change im2sequence for ctc batch inference (#11696)

* change im2sequence for ctc batch inference

* Update im2sequence_op.cc

* change im2sequence for ctc batch inference

* update

* change PR by comment

* fix ocr test error

* fix test_im2sequence

* modify the old name to standard name

* fix test_layers failed

paddle/fluid/operators/im2sequence_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/im2sequence_op.h"
+#include <string>
 #include <vector>
 
 namespace paddle {
@@ -28,20 +29,19 @@ class Im2SequenceOp : public framework::OperatorWithKernel {
                    "Input(X) of Im2SequenceOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of Im2SequenceOp op should not be null.");
-
     auto in_dim = ctx->GetInputDim("X");
+
     PADDLE_ENFORCE_EQ(in_dim.size(), 4,
                       "Input(X) format must be 4D tensor, eg., NCHW.");
-
-    auto kernels = ctx->Attrs().Get<std::vector<int>>("kernels");
-    auto strides = ctx->Attrs().Get<std::vector<int>>("strides");
-    auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
-
     int batch_size = in_dim[0];
     int img_channels = in_dim[1];
     int img_height = in_dim[2];
     int img_width = in_dim[3];
 
+    auto kernels = ctx->Attrs().Get<std::vector<int>>("kernels");
+    auto strides = ctx->Attrs().Get<std::vector<int>>("strides");
+    auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+
     int output_height = Im2SeqOutputSize(img_height, kernels[0], paddings[0],
                                          paddings[2], strides[0]);
     int output_width = Im2SeqOutputSize(img_width, kernels[1], paddings[1],
@@ -61,6 +61,10 @@ class Im2SequenceOpMaker : public framework::OpProtoAndCheckerMaker {
              "C: channels"
              "H: height"
              "W: width");
+    AddInput("Y",
+             "(Tensor) The input tensor of image real size(H, W)."
+             "2-D with shape [batchsize, 2]")
+        .AsDispensable();
     AddOutput("Out", "(LodTensor) The output data of im2sequence op,");
     AddAttr<std::vector<int>>("kernels",
                               "(vector<int>), the "
@@ -73,6 +77,13 @@ class Im2SequenceOpMaker : public framework::OpProtoAndCheckerMaker {
                               "(vector<int> default:{0, 0, 0, 0}), the "
                               "paddings(up_pad, left_pad, down_pad, right_pad)")
         .SetDefault({0, 0, 0, 0});
+    AddAttr<std::vector<int>>("out_stride",
+                              "the attribute is valid only when input(Y)"
+                              "is not NULL.this attribute represents the"
+                              "scaling of the pic through the CNN"
+                              "(vector<int> dedault:{1,1}),the out_stride"
+                              " (out_stride_height, out_stride_width)")
+        .SetDefault({1, 1});
     AddComment(R"DOC(
 This op uses kernels to scan images and converts these images to sequences.
 After expanding, The number of time steps are output_height * output_width
@@ -123,7 +134,7 @@ output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
                [ 7.  1.  7.  9.  2.  1.  3.  5.]
                [ 5.  7.  2.  4.  1.  3.  9.  0.]
                [ 7.  9.  4.  8.  3.  5.  0.  8.]]
-output.dims = {8, 9}
+output.dims = {8, 8}
 output.lod = [[0, 4, 8]]
 
 )DOC");

