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Merged
masahi merged 6 commits into from
Sep 11, 2020
Merged

[Format] Convert all Python code w/o CI #6448

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473d4a2
Add black setup
jroesch Sep 10, 2020
0c1d8cb
Tweak pyproject.toml
jroesch Sep 11, 2020
266c16b
Fix syntax issues
jroesch Sep 11, 2020
6b39a2e
Fix
jroesch Sep 11, 2020
b6d803a
Tweak
jroesch Sep 11, 2020
761deca
Black all Python code
jroesch Sep 11, 2020
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76 changes: 46 additions & 30 deletions apps/android_camera/models/prepare_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,52 +28,62 @@
from tvm.contrib import util, ndk, graph_runtime as runtime
from tvm.contrib.download import download_testdata, download

target = 'llvm -mtriple=arm64-linux-android'
target = "llvm -mtriple=arm64-linux-android"
target_host = None


def del_dir(target: Union[Path, str], only_if_empty: bool = False):
target = Path(target).expanduser()
assert target.is_dir()
for p in sorted(target.glob('**/*'), reverse=True):
for p in sorted(target.glob("**/*"), reverse=True):
if not p.exists():
continue
p.chmod(0o666)
if p.is_dir():
p.rmdir()
else:
if only_if_empty:
raise RuntimeError(f'{p.parent} is not empty!')
raise RuntimeError(f"{p.parent} is not empty!")
p.unlink()
target.rmdir()


def get_model(model_name, batch_size=1):
if model_name == 'resnet18_v1':
if model_name == "resnet18_v1":
import mxnet as mx
from mxnet import gluon
from mxnet.gluon.model_zoo import vision

gluon_model = vision.get_model(model_name, pretrained=True)
img_size = 224
data_shape = (batch_size, 3, img_size, img_size)
net, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape})
return (net, params)
elif model_name == 'mobilenet_v2':
elif model_name == "mobilenet_v2":
import keras
from keras.applications.mobilenet_v2 import MobileNetV2

keras.backend.clear_session() # Destroys the current TF graph and creates a new one.
weights_url = ''.join(['https://github.com/JonathanCMitchell/',
'mobilenet_v2_keras/releases/download/v1.1/',
'mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.5_224.h5'])
weights_file = 'mobilenet_v2_weights.h5'
weights_path = download_testdata(weights_url, weights_file, module='keras')
keras_mobilenet_v2 = MobileNetV2(alpha=0.5, include_top=True, weights=None,
input_shape=(224, 224, 3), classes=1000)
weights_url = "".join(
[
"https://github.com/JonathanCMitchell/",
"mobilenet_v2_keras/releases/download/v1.1/",
"mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.5_224.h5",
]
)
weights_file = "mobilenet_v2_weights.h5"
weights_path = download_testdata(weights_url, weights_file, module="keras")
keras_mobilenet_v2 = MobileNetV2(
alpha=0.5, include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000
)
keras_mobilenet_v2.load_weights(weights_path)

img_size = 224
data_shape = (batch_size, 3, img_size, img_size)
mod, params = relay.frontend.from_keras(keras_mobilenet_v2, {'input_1': data_shape})
mod, params = relay.frontend.from_keras(keras_mobilenet_v2, {"input_1": data_shape})
return (mod, params)


def main(model_str, output_path):
if output_path.exists():
del_dir(output_path)
Expand All @@ -90,34 +100,40 @@ def main(model_str, output_path):
with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(net, target, target_host=target_host, params=params)
print("dumping lib...")
lib.export_library(output_path_str + '/' + 'deploy_lib_cpu.so', ndk.create_shared)
lib.export_library(output_path_str + "/" + "deploy_lib_cpu.so", ndk.create_shared)
print("dumping graph...")
with open(output_path_str + '/' + 'deploy_graph.json', 'w') as f:
with open(output_path_str + "/" + "deploy_graph.json", "w") as f:
f.write(graph)
print("dumping params...")
with open(output_path_str + '/' + 'deploy_param.params', 'wb') as f:
with open(output_path_str + "/" + "deploy_param.params", "wb") as f:
f.write(relay.save_param_dict(params))
print("dumping labels...")
synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
'4d0b62f3d01426887599d4f7ede23ee5/raw/',
'596b27d23537e5a1b5751d2b0481ef172f58b539/',
'imagenet1000_clsid_to_human.txt'])
synset_path = output_path_str + '/image_net_labels'
download(synset_url, output_path_str + '/image_net_labels')
synset_url = "".join(
[
"https://gist.githubusercontent.com/zhreshold/",
"4d0b62f3d01426887599d4f7ede23ee5/raw/",
"596b27d23537e5a1b5751d2b0481ef172f58b539/",
"imagenet1000_clsid_to_human.txt",
]
)
synset_path = output_path_str + "/image_net_labels"
download(synset_url, output_path_str + "/image_net_labels")
with open(synset_path) as fi:
synset = eval(fi.read())
with open(output_path_str + '/image_net_labels.json', "w") as fo:
with open(output_path_str + "/image_net_labels.json", "w") as fo:
json.dump(synset, fo, indent=4)
os.remove(synset_path)

if __name__ == '__main__':
if environ.get('TVM_NDK_CC') is None:

if __name__ == "__main__":
if environ.get("TVM_NDK_CC") is None:
raise RuntimeError("Require environment variable TVM_NDK_CC")
models_path = Path().absolute().parent.joinpath('app/src/main/assets/models/')
models_path = Path().absolute().parent.joinpath("app/src/main/assets/models/")
if not models_path.exists():
models_path.mkdir()
models = {'mobilenet_v2': models_path.joinpath('mobilenet_v2'),
'resnet18_v1': models_path.joinpath('resnet18_v1')
}
models = {
"mobilenet_v2": models_path.joinpath("mobilenet_v2"),
"resnet18_v1": models_path.joinpath("resnet18_v1"),
}
for model, output_path in models.items():
main(model, output_path)
20 changes: 10 additions & 10 deletions apps/android_rpc/tests/android_rpc_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,18 +43,18 @@
# whether enable to execute test on Vulkan target
test_vulkan = False


def test_rpc_module():
# graph
n = tvm.runtime.convert(1024)
A = te.placeholder((n,), name='A')
B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
A = te.placeholder((n,), name="A")
B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name="B")
a_np = np.random.uniform(size=1024).astype(A.dtype)
temp = util.tempdir()

