def test_csv():
tab = Tabular()
tab.add_row([TabularColumn('test1'), TabularColumn('test2'), TabularColumn('test3', fmt="^d")])
tab.add_row(["1", "2", 3])
output = io.StringIO()
tab.render(CSVRenderer(output))
output = output.getvalue()
assert output == 'test1,test2,test3\r\n1,2,3\r\n'
def test_excel():
tab = Tabular()
tab.add_row([
TabularColumn('test1 is very long'),
TabularColumnGroup('group',
[TabularColumn('test2'),
TabularColumn('test3', fmt="^d")])
])
tab.add_row(["1", "2", 3])
renderer = ExcelRenderer("/tmp/test.xls")
tab.render(renderer)
def test_textable():
tab = Tabular()
tab.add_row([TabularColumn('test1'), TabularColumn('test2'), TabularColumn('test3', fmt="^d")])
tab.add_row(["1", "2", 3])
renderer = TextTableRenderer(maxwidth=80)
tab.render(renderer)
output = renderer.get_output()
assert output == ('+-------+-------+-------+\n'
'| test1 | test2 | test3 |\n'
'+=======+=======+=======+\n'
'| 1 | 2 | 3 |\n'
'+-------+-------+-------+')
def report(self, G, stats):
table = Tabular()
table.add_row([
TabularColumn("Step", fmt="^d"),
TabularColumn("Name"),
TabularColumn("In"),
TabularColumn("Out"),
TabularColumn("Weights"),
TabularColumn("Bias"),
TabularColumn("Calc"),
TabularColumn("Acc"),
])
for key, qrec in stats.items():
if not isinstance(key, NodeId):
continue
node = G.node(key.node_name)
if self._step is not None and self._step != node.step_idx:
continue
fnode = node.get_contained_node(
key.fnode_name) if key.fnode_name else None
step_idx = node.step_idx
node = fnode or node
row = [
step_idx, node.name,
emit_qs(qrec.in_qs),
emit_qs(qrec.out_qs)
]
if isinstance(node, FilterParameters):
for i in ["weights", "biases", "calc", "acc"]:
row.append(emit_q(getattr(qrec, i + '_q')))
else:
row += ["", "", "", ""]
table.add_row(row)
return table
def do_dheader(table):
header = [
TabularColumn("name"),
TabularColumn("step", fmt=">d"),
TabularColumn("min I range", fmt=">.3f"),
TabularColumn("max I range", fmt=">.3f"),
TabularColumn("min O range", fmt=">.3f"),
TabularColumn("max O range", fmt=">.3f"),
TabularColumn("min I prec", fmt=">.3f"),
TabularColumn("avg I prec", fmt=">.3f"),
TabularColumn("min O prec", fmt=">.3f"),
TabularColumn("avg O prec", fmt=">.3f"),
]
table.add_row(header)
def report(self, G, stats):
table = Tabular()
header = [
TabularColumn("name"),
TabularColumn("creating node"),
TabularColumn("type"),
TabularColumn("dimensions", fmt="^"),
TabularColumn("size", fmt=">d"),
TabularColumn("count", fmt=">d"),
]
is_quantized = stats and 'qtype' in stats[0]
if is_quantized:
header.append(TabularColumn("Q"))
table.add_row(header)
total = 0
for stat in stats:
row = [str(stat[k]) for k in ['name', 'creating_node', 'type']]
row.append(str(stat['dims']))
row.append(stat['dims'].size())
row.append(stat['count'])
if is_quantized:
row.append(str(stat['qtype']))
table.add_row(row)
total += stat['dims'].size()
if self._do_totals:
row = ["Total", "", "", "", total, ""]
if is_quantized:
row.append("")
table.add_row(row)
return table
def do_dheader(table, one_input):
header = [
TabularColumn("name"),
TabularColumn("op type"),
TabularColumn("step", fmt=">d"),
TabularColumn("avg error", fmt=">.3f"),
TabularColumn("max error", fmt=">.3f"),
TabularColumn("min error", fmt=">.3f")
]
if one_input:
header.extend([TabularColumn("QSNR", fmt=">.0f")])
else:
header.extend([
TabularColumn("av QSNR", fmt=">.0f"),
TabularColumn("max QSNR", fmt=">.0f"),
TabularColumn("min QSNR", fmt=">.0f"),
])
header.extend([
TabularColumn("OverF dot", fmt=">d"),
TabularColumn("OverF acc", fmt=">d"),
])
table.add_row(header)
def do_headers(self, active_order, tab):
headers = [
TabularColumn("Step", fmt="^d"),