paddle/fluid/operators/im2sequence_op.h

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #pragma once
+#include <string>
 #include <vector>
 #include "paddle/fluid/framework/data_layout.h"
 #include "paddle/fluid/framework/eigen.h"
@@ -39,51 +40,107 @@ class Im2SequenceKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     const Tensor* in = ctx.Input<Tensor>("X");
     LoDTensor* out = ctx.Output<LoDTensor>("Out");
-    out->mutable_data<T>(ctx.GetPlace());
-    // TODO(wanghaoshuang): Add layout checker after 'set_layout'
-    // being available for python API
-    // PADDLE_ENFORCE_EQ(in->layout(), framework::DataLayout::kNCHW,
-    //                  "Input(X) layout must be NCHW");
     auto in_dim = in->dims();
     int batch_size = in_dim[0];
     int img_channels = in_dim[1];
     int img_height = in_dim[2];
     int img_width = in_dim[3];
-
     auto kernels = ctx.Attr<std::vector<int>>("kernels");
     auto strides = ctx.Attr<std::vector<int>>("strides");
     auto paddings = ctx.Attr<std::vector<int>>("paddings");
+    if (ctx.HasInput("Y") && batch_size > 1) {
+      const Tensor* imgrealsize = ctx.Input<Tensor>("Y");
+      auto out_stride = ctx.Attr<std::vector<int>>("out_stride");
+      Tensor cpu_shape_tensor;
+      TensorCopySync(*imgrealsize, platform::CPUPlace(), &cpu_shape_tensor);
+      std::vector<int> imgreal_h;
+      std::vector<int> imgreal_w;
+      std::vector<int> output_height;
+      std::vector<int> output_width;
+      int result = 0;
+      for (int i = 0; i < batch_size; i++) {
+        int tmp_real_h = static_cast<int>((cpu_shape_tensor.data<T>())[2 * i]);
+        int tmp_real_w =
+            static_cast<int>((cpu_shape_tensor.data<T>())[2 * i + 1]);
+        if (tmp_real_h % out_stride[0] == 0) {
+          tmp_real_h = tmp_real_h / out_stride[0];
+        } else {
+          tmp_real_h = tmp_real_h / out_stride[0] + 1;
+        }
+        if (tmp_real_w % out_stride[1] == 0) {
+          tmp_real_w = tmp_real_w / out_stride[1];
+        } else {
+          tmp_real_w = tmp_real_w / out_stride[1] + 1;
+        }
+        imgreal_h.push_back(tmp_real_h);
+        imgreal_w.push_back(tmp_real_w);
+        output_height.push_back(Im2SeqOutputSize(
+            imgreal_h[i], kernels[0], paddings[0], paddings[2], strides[0]));
+        output_width.push_back(Im2SeqOutputSize(
+            imgreal_w[i], kernels[1], paddings[1], paddings[3], strides[1]));
+        result += output_height[i] * output_width[i];
+      }
+
+      out->mutable_data<T>({result, img_channels * kernels[0] * kernels[1]},
+                           ctx.GetPlace());
+
+      const std::vector<int> dilations({1, 1});
+      int offset_out = 0;
+      for (int i = 0; i < batch_size; i++) {
+        const Tensor src =
+            in->Slice(i, i + 1).Resize({img_channels, img_height, img_width});
+        Tensor dst = out->Slice(offset_out,
+                                offset_out + output_height[i] * output_width[i])
+                         .Resize({output_height[i], output_width[i],
+                                  img_channels, kernels[0], kernels[1]});
+        offset_out += output_height[i] * output_width[i];
+
+        math::Im2ColFunctor<math::ColFormat::kOCF, DeviceContext, T> f;
+        auto& dev_ctx = ctx.template device_context<DeviceContext>();
+        f(dev_ctx, src, dilations, strides, paddings, &dst);
+      }
+      framework::LoD lod(1);
+      lod[0].reserve(batch_size + 1);
+      int offset = 0;
+      lod[0].push_back(offset);
+      for (int i = 0; i < batch_size; ++i) {
+        offset += output_height[i] * output_width[i];
+        lod[0].push_back(offset);
+      }
+      out->set_lod(lod);
+    } else {
+      out->mutable_data<T>(ctx.GetPlace());
       int output_height = Im2SeqOutputSize(img_height, kernels[0], paddings[0],
                                            paddings[2], strides[0]);
       int output_width = Im2SeqOutputSize(img_width, kernels[1], paddings[1],
                                           paddings[3], strides[1]);
 
