def test_sym_pass(iter_num=10):
inputs_ext = { 'data': {
'shape': (batch_size, 1, 28, 28),
} }
inputs = [mx.sym.var(n) for n in inputs_ext]
data_iter = iter(val_loader)
def data_iter_func():
return next(data_iter)
data, _ = data_iter_func()
net1 = utils.load_model(*load_fname(version), inputs, ctx=ctx)
def graph_func(data):
return net1.forward(data.as_in_context(ctx))
sym_file, param_file = load_fname(version)
sym, params = mx.sym.load(sym_file), nd.load(param_file)
sym, params = spass.sym_quant_prepare(sym, params, inputs_ext)
if True:
mrt = _mrt.MRT(sym, params, inputs_ext)
mrt.set_data('data', data)
mrt.calibrate(ctx=ctx)
mrt.set_output_prec(8)
qsym, qparams, inputs_ext = mrt.quantize()
else:
inputs_ext['data']['data'] = data
th_dict = calib.sym_calibrate(sym, params, inputs_ext, ctx=ctx)
qsym, qparams, precs, _ = calib.sym_simulate(sym, params, inputs_ext, th_dict)
qsym, qparams = calib.sym_realize(qsym, qparams, inputs_ext, precs, "cvm")
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
sim.save_ext(dump_ext, inputs_ext)
nd.save(dump_params, qparams)
open(dump_sym, "w").write(qsym.tojson())
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
(inputs_ext,) = sim.load_ext(dump_ext)
inputs = [mx.sym.var(n) for n in inputs_ext]
net2 = utils.load_model(dump_sym, dump_params, inputs, ctx=ctx)
def cvm_quantize(data):
data = sim.load_real_data(data, 'data', inputs_ext)
return net2.forward(data.as_in_context(ctx))
utils.multi_eval_accuracy(graph_func, data_iter_func,
cvm_quantize,
iter_num=iter_num)
def test_sym_pass(batch_size=10, iter_num=10, quantize=True):
logger = logging.getLogger("log.test.sym.pass")
calib_ctx = mx.gpu(2)
ctx = [mx.gpu(int(i)) for i in "1,2,3,4".split(',') if i.strip()]
input_size = 299
version = "v3"
h, w = input_size, input_size
inputs_ext = {
'data': {
'shape': (batch_size, 3, h, w),
}
}
inputs = [mx.sym.var(name) for name in inputs_ext]
logger.info("load dataset, symbol and parameters")
data_iter = ds.load_imagenet_rec(batch_size, input_size)
def data_iter_func():
data = data_iter.next()
return data.data[0], data.label[0]
net1 = utils.load_model(*load_fname(version), inputs, ctx=ctx)
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
acc_top1.reset()
acc_top5.reset()
def inception_v3(data, label):
data = gluon.utils.split_and_load(data,
ctx_list=ctx,
batch_axis=0,
even_split=False)
res = [net1.forward(d) for d in data]
res = nd.concatenate(res)
acc_top1.update(label, res)
_, top1 = acc_top1.get()
acc_top5.update(label, res)
_, top5 = acc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
if quantize:
sym_file, param_file = load_fname(version)
sym, params = mx.sym.load(sym_file), nd.load(param_file)
sym, params = spass.sym_quant_prepare(sym, params, inputs_ext)
data, _ = data_iter_func()
if True:
dump_sym, dump_params, dump_ext = load_fname(version, "mrt", True)
mrt = _mrt.MRT(sym, params, inputs_ext)
mrt.set_data('data', data)
mrt.calibrate(ctx=calib_ctx)
mrt.set_output_prec(8)
qsym, qparams, inputs_ext = mrt.quantize()
else:
dump_sym, dump_params, dump_ext = load_fname(
version, "sym.quantize", True)
inputs_ext['data']['data'] = data
th_dict = calib.sym_calibrate(sym,
params,
inputs_ext,
ctx=calib_ctx)
qsym, qparams, precs, _ = calib.sym_simulate(
sym, params, inputs_ext, th_dict)
qsym, qparams = calib.sym_realize(qsym, qparams, inputs_ext, precs)
sim.save_ext(dump_ext, inputs_ext)
nd.save(dump_params, qparams)
open(dump_sym, "w").write(qsym.tojson())
dump_sym, dump_params, dump_ext = load_fname(version, "mrt", True)
