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test_torch_train.py
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50 lines (39 loc) · 1.55 KB
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# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import torch
from docarray import BaseDoc, DocList
from docarray.typing import TorchTensor
def test_torch_train():
class Mmdoc(BaseDoc):
text: str
tensor: Optional[TorchTensor[3, 224, 224]] = None
N = 10
batch = DocList[Mmdoc](Mmdoc(text=f'hello{i}') for i in range(N))
batch.tensor = torch.zeros(N, 3, 224, 224)
batch = batch.to_doc_vec()
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(3, 16, 3)
def forward(self, x):
return self.conv(x)
model = Model()
opt = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
for _ in range(2):
loss = model(batch.tensor).sum()
loss.backward()
opt.step()