       const std::vector<int> dilations({1, 1});
-
       auto out_dims = out->dims();
       out->Resize({batch_size, out->numel() / batch_size});
       for (int i = 0; i < batch_size; i++) {
         const Tensor src =
             in->Slice(i, i + 1).Resize({img_channels, img_height, img_width});
-      Tensor dst = out->Slice(i, i + 1).Resize(
-          {output_height, output_width, img_channels, kernels[0], kernels[1]});
+        Tensor dst =
+            out->Slice(i, i + 1).Resize({output_height, output_width,
+                                         img_channels, kernels[0], kernels[1]});
 
         math::Im2ColFunctor<math::ColFormat::kOCF, DeviceContext, T> f;
         auto& dev_ctx = ctx.template device_context<DeviceContext>();
         f(dev_ctx, src, dilations, strides, paddings, &dst);
       }
       out->Resize(out_dims);
-
-    // set lod information
-    // TODO(wanghaoshuang): Move this to InferShape
       framework::LoD lod(1);
       lod[0].reserve(batch_size + 1);
-    for (int i = 0, offset = 0; i < batch_size + 1; ++i) {
+      int offset = 0;
       lod[0].push_back(offset);
+      for (int i = 0; i < batch_size; ++i) {
         offset += output_height * output_width;
+        lod[0].push_back(offset);
       }
       out->set_lod(lod);
     }
+  }
 };
 
 template <typename DeviceContext, typename T>

paddle/fluid/operators/math/im2col.cc

@@ -43,21 +43,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
     int col_height = col->dims()[3];
     int col_width = col->dims()[4];
 
-    PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
-                       ((dilation[0] * (filter_height - 1) + 1))) /
-                              stride[0] +
-                          1,
-                      col_height,
-                      "Output_height and padding(padding_up, padding_down) are "
-                      "inconsistent.");
-    PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
-                       ((dilation[1] * (filter_width - 1) + 1))) /
-                              stride[1] +
-                          1,
-                      col_width,
-                      "Output_height and padding(padding_up, padding_down) are "
-                      "inconsistent.");
-
     int channels_col = im_channels * filter_height * filter_width;
 
     const T* im_data = im.data<T>();
@@ -178,17 +163,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
     int col_height = col->dims()[0];
     int col_width = col->dims()[1];
 
-    PADDLE_ENFORCE_EQ(
-        (im_height + padding[0] + padding[2] - filter_height) / stride[0] + 1,
-        col_height,
-        "Output_height and padding(padding_up, padding_down) are "
-        "inconsistent.");
-    PADDLE_ENFORCE_EQ(
-        (im_width + padding[1] + padding[3] - filter_width) / stride[1] + 1,
-        col_width,
-        "col_width and padding(padding_left, padding_right) are "
-        "inconsistent.");
-
     const T* im_data = im.data<T>();
     T* col_data = col->data<T>();
 

paddle/fluid/operators/math/im2col.cu

@@ -77,21 +77,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
     int col_height = col->dims()[3];
     int col_width = col->dims()[4];
 
-    PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
-                       (dilation[0] * (filter_height - 1) + 1)) /
-                              stride[0] +
-                          1,
-                      col_height,
-                      "Output_height and padding(padding_up, padding_down) are "
-                      "inconsistent.");
-    PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
-                       (dilation[1] * (filter_width - 1) + 1)) /
-                              stride[1] +
-                          1,
-                      col_width,
-                      "col_width and padding(padding_left, padding_right) are "
-                      "inconsistent.");
-
     int num_outputs = im_channels * col_height * col_width;
     int blocks = (num_outputs + 1024 - 1) / 1024;
     int block_x = 512;
@@ -274,21 +259,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
     int col_height = col->dims()[0];
     int col_width = col->dims()[1];
 