TabularColumn("Step name"),
TabularColumn("Operation"),
]
if self._split_dims:
headers.append(
TabularColumnGroup("Input Dims",
[TabularColumn("C", fmt="^s"),
TabularColumn("H", fmt="^s"),
TabularColumn("W", fmt="^s")]))
headers.append(
TabularColumnGroup("Output Dims",
[TabularColumn("C", fmt="^s"),
TabularColumn("H", fmt="^s"),
TabularColumn("W", fmt="^s")]))
else:
headers.append(
TabularColumn("Input Dims\n({})".format(active_order), fmt="^s")
)
headers.append(
TabularColumn("Output Dims\n({})".format(active_order), fmt="^s")
)
headers.extend([
TabularColumn("Inputs", fmt="^s"),
TabularColumn("Active\nsize", fmt="^d"),
TabularColumn("Params\nsize", fmt="^d"),
TabularColumn("Ops", fmt=">s"),
TabularColumn("Params"),
TabularColumn("Hints")
])
tab.add_row(headers)
def do_header(table):
header = [
TabularColumn("step"),
TabularColumn("name"),
TabularColumn("mean", fmt=">.3f"),
TabularColumn("std dev", fmt=">.3f"),
TabularColumn("min", fmt=">.3f"),
TabularColumn("max", fmt=">.3f"),
TabularColumn("min acc", fmt=">.3f"),
TabularColumn("max acc", fmt=">.3f"),
TabularColumn("weak\nOLs", fmt=">d"),
TabularColumn("strong\nOLs", fmt=">d"),
TabularColumn("min\nOL", fmt=">.3f"),
TabularColumn("max\nOL", fmt=">.3f"),
TabularColumn("int\nbits", fmt=">d"),
]
for bit_size in STATS_BITS:
header.append(TabularColumn("{}-bits\nformat".format(bit_size), fmt="^s"))
header.append(TabularColumn("{}-bits\nQSNR".format(bit_size), fmt=">.0f"))
header.append(TabularColumn("size\n(bytes)", fmt=">d"))
table.add_row(header)
def report(self, G, stats):
table = Tabular()
table.add_row([
TabularColumn("Step", fmt="^d"),
TabularColumn("Name"),
TabularColumn("Type"),
TabularColumn("In"),
TabularColumn("Out"),
TabularColumn("Weights"),
TabularColumn("Bias"),
TabularColumn("Mulbias"),
TabularColumn("Calc"),
TabularColumn("Acc"),
])
# TODO - Fix report for weights_q
for key, qrec in stats.sorted_iterator(G):
if not isinstance(key, NodeId):
continue
node = G.node(key.node_name)
if self._step is not None and self._step != node.step_idx:
continue
fnode = node.get_contained_node(
key.fnode_name) if key.fnode_name else None
step_idx = node.step_idx
node = fnode or node
if qrec:
if self._step is None or not isinstance(
qrec, MultScalableFilterQuantizationRecord) or len(
qrec.in_qs[1].scale) == 1:
if isinstance(node, ConstantInputParameters):
row = [
step_idx, node.name, qrec.TYPE, "",
self.emit_qs(qrec.out_qs,
limit=self._step is None), "", "", "",
"", ""
]
else:
row = [
step_idx, node.name, qrec.TYPE,
self.emit_qs(qrec.in_qs, limit=self._step is None),
self.emit_qs(qrec.out_qs, limit=self._step is None)
]
if isinstance(
qrec,
(SymmetricScalableFilterQuantizationRecord,
MultScalableFilterQuantizationRecord)):
for i in [1, 2]:
row.append(self.emit_qs([qrec.in_qs[i]]))
for i in ["mul_biases", "calc", "acc"]:
row.append(
self.emit_qs([getattr(qrec, i + '_q')]))
else:
row += ["", "", "", "", ""]
else:
first = True
for chan in range(len(qrec.in_qs[1].scale)):
if first:
row = [
step_idx,
node.name,
qrec.TYPE,
self.emit_qs(qrec.in_qs,
limit=self._step is None),
self.emit_qs(qrec.out_qs,
limit=self._step is None),
self.emit_q_chan(qrec.in_qs[1], chan),
self.emit_q_chan(qrec.in_qs[2], chan),
self.emit_q_chan(qrec.mul_biases_q, chan),
str(qrec.calc_q),
str(qrec.acc_q),
]
first = False
else:
row = [
chan, "", "", "", "",
self.emit_q_chan(qrec.in_qs[1], chan),
self.emit_q_chan(qrec.in_qs[2], chan),
self.emit_q_chan(qrec.mul_biases_q, chan), "",
""
]
table.add_row(row)
continue
else:
row = [
step_idx, node.name, "IEEE32", "None", "None", "", "", "",
"", ""
]
table.add_row(row)
return table
