def test_graph_execute_complex(ir_graph, ir_images):
G = create_graph(ir_graph, opts={"load_tensors": True})
G.add_dimensions()
input_tensor = import_data(ir_images[0], offset=0, divisor=255)
input_tensor = input_tensor.reshape((80, 80, 1))
executer = GraphExecuter(G)
executer.execute([input_tensor])
def test_cross_mini(two_conv_graph):
G = two_conv_graph
executer = GraphExecuter(G)
output1 = executer.execute([np.full([10, 10, 2], 1)])
groups, neurons = cl.discover_groups(G)
assert groups and neurons, "Nothing discovered"
cl.process_groups(groups)
cl.update_parameters(neurons)
output2 = executer.execute([np.full([10, 10, 2], 1)])
assert np.max(np.abs(output1[3][0] - output2[3][0])) < 0.00001
def do_gen(self, args):
"""
Generate AutoTiler model C code and optionally dump tensors. If no destination file is
given the generated code will be outputed to the screen. Check the 'set' command for
settings related to code generation."""
self._check_graph()
self._check_quantized()
self._check_adjusted()
if args.checksums:
input_args = self._get_input_args(None)
LOG.info("input file %s", args.checksums)
data = import_data(args.checksums, **input_args)
executer = GraphExecuter(self.G, qrecs=self.G.quantization)
executer.execute([data], qmode=QuantizationMode.all())
self.settings['checksum_file'] = args.checksums
self.settings['generate_checksums'] = True
if args.tensor_directory:
self.settings['tensor_directory'] = args.tensor_directory
if args.model_directory:
self.settings['model_directory'] = args.model_directory
self.settings['basic_kernel_source_file'] = args.basic_kernel_source_file
self.settings['basic_kernel_header_file'] = args.basic_kernel_header_file
code_gen = CodeGenerator(self.G, DefaultNamingConvension(self.G), self.settings)
if self.settings['template_file']:
code_template = dynamic_template(self.settings['template_file'])
else:
code_template = default_template
if args.model_file:
with open(os.path.join(self.settings['model_directory'],
args.model_file), "w") as output_fp:
output_fp.write(code_template(self.G, code_generator=code_gen))
if self.G.has_expressions:
with open(os.path.join(self.settings['model_directory'],
args.basic_kernel_source_file), "w") as output_fp:
output_fp.write(basic_kernel_source_template(self.G, code_generator=code_gen))
with open(os.path.join(self.settings['model_directory'],
args.basic_kernel_header_file), "w") as output_fp:
output_fp.write(basic_kernel_header_template(self.G, code_generator=code_gen))
else:
self.ppaged(code_template(self.G, code_generator=code_gen))
if self.G.has_expressions:
self.ppaged(basic_kernel_source_template(self.G, code_generator=code_gen))
self.ppaged(basic_kernel_header_template(self.G, code_generator=code_gen))
if args.output_tensors:
code_gen.write_constants()
if args.header_file:
with open(os.path.join(self.settings['model_directory'], args.header_file), "w") as output_fp:
output_fp.write(header_template(self.G, code_generator=code_gen))
def test_validate_mn1_quantized1(mn1q_graph, mn1f_graph):
tfi = TfliteImporter()
Gf = tfi.create_graph(mn1f_graph, {'load_tensors': True})
Gf.add_dimensions()
Gf.adjust_order()
matcher = get_pow2_match_group()
matcher.match(Gf)
Gf.add_dimensions()
tfi = TfliteImporter()
G = tfi.create_graph(mn1q_graph, {
'load_tensors': True,
'load_quantization': True
})
G.add_dimensions()
G.adjust_order()
matcher = get_pow2_match_group()
matcher.match(G)
G.add_dimensions()
fpnode = Gf.graph_state.steps[2]['node']
fpcnode = fpnode.contained_filters()[0]
qpnode = G.graph_state.steps[2]['node']
qpcnode = qpnode.contained_filters()[0]
nid = NodeId(qpnode, qpcnode)
