Note
Click here to download the full example code
Deploy the Pretrained Model on Adreno¶
Author: Daniil Barinov
This article is a step-by-step tutorial to deploy pretrained Pytorch ResNet-18 model on Adreno (on different precisions).
For us to begin with, PyTorch must be installed. TorchVision is also required since we will be using it as our model zoo.
A quick solution is to install it via pip:
pip install torch
pip install torchvision
Besides that, you should have TVM builded for Android. See the following instructions on how to build it.
After the build section there should be two files in build directory «libtvm_runtime.so» and «tvm_rpc». Let’s push them to the device and run TVM RPC Server.
TVM RPC Server¶
To get the hash of the device use:
adb devices
Then to upload these two files to the device you should use:
adb -s <device_hash> push {libtvm_runtime.so,tvm_rpc} /data/local/tmp
At this moment you will have «libtvm_runtime.so» and «tvm_rpc» on path /data/local/tmp on your device. Sometimes cmake can’t find «libc++_shared.so». Use:
find ${ANDROID_NDK_HOME} -name libc++_shared.so
to find it and also push it with adb on the desired device:
adb -s <device_hash> push libc++_shared.so /data/local/tmp
We are now ready to run the TVM RPC Server. Launch rpc_tracker with following line in 1st console:
python3 -m tvm.exec.rpc_tracker --port 9190
Then we need to run tvm_rpc server from under the desired device in 2nd console:
adb -s <device_hash> reverse tcp:9190 tcp:9190
adb -s <device_hash> forward tcp:9090 tcp:9090
adb -s <device_hash> forward tcp:9091 tcp:9091
adb -s <device_hash> forward tcp:9092 tcp:9092
adb -s <device_hash> forward tcp:9093 tcp:9093
adb -s <device_hash> shell LD_LIBRARY_PATH=/data/local/tmp /data/local/tmp/tvm_rpc server --host=0.0.0.0 --port=9090 --tracker=127.0.0.1:9190 --key=android --port-end=9190
Before proceeding to compile and infer model, specify TVM_TRACKER_HOST and TVM_TRACKER_PORT
export TVM_TRACKER_HOST=0.0.0.0
export TVM_TRACKER_PORT=9190
check that the tracker is running and the device is available
python -m tvm.exec.query_rpc_tracker --port 9190
For example, if we have 1 Android device, the output can be:
Queue Status
----------------------------------
key total free pending
----------------------------------
android 1 1 0
----------------------------------
Load a test image¶
As an example we would use classical cat image from ImageNet
from PIL import Image
from tvm.contrib.download import download_testdata
from matplotlib import pyplot as plt
import numpy as np
img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
img_path = download_testdata(img_url, "cat.png", module="data")
img = Image.open(img_path).resize((224, 224))
plt.imshow(img)
plt.show()
# Preprocess the image and convert to tensor
from torchvision import transforms
my_preprocess = transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
img = my_preprocess(img)
img = np.expand_dims(img, 0)
Load pretrained Pytorch model¶
Create a Relay graph from a Pytorch ResNet-18 model
import os
import torch
import torchvision
import tvm
from tvm import te
from tvm import relay, rpc
from tvm.contrib import utils, ndk
from tvm.contrib import graph_executor
model_name = "resnet18"
model = getattr(torchvision.models, model_name)(pretrained=True)
model = model.eval()
# We grab the TorchScripted model via tracing
input_shape = [1, 3, 224, 224]
input_data = torch.randn(input_shape)
scripted_model = torch.jit.trace(model, input_data).eval()
# Input name can be arbitrary
input_name = "input0"
shape_list = [(input_name, img.shape)]
mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
f"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, "
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
return LooseVersion(torch_ver) > ver
/venv/apache-tvm-py3.7/lib/python3.7/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
other = LooseVersion(other)
Precisions¶
Since TVM support Mixed Precision, we need to register mixed_precision_conversion:
from tvm.relay.op import register_mixed_precision_conversion
conv2d_acc = "float32"
@register_mixed_precision_conversion("nn.conv2d", level=11)
def conv2d_mixed_precision_rule(call_node: "relay.Call", mixed_precision_type: str):
global conv2d_acc
return [
relay.transform.mixed_precision.MIXED_PRECISION_ALWAYS,
conv2d_acc,
mixed_precision_type,
]
@register_mixed_precision_conversion("nn.dense", level=11)
def conv2d_mixed_precision_rule(call_node: "relay.Call", mixed_precision_type: str):
global conv2d_acc
return [
relay.transform.mixed_precision.MIXED_PRECISION_ALWAYS,
conv2d_acc,
mixed_precision_type,
]
and also define the conversion function itself
def convert_to_dtype(mod, dtype):
# downcast to float16
if dtype == "float16" or dtype == "float16_acc32":
global conv2d_acc
conv2d_acc = "float16" if dtype == "float16" else "float32"
from tvm.ir import IRModule
mod = IRModule.from_expr(mod)
seq = tvm.transform.Sequential(
[relay.transform.InferType(), relay.transform.ToMixedPrecision()]
)
with tvm.transform.PassContext(opt_level=3):
mod = seq(mod)
return mod
Let’s choose “float16_acc32” for example.
