Created by: zhenghl1

我使用当前的docker 1.3 版本，运行图片分类示例，出现错误： 代码：

from __future__ import print_function
import paddle
import paddle.fluid as fluid
import numpy
import sys

def vgg_bn_drop(input):
    def conv_block(ipt, num_filter, groups, dropouts):
        return fluid.nets.img_conv_group(
            input=ipt,
            pool_size=2,
            pool_stride=2,
            conv_num_filter=[num_filter] * groups,
            conv_filter_size=3,
            conv_act='relu',
            conv_with_batchnorm=True,
            conv_batchnorm_drop_rate=dropouts,
            pool_type='max')

    conv1=conv_block(input,64,2,[0.3,0])
    conv2=conv_block(conv1,128,2,[0.4,0])
    conv3=conv_block(conv2,256,3,[0.4,0.4,0])
    conv4=conv_block(conv3,512,3,[0.4,0.4,0])
    conv5=conv_block(conv4,512,3,[0.4,0.4,0])
    drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
    fc1= fluid.layers.fc(input=drop,size=512,act=None)
    bn=fluid.layers.batch_norm(input=fc1,act='relu')
    drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
    fc2= fluid.layers.fc(input=drop2,size=512,act=None)
    predict=fluid.layers.fc(input=fc2,size=10,act='softmax')
    return predict

def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu', bias_attr=False):
    tmp=fluid.layers.conv2d(input=input, filter_size=filter_size, num_filters=ch_out, stride=stride, padding=padding, act=None, bias_attr=bias_attr )
    return fluid.layers.batch_norm(input=tmp,act=act)

def shortcut(input,ch_in,ch_out,stride):
    if ch_in != ch_out:
        return conv_bn_layer(input,ch_out,1,stride,0, None)
    else:
        return input

def basicblock(input, ch_in, ch_out, stride):
    tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
    tmp1= conv_bn_layer(tmp, ch_out, 3,stride,1)
    short=shortcut(input, ch_in,ch_out,stride)
    return fluid.layers.elementwise_add(x=tmp1,y=short,act='relu')

def layer_warp(block_func,input,ch_in,ch_out,count,stride):
    tmp=block_func(input, ch_in,ch_out,stride)
    for i in range(1,count):
        tmp=block_func(tmp,ch_out,ch_out,1)
    return tmp
def resnet_cifar10(ipt,depth=32):
    assert(depth-2)%6==0
    n=(depth-2)//6
    nStages={16,64,128}
    conv1=conv_bn_layer(ipt,ch_out=16,filter_size=3,stride=1,padding=1)
    res1=layer_warp(basicblock,conv1,16,16,n,1)
    res2=layer_warp(basicblock,res1,16,32,n,2)
    res3=layer_warp(basicblock,res2,32,64,n,2)
    pool=fluid.layers.pool2d(input=res3, pool_size=4, pool_type='max', pool_stride=1, global_pooling=False)
    predict=fluid.layers.fc(input=pool,size=10,act='softmax')
    return predict

def inference_program():
    data_shape=[3,32,32]
    images=fluid.layers.data(name='pixel',shape=data_shape,dtype='float32')
    predict=resnet_cifar10(images,32)
    #predict=vgg_bn_drop(images)
    return predict

Train Program Config

CODE COPY FROM README

def train_program():
    predict=inference_program()
    label=fluid.layers.data(name='label',shape=[1],dtype='int64')
    cost=fluid.layers.cross_entropy(input=predict,label=label)
    avg_cost=fluid.layers.mean(cost)
    accuracy=fluid.layers.accuracy(input=predict,label=label )
    return [avg_cost,accuracy]

def optimizer_program():
    return fluid.optimizer.Adam(learning_rate=0.001)

BATCH_SIZE=128
print(BATCH_SIZE)
train_reader=paddle.batch( paddle.reader.shuffle(paddle.dataset.cifar.train10(),buf_size=50000),batch_size=BATCH_SIZE )
test_reader=paddle.batch(paddle.dataset.cifar.test10(),batch_size=BATCH_SIZE)

use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
feeder_order=['pixel','label']
main_program=fluid.default_main_program()
star_program=fluid.default_startup_program()
avg_cost,acc=train_program()

test_program=main_program.clone(for_test=True)
optimizer = optimizer_program()
optimizer.minimize(avg_cost)
exe=fluid.Executor(place)
EPOCH_NUM =2
# for traning test cost
def train_test(program,reader):
    count = 0
    feed_var_list = [
        program.global_block().var(var_name) for var_name in feed_order
    ]
    feeder_test = fluid.DataFeeder(
        feed_list=feed_var_list, place=place)
    test_exe = fluid.Executor(place)
    accumulated = len([avg_cost, acc]) * [0]
    for tid, test_data in enumerate(reader()):
        avg_cost_np = test_exe.run(program=program,
                                   feed=feeder_test.feed(test_data),
                                   fetch_list=[avg_cost, acc])
        accumulated = [x[0] + x[1][0] for x in zip(accumulated, avg_cost_np)]
        count += 1
    return [x / count for x in accumulated]