qrec = G.quantization[nid]
dqbiases = qrec.biases_q.get_dequantized(qpcnode.biases)
assert np.max(np.abs(fpcnode.biases - dqbiases)) < 0.1
input_tensor = np.load('tests/mobv1_valid/COCO_val2014_000000362331_0.npy')
input_tensor = input_tensor.reshape((224, 224, 3)).transpose((2, 0, 1))
executer = GraphExecuter(Gf)
foutput_tensors = executer.execute([input_tensor])
foutput_tensor = np.load(
'tests/mobv1_valid/output_COCO_val2014_000000362331_0_float.npy')
assert np.max(np.abs(foutput_tensors[-1][0] - foutput_tensor[0])) < 0.0001
executer = GraphExecuter(G, qrecs=G.quantization)
qfroutput_tensors = executer.execute([input_tensor],
qmode=QuantizationMode.none())
assert np.max(np.abs(qfroutput_tensors[-1][0] - foutput_tensor[0])) < 0.2
executer = GraphExecuter(G, qrecs=G.quantization)
qroutput_tensors = executer.execute(
[input_tensor], qmode=QuantizationMode.all_dequantize())
output_tensor = np.load(
'tests/mobv1_valid/output_COCO_val2014_000000362331_0_quant.npy')
# assert np.max(np.abs(qroutput_tensors[-1][0] - output_tensor[0])) < 0.16
assert np.max(np.abs(qroutput_tensors[-1][0] - output_tensor[0])) < 0.28
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
if not all(cls.is_constant(inp) for inp in inputs):
raise NotImplementedError(
"nntool does not support import of graphs with evaluated loops"
)
importer = kwargs['importer']
sub_G = NNGraph()
all_nodes_clone = all_nodes.copy()
importer.import_subgraph(sub_G,
node.attrs['body'], {},
all_nodes=all_nodes_clone)
if not all(
isinstance(inp, (InputParameters, ConstantInputParameters))
for inp in sub_G.inputs()):
raise NotImplementedError(
"nntool does not support import of graphs with evaluated loops"
)
sub_G.add_dimensions()
for idx, inp in enumerate(sub_G.inputs()):
inp.index = idx
logger.info(f"reducing loop {valid_name} to a constant")
count = inputs[0][0].value
keep_going = inputs[1][0].value
loop_carried = [inp[0].value for inp in inputs[2:]]
outputs = [np.array([])] * len(node.output)
while keep_going and count > 0:
executer = GraphExecuter(sub_G)
output_tensors = executer.execute([count, keep_going] +
loop_carried,
silent=True)
outp_vals = [
output_tensors[node.step_idx][0] for node in sub_G.outputs()
if not isinstance(node, InputParameters)
]
keep_going = outp_vals[0]
for idx, val in enumerate(outp_vals[1:]):
if idx < len(loop_carried):
loop_carried[idx] = outputs[idx] = val
elif outputs[idx] is None:
outputs[idx] = val
else:
outputs[idx] = np.concatenate((outputs[idx], val))
count -= 1
for idx, outp in enumerate(node.output):
params = ConstantInputParameters(
G.unique_name(f'{valid_name}_out{idx}'),
value=outputs[idx],
dims=Dim.unnamed(outputs[idx].shape))
all_nodes[outp] = (params, 0, ProvisionalDim(outputs[idx].shape),
None)
return None
def test_graph_imu_auto_quant_and_execute_quant():
G = create_graph("tests/graph/imu.tflite", opts={"load_tensors": True})
G.add_dimensions()
G.adjust_order()
get_pow2_match_group().match(G)
G.add_dimensions()
stats_collector = ActivationStatsCollector()
for input_file in ['tests/images/imu0.pgm']:
input_tensor = import_data(input_file,
offset=0,
divisor=256,
nptype='int16')
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=16)
qrecs = quantizer.quantize(G)
G.quantization = qrecs
executer = GraphExecuter(G, qrecs=qrecs)
for input_file in ['tests/images/imu0.pgm']:
input_tensor = import_data(input_file,
offset=0,
divisor=256,
nptype='int16')
output_ = executer.execute([input_tensor],
qmode=QuantizationMode.all())
def test_external_biases_sq8(qvww_graph):
# this model has at the end an external biases layer as constant add
tfi = TfliteImporter()
