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Fix ema (#883)
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* fix ema

* update README
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fsx950223 committed Nov 24, 2020
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119 changes: 38 additions & 81 deletions efficientdet/keras/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -3,16 +3,6 @@
[1] Mingxing Tan, Ruoming Pang, Quoc V. Le. EfficientDet: Scalable and Efficient Object Detection. CVPR 2020.
Arxiv link: https://arxiv.org/abs/1911.09070

Updates:

- **Jul20: Added keras/TF2 and new SOTA D7x: 55.1mAP with 153ms**
- Apr22: Sped up end-to-end latency: D0 has up to >200 FPS throughput on Tesla V100.
* A great collaboration with [@fsx950223](https://github.com/fsx950223).
- Apr1: Updated results for test-dev and added EfficientDet-D7.
- Mar26: Fixed a few bugs and updated all checkpoints/results.
- Mar24: Added tutorial with visualization and coco eval.
- Mar 13: Released the initial code and models.

**Quick start tutorial: [tutorial.ipynb](tutorial.ipynb)**

**Quick install dependencies: ```pip install -r requirements.txt```**
Expand All @@ -25,7 +15,7 @@ EfficientDets are a family of object detection models, which achieve state-of-th
EfficientDets are developed based on the advanced backbone, a new BiFPN, and a new scaling technique:

<p align="center">
<img src="./g3doc/network.png" width="800" />
<img src="../g3doc/network.png" width="800" />
</p>

* **Backbone**: we employ [EfficientNets](https://arxiv.org/abs/1905.11946) as our backbone networks.
Expand All @@ -38,10 +28,10 @@ Our model family starts from EfficientDet-D0, which has comparable accuracy as [
<table border="0">
<tr>
<td>
<img src="./g3doc/flops.png" width="100%" />
<img src="../g3doc/flops.png" width="100%" />
</td>
<td>
<img src="./g3doc/params.png", width="100%" />
<img src="../g3doc/params.png", width="100%" />
</td>
</tr>
</table>
Expand All @@ -56,15 +46,15 @@ We have provided a list of EfficientDet checkpoints and results as follows:

