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jonyguo
4333080b6f
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5 years ago | |
|---|---|---|
| .. | ||
| data | 5 years ago | |
| output | 5 years ago | |
| README.md | 5 years ago | |
| create_dataset.py | 5 years ago | |
| gen_mindrecord.py | 5 years ago | |
| run.sh | 5 years ago | |
| run_read.sh | 5 years ago | |
README.md
Guideline to Transfer Caltech-UCSD Birds-200-2011 Dataset to MindRecord
What does the example do
This example is used to read data from Caltech-UCSD Birds-200-2011 dataset and generate mindrecord. It just transfers the Caltech-UCSD Birds-200-2011 dataset to mindrecord without any data preprocessing. You can modify the example or follow the example to implement your own example.
- run.sh: generate MindRecord entry script.
- gen_mindrecord.py : read the Caltech-UCSD Birds-200-2011 data and transfer it to mindrecord.
- run_read.py: create MindDataset by MindRecord entry script.
- create_dataset.py: use MindDataset to read MindRecord to generate dataset.
How to use the example to generate MindRecord
Download Caltech-UCSD Birds-200-2011 dataset, transfer it to mindrecord, use MindDataset to read mindrecord.
Download Caltech-UCSD Birds-200-2011 dataset and unzip
-
Download the training data zip.
Caltech-UCSD Birds-200-2011 dataset download address
1) -> Download -> All Images and Annotations
2) -> Download -> Segmentations -
Unzip the training data to dir example/nlp_to_mindrecord/Caltech-UCSD-Birds-200-2011/data.
tar -zxvf CUB_200_2011.tgz -C {your-mindspore}/example/cv_to_mindrecord/Caltech-UCSD-Birds-200-2011/data/ tar -zxvf segmentations.tgz -C {your-mindspore}/example/cv_to_mindrecord/Caltech-UCSD-Birds-200-2011/data/- The unzip should like this:
$ ls {your-mindspore}/example/cv_to_mindrecord/Caltech-UCSD-Birds-200-2011/data/ attributes.txt CUB_200_2011 README.md segmentations
Generate MindRecord
-
Run the run.sh script.
bash run.sh -
Output like this:
... >> begin generate mindrecord >> sample id: 1, filename: data/CUB_200_2011/images/001.Black_footed_Albatross/Black_Footed_Albatross_0046_18.jpg, bbox: [60.0, 27.0, 325.0, 304.0], label: 1, seg_filename: data/segmentations/001.Black_footed_Albatross/Black_Footed_Albatross_0046_18.png, class: 001.Black_footed_Albatross [INFO] MD(11253,python):2020-05-20-16:21:42.462.686 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:106] OpenDataFiles] Open shard file successfully. [INFO] MD(11253,python):2020-05-20-16:21:43.147.496 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:680] WriteRawData] Write 256 records successfully. >> transformed 256 record... [INFO] MD(11253,python):2020-05-20-16:21:43.842.372 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:680] WriteRawData] Write 256 records successfully. >> transformed 512 record... [INFO] MD(11253,python):2020-05-20-16:21:44.748.585 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:680] WriteRawData] Write 256 records successfully. >> transformed 768 record... [INFO] MD(11253,python):2020-05-20-16:21:45.736.179 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:680] WriteRawData] Write 256 records successfully. >> transformed 1024 record... ... [INFO] MD(11253,python):2020-05-20-16:22:21.207.820 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:680] WriteRawData] Write 12 records successfully. >> transformed 11788 record... [INFO] MD(11253,python):2020-05-20-16:22:21.210.261 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:227] Commit] Write metadata successfully. [INFO] MD(11253,python):2020-05-20-16:22:21.211.688 [mindspore/ccsrc/mindrecord/io/shard_index_generator.cc:59] Build] Init header from mindrecord file for index successfully. [INFO] MD(11253,python):2020-05-20-16:22:21.236.799 [mindspore/ccsrc/mindrecord/io/shard_index_generator.cc:600] DatabaseWriter] Init index db for shard: 0 successfully. [INFO] MD(11253,python):2020-05-20-16:22:21.964.034 [mindspore/ccsrc/mindrecord/io/shard_index_generator.cc:549] ExecuteTransaction] Insert 11788 rows to index db. [INFO] MD(11253,python):2020-05-20-16:22:21.978.087 [mindspore/ccsrc/mindrecord/io/shard_index_generator.cc:620] DatabaseWriter] Generate index db for shard: 0 successfully. [INFO] ME(11253:139923799271232,MainProcess):2020-05-20-16:22:21.979.634 [mindspore/mindrecord/filewriter.py:313] The list of mindrecord files created are: ['output/CUB_200_2011.mindrecord'], and the list of index files are: ['output/CUB_200_2011.mindrecord.db'] -
Generate mindrecord files
$ ls output/ CUB_200_2011.mindrecord CUB_200_2011.mindrecord.db README.md
Create MindDataset By MindRecord
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Run the run_read.sh script.
