| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| from __future__ import unicode_literals |
| |
| r"""Importing this file includes common utility methods and base clases for |
| checking quantization api and properties of resulting modules. |
| """ |
| |
| import io |
| import torch |
| import torch.nn as nn |
| import torch.nn.quantized as nnq |
| import torch.nn.quantized.dynamic as nnqd |
| from common_utils import TestCase |
| from torch.quantization import QuantWrapper, QuantStub, DeQuantStub, \ |
| default_qconfig, default_per_channel_qconfig, QConfig, default_observer, default_weight_observer, \ |
| propagate_qconfig_, convert |
| from torch.quantization.default_mappings import DEFAULT_DYNAMIC_MODULE_MAPPING |
| |
| def test_only_eval_fn(model, calib_data): |
| r""" |
| Default evaluation function takes a torch.utils.data.Dataset or a list of |
| input Tensors and run the model on the dataset |
| """ |
| total, correct = 0, 0 |
| for data, target in calib_data: |
| output = model(data) |
| _, predicted = torch.max(output, 1) |
| total += target.size(0) |
| correct += (predicted == target).sum().item() |
| return correct / total |
| |
| _default_loss_fn = torch.nn.CrossEntropyLoss() |
| def test_only_train_fn(model, train_data, loss_fn=_default_loss_fn): |
| r""" |
| Default train function takes a torch.utils.data.Dataset and train the model |
| on the dataset |
| """ |
| optimizer = torch.optim.Adam(model.parameters(), lr=0.001) |
| train_loss, correct, total = 0, 0, 0 |
| for i in range(10): |
| model.train() |
| for data, target in train_data: |
| optimizer.zero_grad() |
| output = model(data) |
| loss = loss_fn(output, target) |
| loss.backward() |
| optimizer.step() |
| train_loss += loss.item() |
| _, predicted = torch.max(output, 1) |
| total += target.size(0) |
| correct += (predicted == target).sum().item() |
| return train_loss, correct, total |
| |
| def convert_dynamic(module): |
| convert(module, DEFAULT_DYNAMIC_MODULE_MAPPING, inplace=True) |
| |
| def prepare_dynamic(model, qconfig_dict=None): |
| propagate_qconfig_(model, qconfig_dict) |
| |
| # QuantizationTestCase used as a base class for testing quantization on modules |
| class QuantizationTestCase(TestCase): |
| def setUp(self): |
| super(QuantizationTestCase, self).setUp() |
| self.calib_data = [(torch.rand(2, 5, dtype=torch.float), torch.randint(0, 1, (2,), dtype=torch.long)) for _ in range(2)] |
| self.train_data = [(torch.rand(2, 5, dtype=torch.float), torch.randint(0, 1, (2,), dtype=torch.long)) for _ in range(2)] |
| self.img_data = [(torch.rand(2, 3, 10, 10, dtype=torch.float), torch.randint(0, 1, (2,), dtype=torch.long)) |
| for _ in range(2)] |
| |
| def checkNoPrepModules(self, module): |
| r"""Checks the module does not contain child |
| modules for quantization prepration, e.g. |
| quant, dequant and observer |
| """ |
| self.assertFalse(hasattr(module, 'quant')) |
| self.assertFalse(hasattr(module, 'dequant')) |
| |
| def checkHasPrepModules(self, module): |
| r"""Checks the module contains child |
| modules for quantization prepration, e.g. |
| quant, dequant and observer |
| """ |
| self.assertTrue(hasattr(module, 'module')) |
| self.assertTrue(hasattr(module, 'quant')) |
| self.assertTrue(hasattr(module, 'dequant')) |
| |
| def checkObservers(self, module): |
| r"""Checks the module or module's leaf descendants |
| have observers in preperation for quantization |
| """ |
| if hasattr(module, 'qconfig') and module.qconfig is not None and \ |