(inputs_ext, ) = sim.load_ext(dump_ext)
net2 = utils.load_model(dump_sym, dump_params, inputs, ctx=ctx)
qacc_top1 = mx.metric.Accuracy()
qacc_top5 = mx.metric.TopKAccuracy(5)
qacc_top1.reset()
qacc_top5.reset()
def cvm_quantize(data, label):
data = sim.load_real_data(data, 'data', inputs_ext)
data = gluon.utils.split_and_load(data,
ctx_list=ctx,
batch_axis=0,
even_split=False)
res = [net2.forward(d) for d in data]
res = nd.concatenate(res)
qacc_top1.update(label, res)
_, top1 = qacc_top1.get()
qacc_top5.update(label, res)
_, top5 = qacc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
utils.multi_validate(inception_v3,
data_iter_func,
cvm_quantize,
iter_num=iter_num,
logger=logger)
def test_mx_quantize(batch_size=10, iter_num=10):
logger = logging.getLogger("log.test.mx.quantize")
ctx = [mx.gpu(int(i)) for i in "1,3".split(',') if i.strip()]
inputs_ext = { 'data': {
'shape': (batch_size, 3, 224, 224),
}}
inputs = [mx.sym.var(n) for n in inputs_ext]
data_iter = ds.load_imagenet_rec(batch_size)
def data_iter_func():
data = data_iter.next()
return data.data[0], data.label[0]
data, _ = data_iter_func()
net1 = utils.load_model(*load_fname(version), inputs, ctx=ctx)
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
acc_top1.reset()
acc_top5.reset()
def mobilenet(data, label):
data = gluon.utils.split_and_load(data, ctx_list=ctx, batch_axis=0, even_split=False)
res = [net1.forward(d) for d in data]
res = nd.concatenate(res)
acc_top1.update(label, res)
_, top1 = acc_top1.get()
acc_top5.update(label, res)
_, top5 = acc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
calib_ctx = mx.gpu(1)
sym_fname, param_fname = load_fname(version)
sym, params = mx.sym.load(sym_fname), nd.load(param_fname)
sym, params = spass.sym_quant_prepare(sym, params, inputs_ext)
if True:
if True:
mrt = _mrt.MRT(sym, params, inputs_ext)
mrt.set_data('data', data)
mrt.calibrate()
# [ 0.0008745864 0.03330660510427334 ] 0.6670066884888368 0.7753906
# mrt.set_threshold("mobilenet0_dense0_weight", 0.67)
# # [ -0.0036011334 0.054821780899052534 ] 1.100036751338784 1.4626989
# mrt.set_threshold("mobilenet0_conv24_batchnorm24_fwd_weight", 1.1)
# # [ 0.013243316 1.7543557133786065 ] 70.18747185088569 94.66275
# mrt.set_threshold("mobilenet0_conv23_batchnorm23_fwd_weight", 35.10)
# # [ -0.0016149869 0.05713169649243355 ] 1.1442489167675376 1.7122083
# mrt.set_threshold("mobilenet0_conv20_batchnorm20_fwd_weight", 1.144)
# # [ -0.0015804865 0.04523811489343643 ] 0.9063427844084799 1.0745146
# mrt.set_threshold("mobilenet0_conv16_batchnorm16_fwd_weight", 0.90)
# # [ 0.4315614 2.447332109723772 ] 49.37820360490254 63.959927
# mrt.set_threshold("mobilenet0_conv2_batchnorm2_fwd", 49.37)
# # [ 0.9770754 1.3392452512468611 ] 27.761980422905516 40.729546
# mrt.set_threshold("mobilenet0_relu2_fwd", 27.76)
# [ 1.0975745 1.0489919010632773 ] 22.077412493692915 23.784576
# mrt.set_threshold("mobilenet0_relu4_fwd", 22.08)
# # [ 0.9885562 2.360489403014386 ] 48.19834426651407 69.22121
# mrt.set_threshold("mobilenet0_conv5_batchnorm5_fwd", 48.2)
# # [ 0.7895588 1.0544661745870065 ] 21.878882319617176 30.95745
# mrt.set_threshold("mobilenet0_relu17_fwd", 21.88)
# # [ 0.8717863 1.0887600296120434 ] 22.646986888608513 28.265652
# mrt.set_threshold("mobilenet0_relu19_fwd", 22.65)
# # [ 0.35124516 0.6501711574631898 ] 13.354668314135012 20.770807
# mrt.set_threshold("mobilenet0_relu20_fwd", 13.35)
# # [ 0.9378179 1.110470714216975 ] 23.147232155910086 27.886068
# mrt.set_threshold("mobilenet0_relu21_fwd", 23.15)
# # [ 0.36263302 0.6352599878026505 ] 13.067832775738754 17.18809
# mrt.set_threshold("mobilenet0_relu22_fwd", 13.07)
# # [ 0.19875833 0.49999100821358816 ] 10.198578498193196 16.625143
# mrt.set_threshold("mobilenet0_relu24_fwd", 10.2)
# # [ 0.32357717 1.6308352606637138 ] 65.55698759215218 75.84912
# mrt.set_threshold("mobilenet0_conv25_batchnorm25_fwd", 32.94)
# # [ 0.36793178 1.512995992388044 ] 30.62785163096019 49.464615
# mrt.set_threshold("mobilenet0_relu26_fwd", 30.63)
# # [ 18.028658 38.61970520019531 ] 790.4227619171143 805.51886
# mrt.set_threshold("sum0", 790.423)
mrt.set_output_prec(8)
qsym, qparams, inputs_ext = mrt.quantize()
else:
inputs_ext['data']['data'] = data
th_dict = calib.sym_calibrate(sym, params, inputs_ext, ctx=calib_ctx)
qsym, qparams, precs, _ = calib.sym_simulate(sym, params, inputs_ext, th_dict)
qsym, qparams = calib.sym_realize(qsym, qparams, inputs_ext, precs)
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
sim.save_ext(dump_ext, inputs_ext)
nd.save(dump_params, qparams)
open(dump_sym, "w").write(qsym.tojson())
dump_sym, dump_params = load_fname(version, "nnvm.compile")
nnvm_sym, nnvm_params = spass.mxnet_to_nnvm(qsym, qparams, inputs_ext)
spass.cvm_build(nnvm_sym, nnvm_params, inputs_ext, dump_sym, dump_params)
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
(inputs_ext,) = sim.load_ext(dump_ext)
net2 = utils.load_model(dump_sym, dump_params, inputs, ctx=ctx)
qacc_top1 = mx.metric.Accuracy()
qacc_top5 = mx.metric.TopKAccuracy(5)
qacc_top1.reset()
qacc_top5.reset()
def cvm_quantize(data, label):
data = sim.load_real_data(data, 'data', inputs_ext)
data = gluon.utils.split_and_load(data, ctx_list=ctx, batch_axis=0, even_split=False)
res = [net2.forward(d) for d in data]
res = nd.concatenate(res)
qacc_top1.update(label, res)
_, top1 = qacc_top1.get()
qacc_top5.update(label, res)
_, top5 = qacc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
utils.multi_validate(mobilenet, data_iter_func,
cvm_quantize,
iter_num=iter_num, logger=logger)
_, top1 = acc_top1.get()
acc_top5.update(label, res)
_, top5 = acc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
if True:
sym, params = mx.sym.load(sym_file), nd.load(param_file)
sym, params = spass.sym_quant_prepare(sym, params, inputs_ext)
import os
open(os.path.expanduser('~/tvm-cvm/data/test_ryt2.json'),
'w').write(sym.tojson())
exit()
qsym, qparams, precs, _ = calib.sym_simulate(sym, params, inputs_ext, data,
calib_ctx)
qsym, qparams = calib.sym_realize(qsym, qparams, inputs_ext, precs, "tvm")
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
sim.save_ext(dump_ext, inputs_ext)
nd.save(dump_params, qparams)
open(dump_sym, "w").write(qsym.tojson())
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
sym, params = mx.sym.load(dump_sym), nd.load(dump_params)
(inputs_ext, ) = sim.load_ext(dump_ext)
inputs = [mx.sym.var(n) for n in inputs_ext]
net2 = utils.load_model(dump_sym, dump_params, inputs, ctx=ctx)
qacc_top1 = mx.metric.Accuracy()
qacc_top5 = mx.metric.TopKAccuracy(5)
qacc_top1.reset()
qacc_top5.reset()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
if True:
sym, params = mx.sym.load(sym_file), nd.load(param_file)
sym, params = spass.sym_quant_prepare(sym, params, inputs_ext)
if True:
mrt = _mrt.MRT(sym, params, inputs_ext)
mrt.set_data('data', data)
mrt.calibrate(ctx=calib_ctx)
mrt.set_output_prec(8)
qsym, qparams, inputs_ext = mrt.quantize()
else:
inputs_ext['data']['data'] = data
th_dict = calib.sym_calibrate(sym, params, inputs_ext, ctx=calib_ctx)
qsym, qparams, precs, _ = calib.sym_simulate(sym, params, inputs_ext,