def @main(%input0: Tensor[(1, 3, 224, 224), float32] /* ty=Tensor[(1, 3, 224, 224), float32] */, %conv1.weight: Tensor[(64, 3, 7, 7), float32] /* ty=Tensor[(64, 3, 7, 7), float32] */, %bn1.weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %bn1.bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %bn1.running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %bn1.running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.0.conv1.weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] */, %layer1.0.bn1.weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.0.bn1.bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.0.bn1.running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.0.bn1.running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.0.conv2.weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] */, %layer1.0.bn2.weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.0.bn2.bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.0.bn2.running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.0.bn2.running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.1.conv1.weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] */, %layer1.1.bn1.weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.1.bn1.bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.1.bn1.running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.1.bn1.running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.1.conv2.weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] */, %layer1.1.bn2.weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.1.bn2.bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.1.bn2.running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer1.1.bn2.running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %layer2.0.conv1.weight: Tensor[(128, 64, 3, 3), float32] /* ty=Tensor[(128, 64, 3, 3), float32] */, %layer2.0.bn1.weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.bn1.bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.bn1.running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.bn1.running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.conv2.weight: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] */, %layer2.0.bn2.weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.bn2.bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.bn2.running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.bn2.running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.downsample.0.weight: Tensor[(128, 64, 1, 1), float32] /* ty=Tensor[(128, 64, 1, 1), float32] */, %layer2.0.downsample.1.weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.downsample.1.bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.downsample.1.running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.0.downsample.1.running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.1.conv1.weight: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] */, %layer2.1.bn1.weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.1.bn1.bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.1.bn1.running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.1.bn1.running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.1.conv2.weight: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] */, %layer2.1.bn2.weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.1.bn2.bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.1.bn2.running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer2.1.bn2.running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %layer3.0.conv1.weight: Tensor[(256, 128, 3, 3), float32] /* ty=Tensor[(256, 128, 3, 3), float32] */, %layer3.0.bn1.weight: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.bn1.bias: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.bn1.running_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.bn1.running_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.conv2.weight: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] */, %layer3.0.bn2.weight: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.bn2.bias: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.bn2.running_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.bn2.running_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.downsample.0.weight: Tensor[(256, 128, 1, 1), float32] /* ty=Tensor[(256, 128, 1, 