| Model | AP<sup>test</sup> | AP<sub>50</sub> | AP<sub>75</sub> |AP<sub>S</sub> | AP<sub>M</sub> | AP<sub>L</sub> | AP<sup>val</sup> | | #params | #FLOPs |
|---------- |------ |------ |------ | -------- | ------| ------| ------ |------ |------ | :------: |
| EfficientDet-D0 ([h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d0.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d0.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d0_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d0_coco_test-dev2017.txt)) | 34.6 | 53.0 | 37.1 | 12.4 | 39.0 | 52.7 | 34.3 | | 3.9M | 2.54B |
| EfficientDet-D1 ([h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d1.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d1.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d1_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d1_coco_test-dev2017.txt)) | 40.5 | 59.1 | 43.7 | 18.3 | 45.0 | 57.5 | 40.2 | | 6.6M | 6.10B |
| EfficientDet-D2 ([h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d2.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d2.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d2_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d2_coco_test-dev2017.txt)) | 43.0 | 62.3 | 46.2 | 22.5 | 47.0 | 58.4 | 42.5 | | 8.1M | 11.0B |
| EfficientDet-D3 ([h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d3.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d3.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d3_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d3_coco_test-dev2017.txt)) | 47.5 | 66.2 | 51.5 | 27.9 | 51.4 | 62.0 | 47.2 | | 12.0M | 24.9B |
| EfficientDet-D4 ([h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d4.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d4.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d4_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d4_coco_test-dev2017.txt)) | 49.7 | 68.4 | 53.9 | 30.7 | 53.2 | 63.2 | 49.3 | | 20.7M | 55.2B |
| EfficientDet-D5 ([h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d5.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d5.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d5_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d5_coco_test-dev2017.txt)) | 51.5 | 70.5 | 56.1 | 33.9 | 54.7 | 64.1 | 51.2 | | 33.7M | 130B |
| EfficientDet-D6 ([h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d6.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d6.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d6_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d6_coco_test-dev2017.txt)) | 52.6 | 71.5 | 57.2 | 34.9 | 56.0 | 65.4 | 52.1 | | 51.9M | 226B |
| EfficientDet-D7 ([h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d7.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d7.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d7_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d7_coco_test-dev2017.txt)) | 53.7 | 72.4 | 58.4 | 35.8 | 57.0 | 66.3 | 53.4 | | 51.9M | 325B |
| EfficientDet-D7x ([h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d7x.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d7x.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d7x_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d7x_coco_test-dev2017.txt)) | 55.1 | 74.3 | 59.9 | 37.2 | 57.9 | 68.0 | 54.4 | | 77.0M | 410B |
| EfficientDet-D0 ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d0.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d0_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d0_coco_test-dev2017.txt)) | 34.6 | 53.0 | 37.1 | 12.4 | 39.0 | 52.7 | 34.3 | | 3.9M | 2.54B |
| EfficientDet-D1 ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d1.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d1_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d1_coco_test-dev2017.txt)) | 40.5 | 59.1 | 43.7 | 18.3 | 45.0 | 57.5 | 40.2 | | 6.6M | 6.10B |
| EfficientDet-D2 ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d2.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d2_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d2_coco_test-dev2017.txt)) | 43.0 | 62.3 | 46.2 | 22.5 | 47.0 | 58.4 | 42.5 | | 8.1M | 11.0B |
| EfficientDet-D3 ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d3.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d3_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d3_coco_test-dev2017.txt)) | 47.5 | 66.2 | 51.5 | 27.9 | 51.4 | 62.0 | 47.2 | | 12.0M | 24.9B |
| EfficientDet-D4 ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d4.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d4_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d4_coco_test-dev2017.txt)) | 49.7 | 68.4 | 53.9 | 30.7 | 53.2 | 63.2 | 49.3 | | 20.7M | 55.2B |
| EfficientDet-D5 ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d5.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d5_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d5_coco_test-dev2017.txt)) | 51.5 | 70.5 | 56.1 | 33.9 | 54.7 | 64.1 | 51.2 | | 33.7M | 130B |
| EfficientDet-D6 ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d6.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d6_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d6_coco_test-dev2017.txt)) | 52.6 | 71.5 | 57.2 | 34.9 | 56.0 | 65.4 | 52.1 | | 51.9M | 226B |
| EfficientDet-D7 ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d7.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d7_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d7_coco_test-dev2017.txt)) | 53.7 | 72.4 | 58.4 | 35.8 | 57.0 | 66.3 | 53.4 | | 51.9M | 325B |
| EfficientDet-D7x ([ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d7x.tar.gz), [val](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/val/d7x_coco_val.txt), [test-dev](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/testdev/d7x_coco_test-dev2017.txt)) | 55.1 | 74.3 | 59.9 | 37.2 | 57.9 | 68.0 | 54.4 | | 77.0M | 410B |

<sup><em>val</em> denotes validation results, <em>test-dev</em> denotes test-dev2017 results. AP<sup>val</sup> is for validation accuracy, all other AP results in the table are for COCO test-dev2017. All accuracy numbers are for single-model single-scale without ensemble or test-time augmentation. EfficientDet-D0 to D6 are trained for 300 epochs and D7/D7x are trained for 600 epochs.</sup>

Expand All @@ -73,11 +63,11 @@ In addition, the following table includes a list of models trained with fixed 64

| Model | mAP | Latency |
| ------ | ------ | ------ |
| D2(640) [h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d2-640.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d2-640.tar.gz) | 41.7 | 14.8ms |
| D3(640) [h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d3-640.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d3-640.tar.gz) | 44.0 | 18.7ms |
| D4(640) [h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d4-640.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d4-640.tar.gz) | 45.7 | 21.7ms |
| D5(640 [h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d5-640.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d5-640.tar.gz) | 46.6 | 26.6ms |
| D6(640) [h5](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d6-640.h5), [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d6-640.tar.gz) | 47.9 | 33.8ms |
| D2(640) [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d2-640.tar.gz) | 41.7 | 14.8ms |
| D3(640) [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d3-640.tar.gz) | 44.0 | 18.7ms |
| D4(640) [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d4-640.tar.gz) | 45.7 | 21.7ms |
| D5(640) [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d5-640.tar.gz) | 46.6 | 26.6ms |
| D6(640) [ckpt](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco640/efficientdet-d6-640.tar.gz) | 47.9 | 33.8ms |