th_dict)
qsym, qparams = calib.sym_realize(qsym, qparams, inputs_ext, precs,
"cvm")
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
sim.save_ext(dump_ext, inputs_ext)
nd.save(dump_params, qparams)
open(dump_sym, "w").write(qsym.tojson())
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
(inputs_ext, ) = sim.load_ext(dump_ext)
inputs = [mx.sym.var(n) for n in inputs_ext]
net2 = utils.load_model(dump_sym, dump_params, inputs, ctx=ctx)
qacc_top1 = mx.metric.Accuracy()
qacc_top5 = mx.metric.TopKAccuracy(5)
qacc_top1.reset()
qacc_top5.reset()
def test_sym_pass(batch_size=10, iter_num=10):
logger = logging.getLogger("log.test.sym.pass")
base_ctx = mx.gpu(1)
ctx = mx.gpu(2)
input_size = 416
h, w = input_size, input_size
inputs_ext = {
'data': {
'shape': (batch_size, 3, h, w),
}
}
val_data = dataset.load_voc(batch_size, input_size)
val_data_iter = iter(val_data)
def data_iter_func():
data, label = next(val_data_iter)
return data, label
sym_file, param_file = load_fname("_darknet53_voc")
sym, params = mx.sym.load(sym_file), nd.load(param_file)
sym, params = spass.sym_quant_prepare(sym, params, inputs_ext)
if False:
th_dict = {}
for i in range(16):
data, _ = data_iter_func()
for k, v in inputs_ext.items():
v['data'] = data
th_dict = calib.sym_calibrate(sym,
params,
inputs_ext,
old_ths=th_dict,
ctx=ctx)
_, _, dump_ext = load_fname("_darknet53_voc", "dict", True)
sim.save_ext(dump_ext, th_dict)
_, _, dump_ext = load_fname("_darknet53_voc", "dict", True)
(th_dict, ) = sim.load_ext(dump_ext)
inputs = [mx.sym.var(name) for name in inputs_ext]
net1 = mx.gluon.nn.SymbolBlock(sym, inputs)
utils.load_parameters(net1, params, ctx=ctx)
metric = dataset.load_voc_metric()
metric.reset()
def yolov3(data, label):
def net(data):
out = net1(data.as_in_context(ctx))
print([o[0][0][:] for o in out])
return out
acc = validate_data(net, data, label, metric)
return "{:6.2%}".format(acc)
keys = [
'yolov30_yolooutputv30_conv0_fwd',
'yolov30_yolooutputv31_conv0_fwd',
'yolov30_yolooutputv32_conv0_fwd',
]
base, base_params, base_inputs_ext, top, top_params, top_inputs_ext \
= split_model(sym, params, inputs_ext, keys, logger)
dump_sym, dump_params = load_fname("_darknet53_voc", "base")
open(dump_sym, "w").write(base.tojson())
dump_sym, dump_params, dump_ext = load_fname("_darknet53_voc", "top", True)
open(dump_sym, "w").write(top.tojson())
nd.save(dump_params, top_params)
sim.save_ext(dump_ext, top_inputs_ext)
base_inputs = [mx.sym.var(n) for n in base_inputs_ext]
base_graph = mx.gluon.nn.SymbolBlock(base, base_inputs)
utils.load_parameters(base_graph, base_params, ctx=base_ctx)
top_inputs = [mx.sym.var(n) for n in top_inputs_ext]
top_graph = mx.gluon.nn.SymbolBlock(top, top_inputs)
utils.load_parameters(top_graph, top_params, ctx=ctx)
# quantize base graph
if False:
qbase, qbase_params, qbase_prec, base_oscales = calib.sym_simulate(
base, base_params, base_inputs_ext, th_dict)
qbase, qbase_params = calib.sym_realize(qbase, qbase_params,
base_inputs_ext, qbase_prec)
dump_sym, dump_params, dump_ext = load_fname("_darknet53_voc",
"base.quantize", True)
open(dump_sym, "w").write(qbase.tojson())
sim.save_ext(dump_ext, base_inputs_ext, base_oscales)
nd.save(dump_params, qbase_params)
if False:
qb_sym, qb_params, qb_ext = load_fname("_darknet53_voc",
"base.quantize", True)
net2_inputs_ext, base_oscales = sim.load_ext(qb_ext)
net2_inputs = [mx.sym.var(n) for n in net2_inputs_ext]
net2 = utils.load_model(qb_sym, qb_params, net2_inputs, ctx=ctx)
base_metric = dataset.load_voc_metric()
