def check(dim, axis, nstep):
eps = 0.01
x = sym.Variable("x") + 1
beta = sym.Variable("beta")
gamma = sym.Variable("gamma")
moving_var = sym.Variable("moving_var")
moving_mean = sym.Variable("moving_mean")
y1, y2 = x, sym.Variable("xx") + 1
ishape = {"x": tuple(10 for i in range(dim))}
for i in range(nstep):
y1 = sym.batch_norm(y1 + 1,
gamma,
beta,
moving_mean,
moving_var,
epsilon=eps,
axis=axis)
y1 = sym.dropout(y1)
y2 = simple_bn(y2 + 1,
gamma,
beta,
moving_mean,
moving_var,
epsilon=eps,
axis=axis,
shape=ishape["x"])
g = nnvm.graph.create(y1)
g2 = nnvm.graph.create(y2)
graph_attr.set_shape_inputs(g, ishape)
g1 = g.apply("InferShape").apply("SimplifyInference")
# assert graph equals as expected
graph_util.check_graph_equal(g1, g2)
def compare_graph(sym1, sym2, ishape=(2, 3, 224, 224)):
g1 = nnvm.graph.create(sym1)
g2 = nnvm.graph.create(sym2)
graph_attr.set_shape_inputs(g1, {'data':ishape})
graph_attr.set_shape_inputs(g2, {'data':ishape})
g1 = g1.apply("InferShape").apply("SimplifyInference")
g2 = g2.apply("InferShape").apply("SimplifyInference")
graph_util.check_graph_equal(g1, g2)
def compare_graph(sym1, sym2, ishape=(2, 3, 224, 224)):
g1 = nnvm.graph.create(sym1)
g2 = nnvm.graph.create(sym2)
graph_attr.set_shape_inputs(g1, {'data':ishape})
graph_attr.set_shape_inputs(g2, {'data':ishape})
g1 = g1.apply("InferShape").apply("SimplifyInference")
g2 = g2.apply("InferShape").apply("SimplifyInference")
graph_util.check_graph_equal(g1, g2)
def compare_graph(onnx_file, nnvm_sym, ishape):
onnx_graph = onnx.load(onnx_file)
onnx_sym, params = nnvm.frontend.from_onnx(onnx_graph)
g1 = nnvm.graph.create(onnx_sym)
g2 = nnvm.graph.create(nnvm_sym)
ishapes = {'input_0': ishape}
graph_attr.set_shape_inputs(g1, ishapes)
graph_attr.set_shape_inputs(g2, ishapes)
g1 = g1.apply("InferShape").apply("SimplifyInference")
g2 = g2.apply("InferShape").apply("SimplifyInference")
graph_util.check_graph_equal(g1, g2)
def check(shape, channels):
x = sym.Variable("x")
bias = sym.Variable("bias")
scale = sym.Variable("scale")
y1 = before(x, scale, channels)
ishape = {"x": shape, "scale": (channels, ), "bias": (channels, )}
g1 = nnvm.graph.create(y1)
graph_attr.set_shape_inputs(g1, ishape)
g2 = g1.apply("InferShape").apply("FoldScaleAxis")
# assert graph equals as expected
graph_util.check_graph_equal(g1, g2)
def compare_graph(init, predict, nnvm_sym, ishape):
caffe2_sym, params = nnvm.frontend.from_caffe2(init, predict)
g1 = nnvm.graph.create(caffe2_sym)
g2 = nnvm.graph.create(nnvm_sym)
input_name = predict.external_input[0]
ishapes = {input_name: ishape}
graph_attr.set_shape_inputs(g1, ishapes)
graph_attr.set_shape_inputs(g2, ishapes)
g1 = g1.apply("InferShape").apply("SimplifyInference")
g2 = g2.apply("InferShape").apply("SimplifyInference")
graph_util.check_graph_equal(g1, g2)
def compare_graph(onnx_file, nnvm_sym, ishape):
onnx_graph = onnx.load(onnx_file)
onnx_sym, params = nnvm.frontend.from_onnx(onnx_graph)
g1 = nnvm.graph.create(onnx_sym)
g2 = nnvm.graph.create(nnvm_sym)
ishapes = {'input_0': ishape}
graph_attr.set_shape_inputs(g1, ishapes)
graph_attr.set_shape_inputs(g2, ishapes)
g1 = g1.apply("InferShape").apply("SimplifyInference")
g2 = g2.apply("InferShape").apply("SimplifyInference")
graph_util.check_graph_equal(g1, g2)
def check(shape, channels):
x = sym.Variable("x")
bias = sym.Variable("bias")
scale = sym.Variable("scale")
y1 = before(x, scale, channels)
ishape = {"x": shape, "scale": (channels,), "bias": (channels,)}
g1 = nnvm.graph.create(y1)
graph_attr.set_shape_inputs(g1, ishape)
g2 = g1.apply("InferShape").apply("FoldScaleAxis")
# assert graph equals as expected
graph_util.check_graph_equal(g1, g2)
def compare_graph(init, predict, nnvm_sym, ishape):
caffe2_sym, params = nnvm.frontend.from_caffe2(init, predict)
g1 = nnvm.graph.create(caffe2_sym)
g2 = nnvm.graph.create(nnvm_sym)
input_name = predict.external_input[0]
ishapes = {input_name: ishape}
