From a0e82c2bdf23b96766eed7e1a9b23435aab835cf Mon Sep 17 00:00:00 2001 From: huangxu96 <46740794+huangxu96@users.noreply.github.com> Date: Wed, 20 Jan 2021 10:27:28 +0800 Subject: [PATCH] [Cherry-pick]Implemented AddQuantDequantPass in imperative quantization. (#26692) (#30525) * Implemented AddQuantDequantPass in imperative quantization. * support 2.0 API such as Pool2D and ReLU --- .../slim/quantization/imperative/qat.py | 21 +- .../slim/quantization/imperative/quant_nn.py | 30 +- .../fluid/contrib/slim/tests/CMakeLists.txt | 25 + .../test_imperative_qat_addquantdequant.py | 477 ++++++++++++++++++ .../tests/test_imperative_qat_channelwise.py | 4 +- 5 files changed, 549 insertions(+), 8 deletions(-) create mode 100644 python/paddle/fluid/contrib/slim/tests/test_imperative_qat_addquantdequant.py diff --git a/python/paddle/fluid/contrib/slim/quantization/imperative/qat.py b/python/paddle/fluid/contrib/slim/quantization/imperative/qat.py index 6526cf11f06..26fa0f0d484 100644 --- a/python/paddle/fluid/contrib/slim/quantization/imperative/qat.py +++ b/python/paddle/fluid/contrib/slim/quantization/imperative/qat.py @@ -86,7 +86,7 @@ class ImperativeQuantAware(object): 'moving_average_abs_max', the static quantization scale will be calculated during training and used in inference. moving_rate(float): the parameter for 'moving_average_abs_max' quantization. - quantizable_op_type(list[str]): List the type of layers that will be quantized. + quantizable_layer_type(list[str]): List the type of layers that will be quantized. Default is ['Conv2D', 'Linear']. The quantizable_op_type in QuantizationFreezePass and ConvertToInt8Pass must be the same as this. weight_preprocess_layer(paddle.nn.Layer, optional): A paddle Layer that defines how to preprocess @@ -229,7 +229,17 @@ class ImperativeQuantAware(object): "'abs_max' or 'moving_average_abs_max' or 'channel_wise_abs_max' now." % (str(weight_quantize_type))) - self._quant_layers_map = {'Conv2D': Conv2D, 'Linear': Linear} + self._quant_layers_map = { + 'Conv2D': Conv2D, + 'Linear': Linear, + 'Pool2D': Pool2D, + 'ReLU': ReLU, + 'LeakyReLU': LeakyReLU, + 'ReLU6': ReLU6, + 'Softmax': Softmax, + 'Tanh': Tanh, + 'Swish': Swish + } self._quantizable_layer_type = tuple( self._quant_layers_map[layer] if layer in self._quant_layers_map else layer @@ -291,7 +301,12 @@ class ImperativeQuantAware(object): layer.full_name())) sys.exit(-1) - quantized_layer = quant_nn.__dict__[quantized_counterpart[index]]( + layer_with_weight = ['QuantizedConv2D', 'QuantizedLinear'] + if quantized_counterpart[index] not in layer_with_weight: + quant_layer_class_name = 'QuantizedNoweightLayer' + else: + quant_layer_class_name = quantized_counterpart[index] + quantized_layer = quant_nn.__dict__[quant_layer_class_name]( layer, self._weight_bits, self._activation_bits, self._moving_rate, self._weight_quantize_type, self._activation_quantize_type, self._weight_pre_layer, self._act_pre_layer, diff --git a/python/paddle/fluid/contrib/slim/quantization/imperative/quant_nn.py b/python/paddle/fluid/contrib/slim/quantization/imperative/quant_nn.py index 3b3e0abf45c..0469de7aef2 100644 --- a/python/paddle/fluid/contrib/slim/quantization/imperative/quant_nn.py +++ b/python/paddle/fluid/contrib/slim/quantization/imperative/quant_nn.py @@ -24,9 +24,9 @@ from paddle.fluid.data_feeder import check_variable_and_dtype from paddle.nn import functional as F __all__ = [ - 'FakeQuantMovingAverage', 'FakeQuantAbsMax', 'QuantizedConv2D', - 'QuantizedLinear', 'FakeChannelWiseQuantDequantAbsMax', - 'MovingAverageAbsMaxScale' + 'FakeQuantMovingAverage', 'FakeQuantAbsMax', + 'FakeChannelWiseQuantDequantAbsMax', 'QuantizedConv2D', 'QuantizedLinear', + 'QuantizedNoweightLayer', 'MovingAverageAbsMaxScale' ] @@ -478,6 +478,30 @@ class QuantizedLinear(layers.Layer): return out +class QuantizedNoweightLayer(layers.Layer): + def __init__(self, + layer, + weight_bits=8, + activation_bits=8, + moving_rate=0.9, + *args, + **kwargs): + + super(QuantizedNoweightLayer, self).