G = tfi.create_graph(qvww_graph, {"load_quantization": True, "load_tensors": True})
G.add_dimensions()
matcher = get_scale8_match_group()
matcher.match(G)
G.add_dimensions()
image = 'tests/vwwimages/COCO_val2014_000000174838_1.png'
img_in = Image.open(image)
img_in = img_in.resize((238, 208))
input_tensor = np.array(img_in, dtype=np.uint8)
input_tensor = (input_tensor.astype(np.float32) - 128) / 128
executer = GraphExecuter(G, qrecs=G.quantization)
# check if nntool can execute
qoutput_tensors = executer.execute([input_tensor], qmode=QuantizationMode.all_dequantize())
foutput_tensors = executer.execute([input_tensor], qmode=None)
diff = [q[0]-f[0] for q,f in zip(qoutput_tensors, foutput_tensors)]
assert max([np.max(d) for d in diff]) < 2.2
def test_graph_calc_quantize_one_2(mnist_unfused_16bit_state, mnist_images):
G = load_state(mnist_unfused_16bit_state)
input_tensor = import_data(mnist_images[0],
height=28,
width=28,
offset=0,
divisor=255)
input_tensor = input_tensor.reshape((28, 28, 1))
executer = GraphExecuter(G, qrecs=G.quantization)
output1 = executer.execute([input_tensor])
input_tensor = import_data(mnist_images[0],
height=28,
width=28,
offset=0,
divisor=255)
input_tensor = input_tensor.reshape((28, 28, 1))
output2 = executer.execute([input_tensor], qmode=QuantizationMode.step(4))
diffs = []
for i, out1 in enumerate(output1):
diffs.append(out1[0] - output2[i][0])
assert np.min(diffs[7]) > -2 and np.max(diffs[7]) < 2
def test_validate_mn1_dequant_quantfloat(mn1q_graph):
# load dequantized graph same results as quant graph and float execution
tfi = TfliteImporter()
G = tfi.create_graph(mn1q_graph, {
'load_tensors': True,
'load_quantization': True
})
G.add_dimensions()
G.adjust_order()
matcher = get_pow2_match_group()
matcher.match(G)
G.add_dimensions()
Gdq = tfi.create_graph(mn1q_graph, {
'load_tensors': True,
'load_dequantized': True
})
Gdq.add_dimensions()
Gdq.adjust_order()
matcher = get_pow2_match_group()
matcher.match(Gdq)
Gdq.add_dimensions()
input_tensor = np.load('tests/mobv1_valid/COCO_val2014_000000362331_0.npy')
input_tensor = input_tensor.reshape((224, 224, 3)).transpose((2, 0, 1))
executer = GraphExecuter(G, qrecs=G.quantization)
qfoutput_tensors = executer.execute([input_tensor],
qmode=QuantizationMode.none())
executer = GraphExecuter(Gdq)
dfoutput_tensors = executer.execute([input_tensor])
diff_list = [
np.abs(df[0] - qf[0])
for df, qf in zip(dfoutput_tensors, qfoutput_tensors)
]
max_diff = [np.max(elem) for elem in diff_list]
assert max(max_diff) < 0.003
def test_graph_calc_quantized8(mnist_unfused_8bit_state, mnist_images):
G = load_state(mnist_unfused_8bit_state)
input_tensor = import_data(mnist_images[0],
height=28,
width=28,
offset=0,
divisor=255)
input_tensor = input_tensor.reshape((28, 28, 1))
executer = GraphExecuter(G, qrecs=G.quantization)
output1 = executer.execute([input_tensor], step_idx_limit=7)
input_tensor = import_data(mnist_images[0],
height=28,
width=28,
offset=0,
divisor=255)
input_tensor = input_tensor.reshape((28, 28, 1))
output2 = executer.execute([input_tensor],
qmode=QuantizationMode.all_dequantize(),
step_idx_limit=7)
diffs = []
for i in range(8):
diffs.append(output1[i][0] - output2[i][0])
assert np.max(np.abs(diffs[7])) < 9
def test_validate_mn1_float(mn1f_graph):
tfi = TfliteImporter()
G = tfi.create_graph(mn1f_graph, {'load_tensors': True})
G.add_dimensions()
matcher = get_pow2_match_group()
matcher.match(G)
G.add_dimensions()
input_tensor = np.load('tests/mobv1_valid/COCO_val2014_000000362331_0.npy')
input_tensor = input_tensor.reshape((224, 224, 3))
executer = GraphExecuter(G, qrecs=G.quantization)
