Fix some lint errors in image_ops_test.py and word2vec_basic.py

Change: 110727357

README.md

@@ -16,11 +16,8 @@ organization for the purposes of conducting machine learning and deep neural
 networks research.  The system is general enough to be applicable in a wide
 variety of other domains, as well.
 
-
-**Note: Currently we do not accept pull requests on github -- see
-[CONTRIBUTING.md](CONTRIBUTING.md) for information on how to contribute code
-changes to TensorFlow through
-[tensorflow.googlesource.com](https://tensorflow.googlesource.com/tensorflow)**
+**If you'd like to contribute to tensorflow, be sure to review the [contribution
+guidelines](CONTRIBUTING.md).**
 
 **We use [github issues](https://github.com/tensorflow/tensorflow/issues) for
 tracking requests and bugs, but please see
@@ -29,35 +26,7 @@ and discussion.**
 
 # Download and Setup
 
-To install the CPU version of TensorFlow using a binary package, see the
-instructions below.  For more detailed installation instructions, including
-installing from source, GPU-enabled support, etc., see
-[here](tensorflow/g3doc/get_started/os_setup.md).
-
-## Binary Installation
-
-The TensorFlow Python API supports Python 2.7 and Python 3.3+.
-
-The simplest way to install TensorFlow is using
-[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
-
-For the GPU-enabled version, or if you encounter installation errors, or for
-more detailed installation instructions, see
-[here](tensorflow/g3doc/get_started/os_setup.md#detailed_install).
-
-### Ubuntu/Linux 64-bit
-
-```bash
-# For CPU-only version
-$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
-```
-
-### Mac OS X
-
-```bash
-# Only CPU-version is available at the moment.
-$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
-```
+See [install instructions](tensorflow/g3doc/get_started/os_setup.md).
 
 ### Try your first TensorFlow program
 

tensorflow/core/BUILD

@@ -349,6 +349,7 @@ tf_gpu_kernel_library(
     visibility = ["//visibility:public"],
     deps = [
         ":cuda",
+        ":framework",
         "//third_party/eigen3",
     ],
 )

tensorflow/core/kernels/resize_area_op.cc

@@ -144,11 +144,8 @@ class ResizeAreaOp : public OpKernel {
                               .HostMemory("size"),    \
                           ResizeAreaOp<CPUDevice, T>);
 
-REGISTER_KERNEL(uint8);
-REGISTER_KERNEL(int8);
-REGISTER_KERNEL(int32);
-REGISTER_KERNEL(float);
-REGISTER_KERNEL(double);
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
+
 #undef REGISTER_KERNEL
 
 }  // namespace tensorflow

tensorflow/core/kernels/resize_bicubic_op.cc

@@ -131,11 +131,8 @@ class ResizeBicubicOp : public OpKernel {
                               .HostMemory("size"),    \
                           ResizeBicubicOp<CPUDevice, T>);
 
-REGISTER_KERNEL(uint8);
-REGISTER_KERNEL(int8);
-REGISTER_KERNEL(int32);
-REGISTER_KERNEL(float);
-REGISTER_KERNEL(double);
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
+
 #undef REGISTER_KERNEL
 
 }  // namespace tensorflow

tensorflow/core/kernels/resize_bilinear_op.cc

@@ -215,11 +215,8 @@ class ResizeBilinearOpGrad : public OpKernel {
                               .HostMemory("size"),    \
                           ResizeBilinearOp<CPUDevice, T>);
 
-REGISTER_KERNEL(uint8);
-REGISTER_KERNEL(int8);
-REGISTER_KERNEL(int32);
-REGISTER_KERNEL(float);
-REGISTER_KERNEL(double);
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
+
 #undef REGISTER_KERNEL
 
 REGISTER_KERNEL_BUILDER(Name("ResizeBilinearGrad")

tensorflow/core/kernels/resize_nearest_neighbor_op.cc

@@ -178,11 +178,8 @@ class ResizeNearestNeighborOpGrad : public OpKernel {
                               .HostMemory("size"),                \
                           ResizeNearestNeighborOpGrad<CPUDevice, T>);
 