# Establish remote connection with target hardware
tracker = rpc.connect_tracker(tracker_host, tracker_port)
remote = tracker.request(key, priority=0,
session_timeout=60)
remote = tracker.request(key, priority=0, session_timeout=60)

# Compile the Graph for CPU target
s = te.create_schedule(B.op)
Expand All @@ -67,15 +67,15 @@ def test_rpc_module():
f.export_library(path_dso_cpu, ndk.create_shared)

# Execute the portable graph on cpu target
print('Run CPU test ...')
print("Run CPU test ...")
ctx = remote.cpu(0)
remote.upload(path_dso_cpu)
f2 = remote.load_module("cpu_lib.so")
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f2.time_evaluator(f2.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op\n' % cost)
print("%g secs/op\n" % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)

# Compile the Graph for OpenCL target
Expand All @@ -90,15 +90,15 @@ def test_rpc_module():
path_dso_cl = temp.relpath("dev_lib_cl.so")
f.export_library(path_dso_cl, ndk.create_shared)

print('Run GPU(OpenCL Flavor) test ...')
print("Run GPU(OpenCL Flavor) test ...")
ctx = remote.cl(0)
remote.upload(path_dso_cl)
f1 = remote.load_module("dev_lib_cl.so")
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op\n' % cost)
print("%g secs/op\n" % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)

# Compile the Graph for Vulkan target
Expand All @@ -113,15 +113,15 @@ def test_rpc_module():
path_dso_vulkan = temp.relpath("dev_lib_vulkan.so")
f.export_library(path_dso_vulkan, ndk.create_shared)

print('Run GPU(Vulkan Flavor) test ...')
print("Run GPU(Vulkan Flavor) test ...")
ctx = remote.vulkan(0)
remote.upload(path_dso_vulkan)
f1 = remote.load_module("dev_lib_vulkan.so")
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op\n' % cost)
print("%g secs/op\n" % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)


Expand Down
54 changes: 35 additions & 19 deletions apps/benchmark/arm_cpu_imagenet_bench.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -40,12 +40,12 @@ def evaluate_network(network, target, target_host, repeat):

print_progress("%-20s building..." % network)
with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(
net, target=target, target_host=target_host, params=params)
graph, lib, params = relay.build(net, target=target, target_host=target_host, params=params)

tmp = tempdir()
if 'android' in str(target):
if "android" in str(target):
from tvm.contrib import ndk

filename = "%s.so" % network
lib.export_library(tmp.relpath(filename), ndk.create_shared)
else:
Expand All @@ -60,38 +60,55 @@ def evaluate_network(network, target, target_host, repeat):
rlib = remote.load_module(filename)
module = runtime.create(graph, rlib, ctx)
data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
module.set_input('data', data_tvm)
module.set_input("data", data_tvm)
module.set_input(**params)

# evaluate
print_progress("%-20s evaluating..." % network)
ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=repeat)
prof_res = np.array(ftimer().results) * 1000 # multiply 1000 for converting to millisecond
print("%-20s %-19s (%s)" % (network, "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res)))
print(
"%-20s %-19s (%s)" % (network, "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res))
)


if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--network", type=str, choices=
['resnet-18', 'resnet-34', 'resnet-50',
'vgg-16', 'vgg-19', 'densenet-121', 'inception_v3',
'mobilenet', 'squeezenet_v1.0', 'squeezenet_v1.1'],
help='The name of neural network')
parser.add_argument("--model", type=str, choices=
['rk3399', 'mate10', 'mate10pro', 'p20', 'p20pro',
'pixel2', 'rasp3b', 'pynq'], default='rk3399',
help="The model of the test device. If your device is not listed in "
"the choices list, pick the most similar one as argument.")
parser.add_argument("--host", type=str, default='localhost')
parser.add_argument(
"--network",
type=str,
choices=[
"resnet-18",
"resnet-34",
"resnet-50",
"vgg-16",
"vgg-19",
"densenet-121",
"inception_v3",
"mobilenet",
"squeezenet_v1.0",
"squeezenet_v1.1",
],
help="The name of neural network",
)
parser.add_argument(
"--model",
type=str,
choices=["rk3399", "mate10", "mate10pro", "p20", "p20pro", "pixel2", "rasp3b", "pynq"],
default="rk3399",
help="The model of the test device. If your device is not listed in "
"the choices list, pick the most similar one as argument.",
)
parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--port", type=int, default=9190)
parser.add_argument("--rpc-key", type=str, required=True)
parser.add_argument("--repeat", type=int, default=10)
args = parser.parse_args()

dtype = 'float32'
dtype = "float32"

if args.network is None:
networks = ['squeezenet_v1.1', 'mobilenet', 'resnet-18', 'vgg-16']
networks = ["squeezenet_v1.1", "mobilenet", "resnet-18", "vgg-16"]
else:
networks = [args.network]

Expand All @@ -103,4 +120,3 @@ def evaluate_network(network, target, target_host, repeat):
print("--------------------------------------------------")
for network in networks:
evaluate_network(network, target, target_host, args.repeat)

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