本文整理汇总了Python中tvm.relay.testing.ctx_list函数的典型用法代码示例。如果您正苦于以下问题：Python ctx_list函数的具体用法？Python ctx_list怎么用？Python ctx_list使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了ctx_list函数的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: test_avg_pool2d_no_count_pad
def test_avg_pool2d_no_count_pad():
kh, kw = (4, 4)
sh, sw = (2, 2)
ph, pw = (2, 2)
n = 1
(ic, ih, iw) = (3, 28, 28)
(oc, oh, ow) = (3, 15, 15)
dshape = (n, ic, ih, iw)
x = relay.var("x", shape=dshape)
y = relay.nn.avg_pool2d(x,
pool_size=(kh, kw),
strides=(sw, sw),
padding=(ph, pw),
count_include_pad=False)
func = relay.Function([x], y)
dtype = "float32"
a_np = np.random.uniform(low=0.001, size=(n, ic, ih, iw)).astype(dtype)
pad_np = np.zeros(shape=(n, ic, ih+2*ph, iw+2*pw)).astype(dtype)
no_zero = (range(n), range(ic), (range(ph, ih+ph)), (range(pw, iw+pw)))
pad_np[np.ix_(*no_zero)] = a_np
b_np = np.zeros(shape=(n, oc, oh, ow)).astype(dtype)
for i in range(oh):
for j in range(ow):
pad_count = np.sum(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw] > 0, axis=(2,3))
b_np[:,:,i,j] = np.sum(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw],
axis=(2,3)) / np.maximum(pad_count, 1)
ref_res = np.maximum(b_np, 0.0)
data = a_np
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:33，代码来源:test_op_level2.py
示例2: test_binary_int_broadcast
def test_binary_int_broadcast():
for op, ref in [(relay.right_shift, np.right_shift),
(relay.left_shift, np.left_shift),
(relay.mod, np.mod),
(relay.maximum, np.maximum),
(relay.minimum, np.minimum)]:
x = relay.var("x", relay.TensorType((10, 4), "int32"))
y = relay.var("y", relay.TensorType((5, 10, 1), "int32"))
z = op(x, y)
zz = relay.ir_pass.infer_type(z)
assert zz.checked_type == relay.TensorType((5, 10, 4), "int32")
if ref is not None:
x_shape = (10, 4)
y_shape = (5, 10, 1)
t1 = relay.TensorType(x_shape, 'int32')
t2 = relay.TensorType(y_shape, 'int32')
x_data = np.random.rand(*x_shape).astype(t1.dtype)
y_data = np.random.rand(*y_shape).astype(t2.dtype)
func = relay.Function([x, y], z)
ref_res = ref(x_data, y_data)
for target, ctx in ctx_list():
intrp = relay.create_executor("graph", ctx=ctx, target=target)
op_res = intrp.evaluate(func)(x_data, y_data)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)
开发者ID:bddppq，项目名称:tvm，代码行数:26，代码来源:test_op_level4.py
示例3: test_cmp_type
def test_cmp_type():
for op, ref in ((relay.greater, np.greater),
(relay.greater_equal, np.greater_equal),
(relay.less, np.less),
(relay.less_equal, np.less_equal),
(relay.equal, np.equal),
(relay.not_equal, np.not_equal)):
x = relay.var("x", relay.TensorType((10, 4), "float32"))
y = relay.var("y", relay.TensorType((5, 10, 1), "float32"))
z = op(x, y)
z.astext()
zz = relay.ir_pass.infer_type(z)
assert zz.checked_type == relay.TensorType((5, 10, 4), "bool")
if ref is not None:
x_shape = (10, 4)
y_shape = (5, 10, 1)
t1 = relay.TensorType(x_shape)
t2 = relay.TensorType(y_shape)
x = relay.var("x", t1)
y = relay.var("y", t2)
z = op(x, y)
x_data = np.random.rand(*x_shape).astype(t1.dtype)
y_data = np.random.rand(*y_shape).astype(t2.dtype)
ref_res = ref(x_data, y_data)
func = relay.Function([x, y], z)
for target, ctx in ctx_list():
intrp = relay.create_executor("graph", ctx=ctx, target=target)
