[Dy2stat] Add Resnet Test for V2 APIs (#27459)

* Add test_resnet_v2.py test=develop

python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_v2.py

+# Copyright (c) 2020 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 math
+import time
+import unittest
+
+import numpy as np
+
+import paddle
+
+from predictor_utils import PredictorTools
+
+SEED = 2020
+IMAGENET1000 = 1281167
+base_lr = 0.001
+momentum_rate = 0.9
+l2_decay = 1e-4
+# NOTE: Reduce batch_size from 8 to 2 to avoid unittest timeout.
+batch_size = 2
+epoch_num = 1
+place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() \
+    else paddle.CPUPlace()
+MODEL_SAVE_PATH = "./resnet_v2.inference.model"
+DY_STATE_DICT_SAVE_PATH = "./resnet_v2.dygraph"
+program_translator = paddle.jit.ProgramTranslator()
+
+if paddle.is_compiled_with_cuda():
+    paddle.fluid.set_flags({'FLAGS_cudnn_deterministic': True})
+
+
+def optimizer_setting(parameter_list=None):
+    optimizer = paddle.optimizer.Momentum(
+        learning_rate=base_lr,
+        momentum=momentum_rate,
+        weight_decay=paddle.regularizer.L2Decay(l2_decay),
+        parameters=parameter_list)
+
+    return optimizer
+
+
+class ConvBNLayer(paddle.nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = paddle.nn.Conv2d(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            bias_attr=False)
+
+        self._batch_norm = paddle.nn.BatchNorm(num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class BottleneckBlock(paddle.nn.Layer):
+    def __init__(self, num_channels, num_filters, stride, shortcut=True):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu')
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = paddle.add(x=short, y=conv2)
+
+        layer_helper = paddle.fluid.layer_helper.LayerHelper(
+            self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet(paddle.nn.Layer):
+    def __init__(self, layers=50, class_dim=102):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512, 1024]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv = ConvBNLayer(
+            num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
+        self.pool2d_max = paddle.nn.Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+
+        self.bottleneck_block_list = []
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels[block]
+                        if i == 0 else num_filters[block] * 4,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        shortcut=shortcut))
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = paddle.nn.Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 4 * 1 * 1
+
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = paddle.nn.Linear(
+            in_features=self.pool2d_avg_output,
+            out_features=class_dim,
+            weight_attr=paddle.ParamAttr(
+                initializer=paddle.nn.initializer.Uniform(-stdv, stdv)))
+
+    @paddle.jit.to_static
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = paddle.reshape(y, shape=[-1, self.pool2d_avg_output])
+        pred = self.out(y)
+        pred = paddle.nn.functional.softmax(pred)
+
+        return pred
+
+
+def reader_decorator(reader):
+    def __reader__():
+        for item in reader():
+            img = np.array(item[0]).astype('float32').reshape(3, 224, 224)
+            label = np.array(item[1]).astype('int64').reshape(1)
+            yield img, label
+
+    return __reader__
+
+
+def train(to_static):
+    """
+    Tests model decorated by `dygraph_to_static_output` in static mode. For users, the model is defined in dygraph mode and trained in static mode.
+    """
+    paddle.disable_static(place)
+    np.random.seed(SEED)
+    paddle.manual_seed(SEED)
+    paddle.framework.random._manual_program_seed(SEED)
+
+    train_reader = paddle.batch(
+        reader_decorator(paddle.dataset.flowers.train(use_xmap=False)),
+        batch_size=batch_size,
+        drop_last=True)
+    data_loader = paddle.io.DataLoader.from_generator(capacity=5, iterable=True)
+    data_loader.set_sample_list_generator(train_reader)
+
+    resnet = ResNet()
+    optimizer = optimizer_setting(parameter_list=resnet.parameters())
+
+    for epoch in range(epoch_num):
+        total_loss = 0.0
+        total_acc1 = 0.0
+        total_acc5 = 0.0
+        total_sample = 0
+
+        for batch_id, data in enumerate(data_loader()):
+            start_time = time.time()
+            img, label = data
+