base_metric.reset()
def base_quantize(data, label):
def net(data):
data = sim.load_real_data(data, 'data', net2_inputs_ext)
tmp = list(net2(data.as_in_context(ctx)))
tmp = [t / base_oscales[i] for i, t in enumerate(tmp)]
return top_graph(*tmp)
acc = validate_data(net, data, label, base_metric)
return "{:6.2%}".format(acc)
# quantize top graph
if False:
in_bit, out_bit = 8, 30
outputs_ext = {
'yolov30_yolooutputv30_expand_dims0': {
'threshold': 1,
'type': 'score'
},
'yolov30_yolooutputv31_expand_dims0': {
'threshold': 1,
'type': 'score'
},
'yolov30_yolooutputv32_expand_dims0': {
'threshold': 1,
'type': 'score'
},
'yolov30_yolooutputv30_tile0': {
'threshold': 416,
'type': 'bbox'
},
'yolov30_yolooutputv31_tile0': {
'threshold': 416,
'type': 'bbox'
},
'yolov30_yolooutputv32_tile0': {
'threshold': 416,
'type': 'bbox'
},
'yolov30_yolooutputv30_broadcast_add1': {
'fixed': True,
'type': 'ids'
},
'yolov30_yolooutputv31_broadcast_add1': {
'fixed': True,
'type': 'ids'
},
'yolov30_yolooutputv32_broadcast_add1': {
'fixed': True,
'type': 'ids'
},
}
qsym, qparams, type_ext = anno.mixed_precision(top,
top_params,
top_inputs_ext,
th_dict,
in_bit=in_bit,
out_bit=out_bit,
out_ext=outputs_ext,
runtime="cvm")
out_scales = [type_ext['ids'], type_ext['score'], type_ext['bbox']]
dump_sym, dump_params, dump_ext = load_fname("_darknet53_voc",
"top.quantize", True)
open(dump_sym, "w").write(qsym.tojson())
sim.save_ext(dump_ext, top_inputs_ext, out_scales)
nd.save(dump_params, qparams)
if True:
sym_file, param_file, ext_file = load_fname("_darknet53_voc",
"top.quantize", True)
net3_inputs_ext, net3_scales = sim.load_ext(ext_file)
top_sym = base_graph(mx.sym.Group(base_inputs))
top_names = [c.attr('name') for c in top_sym]
net3_inputs = [mx.sym.var(n) for n in net3_inputs_ext]
net3 = utils.load_model(sym_file, param_file, net3_inputs, ctx=ctx)
top_qmetric = dataset.load_voc_metric()
top_qmetric.reset()
def top_quantize(data, label):
def net(data):
tmp = base_graph(data.as_in_context(base_ctx))
tmp = [t.as_in_context(ctx) for t in tmp]
tmp = [
sim.load_real_data(tmp[i], n, net3_inputs_ext)
for i, n in enumerate(top_names)
]
out = net3(*tmp)
out = [(t / net3_scales[i]) for i, t in enumerate(out)]
print([o[0][0][:] for o in out])
return out
acc = validate_data(net, data, label, top_qmetric)
return "{:6.2%}".format(acc)
# merge quantize model
if False:
qb_sym, qb_params, qb_ext = load_fname("_darknet53_voc",
"base.quantize", True)
qbase, qbase_params = mx.sym.load(qb_sym), nd.load(qb_params)
qbase_inputs_ext, _ = sim.load_ext(qb_ext)
qt_sym, qt_params, qt_ext = load_fname("_darknet53_voc",
"top.quantize", True)
qtop, qtop_params = mx.sym.load(qt_sym), nd.load(qt_params)
_, out_scales = sim.load_ext(qt_ext)
maps = dict(
zip([c.attr('name') for c in qbase],
[c.attr('name') for c in base]))
qsym, qparams = merge_model(qbase, qbase_params, qbase_inputs_ext,
qtop, qtop_params, maps)
sym_file, param_file, ext_file = load_fname("_darknet53_voc",
"all.quantize", True)
open(sym_file, "w").write(qsym.tojson())
nd.save(param_file, qparams)
sim.save_ext(ext_file, qbase_inputs_ext, out_scales)
if False:
sym_file, param_file, ext_file = load_fname("_darknet53_voc",
"all.quantize", True)
net4_inputs_ext, net4_scales = sim.load_ext(ext_file)
net4_inputs = [mx.sym.var(n) for n in net4_inputs_ext]
net4 = utils.load_model(sym_file, param_file, net4_inputs, ctx=ctx)
all_qmetric = dataset.load_voc_metric()
all_qmetric.reset()
def all_quantize(data, label):
def net(data):
data = sim.load_real_data(data, 'data', net4_inputs_ext)
