Remove fluid API (PaddlePaddle#1567)

ce_tests/dygraph/quant/src/save_quant_model.py

@@ -24,7 +24,7 @@
 import paddle
 from paddle.fluid.framework import IrGraph
 from paddle.fluid.contrib.slim.quantization import Quant2Int8MkldnnPass
-from paddle.fluid import core
+from paddle.framework import core
 
 paddle.enable_static()
 

demo/darts/model.py

@@ -74,7 +74,7 @@ def __init__(self, input_dim, num_classes, name=None):
 
     def forward(self, x):
         x = self.pool2d(x)
-        x = fluid.layers.squeeze(x, axes=[2, 3])
+        x = paddle.squeeze(x, axes=[2, 3])
         out = self.fc(x)
         return out
 
@@ -85,7 +85,7 @@ def drop_path(x, drop_prob):
     mask = 1 - np.random.binomial(
         1, drop_prob, size=[x.shape[0]]).astype(np.float32)
     mask = to_variable(mask)
-    x = fluid.layers.elementwise_mul(x / keep_prob, mask, axis=0)
+    x = paddle.multiply(x / keep_prob, mask)
     return x
 
 
@@ -122,7 +122,7 @@ def _compile(self, c_curr, op_names, indices, multiplier, reduction):
             op = OPS[op_name](c_curr, stride, True)
             ops += [op]
             edge_index += 1
-        self._ops = fluid.dygraph.LayerList(ops)
+        self._ops = paddle.nn.LayerList(ops)
         self._indices = indices
 
     def forward(self, s0, s1, drop_prob, training):
@@ -206,7 +206,7 @@ def __init__(self, C, num_classes, layers, auxiliary, genotype):
             c_prev_prev, c_prev = c_prev, cell._multiplier * c_curr
             if i == 2 * layers // 3:
                 c_to_auxiliary = c_prev
-        self.cells = fluid.dygraph.LayerList(cells)
+        self.cells = paddle.nn.LayerList(cells)
 
         if auxiliary:
             self.auxiliary_head = AuxiliaryHeadCIFAR(c_to_auxiliary,
@@ -288,7 +288,7 @@ def __init__(self, C, num_classes, layers, auxiliary, genotype):
             c_prev_prev, c_prev = c_prev, cell._multiplier * c_curr
             if i == 2 * layers // 3:
                 c_to_auxiliary = c_prev
-        self.cells = fluid.dygraph.LayerList(cells)
+        self.cells = paddle.nn.LayerList(cells)
 
         if auxiliary:
             self.auxiliary_head = AuxiliaryHeadImageNet(c_to_auxiliary,

demo/darts/model_search.py

@@ -16,12 +16,14 @@
 from __future__ import division
 from __future__ import print_function
 
+import paddle
 import paddle.fluid as fluid
 from paddle.nn.initializer import Normal, KaimingUniform, Constant
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.nn import Conv2D, Pool2D, BatchNorm, Linear
 from paddle.fluid.dygraph.base import to_variable
 from genotypes import PRIMITIVES
 from operations import *
+import paddleslim
 
 
 def channel_shuffle(x, groups):
@@ -31,7 +33,7 @@ def channel_shuffle(x, groups):
     # reshape
     x = paddle.reshape(x,
                        [batchsize, groups, channels_per_group, height, width])
-    x = fluid.layers.transpose(x, [0, 2, 1, 3, 4])
+    x = paddle.transpose(x, [0, 2, 1, 3, 4])
 
     # flatten
     x = paddle.reshape(x, [batchsize, num_channels, height, width])
@@ -59,12 +61,12 @@ def __init__(self, c_cur, stride, method):
                     trainable=False)
                 BN = BatchNorm(
                     c_cur // self._k, param_attr=gama, bias_attr=beta)
-                op = fluid.dygraph.Sequential(op, BN)
+                op = paddle.nn.Sequential(op, BN)
             ops.append(op)
-        self._ops = fluid.dygraph.LayerList(ops)
+        self._ops = paddle.nn.LayerList(ops)
 
     def forward(self, x, weights):
-        return fluid.layers.sums(
+        return paddle.add_n(
             [weights[i] * op(x) for i, op in enumerate(self._ops)])
 