routput_tensors = executer.execute([input_tensor])
output_tensor = np.load(
'tests/mobv1_valid/output_COCO_val2014_000000362331_0_float.npy')
assert np.max(np.abs(routput_tensors[-1][0] - output_tensor[0])) < 0.0001
def test_equivalence(mnist_graph, mnist_images):
G = create_graph(mnist_graph, opts={"load_tensors": True})
G.add_dimensions()
G.adjust_order()
G.add_dimensions()
input_tensor = import_data(mnist_images[0],
height=28,
width=28,
divisor=255,
offset=0,
transpose=False)
executer = GraphExecuter(G)
output_ = executer.execute([input_tensor])
with open("tests/h5_pickles/weights.pickle", 'rb') as fp:
verif_weights = pickle.load(fp)
assert np.array_equal(verif_weights[0]['weights'],
G.graph_state.steps[1]['node'].weights)
assert np.array_equal(verif_weights[0]['biases'],
G.graph_state.steps[1]['node'].biases)
assert np.array_equal(verif_weights[3]['weights'],
G.graph_state.steps[4]['node'].weights)
assert np.array_equal(verif_weights[3]['biases'],
G.graph_state.steps[4]['node'].biases)
assert np.array_equal(verif_weights[7]['weights'],
G.graph_state.steps[7]['node'].weights)
assert np.array_equal(verif_weights[7]['biases'],
G.graph_state.steps[7]['node'].biases)
with open(
os.path.join("tests/h5_pickles",
os.path.basename(mnist_images[0]) + '.pickle'),
'rb') as fp:
verif = pickle.load(fp)
assert all([
np.max(np.abs(verif[idx][0] - output_[idx][0])) < 0.00001
for idx in range(7)
])
# checking the Flatten layer doesn't work because the layout was not changed in the run tool
# the layout for the output of the linear layer is a little different
assert np.max(np.abs(verif[8][0] - output_[7][0].flatten())) < 0.00001
assert np.array_equal(np.round(output_[-1][0].flatten()),
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
def validate(self,
qmode,
break_on_error=False,
inputs=None,
progress=None):
if inputs is None:
inputs = self._input_files
good_inputs = []
bad_inputs = []
good_margin = 0.0
bad_margin = 0.0
for file_per_input in inputs:
data = [
import_data(input_file, **self._input_args)
for input_file in file_per_input
]
executer = GraphExecuter(self._graph,
qrecs=self._graph.quantization)
outputs = executer.execute(data, qmode=qmode, silent=True)
predicted_values = np.asarray(outputs[self._prediction_step_idx])
good_prediction, _, _, margin = self._validation.validate(
file_per_input[0], predicted_values)
if good_prediction:
good_margin += margin
good_inputs.append(file_per_input)
else:
bad_margin += margin
bad_inputs.append(file_per_input)
if break_on_error:
break
if progress:
progress(good_prediction)
if good_inputs:
good_margin /= len(good_inputs)
if bad_inputs:
bad_margin /= len(bad_inputs)
return good_inputs, good_margin, bad_inputs, bad_margin
def gen_project(G,
settings,
project_folder,
script_commands,
overwrite=False,
performance=False,
quantized=False,
test_results=False,
save_inputs=False,
input_file=None,
input_args=None,
gen_atproject=False,
dump_tensors=False,
input_tensors=None,
tolerance=0.0):
settings = deepcopy(settings)
settings['graph_monitor_cycles'] = True
settings['graph_produce_node_names'] = True
settings['graph_produce_operinfos'] = True
code_gen = CodeGenerator(G, DefaultNamingConvension(G), settings)
if not os.path.exists(project_folder):
os.mkdir(project_folder)
qoutputs = None
if test_results:
np.random.seed(12345)
finput_tensors = []
input_tensors = []
for i, node in enumerate(G.input_nodes()):
out_q = G.quantization[NodeId(node)].out_qs[0]
if input_file:
file_per_input = glob_input_files(input_file, G.num_inputs)[0]
finput = import_data(file_per_input[i], **input_args)
else:
