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Add EfficientNet-EdgeTPU-M (efficientnet_em) model trained natively in PyTorch. More sotabench fiddling.

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Ross Wightman 2020-09-23 17:12:07 -07:00
parent 3681c5c4dd
commit 9c406532bd
3 changed files with 22 additions and 13 deletions
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25
sotabench.py
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@ -56,6 +56,8 @@ model_list = [
model_desc='Trained from scratch in PyTorch w/ RandAugment'),
_entry('efficientnet_es', 'EfficientNet-EdgeTPU-S', '1905.11946',
model_desc='Trained from scratch in PyTorch w/ RandAugment'),
_entry('efficientnet_em', 'EfficientNet-EdgeTPU-M', '1905.11946',
model_desc='Trained from scratch in PyTorch w/ RandAugment'),
_entry('gluon_inception_v3', 'Inception V3', '1512.00567', model_desc='Ported from GluonCV Model Zoo'),
_entry('gluon_resnet18_v1b', 'ResNet-18', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
@ -111,8 +113,11 @@ model_list = [
model_desc="'D' variant (3x3 deep stem w/ avg-pool downscale). Trained with "
"SGD w/ cosine LR decay, random-erasing (gaussian per-pixel noise) and label-smoothing"),
_entry('wide_resnet50_2', 'Wide-ResNet-50', '1605.07146'),
_entry('seresnet18', 'SE-ResNet-18', '1709.01507'),
_entry('seresnet34', 'SE-ResNet-34', '1709.01507'),
_entry('seresnet50', 'SE-ResNet-50', '1709.01507'),
_entry('seresnext26_32x4d', 'SE-ResNeXt-26 32x4d', '1709.01507',
model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck'),
_entry('seresnext26d_32x4d', 'SE-ResNeXt-26-D 32x4d', '1812.01187',
@ -121,6 +126,7 @@ model_list = [
model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered stem, and avg-pool in downsample layers.'),
_entry('seresnext26tn_32x4d', 'SE-ResNeXt-26-TN 32x4d', '1812.01187',
model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered narrow stem, and avg-pool in downsample layers.'),
_entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
_entry('skresnet18', 'SK-ResNet-18', '1903.06586'),
_entry('skresnet34', 'SK-ResNet-34', '1903.06586'),
@ -139,6 +145,7 @@ model_list = [
_entry('densenetblur121d', 'DenseNet-Blur-121D', '1904.11486',
model_desc='DenseNet with blur pooling and deep stem'),
_entry('ese_vovnet19b_dw', 'VoVNet-19-DW-V2', '1911.06667'),
_entry('ese_vovnet39b', 'VoVNet-39-V2', '1911.06667'),
_entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946',
@ -247,13 +254,13 @@ model_list = [
_entry('inception_v4', 'Inception V4', '1602.07261'),
_entry('nasnetalarge', 'NASNet-A Large', '1707.07012', batch_size=BATCH_SIZE // 4),
_entry('pnasnet5large', 'PNASNet-5', '1712.00559', batch_size=BATCH_SIZE // 4),
_entry('seresnet50', 'SE-ResNet-50', '1709.01507'),
_entry('seresnet101', 'SE-ResNet-101', '1709.01507'),
_entry('seresnet152', 'SE-ResNet-152', '1709.01507'),
_entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
_entry('seresnext101_32x4d', 'SE-ResNeXt-101 32x4d', '1709.01507'),
_entry('senet154', 'SENet-154', '1709.01507'),
_entry('xception', 'Xception', '1610.02357', batch_size=BATCH_SIZE//2),
_entry('legacy_seresnet50', 'SE-ResNet-50', '1709.01507'),
_entry('legacy_seresnet101', 'SE-ResNet-101', '1709.01507'),
_entry('legacy_seresnet152', 'SE-ResNet-152', '1709.01507'),
_entry('legacy_seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
_entry('legacy_seresnext101_32x4d', 'SE-ResNeXt-101 32x4d', '1709.01507'),
_entry('legacy_senet154', 'SENet-154', '1709.01507'),
## Torchvision weights
# _entry('densenet121'),
@ -443,12 +450,6 @@ model_list = [
]
# FIXME debug sotabench dataset issues
from pprint import pprint
from glob import glob
pprint([glob('./**', recursive=True)])
pprint([glob('./.data/vision/**', recursive=True)])
for m in model_list:
model_name = m['model']
# create model from name

7
sotabench_setup.sh
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@ -3,3 +3,10 @@ source /workspace/venv/bin/activate
pip install -r requirements-sotabench.txt
pip uninstall -y pillow
CC="cc -mavx2" pip install -U --force-reinstall pillow-simd
# FIXME this shouldn't be needed but sb dataset upload functionality doesn't seem to work
apt-get install wget
wget https://onedrive.hyper.ai/down/ImageNet/data/ImageNet2012/ILSVRC2012_devkit_t12.tar.gz -P ./.data/vision/imagenet
wget https://onedrive.hyper.ai/down/ImageNet/data/ImageNet2012/ILSVRC2012_img_val.tar -P ./.data/vision/imagenet

3
timm/models/efficientnet.py
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@ -114,7 +114,8 @@ default_cfgs = {
'efficientnet_es': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth'),
'efficientnet_em': _cfg(
url='', input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth',
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'efficientnet_el': _cfg(
url='', input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),