+            pred = resnet(img)
+            loss = paddle.nn.functional.cross_entropy(input=pred, label=label)
+            avg_loss = paddle.mean(x=loss)
+            acc_top1 = paddle.metric.accuracy(input=pred, label=label, k=1)
+            acc_top5 = paddle.metric.accuracy(input=pred, label=label, k=5)
+
+            avg_loss.backward()
+            optimizer.minimize(avg_loss)
+            resnet.clear_gradients()
+
+            total_loss += avg_loss
+            total_acc1 += acc_top1
+            total_acc5 += acc_top5
+            total_sample += 1
+
+            end_time = time.time()
+            if batch_id % 2 == 0:
+                print( "epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f" % \
+                    ( epoch, batch_id, total_loss.numpy() / total_sample, \
+                        total_acc1.numpy() / total_sample, total_acc5.numpy() / total_sample, end_time-start_time))
+            if batch_id == 10:
+                if to_static:
+                    paddle.jit.save(resnet, MODEL_SAVE_PATH)
+                else:
+                    paddle.fluid.dygraph.save_dygraph(resnet.state_dict(),
+                                                      DY_STATE_DICT_SAVE_PATH)
+                    # avoid dataloader throw abort signaal
+                data_loader._reset()
+                break
+    paddle.enable_static()
+
+    return total_loss.numpy()
+
+
+def predict_dygraph(data):
+    program_translator.enable(False)
+    paddle.disable_static(place)
+    resnet = ResNet()
+
+    model_dict, _ = paddle.fluid.dygraph.load_dygraph(DY_STATE_DICT_SAVE_PATH)
+    resnet.set_dict(model_dict)
+    resnet.eval()
+
+    pred_res = resnet(
+        paddle.to_tensor(
+            data=data, dtype=None, place=None, stop_gradient=True))
+
+    ret = pred_res.numpy()
+    paddle.enable_static()
+    return ret
+
+
+def predict_static(data):
+    exe = paddle.static.Executor(place)
+    [inference_program, feed_target_names,
+     fetch_targets] = paddle.static.load_inference_model(
+         MODEL_SAVE_PATH,
+         executor=exe,
+         params_filename=paddle.fluid.dygraph.io.VARIABLE_FILENAME)
+
+    pred_res = exe.run(inference_program,
+                       feed={feed_target_names[0]: data},
+                       fetch_list=fetch_targets)
+
+    return pred_res[0]
+
+
+def predict_dygraph_jit(data):
+    paddle.disable_static(place)
+    resnet = paddle.jit.load(MODEL_SAVE_PATH)
+    resnet.eval()
+
+    pred_res = resnet(data)
+
+    ret = pred_res.numpy()
+    paddle.enable_static()
+    return ret
+
+
+def predict_analysis_inference(data):
+    output = PredictorTools(MODEL_SAVE_PATH,
+                            paddle.fluid.dygraph.io.VARIABLE_FILENAME, [data])
+    out = output()
+    return out
+
+
+class TestResnet(unittest.TestCase):
+    def train(self, to_static):
+        program_translator.enable(to_static)
+        return train(to_static)
+
+    def verify_predict(self):
+        image = np.random.random([1, 3, 224, 224]).astype('float32')
+        dy_pre = predict_dygraph(image)
+        st_pre = predict_static(image)
+        dy_jit_pre = predict_dygraph_jit(image)
+        predictor_pre = predict_analysis_inference(image)
+        self.assertTrue(
+            np.allclose(dy_pre, st_pre),
+            msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
+        self.assertTrue(
+            np.allclose(dy_jit_pre, st_pre),
+            msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre, st_pre))
+        self.assertTrue(
+            np.allclose(predictor_pre, st_pre),
+            msg="predictor_pre:\n {}\n, st_pre: \n{}.".format(predictor_pre,
+                                                              st_pre))
+
+    def test_resnet(self):
+        static_loss = self.train(to_static=True)
+        dygraph_loss = self.train(to_static=False)
+        self.assertTrue(
+            np.allclose(static_loss, dygraph_loss),
+            msg="static_loss: {} \n dygraph_loss: {}".format(static_loss,
+                                                             dygraph_loss))
+        self.verify_predict()
+
+    def test_in_static_mode_mkldnn(self):
+        paddle.fluid.set_flags({'FLAGS_use_mkldnn': True})
+        try:
+            train(to_static=True)
+        finally:
+            paddle.fluid.set_flags({'FLAGS_use_mkldnn': False})
+
+
+if __name__ == '__main__':
+    unittest.main()