Expand All @@ -96,10 +86,9 @@ Then you will get:
- saved model under `savedmodeldir/`
- frozen graph with name `savedmodeldir/efficientdet-d0_frozen.pb`
- TensorRT saved model under `savedmodeldir/tensorrt_fp32/`
- tflite file with name `efficientdet-d0.tflite`
- tflite file with name `savedmodeldir/fp32.tflite`

Notably,
--tflite_path only works after 2.3.0-dev20200521 ,
--model_dir=xx/archive is the folder for exporting the best model.


Expand Down Expand Up @@ -154,12 +143,12 @@ latency and throughput are:

# Step2: inference image.
!python inspector.py --mode=infer \
--model_name=efficientdet-d0 --model_dir=efficientdet-d0 \
--model_name=efficientdet-d0 --saved_model_dir=/tmp/saved_model \
--hparams="image_size=1920x1280" \
--input_image=img.png --output_image_dir=/tmp/


Alternatively, if you want to do inference using frozen graph instead of saved model, you can run
If you want to do inference using frozen graph, you can run

# Step 1 is the same as before.
# Step 2: do inference with frozen graph.
Expand All @@ -168,18 +157,28 @@ Alternatively, if you want to do inference using frozen graph instead of saved m
--saved_model_dir=/tmp/saved_model/efficientdet-d0_frozen.pb \
--input_image=img.png --output_image_dir=/tmp/

If you want to do inference using tflite, you can run

# Step 1 is the same as before.
# Step 2: do inference with frozen graph.
!python inspector.py --mode=infer \
--model_name=efficientdet-d0 \
--saved_model_dir=/tmp/saved_model/fp32.tflite \
--input_image=img.png --output_image_dir=/tmp/

Lastly, if you only have one image and just want to run a quick test, you can also run the following command (it is slow because it needs to construct the graph from scratch):

# Run inference for a single image.
!python inspector.py --mode=infer --model_name=$MODEL \
!python inspector.py --mode=infer \
--model_name=efficientdet-d0 --model_dir=$CKPT_PATH \
--hparams="image_size=1920x1280" \
--model_dir=$CKPT_PATH --input_image=img.png --output_image_dir=/tmp
--input_image=img.png --output_image_dir=/tmp/
# you can visualize the output /tmp/0.jpg

Here is an example of EfficientDet-D0 visualization: more on [tutorial](tutorial.ipynb)

<p align="center">
<img src="./g3doc/street.jpg" width="800" />
<img src="../g3doc/street.jpg" width="800" />
</p>

## 6. Inference for videos.
Expand Down Expand Up @@ -243,67 +242,25 @@ Create a config file for the PASCAL VOC dataset called voc_config.yaml and put t
var_freeze_expr: '(efficientnet|fpn_cells|resample_p6)'
label_map: {1: aeroplane, 2: bicycle, 3: bird, 4: boat, 5: bottle, 6: bus, 7: car, 8: cat, 9: chair, 10: cow, 11: diningtable, 12: dog, 13: horse, 14: motorbike, 15: person, 16: pottedplant, 17: sheep, 18: sofa, 19: train, 20: tvmonitor}

Finetune needs to use --ckpt rather than --backbone_ckpt.
Finetune needs to use --pretrained_ckpt.

!python train.py
--training_file_pattern=tfrecord/pascal*.tfrecord \
--val_file_pattern=tfrecord/pascal*.tfrecord \
--val_file_pattern=tfrecord/*.json \
--model_name=efficientdet-d0 \
--model_dir=/tmp/efficientdet-d0-finetune \
--ckpt=efficientdet-d0 \
--pretrained_ckpt=efficientdet-d0 \
--batch_size=64 \
--eval_samples=1024 \
--num_examples_per_epoch=5717 --num_epochs=50 \
--hparams=voc_config.yaml

If you want to continue to train the model, simply re-run the above command because the `num_epochs` is a maximum number of epochs. For example, to reproduce the result of efficientdet-d0, set `--num_epochs=300` then run the command multiple times until the training is finished.