-    PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
-                       (dilation[0] * (filter_height - 1) + 1)) /
-                              stride[0] +
-                          1,
-                      col_height,
-                      "Output_height and padding(padding_up, padding_down) are "
-                      "inconsistent.");
-    PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
-                       (dilation[1] * (filter_width - 1) + 1)) /
-                              stride[1] +
-                          1,
-                      col_width,
-                      "col_width and padding(padding_left, padding_right) are "
-                      "inconsistent.");
-
     int block_dim_x = 0;
     int block_dim_y = 0;
     if (filter_height <= 4 && filter_width <= 4) {

python/paddle/fluid/layers/nn.py

@@ -11,6 +11,20 @@
 # 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.
+
+#   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.
 """
 All layers just related to the neural network.
 """
@@ -3900,7 +3914,13 @@ def transpose(x, perm, name=None):
     return out
 
 
-def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
+def im2sequence(input,
+                filter_size=1,
+                stride=1,
+                padding=0,
+                input_image_size=None,
+                out_stride=1,
+                name=None):
     """
     Extracts image patches from the input tensor to form a tensor of shape
     {input.batch_size * output_height * output_width, filter_size_H *
@@ -3937,6 +3957,15 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
             padding_up = padding_down = padding_left = padding_right = padding
             Default: padding = 0.
 
+        input_image_size(Variable): the input contains image real size.It's dim
+            is [batchsize, 2]. It is dispensable.It is just for batch inference.
+
+        out_stride(int|tuple): The scaling of image through CNN. It is
+            dispensable. It is valid only when input_image_size is not null.
+            If out_stride is tuple,  it must contain two intergers,
+            (out_stride_H, out_stride_W). Otherwise,
+            the out_stride_H = out_stride_W = out_stride.
+
         name (int): The name of this layer. It is optional.
 
     Returns:
@@ -3987,7 +4016,7 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
                            [ 5.  7.  2.  4.  1.  3.  9.  0.]
                            [ 7.  9.  4.  8.  3.  5.  0.  8.]]
 
-            output.dims = {8, 9}
+            output.dims = {8, 8}
 
             output.lod = [[4, 4]]
 
@@ -4009,18 +4038,17 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
     if len(padding) == 2:
         padding.append(padding[0])
         padding.append(padding[1])
-
+    inputs = {"X": input}
+    attrs = {"kernels": filter_size, "strides": stride, "padding": padding}
+    if input_image_size:
+        if isinstance(out_stride, int):
+            out_stride = [out_stride, out_stride]
+        inputs["Y"] = input_image_size
+        attrs["out_stride"] = out_stride
     helper = LayerHelper('im2sequence', **locals())
     out = helper.create_tmp_variable(dtype=helper.input_dtype())
     helper.append_op(
-        type='im2sequence',
-        inputs={'X': input},
-        outputs={'Out': out},
-        attrs={
-            'kernels': filter_size,
-            'strides': stride,
-            'paddings': padding,
-        })
+        type='im2sequence', inputs=inputs, outputs={'Out': out}, attrs=attrs)
     return out
 
 

python/paddle/fluid/tests/unittests/test_im2sequence_op.py

@@ -16,20 +16,45 @@ import numpy as np
 from op_test import OpTest
 
 
-def get_output_shape(attrs, in_shape):
+def get_output_shape(attrs, in_shape, img_real_size):
+    batchsize = in_shape[0]
     img_height = in_shape[2]
     img_width = in_shape[3]
+    paddings = np.array(attrs['paddings']).astype("int32")
+    kernels = np.array(attrs['kernels']).astype("int32")
+    strides = np.array(attrs['strides']).astype("int32")
+    output_height = np.zeros((1, batchsize)).astype("int32")
+    output_width = np.zeros((1, batchsize)).astype("int32")
+    if len(img_real_size):
+        out_stride = np.array(attrs['out_stride']).astype("int32")
+        imgreal_h = 0
+        imgreal_w = 0
+        for index in range(batchsize):
+            if img_real_size[index, 0] % out_stride[0] == 0:
+                imgreal_h = img_real_size[index, 0] / out_stride[0]
+            else:
+                imgreal_h = img_real_size[index, 0] / out_stride[0] + 1
+            if img_real_size[index, 0] % out_stride[1] == 0:
+                imgreal_w = img_real_size[index, 1] / out_stride[1]
+            else:
+                imgreal_w = img_real_size[index, 0] / out_stride[1] + 1
+            output_height[0,index] = \
+              1 +  \
+              (imgreal_h + paddings[0] + paddings[2] - kernels[0] + strides[0] - 1) / \
+                  strides[0]
 