bash run_read.sh -
Output like this:
[INFO] MD(12469,python):2020-05-20-16:26:38.308.797 [mindspore/ccsrc/dataset/util/task.cc:31] operator()] Op launched, OperatorId:0 Thread ID 139702598620928 Started. [INFO] MD(12469,python):2020-05-20-16:26:38.322.433 [mindspore/ccsrc/mindrecord/io/shard_reader.cc:343] ReadAllRowsInShard] Get 11788 records from shard 0 index. [INFO] MD(12469,python):2020-05-20-16:26:38.386.904 [mindspore/ccsrc/mindrecord/io/shard_reader.cc:1058] CreateTasks] Total rows is 11788 [INFO] MD(12469,python):2020-05-20-16:26:38.387.068 [mindspore/ccsrc/dataset/util/task.cc:31] operator()] Parallel Op Worker Thread ID 139702590228224 Started. [INFO] MD(12469,python):2020-05-20-16:26:38.387.272 [mindspore/ccsrc/dataset/util/task.cc:31] operator()] Parallel Op Worker Thread ID 139702581044992 Started. [INFO] MD(12469,python):2020-05-20-16:26:38.387.465 [mindspore/ccsrc/dataset/util/task.cc:31] operator()] Parallel Op Worker Thread ID 139702572652288 Started. [INFO] MD(12469,python):2020-05-20-16:26:38.387.617 [mindspore/ccsrc/dataset/util/task.cc:31] operator()] Parallel Op Worker Thread ID 139702564259584 Started. example 0: {'image': array([255, 216, 255, ..., 47, 255, 217], dtype=uint8), 'bbox': array([ 70., 120., 168., 150.], dtype=float32), 'label': array(199, dtype=int32), 'image_filename': array([ 87, 105, 110, 116, 101, 114, 95, 87, 114, 101, 110, 95, 48, 49, 49, 54, 95, 49, 56, 57, 56, 51, 52, 46, 106, 112, 103], dtype=uint8), 'segmentation_mask': array([137, 80, 78, ..., 66, 96, 130], dtype=uint8), 'label_name': array([ 49, 57, 57, 46, 87, 105, 110, 116, 101, 114, 95, 87, 114, 101, 110], dtype=uint8)} example 1: {'image': array([255, 216, 255, ..., 3, 255, 217], dtype=uint8), 'bbox': array([ 51., 51., 235., 322.], dtype=float32), 'label': array(170, dtype=int32), 'image_filename': array([ 77, 111, 117, 114, 110, 105, 110, 103, 95, 87, 97, 114, 98, 108, 101, 114, 95, 48, 48, 55, 52, 95, 55, 57, 53, 51, 54, 55, 46, 106, 112, 103], dtype=uint8), 'segmentation_mask': array([137, 80, 78, ..., 66, 96, 130], dtype=uint8), 'label_name': array([ 49, 55, 48, 46, 77, 111, 117, 114, 110, 105, 110, 103, 95, 87, 97, 114, 98, 108, 101, 114], dtype=uint8)} example 2: {'image': array([255, 216, 255, ..., 35, 255, 217], dtype=uint8), 'bbox': array([ 57., 56., 285., 248.], dtype=float32), 'label': array(148, dtype=int32), 'image_filename': array([ 71, 114, 101, 101, 110, 95, 84, 97, 105, 108, 101, 100, 95, 84, 111, 119, 104, 101, 101, 95, 48, 48, 53, 52, 95, 49, 53, 52, 57, 51, 56, 