| len(module._modules) == 0 and not isinstance(module, torch.nn.Sequential): |
| self.assertTrue(hasattr(module, 'activation_post_process'), |
| 'module: ' + str(type(module)) + ' do not have observer') |
| for child in module.children(): |
| self.checkObservers(child) |
| |
| def checkQuantDequant(self, mod): |
| r"""Checks that mod has nn.Quantize and |
| nn.DeQuantize submodules inserted |
| """ |
| self.assertEqual(type(mod.quant), nnq.Quantize) |
| self.assertEqual(type(mod.dequant), nnq.DeQuantize) |
| |
| def checkWrappedQuantizedLinear(self, mod): |
| r"""Checks that mod has been swapped for an nnq.Linear |
| module, the bias is qint32, and that the module |
| has Quantize and DeQuantize submodules |
| """ |
| self.assertEqual(type(mod.module), nnq.Linear) |
| self.checkQuantDequant(mod) |
| |
| def checkQuantizedLinear(self, mod): |
| self.assertEqual(type(mod), nnq.Linear) |
| |
| def checkDynamicQuantizedLinear(self, mod): |
| r"""Checks that mod has been swapped for an nnqd.Linear |
| module, the bias is float. |
| """ |
| self.assertEqual(type(mod), nnqd.Linear) |
| |
| def checkLinear(self, mod): |
| self.assertEqual(type(mod), torch.nn.Linear) |
| |
| # calib_data follows the same schema as calib_data for |
| # test_only_eval_fn, i.e. (input iterable, output iterable) |
| def checkScriptable(self, orig_mod, calib_data, check_save_load=False): |
| scripted = torch.jit.script(orig_mod) |
| self._checkScriptable(orig_mod, scripted, calib_data, check_save_load) |
| |
| # Use first calib_data entry as trace input |
| # |
| # TODO: Trace checking is blocked on this issue: |
| # https://github.com/pytorch/pytorch/issues/23986 |
| # |
| # Once that's resolved we can remove `check_trace=False` |
| traced = torch.jit.trace(orig_mod, calib_data[0][0], check_trace=False) |
| self._checkScriptable(orig_mod, traced, calib_data, check_save_load) |
| |
| # Call this twice: once for a scripted module and once for a traced module |
| def _checkScriptable(self, orig_mod, script_mod, calib_data, check_save_load): |
| self._checkModuleCorrectnessAgainstOrig(orig_mod, script_mod, calib_data) |
| |
| # Test save/load |
| buffer = io.BytesIO() |
| torch.jit.save(script_mod, buffer) |
| |
| buffer.seek(0) |
| loaded_mod = torch.jit.load(buffer) |
| |
| # Pending __get_state_ and __set_state__ support |
| # See tracking task https://github.com/pytorch/pytorch/issues/23984 |
| if check_save_load: |
| self._checkModuleCorrectnessAgainstOrig(orig_mod, loaded_mod, calib_data) |
| |
| def _checkModuleCorrectnessAgainstOrig(self, orig_mod, test_mod, calib_data): |
| for (inp, _) in calib_data: |
| ref_output = orig_mod(inp) |
| scripted_output = test_mod(inp) |
| self.assertEqual(scripted_output, ref_output) |
| |
| # Below are a series of neural net models to use in testing quantization |
| class SingleLayerLinearModel(torch.nn.Module): |
| def __init__(self): |
| super(SingleLayerLinearModel, self).__init__() |
| self.fc1 = torch.nn.Linear(5, 5).to(dtype=torch.float) |
| |
| def forward(self, x): |
| x = self.fc1(x) |
| return x |
| |
| class AnnotatedSingleLayerLinearModel(torch.nn.Module): |
| def __init__(self): |
| super(AnnotatedSingleLayerLinearModel, self).__init__() |
| self.qconfig = default_qconfig |
| self.fc1 = QuantWrapper(torch.nn.Linear(5, 5).to(dtype=torch.float)) |
| |
| def forward(self, x): |
| x = self.fc1(x) |
| return x |
| |
| class SingleLayerLinearDynamicModel(torch.nn.Module): |
| def __init__(self): |