out = net4(data.as_in_context(ctx))
out = [(t / net4_scales[i]) for i, t in enumerate(out)]
return out
acc = validate_data(net, data, label, all_qmetric)
return "{:6.2%}".format(acc)
if False:
sym_file, param_file, ext_file = load_fname("_darknet53_voc",
"all.quantize", True)
net4_inputs_ext, net4_scales = sim.load_ext(ext_file)
datadir = "/data/voc/data/"
for i in range(50):
countdir = datadir + "/" + str(i)
os.makedirs(countdir, exist_ok=True)
data, label = data_iter_func()
data = sim.load_real_data(data, 'data', net4_inputs_ext)
np.save(countdir + "/data.npy", data.asnumpy().astype('int8'))
np.save(countdir + "/label.npy", label.asnumpy())
# data = sim.load_real_data(data, 'data', net4_inputs_ext)
# np.save("/tmp/yolo/data", data.asnumpy().astype('int8'))
# out = net4(data.as_in_context(ctx))
# for i, o in enumerate(out):
# np.save("/tmp/yolo/result"+str(i), o.asnumpy().astype('int32'))
exit()
utils.multi_validate(
yolov3,
data_iter_func,
top_quantize,
# base_quantize, # top_quantize, all_quantize,
iter_num=iter_num,
logger=logger)
def test_sym_pass(batch_size=10, iter_num=10):
logger = logging.getLogger("log.test.sym.pass")
version = ""
sym_fname, param_fname = load_fname(version)
sym, params = mx.sym.load(sym_fname), nd.load(param_fname)
params = {k.split(':')[1]: v for k, v in params.items()}
calib_ctx = mx.gpu(2)
ctx = [mx.gpu(int(i)) for i in "1,2,3,4,5,6,7".split(',') if i.strip()]
inputs_ext = {
'data': {
'shape': (batch_size, 3, 224, 224),
}
}
inputs = [mx.sym.var(name) for name in inputs_ext]
logger.info("load dataset, symbol and parameters")
order = sutils.topo_sort(sym)
for op_head in order:
if op_head.attr('name') == 'classifier':
break
sym = op_head
net = mx.gluon.nn.SymbolBlock(sym, inputs)
load_parameters(net, params, ctx=ctx)
data_iter = ds.load_imagenet_rec(batch_size)
def data_iter_func():
data = data_iter.next()
return data.data[0], data.label[0]
for i in range(10):
if i == 3:
break
data, _ = data_iter_func()
data_iter.reset()
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
acc_top1.reset()
acc_top5.reset()
def resnet(data, label):
data = gluon.utils.split_and_load(data,
ctx_list=ctx,
batch_axis=0,
even_split=False)
res = [net.forward(d) for d in data]
res = nd.concatenate(res)
acc_top1.update(label, res)
_, top1 = acc_top1.get()
acc_top5.update(label, res)
_, top5 = acc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
sym, params = spass.sym_quant_prepare(sym, params, inputs_ext)
qsym, qparams, precs, _ = calib.sym_simulate(sym, params, inputs_ext, data,
calib_ctx)
qsym, qparams = calib.sym_realize(qsym, qparams, inputs_ext, precs, "cvm")
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
sim.save_ext(dump_ext, inputs_ext)
nd.save(dump_params, qparams)
open(dump_sym, "w").write(qsym.tojson())
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
(inputs_ext, ) = sim.load_ext(dump_ext)
net3 = utils.load_model(dump_sym, dump_params, inputs, ctx=ctx)
qacc_top1 = mx.metric.Accuracy()
qacc_top5 = mx.metric.TopKAccuracy(5)
qacc_top1.reset()
qacc_top5.reset()
def cvm_quantize(data, label):
data = sim.load_real_data(data, 'data', inputs_ext)
data = gluon.utils.split_and_load(data,
ctx_list=ctx,
batch_axis=0,
even_split=False)
res = [net3.forward(d) for d in data]
res = nd.concatenate(res)
qacc_top1.update(label, res)
_, top1 = qacc_top1.get()
qacc_top5.update(label, res)
_, top5 = qacc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
utils.multi_validate(resnet,
data_iter_func,
cvm_quantize,
iter_num=iter_num,
logger=logger)