def report(self, G, stats, nodes=None):
if nodes is None:
nodes = G.nodes()
nodes = sorted(nodes, key=lambda x: x.step_idx)
table = Tabular()
table.add_row([
TabularColumn("Step", fmt="^d"),
TabularColumn("Name"),
TabularColumn("Type"),
TabularColumn("In"),
TabularColumn("Out"),
TabularColumn("Weights"),
TabularColumn("Bias"),
TabularColumn("Mulbias"),
TabularColumn("Calc"),
TabularColumn("Acc"),
])
# TODO - Fix report for weights_q
single = nodes[0].step_idx == nodes[-1].step_idx
for key, qrec in stats.sorted_iterator(G):
if not isinstance(key, NodeId):
continue
node = G.node(key.node_name)
if node.step_idx < nodes[0].step_idx or node.step_idx > nodes[
-1].step_idx:
continue
fnode = node.get_contained_node(
key.fnode_name) if key.fnode_name else None
step_idx = node.step_idx
node = fnode or node
if qrec:
if (not single or not qrec.ktype == 'scaled'
or not isinstance(node, FilterParameters)
or len(qrec.in_qs[1].scale) == 1):
if isinstance(node, ConstantInputParameters):
row = [
step_idx, node.name, qrec.ktype, "",
self.emit_qs(qrec.out_qs,
limit=self._step is None), "", "", "",
"", ""
]
else:
row = [
step_idx, node.name, qrec.ktype +
(" NE16" if qrec.cache.get('ne16') else ""),
self.emit_qs(qrec.in_qs, limit=self._step is None),
self.emit_qs(qrec.out_qs, limit=self._step is None)
]
if isinstance(node, FilterParameters):
for i in [1, 2]:
row.append(self.emit_qs([qrec.in_qs[i]]))
for i in ["mul_biases", "calc", "acc"]:
key = f'{i}_q'
if key in qrec.cache:
row.append(self.emit_qs([qrec.cache[key]]))
else:
row.append("")
else:
row += ["", "", "", "", ""]
else:
first = True
for chan in range(len(qrec.in_qs[1].scale)):
if first:
row = [
step_idx,
node.name,
qrec.ktype,
self.emit_qs(qrec.in_qs,
limit=self._step is None),
self.emit_qs(qrec.out_qs,
limit=self._step is None),
self.emit_q_chan(qrec.in_qs[1], chan),
self.emit_q_chan(qrec.in_qs[2], chan),
self.emit_q_chan(qrec.cache['mul_biases_q'],
chan),
str(qrec.cache['calc_q']),
str(qrec.cache['acc_q']),
]
first = False
else:
row = [
chan, "", "", "", "",
self.emit_q_chan(qrec.in_qs[1], chan),
self.emit_q_chan(qrec.in_qs[2], chan),
self.emit_q_chan(qrec.cache['mul_biases_q'],
chan), "", ""
]
table.add_row(row)
continue
else:
row = [
step_idx, node.name, "IEEE32", "None", "None", "", "", "",
"", ""
]
table.add_row(row)
return table
def do_dheader(table, one_input, with_chan):
header = [
TabularColumn("name"),
TabularColumn("op type"),
TabularColumn("step", fmt=">"),
TabularColumn("avg error", fmt=">.3f"),
TabularColumn("max error", fmt=">.3f"),
TabularColumn("min error", fmt=">.3f")
]
if with_chan:
header.append(TabularColumn("max ch error", fmt=">.3f"))
if one_input:
header.extend([
TabularColumn("QSNR", fmt=">.0f")
])
else:
header.extend([
TabularColumn("av QSNR", fmt=">.0f"),
TabularColumn("max QSNR", fmt=">.0f"),
TabularColumn("min QSNR", fmt=">.0f"),
])
table.add_row(header)
def do_header(self, table):
header = [
TabularColumn("step"),
TabularColumn("name")
if self._step_idx is None else TabularColumn('channel'),
TabularColumn("mean", fmt=">.3f"),
TabularColumn("std dev", fmt=">.3f"),
TabularColumn("min", fmt=">.3f"),
TabularColumn("max", fmt=">.3f"),
TabularColumn("weak\nOLs", fmt=">d"),
TabularColumn("strong\nOLs", fmt=">d"),
TabularColumn("min\nOL", fmt=">.3f"),
TabularColumn("max\nOL", fmt=">.3f"),
TabularColumn("int\nbits", fmt=">d"),
TabularColumn("avg\nprec", fmt=">.3f"),
]
for bit_size in STATS_BITS:
header.append(
TabularColumn("{}-bits\nformat".format(bit_size), fmt="^s"))
header.append(
TabularColumn("{}-bits\nQSNR".format(bit_size), fmt=">.1f"))
header.append(TabularColumn("size\n(bytes)", fmt=">d"))
table.add_row(header)
