aparler/PyNorch
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Fix tensor creation nested_list

Browse source
This commit is contained in:
lucasdelimanogueira 2024-04-26 00:56:47 -03:00
parent d5fd914a8d
commit dbc45488ff
3 changed files with 42 additions and 24 deletions
Download patch file Download diff file Expand all files Collapse all files

BIN
python/__pycache__/tensor.cpython-39.pyc
View file

Binary file not shown.

12
python/tensor.py
View file

@ -9,7 +9,9 @@ class CTensor(ctypes.Structure):
]
class Tensor:
def __init__(self, data, shape):
def __init__(self, data):
data, shape = self.flatten(data)
print(data, shape)
self.lib = ctypes.CDLL("../build/libtensor.so") # Adjust the path to the shared library
self.data = (ctypes.c_float * len(data))(*data)
self.shape = shape
@ -23,6 +25,14 @@ class Tensor:
(ctypes.c_int * len(shape))(*shape),
ctypes.c_int(len(shape))
)
def flatten(self, nested_list):
flat_data = []
shape = [len(nested_list), len(nested_list[0])]
for sublist in nested_list:
for item in sublist:
flat_data.append(item)
return flat_data, shape
def __getitem__(self, indices):
if len(indices) != self.ndim:

54
python/tests.ipynb
View file

@ -9,36 +9,23 @@
"name": "stdout",
"output_type": "stream",
"text": [
"kkkk <tensor.c_int_Array_2 object at 0x7f990806fcc0> @@ c_int(2)\n",
"[0, 1, 2, 0, 2, 2] [2, 3, 3]\n",
"Tensor shape: [2, 3, 3]\n",
"Tensor created successfully\n",
"Tensor information:\n",
"Number of dimensions: 2\n",
"Shape: [3, 3]\n",
"Number of dimensions: 3\n",
"Shape: [2, 3, 3]\n",
"Data:\n",
"[0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90]\n"
]
},
{
"ename": "ValueError",
"evalue": "Number of indices must match the number of dimensions",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m shape \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m3\u001b[39m]\n\u001b[1;32m 5\u001b[0m tensor \u001b[38;5;241m=\u001b[39m Tensor(data, shape)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mTensor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documentos/recreate_pytorch/foo/python/tensor.py:15\u001b[0m, in \u001b[0;36mTensor.__getitem__\u001b[0;34m(self, indices)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, indices):\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(indices) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim:\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of indices must match the number of dimensions\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 16\u001b[0m indices \u001b[38;5;241m=\u001b[39m (ctypes\u001b[38;5;241m.\u001b[39mc_int \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(indices))(\u001b[38;5;241m*\u001b[39mindices)\n\u001b[1;32m 17\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlib\u001b[38;5;241m.\u001b[39mget_item(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtensor, indices) \n",
"\u001b[0;31mValueError\u001b[0m: Number of indices must match the number of dimensions"
"[0.00, 1.00, 2.00, 0.00, 2.00, 2.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00]\n"
]
}
],
"source": [
"from tensor import Tensor\n",
"\n",
"data = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
"shape = [3, 3]\n",
"tensor = Tensor(data, shape)\n",
"tensor.shape\n",
"Tensor.__getitem__(tensor, [0, 0])"
"data = [[0, 1, 2], [0, 2, 2], [0, 1, 2]]\n",
"tensor = Tensor(data)\n",
"print(\"Tensor shape:\", tensor.shape)"
]
},
{
@ -50,12 +37,33 @@
"name": "stdout",
"output_type": "stream",
"text": [
"-648269760\n"
"Tensor created successfully\n",
"Tensor information:\n",
"Number of dimensions: 2\n",
"Shape: [3, 3]\n",
"Data:\n",
"[0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90]\n"
]
},
{
"data": {
"text/plain": [
"0.10000000149011612 0.20000000298023224 0.30000001192092896 \n",
"0.4000000059604645 0.5 0.6000000238418579 \n",
"0.699999988079071 0.800000011920929 0.8999999761581421"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(tensor[1, 1]) # Accessing an element"
"\n",
"data = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
"shape = [3, 3]\n",
"tensor = Tensor(data, shape)\n",
"tensor\n"
]
},
{