Skip to content

Fix trt Test #7016

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
anijain2305 merged 6 commits into from
Dec 3, 2020
Merged

Fix trt Test #7016

Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
81e4c7f
Fix trt Test
codeislife99 Dec 2, 2020
8e2ce9a
Fixed stuff
Dec 2, 2020
d719346
Test TRT
Dec 2, 2020
4e160e9
Done
Dec 2, 2020
3104113
fix 0
Dec 2, 2020
24116fd
Trigger Build
Dec 3, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 8 additions & 10 deletions tests/python/contrib/test_tensorrt.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1050,26 +1050,26 @@ def test_tensorrt_dynamic_batch():
batches_to_test = [1, 1, 0, 2, 3, 0, 1, 3, 2]
x_shape = (relay.Any(), 1, 8, 8)
x_data = np.ones([max(batches_to_test)] + list(x_shape)[1:]).astype("float32")
result_dict = {}
result_arr = [{} for _ in range(len(batches_to_test))]
for use_trt in [True, False]:
x = relay.var("x", shape=x_shape, dtype="float32")
out = relay.nn.relu(x)
f = relay.Function([x], out)
mod = tvm.IRModule()
mod["main"] = f
if use_trt:
mod = relay.tensorrt.EnableTrt(mod)
mod, _ = tensorrt.partition_for_tensorrt(mod)

if not skip_runtime_test():
with relay.build_config(opt_level=3):
relay_exec = relay.create_executor("vm", mod=mod, ctx=tvm.cpu(0), target="llvm")

for i, batch_size in enumerate(batches_to_test):
result_dict[(i, use_trt)] = relay_exec.evaluate()(x_data[:batch_size, ...])
result_arr[i][use_trt] = relay_exec.evaluate()(x_data[:batch_size, ...])

if not skip_runtime_test():
for i in range(len(batches_to_test)):
assert_result_matches(result_dict[(i, True)], result_dict[(i, False)])
assert_result_dict_holds(result_arr[i])


def test_tensorrt_dynamic_batch_conv():
Expand All @@ -1080,7 +1080,7 @@ def test_tensorrt_dynamic_batch_conv():
x_data = np.ones([max(batches_to_test)] + list(x_shape)[1:]).astype("float32")
k_shape = (16, 32, 3, 3)
params = {"kernel": np.random.uniform(-1, 1, k_shape).astype("float32")}
result_dict = {}
result_arr = [{} for _ in range(len(batches_to_test))]
for use_trt in [True, False]:
x = relay.var("x", shape=x_shape, dtype="float32")
kernel = relay.var("kernel", shape=k_shape, dtype="float32")
Expand All @@ -1089,20 +1089,18 @@ def test_tensorrt_dynamic_batch_conv():
mod = tvm.IRModule()
mod["main"] = f
if use_trt:
mod = tensorrt.partition_for_tensorrt(mod, params)
mod, _ = tensorrt.partition_for_tensorrt(mod, params)

if not skip_runtime_test():
with relay.build_config(opt_level=3):
relay_exec = relay.create_executor("vm", mod=mod, ctx=tvm.cpu(0), target="llvm")

for i, batch_size in enumerate(batches_to_test):
result_dict[(i, use_trt)] = relay_exec.evaluate()(
x=x_data[:batch_size, ...], **params
)
result_arr[i][use_trt] = relay_exec.evaluate()(x_data[:batch_size, ...], **params)

if not skip_runtime_test():
for i in range(len(batches_to_test)):
assert_result_matches(result_dict[(i, True)], result_dict[(i, False)])
assert_result_dict_holds(result_arr[i])


def test_maskrcnn_resnet50() -> None:
Expand Down