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run_pretrained_models.yaml
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run_pretrained_models.yaml
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#
# simple models for basic functional test
#
regression-graphdef:
model: models/regression/graphdef/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1]
outputs:
- pred:0
regression-checkpoint:
model: models/regression/checkpoint/model.meta
model_type: checkpoint
input_get: get_ramp
inputs:
"X:0": [1]
outputs:
- pred:0
regression-saved-model:
model: models/regression/saved_model
model_type: saved_model
tag: serve
input_get: get_ramp
inputs:
"X:0": [1]
outputs:
- pred:0
saved_model_with_redundant_inputs:
disabled: true # grappler will remove the unconnected inputs - no chance to test this
model: models/saved_model_with_redundant_inputs
model_type: saved_model
input_get: get_ramp
inputs:
"X:0": [1, 10]
"Placeholder:0": [1, 10]
outputs:
- Add:0
graphdef_with_redundant_inputs:
disabled: true # grappler will remove the unconnected inputs - no chance to test this
model: models/regression/graphdef/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 10]
"Placeholder:0": [1, 10]
outputs:
- Add:0
checkpoint_with_redundant_inputs:
disabled: true # grappler will remove the unconnected inputs - no chance to test this
model: models/regression/checkpoint/model.meta
model_type: checkpoint
input_get: get_ramp
inputs:
"X:0": [1]
"Placeholder:0": [1, 10]
outputs:
- pred:0
benchtf-fc:
model: models/fc-layers/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
outputs:
- output:0
benchtf-conv:
model: models/conv-layers/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
outputs:
- output:0
benchtf-convbn:
disabled: true # some if from training isn't removed
model: models/convbn-layers/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
outputs:
- output:0
benchtf-ae0:
model: models/ae0/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
outputs:
- output:0
benchtf-lstm:
disabled: true
model: models/lstm/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
outputs:
- output:0
benchtf-gru:
disabled: true
model: models/gru/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
"keep_prob:0": [1]
outputs:
- output:0
##
## standard image nets
##
esrgan-tf2:
# url: https://tfhub.dev/captain-pool/esrgan-tf2/1/esrgan-tf2_1.tar.gz
url: https://github.com/captain-pool/GSOC/releases/download/1.0.0/esrgan.tar.gz
model: "."
model_type: saved_model
input_get: get_beach
opset_constraints:
"onnx":
"min": 10
inputs:
"input_0:0": [1, 50, 50, 3]
outputs:
- Identity:0
rtol: 0.02
atol: 0.0005
tf_min_version: 2.1
inception_v3_slim:
url: https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz
model: inception_v3_2016_08_28_frozen.pb
input_get: get_beach
inputs:
"input:0": [1, 299, 299, 3]
outputs:
- InceptionV3/Predictions/Softmax:0
rtol: 0.02
atol: 0.00001
googlenet_v1_nonslim:
disabled: true
url: https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
model: tensorflow_inception_graph.pb
input_get: get_beach
inputs:
"input:0": [1, 224, 224, 3]
outputs:
- softmax2:0
googlenet_resnet_v2:
url: https://storage.googleapis.com/download.tensorflow.org/models/inception_resnet_v2_2016_08_30_frozen.pb.tar.gz
model: inception_resnet_v2_2016_08_30_frozen.pb
input_get: get_beach
inputs:
"input:0": [1, 299, 299, 3]
outputs:
- InceptionResnetV2/Logits/Predictions:0
rtol: 0.05
googlenet_v1_slim:
url: https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz
model: inception_v1_2016_08_28_frozen.pb
input_get: get_beach
inputs:
"input:0": [1, 224, 224, 3]
outputs:
- InceptionV1/Logits/Predictions/Softmax:0
rtol: 0.05
googlenet_v2_slim:
# FIXME: fails because of 0.29% missmatch
disabled: true
url: https://storage.googleapis.com/download.tensorflow.org/models/inception_v2_2016_08_28_frozen.pb.tar.gz
model: inception_v2_2016_08_28_frozen.pb
input_get: get_beach
inputs:
"input:0": [1, 224, 224, 3]
outputs:
- InceptionV2/Predictions/Softmax:0
rtol: 0.05
atol: 0.00005
googlenet_v4_slim:
url: https://storage.googleapis.com/download.tensorflow.org/models/inception_v4_2016_09_09_frozen.pb.tar.gz
model: inception_v4_2016_09_09_frozen.pb
input_get: get_beach
inputs:
"input:0": [1, 299, 299, 3]
outputs:
- InceptionV4/Logits/Predictions:0
rtol: 0.1
mobilenet_v3_large_float:
url: https://storage.googleapis.com/mobilenet_v3/checkpoints/v3-large_224_1.0_float.tgz
model: v3-large_224_1.0_float/v3-large_224_1.0_float.pb
input_get: get_beach
inputs:
"input:0": [1, 224, 224, 3]
outputs:
- MobilenetV3/Predictions/Softmax:0
mobilenet_v2_1.4_224:
url: https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz
model: mobilenet_v2_1.4_224_frozen.pb
input_get: get_beach
force_input_shape: true
inputs:
"input:0": [1, 224, 224, 3]
outputs:
- MobilenetV2/Predictions/Reshape_1:0
mobilenet_v1_100_224:
url: https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz
model: mobilenet_v1_1.0_224/frozen_graph.pb
input_get: get_beach
inputs:
"input:0": [1, 224, 224, 3]
outputs:
- MobilenetV1/Predictions/Softmax:0
mobilenet_v1_75_192:
url: https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.75_192_frozen.tgz
