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Fix ctx_list
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Wang committed Jan 28, 2019
1 parent af874ad commit 3907c10
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Showing 2 changed files with 75 additions and 71 deletions.
82 changes: 42 additions & 40 deletions nnvm/tests/python/compiler/test_top_level4.py
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Original file line number Diff line number Diff line change
Expand Up @@ -528,14 +528,13 @@ def verify_multibox_prior(dshape, sizes=(1,), ratios=(1,), steps=(-1, -1),
if clip:
np_out = np.clip(np_out, 0, 1)

target = "llvm"
ctx = tvm.cpu()
graph, lib, _ = nnvm.compiler.build(out, target, {"data": dshape})
m = graph_runtime.create(graph, lib, ctx)
m.set_input("data", np.random.uniform(size=dshape).astype(dtype))
m.run()
out = m.get_output(0, tvm.nd.empty(np_out.shape, dtype))
tvm.testing.assert_allclose(out.asnumpy(), np_out, atol=1e-5, rtol=1e-5)
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(out, target, {"data": dshape})
m = graph_runtime.create(graph, lib, ctx)
m.set_input("data", np.random.uniform(size=dshape).astype(dtype))
m.run()
out = m.get_output(0, tvm.nd.empty(np_out.shape, dtype))
tvm.testing.assert_allclose(out.asnumpy(), np_out, atol=1e-5, rtol=1e-5)

def test_multibox_prior():
verify_multibox_prior((1, 3, 50, 50))
Expand All @@ -562,17 +561,18 @@ def test_multibox_transform_loc():
[0, 0.44999999, 1, 1, 1, 1],
[0, 0.30000001, 0, 0, 0.22903419, 0.20435292]]])

target = "llvm"
dtype = "float32"
ctx = tvm.cpu()
graph, lib, _ = nnvm.compiler.build(out, target, {"cls_prob": (batch_size, num_anchors, num_classes),
"loc_preds": (batch_size, num_anchors * 4),
"anchors": (1, num_anchors, 4)})
m = graph_runtime.create(graph, lib, ctx)
m.set_input(**{"cls_prob": np_cls_prob.astype(dtype), "loc_preds": np_loc_preds.astype(dtype), "anchors": np_anchors.astype(dtype)})
m.run()
out = m.get_output(0, tvm.nd.empty(expected_np_out.shape, dtype))
tvm.testing.assert_allclose(out.asnumpy(), expected_np_out, atol=1e-5, rtol=1e-5)
for target, ctx in ctx_list():
if target == "cuda":
continue
graph, lib, _ = nnvm.compiler.build(out, target, {"cls_prob": (batch_size, num_anchors, num_classes),
"loc_preds": (batch_size, num_anchors * 4),
"anchors": (1, num_anchors, 4)})
m = graph_runtime.create(graph, lib, ctx)
m.set_input(**{"cls_prob": np_cls_prob.astype(dtype), "loc_preds": np_loc_preds.astype(dtype), "anchors": np_anchors.astype(dtype)})
m.run()
out = m.get_output(0, tvm.nd.empty(expected_np_out.shape, dtype))
tvm.testing.assert_allclose(out.asnumpy(), expected_np_out, atol=1e-5, rtol=1e-5)

def verify_get_valid_counts(dshape, score_threshold):
dtype = "float32"
Expand All @@ -594,19 +594,20 @@ def verify_get_valid_counts(dshape, score_threshold):
for k in range(elem_length):
np_out2[i, j, k] = -1

target = "llvm"
ctx = tvm.cpu()
data = sym.Variable("data", dtype=dtype)
valid_counts, inter_data = sym.get_valid_counts(data, score_threshold=score_threshold)
out = sym.Group([valid_counts, inter_data])
graph, lib, _ = nnvm.compiler.build(out, target, {"data": dshape})
m = graph_runtime.create(graph, lib, ctx)
m.set_input("data", np_data)
m.run()
out1 = m.get_output(0, tvm.nd.empty(np_out1.shape, "int32"))
out2 = m.get_output(1, tvm.nd.empty(dshape, dtype))
tvm.testing.assert_allclose(out1.asnumpy(), np_out1, rtol=1e-3)
tvm.testing.assert_allclose(out2.asnumpy(), np_out2, rtol=1e-3)
for target, ctx in ctx_list():
if target == "cuda":
continue
data = sym.Variable("data", dtype=dtype)
valid_counts, inter_data = sym.get_valid_counts(data, score_threshold=score_threshold)
out = sym.Group([valid_counts, inter_data])
graph, lib, _ = nnvm.compiler.build(out, target, {"data": dshape})
m = graph_runtime.create(graph, lib, ctx)
m.set_input("data", np_data)
m.run()
out1 = m.get_output(0, tvm.nd.empty(np_out1.shape, "int32"))
out2 = m.get_output(1, tvm.nd.empty(dshape, dtype))
tvm.testing.assert_allclose(out1.asnumpy(), np_out1, rtol=1e-3)
tvm.testing.assert_allclose(out2.asnumpy(), np_out2, rtol=1e-3)