op_res = intrp.evaluate(func)(x_data, y_data)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)
开发者ID:bddppq，项目名称:tvm，代码行数:31，代码来源:test_op_level4.py
示例4: check_binary_op
def check_binary_op(opfunc, ref):
n = tvm.var("n")
t1 = relay.TensorType((5, n, 5))
t2 = relay.TensorType((n, 1))
x = relay.var("x", t1)
y = relay.var("y", t2)
z = opfunc(x, y)
# test printer
assert ("{}(%x, %y)".format(z.op.name)) in z.astext()
assert relay.ir_pass.infer_type(z).checked_type == t1
if ref is not None:
t1 = relay.TensorType((5, 10, 5))
t2 = relay.TensorType((5, 10, 5))
x = relay.var("x", t1)
y = relay.var("y", t2)
z = opfunc(x, y)
x_data = np.random.rand(5, 10, 5).astype(t1.dtype)
y_data = np.random.rand(5, 10, 5).astype(t2.dtype)
ref_res = ref(x_data, y_data)
func = relay.Function([x, y], z)
for target, ctx in ctx_list():
intrp = relay.create_executor("graph", ctx=ctx, target=target)
op_res = intrp.evaluate(func)(x_data, y_data)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)
开发者ID:bddppq，项目名称:tvm，代码行数:26，代码来源:test_op_level4.py
示例5: test_flatten_infer_type
def test_flatten_infer_type():
d1, d2, d3, d4 = tvm.var("d1"), tvm.var("d2"), tvm.var("d3"), tvm.var("d4")
x = relay.var("x", relay.TensorType((d1, d2, d3, d4), "float32"))
y = relay.nn.batch_flatten(x)
yy = relay.ir_pass.infer_type(y)
assert yy.checked_type == relay.TensorType((d1, ((d2*d3)*d4)), "float32")
x = relay.var("x", relay.TensorType((3, 2, 4, 3), "float32"))
y = relay.nn.batch_flatten(x)
yy = relay.ir_pass.infer_type(y)
assert yy.checked_type == relay.TensorType((3, 24), "float32")
x = relay.var("x", relay.TensorType((d1, 2, d3, 3), "float32"))
y = relay.nn.batch_flatten(x)
yy = relay.ir_pass.infer_type(y)
assert yy.checked_type == relay.TensorType((d1, ((2*d3)*3)), "float32")
shape = (1, 5, 10, 10)
o_shape = (1, 500)
dtype = "float32"
x = relay.var("x", relay.TensorType(shape, dtype))
z = relay.nn.batch_flatten(x)
yy = relay.ir_pass.infer_type(z)
assert yy.checked_type == relay.TensorType(o_shape, dtype)
func = relay.Function([x], z)
x_data = np.random.uniform(low=-1, high=1, size=shape).astype(dtype)
ref_res = x_data.flatten().reshape(o_shape)
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(x_data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5)
op_res2 = intrp2.evaluate(func)(x_data)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:35，代码来源:test_op_level2.py
示例6: verify_get_valid_counts
def verify_get_valid_counts(dshape, score_threshold):
dtype = "float32"
batch_size, num_anchor, elem_length = dshape
np_data = np.random.uniform(size=dshape).astype(dtype)
np_out1 = np.zeros(shape=(batch_size,))
np_out2 = np.zeros(shape=dshape).astype(dtype)
for i in range(batch_size):
np_out1[i] = 0
inter_idx = 0
for j in range(num_anchor):
score = np_data[i, j, 1]
if score >= score_threshold:
for k in range(elem_length):
np_out2[i, inter_idx, k] = np_data[i, j, k]
np_out1[i] += 1
inter_idx += 1
if j >= np_out1[i]:
for k in range(elem_length):
np_out2[i, j, k] = -1
x = relay.var("x", relay.ty.TensorType(dshape, dtype))
z = relay.vision.get_valid_counts(x, score_threshold)
assert "score_threshold" in z.astext()
func = relay.Function([x], z.astuple())