1), float32] */, %layer3.0.downsample.1.weight: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.downsample.1.bias: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.downsample.1.running_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.0.downsample.1.running_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.1.conv1.weight: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] */, %layer3.1.bn1.weight: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.1.bn1.bias: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.1.bn1.running_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.1.bn1.running_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.1.conv2.weight: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] */, %layer3.1.bn2.weight: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.1.bn2.bias: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.1.bn2.running_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer3.1.bn2.running_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %layer4.0.conv1.weight: Tensor[(512, 256, 3, 3), float32] /* ty=Tensor[(512, 256, 3, 3), float32] */, %layer4.0.bn1.weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.bn1.bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.bn1.running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.bn1.running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.conv2.weight: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] */, %layer4.0.bn2.weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.bn2.bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.bn2.running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.bn2.running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.downsample.0.weight: Tensor[(512, 256, 1, 1), float32] /* ty=Tensor[(512, 256, 1, 1), float32] */, %layer4.0.downsample.1.weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.downsample.1.bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.downsample.1.running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.0.downsample.1.running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.1.conv1.weight: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] */, %layer4.1.bn1.weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.1.bn1.bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.1.bn1.running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.1.bn1.running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.1.conv2.weight: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] */, %layer4.1.bn2.weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.1.bn2.bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.1.bn2.running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %layer4.1.bn2.running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %fc.weight: Tensor[(1000, 512), float32] /* ty=Tensor[(1000, 512), float32] */, %fc.bias: Tensor[(1000), float32] /* ty=Tensor[(1000), float32] */) -> Tensor[(1, 1000), float16] {
%0 = cast(%input0, dtype="float16") /* ty=Tensor[(1, 3, 224, 224), float16] */;
%1 = cast(%conv1.weight, dtype="float16") /* ty=Tensor[(64, 3, 7, 7), float16] */;
%2 = nn.conv2d(%0, %1, strides=[2, 2], padding=[3, 3, 3, 3], channels=64, kernel_size=[7, 7], out_dtype="float32") /* ty=Tensor[(1, 64, 112, 112), float32] */;
%3 = cast(%2, dtype="float16") /* ty=Tensor[(1, 64, 112, 112), float16] */;
%4 = cast(%bn1.weight, dtype="float16") /* ty=Tensor[(64), float16] */;
%5 = cast(%bn1.bias, dtype="float16") /* ty=Tensor[(64), float16] */;
%6 = cast(%bn1.running_mean, dtype="float16") /* ty=Tensor[(64), float16] */;
%7 = cast(%bn1.running_var, dtype="float16") /* ty=Tensor[(64), float16] */;
%8 = nn.batch_norm(%3, %4, %5, %6, %7) /* ty=(Tensor[(1, 64, 112, 112), float16], Tensor[(64), float16], Tensor[(64), float16]) */;
%9 = %8.0 /* ty=Tensor[(1, 64, 112, 112), float16] */;
%10 = nn.relu(%9) /* ty=Tensor[(1, 64, 112, 112), float16] */;
%11 = nn.max_pool2d(%10, pool_size=[3, 3], strides=[2, 2], padding=[1, 1, 1, 1]) /* ty=Tensor[(1, 64, 56, 56), float16] */;
%12 = cast(%layer1.0.conv1.weight, dtype="float16") /* ty=Tensor[(64, 64, 3, 3), float16] */;
%13 = nn.conv2d(%11, %12, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 64, 56, 56), float32] */;
%14 = cast(%13, dtype="float16") /* ty=Tensor[(1, 64, 56, 56), float16] */;
%15 = cast(%layer1.0.bn1.weight, dtype="float16") /* ty=Tensor[(64), float16] */;
%16 = cast(%layer1.0.bn1.bias, dtype="float16") /* ty=Tensor[(64), float16] */;
%17 = cast(%layer1.0.bn1.running_mean, dtype="float16") /* ty=Tensor[(64), float16] */;
