islavutin commented on issue #9242:
URL: https://github.com/apache/tvm/issues/9242#issuecomment-982660399
I slightly modified your script and getting very much close output by
running on CPU and on Adreno GPU
```
maximum element difference: 5.4836273193359375e-06, l2 diff:
4.9437097914051265e-05
+-------------+--------------------------------+----------+-------------+
| TVM Results | Label | Class ID | Probability |
+-------------+--------------------------------+----------+-------------+
| Top1 | tiger cat | 282 | 835.40 |
| Top2 | Egyptian cat | 285 | 738.12 |
| Top3 | tabby, tabby cat | 281 | 726.89 |
| Top4 | kit fox, Vulpes macrotis | 278 | 601.60 |
| Top5 | Pembroke, Pembroke Welsh corgi | 263 | 598.17 |
+-------------+--------------------------------+----------+-------------+
+-------------------+--------------------------------+----------+-------------+
| TVM Results (ref) | Label | Class ID |
Probability |
+-------------------+--------------------------------+----------+-------------+
| Top1 | tiger cat | 282 | 835.40
|
| Top2 | Egyptian cat | 285 | 738.12
|
| Top3 | tabby, tabby cat | 281 | 726.89
|
| Top4 | kit fox, Vulpes macrotis | 278 | 601.60
|
| Top5 | Pembroke, Pembroke Welsh corgi | 263 | 598.17
|
+-------------------+--------------------------------+----------+-------------+
```
Here is the modified code:
```
import os
import cv2
import numpy as np
import tvm
from mxnet.gluon.model_zoo.vision import get_model
from prettytable import PrettyTable
from tvm import auto_scheduler, rpc
from tvm import relay
from tvm.contrib import utils, ndk, graph_executor
from tvm.contrib.download import download_testdata
def preprocess(img_data):
mean_vec = np.array([0.485, 0.456, 0.406])
stddev_vec = np.array([0.229, 0.224, 0.225])
norm_img_data = np.zeros(img_data.shape).astype('float32')
for i in range(img_data.shape[0]):
# for each pixel in each channel, divide the value by 255 to get
value between [0, 1] and then normalize
norm_img_data[i, :, :] = (img_data[i, :, :] / 255.0 - mean_vec[i]) /
stddev_vec[i]
return norm_img_data
def get_img(batch=1):
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 = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (224, 224))
in_data = np.asarray(img[:, :])
# hwc to chw
in_data = in_data.transpose((2, 0, 1))
shape = in_data.shape
in_data = preprocess(in_data)
in_data = np.broadcast_to(in_data.astype("float32"), shape=(batch,
*shape))
return in_data
# Download class labels (from a separate file)
def load_class_labels():
synset_url = "".join(
[
"https://gist.githubusercontent.com/zhreshold/";,
"4d0b62f3d01426887599d4f7ede23ee5/raw/",
"596b27d23537e5a1b5751d2b0481ef172f58b539/",
"imagenet1000_clsid_to_human.txt",
]
)
synset_name = "imagenet1000_clsid_to_human.txt"
synset_path = download_testdata(synset_url, synset_name, module="data")
with open(synset_path) as f:
synset = eval(f.read())
return synset
if __name__ == "__main__":
block = get_model("mobilenet1.0", pretrained=True)
shape_dict = {"data": (1, 3, 224, 224)}
mod, params = relay.frontend.from_mxnet(block, shape_dict)
tracker_host = "0.0.0.0"
tracker_port = 9191
key = 'Android'
os.environ[
"TVM_NDK_CC"] =
'/home/iliya/Android/Sdk/ndk/22.0.6917172/android-toolchain-arm64/bin/aarch64-linux-android-g++'
output_file = 'auto_scheduled_model_mxnet.json'
target = 'opencl -device=mali'
target_host = 'llvm -mtriple=arm64-linux-android'
tasks, task_weights = tvm.auto_scheduler.extract_tasks(mod['main'],
params, target, target_host)
tuner = tvm.auto_scheduler.TaskScheduler(tasks, task_weights)
builder = tvm.auto_scheduler.LocalBuilder(build_func='ndk')
runner = tvm.auto_scheduler.RPCRunner(key=key, host=tracker_host,
port=tracker_port, priority=0, number=3, repeat=1)
tune_options = tvm.auto_scheduler.TuningOptions(num_measure_trials=35,
builder=builder,
runner=runner,
measure_callbacks=[tvm.auto_scheduler.RecordToFile(output_file)])
tuner.tune(tune_options)
with tvm.auto_scheduler.ApplyHistoryBest(output_file):
with tvm.transform.PassContext(opt_level=3,
config={"relay.backend.use_auto_scheduler": True}):
lib = relay.build(mod, target=target, target_host=target_host,
params=params)
tracker = rpc.connect_tracker(tracker_host, tracker_port)
remote = tracker.request(key, priority=0, session_timeout=0)
device = remote.cl(0)
tmp = utils.tempdir()
lib_fname = tmp.relpath(f"net_gpu.so")
fcompile = ndk.create_shared
lib.export_library(lib_fname, fcompile)
remote.upload(lib_fname)
exported_lib = remote.load_module(f"net_gpu.so")
module = graph_executor.GraphModule(exported_lib["default"](device))
local_device_ref = tvm.device(str(tvm.target.Target('llvm')), 0)
with tvm.transform.PassContext(opt_level=3):
ref_lib = relay.build(mod, target=tvm.target.Target('llvm
-mcpu=core-avx2'), params=params)
unoptimized_module =
graph_executor.GraphModule(ref_lib["default"](local_device_ref))
dummy_input = {'data': get_img().astype("float32")}
tvm_dummy = {key: tvm.nd.array(input) for key, input in
dummy_input.items()}
module.set_input(**tvm_dummy)
module.run()
module_output = module.get_output(0).asnumpy()[0]
unoptimized_module.set_input(**tvm_dummy)
unoptimized_module.run()
unoptimized_module_output = unoptimized_module.get_output(0).asnumpy()[0]
diff = np.abs(module_output - unoptimized_module_output)
print(f'maximum element difference: {np.amax(diff)}, l2 diff:
{np.linalg.norm(diff)}')
# cut last 5 values after argsort and reverse
ref_res5 = unoptimized_module_output.argsort()[-5:][::-1]
res5 = module_output.argsort()[-5:][::-1]
class_labels = load_class_labels()
t = PrettyTable(['TVM Results', 'Label', 'Class ID', 'Probability'],
float_format=".2")
for i, idx in enumerate(res5):
t.add_row([f"Top{i + 1}", class_labels[idx], idx, module_output[idx]
* 100])
print(t)
t = PrettyTable(['TVM Results (ref)', 'Label', 'Class ID',
'Probability'], float_format=".2")
for i, idx in enumerate(ref_res5):
t.add_row([f"Top{i + 1}", class_labels[idx], idx,
unoptimized_module_output[idx] * 100])
print(t)
```
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