def test_quantize_network(self, model_name, model_builder, input_size):
net = model_builder()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
qnet = QuantizedNetwork(net, input_size)
_ = qnet(torch.zeros(*input_size))
_ = qnet(torch.zeros(*input_size))
check_graph(to_networkx(ctx), model_name, 'quantized')
def test_quantize_network(self, model_name, model_builder, input_size, _quantize_config):
net = model_builder()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
qnet = QuantizedNetwork(net, _quantize_config.quantizer, input_size,
dummy_forward_fn=create_dummy_forward_fn(input_size))
_ = qnet(torch.zeros(*input_size))
_ = qnet(torch.zeros(*input_size))
check_graph(to_networkx(ctx), model_name, _quantize_config.graph_dir)
def test_build_graph(self, model_name, model_builder, input_size):
net = model_builder()
ctx = reset_context('test')
with context('test') as c:
_ = net(torch.zeros(input_size))
c.reset_scope_operator_call_counters()
_ = net(torch.zeros(input_size))
check_graph(to_networkx(ctx), model_name, 'original')
def test_resnet18__with_qinput():
net = test_models.ResNet18()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
input_shape = (1, 3, 32, 32)
qnet = QuantizedNetwork(net, input_shape, quantize_inputs=True)
_ = qnet(torch.zeros(*input_shape))
_ = qnet(torch.zeros(*input_shape))
check_graph(to_networkx(ctx), 'resnet18_qinput.dot', 'quantized')
def test_resnet18__with_ignore():
net = test_models.ResNet18()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
input_shape = (1, 3, 32, 32)
qnet = QuantizedNetwork(net,
input_shape,
ignored_scopes=['ResNet/Sequential[layer3]'])
_ = qnet(torch.zeros(*input_shape))
_ = qnet(torch.zeros(*input_shape))
check_graph(to_networkx(ctx), 'resnet18_ignore.dot', 'quantized')
def test_output_quantization(_quantize_config):
net = test_models.UNet()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
input_shape = (1, 3, 360, 480)
qnet = QuantizedNetwork(net, _quantize_config.quantizer, input_shape,
dummy_forward_fn=create_dummy_forward_fn(input_shape),
quantize_outputs=True)
_ = qnet(torch.zeros(*input_shape))
_ = qnet(torch.zeros(*input_shape))
check_graph(to_networkx(ctx), 'unet_qoutput.dot', _quantize_config.graph_dir)
def test_resnet18__with_ignore(_quantize_config):
net = test_models.ResNet18()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
input_shape = (1, 3, 32, 32)
qnet = QuantizedNetwork(net, _quantize_config.quantizer, input_shape,
dummy_forward_fn=create_dummy_forward_fn(input_shape),
ignored_scopes=['ResNet/Sequential[layer3]'])
_ = qnet(torch.zeros(*input_shape))
_ = qnet(torch.zeros(*input_shape))
check_graph(to_networkx(ctx), 'resnet18_ignore.dot', _quantize_config.graph_dir)
def test_resnet18__with_not_qinput(_quantize_config):
net = test_models.ResNet18()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
input_shape = (1, 3, 32, 32)
qnet = QuantizedNetwork(net, _quantize_config.quantizer, input_shape,
dummy_forward_fn=create_dummy_forward_fn(input_shape),
quantize_inputs=False)
_ = qnet(torch.zeros(*input_shape))
_ = qnet(torch.zeros(*input_shape))
check_graph(to_networkx(ctx), 'resnet18_no_qinput.dot', _quantize_config.graph_dir)
def test_custom_quantizable_subgraph_patterns(_quantize_config):
net = test_models.SENet18()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
input_shape = (1, 3, 32, 32)
qnet = QuantizedNetwork(net, _quantize_config.quantizer, input_shape,
dummy_forward_fn=create_dummy_forward_fn(input_shape),
quantize_outputs=False,
quantizable_subgraph_patterns=(("sigmoid", "__mul__"),
("__iadd__", "batch_norm")))
_ = qnet(torch.zeros(*input_shape))
_ = qnet(torch.zeros(*input_shape))
check_graph(to_networkx(ctx), 'senet_custom_patterns.dot', _quantize_config.graph_dir)
def test_iterate_module_list():
class Net(nn.Module):
def __init__(self):
super().__init__()
self.ml = nn.ModuleList([nn.Conv2d(1, 1, 1), nn.Conv2d(1, 1, 1)])
def forward(self, x):
return [self.ml[0](x), self.ml[1](x)]
net = Net()
ctx = reset_context('test')
with context('test'):
_ = net(torch.zeros(1, 1, 1, 1))
check_graph(to_networkx(ctx), 'case_iterate_module_list.dot', 'original')
def test_sparse_network(self, model_name, model_builder, input_size, algo, params):
model = model_builder()
from nncf.layers import NNCF_MODULES_MAP
sparsifiable_modules = list(NNCF_MODULES_MAP.values())
ref_num_sparsed = len(get_all_modules_by_type(model, sparsifiable_modules))
ctx = reset_context('test')
config = get_empty_config(input_sample_size=input_size)
config["compression"] = {"algorithm": algo, "params": params}
compression_algo = create_compression_algorithm(model, config)
assert ref_num_sparsed == len(compression_algo.sparsified_module_info)
model = compression_algo.model
with context('test') as c:
_ = model(torch.zeros(input_size))
c.reset_scope_operator_call_counters()
_ = model(torch.zeros(input_size))
check_graph(to_networkx(ctx), model_name, algo)