%18 = cast(%layer1.0.bn1.running_var, dtype="float16") /* ty=Tensor[(64), float16] */;
%19 = nn.batch_norm(%14, %15, %16, %17, %18) /* ty=(Tensor[(1, 64, 56, 56), float16], Tensor[(64), float16], Tensor[(64), float16]) */;
%20 = %19.0 /* ty=Tensor[(1, 64, 56, 56), float16] */;
%21 = nn.relu(%20) /* ty=Tensor[(1, 64, 56, 56), float16] */;
%22 = cast(%layer1.0.conv2.weight, dtype="float16") /* ty=Tensor[(64, 64, 3, 3), float16] */;
%23 = nn.conv2d(%21, %22, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 64, 56, 56), float32] */;
%24 = cast(%23, dtype="float16") /* ty=Tensor[(1, 64, 56, 56), float16] */;
%25 = cast(%layer1.0.bn2.weight, dtype="float16") /* ty=Tensor[(64), float16] */;
%26 = cast(%layer1.0.bn2.bias, dtype="float16") /* ty=Tensor[(64), float16] */;
%27 = cast(%layer1.0.bn2.running_mean, dtype="float16") /* ty=Tensor[(64), float16] */;
%28 = cast(%layer1.0.bn2.running_var, dtype="float16") /* ty=Tensor[(64), float16] */;
%29 = nn.batch_norm(%24, %25, %26, %27, %28) /* ty=(Tensor[(1, 64, 56, 56), float16], Tensor[(64), float16], Tensor[(64), float16]) */;
%30 = %29.0 /* ty=Tensor[(1, 64, 56, 56), float16] */;
%31 = add(%30, %11) /* ty=Tensor[(1, 64, 56, 56), float16] */;
%32 = nn.relu(%31) /* ty=Tensor[(1, 64, 56, 56), float16] */;
%33 = cast(%layer1.1.conv1.weight, dtype="float16") /* ty=Tensor[(64, 64, 3, 3), float16] */;
%34 = nn.conv2d(%32, %33, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 64, 56, 56), float32] */;
%35 = cast(%34, dtype="float16") /* ty=Tensor[(1, 64, 56, 56), float16] */;
%36 = cast(%layer1.1.bn1.weight, dtype="float16") /* ty=Tensor[(64), float16] */;
%37 = cast(%layer1.1.bn1.bias, dtype="float16") /* ty=Tensor[(64), float16] */;
%38 = cast(%layer1.1.bn1.running_mean, dtype="float16") /* ty=Tensor[(64), float16] */;
%39 = cast(%layer1.1.bn1.running_var, dtype="float16") /* ty=Tensor[(64), float16] */;
%40 = nn.batch_norm(%35, %36, %37, %38, %39) /* ty=(Tensor[(1, 64, 56, 56), float16], Tensor[(64), float16], Tensor[(64), float16]) */;
%41 = %40.0 /* ty=Tensor[(1, 64, 56, 56), float16] */;
%42 = nn.relu(%41) /* ty=Tensor[(1, 64, 56, 56), float16] */;
%43 = cast(%layer1.1.conv2.weight, dtype="float16") /* ty=Tensor[(64, 64, 3, 3), float16] */;
%44 = nn.conv2d(%42, %43, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 64, 56, 56), float32] */;
%45 = cast(%44, dtype="float16") /* ty=Tensor[(1, 64, 56, 56), float16] */;
%46 = cast(%layer1.1.bn2.weight, dtype="float16") /* ty=Tensor[(64), float16] */;
%47 = cast(%layer1.1.bn2.bias, dtype="float16") /* ty=Tensor[(64), float16] */;
%48 = cast(%layer1.1.bn2.running_mean, dtype="float16") /* ty=Tensor[(64), float16] */;
%49 = cast(%layer1.1.bn2.running_var, dtype="float16") /* ty=Tensor[(64), float16] */;
%50 = nn.batch_norm(%45, %46, %47, %48, %49) /* ty=(Tensor[(1, 64, 56, 56), float16], Tensor[(64), float16], Tensor[(64), float16]) */;
%51 = %50.0 /* ty=Tensor[(1, 64, 56, 56), float16] */;
%52 = add(%51, %32) /* ty=Tensor[(1, 64, 56, 56), float16] */;
%53 = nn.relu(%52) /* ty=Tensor[(1, 64, 56, 56), float16] */;
%54 = cast(%layer2.0.conv1.weight, dtype="float16") /* ty=Tensor[(128, 64, 3, 3), float16] */;
%55 = nn.conv2d(%53, %54, strides=[2, 2], padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 128, 28, 28), float32] */;
%56 = cast(%55, dtype="float16") /* ty=Tensor[(1, 128, 28, 28), float16] */;
%57 = cast(%layer2.0.bn1.weight, dtype="float16") /* ty=Tensor[(128), float16] */;
%58 = cast(%layer2.0.bn1.bias, dtype="float16") /* ty=Tensor[(128), float16] */;
%59 = cast(%layer2.0.bn1.running_mean, dtype="float16") /* ty=Tensor[(128), float16] */;
%60 = cast(%layer2.0.bn1.running_var, dtype="float16") /* ty=Tensor[(128), float16] */;
%61 = nn.batch_norm(%56, %57, %58, %59, %60) /* ty=(Tensor[(1, 128, 28, 28), float16], Tensor[(128), float16], Tensor[(128), float16]) */;
%62 = %61.0 /* ty=Tensor[(1, 128, 28, 28), float16] */;
%63 = nn.relu(%62) /* ty=Tensor[(1, 128, 28, 28), float16] */;
%64 = cast(%layer2.0.conv2.weight, dtype="float16") /* ty=Tensor[(128, 128, 3, 3), float16] */;
%65 = nn.conv2d(%63, %64, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 128, 28, 28), float32] */;
%66 = cast(%65, dtype="float16") /* ty=Tensor[(1, 128, 28, 28), float16] */;
%67 = cast(%layer2.0.bn2.weight, dtype="float16") /* ty=Tensor[(128), float16] */;
%68 = cast(%layer2.0.bn2.bias, dtype="float16") /* ty=Tensor[(128), float16] */;
%69 = cast(%layer2.0.bn2.running_mean, dtype="float16") /* ty=Tensor[(128), float16] */;