-REGISTER_KERNEL(uint8);
-REGISTER_KERNEL(int8);
-REGISTER_KERNEL(int32);
-REGISTER_KERNEL(float);
-REGISTER_KERNEL(double);
+TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
+
 #undef REGISTER_KERNEL
 
 }  // namespace tensorflow

tensorflow/core/ops/image_ops.cc

@@ -22,7 +22,7 @@ REGISTER_OP("ResizeArea")
     .Input("images: T")
     .Input("size: int32")
     .Output("resized_images: float")
-    .Attr("T: {uint8, int8, int32, float, double}")
+    .Attr("T: {uint8, int8, int16, int32, int64, float, double}")
     .Doc(R"doc(
 Resize `images` to `size` using area interpolation.
 
@@ -40,7 +40,7 @@ REGISTER_OP("ResizeBicubic")
     .Input("images: T")
     .Input("size: int32")
     .Output("resized_images: float")
-    .Attr("T: {uint8, int8, int32, float, double}")
+    .Attr("T: {uint8, int8, int16, int32, int64, float, double}")
     .Doc(R"doc(
 Resize `images` to `size` using bicubic interpolation.
 
@@ -58,7 +58,7 @@ REGISTER_OP("ResizeBilinear")
     .Input("images: T")
     .Input("size: int32")
     .Output("resized_images: float")
-    .Attr("T: {uint8, int8, int32, float, double}")
+    .Attr("T: {uint8, int8, int16, int32, int64, float, double}")
     .Doc(R"doc(
 Resize `images` to `size` using bilinear interpolation.
 
@@ -93,7 +93,7 @@ REGISTER_OP("ResizeNearestNeighbor")
     .Input("images: T")
     .Input("size: int32")
     .Output("resized_images: T")
-    .Attr("T: {uint8, int8, int32, float, double}")
+    .Attr("T: {uint8, int8, int16, int32, int64, float, double}")
     .Doc(R"doc(
 Resize `images` to `size` using nearest neighbor interpolation.
 

tensorflow/core/ops/ops.pbtxt

@@ -5949,7 +5949,9 @@ op {
       list {
         type: DT_UINT8
         type: DT_INT8
+        type: DT_INT16
         type: DT_INT32
+        type: DT_INT64
         type: DT_FLOAT
         type: DT_DOUBLE
       }
@@ -5982,7 +5984,9 @@ op {
       list {
         type: DT_UINT8
         type: DT_INT8
+        type: DT_INT16
         type: DT_INT32
+        type: DT_INT64
         type: DT_FLOAT
         type: DT_DOUBLE
       }
@@ -6015,7 +6019,9 @@ op {
       list {
         type: DT_UINT8
         type: DT_INT8
+        type: DT_INT16
         type: DT_INT32
+        type: DT_INT64
         type: DT_FLOAT
         type: DT_DOUBLE
       }
@@ -6077,7 +6083,9 @@ op {
       list {
         type: DT_UINT8
         type: DT_INT8
+        type: DT_INT16
         type: DT_INT32
+        type: DT_INT64
         type: DT_FLOAT
         type: DT_DOUBLE
       }

tensorflow/examples/tutorials/word2vec/word2vec_basic.py

@@ -141,16 +141,18 @@ with graph.as_default():
   train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
   valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
 
-  # Construct the variables.
-  embeddings = tf.Variable(
-      tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
-  nce_weights = tf.Variable(
-      tf.truncated_normal([vocabulary_size, embedding_size],
-                          stddev=1.0 / math.sqrt(embedding_size)))
-  nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
-
-  # Look up embeddings for inputs.
-  embed = tf.nn.embedding_lookup(embeddings, train_inputs)
+  # Ops and variables pinned to the CPU because of missing GPU implementation
+  with tf.device('/cpu:0'):
+    # Look up embeddings for inputs.
+    embeddings = tf.Variable(
+        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
+    embed = tf.nn.embedding_lookup(embeddings, train_inputs)
+
+    # Construct the variables for the NCE loss
+    nce_weights = tf.Variable(
+        tf.truncated_normal([vocabulary_size, embedding_size],
+                            stddev=1.0 / math.sqrt(embedding_size)))
+    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
 
