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run_pretrained_models.yaml
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#
# simple models for basic functional test
#
regression-graphdef:
model: tests/models/regression/graphdef/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1]
outputs:
- pred:0
regression-checkpoint:
model: tests/models/regression/checkpoint/model.meta
model_type: checkpoint
input_get: get_ramp
inputs:
"X:0": [1]
outputs:
- pred:0
regression-saved-model:
model: tests/models/regression/saved_model
model_type: saved_model
input_get: get_ramp
inputs:
"X:0": [1]
outputs:
- pred:0
saved_model_with_redundant_inputs:
model: tests/models/saved_model_with_redundant_inputs
model_type: saved_model
input_get: get_ramp
inputs:
"Placeholder:0": [1, 10]
outputs:
- Add:0
benchtf-fc:
model: tests/models/fc-layers/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
outputs:
- output:0
benchtf-conv:
model: tests/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: tests/models/convbn-layers/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
outputs:
- output:0
benchtf-ae0:
model: tests/models/ae0/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
outputs:
- output:0
benchtf-lstm:
disabled: true
model: tests/models/lstm/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
outputs:
- output:0
benchtf-gru:
disabled: true
model: tests/models/gru/frozen.pb
input_get: get_ramp
inputs:
"X:0": [1, 784]
"keep_prob:0": [1]
outputs:
- output:0
##
## standard image nets
##
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_v2_1.4_224:
# fails because of input[1] in squeeze is 0 instead of 1
disabled: true
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
tiny-yolo:
# works but local file
disabled: true
model: c:/src/darkflow/built_graph/tiny-yolo.pb
input_get: get_beach
inputs:
"input:0": [1, 416, 416, 3]
outputs:
- output:0
rtol: 0.6
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
model_type: saved_model
url: http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp32_savedmodel_NCHW.tar.gz
model: resnet_v2_fp32_savedmodel_NCHW/1538687196
input_get: get_beach
inputs:
"input_tensor:0": [64, 224, 224, 3]
outputs:
- ArgMax:0
- softmax_tensor:0
resnet50_v2_nhwc:
disabled: true # FIXME: numerical results are correct but shape=(64,) for tf and (64,1) for lotus
model_type: saved_model
url: http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp32_savedmodel_NHWC.tar.gz
model: resnet_v2_fp32_savedmodel_NHWC/1538687283
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
#
# models that will never 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
ssd_mobilenet_v1_coco:
# issues with control flow
disabled: true
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
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:
# issues with control flow
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
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:
# issues with control flow
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
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:
# issues with control flow
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
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