%70 = cast(%layer2.0.bn2.running_var, dtype="float16") /* ty=Tensor[(128), float16] */;
%71 = nn.batch_norm(%66, %67, %68, %69, %70) /* ty=(Tensor[(1, 128, 28, 28), float16], Tensor[(128), float16], Tensor[(128), float16]) */;
%72 = cast(%layer2.0.downsample.0.weight, dtype="float16") /* ty=Tensor[(128, 64, 1, 1), float16] */;
%73 = nn.conv2d(%53, %72, strides=[2, 2], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], out_dtype="float32") /* ty=Tensor[(1, 128, 28, 28), float32] */;
%74 = cast(%73, dtype="float16") /* ty=Tensor[(1, 128, 28, 28), float16] */;
%75 = cast(%layer2.0.downsample.1.weight, dtype="float16") /* ty=Tensor[(128), float16] */;
%76 = cast(%layer2.0.downsample.1.bias, dtype="float16") /* ty=Tensor[(128), float16] */;
%77 = cast(%layer2.0.downsample.1.running_mean, dtype="float16") /* ty=Tensor[(128), float16] */;
%78 = cast(%layer2.0.downsample.1.running_var, dtype="float16") /* ty=Tensor[(128), float16] */;
%79 = nn.batch_norm(%74, %75, %76, %77, %78) /* ty=(Tensor[(1, 128, 28, 28), float16], Tensor[(128), float16], Tensor[(128), float16]) */;
%80 = %71.0 /* ty=Tensor[(1, 128, 28, 28), float16] */;
%81 = %79.0 /* ty=Tensor[(1, 128, 28, 28), float16] */;
%82 = add(%80, %81) /* ty=Tensor[(1, 128, 28, 28), float16] */;
%83 = nn.relu(%82) /* ty=Tensor[(1, 128, 28, 28), float16] */;
%84 = cast(%layer2.1.conv1.weight, dtype="float16") /* ty=Tensor[(128, 128, 3, 3), float16] */;
%85 = nn.conv2d(%83, %84, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 128, 28, 28), float32] */;
%86 = cast(%85, dtype="float16") /* ty=Tensor[(1, 128, 28, 28), float16] */;
%87 = cast(%layer2.1.bn1.weight, dtype="float16") /* ty=Tensor[(128), float16] */;
%88 = cast(%layer2.1.bn1.bias, dtype="float16") /* ty=Tensor[(128), float16] */;
%89 = cast(%layer2.1.bn1.running_mean, dtype="float16") /* ty=Tensor[(128), float16] */;
%90 = cast(%layer2.1.bn1.running_var, dtype="float16") /* ty=Tensor[(128), float16] */;
%91 = nn.batch_norm(%86, %87, %88, %89, %90) /* ty=(Tensor[(1, 128, 28, 28), float16], Tensor[(128), float16], Tensor[(128), float16]) */;
%92 = %91.0 /* ty=Tensor[(1, 128, 28, 28), float16] */;
%93 = nn.relu(%92) /* ty=Tensor[(1, 128, 28, 28), float16] */;
%94 = cast(%layer2.1.conv2.weight, dtype="float16") /* ty=Tensor[(128, 128, 3, 3), float16] */;
%95 = nn.conv2d(%93, %94, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 128, 28, 28), float32] */;
%96 = cast(%95, dtype="float16") /* ty=Tensor[(1, 128, 28, 28), float16] */;
%97 = cast(%layer2.1.bn2.weight, dtype="float16") /* ty=Tensor[(128), float16] */;
%98 = cast(%layer2.1.bn2.bias, dtype="float16") /* ty=Tensor[(128), float16] */;
%99 = cast(%layer2.1.bn2.running_mean, dtype="float16") /* ty=Tensor[(128), float16] */;
%100 = cast(%layer2.1.bn2.running_var, dtype="float16") /* ty=Tensor[(128), float16] */;
%101 = nn.batch_norm(%96, %97, %98, %99, %100) /* ty=(Tensor[(1, 128, 28, 28), float16], Tensor[(128), float16], Tensor[(128), float16]) */;
%102 = %101.0 /* ty=Tensor[(1, 128, 28, 28), float16] */;
%103 = add(%102, %83) /* ty=Tensor[(1, 128, 28, 28), float16] */;
%104 = nn.relu(%103) /* ty=Tensor[(1, 128, 28, 28), float16] */;
%105 = cast(%layer3.0.conv1.weight, dtype="float16") /* ty=Tensor[(256, 128, 3, 3), float16] */;
%106 = nn.conv2d(%104, %105, strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 256, 14, 14), float32] */;
%107 = cast(%106, dtype="float16") /* ty=Tensor[(1, 256, 14, 14), float16] */;
%108 = cast(%layer3.0.bn1.weight, dtype="float16") /* ty=Tensor[(256), float16] */;
%109 = cast(%layer3.0.bn1.bias, dtype="float16") /* ty=Tensor[(256), float16] */;
%110 = cast(%layer3.0.bn1.running_mean, dtype="float16") /* ty=Tensor[(256), float16] */;
%111 = cast(%layer3.0.bn1.running_var, dtype="float16") /* ty=Tensor[(256), float16] */;
%112 = nn.batch_norm(%107, %108, %109, %110, %111) /* ty=(Tensor[(1, 256, 14, 14), float16], Tensor[(256), float16], Tensor[(256), float16]) */;
%113 = %112.0 /* ty=Tensor[(1, 256, 14, 14), float16] */;
%114 = nn.relu(%113) /* ty=Tensor[(1, 256, 14, 14), float16] */;
%115 = cast(%layer3.0.conv2.weight, dtype="float16") /* ty=Tensor[(256, 256, 3, 3), float16] */;
%116 = nn.conv2d(%114, %115, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 256, 14, 14), float32] */;
%117 = cast(%116, dtype="float16") /* ty=Tensor[(1, 256, 14, 14), float16] */;
%118 = cast(%layer3.0.bn2.weight, dtype="float16") /* ty=Tensor[(256), float16] */;
%119 = cast(%layer3.0.bn2.bias, dtype="float16") /* ty=Tensor[(256), float16] */;
