]]], device=self.device)),
(norch.Tensor([[[1., -1, 0], [2, -2, 0]]]).to(self.device), torch.tensor([[[1., -1, 0], [2, -2, 0]]], device=self.device)),
(norch.Tensor([[[0., 0, 0], [0, 0, 0]]]).to(self.device), torch.tensor([[[0., 0, 0], [0, 0, 0]]], device=self.device))
]
for dim in axes:
@ -215,12 +191,7 @@ class TestNNModuleActivationFn(unittest.TestCase):
softmax_fn_torch = torch.nn.Softmax(dim=dim)
for norch_input, torch_input in test_cases:
# Move tensors to the correct device
norch_input = norch_input.to(self.device)
torch_input = torch_input
torch_input.to(self.device)
# Forward pass using norch
softmax_norch = softmax_fn_norch.forward(norch_input)
softmax_torch_result = utils.to_torch(softmax_norch).to(self.device)