func = relay.ir_pass.infer_type(func)
for target, ctx in ctx_list():
if target == 'cuda':
return
intrp = relay.create_executor("debug", ctx=ctx, target=target)
out = intrp.evaluate(func)(np_data)
tvm.testing.assert_allclose(out[0].asnumpy(), np_out1, rtol=1e-3, atol=1e-04)
tvm.testing.assert_allclose(out[1].asnumpy(), np_out2, rtol=1e-3, atol=1e-04)
开发者ID:bddppq，项目名称:tvm，代码行数:32，代码来源:test_op_level5.py
示例7: test_infer_type_leaky_relu
def test_infer_type_leaky_relu():
n, c , h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
x = relay.var("x", relay.TensorType((n, c, h, w), "float32"))
y = relay.nn.leaky_relu(x, alpha=0.1)
"alpha=0.1" in y.astext()
yy = relay.ir_pass.infer_type(y)
assert yy.checked_type == relay.TensorType((n, c, h, w), "float32")
shape = (1, 5, 10, 10)
dtype = "float32"
x = relay.var("x", relay.TensorType(shape, dtype))
z = relay.nn.leaky_relu(x, alpha=0.1)
assert "alpha=0.1" in z.astext()
yy = relay.ir_pass.infer_type(z)
assert yy.checked_type == relay.TensorType(shape, dtype)
func = relay.Function([x], z)
x_data = np.random.uniform(low=-1, high=1, size=shape).astype(dtype)
ref_res = np.where(x_data > 0, x_data, x_data * 0.1)
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(x_data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5)
op_res2 = intrp2.evaluate(func)(x_data)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5)
开发者ID:LANHUIYING，项目名称:tvm，代码行数:26，代码来源:test_op_level3.py
示例8: test_pass_run
def test_pass_run():
function_pass = transform
assert pass_name in function_pass.astext()
updated_mod = function_pass(mod)
assert isinstance(updated_mod, relay.Module)
# Check the log function in the updated module.
new_v_log = updated_mod.get_global_var(v_log.name_hint)
new_log = updated_mod[new_v_log]
check_func(new_log, get_ref_log())
# Check the log function in the python transformed function.
ret = opt_tester.transform(log, pass_ctx)
check_func(new_log, ret)
# Execute the add function.
x_nd = get_rand(shape, dtype)
ref_res = np.log(x_nd.asnumpy() * 2)
for target, ctx in ctx_list():
exe1 = relay.create_executor("graph", ctx=ctx, target=target)
exe2 = relay.create_executor("debug", ctx=ctx, target=target)
res1 = exe1.evaluate(new_log)(x_nd)
tvm.testing.assert_allclose(res1.asnumpy(), ref_res, rtol=1e-5)
res2 = exe2.evaluate(new_log)(x_nd)
tvm.testing.assert_allclose(res2.asnumpy(), ref_res, rtol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:26，代码来源:test_pass_manager.py
示例9: check_binary_op
def check_binary_op(opfunc, ref):
# TODO(@jroesch): this piece of code improperly uses type variables.
n = tvm.var("n")
s1 = (5, n, 5)
s2 = (n, 1)
t1 = relay.TensorType(s1)
t2 = relay.TensorType(s2)
x = relay.var("x", t1)
y = relay.var("y", t2)
z = opfunc(x, y)
# test printer
assert ("{}(%x, %y)".format(z.op.name)) in z.astext()
assert relay.ir_pass.infer_type(z).checked_type == t1
if ref is not None:
t1 = relay.TensorType((5, 10, 5))
t2 = relay.TensorType((5, 10, 5))
x = relay.var("x", t1)
y = relay.var("y", t2)
z = opfunc(x, y)
x_data = np.random.rand(5, 10, 5).astype(t1.dtype)
y_data = np.random.rand(5, 10, 5).astype(t2.dtype)
ref_res = ref(x_data, y_data)
func = relay.Function([x, y], z)
for target, ctx in ctx_list():
