fix program

ppcls/modeling/architectures/resnet.py

@@ -19,7 +19,6 @@ from __future__ import print_function
 import numpy as np
 import paddle
 from paddle import ParamAttr
-# from paddle.fluid.param_attr import ParamAttr
 import paddle.nn as nn
 from paddle.nn import Conv2d, Pool2D, BatchNorm, Linear, Dropout
 

tools/program.py

@@ -18,11 +18,12 @@ from __future__ import print_function
 
 import os
 import time
-
 from collections import OrderedDict
 
 import paddle
-import paddle.fluid as fluid
+from paddle import to_tensor
+import paddle.nn as nn
+import paddle.nn.functional as F
 
 from ppcls.optimizer import LearningRateBuilder
 from ppcls.optimizer import OptimizerBuilder
@@ -34,8 +35,6 @@ from ppcls.modeling.loss import GoogLeNetLoss
 from ppcls.utils.misc import AverageMeter
 from ppcls.utils import logger
 
-from paddle.fluid.dygraph.base import to_variable
-
 
 def create_dataloader():
     """
@@ -45,11 +44,11 @@ def create_dataloader():
         feeds(dict): dict of model input variables
 
     Returns:
-        dataloader(fluid dataloader):
+        dataloader(paddle dataloader):
     """
     trainer_num = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
     capacity = 64 if trainer_num == 1 else 8
-    dataloader = fluid.io.DataLoader.from_generator(
+    dataloader = paddle.io.DataLoader.from_generator(
         capacity=capacity, use_double_buffer=True, iterable=True)
 
     return dataloader
@@ -149,15 +148,15 @@ def create_metric(out,
         # just need student label to get metrics
         if use_distillation:
             out = out[1]
-        softmax_out = fluid.layers.softmax(out, use_cudnn=False)
+        softmax_out = F.softmax(out)
 
     fetchs = OrderedDict()
     # set top1 to fetchs
-    top1 = fluid.layers.accuracy(softmax_out, label=label, k=1)
+    top1 = paddle.metric.accuracy(softmax_out, label=label, k=1)
     fetchs['top1'] = top1
     # set topk to fetchs
     k = min(topk, classes_num)
-    topk = fluid.layers.accuracy(softmax_out, label=label, k=k)
+    topk = paddle.metric.accuracy(softmax_out, label=label, k=k)
     topk_name = 'top{}'.format(k)
     fetchs[topk_name] = topk
 
@@ -244,12 +243,12 @@ def create_optimizer(config, parameter_list=None):
 def create_feeds(batch, use_mix):
     image = batch[0]
     if use_mix:
-        y_a = to_variable(batch[1].numpy().astype("int64").reshape(-1, 1))
-        y_b = to_variable(batch[2].numpy().astype("int64").reshape(-1, 1))
-        lam = to_variable(batch[3].numpy().astype("float32").reshape(-1, 1))
+        y_a = to_tensor(batch[1].numpy().astype("int64").reshape(-1, 1))
+        y_b = to_tensor(batch[2].numpy().astype("int64").reshape(-1, 1))
+        lam = to_tensor(batch[3].numpy().astype("float32").reshape(-1, 1))
         feeds = {"image": image, "y_a": y_a, "y_b": y_b, "lam": lam}
     else:
-        label = to_variable(batch[1].numpy().astype('int64').reshape(-1, 1))
+        label = to_tensor(batch[1].numpy().astype('int64').reshape(-1, 1))
         feeds = {"image": image, "label": label}
     return feeds
 
@@ -259,7 +258,7 @@ def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
     Feed data to the model and fetch the measures and loss
 
     Args:
-        dataloader(fluid dataloader):
+        dataloader(paddle dataloader):
         exe():
         program():
         fetchs(dict): dict of measures and the loss