   # Compute the average NCE loss for the batch.
   # tf.nce_loss automatically draws a new sample of the negative labels each

tensorflow/g3doc/api_docs/python/framework.md

@@ -1408,7 +1408,7 @@ and Python scalars. For example:
 
 ```python
 import numpy as np
-array = np.random.rand((32, 100, 100))
+array = np.random.rand(32, 100, 100)
 
 def my_func(arg):
   arg = tf.convert_to_tensor(arg, dtype=tf.float32)

tensorflow/g3doc/resources/dims_types.md

@@ -54,14 +54,14 @@ Data type | Python type | Description
 --- | --- | ---
 `DT_FLOAT` | `tf.float32` | 32 bits floating point.
 `DT_DOUBLE` | `tf.float64` | 64 bits floating point.
-`DT_INT64` | `tf.int64` | 64 bits signed integer.
-`DT_INT32` | `tf.int32` | 32 bits signed integer.
-`DT_INT16` | `tf.int16` | 16 bits signed integer.
 `DT_INT8` | `tf.int8` | 8 bits signed integer.
+`DT_INT16` | `tf.int16` | 16 bits signed integer.
+`DT_INT32` | `tf.int32` | 32 bits signed integer.
+`DT_INT64` | `tf.int64` | 64 bits signed integer.
 `DT_UINT8` | `tf.uint8` | 8 bits unsigned integer.
 `DT_STRING` | `tf.string` | Variable length byte arrays.  Each element of a Tensor is a byte array.
 `DT_BOOL` | `tf.bool` | Boolean.
 `DT_COMPLEX64` | `tf.complex64` | Complex number made of two 32 bits floating points: real and imaginary parts.
-`DT_QINT32` | `tf.qint32` | 32 bits signed integer used in quantized Ops.
 `DT_QINT8` | `tf.qint8` | 8 bits signed integer used in quantized Ops.
-`DT_QUINT8` | `tf.quint8` | 8 bits unsigned integer used in quantized Ops.
+`DT_QINT32` | `tf.qint32` | 32 bits signed integer used in quantized Ops.
+`DT_QUINT8` | `tf.quint8` | 8 bits unsigned integer used in quantized Ops.
\ No newline at end of file

tensorflow/g3doc/tutorials/deep_cnn/index.md

@@ -126,7 +126,7 @@ artificially increase the data set size:
 
 * [Randomly flip](../../api_docs/python/image.md#random_flip_left_right) the image from left to right.
 * Randomly distort the [image brightness](../../api_docs/python/image.md#random_brightness).
-* Randomly distort the [image contrast](../../api_docs/python/image.md#tf_image_random_contrast).
+* Randomly distort the [image contrast](../../api_docs/python/image.md#random_contrast).
 
 Please see the [Images](../../api_docs/python/image.md) page for the list of
 available distortions. We also attach an

tensorflow/python/framework/ops.py

@@ -486,7 +486,7 @@ def convert_to_tensor(value, dtype=None, name=None, as_ref=False):
 
   ```python
   import numpy as np
-  array = np.random.rand((32, 100, 100))
+  array = np.random.rand(32, 100, 100)
 
   def my_func(arg):
     arg = tf.convert_to_tensor(arg, dtype=tf.float32)

tensorflow/python/ops/image_ops_test.py

@@ -564,49 +564,56 @@ class ResizeImagesTest(test_util.TensorFlowTestCase):
              image_ops.ResizeMethod.BICUBIC,
              image_ops.ResizeMethod.AREA]
 
+  TYPES = [np.uint8, np.int8, np.int16, np.int32, np.int64,
+           np.float, np.double]
+
   def testNoOp(self):
     img_shape = [1, 6, 4, 1]
     single_shape = [6, 4, 1]
-    data = [128, 128, 64, 64,
-            128, 128, 64, 64,
-            64, 64, 128, 128,
-            64, 64, 128, 128,
+    # This test is also conducted with int8, so 127 is the maximum
+    # value that can be used.
+    data = [127, 127, 64, 64,
+            127, 127, 64, 64,
+            64, 64, 127, 127,
+            64, 64, 127, 127,
             50, 50, 100, 100,
             50, 50, 100, 100]
-    img_np = np.array(data, dtype=np.uint8).reshape(img_shape)
-
     target_height = 6
     target_width = 4
 