# use graph by execuor default for testing, as we need
# create function explicitly to avoid constant-folding.
intrp = relay.create_executor("graph", ctx=ctx, target=target)
op_res = intrp.evaluate(func)(x_data, y_data)
np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=0.01)
开发者ID:bddppq，项目名称:tvm，代码行数:31，代码来源:test_op_level1.py
示例10: verify_roi_pool
def verify_roi_pool(data_shape, rois_shape, pooled_size, spatial_scale):
data = relay.var("data", relay.ty.TensorType(data_shape, "float32"))
rois = relay.var("rois", relay.ty.TensorType(rois_shape, "float32"))
z = relay.vision.roi_pool(data, rois, pooled_size=(pooled_size, pooled_size),
spatial_scale=spatial_scale, layout="NCHW")
zz = relay.ir_pass.infer_type(z)
batch, channel, in_size, _ = data_shape
num_roi = rois_shape[0]
assert zz.checked_type == relay.ty.TensorType(
(num_roi, channel, pooled_size, pooled_size), "float32")
func = relay.Function([data, rois], z)
func = relay.ir_pass.infer_type(func)
np_data = np.random.uniform(size=data_shape).astype("float32")
np_rois = np.random.uniform(size=rois_shape).astype('float32') * in_size
np_rois[:, 0] = np.random.randint(low = 0, high = batch, size = num_roi).astype('float32')
ref_res = topi.testing.roi_pool_nchw_python(np_data, np_rois, pooled_size=pooled_size,
spatial_scale=spatial_scale)
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(np_data, np_rois)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-4)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res2 = intrp2.evaluate(func)(np_data, np_rois)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-4)
开发者ID:bddppq，项目名称:tvm，代码行数:26，代码来源:test_op_level5.py
示例11: test_lrn
def test_lrn():
n, c , h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
x = relay.var("x", shape=(n, c , h, w))
y = relay.nn.lrn(x, size=10, axis=2, bias=0.5, alpha=.00001, beta=0.75)
"alpha=" in y.astext()
yy = relay.ir_pass.infer_type(y)
assert yy.checked_type == relay.TensorType((n, c , h, w))
shape = (1, 5, 10, 10)
dtype = "float32"
x = relay.var("x", relay.TensorType(shape, dtype))
size=5
axis=1
bias=0.5
alpha=.00001
beta=0.75
z = relay.nn.lrn(x, size=size, axis=axis, bias=bias, alpha=alpha, beta=beta)
yy = relay.ir_pass.infer_type(z)
assert yy.checked_type == relay.TensorType(shape, dtype)
func = relay.Function([x], z)
x_data = np.random.uniform(low=-1, high=1, size=shape).astype(dtype)
ref_res = topi.testing.lrn_python(x_data, size, axis, bias, alpha, beta)
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(x_data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5)
op_res2 = intrp2.evaluate(func)(x_data)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:30，代码来源:test_op_level2.py
示例12: verify_infer_type_prelu
def verify_infer_type_prelu(data, alpha, axis, output, dtype="float32"):
x = relay.var("data", relay.TensorType(data, dtype))
if alpha:
y = relay.var("alpha", relay.TensorType(alpha, dtype))
else:
y = relay.var("alpha", relay.IncompleteType())
z = relay.nn.prelu(x, y, axis=axis)
zz = relay.ir_pass.infer_type(z)
if axis != 1:
assert "axis" in z.astext()
assert zz.checked_type == relay.ty.TensorType(output, dtype)
if not alpha:
axis = axis if axis else 1
alpha_shape = (data[axis],)
assert zz.args[1].checked_type == relay.TensorType(alpha_shape, "float32")
if all(isinstance(v, tvm.expr.Var) == 1 for v in data) or not alpha:
return
func = relay.Function([x, y], z)
x_data = np.random.uniform(low=-1, high=1, size=data).astype(dtype)
a_data = np.random.uniform(low=-1, high=1, size=alpha).astype(dtype)
if axis == 1:
ref_res = (x_data < 0) * (x_data * a_data.reshape(3, 1, 1)) + (x_data>=0) * x_data
else:
ref_res = (x_data < 0) * (x_data * a_data.reshape(1, 1, 3)) + (x_data>=0) * x_data
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(x_data, a_data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5)
op_res2 = intrp2.evaluate(func)(x_data, a_data)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5)