__init__() + self._layer = layer + self._fake_quant_input = _get_fake_quant_type( + 'moving_average_abs_max', + name=layer.full_name(), + moving_rate=moving_rate, + quant_bits=activation_bits, + dtype=self._dtype, + quant_on_weight=False) + + def forward(self, input): + quant_input = self._fake_quant_input(input) + return self._layer.forward(quant_input) + + class MovingAverageAbsMaxScale(layers.Layer): def __init__(self, name=None, moving_rate=0.9, dtype='float32'): r""" diff --git a/python/paddle/fluid/contrib/slim/tests/CMakeLists.txt b/python/paddle/fluid/contrib/slim/tests/CMakeLists.txt index ca8ef452cfc..743547b2708 100644 --- a/python/paddle/fluid/contrib/slim/tests/CMakeLists.txt +++ b/python/paddle/fluid/contrib/slim/tests/CMakeLists.txt @@ -270,6 +270,30 @@ list(REMOVE_ITEM TEST_OPS LIST(REMOVE_ITEM TEST_OPS test_auto_pruning) LIST(REMOVE_ITEM TEST_OPS test_filter_pruning) +# only tests on singal GPU environment +LIST(REMOVE_ITEM TEST_OPS test_imperative_qat_addquantdequant) + +py_test_modules(test_imperative_qat_addquantdequant MODULES test_imperative_qat_addquantdequant ENVS + CUDA_VISIBLE_DEVICES=0) + +# fix +if(WIN32) + SET(SINGLE_CARD_TEST_OPS + test_user_defined_quantization + test_quantization_scale_pass + test_quantization_pass + test_moving_average_abs_max_scale_op + test_imperative_qat_channelwise + test_imperative_qat + test_imperative_out_scale + test_graph) + LIST(REMOVE_ITEM TEST_OPS ${SINGLE_CARD_TEST_OPS}) + foreach(src ${SINGLE_CARD_TEST_OPS}) + py_test(${src} SRCS ${src}.py ENVS CUDA_VISIBLE_DEVICES=0) + endforeach() +endif() + + foreach(src ${TEST_OPS}) py_test(${src} SRCS ${src}.py) endforeach() @@ -288,6 +312,7 @@ set_tests_properties(test_quantization_pass PROPERTIES TIMEOUT 120) set_tests_properties(test_imperative_qat_channelwise PROPERTIES TIMEOUT 120) set_tests_properties(test_user_defined_quantization PROPERTIES TIMEOUT 120) set_tests_properties(test_imperative_qat PROPERTIES TIMEOUT 120) +set_tests_properties(test_imperative_qat_addquantdequant PROPERTIES TIMEOUT 120) set_tests_properties(test_imperative_out_scale PROPERTIES TIMEOUT 120) if(LINUX AND WITH_MKLDNN) set_tests_properties(test_quant2_int8_mobilenetv1_mkldnn PROPERTIES TIMEOUT 120) diff --git a/python/paddle/fluid/contrib/slim/tests/test_imperative_qat_addquantdequant.py b/python/paddle/fluid/contrib/slim/tests/test_imperative_qat_addquantdequant.py new file mode 100644 index 00000000000..9d2b2d726e3 --- /dev/null +++ b/python/paddle/fluid/contrib/slim/tests/test_imperative_qat_addquantdequant.py @@ -0,0 +1,477 @@ +# copyright (c) 2018 paddlepaddle authors. all rights reserved. +# +# licensed under the apache license, version 2.0 (the "license"); +# you may not use this file except in compliance with the license. +# you may obtain a copy of the license at +# +# http://www.apache.org/licenses/license-2.0 +# +# unless required by applicable law or agreed to in writing, software +# distributed under the license is distributed on an "as is" basis, +# without warranties or conditions of any kind, either express or implied. +# see the license for the specific language governing permissions and +# limitations