| super(SingleLayerLinearDynamicModel, self).__init__() |
| self.qconfig = default_qconfig |
| self.fc1 = torch.nn.Linear(5, 5).to(dtype=torch.float) |
| |
| def forward(self, x): |
| x = self.fc1(x) |
| return x |
| |
| class LSTMDynamicModel(torch.nn.Module): |
| def __init__(self): |
| super(LSTMDynamicModel, self).__init__() |
| self.qconfig = default_qconfig |
| self.lstm = torch.nn.LSTM(2, 2).to(dtype=torch.float) |
| |
| def forward(self, x): |
| x = self.lstm(x) |
| return x |
| |
| class TwoLayerLinearModel(torch.nn.Module): |
| def __init__(self): |
| super(TwoLayerLinearModel, self).__init__() |
| self.fc1 = torch.nn.Linear(5, 8).to(dtype=torch.float) |
| self.fc2 = torch.nn.Linear(8, 5).to(dtype=torch.float) |
| |
| def forward(self, x): |
| x = self.fc1(x) |
| x = self.fc2(x) |
| return x |
| |
| class AnnotatedTwoLayerLinearModel(torch.nn.Module): |
| def __init__(self): |
| super(AnnotatedTwoLayerLinearModel, self).__init__() |
| self.fc1 = torch.nn.Linear(5, 8).to(dtype=torch.float) |
| self.fc2 = QuantWrapper(torch.nn.Linear(8, 5).to(dtype=torch.float)) |
| self.fc2.qconfig = torch.quantization.get_default_qconfig("fbgemm") |
| |
| def forward(self, x): |
| x = self.fc1(x) |
| x = self.fc2(x) |
| return x |
| |
| class LinearReluModel(torch.nn.Module): |
| def __init__(self): |
| super(LinearReluModel, self).__init__() |
| self.fc = torch.nn.Linear(5, 5).to(dtype=torch.float) |
| self.relu = torch.nn.ReLU() |
| |
| def forward(self, x): |
| x = self.relu(self.fc(x)) |
| return x |
| |
| class NestedModel(torch.nn.Module): |
| def __init__(self): |
| super(NestedModel, self).__init__() |
| self.sub1 = LinearReluModel() |
| self.sub2 = TwoLayerLinearModel() |
| self.fc3 = torch.nn.Linear(5, 5).to(dtype=torch.float) |
| |
| def forward(self, x): |
| x = self.sub1(x) |
| x = self.sub2(x) |
| x = self.fc3(x) |
| return x |
| |
| class AnnotatedNestedModel(torch.nn.Module): |
| def __init__(self): |
| super(AnnotatedNestedModel, self).__init__() |
| self.sub1 = LinearReluModel() |
| self.sub2 = TwoLayerLinearModel() |
| self.fc3 = QuantWrapper(torch.nn.Linear(5, 5).to(dtype=torch.float)) |
| self.fc3.qconfig = default_qconfig |
| self.sub2.fc1 = QuantWrapper(self.sub2.fc1) |
| self.sub2.fc1.qconfig = default_per_channel_qconfig |
| |
| def forward(self, x): |
| x = self.sub1(x) |
| x = self.sub2(x) |
| x = self.fc3(x) |
| return x |
| |
| class AnnotatedSubNestedModel(torch.nn.Module): |
| def __init__(self): |
| super(AnnotatedSubNestedModel, self).__init__() |
| self.sub1 = LinearReluModel() |
| self.sub2 = QuantWrapper(TwoLayerLinearModel()) |
| self.fc3 = QuantWrapper(torch.nn.Linear(5, 5).to(dtype=torch.float)) |
| self.fc3.qconfig = default_qconfig |
| self.sub2.qconfig = default_qconfig |
| |
| def forward(self, x): |
| x = self.sub1(x) |
| x = self.sub2(x) |
| x = self.fc3(x) |
| return x |
| |
| class AnnotatedCustomConfigNestedModel(torch.nn.Module): |
| def __init__(self): |
| super(AnnotatedCustomConfigNestedModel, self).__init__() |
| self.sub1 = LinearReluModel() |
| self.sub2 = TwoLayerLinearModel() |
| self.fc3 = QuantWrapper(torch.nn.Linear(5, 5).to(dtype=torch.float)) |
| self.fc3.qconfig = default_qconfig |
| self.sub2.qconfig = default_qconfig |
| |
| custom_options = { |
| 'dtype': torch.quint8, |
| 'qscheme': torch.per_tensor_affine |
| } |
| custom_qconfig = QConfig(activation=default_observer.with_args(**custom_options), |
| weight=default_weight_observer) |
| self.sub2.fc1.qconfig = custom_qconfig |