开发者ID:LANHUIYING，项目名称:tvm，代码行数:35，代码来源:test_op_level3.py
示例13: verify_adaptive_pool2d
def verify_adaptive_pool2d(dshape, out_size, pool_type, layout="NCHW", dtype="float32"):
def start_index(index, odim, idim):
return int(np.floor(index * idim / odim))
def end_index(index, odim, idim):
return int(np.ceil((index + 1) * idim / odim))
np_data = np.random.uniform(low=0, high=255, size=dshape).astype(dtype)
n, c, h, w = dshape
oh, ow = out_size
oshape = (n, c) + out_size
np_out = np.zeros(oshape).astype(dtype)
np_op = np.mean if pool_type == "avg" else np.max
for i in range(n):
for j in range(c):
for k in range(oh):
k_start = start_index(k, oh, h)
k_end = end_index(k, oh, h)
k_sl = slice(k_start, k_end)
for l in range(ow):
l_start = start_index(l, ow, w)
l_end = end_index(l, ow, w)
l_sl = slice(l_start, l_end)
np_out[i, j, k, l] = np_op(np_data[i, j, k_sl, l_sl])
opfunc = relay.contrib.adaptive_avg_pool2d if pool_type == "avg" else relay.contrib.adaptive_max_pool2d
x = relay.var("x", relay.TensorType((n, c, h, w), "float32"))
y = opfunc(x, out_size, layout)
func = relay.Function([x], y)
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
relay_out = intrp1.evaluate(func)(np_data)
tvm.testing.assert_allclose(relay_out.asnumpy(), np_out, rtol=1e-5, atol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:34，代码来源:test_op_level10.py
示例14: run_test_conv2d
def run_test_conv2d(dtype, out_dtype, scale, dshape, kshape,
padding=(1, 1),
fref=None,
groups=1,
dilation=(1, 1),
except_targets=None,
**attrs):
if except_targets is None:
except_targets = []
x = relay.var("x", shape=dshape, dtype=dtype)
w = relay.var("w", dtype=dtype)
y = relay.nn.conv2d(x, w,
padding=padding,
dilation=dilation,
groups=groups,
**attrs)
func = relay.Function([x, w], y)
data = np.random.uniform(-scale, scale, size=dshape).astype(dtype)
kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype)
dkernel = topi.testing.dilate_python(kernel, (1, 1) + dilation)
if fref is None:
ref_res = topi.testing.conv2d_nchw_python(
data.astype(out_dtype), dkernel.astype(out_dtype), 1, padding,
groups=groups)
else:
ref_res = fref(data.astype(out_dtype), dkernel.astype(out_dtype))
for target, ctx in ctx_list():
if target in except_targets:
continue
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(data, kernel)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:35，代码来源:test_op_level2.py
示例15: verify_multibox_prior
def verify_multibox_prior(x, dshape, ref_res, sizes=(1.0,),
ratios=(1.0,), steps=(-1.0, -1.0),
offsets=(0.5, 0.5), clip=True, check_size=False,
check_type_only=False):
z = relay.vision.multibox_prior(x, sizes, ratios, steps, offsets, clip)
zz = relay.ir_pass.infer_type(z)
if check_size:
assert "sizes=" in z.astext()
assert zz.checked_type == relay.TensorType(
(1, dshape[2] * dshape[3] * (len(sizes) + len(ratios) - 1), 4),
"float32")
if check_type_only:
return
data = np.random.uniform(low=-1, high=1, size=dshape).astype("float32")
func = relay.Function([x], z)
func = relay.ir_pass.infer_type(func)
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res2 = intrp2.evaluate(func)(data)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:26，代码来源:test_op_level5.py
示例16: _test_upsampling
def _test_upsampling(layout, method):
n, c, h, w = tvm.var("n"), 16, 32, 32
scale = 2
dtype = "float32"
def get_shape():
if layout == "NCHW":
return (c, h, w), (c, h*scale, w*scale)
else:
return (h, w, c), (h*scale, w*scale, c)
ishape, oshape = get_shape()
x = relay.var("x", relay.TensorType((n,) + ishape, dtype))
y = relay.nn.upsampling(x, scale=scale, layout=layout, method=method)
yy = relay.ir_pass.infer_type(y)
assert yy.checked_type == relay.TensorType((n,) + oshape, dtype)
dshape = (1,) + ishape
x = relay.var("x", shape=dshape)
y = relay.nn.upsampling(x, scale=scale, layout=layout, method=method)
func = relay.Function([x], y)
data = np.random.uniform(size=dshape).astype(dtype)