min_val = out_q.min if not out_q.is_floating else -1.0
max_val = out_q.max if not out_q.is_floating else 1.0
finput = get_rand(node.out_dims[0].shape,
low_high=(min_val, max_val))
finput_tensors.append(finput)
executer = GraphExecuter(G, qrecs=G.quantization)
qoutput_tensors = executer.execute(finput_tensors.copy(),
qmode=QuantizationMode.all())
qoutputs = []
for params in G.outputs():
outp = qoutput_tensors[params.step_idx][0]
qoutputs.append(outp)
for i, params in enumerate(G.input_nodes()):
inp = qoutput_tensors[params.step_idx][0]
input_tensors.append(inp)
if save_inputs:
nodeq = G.quantization[NodeId(params, None)].out_qs[0]
np.save(os.path.join(project_folder, f"fake_input_{i}.npy"),
nodeq.dequantize(inp))
main = os.path.join(project_folder, f"{code_gen.project_name}")
main_c = main + '.c'
main_h = main + '.h'
common_mk = os.path.join(project_folder, "common.mk")
nntool_script = os.path.join(project_folder, "nntool_script")
if overwrite or not os.path.exists(main_c):
with open(os.path.join(project_folder, f"{code_gen.project_name}.c"),
"w") as output_fp:
output_fp.write(
generate_main_appl_template(G, code_gen, input_tensors,
qoutputs, tolerance))
if overwrite or not os.path.exists(main_h):
with open(os.path.join(project_folder, f"{code_gen.project_name}.h"),
"w") as output_fp:
output_fp.write(generate_main_appl_header(G, code_gen))
if overwrite or not os.path.exists(common_mk):
open_args = parse_last_open(script_commands)
open_args = build_last_open_args(open_args) if open_args else ""
with open(os.path.join(project_folder, "common.mk"), "w") as output_fp:
if gen_atproject:
output_fp.write(
generate_main_appl_make_atproject(G, code_gen, quantized,
'Model.c'))
else:
output_fp.write(
generate_main_appl_make(G,
code_gen,
quantized,
open_args=open_args))
if overwrite or not os.path.exists(nntool_script):
with open(nntool_script, 'w') as fp:
# NOTE - gen_template_project is excluded so that tests work. Normally it will not be in the
# history.
fp.writelines(process_script(script_commands))
# always add performance since the main template uses it
for setting in [
'set graph_produce_node_names true',
'set graph_produce_operinfos true',
'set graph_monitor_cycles true'
]:
fp.write(f'{setting}\n')
if dump_tensors:
fp.write('set graph_dump_tensor 7\n')
if script_commands[-1] != "save_state":
fp.write('save_state\n')
if gen_atproject:
code_gen = CodeGenerator(G, DefaultNamingConvension(G), settings)
with open(os.path.join(project_folder, 'Model.c'), "w") as output_fp:
output_fp.write(default_template(G, code_generator=code_gen))
if G.has_expressions:
with open(os.path.join(project_folder, "Expression_Kernels.c"),
"w") as output_fp:
output_fp.write(
basic_kernel_source_template(G, code_generator=code_gen))
with open(os.path.join(project_folder, "Expression_Kernels.h"),
"w") as output_fp:
output_fp.write(
basic_kernel_header_template(G, code_generator=code_gen))
code_gen.write_constants(tensor_directory=project_folder)
ignore_function = None if overwrite else skip_existing_files(
project_folder)
shutil.copytree(os.path.join(os.environ.get("NNTOOL_PATH"),
'generation/project_template'),
project_folder,
dirs_exist_ok=True,
ignore=ignore_function)
if not gen_atproject:
try:
shutil.copy(
G.graph_identity.filename,
os.path.join(project_folder,
os.path.split(G.graph_identity.filename)[1]))
except shutil.SameFileError:
pass
def do_dump(self, args: argparse.Namespace):
"""
Dump the activations resulting from running an input file through the graph.
You can use the current quantization settings and can also just quantify one
specific step of the graph."""
