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Commit 16491dc0 authored by traveller59's avatar traveller59
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add two fake scripts for debugging

parent 5073a413
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test/fake_dist_train.py 0 → 100644
View file @ 16491dc0
import horovod.torch as hvd
import time
from pathlib import Path
import fire
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
from torch import distributed, nn
from torch.utils import data
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
import spconv
from spconv.test_utils import generate_sparse_data
class FakeSparseDataset(Dataset):
def __len__(self):
return 500
def __getitem__(self, idx):
data_ranges = {
0: [-1, 1],
1: [0, 2],
2: [-2, 0],
3: [-2, -2],
}
l = np.random.randint(0, 4, size=[2])
data = generate_sparse_data([16, 64, 64], [16 * 64 * 64 // 2],
3,
data_range=data_ranges[l[0]],
with_dense=False)
data2 = generate_sparse_data([16, 64, 64], [16 * 64 * 64 // 2],
3,
data_range=data_ranges[l[1]],
with_dense=False)
features = np.ascontiguousarray(data["features"]).astype(np.float32)
indices = np.ascontiguousarray(
data["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
features2 = np.ascontiguousarray(data2["features"]).astype(np.float32)
indices2 = np.ascontiguousarray(
data2["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
features = np.ascontiguousarray(np.concatenate([features, features2]))
indices = np.ascontiguousarray(np.concatenate([indices, indices2]))
return features, indices, l
class FakeClassifier(nn.Module):
def __init__(self):
super().__init__()
self.net = spconv.SparseSequential(
spconv.SubMConv3d(3, 8, 3, indice_key="subm1", padding=1, use_hash=False),
nn.BatchNorm1d(8),
nn.ReLU(),
spconv.SparseConv3d(8, 16, 3, stride=2, padding=1, use_hash=False),
nn.BatchNorm1d(16),
nn.ReLU(),
spconv.SubMConv3d(16, 16, 3, indice_key="subm2", padding=1, use_hash=False),
nn.BatchNorm1d(16),
nn.ReLU(),
spconv.SparseConv3d(16, 32, 3, stride=2, padding=1, use_hash=False),
nn.BatchNorm1d(32),
nn.ReLU(),
spconv.SubMConv3d(32, 32, 3, indice_key="subm3", padding=1, use_hash=False),
nn.BatchNorm1d(32),
nn.ReLU(),
spconv.SparseConv3d(32, 64, 3, stride=2, padding=1, use_hash=False),
nn.BatchNorm1d(64),
nn.ReLU(),
spconv.SubMConv3d(64, 64, 3, indice_key="subm4", padding=1, use_hash=False),
nn.BatchNorm1d(64),
nn.ReLU(),
spconv.ToDense() # [64, 2, 8, 8]
)
self.linear = nn.Linear(64 * 2 * 8 * 8, 4)
def forward(self, features, indices):
indices = indices.int()
x = spconv.SparseConvTensor(features, indices, [16, 64, 64], 2)
x = self.net(x)
x = x.view(2, -1)
x = self.linear(x)
return x
def run():
hvd.init()
torch.cuda.set_device(hvd.local_rank())
np.random.seed(50051 + hvd.local_rank())
ds = FakeSparseDataset()
device = torch.device('cuda')
model = FakeClassifier()
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
compression = hvd.Compression.none
optimizer = hvd.DistributedOptimizer(optimizer,
named_parameters=model.named_parameters(),
compression=compression,
op=hvd.Average)
for i in tqdm.tqdm(list(range(100))):
# for j in range(4):
# features, indices, label = ds[(i * 4 + j) % len(ds)]
features, indices, label = ds[i % len(ds)]
features_t = torch.from_numpy(features)
indices_t = torch.from_numpy(indices)
features_t = features_t.to(device)
indices_t = indices_t.to(device)
target = torch.from_numpy(label).to(device)
output = model(features_t, indices_t)
# print(output.shape)
loss = F.cross_entropy(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
def dev():
ds = FakeSparseDataset()
for i in range(10):
features, indices, label = ds[i]
print(indices[:10])