if method == "NEAREST_NEIGHBOR":
ref = topi.testing.upsampling_python(data, scale, layout)
else:
ref = topi.testing.bilinear_resize_python(data, (h*scale, w*scale), layout)
for target, ctx in ctx_list():
executor = relay.create_executor("graph", ctx=ctx, target=target)
out = executor.evaluate(func)(data)
tvm.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5, atol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:27，代码来源:test_op_level2.py
示例17: test_l2_normalize
def test_l2_normalize():
n, c , h, w = tvm.var("n"), tvm.var("c"), tvm.var("h"), tvm.var("w")
x = relay.var("x", shape=(n, c , h, w))
y = relay.nn.l2_normalize(x, eps=0.001, axis=[1])
"axis=" in y.astext()
yy = relay.ir_pass.infer_type(y)
assert yy.checked_type == relay.TensorType((n, c , h, w))
shape = (1, 5, 10, 10)
dtype = "float32"
x = relay.var("x", relay.TensorType(shape, dtype))
eps=0.001
axis=1
z = relay.nn.l2_normalize(x, eps=0.001, axis=[axis])
yy = relay.ir_pass.infer_type(z)
assert yy.checked_type == relay.TensorType(shape, dtype)
func = relay.Function([x], z)
x_data = np.random.uniform(low=-1, high=1, size=shape).astype(dtype)
ref_res = topi.testing.l2_normalize_python(x_data, eps, axis)
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(x_data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5)
op_res2 = intrp2.evaluate(func)(x_data)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:27，代码来源:test_op_level2.py
示例18: test_run
def test_run(batch, in_channel, size, out_channel, deformable_groups, groups):
kernel_size = (3, 3)
data_shape = (batch, in_channel, size, size)
offset_shape = (batch, 2 * kernel_size[0] * kernel_size[1] * deformable_groups, size, size)
kernel_shape = (out_channel, in_channel // groups, kernel_size[0], kernel_size[1])
dtype = 'float32'
data = relay.var("data", shape=data_shape, dtype=dtype)
offset = relay.var("offset")
kernel = relay.var("kernel")
y = relay.nn.deformable_conv2d(data, offset, kernel,
strides=(1, 1),
padding=(1, 1),
dilation=(1, 1),
kernel_size=kernel_size,
deformable_groups=deformable_groups,
groups=groups,
channels=out_channel)
func = relay.Function([data, offset, kernel], y)
data = np.random.uniform(size=data_shape).astype(dtype)
offset = np.random.uniform(size=offset_shape).astype(dtype)
kernel = np.random.uniform(size=kernel_shape).astype(dtype)
ref_res = topi.testing.deformable_conv2d_nchw_python(data, offset, kernel, stride=(1, 1), padding=(1, 1), dilation=(1, 1), deformable_groups=deformable_groups, groups=groups)
for target, ctx in ctx_list():
for kind in ["graph", "debug"]:
intrp1 = relay.create_executor(kind, ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(data, offset, kernel)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:28，代码来源:test_op_level5.py
示例19: verify_expand_dims
def verify_expand_dims(dshape, dtype, oshape, axis, num_newaxis):
x = relay.Var("x", relay.TensorType(dshape, dtype))
func = relay.Function([x], relay.expand_dims(x, axis, num_newaxis))
for target, ctx in ctx_list():
data = np.random.uniform(size=dshape).astype(dtype)
ref_res = data.reshape(oshape)
intrp = relay.create_executor("graph", ctx=ctx, target=target)
op_res = intrp.evaluate(func)(data)
np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=0.01)
开发者ID:bddppq，项目名称:tvm，代码行数:9，代码来源:test_op_level1.py
示例20: verify_full
def verify_full(fill_value, src_shape, dtype):
x = relay.var("x", relay.scalar_type(dtype))
z = relay.full(x, src_shape, dtype)
func = relay.Function([x], z)
ref_res = np.full(src_shape, fill_value)
for target, ctx in ctx_list():
for kind in ["graph", "debug"]:
intrp = relay.create_executor(kind, ctx=ctx, target=target)
op_res = intrp.evaluate(func)(np.array(fill_value, dtype))
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
开发者ID:bddppq，项目名称:tvm，代码行数:10，代码来源:test_op_level3.py
注：本文中的tvm.relay.testing.ctx_list函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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