%120 = cast(%layer3.0.bn2.running_mean, dtype="float16") /* ty=Tensor[(256), float16] */;
%121 = cast(%layer3.0.bn2.running_var, dtype="float16") /* ty=Tensor[(256), float16] */;
%122 = nn.batch_norm(%117, %118, %119, %120, %121) /* ty=(Tensor[(1, 256, 14, 14), float16], Tensor[(256), float16], Tensor[(256), float16]) */;
%123 = cast(%layer3.0.downsample.0.weight, dtype="float16") /* ty=Tensor[(256, 128, 1, 1), float16] */;
%124 = nn.conv2d(%104, %123, strides=[2, 2], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], out_dtype="float32") /* ty=Tensor[(1, 256, 14, 14), float32] */;
%125 = cast(%124, dtype="float16") /* ty=Tensor[(1, 256, 14, 14), float16] */;
%126 = cast(%layer3.0.downsample.1.weight, dtype="float16") /* ty=Tensor[(256), float16] */;
%127 = cast(%layer3.0.downsample.1.bias, dtype="float16") /* ty=Tensor[(256), float16] */;
%128 = cast(%layer3.0.downsample.1.running_mean, dtype="float16") /* ty=Tensor[(256), float16] */;
%129 = cast(%layer3.0.downsample.1.running_var, dtype="float16") /* ty=Tensor[(256), float16] */;
%130 = nn.batch_norm(%125, %126, %127, %128, %129) /* ty=(Tensor[(1, 256, 14, 14), float16], Tensor[(256), float16], Tensor[(256), float16]) */;
%131 = %122.0 /* ty=Tensor[(1, 256, 14, 14), float16] */;
%132 = %130.0 /* ty=Tensor[(1, 256, 14, 14), float16] */;
%133 = add(%131, %132) /* ty=Tensor[(1, 256, 14, 14), float16] */;
%134 = nn.relu(%133) /* ty=Tensor[(1, 256, 14, 14), float16] */;
%135 = cast(%layer3.1.conv1.weight, dtype="float16") /* ty=Tensor[(256, 256, 3, 3), float16] */;
%136 = nn.conv2d(%134, %135, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 256, 14, 14), float32] */;
%137 = cast(%136, dtype="float16") /* ty=Tensor[(1, 256, 14, 14), float16] */;
%138 = cast(%layer3.1.bn1.weight, dtype="float16") /* ty=Tensor[(256), float16] */;
%139 = cast(%layer3.1.bn1.bias, dtype="float16") /* ty=Tensor[(256), float16] */;
%140 = cast(%layer3.1.bn1.running_mean, dtype="float16") /* ty=Tensor[(256), float16] */;
%141 = cast(%layer3.1.bn1.running_var, dtype="float16") /* ty=Tensor[(256), float16] */;
%142 = nn.batch_norm(%137, %138, %139, %140, %141) /* ty=(Tensor[(1, 256, 14, 14), float16], Tensor[(256), float16], Tensor[(256), float16]) */;
%143 = %142.0 /* ty=Tensor[(1, 256, 14, 14), float16] */;
%144 = nn.relu(%143) /* ty=Tensor[(1, 256, 14, 14), float16] */;
%145 = cast(%layer3.1.conv2.weight, dtype="float16") /* ty=Tensor[(256, 256, 3, 3), float16] */;
%146 = nn.conv2d(%144, %145, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 256, 14, 14), float32] */;
%147 = cast(%146, dtype="float16") /* ty=Tensor[(1, 256, 14, 14), float16] */;
%148 = cast(%layer3.1.bn2.weight, dtype="float16") /* ty=Tensor[(256), float16] */;
%149 = cast(%layer3.1.bn2.bias, dtype="float16") /* ty=Tensor[(256), float16] */;
%150 = cast(%layer3.1.bn2.running_mean, dtype="float16") /* ty=Tensor[(256), float16] */;
%151 = cast(%layer3.1.bn2.running_var, dtype="float16") /* ty=Tensor[(256), float16] */;
%152 = nn.batch_norm(%147, %148, %149, %150, %151) /* ty=(Tensor[(1, 256, 14, 14), float16], Tensor[(256), float16], Tensor[(256), float16]) */;
%153 = %152.0 /* ty=Tensor[(1, 256, 14, 14), float16] */;
%154 = add(%153, %134) /* ty=Tensor[(1, 256, 14, 14), float16] */;
%155 = nn.relu(%154) /* ty=Tensor[(1, 256, 14, 14), float16] */;
%156 = cast(%layer4.0.conv1.weight, dtype="float16") /* ty=Tensor[(512, 256, 3, 3), float16] */;
%157 = nn.conv2d(%155, %156, strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 512, 7, 7), float32] */;
%158 = cast(%157, dtype="float16") /* ty=Tensor[(1, 512, 7, 7), float16] */;
%159 = cast(%layer4.0.bn1.weight, dtype="float16") /* ty=Tensor[(512), float16] */;
%160 = cast(%layer4.0.bn1.bias, dtype="float16") /* ty=Tensor[(512), float16] */;
%161 = cast(%layer4.0.bn1.running_mean, dtype="float16") /* ty=Tensor[(512), float16] */;
%162 = cast(%layer4.0.bn1.running_var, dtype="float16") /* ty=Tensor[(512), float16] */;
%163 = nn.batch_norm(%158, %159, %160, %161, %162) /* ty=(Tensor[(1, 512, 7, 7), float16], Tensor[(512), float16], Tensor[(512), float16]) */;
%164 = %163.0 /* ty=Tensor[(1, 512, 7, 7), float16] */;
%165 = nn.relu(%164) /* ty=Tensor[(1, 512, 7, 7), float16] */;
%166 = cast(%layer4.0.conv2.weight, dtype="float16") /* ty=Tensor[(512, 512, 3, 3), float16] */;
%167 = nn.conv2d(%165, %166, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 512, 7, 7), float32] */;
%168 = cast(%167, dtype="float16") /* ty=Tensor[(1, 512, 7, 7), float16] */;