features_t = torch.from_numpy(features.astype(np.float32)).cuda()
indices_t = torch.from_numpy(indices.astype(np.int32)).cuda()
net = FakeClassifier().cuda()
net(features_t, indices_t)
def main():
run()
if __name__ == "__main__":
fire.Fire(main)
test/fake_train.py 0 → 100644
View file @ 16491dc0
import time
from pathlib import Path
import fire
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
from torch import distributed, nn
from torch.utils import data
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
import spconv
from spconv.test_utils import generate_sparse_data
class FakeSparseDataset(Dataset):
def __len__(self):
return 500
def __getitem__(self, idx):
data_ranges = {
0: [-1, 1],
1: [0, 2],
2: [-2, 0],
3: [-2, -2],
}
l = np.random.randint(0, 4, size=[2])
data = generate_sparse_data([16, 64, 64], [16 * 64 * 64 // 2],
3,
data_range=data_ranges[l[0]],
with_dense=False)
data2 = generate_sparse_data([16, 64, 64], [16 * 64 * 64 // 2],
3,
data_range=data_ranges[l[1]],
with_dense=False)
features = np.ascontiguousarray(data["features"]).astype(np.float32)
indices = np.ascontiguousarray(
data["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
features2 = np.ascontiguousarray(data2["features"]).astype(np.float32)
indices2 = np.ascontiguousarray(
data2["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
features = np.ascontiguousarray(np.concatenate([features, features2]))
indices = np.ascontiguousarray(np.concatenate([indices, indices2]))
return features, indices, l
class FakeClassifier(nn.Module):
def __init__(self):
super().__init__()
self.net = spconv.SparseSequential(
spconv.SubMConv3d(3, 8, 3, indice_key="subm1", padding=1, use_hash=False),
nn.BatchNorm1d(8),
nn.ReLU(),
spconv.SparseConv3d(8, 16, 3, stride=2, padding=1, use_hash=False),
nn.BatchNorm1d(16),
nn.ReLU(),
spconv.SubMConv3d(16, 16, 3, indice_key="subm2", padding=1, use_hash=False),
nn.BatchNorm1d(16),
nn.ReLU(),
spconv.SparseConv3d(16, 32, 3, stride=2, padding=1, use_hash=False),
nn.BatchNorm1d(32),
nn.ReLU(),
spconv.SubMConv3d(32, 32, 3, indice_key="subm3", padding=1, use_hash=False),
nn.BatchNorm1d(32),
nn.ReLU(),
spconv.SparseConv3d(32, 64, 3, stride=2, padding=1, use_hash=False),
nn.BatchNorm1d(64),
nn.ReLU(),
spconv.SubMConv3d(64, 64, 3, indice_key="subm4", padding=1, use_hash=False),
nn.BatchNorm1d(64),
nn.ReLU(),
spconv.ToDense() # [64, 2, 8, 8]
)
self.linear = nn.Linear(64 * 2 * 8 * 8, 4)
def forward(self, features, indices):
indices = indices.int()
x = spconv.SparseConvTensor(features, indices, [16, 64, 64], 2)
x = self.net(x)
x = x.view(2, -1)
x = self.linear(x)
return x
def run():
np.random.seed(50051)
ds = FakeSparseDataset()
device = torch.device('cuda')
model = FakeClassifier()
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
for i in tqdm.tqdm(list(range(100))):
# for j in range(4):
# features, indices, label = ds[(i * 4 + j) % len(ds)]
features, indices, label = ds[i % len(ds)]
features_t = torch.from_numpy(features)
indices_t = torch.from_numpy(indices)
features_t = features_t.to(device)
indices_t = indices_t.to(device)
target = torch.from_numpy(label).to(device)
output = model(features_t, indices_t)
# print(output.shape)
loss = F.cross_entropy(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
def dev():
ds = FakeSparseDataset()
for i in range(10):
features, indices, label = ds[i]
print(indices[:10])
features_t = torch.from_numpy(features.astype(np.float32)).cuda()
indices_t = torch.from_numpy(indices.astype(np.int32)).cuda()
net = FakeClassifier().cuda()
net(features_t, indices_t)
def main():
run()
if __name__ == "__main__":
fire.Fire(main)
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