 
@@ -89,7 +91,7 @@ def __init__(self, steps, multiplier, c_prev_prev, c_prev, c_cur, reduction,
                 stride = 2 if reduction and j < 2 else 1
                 op = MixedOp(c_cur, stride, method)
                 ops.append(op)
-        self._ops = fluid.dygraph.LayerList(ops)
+        self._ops = paddle.nn.LayerList(ops)
 
     def forward(self, s0, s1, weights, weights2=None):
         s0 = self.preprocess0(s0)
@@ -98,7 +100,7 @@ def forward(self, s0, s1, weights, weights2=None):
         states = [s0, s1]
         offset = 0
         for i in range(self._steps):
-            s = fluid.layers.sums([
+            s = paddle.add_n([
                 self._ops[offset + j](h, weights[offset + j])
                 for j, h in enumerate(states)
             ])
@@ -127,7 +129,7 @@ def __init__(self,
         self._method = method
 
         c_cur = stem_multiplier * c_in
-        self.stem = fluid.dygraph.Sequential(
+        self.stem = paddle.nn.Sequential(
             Conv2D(
                 num_channels=3,
                 num_filters=c_cur,
@@ -154,7 +156,7 @@ def __init__(self,
             reduction_prev = reduction
             cells.append(cell)
             c_prev_prev, c_prev = c_prev, multiplier * c_cur
-        self.cells = fluid.dygraph.LayerList(cells)
+        self.cells = paddle.nn.LayerList(cells)
         self.global_pooling = Pool2D(pool_type='avg', global_pooling=True)
         self.classifier = Linear(
             input_dim=c_prev,
@@ -174,13 +176,13 @@ def forward(self, input):
                 weights = paddle.nn.functional.softmax(self.alphas_normal)
             s0, s1 = s1, cell(s0, s1, weights, weights2)
         out = self.global_pooling(s1)
-        out = fluid.layers.squeeze(out, axes=[2, 3])
+        out = paddle.squeeze(out, axes=[2, 3])
         logits = self.classifier(out)
         return logits
 
     def _loss(self, input, target):
         logits = self(input)
-        loss = fluid.layers.reduce_mean(
+        loss = paddle.mean(
             paddle.nn.functional.softmax_with_cross_entropy(logits, target))
         return loss
 

demo/darts/operations.py

@@ -71,8 +71,8 @@ def __init__(self, stride):
 
     def forward(self, x):
         pooled = self.pool(x)
-        x = fluid.layers.zeros_like(
-            x) if self.stride == 1 else fluid.layers.zeros_like(pooled)
+        x = paddle.zeros_like(x) if self.stride == 1 else paddle.zeros_like(
+            pooled)
         return x
 
 

demo/darts/search.py

@@ -63,7 +63,8 @@ def main(args):
     elif not args.use_data_parallel:
         place = paddle.CUDAPlace(0)
     else:
-        place = paddle.CUDAPlace(fluid.dygraph.parallel.Env().dev_id)
+        place = paddle.CUDAPlace(paddle.distributed.parallel.ParallelEnv()
+                                 .dev_id)
 
     train_reader, valid_reader = reader.train_search(
         batch_size=args.batch_size,

demo/darts/train.py

@@ -79,10 +79,10 @@ def train(model, train_reader, optimizer, epoch, drop_path_prob, args):
 
         prec1 = paddle.static.accuracy(input=logits, label=label, k=1)
         prec5 = paddle.static.accuracy(input=logits, label=label, k=5)
-        loss = fluid.layers.reduce_mean(
+        loss = paddle.mean(
             paddle.nn.functional.softmax_with_cross_entropy(logits, label))
         if args.auxiliary:
-            loss_aux = fluid.layers.reduce_mean(
+            loss_aux = paddle.mean(
                 paddle.nn.functional.softmax_with_cross_entropy(logits_aux,
                                                                 label))
             loss = loss + args.auxiliary_weight * loss_aux
@@ -122,7 +122,7 @@ def valid(model, valid_reader, epoch, args):
         logits, _ = model(image, 0, False)
         prec1 = paddle.static.accuracy(input=logits, label=label, k=1)
         prec5 = paddle.static.accuracy(input=logits, label=label, k=5)
-        loss = fluid.layers.reduce_mean(
+        loss = paddle.mean(
             paddle.nn.functional.softmax_with_cross_entropy(logits, label))
 
