"examples/llm/benchmarks/__init__.py" did not exist on "4b1867c53ebbf98dea54623af24d2424ead56573"
Commit 68661967 authored by limm's avatar limm
Browse files

add config module

parent 4353fa59
Pipeline #2808 canceled with stages
# ABINet models use static input 32x128
_base_ = ['./text-recognition_static.py', '../../_base_/backends/tensorrt.py']
onnx_config = dict(input_shape=[128, 32])
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 32, 128],
opt_shape=[1, 3, 32, 128],
max_shape=[1, 3, 32, 128])))
])
_base_ = ['./text-recognition_static.py', '../../_base_/backends/tensorrt.py']
onnx_config = dict(input_shape=[32, 32])
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 32, 32],
opt_shape=[1, 3, 32, 32],
max_shape=[1, 3, 32, 32])))
])
_base_ = [
'../../_base_/torchscript_config.py',
'../../_base_/backends/torchscript.py'
]
ir_config = dict(input_shape=None)
codebase_config = dict(type='mmocr', task='TextRecognition')
_base_ = ['./pose-detection_static.py', '../_base_/backends/ncnn-int8.py']
onnx_config = dict(input_shape=[256, 256])
_base_ = ['./pose-detection_static.py', '../_base_/backends/ncnn.py']
onnx_config = dict(input_shape=[192, 256])
_base_ = ['./pose-detection_static.py', '../_base_/backends/ncnn.py']
onnx_config = dict(input_shape=[256, 256])
_base_ = [
'./pose-detection_static.py', '../_base_/backends/onnxruntime-fp16.py'
]
_base_ = ['./pose-detection_static.py', '../_base_/backends/onnxruntime.py']
onnx_config = dict(input_shape=None)
_base_ = ['./pose-detection_static.py', '../_base_/backends/openvino.py']
onnx_config = dict(input_shape=[192, 256])
backend_config = dict(
model_inputs=[dict(opt_shapes=dict(input=[1, 3, 256, 192]))])
_base_ = ['./pose-detection_static.py', '../_base_/backends/openvino.py']
onnx_config = dict(input_shape=[256, 256])
backend_config = dict(
model_inputs=[dict(opt_shapes=dict(input=[1, 3, 256, 256]))])
_base_ = ['./pose-detection_static.py', '../_base_/backends/pplnn.py']
onnx_config = dict(input_shape=[192, 256])
backend_config = dict(model_inputs=dict(opt_shape=[1, 3, 256, 192]))
_base_ = ['./pose-detection_static.py', '../_base_/backends/rknn.py']
onnx_config = dict(input_shape=[192, 256])
codebase_config = dict(model_type='end2end')
backend_config = dict(
input_size_list=[[3, 256, 192]],
quantization_config=dict(do_quantization=False),
common_config=dict(target_platform='rv1126'))
_base_ = ['./pose-detection_static.py', '../_base_/backends/rknn.py']
onnx_config = dict(input_shape=[256, 256])
codebase_config = dict(model_type='end2end')
backend_config = dict(
input_size_list=[[3, 256, 256]],
quantization_config=dict(do_quantization=False),
common_config=dict(target_platform='rv1126'))
_base_ = ['./pose-detection_static.py', '../_base_/backends/rknn.py']
onnx_config = dict(input_shape=[192, 256])
codebase_config = dict(model_type='end2end')
backend_config = dict(
input_size_list=[[3, 256, 192]],
common_config=dict(target_platform='rv1126'))
_base_ = ['./pose-detection_static.py', '../_base_/backends/rknn.py']
onnx_config = dict(input_shape=[256, 256])
codebase_config = dict(model_type='end2end')
backend_config = dict(
input_size_list=[[3, 256, 256]],
common_config=dict(target_platform='rv1126'))
_base_ = ['./pose-detection_static.py', '../_base_/backends/onnxruntime.py']
onnx_config = dict(
output_names=['dets', 'keypoints'],
dynamic_axes={
'input': {
0: 'batch',
},
'dets': {
0: 'batch',
},
'keypoints': {
0: 'batch'
}
})
codebase_config = dict(
post_processing=dict(
score_threshold=0.05,
iou_threshold=0.5,
max_output_boxes_per_class=200,
pre_top_k=2000,
keep_top_k=50,
background_label_id=-1,
))
_base_ = ['./pose-detection_static.py', '../_base_/backends/tensorrt-fp16.py']
onnx_config = dict(
output_names=['dets', 'keypoints'],
dynamic_axes={
'input': {
0: 'batch',
},
'dets': {
0: 'batch',
},
'keypoints': {
0: 'batch'
}
})
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 640, 640],
opt_shape=[1, 3, 640, 640],
max_shape=[1, 3, 640, 640])))
])
codebase_config = dict(
post_processing=dict(
score_threshold=0.05,
iou_threshold=0.5,
max_output_boxes_per_class=200,
pre_top_k=2000,
keep_top_k=50,
background_label_id=-1,
))
_base_ = ['./pose-detection_static.py', '../_base_/backends/sdk.py']
codebase_config = dict(model_type='sdk')
backend_config = dict(pipeline=[
dict(type='LoadImageFromFile'),
dict(type='GetBBoxCenterScale'),
dict(type='PackPoseInputs')
])
ext_info = dict(image_size=[192, 256], padding=1.25)
_base_ = ['../_base_/torchscript_config.py', '../_base_/backends/coreml.py']
ir_config = dict(input_shape=[192, 256], output_names=['simcc_x', 'simcc_y'])
codebase_config = dict(type='mmpose', task='PoseDetection')
backend_config = dict(model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 256, 192],
max_shape=[1, 3, 256, 192],
default_shape=[1, 3, 256, 192])))
])
_base_ = ['./pose-detection_static.py', '../_base_/backends/ncnn.py']
backend_config = dict(precision='FP16')
onnx_config = dict(input_shape=[192, 256], output_names=['simcc_x', 'simcc_y'])
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