self._check_graph()
dequantize = args.dequantize if args.dequantize is not None\
else not (args.pickle or args.save)
if args.quantize or args.quantize_step or args.quantize_all_steps:
self._check_quantized()
if args.quantize:
if dequantize:
qmode = QuantizationMode.all_dequantize()
else:
qmode = QuantizationMode.all()
elif args.quantize_all_steps:
qmode = QuantizationMode.step_all()
dequantize = True
else:
qmode = QuantizationMode.step(args.quantize_step)
elif args.quantize_and_dequantize:
qmode = QuantizationMode.all_float_quantize_dequantize()
else:
qmode = QuantizationMode.none()
if args.step is not None:
step = args.step
num_steps = len(self.G.graph_state.steps)
if step < 0:
step = num_steps + step
if step < 0 or step > num_steps:
self.perror("step must be from {} to {}".format(
-num_steps, num_steps))
return
else:
step = None
input_args = self._get_input_args(args)
pickles = []
for file_per_input in glob_input_files(args.input_files,
self.G.num_inputs):
LOG.info("input file %s", file_per_input)
data = [
import_data(input_file, **input_args)
for input_file in file_per_input
]
executer = GraphExecuter(self.G, qrecs=self.G.quantization)
outputs = executer.execute(data, step_idx_limit=step, qmode=qmode)
if args.pickle or self._in_py or args.save:
pickles.append(outputs)
else:
self.G.print_intermediates(outputs,
limit=step,
width=args.number_width,
precision=args.precision,
channel=args.channel,
order=['c', 'h', 'w'],
checksum=args.checksum)
if args.visualize_detection:
img_in = Image.open(file_per_input[0]).convert('RGBA')
height = img_in.size[1] if input_args[
'height'] == -1 else input_args['height']
width = img_in.size[0] if input_args[
'width'] == -1 else input_args['width']
img_in = img_in.resize((width, height))
if self.G.has_ssd_postprocess:
bboxes, classes, scores, _ = [
outputs[graph_out.step_idx][0]
for graph_out in self.G.outputs()
]
draw = ImageDraw.Draw(img_in, 'RGBA')
for box, score, class_id in zip(bboxes, scores, classes):
if args.quantize and not args.dequantize:
ssd_node = [
node for node in self.G.nodes()
if isinstance(node, SSDDetectorParameters)
][0]
ssd_qrec = self.G.quantization[NodeId(ssd_node)]
x0, x1 = int(box[1] * width *
ssd_qrec.out_qs[0].scale), int(
box[3] * width *
ssd_qrec.out_qs[0].scale)
y0, y1 = int(box[0] * height *
ssd_qrec.out_qs[0].scale), int(
box[2] * height *
ssd_qrec.out_qs[0].scale)
score = score * ssd_qrec.out_qs[2].scale
else:
x0, x1 = int(box[1] * width), int(box[3] * width)
y0, y1 = int(box[0] * height), int(box[2] * height)
rect_points = (x0, y0), (x1, y0), (x1, y1), (x0,
y1), (x0,
y0)
draw.line(rect_points, fill='red', width=2)
txt = '{}@{}%'.format(class_id, int(score * 100))
draw.text([x0, y0 - 10], txt, fill=(0, 255, 0))
img_in.show()
if args.pickle or args.save or self._in_py:
if not pickles:
self.perror("no input files found")
return
if len(args.input_files) == self.G.num_inputs:
pickles = pickles[0]
if args.pickle:
with open(args.pickle, 'wb') as pickle_fp:
pickle.dump(pickles, pickle_fp)
if args.save:
if len(args.input_files) != self.G.num_inputs:
self.perror(
"can only save dumps on one input to tensor store")
return
self.tensor_store[args.save] = pickles
if self._in_py:
self.last_result = pickles
def do_validate(self, args: argparse.Namespace):
"""
Validate the model (quantized [-q] or not) in terms of prediction accuracy rate on a given dataset (images
folder). Ground truth labels can be embedded in files names ("filename_03.[png, ppm, pgm]", the number of
digits must be coherent with the number of networks outputs: e.g. in a 1000 classes problem the last digits
must be 3, "file_45.png" will raise an error) or can be written in a .json object (example: {'file0':label0,
'file1':label1, ...}) and given to the function with --label_json
"""
self._check_graph()
if args.quantize:
self._check_quantized()
qmode = QuantizationMode.all_dequantize()
else:
qmode = QuantizationMode.none()
LOG.info("quantization mode - %s", qmode)
input_args = self._get_input_args(args)
good_predictions = []
good_margin = 0
bad_margin = 0
number_samples = sum(1 for _ in glob_input_files(args.input_files))
if args.vww_instances_file:
validation = ValidateFromVWWInstances(