-    paddings = attrs['paddings']
-    kernels = attrs['kernels']
-    strides = attrs['strides']
-
-    output_height = \
+            output_width[0,index] = \
+              1 + \
+              (imgreal_w + paddings[1] + paddings[3] - kernels[1] + strides[1] - 1) / \
+                  strides[1]
+    else:
+        for index in range(batchsize):
+            output_height[0,index] = \
               1 +  \
               (img_height + paddings[0] + paddings[2] - kernels[0] + strides[0] - 1) / \
                   strides[0]
 
-    output_width = \
+            output_width[0,index] = \
               1 + \
               (img_width + paddings[1] + paddings[3] - kernels[1] + strides[1] - 1) / \
                   strides[1]
@@ -75,22 +100,25 @@ def im2col(attrs, im, col):
                                     im_row_offset][im_col_offset]
 
 
-def Im2Sequence(inputs, attrs):
-    output_height, output_width = get_output_shape(attrs, inputs.shape)
+def Im2Sequence(inputs, img_real_size, attrs):
+    output_height, output_width = get_output_shape(attrs, inputs.shape,
+                                                   img_real_size)
     img_channels = inputs.shape[1]
     batch_size = inputs.shape[0]
-    out = np.zeros([
-        batch_size, output_height, output_width, img_channels,
+    out = []
+    for index in range(batch_size):
+        tmp = np.zeros([
+            output_height[0, index], output_width[0, index], img_channels,
             attrs['kernels'][0], attrs['kernels'][1]
         ]).astype("float32")
-
-    for i in range(len(inputs)):
-        im2col(attrs, inputs[i], out[i])
-
-    out = out.reshape([
-        batch_size * output_height * output_width,
+        out.append(tmp)
+    for index in range(len(inputs)):
+        im2col(attrs, inputs[index], out[index])
+        out[index] = out[index].reshape([
+            output_height[0, index] * output_width[0, index],
             img_channels * attrs['kernels'][0] * attrs['kernels'][1]
         ])
+    out = np.concatenate(out, axis=0)
     return out
 
 
@@ -103,7 +131,7 @@ class TestBlockExpandOp(OpTest):
         self.attrs = {
             'kernels': [2, 2],
             'strides': [1, 1],
-            'paddings': [1, 1, 1, 1]
+            'paddings': [1, 1, 1, 1],
         }
 
     def setUp(self):
@@ -113,7 +141,8 @@ class TestBlockExpandOp(OpTest):
             self.batch_size, self.img_channels, self.img_height, self.img_width
         ]).astype("float32")
 
-        out = Im2Sequence(x, self.attrs)
+        real_size = np.array([]).astype("float32")
+        out = Im2Sequence(x, real_size, self.attrs)
         self.inputs = {'X': x}
         self.outputs = {'Out': out}
 
@@ -133,20 +162,20 @@ class TestBlockExpandOpCase2(TestBlockExpandOp):
         self.attrs = {
             'kernels': [2, 1],
             'strides': [2, 1],
-            'paddings': [2, 1, 2, 1]
+            'paddings': [2, 1, 2, 1],
         }
 
 
 class TestBlockExpandOpCase3(TestBlockExpandOp):
     def config(self):
-        self.batch_size = 3
+        self.batch_size = 2
         self.img_channels = 1
         self.img_height = 4
         self.img_width = 5
         self.attrs = {
             'kernels': [2, 1],
             'strides': [2, 1],
-            'paddings': [2, 0, 2, 0]
+            'paddings': [2, 0, 2, 0],
         }
 