under the license. + +from __future__ import print_function + +import os +import numpy as np +import random +import unittest +import logging +import paddle +import six +import paddle.fluid as fluid +from paddle.nn import functional +from paddle.nn import Linear, Conv2D, Softmax, BatchNorm +from paddle.fluid.layers import nn +from paddle.fluid import core +from paddle.fluid.layer_helper import LayerHelper +from paddle.fluid.optimizer import AdamOptimizer +from paddle.fluid.framework import IrGraph +from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware, QuantizationTransformPass, AddQuantDequantPass +from paddle.fluid.dygraph.container import Sequential +from paddle.fluid.dygraph.nn import Pool2D +from paddle.nn.layer.activation import ReLU, LeakyReLU, ReLU6, Tanh, Swish +from paddle.fluid.log_helper import get_logger +from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX + +paddle.enable_static() + +os.environ["CPU_NUM"] = "1" +if core.is_compiled_with_cuda(): + fluid.set_flags({"FLAGS_cudnn_deterministic": True}) + +_logger = get_logger( + __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') + + +def StaticLenet(data, num_classes=10): + conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1") + conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2") + conv2d_w3_attr = fluid.ParamAttr(name="conv2d_w_3") + fc_w1_attr = fluid.ParamAttr(name="fc_w_1") + fc_w2_attr = fluid.ParamAttr(name="fc_w_2") + fc_w3_attr = fluid.ParamAttr(name="fc_w_3") + conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1") + conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2") + conv2d_b3_attr = fluid.ParamAttr(name="conv2d_b_3") + fc_b1_attr = fluid.ParamAttr(name="fc_b_1") + fc_b2_attr = fluid.ParamAttr(name="fc_b_2") + fc_b3_attr = fluid.ParamAttr(name="fc_b_3") + + conv1 = fluid.layers.conv2d( + data, + num_filters=6, + filter_size=3, + stride=1, + padding=1, + param_attr=conv2d_w1_attr, + bias_attr=conv2d_b1_attr) + conv1 = fluid.layers.leaky_relu(conv1, alpha=0.02) + pool1 = fluid.layers.pool2d( + conv1, pool_size=2, pool_type='max', pool_stride=2) + conv2 = fluid.layers.conv2d( + pool1, + num_filters=16, + filter_size=5, + stride=1, + padding=0, + param_attr=conv2d_w2_attr, + bias_attr=conv2d_b2_attr) + pool2 = fluid.layers.pool2d( + conv2, pool_size=2, pool_type='max', pool_stride=2) + pool2 = fluid.layers.relu(pool2) + pool2 = fluid.layers.swish(pool2) + conv3 = fluid.layers.conv2d( + pool2, + num_filters=16, + filter_size=1, + stride=1, + padding=0, + param_attr=conv2d_w3_attr, + bias_attr=conv2d_b3_attr) + conv3 = fluid.layers.relu6(conv3) + conv3 = paddle.tensor.math.tanh(conv3) + fc1 = fluid.layers.fc(input=conv3, + size=120, + param_attr=fc_w1_attr, + bias_attr=fc_b1_attr) + fc2 = fluid.layers.fc(input=fc1, + size=84, + param_attr=fc_w2_attr, + bias_attr=fc_b2_attr) + fc3 = fluid.layers.fc(input=fc2, + size=num_classes, + param_attr=fc_w3_attr, + bias_attr=fc_b3_attr) + fc3 = fluid.layers.softmax(fc3, use_cudnn=True) + + return fc3 + + +class ImperativeLenet(fluid.dygraph.Layer): + def __init__(self, num_classes=10): + super(ImperativeLenet, self).