         n = image.shape[0]
@@ -137,7 +137,7 @@ def valid(model, valid_reader, epoch, args):
 
 
 def main(args):
-    place = paddle.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+    place = paddle.CUDAPlace(paddle.distributed.parallel.ParallelEnv().dev_id) \
         if args.use_data_parallel else paddle.CUDAPlace(0)
 
     with fluid.dygraph.guard(place):
@@ -152,7 +152,7 @@ def main(args):
         logger.info("param size = {:.6f}MB".format(
             count_parameters_in_MB(model.parameters())))
 
-        device_num = fluid.dygraph.parallel.Env().nranks
+        device_num = paddle.distributed.parallel.ParallelEnv().nranks
         step_per_epoch = int(args.trainset_num / (args.batch_size * device_num))
         learning_rate = fluid.dygraph.CosineDecay(args.learning_rate,
                                                   step_per_epoch, args.epochs)
@@ -200,7 +200,7 @@ def main(args):
 
         save_parameters = (not args.use_data_parallel) or (
             args.use_data_parallel and
-            fluid.dygraph.parallel.Env().local_rank == 0)
+            paddle.distributed.parallel.ParallelEnv().local_rank == 0)
         best_acc = 0
         for epoch in range(args.epochs):
             drop_path_prob = args.drop_path_prob * epoch / args.epochs

demo/darts/train_imagenet.py

@@ -69,9 +69,9 @@
 def cross_entropy_label_smooth(preds, targets, epsilon):
     preds = paddle.nn.functional.softmax(preds)
     targets_one_hot = fluid.one_hot(input=targets, depth=args.class_num)
-    targets_smooth = fluid.layers.label_smooth(
+    targets_smooth = paddle.nn.functional.label_smooth(
         targets_one_hot, epsilon=epsilon, dtype="float32")
-    loss = fluid.layers.cross_entropy(
+    loss = paddle.nn.functional.cross_entropy(
         input=preds, label=targets_smooth, soft_label=True)
     return loss
 
@@ -91,11 +91,11 @@ def train(model, train_reader, optimizer, epoch, args):
 
         prec1 = paddle.static.accuracy(input=logits, label=label, k=1)
         prec5 = paddle.static.accuracy(input=logits, label=label, k=5)
-        loss = fluid.layers.reduce_mean(
+        loss = paddle.mean(
             cross_entropy_label_smooth(logits, label, args.label_smooth))
 
         if args.auxiliary:
-            loss_aux = fluid.layers.reduce_mean(
+            loss_aux = paddle.mean(
                 cross_entropy_label_smooth(logits_aux, label,
                                            args.label_smooth))
             loss = loss + args.auxiliary_weight * loss_aux
@@ -135,7 +135,7 @@ def valid(model, valid_reader, epoch, args):
         logits, _ = model(image, False)
         prec1 = paddle.static.accuracy(input=logits, label=label, k=1)
         prec5 = paddle.static.accuracy(input=logits, label=label, k=5)
-        loss = fluid.layers.reduce_mean(
+        loss = paddle.mean(
             cross_entropy_label_smooth(logits, label, args.label_smooth))
 
         n = image.shape[0]
@@ -150,7 +150,7 @@ def valid(model, valid_reader, epoch, args):
 
 
 def main(args):
-    place = paddle.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+    place = paddle.CUDAPlace(paddle.distributed.parallel.ParallelEnv().dev_id) \
         if args.use_data_parallel else paddle.CUDAPlace(0)
 
     with fluid.dygraph.guard(place):
@@ -165,7 +165,7 @@ def main(args):
         logger.info("param size = {:.6f}MB".format(
             count_parameters_in_MB(model.parameters())))
 