def test_get_valid_counts():
Expand All @@ -633,15 +634,16 @@ def test_nms():
[-1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1]]])

target = "llvm"
ctx = tvm.cpu()
graph, lib, _ = nnvm.compiler.build(out, target, {"data": dshape, "valid_count": (dshape[0],)},
dtype={"data": "float32", "valid_count": "int32"})
m = graph_runtime.create(graph, lib, ctx)
m.set_input(**{"data": np_data, "valid_count": np_valid_count})
m.run()
out = m.get_output(0, tvm.nd.empty(np_result.shape, "float32"))
tvm.testing.assert_allclose(out.asnumpy(), np_result, atol=1e-5, rtol=1e-5)
for target, ctx in ctx_list():
if target == "cuda":
continue
graph, lib, _ = nnvm.compiler.build(out, target, {"data": dshape, "valid_count": (dshape[0],)},
dtype={"data": "float32", "valid_count": "int32"})
m = graph_runtime.create(graph, lib, ctx)
m.set_input(**{"data": np_data, "valid_count": np_valid_count})
m.run()
out = m.get_output(0, tvm.nd.empty(np_result.shape, "float32"))
tvm.testing.assert_allclose(out.asnumpy(), np_result, atol=1e-5, rtol=1e-5)

def np_slice_like(np_data, np_shape_like, axis=[]):
begin_idx = [0 for _ in np_data.shape]
Expand Down
64 changes: 33 additions & 31 deletions tutorials/nnvm/deploy_ssd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
from matplotlib import pyplot as plt
from nnvm import compiler
from nnvm.frontend import from_mxnet
from nnvm.testing.config import ctx_list
from tvm import relay
from tvm.contrib import graph_runtime
from gluoncv import model_zoo, data, utils
Expand Down Expand Up @@ -52,8 +53,8 @@
model_name = "ssd_512_resnet50_v1_voc"
dshape = (1, 3, 512, 512)
dtype = "float32"
target = "llvm"
ctx = tvm.cpu()
target_list = ctx_list()
frontend_list = ["nnvm", "relay"]

######################################################################
# Download and pre-process demo image
Expand All @@ -62,45 +63,46 @@
'gluoncv/detection/street_small.jpg?raw=true',
path='street_small.jpg')
x, img = data.transforms.presets.ssd.load_test(im_fname, short=512)
tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)

######################################################################
# Convert and compile model with NNVM or Relay for CPU.

block = model_zoo.get_model(model_name, pretrained=True)

import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--frontend",
help="Frontend for compilation, nnvm or relay",
type=str,
default="nnvm")
args = parser.parse_args()
if args.frontend == "relay":
net, params = relay.frontend.from_mxnet(block, {"data": dshape})
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(net, target, params=params)
elif args.frontend == "nnvm":
net, params = from_mxnet(block)
with compiler.build_config(opt_level=3):
graph, lib, params = compiler.build(
net, target, {"data": dshape}, params=params)
else:
parser.print_help()
parser.exit()
def compile(frontend, target):
if frontend == "relay":
net, params = relay.frontend.from_mxnet(block, {"data": dshape})
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(net, target, params=params)
else:
net, params = from_mxnet(block)
with compiler.build_config(opt_level=3):
graph, lib, params = compiler.build(
net, target, {"data": dshape}, params=params)
return graph, lib, params

######################################################################
# Create TVM runtime and do inference

# Build TVM runtime
m = graph_runtime.create(graph, lib, ctx)
m.set_input('data', tvm_input)
m.set_input(**params)
# execute
m.run()
# get outputs
class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)
def run(graph, lib, params, ctx):
# Build TVM runtime
m = graph_runtime.create(graph, lib, ctx)
tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)
m.set_input('data', tvm_input)
m.set_input(**params)
# execute
m.run()
# get outputs
class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)
return class_IDs, scores, bounding_boxs

for target, ctx in target_list:
if target == "cuda":
print("GPU not supported yet, skip.")
continue
for frontend in frontend_list:
graph, lib, params = compile(frontend, target)
class_IDs, scores, bounding_boxs = run(graph, lib, params, ctx)

######################################################################
# Display result
Expand Down

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