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import pytest
from collections import namedtuple
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import treetensor.torch as ttorch
from ding.torch_utils import CudaFetcher, to_device, to_dtype, to_tensor, to_ndarray, to_list, \
tensor_to_list, same_shape, build_log_buffer, get_tensor_data, to_item
from ding.utils import EasyTimer
@pytest.fixture(scope='function')
def setup_data_dict():
return {
'tensor': torch.randn(4),
'list': [True, False, False],
'tuple': (4, 5, 6),
'bool': True,
'int': 10,
'float': 10.,
'array': np.random.randn(4),
'str': "asdf",
'none': None,
}
@pytest.mark.unittest
class TestDataFunction:
def test_to_dtype(self):
t = torch.randint(0, 10, (3, 5))
tfloat = to_dtype(t, torch.float)
assert tfloat.dtype == torch.float
tlist = [t]
tlfloat = to_dtype(tlist, torch.float)
assert tlfloat[0].dtype == torch.float
tdict = {'t': t}
tdictf = to_dtype(tdict, torch.float)
assert tdictf['t'].dtype == torch.float
with pytest.raises(TypeError):
to_dtype(EasyTimer(), torch.float)
def test_to_tensor(self, setup_data_dict):
i = 10
t = to_tensor(i)
assert t.item() == i
d = {'i': i}
dt = to_tensor(d, torch.int)
assert dt['i'].item() == i
with pytest.raises(TypeError):
_ = to_tensor({1, 2}, torch.int)
data_type = namedtuple('data_type', ['x', 'y'])
inputs = data_type(np.random.random(3), 4)
outputs = to_tensor(inputs, torch.float32)
assert type(outputs) == data_type
assert isinstance(outputs.x, torch.Tensor)
assert isinstance(outputs.y, torch.Tensor)
assert outputs.x.dtype == torch.float32
assert outputs.y.dtype == torch.float32
transformed_tensor = to_tensor(setup_data_dict)
with pytest.raises(TypeError):
to_tensor(EasyTimer(), torch.float)
def test_to_ndarray(self, setup_data_dict):
t = torch.randn(3, 5)
tarray1 = to_ndarray(t)
assert tarray1.shape == (3, 5)
assert isinstance(tarray1, np.ndarray)
t = [torch.randn(5, ) for i in range(3)]
tarray1 = to_ndarray(t, np.float32)
assert isinstance(tarray1, list)
assert tarray1[0].shape == (5, )
assert isinstance(tarray1[0], np.ndarray)
transformed_array = to_ndarray(setup_data_dict)
with pytest.raises(TypeError):
to_ndarray(EasyTimer(), np.float32)
def test_to_list(self, setup_data_dict):
# tensor_to_list
t = torch.randn(3, 5)
tlist1 = tensor_to_list(t)
assert len(tlist1) == 3
assert len(tlist1[0]) == 5
t = torch.randn(3, )
tlist1 = tensor_to_list(t)
assert len(tlist1) == 3
t = [torch.randn(5, ) for i in range(3)]
tlist1 = tensor_to_list(t)
assert len(tlist1) == 3
assert len(tlist1[0]) == 5
td = {'t': t}
tdlist1 = tensor_to_list(td)
assert len(tdlist1['t']) == 3
assert len(tdlist1['t'][0]) == 5
tback = to_tensor(tlist1, torch.float)
for i in range(3):
assert (tback[i] == t[i]).all()
with pytest.raises(TypeError):
tensor_to_list(EasyTimer())
# to_list
transformed_list = to_list(setup_data_dict)
with pytest.raises(TypeError):
to_ndarray(EasyTimer())
def test_to_item(self):
data = {
'tensor': torch.randn(1),
'list': [True, False, torch.randn(1)],
'tuple': (4, 5, 6),
'bool': True,
'int': 10,
'float': 10.,
'array': np.random.randn(1),
'str': "asdf",
'none': None,
}
assert not np.isscalar(data['tensor'])
assert not np.isscalar(data['array'])
assert not np.isscalar(data['list'][-1])
new_data = to_item(data)
assert np.isscalar(new_data['tensor'])
assert np.isscalar(new_data['array'])
assert np.isscalar(new_data['list'][-1])
data = ttorch.randn({'a': 1})
new_data = to_item(data)
assert np.isscalar(new_data.a)
with pytest.raises((ValueError, RuntimeError)):
to_item({'a': torch.randn(4), 'b': torch.rand(1)}, ignore_error=False)
output = to_item({'a': torch.randn(4), 'b': torch.rand(1)}, ignore_error=True)
assert 'a' not in output
assert 'b' in output
def test_same_shape(self):
tlist = [torch.randn(3, 5) for i in range(5)]
assert same_shape(tlist)
tlist = [torch.randn(3, 5), torch.randn(4, 5)]
assert not same_shape(tlist)
def test_get_tensor_data(self):
a = {
'tensor': torch.tensor([1, 2, 3.], requires_grad=True),
'list': [torch.tensor([1, 2, 3.], requires_grad=True) for _ in range(2)],
'none': None
}
tensor_a = get_tensor_data(a)
assert not tensor_a['tensor'].requires_grad
for t in tensor_a['list']:
assert not t.requires_grad
with pytest.raises(TypeError):
get_tensor_data(EasyTimer())
@pytest.mark.unittest
def test_log_dict():
log_buffer = build_log_buffer()
log_buffer['not_tensor'] = torch.randn(3)
assert isinstance(log_buffer['not_tensor'], list)
assert len(log_buffer['not_tensor']) == 3
log_buffer.update({'not_tensor': 4, 'a': 5})
assert log_buffer['not_tensor'] == 4
@pytest.mark.cudatest
class TestCudaFetcher:
def get_dataloader(self):
class Dataset(object):
def __init__(self):
self.data = torch.randn(2560, 2560)
def __len__(self):
return 100
def __getitem__(self, idx):
return self.data
return DataLoader(Dataset(), batch_size=3)
def get_model(self):
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.main = [nn.Linear(2560, 2560) for _ in range(100)]
self.main = nn.Sequential(*self.main)
def forward(self, x):
x = self.main(x)
return x
return Model()
def test_naive(self):
model = self.get_model()
model.cuda()
timer = EasyTimer()
dataloader = iter(self.get_dataloader())
dataloader = CudaFetcher(dataloader, device='cuda', sleep=0.1)
dataloader.run()
count = 0
while True:
with timer:
data = next(dataloader)
model(data)
print('count {}, run_time: {}'.format(count, timer.value))
count += 1
if count == 10:
break
dataloader.close()
@pytest.mark.cudatest
def test_to_device_cuda(setup_data_dict):
setup_data_dict['module'] = nn.Linear(3, 5)
device = 'cuda'
cuda_d = to_device(setup_data_dict, device, ignore_keys=['module'])
assert cuda_d['module'].weight.device == torch.device('cpu')
other = EasyTimer()
with pytest.raises(TypeError):
to_device(other)
@pytest.mark.unittest
def test_to_device_cpu(setup_data_dict):
setup_data_dict['module'] = nn.Linear(3, 5)
device = 'cpu'
cuda_d = to_device(setup_data_dict, device, ignore_keys=['module'])
assert cuda_d['module'].weight.device == torch.device('cpu')
other = EasyTimer()
with pytest.raises(TypeError):
to_device(other)
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