%169 = cast(%layer4.0.bn2.weight, dtype="float16") /* ty=Tensor[(512), float16] */;
%170 = cast(%layer4.0.bn2.bias, dtype="float16") /* ty=Tensor[(512), float16] */;
%171 = cast(%layer4.0.bn2.running_mean, dtype="float16") /* ty=Tensor[(512), float16] */;
%172 = cast(%layer4.0.bn2.running_var, dtype="float16") /* ty=Tensor[(512), float16] */;
%173 = nn.batch_norm(%168, %169, %170, %171, %172) /* ty=(Tensor[(1, 512, 7, 7), float16], Tensor[(512), float16], Tensor[(512), float16]) */;
%174 = cast(%layer4.0.downsample.0.weight, dtype="float16") /* ty=Tensor[(512, 256, 1, 1), float16] */;
%175 = nn.conv2d(%155, %174, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], out_dtype="float32") /* ty=Tensor[(1, 512, 7, 7), float32] */;
%176 = cast(%175, dtype="float16") /* ty=Tensor[(1, 512, 7, 7), float16] */;
%177 = cast(%layer4.0.downsample.1.weight, dtype="float16") /* ty=Tensor[(512), float16] */;
%178 = cast(%layer4.0.downsample.1.bias, dtype="float16") /* ty=Tensor[(512), float16] */;
%179 = cast(%layer4.0.downsample.1.running_mean, dtype="float16") /* ty=Tensor[(512), float16] */;
%180 = cast(%layer4.0.downsample.1.running_var, dtype="float16") /* ty=Tensor[(512), float16] */;
%181 = nn.batch_norm(%176, %177, %178, %179, %180) /* ty=(Tensor[(1, 512, 7, 7), float16], Tensor[(512), float16], Tensor[(512), float16]) */;
%182 = %173.0 /* ty=Tensor[(1, 512, 7, 7), float16] */;
%183 = %181.0 /* ty=Tensor[(1, 512, 7, 7), float16] */;
%184 = add(%182, %183) /* ty=Tensor[(1, 512, 7, 7), float16] */;
%185 = nn.relu(%184) /* ty=Tensor[(1, 512, 7, 7), float16] */;
%186 = cast(%layer4.1.conv1.weight, dtype="float16") /* ty=Tensor[(512, 512, 3, 3), float16] */;
%187 = nn.conv2d(%185, %186, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 512, 7, 7), float32] */;
%188 = cast(%187, dtype="float16") /* ty=Tensor[(1, 512, 7, 7), float16] */;
%189 = cast(%layer4.1.bn1.weight, dtype="float16") /* ty=Tensor[(512), float16] */;
%190 = cast(%layer4.1.bn1.bias, dtype="float16") /* ty=Tensor[(512), float16] */;
%191 = cast(%layer4.1.bn1.running_mean, dtype="float16") /* ty=Tensor[(512), float16] */;
%192 = cast(%layer4.1.bn1.running_var, dtype="float16") /* ty=Tensor[(512), float16] */;
%193 = nn.batch_norm(%188, %189, %190, %191, %192) /* ty=(Tensor[(1, 512, 7, 7), float16], Tensor[(512), float16], Tensor[(512), float16]) */;
%194 = %193.0 /* ty=Tensor[(1, 512, 7, 7), float16] */;
%195 = nn.relu(%194) /* ty=Tensor[(1, 512, 7, 7), float16] */;
%196 = cast(%layer4.1.conv2.weight, dtype="float16") /* ty=Tensor[(512, 512, 3, 3), float16] */;
%197 = nn.conv2d(%195, %196, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], out_dtype="float32") /* ty=Tensor[(1, 512, 7, 7), float32] */;
%198 = cast(%197, dtype="float16") /* ty=Tensor[(1, 512, 7, 7), float16] */;
%199 = cast(%layer4.1.bn2.weight, dtype="float16") /* ty=Tensor[(512), float16] */;
%200 = cast(%layer4.1.bn2.bias, dtype="float16") /* ty=Tensor[(512), float16] */;
%201 = cast(%layer4.1.bn2.running_mean, dtype="float16") /* ty=Tensor[(512), float16] */;
%202 = cast(%layer4.1.bn2.running_var, dtype="float16") /* ty=Tensor[(512), float16] */;
%203 = nn.batch_norm(%198, %199, %200, %201, %202) /* ty=(Tensor[(1, 512, 7, 7), float16], Tensor[(512), float16], Tensor[(512), float16]) */;
%204 = %203.0 /* ty=Tensor[(1, 512, 7, 7), float16] */;
%205 = add(%204, %185) /* ty=Tensor[(1, 512, 7, 7), float16] */;
%206 = nn.relu(%205) /* ty=Tensor[(1, 512, 7, 7), float16] */;
%207 = cast(%206, dtype="float32") /* ty=Tensor[(1, 512, 7, 7), float32] */;
%208 = nn.adaptive_avg_pool2d(%207, output_size=[1, 1]) /* ty=Tensor[(1, 512, 1, 1), float32] */;
%209 = reshape(%208, newshape=[0, -1, 1, 1]) /* ty=Tensor[(1, 512, 1, 1), float32] */;
%210 = squeeze(%209, axis=[2, 3]) /* ty=Tensor[(1, 512), float32] */;
%211 = cast(%210, dtype="float16") /* ty=Tensor[(1, 512), float16] */;
%212 = cast(%fc.weight, dtype="float16") /* ty=Tensor[(1000, 512), float16] */;
%213 = nn.dense(%211, %212, units=None, out_dtype="float32") /* ty=Tensor[(1, 1000), float32] */;
%214 = cast(%213, dtype="float16") /* ty=Tensor[(1, 1000), float16] */;
%215 = cast(%fc.bias, dtype="float16") /* ty=Tensor[(1000), float16] */;
nn.bias_add(%214, %215, axis=-1) /* ty=Tensor[(1, 1000), float16] */
}
As you can see in the IR, the architecture now contains cast operations, which are needed to convert to FP16 precision. You can also use “float16” or “float32” precisions as other dtype options.