__init__() + conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1") + conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2") + conv2d_w3_attr = fluid.ParamAttr(name="conv2d_w_3") + fc_w1_attr = fluid.ParamAttr(name="fc_w_1") + fc_w2_attr = fluid.ParamAttr(name="fc_w_2") + fc_w3_attr = fluid.ParamAttr(name="fc_w_3") + conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1") + conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2") + conv2d_b3_attr = fluid.ParamAttr(name="conv2d_b_3") + fc_b1_attr = fluid.ParamAttr(name="fc_b_1") + fc_b2_attr = fluid.ParamAttr(name="fc_b_2") + fc_b3_attr = fluid.ParamAttr(name="fc_b_3") + self.features = Sequential( + Conv2D( + in_channels=1, + out_channels=6, + kernel_size=3, + stride=1, + padding=1, + weight_attr=conv2d_w1_attr, + bias_attr=conv2d_b1_attr), + LeakyReLU(negative_slope=0.02), + Pool2D( + pool_size=2, pool_type='max', pool_stride=2), + Conv2D( + in_channels=6, + out_channels=16, + kernel_size=5, + stride=1, + padding=0, + weight_attr=conv2d_w2_attr, + bias_attr=conv2d_b2_attr), + Pool2D( + pool_size=2, pool_type='max', pool_stride=2), + ReLU(), + Swish(), + Conv2D( + in_channels=16, + out_channels=16, + kernel_size=1, + stride=1, + padding=0, + weight_attr=conv2d_w3_attr, + bias_attr=conv2d_b3_attr), + ReLU6(), + Tanh()) + self.fc = Sequential( + Linear( + in_features=400, + out_features=120, + weight_attr=fc_w1_attr, + bias_attr=fc_b1_attr), + Linear( + in_features=120, + out_features=84, + weight_attr=fc_w2_attr, + bias_attr=fc_b2_attr), + Linear( + in_features=84, + out_features=num_classes, + weight_attr=fc_w3_attr, + bias_attr=fc_b3_attr), + Softmax()) + + def forward(self, inputs): + x = self.features(inputs) + x = fluid.layers.flatten(x, 1) + x = self.fc(x) + return x + + +class TestImperativeAddQuantDequant(unittest.TestCase): + def test_qat_save(self): + + imperative_qat = ImperativeQuantAware( + weight_quantize_type='abs_max', + activation_quantize_type='moving_average_abs_max', + quantizable_layer_type=[ + 'Conv2D', 'Linear', 'ReLU', 'Pool2D', 'LeakyReLU', 'ReLU6', + 'Tanh', 'Swish' + ]) + + with fluid.dygraph.guard(): + lenet = ImperativeLenet() + imperative_qat.quantize(lenet) + adam = AdamOptimizer( + learning_rate=0.001, parameter_list=lenet.parameters()) + train_reader = paddle.batch( + paddle.dataset.mnist.train(), batch_size=32, drop_last=True) + test_reader = paddle.batch( + paddle.dataset.mnist.test(), batch_size=32) + + epoch_num = 1 + for epoch in range(epoch_num): + lenet.train() + for batch_id, data in enumerate(train_reader()): + x_data = np.array([x[0].reshape(1, 28, 28) + for x in data]).astype('float32') + y_data = np.array( + [x[1] for x in data]).astype('int64').reshape(-1, 1) + + img = fluid.dygraph.to_variable(x_data) + label = fluid.dygraph.to_variable(y_data) + out = lenet(img) + acc = fluid.layers.accuracy(out, label) + loss = fluid.layers.cross_entropy(out, label) + avg_loss = fluid.layers.mean(loss) + avg_loss.backward() + adam.minimize(avg_loss) + lenet.clear_gradients() + if batch_id % 100 == 0: + _logger.info( + "Train | At epoch {} step {}: loss = {:}, acc= {:}". + format(epoch, batch_id, + avg_loss.numpy(), acc.numpy())) + + lenet.eval() + for batch_id, data in enumerate(test_reader()): + x_data = np.array([x[0].reshape(1, 28, 28) + for x in data]).astype('float32') + y_data = np.array( + [x[1] for x in data]).astype('int64').reshape(-1, 1) + + img = fluid.dygraph.to_variable(x_data) + label = fluid.dygraph.to_variable(y_data) + + out = lenet(img) + acc_top1 = fluid.layers.accuracy( + input=out, label=label, k=1) + acc_top5 = fluid.layers.accuracy( + input=out, label=label, k=5) + + if batch_id % 100 == 0: + _logger.info( + "Test | At epoch {} step {}: acc1 = {:}, acc5 = {:}". + format(epoch, batch_id, + acc_top1.numpy(), acc_top5.numpy())) + + # save weights + model_dict = lenet.state_dict() + fluid.save_dygraph(model_dict, "save_temp") + + # test the correctness of `paddle.jit.save` + data = next(test_reader()) + test_data = np.array([x[0].reshape(1, 28, 28) + for