46, 106, 112, 103], dtype=uint8), 'segmentation_mask': array([137, 80, 78, ..., 66, 96, 130], dtype=uint8), 'label_name': array([ 49, 52, 56, 46, 71, 114, 101, 101, 110, 95, 116, 97, 105, 108, 101, 100, 95, 84, 111, 119, 104, 101, 101], dtype=uint8)} example 3: {'image': array([255, 216, 255, ..., 85, 255, 217], dtype=uint8), 'bbox': array([ 95., 61., 333., 323.], dtype=float32), 'label': array(176, dtype=int32), 'image_filename': array([ 80, 114, 97, 105, 114, 105, 101, 95, 87, 97, 114, 98, 108, 101, 114, 95, 48, 49, 48, 53, 95, 49, 55, 50, 57, 56, 50, 46, 106, 112, 103], dtype=uint8), 'segmentation_mask': array([137, 80, 78, ..., 66, 96, 130], dtype=uint8), 'label_name': array([ 49, 55, 54, 46, 80, 114, 97, 105, 114, 105, 101, 95, 87, 97, 114, 98, 108, 101, 114], dtype=uint8)} ... example 11786: {'image': array([255, 216, 255, ..., 199, 255, 217], dtype=uint8), 'bbox': array([180., 61., 153., 162.], dtype=float32), 'label': array(75, dtype=int32), 'image_filename': array([ 71, 114, 101, 101, 110, 95, 74, 97, 121, 95, 48, 48, 55, 49, 95, 54, 53, 55, 57, 57, 46, 106, 112, 103], dtype=uint8), 'segmentation_mask': array([137, 80, 78, ..., 66, 96, 130], dtype=uint8), 'label_name': array([ 48, 55, 53, 46, 71, 114, 101, 101, 110, 95, 74, 97, 121], dtype=uint8)} example 11787: {'image': array([255, 216, 255, ..., 127, 255, 217], dtype=uint8), 'bbox': array([ 49., 33., 276., 216.], dtype=float32), 'label': array(27, dtype=int32), 'image_filename': array([ 83, 104, 105, 110, 121, 95, 67, 111, 119, 98, 105, 114, 100, 95, 48, 48, 51, 49, 95, 55, 57, 54, 56, 53, 49, 46, 106, 112, 103], dtype=uint8), 'segmentation_mask': array([137, 80, 78, ..., 66, 96, 130], dtype=uint8), 'label_name': array([ 48, 50, 55, 46, 83, 104, 105, 110, 121, 95, 67, 111, 119, 98, 105, 114, 100], dtype=uint8)} >> total rows: 11788 [INFO] MD(12469,python):2020-05-20-16:26:49.582.298 [mindspore/ccsrc/dataset/util/task.cc:128] Join] Watchdog Thread ID 139702607013632 Stopped.- bbox : coordinate value of the bounding box in the picture.
- image: the image bytes which is from like "data/CUB_200_2011/images/001.Black_footed_Albatross/Black_Footed_Albatross_0001_796111.jpg".
- image_filename: the image name which is like "Black_Footed_Albatross_0001_796111.jpg"
- label : the picture label which is in [1, 200].
- lable_name : object which is like "016.Painted_Bunting" corresponding to label.
- segmentation_mask : the image bytes which is from like "data/segmentations/001.Black_footed_Albatross/Black_Footed_Albatross_0001_796111.png".