def verify_lrn(ishape, size, axis, bias, alpha, beta):
x = sym.Variable("x")
y = sym.lrn(x, size=size, axis=axis, bias=bias, alpha=alpha, beta=beta)
dtype = "float32"
x_np = np.random.uniform(size=ishape).astype(dtype)
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(y, target, {"x": ishape})
m = graph_runtime.create(graph, lib, ctx)
m.run(x=x_np)
out = m.get_output(0, tvm.nd.empty(ishape))
out_np = topi.testing.lrn_python(x_np, size, axis, bias, alpha, beta)
np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
#Checking LRN op followed by elementwise op relu
z = sym.relu(y)
x_np = np.random.uniform(low=-10.0, high=10.0, size=ishape).astype(dtype)
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(z, target, {"x": ishape})
m = graph_runtime.create(graph, lib, ctx)
m.run(x=x_np)
out = m.get_output(0, tvm.nd.empty(ishape))
out_np = topi.testing.lrn_python(x_np, size, axis, bias, alpha, beta)
out_np = (out_np > 0) * out_np
np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
def verify_lrn(ishape, size, axis, bias, alpha, beta):
x = sym.Variable("x", shape=ishape)
y = sym.lrn(x, size=size, axis=axis, bias=bias, alpha=alpha, beta=beta)
def forward1(x):
return topi.testing.lrn_python(x, size, axis, bias, alpha, beta)
check_function(y, forward1)
def forward2(x):
y = forward1(x)
return (y > 0) * y
#Checking LRN op followed by elementwise op relu
check_function(sym.relu(y), forward2, in_range={'x': (-10.0, 10.0)})
def verify_lrn(ishape, size, axis, bias, alpha, beta):
x = sym.Variable("x", shape=ishape)
y = sym.lrn(x, size=size, axis=axis, bias=bias, alpha=alpha, beta=beta)
def forward1(x):
return topi.testing.lrn_python(x, size, axis, bias, alpha, beta)
check_function(y, forward1)
def forward2(x):
y = forward1(x)
return (y > 0)*y
#Checking LRN op followed by elementwise op relu
check_function(sym.relu(y), forward2, in_range={'x': (-10.0, 10.0)})