model: mobilenet_v1_0.75_192/frozen_graph.pb
input_get: get_beach
inputs:
"input:0": [1, 192, 192, 3]
outputs:
- MobilenetV1/Predictions/Softmax:0
nasnet-a_mobile_224:
# has only checkpoint format
disabled: true
url: https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_mobile_04_10_2017.tar.gz
model: fixme
input_get: get_beach
inputs:
"input:0": [1, 416, 416, 3]
outputs:
- output:0
vgg-16:
# has only checkpoint format
disabled: true
url: http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz
model: fixme
input_get: get_beach
inputs:
"input:0": [1, 416, 416, 3]
outputs:
- output:0
resnet50_v2_nchw: # NOTE: Tensorflow 1.9.0 fails
skip_tensorflow: true # tensorflow fails: Default MaxPoolingOp only supports NHWC on device type CPU
url: http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp32_savedmodel_NCHW.tar.gz
model: resnet_v2_fp32_savedmodel_NCHW/1538687196
model_type: saved_model
tag: serve
input_get: get_beach
inputs:
"input_tensor:0": [64, 224, 224, 3]
outputs:
- ArgMax:0
- softmax_tensor:0
resnet50_v2_nhwc:
url: http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp32_savedmodel_NHWC.tar.gz
model: resnet_v2_fp32_savedmodel_NHWC/1538687283
model_type: saved_model
tag: serve
input_get: get_beach
inputs:
"input_tensor:0": [64, 224, 224, 3]
outputs:
- ArgMax:0
- softmax_tensor:0
resnet50_fp16_v2:
disabled: true # FIXME: need to handle float16 constants
model_type: saved_model
url: http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp16_savedmodel_NHWC.tar.gz
model: resnet_v2_fp16_savedmodel_NHWC/1538686978
input_get: get_beach
inputs:
"input_tensor:0": [64, 224, 224, 3]
outputs:
- ArgMax:0
- softmax_tensor:0
resnet50_v1:
disabled: true # works, disabled because its nearly the same as resnet50_v2_nchw
skip_tensorflow: true # tensorflow fails: Default MaxPoolingOp only supports NHWC on device type CPU
model_type: saved_model
url: http://download.tensorflow.org/models/official/20180601_resnet_v1_imagenet_savedmodel.tar.gz
model: 20180601_resnet_v1_imagenet_savedmodel/1527888778
input_get: get_beach
inputs:
"input_tensor:0": [128, 224, 224, 3]
outputs:
- ArgMax:0
- softmax_tensor:0
ssd_mobilenet_v3_large_coco:
url: http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v3_large_coco_2019_08_14.tar.gz
model: ssd_mobilenet_v3_large_coco_2019_08_14/frozen_inference_graph.pb
opset_constraints:
"onnx":
"min": 10
input_get: get_beach
inputs:
"normalized_input_image_tensor:0": [1, 320, 320, 3]
outputs:
- raw_outputs/box_encodings:0
- raw_outputs/class_predictions:0
ssd_mobilenet_v1_coco:
url: http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz
model: ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb
opset_constraints:
"onnx":
"min": 10
input_get: get_beach
inputs:
"image_tensor:0": [1, 224, 224, 3]
outputs:
- detection_boxes:0
- detection_scores:0
- num_detections:0
- detection_classes:0
ssd_mobilenet_v2_coco:
# works with opset-10
disabled: true
url: http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz
model: ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb
opset_constraints:
"onnx":
"min": 10
input_get: get_beach
inputs:
"image_tensor:0": [1, 224, 224, 3]
outputs:
- detection_boxes:0
- detection_scores:0
- num_detections:0
- detection_classes:0
ssdlite_mobilenet_v2_coco:
# works with opset-10
disabled: true
url: http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz
model: ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb
opset_constraints:
"onnx":
"min": 10
input_get: get_beach
inputs:
"image_tensor:0": [1, 224, 224, 3]
outputs:
- detection_boxes:0
- detection_scores:0
- num_detections:0
- detection_classes:0
ssd_inception_v2_coco:
disabled: true
url: http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz
model: ssd_inception_v2_coco_2017_11_17/frozen_inference_graph.pb
opset_constraints:
"onnx":
"min": 10
input_get: get_beach
inputs:
"image_tensor:0": [1, 224, 224, 3]
outputs:
- detection_boxes:0
- detection_scores:0
- num_detections:0
- detection_classes:0
#
# models that will not work
#
style-transfer:
# quantitized model
disabled: true
url: https://storage.googleapis.com/download.tensorflow.org/models/stylize_v1.zip
model: stylize_quantized.pb
input_get: get_beach
inputs:
"input:0": [1, 416, 416, 3]
outputs:
- output:0
faster_rcnn_inception_v2_coco:
url: http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz
model: faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb
opset_constraints:
"onnx":
"min": 11
input_get: get_beach
inputs:
"image_tensor:0": [1, 224, 224, 3]
outputs:
- detection_boxes:0
- detection_classes:0
- detection_scores:0
- num_detections:0
keras_resnet50:
tf_min_version: 2.1
disabled: false
url: module://tensorflow.keras.applications.resnet50/ResNet50
model: ResNet50
model_type: keras
input_get: get_ramp
inputs:
"input_1:0": [1, 224, 224, 3]
outputs:
- Identity:0
keras_mobilenet_v2:
tf_min_version: 2.1
disabled: false
url: module://tensorflow.keras.applications.mobilenet_v2/MobileNetV2
model: MobileNetV2
model_type: keras
input_get: get_ramp
inputs:
"input_1:0": [1, 224, 224, 3]
outputs:
- Identity:0