 
@@ -159,9 +188,94 @@ class TestBlockExpandOpCase4(TestBlockExpandOp):
         self.attrs = {
             'kernels': [2, 2],
             'strides': [1, 1],
-            'paddings': [0, 0, 0, 0]
+            'paddings': [0, 0, 0, 0],
+        }
+
+
+class TestBlockExpandOpCase5(OpTest):
+    def config(self):
+        self.batch_size = 1
+        self.img_channels = 3
+        self.img_height = 4
+        self.img_width = 5
+        self.attrs = {
+            'kernels': [2, 1],
+            'strides': [2, 1],
+            'paddings': [2, 1, 2, 1],
+            'out_stride': [2, 2],
+        }
+
+    def setUp(self):
+        self.config()
+        self.op_type = "im2sequence"
+        x = np.random.uniform(0.1, 1, [
+            self.batch_size, self.img_channels, self.img_height, self.img_width
+        ]).astype("float32")
+        real_size = np.array([[8, 10], [5, 8]]).astype("float32")
+        out = np.array(Im2Sequence(x, real_size, self.attrs))
+        self.inputs = {'X': x, 'Y': real_size}  #l ??
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestBlockExpandOpCase6(OpTest):
+    def config(self):
+        self.batch_size = 3
+        self.img_channels = 1
+        self.img_height = 4
+        self.img_width = 5
+        self.attrs = {
+            'kernels': [2, 1],
+            'strides': [1, 1],
+            'paddings': [0, 0, 0, 0],
+            'out_stride': [1, 1],
+        }
+
+    def setUp(self):
+        self.config()
+        self.op_type = "im2sequence"
+        x = np.random.uniform(0.1, 1, [
+            self.batch_size, self.img_channels, self.img_height, self.img_width
+        ]).astype("float32")
+        real_size = np.array([[8, 10], [5, 8], [5, 8]]).astype("float32")
+        out = np.array(Im2Sequence(x, real_size, self.attrs))
+        self.inputs = {'X': x, 'Y': real_size}  #l ??
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestBlockExpandOpCase7(OpTest):
+    def config(self):
+        self.batch_size = 2
+        self.img_channels = 2
+        self.img_height = 3
+        self.img_width = 3
+        self.attrs = {
+            'kernels': [2, 2],
+            'strides': [1, 1],
+            'paddings': [1, 0, 1, 0],
+            'out_stride': [2, 2],
         }
 
+    def setUp(self):
+        self.config()
+        self.op_type = "im2sequence"
+        x = np.random.uniform(0.1, 1, [
+            self.batch_size, self.img_channels, self.img_height, self.img_width
+        ]).astype("float32")
+        real_size = np.array([[6, 6], [4, 4]]).astype("float32")
+        out = np.array(Im2Sequence(x, real_size, self.attrs))
+        self.inputs = {'X': x, 'Y': real_size}
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
 
 if __name__ == '__main__':
     unittest.main()
+#set shiftwidth=4 set expandtab set tabstop=4

python/paddle/fluid/tests/unittests/test_layers.py

@@ -251,12 +251,16 @@ class TestBook(unittest.TestCase):
         print(str(program))
 
     def test_im2sequence(self):
-        print("test_im2sequence")
         program = Program()
         with program_guard(program):
             x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
+            y = layers.data(name='y', shape=[], dtype='float32')
             output = layers.im2sequence(
-                input=x, stride=[1, 1], filter_size=[2, 2])
+                input=x,
+                input_image_size=y,
+                stride=[1, 1],
+                filter_size=[2, 2],
+                out_stride=[1, 1])
             self.assertIsNotNone(output)
         print(str(program))