| |
| self.sub2.fc1 = QuantWrapper(self.sub2.fc1) |
| self.sub2.fc2 = QuantWrapper(self.sub2.fc2) |
| |
| def forward(self, x): |
| x = self.sub1(x) |
| x = self.sub2(x) |
| x = self.fc3(x) |
| return x |
| |
| class QuantSubModel(torch.nn.Module): |
| def __init__(self): |
| super(QuantSubModel, self).__init__() |
| self.sub1 = LinearReluModel() |
| self.sub2 = QuantWrapper(TwoLayerLinearModel()) |
| self.sub2.qconfig = default_qconfig |
| self.fc3 = torch.nn.Linear(5, 5).to(dtype=torch.float) |
| self.fc3.qconfig = default_qconfig |
| |
| def forward(self, x): |
| x = self.sub1(x) |
| x = self.sub2(x) |
| x = self.fc3(x) |
| return x |
| |
| class InnerModule(torch.nn.Module): |
| def __init__(self): |
| super(InnerModule, self).__init__() |
| self.fc1 = torch.nn.Linear(5, 8).to(dtype=torch.float) |
| self.relu = torch.nn.ReLU() |
| self.fc2 = torch.nn.Linear(8, 5).to(dtype=torch.float) |
| |
| def forward(self, x): |
| return self.relu(self.fc2(self.relu(self.fc1(x)))) |
| |
| class SkipQuantModel(torch.nn.Module): |
| r"""We can skip quantization by explicitly |
| setting qconfig of a submodule to None |
| """ |
| def __init__(self): |
| super(SkipQuantModel, self).__init__() |
| self.qconfig = default_qconfig |
| self.sub = QuantWrapper(InnerModule()) |
| self.fc = torch.nn.Linear(5, 5).to(dtype=torch.float) |
| # don't quantize this fc |
| self.fc.qconfig = None |
| |
| def forward(self, x): |
| return self.fc(self.sub(x)) |
| |
| class QuantStubModel(torch.nn.Module): |
| r"""A Module with manually inserted `QuantStub` and `DeQuantStub` |
| """ |
| def __init__(self): |
| super(QuantStubModel, self).__init__() |
| self.qconfig = torch.quantization.get_default_qconfig("qnnpack") |
| self.quant = QuantStub() |
| self.dequant = DeQuantStub() |
| self.fc = torch.nn.Linear(5, 5).to(dtype=torch.float) |
| |
| def forward(self, x): |
| x = self.quant(x) |
| x = self.fc(x) |
| return self.dequant(x) |
| |
| class ManualLinearQATModel(torch.nn.Module): |
| r"""A Module with manually inserted `QuantStub` and `DeQuantStub` |
| """ |
| def __init__(self): |
| super(ManualLinearQATModel, self).__init__() |
| self.qconfig = torch.quantization.get_default_qat_qconfig("fbgemm") |
| self.quant = QuantStub() |
| self.dequant = DeQuantStub() |
| self.fc1 = torch.nn.Linear(5, 1).to(dtype=torch.float) |
| self.fc2 = torch.nn.Linear(1, 10).to(dtype=torch.float) |
| |
| def forward(self, x): |
| x = self.quant(x) |
| x = self.fc1(x) |
| x = self.fc2(x) |
| return self.dequant(x) |
| |
| class ManualConvLinearQATModel(torch.nn.Module): |
| r"""A module with manually inserted `QuantStub` and `DeQuantStub` |
| and contains both linear and conv modules |
| """ |
| def __init__(self): |
| super(ManualConvLinearQATModel, self).__init__() |
| self.qconfig = torch.quantization.get_default_qat_qconfig("qnnpack") |
| self.quant = QuantStub() |
| self.dequant = DeQuantStub() |
| self.conv = torch.nn.Conv2d(3, 1, kernel_size=3).to(dtype=torch.float) |
| self.fc1 = torch.nn.Linear(64, 10).to(dtype=torch.float) |
| self.fc2 = torch.nn.Linear(10, 10).to(dtype=torch.float) |
| |
| def forward(self, x): |
| x = self.quant(x) |
| x = self.conv(x) |
| x = x.view(-1, 64).contiguous() |
| x = self.fc1(x) |
| x = self.fc2(x) |
| return self.dequant(x) |
| |
| |
| class SubModelForFusion(nn.Module): |
| def __init__(self): |
| super(SubModelForFusion, self).__init__() |
| self.conv = nn.Conv2d(2, 2, 1, bias=None).to(dtype=torch.float) |
