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import numpy as np
import unittest
import caffe2.python.fakelowp.init_shared_libs # noqa
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python import workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
from caffe2.python.fakelowp.test_utils import print_test_debug_info
import caffe2.python.serialized_test.serialized_test_util as serial
import datetime
core.GlobalInit(["caffe2", "--glow_global_fp16=1",
"--glow_global_fused_scale_offset_fp16=1",
"--glow_global_force_sls_fp16_accum=1"])
GLOW_LOWERED_BATCHNORM = False
def reference_spatialbn_test16(X, scale, bias, mean, var, epsilon, order):
X = X.astype(np.float16)
scale = scale.astype(np.float16)
bias = bias.astype(np.float16)
mean = mean.astype(np.float16)
# var = var.astype(np.float16)
assert(order == "NCHW")
scale = scale[np.newaxis, :, np.newaxis, np.newaxis]
bias = bias[np.newaxis, :, np.newaxis, np.newaxis]
mean = mean[np.newaxis, :, np.newaxis, np.newaxis]
var = var[np.newaxis, :, np.newaxis, np.newaxis]
Y = ((X - mean) * (scale / np.sqrt(var + epsilon).astype(np.float16))) + bias
return Y.astype(np.float32)
# Test the lowered BN op
class BatchnormTest(serial.SerializedTestCase):
# TODO: using hypothesis seed, sweep dimensions
@given(seed=st.integers(0, 65535),
size=st.integers(2, 30),
input_channels=st.integers(2, 40),
batch_size=st.integers(2, 20))
@settings(deadline=datetime.timedelta(seconds=10))
def test_bn(self, seed, size, input_channels, batch_size):
workspace.ResetWorkspace()
np.random.seed(seed)
order = "NCHW"
epsilon = 1e-3
pred_net = caffe2_pb2.NetDef()
pred_net.name = "pred"
pred_net.external_input.extend(["X", "scale", "bias", "mean", "var"])
pred_net.external_output.append("Y")
pred_net.op.add().CopyFrom(
core.CreateOperator(
"SpatialBN",
["X", "scale", "bias", "mean", "var"],
["Y"],
order=order,
is_test=True,
epsilon=epsilon
)
)
if GLOW_LOWERED_BATCHNORM:
refopname = "SpatialBNFakeLoweredFp16NNPI"
else:
refopname = "SpatialBNFakeFp16NNPI"
pred_net_ref = caffe2_pb2.NetDef()
pred_net_ref.name = "pred"
pred_net_ref.external_input.extend(["X", "scale", "bias", "mean", "var"])
pred_net_ref.external_output.append("X")
pred_net_ref.op.add().CopyFrom(
core.CreateOperator(
refopname,
["X", "scale", "bias", "mean", "var"],
["Y"],
order=order,
is_test=True,
epsilon=epsilon
)
)
scale = np.random.rand(input_channels).astype(np.float32) + 0.5
bias = np.random.rand(input_channels).astype(np.float32) - 0.5
mean = np.random.randn(input_channels).astype(np.float32)
var = np.random.rand(input_channels).astype(np.float32) + 0.5
X = np.random.rand(
batch_size, input_channels, size, size).astype(np.float32) - 0.5
workspace.FeedBlob("scale", scale)
workspace.FeedBlob("bias", bias)
workspace.FeedBlob("mean", mean)
workspace.FeedBlob("var", var)
# Use for reference to debug
# Y_np = reference_spatialbn_test16(X, scale, bias, mean, var, epsilon, order)
pred_net_onnxified = onnxifi_caffe2_net(
pred_net,
{"X": [batch_size, input_channels, size, size],
"scale": [input_channels],
"bias": [input_channels],
"mean": [input_channels],
"var": [input_channels]},
debug=True,
adjust_batch=False,
use_onnx=False
)
num_onnxified_ops = sum(
1 if o.type == "Onnxifi" else 0 for o in pred_net_onnxified.op)
np.testing.assert_equal(num_onnxified_ops, 1)
workspace.FeedBlob("X", X)
workspace.CreateNet(pred_net_onnxified)
workspace.CreateNet(pred_net_ref)
workspace.RunNet(pred_net_ref.name)
Y_c2 = workspace.FetchBlob("Y")
workspace.RunNet(pred_net_onnxified.name)
Y_glow = workspace.FetchBlob("Y")
if not np.allclose(Y_glow.astype(np.float16), Y_c2.astype(np.float16)):
diff = np.abs(Y_glow - Y_c2).astype(np.float16)
print_test_debug_info(
"bn",
{
"seed": seed,
"scale": scale,
"bias": bias,
"mean": mean,
"var": var,
"Y_np": Y_c2,
"Y_glow": Y_glow,
"diff": diff,
"rowwise_diff": np.max(np.abs(diff), -1)})
assert(0)
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