If you want to do inference for custom data, you can run

# Setting hparams-flag is needed sometimes.
!python inspector.py --mode=infer \
--model_name=efficientdet-d0 --model_dir=efficientdet-d0 \
--hparams=voc_config.yaml \
--input_image=img.png --output_image_dir=/tmp/

You should check more details of runmode which is written in caption-4.

## 9. Train on multi GPUs.

Create a config file for the PASCAL VOC dataset called voc_config.yaml and put this in it.

num_classes: 21
var_freeze_expr: '(efficientnet|fpn_cells|resample_p6)'
label_map: {1: aeroplane, 2: bicycle, 3: bird, 4: boat, 5: bottle, 6: bus, 7: car, 8: cat, 9: chair, 10: cow, 11: diningtable, 12: dog, 13: horse, 14: motorbike, 15: person, 16: pottedplant, 17: sheep, 18: sofa, 19: train, 20: tvmonitor}

Download efficientdet coco checkpoint.

!wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-d0.tar.gz
!tar xf efficientdet-d0.tar.gz

Finetune needs to use --ckpt rather than --backbone_ckpt.

python train.py \
--training_file_pattern=tfrecord/pascal*.tfrecord \
--val_file_pattern=tfrecord/pascal*.tfrecord \
--model_name=efficientdet-d0 \
--model_dir=/tmp/efficientdet-d0-finetune \
--ckpt=efficientdet-d0 \
--batch_size=64 \
--eval_samples=1024 \
--num_examples_per_epoch=5717 --num_epochs=50 \
--hparams=voc_config.yaml \
--strategy=gpus

If you want to do inference for custom data, you can run

# Setting hparams-flag is needed sometimes.
!python inspector.py --mode=infer \
--model_name=efficientdet-d0 --model_dir=efficientdet-d0 \
--hparams=voc_config.yaml \
--input_image=img.png --output_image_dir=/tmp/

You should check more details of runmode which is written in caption-4.
Just add ```--strategy=gpus```

## 10. Training EfficientDets on TPUs.

Expand Down Expand Up @@ -335,7 +292,7 @@ EfficientDets use a lot of GPU memory for a few reasons:
* Large internal activations for backbone: our backbone uses a relatively large expansion ratio (6), causing the large expanded activations.
* Deep BiFPN: our BiFPN has multiple top-down and bottom-up paths, which leads to a lot of intermediate memory usage during training.

To train this model on GPU with low memory there is an experimental option gradient_checkpointing.
To train this model on GPU with low memory there is an experimental option grad_checkpoint.

Check these links for a high-level idea of what gradient checkpointing is doing:
1. https://medium.com/tensorflow/fitting-larger-networks-into-memory-583e3c758ff9
Expand Down
1 change: 1 addition & 0 deletions efficientdet/keras/eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,6 +76,7 @@ def main(_):
model.build((None, *config.image_size, 3))
util_keras.restore_ckpt(model,
tf.train.latest_checkpoint(FLAGS.model_dir),
config.moving_average_decay,
skip_mismatch=False)
@tf.function
def model_fn(images, labels):
Expand Down
4 changes: 3 additions & 1 deletion efficientdet/keras/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,7 +202,9 @@ def build(self, params_override=None):
self.model = efficientdet_keras.EfficientDetModel(config=config)
image_size = utils.parse_image_size(params['image_size'])
self.model.build((self.batch_size, *image_size, 3))
util_keras.restore_ckpt(self.model, self.ckpt_path, skip_mismatch=False)
util_keras.restore_ckpt(self.model, self.ckpt_path,
self.params['moving_average_decay'],
skip_mismatch=False)

def visualize(self, image, boxes, classes, scores, **kwargs):
"""Visualize prediction on image."""
Expand Down
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