graph_attr.set_shape_inputs(g1, ishapes)
graph_attr.set_shape_inputs(g2, ishapes)
g1 = g1.apply("InferShape").apply("SimplifyInference")
g2 = g2.apply("InferShape").apply("SimplifyInference")
graph_util.check_graph_equal(g1, g2)
def compare_graph(onnx_file, nnvm_sym, ishape):
onnx_model = onnx.load_model(onnx_file)
onnx_sym, params = nnvm.frontend.from_onnx(onnx_model)
g1 = nnvm.graph.create(onnx_sym)
g2 = nnvm.graph.create(nnvm_sym)
input_name = onnx_model.graph.input[0].name
ishapes = {input_name: ishape}
graph_attr.set_shape_inputs(g1, ishapes)
graph_attr.set_shape_inputs(g2, ishapes)
g1 = g1.apply("InferShape").apply("SimplifyInference")
g2 = g2.apply("InferShape").apply("SimplifyInference")
graph_util.check_graph_equal(g1, g2)
def compare_graph(onnx_file, nnvm_sym, ishape):
onnx_model = onnx.load_model(onnx_file)
onnx_sym, params = nnvm.frontend.from_onnx(onnx_model)
g1 = nnvm.graph.create(onnx_sym)
g2 = nnvm.graph.create(nnvm_sym)
input_name = onnx_model.graph.input[0].name
ishapes = {input_name: ishape}
graph_attr.set_shape_inputs(g1, ishapes)
graph_attr.set_shape_inputs(g2, ishapes)
g1 = g1.apply("InferShape").apply("SimplifyInference")
g2 = g2.apply("InferShape").apply("SimplifyInference")
graph_util.check_graph_equal(g1, g2)
def check(shape, channels):
x = sym.Variable("x") + 1
weight = sym.Variable("weight")
bias = sym.Variable("bias")
in_scale = sym.Variable("in_scale")
out_scale = sym.Variable("out_scale")
y1 = before(x, weight, bias, in_scale, out_scale, channels)
y2 = expected(x, weight, bias, in_scale, out_scale, channels)
ishape = {"x": shape, "out_scale": (channels,), "in_scale": (shape[1],)}
g1 = nnvm.graph.create(y1)
g2 = nnvm.graph.create(y2)
graph_attr.set_shape_inputs(g1, ishape)
g1 = g1.apply("InferShape").apply("FoldScaleAxis")
# assert graph equals as expected
graph_util.check_graph_equal(g1, g2)
def check(shape, channels):
x = sym.Variable("x") + 1
weight = sym.Variable("weight")
bias = sym.Variable("bias")
in_scale = sym.Variable("in_scale")
out_scale = sym.Variable("out_scale")
y1 = before(x, weight, bias, in_scale, out_scale, channels)
y2 = expected(x, weight, bias, in_scale, out_scale, channels)
ishape = {"x": shape, "out_scale": (channels,), "in_scale": (shape[1],)}
g1 = nnvm.graph.create(y1)
g2 = nnvm.graph.create(y2)
graph_attr.set_shape_inputs(g1, ishape)
g1 = g1.apply("InferShape").apply("FoldScaleAxis")
# assert graph equals as expected
graph_util.check_graph_equal(g1, g2)
def compare_graph(onnx_file, nnvm_sym, ishape):
onnx_vars = [int(n) for n in onnx.__version__.split('.')] if hasattr(onnx, "__version__") else []
if len(onnx_vars) >= 2 and (onnx_vars[0] > 0 or onnx_vars[1] >= 2): # version >= 0.2
onnx_model = onnx.load(onnx_file)
onnx_sym, params = nnvm.frontend.from_onnx(onnx_model.graph)
else:
onnx_graph = onnx.load(onnx_file)
onnx_sym, params = nnvm.frontend.from_onnx(onnx_graph)
g1 = nnvm.graph.create(onnx_sym)
g2 = nnvm.graph.create(nnvm_sym)
ishapes = {'input_0': ishape}
graph_attr.set_shape_inputs(g1, ishapes)
graph_attr.set_shape_inputs(g2, ishapes)
g1 = g1.apply("InferShape").apply("SimplifyInference")
g2 = g2.apply("InferShape").apply("SimplifyInference")
graph_util.check_graph_equal(g1, g2)
def check(dim, axis, nstep):
eps = 0.01
x = sym.Variable("x") + 1
beta = sym.Variable("beta")
gamma = sym.Variable("gamma")
moving_var = sym.Variable("moving_var")
moving_mean = sym.Variable("moving_mean")
y1, y2 = x, sym.Variable("xx") + 1
ishape = {"x": tuple(10 for i in range(dim))}
for i in range(nstep):
y1 = sym.batch_norm(
y1 + 1, gamma, beta, moving_mean, moving_var, epsilon=eps, axis=axis)
y1 = sym.dropout(y1)
y2 = simple_bn(y2 + 1, gamma, beta, moving_mean, moving_var,
epsilon=eps, axis=axis, shape=ishape["x"])
g = nnvm.graph.create(y1)
g2 = nnvm.graph.create(y2)
graph_attr.set_shape_inputs(g, ishape)
g1 = g.apply("InferShape").apply("SimplifyInference")
# assert graph equals as expected
graph_util.check_graph_equal(g1, g2)
