def output_table(table, args):
fmt = ('tab' if args.output is None else args.output['fmt'])
if fmt == "xls":
table.render(ExcelRenderer(args.output['filename']))
elif fmt == "csv":
with open(args.output['filename'], "w") as file:
table.render(CSVRenderer(file))
else:
renderer = TextTableRenderer(args.table_width)
table.render(renderer)
print(renderer.get_output())
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 test_temps_report_quantized(mnist_unfused_8bit_state):
G = load_state(mnist_unfused_8bit_state)
G.add_dimensions()
stats_collector = TempsStatsCollector(qrecs=G.quantization)
stats = stats_collector.collect_stats(G)
report = TempsReporter().report(G, stats)
renderer = TextTableRenderer(maxwidth=200)
print(report.render(renderer))
def test_temps_report(mnist_graph):
G = create_graph(mnist_graph, opts={"load_tensors": True})
G.add_dimensions()
stats_collector = TempsStatsCollector()
stats = stats_collector.collect_stats(G)
report = TempsReporter().report(G, stats)
renderer = TextTableRenderer(maxwidth=200)
print(report.render(renderer))
def test_filter_detailed_report(mnist_graph, mnist_images):
G = create_graph(mnist_graph, opts={"load_tensors": True})
G.add_dimensions()
stats_collector = FilterDetailedStatsCollector()
stats = stats_collector.collect_stats(G)
report = FilterDetailedStatsReporter().report(G, stats)
renderer = TextTableRenderer(maxwidth=200)
print(report.render(renderer))
def test_activation_report(mnist_graph, mnist_images):
G = create_graph(mnist_graph, opts={"load_tensors": True})
G.add_dimensions()
input_tensor = import_data(mnist_images[0],
height=28,
width=28,
offset=0,
divisor=255)
input_tensor = input_tensor.reshape((28, 28, 1))
stats_collector = ActivationStatsCollector()
stats_collector.collect_stats(G, [input_tensor])
report = ActivationReporter().report(G, stats_collector.reduce_stats())
renderer = TextTableRenderer(maxwidth=200)
print(report.render(renderer))
def test_error_report(mnist_unfused_8bit_state, mnist_images):
G = load_state(mnist_unfused_8bit_state)
G.add_dimensions()
input_tensor = import_data(mnist_images[0],
height=28,
width=28,
offset=0,
divisor=255)
input_tensor = input_tensor.reshape((28, 28, 1))
stats_collector = ErrorStatsCollector()
stats_collector.collect_stats(G, [input_tensor])
stats_collector.collect_stats(G, [input_tensor])
report = ErrorReporter().report(G, stats_collector.reduce_stats())
renderer = TextTableRenderer(maxwidth=200)
print(report.render(renderer))
def test_adjust7(concat_test_graph):
tfi = TfliteImporter()
G = tfi.create_graph(concat_test_graph, {'load_tensors': True})
G.node('input_1').fixed_order = True
G.node('output_1').fixed_order = True
G.node('output_2').fixed_order = True
G.add_dimensions()
G.adjust_order()
matcher = get_pow2_match_group()
matcher.match(G)
G.add_dimensions()
report = GraphReporter().report(G, None)
renderer = TextTableRenderer(maxwidth=200)
print(report.render(renderer))
report = GraphReporter(split_dims=True).report(G, None)
def test_simple_quantization(mnist_graph, mnist_images):
G = create_graph(mnist_graph, opts={"load_tensors": True})
G.add_dimensions()
input_tensor = import_data(mnist_images[0],
height=28,
width=28,
offset=0,
divisor=255)
input_tensor = input_tensor.reshape((28, 28, 1))
stats_collector = ActivationStatsCollector()
stats_collector.collect_stats(G, [input_tensor])
astats = stats_collector.reduce_stats()
stats_collector = FilterStatsCollector()
fstats = stats_collector.collect_stats(G)
quantizer = SymmetricQuantizer(astats, fstats, force_width=8)
qrecs = quantizer.quantize(G)
assert len(qrecs) == 11 # One more for saved quantizer
report = QuantizationReporter().report(G, qrecs)
renderer = TextTableRenderer(maxwidth=200)
print(report.render(renderer))
def test_graph_report(mnist_graph):
G = create_graph(mnist_graph, opts={"load_tensors": True})
G.add_dimensions()
report = GraphReporter().report(G, None)
renderer = TextTableRenderer(maxwidth=200)
print(report.render(renderer))