x in data]).astype('float32') + test_img = fluid.dygraph.to_variable(test_data) + lenet.eval() + before_save = lenet(test_img) + + # save inference quantized model + path = "./qat_infer_model/lenet" + save_dir = "./qat_infer_model" + paddle.jit.save( + layer=lenet, + path=path, + input_spec=[ + paddle.static.InputSpec( + shape=[None, 1, 28, 28], dtype='float32') + ]) + if core.is_compiled_with_cuda(): + place = core.CUDAPlace(0) + else: + place = core.CPUPlace() + exe = fluid.Executor(place) + [inference_program, feed_target_names, + fetch_targets] = fluid.io.load_inference_model( + dirname=save_dir, + executor=exe, + model_filename="lenet" + INFER_MODEL_SUFFIX, + params_filename="lenet" + INFER_PARAMS_SUFFIX) + after_save, = exe.run(inference_program, + feed={feed_target_names[0]: test_data}, + fetch_list=fetch_targets) + + self.assertTrue( + np.allclose(after_save, before_save.numpy()), + msg='Failed to save the inference quantized model.') + + def test_qat_acc(self): + def _build_static_lenet(main, startup, is_test=False, seed=1000): + with fluid.unique_name.guard(): + with fluid.program_guard(main, startup): + main.random_seed = seed + startup.random_seed = seed + img = fluid.layers.data( + name='image', shape=[1, 28, 28], dtype='float32') + label = fluid.layers.data( + name='label', shape=[1], dtype='int64') + prediction = StaticLenet(img) + if not is_test: + loss = fluid.layers.cross_entropy( + input=prediction, label=label) + avg_loss = fluid.layers.mean(loss) + else: + avg_loss = prediction + return img, label, avg_loss + + reader = paddle.batch( + paddle.dataset.mnist.test(), batch_size=32, drop_last=True) + weight_quantize_type = 'abs_max' + activation_quant_type = 'moving_average_abs_max' + param_init_map = {} + seed = 1000 + lr = 0.001 + + # imperative train + _logger.info( + "--------------------------dynamic graph qat--------------------------" + ) + imperative_qat = ImperativeQuantAware( + weight_quantize_type=weight_quantize_type, + activation_quantize_type=activation_quant_type, + quantizable_layer_type=[ + 'Conv2D', 'Linear', 'ReLU', 'LeakyReLU', 'ReLU6', 'Tanh', + 'Swish' + ]) + + with fluid.dygraph.guard(): + np.random.seed(seed) + fluid.default_main_program().random_seed = seed + fluid.default_startup_program().random_seed = seed + lenet = ImperativeLenet() + fixed_state = {} + for name, param in lenet.named_parameters(): + p_shape = param.numpy().shape + p_value = param.numpy() + if name.endswith("bias"): + value = np.zeros_like(p_value).astype('float32') + else: + value = np.random.normal( + loc=0.0, scale=0.01, size=np.product(p_shape)).reshape( + p_shape).astype('float32') + fixed_state[name] = value + param_init_map[param.name] = value + lenet.set_dict(fixed_state) + + imperative_qat.quantize(lenet) + adam = AdamOptimizer( + learning_rate=lr, parameter_list=lenet.parameters()) + dynamic_loss_rec = [] + lenet.train() + for batch_id, data in enumerate(reader()): + x_data = np.array([x[0].reshape(1, 28, 28) + for x in data]).astype('float32') + y_data = np.array( + [x[1] for x in data]).astype('int64').reshape(-1, 1) + + img = fluid.dygraph.to_variable(x_data) + label = fluid.dygraph.to_variable(y_data) + + out = lenet(img) + loss = fluid.layers.cross_entropy(out, label) + avg_loss = fluid.layers.mean(loss) + avg_loss.backward() + adam.minimize(avg_loss) + lenet.clear_gradients() + dynamic_loss_rec.append(avg_loss.numpy()[0]) + if batch_id % 100 == 0: + _logger.info('{}: {}'.format('loss', avg_loss.numpy())) + if batch_id > 500: + break + lenet.eval() + paddle.jit.save( + layer=lenet, + path="./dynamic_mnist/model", + input_spec=[ + paddle.static.InputSpec( + shape=[None, 1, 28, 28], dtype='float32') + ]) + + # static graph train + _logger.info( + "--------------------------static graph qat--------------------------" + ) + static_loss_rec = [] + if core.is_compiled_with_cuda(): + place = core.CUDAPlace(0) + else: + place = core.CPUPlace() + exe = fluid.Executor(place) + + main = fluid.Program() + infer = fluid.Program() + startup = fluid.Program() + static_img, static_label, static_loss = _build_static_lenet( + main, startup, False, seed) + infer_img, _, infer_pre = _build_static_lenet(infer, startup, True, + seed) + with fluid.unique_name.guard(): + with fluid.program_guard(main, startup): + opt = AdamOptimizer(learning_rate=lr) + opt.minimize(static_loss) + + scope = core.Scope() + with fluid.scope_guard(scope): + exe.run(startup) + for param in main.all_parameters(): + param_tensor = scope.var(param.name).get_tensor() + param_tensor.set(param_init_map[param.name], place) + + main_graph = IrGraph(core.Graph(main.desc), for_test=False) + infer_graph = IrGraph(core.Graph(infer.desc), for_test=True) + transform_pass = QuantizationTransformPass( + scope=scope, + place=place, + activation_quantize_type=activation_quant_type, + weight_quantize_type=weight_quantize_type, + quantizable_op_type=['conv2d', 'depthwise_conv2d', 'mul']) + add_quant_dequant_pass = AddQuantDequantPass( + scope=scope, + place=place, + quantizable_op_type=[ + 'relu', 'leaky_relu', 'relu6', 'tanh', 'swish' + ]) + transform_pass.apply(main_graph) + transform_pass.apply(infer_graph) + add_quant_dequant_pass.apply(main_graph) + add_quant_dequant_pass.apply(infer_graph) + build_strategy = fluid.BuildStrategy() + build_strategy.fuse_all_reduce_ops = False + binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel( + loss_name=static_loss.name, build_strategy=build_strategy) + + feeder = fluid.DataFeeder( + feed_list=[static_img, static_label], place=place) + with fluid.scope_guard(scope): + for batch_id, data in enumerate(reader()): + loss_v, = exe.run(binary, + feed=feeder.feed(data), + fetch_list=[static_loss]) + static_loss_rec.append(loss_v[0]) + if batch_id % 100 == 0: + _logger.info('{}: {}'.format('loss', loss_v)) + + save_program = infer_graph.to_program() + with fluid.scope_guard(scope): + fluid.io.save_inference_model("./static_mnist", [infer_img.name], + [infer_pre], exe, save_program) + rtol = 1e-08 + atol = 1e-10 + for i, (loss_d, + loss_s) in enumerate(zip(dynamic_loss_rec, static_loss_rec)): + diff = np.abs(loss_d - loss_s) + if diff > (atol + rtol * np.abs(loss_s)): + _logger.info( + "diff({}) at {}, dynamic loss = {}, static loss = {}". + format(diff, i, loss_d, loss_s)) + break + + self.assertTrue( + np.allclose( + np.array(dynamic_loss_rec), + np.array(static_loss_rec), + rtol=rtol, + atol=atol, + equal_nan=True), + msg='Failed to do the imperative qat.') + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/contrib/slim/tests/test_imperative_qat_channelwise.py b/python/paddle/fluid/contrib/slim/tests/test_imperative_qat_channelwise.py index caa9ea5b4d7..f888edfcc97 100644 --- a/python/paddle/fluid/contrib/slim/tests/test_imperative_qat_channelwise.py +++ b/python/paddle/fluid/contrib/slim/tests/test_imperative_qat_channelwise.py @@ -86,9 +86,9 @@ def StaticLenet(data, num_classes=10): size=num_classes, param_attr=fc_w3_attr, bias_attr=fc_b3_attr) - fc4 = fluid.layers.softmax(fc3, use_cudnn=True) + fc3 = fluid.layers.softmax(fc3, use_cudnn=True) - return fc4 + return fc3 class ImperativeLenet(fluid.dygraph.Layer): -- GitLab