Compile the model with relay¶
Specify Adreno target before compiling to generate texture
leveraging kernels and get all the benefits of textures
Note: This generated example running on our x86 server for demonstration.
If running it on the Android device, we need to
specify its instruction set. Set local_demo to False if you want
to run this tutorial with a real device.
local_demo = True
# by default on CPU target will execute.
# select 'cpu', 'opencl' and 'vulkan'
test_target = "cpu"
# Change target configuration.
# Run `adb shell cat /proc/cpuinfo` to find the arch.
arch = "arm64"
target = tvm.target.Target("llvm -mtriple=%s-linux-android" % arch)
if local_demo:
target = tvm.target.Target("llvm")
elif test_target == "opencl":
target = tvm.target.Target("opencl", host=target)
elif test_target == "vulkan":
target = tvm.target.Target("vulkan", host=target)
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target=target, params=params)
Deploy the Model Remotely by RPC¶
Using RPC you can deploy the model from host machine to the remote Adreno device
rpc_tracker_host = os.environ.get("TVM_TRACKER_HOST", "127.0.0.1")
rpc_tracker_port = int(os.environ.get("TVM_TRACKER_PORT", 9190))
key = "android"
if local_demo:
remote = rpc.LocalSession()
else:
tracker = rpc.connect_tracker(rpc_tracker_host, rpc_tracker_port)
# When running a heavy model, we should increase the `session_timeout`
remote = tracker.request(key, priority=0, session_timeout=60)
if local_demo:
dev = remote.cpu(0)
elif test_target == "opencl":
dev = remote.cl(0)
elif test_target == "vulkan":
dev = remote.vulkan(0)
else:
dev = remote.cpu(0)
temp = utils.tempdir()
dso_binary = "dev_lib_cl.so"
dso_binary_path = temp.relpath(dso_binary)
fcompile = ndk.create_shared if not local_demo else None
lib.export_library(dso_binary_path, fcompile)
remote_path = "/data/local/tmp/" + dso_binary
remote.upload(dso_binary_path)
rlib = remote.load_module(dso_binary)
m = graph_executor.GraphModule(rlib["default"](dev))
Run inference¶
We now can set inputs, infer our model and get predictions as output
m.set_input(input_name, tvm.nd.array(img.astype("float32")))
m.run()
tvm_output = m.get_output(0)
Get predictions and performance statistic¶
This piece of code displays the top-1 and top-5 predictions, as well as provides information about the model’s performance
from os.path import join, isfile
from matplotlib import pyplot as plt
from tvm.contrib import download
# Download ImageNet categories
categ_url = "https://github.com/uwsampl/web-data/raw/main/vta/models/"
categ_fn = "synset.txt"
download.download(join(categ_url, categ_fn), categ_fn)
synset = eval(open(categ_fn).read())
top_categories = np.argsort(tvm_output.asnumpy()[0])
top5 = np.flip(top_categories, axis=0)[:5]
# Report top-1 classification result
print("Top-1 id: {}, class name: {}".format(top5[1 - 1], synset[top5[1 - 1]]))
# Report top-5 classification results
print("\nTop5 predictions: \n")
print("\t#1:", synset[top5[1 - 1]])
print("\t#2:", synset[top5[2 - 1]])
print("\t#3:", synset[top5[3 - 1]])
print("\t#4:", synset[top5[4 - 1]])
print("\t#5:", synset[top5[5 - 1]])
print("\t", top5)
ImageNetClassifier = False
for k in top_categories[-5:]:
if "cat" in synset[k]:
ImageNetClassifier = True
assert ImageNetClassifier, "Failed ImageNet classifier validation check"
print("Evaluate inference time cost...")
print(m.benchmark(dev, number=1, repeat=10))
/workspace/python/tvm/runtime/ndarray.py:200: DeprecationWarning: NDArray.asnumpy() will be deprecated in TVM v0.8 release. Please use NDArray.numpy() instead.
DeprecationWarning,
Top-1 id: 281, class name: tabby, tabby cat
Top5 predictions:
#1: tabby, tabby cat
#2: tiger cat
#3: lynx, catamount
#4: red fox, Vulpes vulpes
#5: Egyptian cat
[281 282 287 277 285]
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
2523.3301 2520.1300 2548.5365 2519.0336 8.5041