args.vww_instances_file,
class_thr=args.class_thr,
binary_classification=args.binary_classification)
elif args.label_json:
validation = ValidateFromJSON(
args.label_json,
class_thr=args.class_thr,
binary_classification=args.binary_classification)
elif args.class_number is not None:
validation = ValidateFromClass(
args.class_number,
class_thr=args.class_thr,
binary_classification=args.binary_classification)
else:
validation = ValidateFromName(
class_thr=args.class_thr,
binary_classification=args.binary_classification)
try:
ExecutionProgress.start()
for i, file_per_input in enumerate(
glob_input_files(args.input_files, self.G.num_inputs)):
if not args.silent:
LOG.info("input file %s", file_per_input)
data = [
import_data(input_file, **input_args)
for input_file in file_per_input
]
executer = GraphExecuter(self.G, qrecs=self.G.quantization)
outputs = executer.execute(data,
qmode=qmode,
silent=args.silent)
predicted_values = np.asarray(
outputs[args.prediction_step_idx])
good_prediction, class_predicted, real_class, margin = validation.validate(
file_per_input[0], predicted_values)
good_predictions.append(good_prediction)
if good_prediction:
good_margin += margin
else:
bad_margin += margin
if not args.silent:
LOG.info(
'Prediction is %s predicted %s correct %s margin %s',
good_prediction, class_predicted, real_class, margin)
if not i % args.progress_every and i > 0:
LOG.info(
'ACCURACY: %.3f %%',
100 * sum(good_predictions) / len(good_predictions))
ExecutionProgress.progress(i, number_samples)
ExecutionProgress.end()
except (KeyboardInterrupt, SystemExit):
pass
self.py_locals['labels'] = validation.labels
self.py_locals['predictions'] = validation.predictions
cnt = len(good_predictions)
if cnt:
ngood = sum(good_predictions)
nbad = cnt - ngood
if nbad:
LOG.info(
"%s out of %s predicted falsly with %s average margin",
nbad, cnt, bad_margin / nbad)
if ngood:
LOG.info(
"%s out of %s predicted correctly with %s average margin",
ngood, cnt, good_margin / ngood)
accuracy_rate = 100 * sum(good_predictions) / len(good_predictions)
LOG.info('Total accuracy: %.3f %%', accuracy_rate)
def do_dump(self, args: argparse.Namespace):
"""
Dump the activations resulting from running an input file through the graph.
You can use the current quantization settings and can also just quantify one
specific step of the graph."""
self._check_graph()
dequantize = args.dequantize if args.dequantize is not None\
else not (args.pickle or args.save)
if args.quantize or args.quantize_step or args.quantize_all_steps:
self._check_quantized()
if args.quantize:
if dequantize:
qmode = QuantizationMode.all_dequantize()
else:
qmode = QuantizationMode.all()
elif args.quantize_all_steps:
qmode = QuantizationMode.step_all()
dequantize = True
else:
qmode = QuantizationMode.step(args.quantize_step)
elif args.quantize_and_dequantize:
qmode = QuantizationMode.all_float_quantize_dequantize()
else:
qmode = QuantizationMode.none()
if args.step is not None:
step = args.step
num_steps = len(self.G.graph_state.steps)
if step < 0:
step = num_steps + step
if step < 0 or step > num_steps:
self.perror("step must be from {} to {}".format(-num_steps, num_steps))
return
else:
step = None
input_args = self._get_input_args(args)
pickles = []
for file_per_input in glob_input_files(args.input_files, self.G.num_inputs):
LOG.info("input file %s", file_per_input)
data = [import_data(input_file, **input_args) for input_file in file_per_input]
executer = GraphExecuter(self.G, qrecs=self.G.quantization)
outputs = executer.execute(data, step_idx_limit=step,
qmode=qmode)
if args.pickle or self._in_py or args.save:
pickles.append(format_dump_file(self.G, outputs, not qmode.is_none,
args.dequantize, args.quantize_step))
else:
self.G.print_intermediates(outputs, limit=step, width=args.number_width,
precision=args.precision, channel=args.channel,
order=['c', 'h', 'w'])
if args.pickle or args.save or self._in_py:
if not pickles:
self.perror("no input files found")
return
if len(args.input_files) == 1:
pickles = pickles[0]
if args.pickle:
with open(args.pickle, 'wb') as pickle_fp:
pickle.dump(pickles, pickle_fp)
if args.save:
self.tensor_store[args.save] = pickles
if self._in_py:
self.last_result = pickles