params_dirname = "image_classification_resnet.inference.model"
def train_loop():
    feed_var_list_loop = [main_program.global_block().var(var_name) for var_name in feeder_order]
    feeder = fluid.DataFeeder( feed_list=feed_var_list_loop, place=place )
    exe.run(star_program)

    step=0
    for pass_id in range( EPOCH_NUM ):
        for step_id,data_train in enumerate( train_reader() ):
            avg_loss_value = exe.run(main_program, feed=feeder.feed(data_train), fetch_list=[avg_cost, acc])
            if step%1 == 0:
                print("step:")
                print(step)
                print(avg_cost)
            step += 1
        
        avg_cost_test, accuracy_test = train_test(test_program, reader=test_reader)

        if params_dirname is not None:
            fluid.io.save_inference_model(params_dirname,["pixel"],[predict], exe)


train_loop()            

报错： File "paddle_iamgeclassify.py", line 95, in avg_cost,acc=train_program() File "paddle_iamgeclassify.py", line 75, in train_program predict=inference_program() File "paddle_iamgeclassify.py", line 69, in inference_program predict=resnet_cifar10(images,32) File "paddle_iamgeclassify.py", line 62, in resnet_cifar10 pool=fluid.layers.pool2d(input=res3, pool_size=4, pool_type='max', pool_stride=1, global_pooling=False) File "/usr/local/lib/python2.7/dist-packages/paddle/fluid/layers/nn.py", line 2383, in pool2d "exclusive": exclusive, File "/usr/local/lib/python2.7/dist-packages/paddle/fluid/layer_helper.py", line 50, in append_op return self.main_program.current_block().append_op(*args, **kwargs) File "/usr/local/lib/python2.7/dist-packages/paddle/fluid/framework.py", line 1208, in append_op op = Operator(block=self, desc=op_desc, *args, kwargs) File "/usr/local/lib/python2.7/dist-packages/paddle/fluid/framework.py", line 657, in init self.desc.infer_shape(self.block.desc) paddle.fluid.core.EnforceNotMet: Due to the settings of padding(0), filter_size(4) and stride(1), the output size is less than 0, please check again. Input_size:2 at [/paddle/paddle/fluid/operators/pool_op.cc:39] PaddlePaddle Call Stacks: 0 0x7f8971787a96p paddle::platform::EnforceNotMet::EnforceNotMet(std::__exception_ptr::exception_ptr, char const, int) + 486 1 0x7f8971dcef1cp paddle::operators::PoolOutputSize(int, int, int, int, bool) + 252 2 0x7f8971dd1e81p paddle::operators::PoolOp::InferShape(paddle::framework::InferShapeContext) const + 1537 3 0x7f897183c376p paddle::framework::OpDesc::InferShape(paddle::framework::BlockDesc const&) const + 902 4 0x7f89717f054cp 5 0x7f89717c18d0p 6 0x4c5326p PyEval_EvalFrameEx + 37958 7 0x4b9b66p PyEval_EvalCodeEx + 774 8 0x4d57a3p 9 0x4eef5ep 10 0x4eeb66p 11 0x4aaafbp 12 0x4a587ep PyObject_Call + 62 13 0x4be51ep PyEval_EvalFrameEx + 9790 14 0x4b9b66p PyEval_EvalCodeEx + 774 15 0x4d57a3p 16 0x4a587ep PyObject_Call + 62 17 0x4be51ep PyEval_EvalFrameEx + 9790 18 0x4b9b66p PyEval_EvalCodeEx + 774 19 0x4c1f56p PyEval_EvalFrameEx + 24694 20 0x4b9b66p PyEval_EvalCodeEx + 774 21 0x4c17c6p PyEval_EvalFrameEx + 22758 22 0x4b9b66p PyEval_EvalCodeEx + 774 23 0x4c17c6p PyEval_EvalFrameEx + 22758 24 0x4b9b66p PyEval_EvalCodeEx + 774 25 0x4c1f56p PyEval_EvalFrameEx + 24694 26 0x4b9b66p PyEval_EvalCodeEx + 774 27 0x4c1f56p PyEval_EvalFrameEx + 24694 28 0x4b9b66p PyEval_EvalCodeEx + 774 29 0x4eb69fp 30 0x4e58f2p PyRun_FileExFlags + 130 31 0x4e41a6p PyRun_SimpleFileExFlags + 390 32 0x4938cep Py_Main + 1358 33 0x7f8a4d522830p __libc_start_main + 240 34 0x493299p _start + 41

在我学习语义角色标注的示例时出现con1105 模块不存在的情况