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add object detection prediction example and fix batch #283
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9ebe92e
add object detection prediction example and fix batch
edgarriba 4e90e8f
Merge branch 'master' into feat/object_detection
edgarriba 95aed15
add tests for default_unciollate
edgarriba 9cf62ee
Merge branch 'master' into feat/object_detection
mergify[bot] 2dc2410
Fix a test
ethanwharris File filter
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# Copyright The PyTorch Lightning team. | ||
# | ||
# Licensed 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 flash import Trainer | ||
from flash.data.utils import download_data | ||
from flash.vision import ObjectDetector | ||
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# 1. Download the data | ||
# Dataset Credit: https://www.kaggle.com/ultralytics/coco128 | ||
download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip", "data/") | ||
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# 2. Load the model from a checkpoint | ||
model = ObjectDetector.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/object_detection_model.pt") | ||
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# 3. Detect the object on the images | ||
predictions = model.predict([ | ||
"data/coco128/images/train2017/000000000025.jpg", | ||
"data/coco128/images/train2017/000000000520.jpg", | ||
"data/coco128/images/train2017/000000000532.jpg", | ||
]) | ||
print(predictions) |
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# Copyright The PyTorch Lightning team. | ||
# | ||
# Licensed 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. | ||
import torch | ||
from torch.tensor import Tensor | ||
from torch.testing import assert_allclose | ||
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from flash.data.batch import default_uncollate | ||
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class TestDefaultUncollate: | ||
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def test_smoke(self): | ||
batch = torch.rand(2, 1) | ||
assert default_uncollate(batch) is not None | ||
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def test_tensor_zero(self): | ||
batch = torch.tensor(1) | ||
output = default_uncollate(batch) | ||
assert_allclose(batch, output) | ||
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def test_tensor_batch(self): | ||
batch = torch.rand(2, 1) | ||
output = default_uncollate(batch) | ||
assert isinstance(output, list) | ||
assert all([isinstance(x, torch.Tensor) for x in output]) | ||
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def test_sequence(self): | ||
B = 3 # batch_size | ||
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batch = {} | ||
batch['a'] = torch.rand(B, 4) | ||
batch['b'] = torch.rand(B, 2) | ||
batch['c'] = torch.rand(B) | ||
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output = default_uncollate(batch) | ||
assert isinstance(output, list) | ||
assert len(batch) == B | ||
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for sample in output: | ||
assert list(sample.keys()) == ['a', 'b', 'c'] | ||
assert isinstance(sample['a'], list) | ||
assert len(sample['a']) == 4 | ||
assert isinstance(sample['b'], list) | ||
assert len(sample['b']) == 2 | ||
assert isinstance(sample['c'], torch.Tensor) | ||
assert len(sample['c'].shape) == 0 |
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Any reason not to return a list here? (like
[batch]
)