| self.bn = nn.BatchNorm2d(2).to(dtype=torch.float) |
| |
| def forward(self, x): |
| x = self.conv(x) |
| x = self.bn(x) |
| return x |
| |
| |
| class SubModelWithoutFusion(nn.Module): |
| def __init__(self): |
| super(SubModelWithoutFusion, self).__init__() |
| self.conv = nn.Conv2d(2, 2, 1, bias=None).to(dtype=torch.float) |
| self.relu = nn.ReLU(inplace=False).to(dtype=torch.float) |
| |
| def forward(self, x): |
| return self.relu(self.conv(x)) |
| |
| class ModelForFusion(nn.Module): |
| def __init__(self, qconfig): |
| super(ModelForFusion, self).__init__() |
| self.conv1 = nn.Conv2d(3, 2, 5, bias=None).to(dtype=torch.float) |
| self.bn1 = nn.BatchNorm2d(2).to(dtype=torch.float) |
| self.relu1 = nn.ReLU(inplace=False).to(dtype=torch.float) |
| self.sub1 = SubModelForFusion() |
| self.sub2 = SubModelWithoutFusion() |
| self.fc = nn.Linear(72, 10).to(dtype=torch.float) |
| self.quant = QuantStub() |
| self.dequant = DeQuantStub() |
| self.qconfig = qconfig |
| # don't quantize sub2 |
| self.sub2.qconfig = None |
| self.fc.qconfig = None |
| |
| def forward(self, x): |
| x = self.quant(x) |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.relu1(x) |
| x = self.sub1(x) |
| x = self.dequant(x) |
| x = self.sub2(x) |
| x = x.view(-1, 72).contiguous() |
| x = self.fc(x) |
| return x |
| |
| class ConvBNReLU(nn.Sequential): |
| def __init__(self): |
| super(ConvBNReLU, self).__init__( |
| nn.Conv2d(3, 3, 1, 1, bias=False), |
| nn.BatchNorm2d(3), |
| nn.ReLU(inplace=False) |
| ) |
| |
| class ModelWithSequentialFusion(nn.Module): |
| def __init__(self): |
| super(ModelWithSequentialFusion, self).__init__() |
| self.conv1 = nn.Conv2d(3, 3, 1) |
| self.relu1 = nn.ReLU(inplace=False) |
| layers = [] |
| for i in range(3): |
| layers.append(ConvBNReLU()) |
| self.features = nn.Sequential(*layers) |
| head = [nn.Linear(300, 10), nn.ReLU(inplace=False)] |
| self.classifier = nn.Sequential(*head) |
| self.seq = nn.Sequential() |
| self.quant = QuantStub() |
| self.dequant = DeQuantStub() |
| |
| def forward(self, x): |
| x = self.quant(x) |
| x = self.conv1(x) |
| x = self.relu1(x) |
| x = self.features(x) |
| x = torch.reshape(x, (-1, 3 * 10 * 10)) |
| x = self.classifier(x) |
| x = self.seq(x) |
| x = self.dequant(x) |
| return x |
| |
| |
| class DummyObserver(torch.nn.Module): |
| def calculate_qparams(self): |
| return 1.0, 0 |
| |
| def forward(self, x): |
| return x |
| |
| |
| class ModelWithFunctionals(torch.nn.Module): |
| def __init__(self): |
| super(ModelWithFunctionals, self).__init__() |
| self.mycat = nnq.FloatFunctional() |
| self.myadd = nnq.FloatFunctional() |
| self.myadd_relu = nnq.FloatFunctional() |
| # Tracing doesnt work yet for c10 ops with scalar inputs |
| # https://github.com/pytorch/pytorch/issues/27097 |
| # self.my_scalar_add = nnq.FloatFunctional() |
| # self.my_scalar_mul = nnq.FloatFunctional() |
| |
| def forward(self, x): |
| y = self.mycat.cat([x, x, x]) |
| z = self.myadd.add(y, y) |
| w = self.myadd_relu.add_relu(z, z) |
| # Tracing doesnt work yet for c10 ops with scalar inputs |
| # https://github.com/pytorch/pytorch/issues/27097 |
| # w = self.my_scalar_add.add_scalar(w, -0.5) |
| # w = self.my_scalar_mul.mul_scalar(w, 0.5) |
| return w |
| |
| |
| class ResNetBase(torch.nn.Module): |
| def __init__(self): |
| super(ResNetBase, self).__init__() |
| norm_layer = nn.BatchNorm2d |
| inplanes = 3 |
| self.conv1 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False) |
| self.bn1 = norm_layer(inplanes) |