-        device_num = fluid.dygraph.parallel.Env().nranks
+        device_num = paddle.distributed.parallel.ParallelEnv().nranks
         step_per_epoch = int(args.trainset_num / (args.batch_size * device_num))
         learning_rate = fluid.dygraph.ExponentialDecay(
             args.learning_rate, step_per_epoch, args.decay_rate, staircase=True)
@@ -209,7 +209,7 @@ def main(args):
 
         save_parameters = (not args.use_data_parallel) or (
             args.use_data_parallel and
-            fluid.dygraph.parallel.Env().local_rank == 0)
+            paddle.distributed.parallel.ParallelEnv().local_rank == 0)
         best_top1 = 0
         for epoch in range(args.epochs):
             logger.info('Epoch {}, lr {:.6f}'.format(epoch, optimizer.get_lr()))

demo/distillation/distill.py

@@ -13,7 +13,7 @@
 sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir)
 import models
 from utility import add_arguments, print_arguments, _download, _decompress
-from paddleslim.dist import merge, l2, soft_label, fsp
+from paddleslim.dist import merge, l2, soft_label
 
 logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
 _logger = logging.getLogger(__name__)
@@ -99,11 +99,11 @@ def compress(args):
     ) if args.use_gpu else paddle.static.cpu_places()
     place = places[0]
     if args.use_gpu:
-        devices_num = paddle.fluid.core.get_cuda_device_count()
+        devices_num = paddle.framework.core.get_cuda_device_count()
     else:
         devices_num = int(os.environ.get('CPU_NUM', 1))
     with paddle.static.program_guard(student_program, s_startup):
-        with paddle.fluid.unique_name.guard():
+        with paddle.utils.unique_name.guard():
             image = paddle.static.data(
                 name='image', shape=[None] + image_shape, dtype='float32')
             label = paddle.static.data(
@@ -144,7 +144,7 @@ def compress(args):
     teacher_program = paddle.static.Program()
     t_startup = paddle.static.Program()
     with paddle.static.program_guard(teacher_program, t_startup):
-        with paddle.fluid.unique_name.guard():
+        with paddle.utils.unique_name.guard():
             image = paddle.static.data(
                 name='image', shape=[None] + image_shape, dtype='float32')
             predict = teacher_model.net(image, class_dim=class_dim)

demo/mkldnn_quant/sample_tester.py

@@ -24,7 +24,7 @@
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.framework import IrGraph
-from paddle.fluid import core
+from paddle.framework import core
 
 logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
 _logger = logging.getLogger(__name__)
@@ -244,7 +244,7 @@ def _predict(self,
             return outputs, acc1_avg, acc5_avg, fps_avg, latency_avg
 
     def test_graph_transformation(self):
-        if not fluid.core.is_compiled_with_mkldnn():
+        if not paddle.fluid.core.is_compiled_with_mkldnn():
             return
 
         infer_model_path = test_case_args.infer_model

demo/models/mobilenet.py

@@ -2,7 +2,6 @@
 from __future__ import division
 from __future__ import print_function
 import paddle
-import paddle.fluid as fluid
 from paddle.nn.initializer import KaimingUniform
 
 __all__ = ['MobileNet']
@@ -121,13 +120,8 @@ def net(self, input, class_dim=1000, scale=1.0):
             scale=scale,
             name="conv6")
 
-        input = fluid.layers.pool2d(
-            input=input,
-            pool_size=0,
-            pool_stride=1,
-            pool_type='avg',
-            global_pooling=True)
-        with fluid.name_scope('last_fc'):
+        input = paddle.nn.functional.adaptive_avg_pool2d(input, 1)
+        with paddle.static.name_scope('last_fc'):
             output = paddle.static.nn.fc(
                 input,
                 class_dim,

demo/models/mobilenet_v2.py

@@ -16,7 +16,6 @@
 from __future__ import division
 from __future__ import print_function
 import paddle
-import paddle.fluid as fluid
 from paddle.nn.initializer import KaimingUniform
 
 __all__ = [
@@ -101,12 +100,7 @@ def net(self, input, class_dim=1000):
             if_act=True,
             name='conv9')
 
-        input = fluid.layers.pool2d(
-            input=input,
-            pool_size=7,
-            pool_stride=1,
-            pool_type='avg',
-            global_pooling=True)
+        input = paddle.nn.functional.adaptive_avg_pool2d(input, 1)
 
         output = paddle.static.nn.fc(
             input,
@@ -150,7 +144,7 @@ def conv_bn_layer(self,
             return bn
 
     def shortcut(self, input, data_residual):
-        return fluid.layers.elementwise_add(input, data_residual)
+        return paddle.add(input, data_residual)
 
     def inverted_residual_unit(self,
                                input,