| self.relu1 = nn.ReLU() |
| self.relu2 = nn.ReLU() |
| self.downsample = torch.nn.Identity() |
| self.myop = nn.quantized.FloatFunctional() |
| self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
| |
| |
| def forward(self, x): |
| out = self.conv1(x) |
| out = self.bn1(out) |
| out = self.relu1(out) |
| identity = self.downsample(x) |
| out = self.myop.add(out, identity) |
| out = self.relu2(out) |
| out = self.avgpool(out) |
| return out |
| |
| class ModelMultipleOps(torch.nn.Module): |
| def __init__(self): |
| super(ModelMultipleOps, self).__init__() |
| norm_layer = nn.BatchNorm2d |
| inplanes = 3 |
| self.conv1 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False) |
| self.conv2 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False) |
| self.bn1 = norm_layer(inplanes) |
| self.relu1 = nn.ReLU() |
| self.relu2 = nn.ReLU() |
| self.downsample = torch.nn.Identity() |
| self.skip_add = nn.quantized.FloatFunctional() |
| self.cat = nn.quantized.FloatFunctional() |
| self.avgpool = nn.AdaptiveAvgPool2d((4, 4)) |
| self.fc = nn.Linear(12, 6) |
| |
| def forward(self, x): |
| out = self.conv1(x) |
| out = self.bn1(out) |
| out = self.relu1(out) |
| identity = self.downsample(x) |
| out = self.skip_add.add(out, identity) |
| out = self.relu2(out) |
| out = self.avgpool(out) |
| out = self.conv2(out) |
| out = torch.nn.functional.max_pool2d(out, 2, 2) |
| out = self.cat.cat([out, out]) |
| out = out.view(-1, 3 * 2 * 2) |
| out = self.fc(out) |
| return out |
| |
| # Model to ensure consistency of fake quant with true quant |
| # Average pooling and mean operations are not modelled |
| # accurately with fake-quant so this model does not |
| # contain those operations |
| class ModelMultipleOpsNoAvgPool(torch.nn.Module): |
| def __init__(self): |
| super(ModelMultipleOpsNoAvgPool, self).__init__() |
| norm_layer = nn.BatchNorm2d |
| inplanes = 3 |
| self.conv1 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False) |
| self.conv2 = nn.Conv2d(inplanes, inplanes, (1, 1), bias=False) |
| self.bn1 = norm_layer(inplanes) |
| self.relu1 = nn.ReLU() |
| self.relu2 = nn.ReLU() |
| self.skip_add = nn.quantized.FloatFunctional() |
| self.cat = nn.quantized.FloatFunctional() |
| self.maxpool = nn.MaxPool2d((4, 4)) |
| self.fc = nn.Linear(12, 6) |
| |
| def forward(self, x): |
| out = self.conv1(x) |
| out = self.bn1(out) |
| out = self.relu1(out) |
| skip = self.conv2(x) |
| out = self.skip_add.add(out, skip) |
| out = self.relu2(out) |
| out = self.maxpool(out) |
| out = self.conv2(out) |
| out = torch.nn.functional.max_pool2d(out, 2, 2) |
| out = self.cat.cat([out, out]) |
| out = out.view(-1, 3 * 2 * 2) |
| out = self.fc(out) |
| return out |
| |
| """Model to make sure that the observers are not inserted into custom modules. |
| """ |
| class ModelWithNoQconfigPropagation(nn.Module): |
| class ListOutModule(nn.Module): |
| def __init__(self): |
| super(ModelWithNoQconfigPropagation.ListOutModule, self).__init__() |
| |
| def forward(self, x): |
| # returns a list of tensors, not supported by observers |
| return [x] |
| |
| def __init__(self): |
| super(ModelWithNoQconfigPropagation, self).__init__() |
| self.fc1 = nn.Linear(5, 5).to(dtype=torch.float) |
| self.quant = QuantStub() |
| self.dequant = DeQuantStub() |
| self.no_quant_module = self.ListOutModule() |
| |
| def forward(self, x): |
| x = self.quant(x) |
| x = self.fc1(x) |
| x = self.dequant(x) |
| x = self.no_quant_module(x) |
| return x |