-    for opt in self.OPTIONS:
-      with self.test_session() as sess:
-        image = constant_op.constant(img_np, shape=img_shape)
-        y = image_ops.resize_images(image, target_height, target_width, opt)
-        yshape = array_ops.shape(y)
-        resized, newshape = sess.run([y, yshape])
-        self.assertAllEqual(img_shape, newshape)
-        self.assertAllClose(resized, img_np, atol=1e-5)
+    for nptype in self.TYPES:
+      img_np = np.array(data, dtype=nptype).reshape(img_shape)
 
-    # Resizing with a single image must leave the shape unchanged also.
-    with self.test_session():
-      img_single = img_np.reshape(single_shape)
-      image = constant_op.constant(img_single, shape=single_shape)
-      y = image_ops.resize_images(image, target_height, target_width,
-                                  self.OPTIONS[0])
-      yshape = array_ops.shape(y)
-      newshape = yshape.eval()
-      self.assertAllEqual(single_shape, newshape)
+      for opt in self.OPTIONS:
+        with self.test_session() as sess:
+          image = constant_op.constant(img_np, shape=img_shape)
+          y = image_ops.resize_images(image, target_height, target_width, opt)
+          yshape = array_ops.shape(y)
+          resized, newshape = sess.run([y, yshape])
+          self.assertAllEqual(img_shape, newshape)
+          self.assertAllClose(resized, img_np, atol=1e-5)
 
-  def testResizeDown(self):
+      # Resizing with a single image must leave the shape unchanged also.
+      with self.test_session():
+        img_single = img_np.reshape(single_shape)
+        image = constant_op.constant(img_single, shape=single_shape)
+        y = image_ops.resize_images(image, target_height, target_width,
+                                    self.OPTIONS[0])
+        yshape = array_ops.shape(y)
+        newshape = yshape.eval()
+        self.assertAllEqual(single_shape, newshape)
 
-    data = [128, 128, 64, 64,
-            128, 128, 64, 64,
-            64, 64, 128, 128,
-            64, 64, 128, 128,
+  def testResizeDown(self):
+    # This test is also conducted with int8, so 127 is the maximum
+    # value that can be used.
+    data = [127, 127, 64, 64,
+            127, 127, 64, 64,
+            64, 64, 127, 127,
+            64, 64, 127, 127,
             50, 50, 100, 100,
             50, 50, 100, 100]
-    expected_data = [128, 64,
-                     64, 128,
+    expected_data = [127, 64,
+                     64, 127,
                      50, 100]
     target_height = 3
     target_width = 2
@@ -617,59 +624,61 @@ class ResizeImagesTest(test_util.TensorFlowTestCase):
                      [target_height, target_width, 1]]
 
     for target_shape, img_shape in zip(target_shapes, img_shapes):
-      img_np = np.array(data, dtype=np.uint8).reshape(img_shape)
 
-      for opt in self.OPTIONS:
-        with self.test_session():
-          image = constant_op.constant(img_np, shape=img_shape)
-          y = image_ops.resize_images(image, target_height, target_width, opt)
-          expected = np.array(expected_data).reshape(target_shape)
-          resized = y.eval()
-          self.assertAllClose(resized, expected, atol=1e-5)
+      for nptype in self.TYPES:
+        img_np = np.array(data, dtype=nptype).reshape(img_shape)
+
+        for opt in self.OPTIONS:
+          with self.test_session():
+            image = constant_op.constant(img_np, shape=img_shape)
+            y = image_ops.resize_images(image, target_height, target_width, opt)
+            expected = np.array(expected_data).reshape(target_shape)
+            resized = y.eval()
+            self.assertAllClose(resized, expected, atol=1e-5)
 
   def testResizeUp(self):
     img_shape = [1, 3, 2, 1]
-    data = [128, 64,
-            64, 128,
+    data = [64, 32,
+            32, 64,
             50, 100]
-    img_np = np.array(data, dtype=np.uint8).reshape(img_shape)
-
     target_height = 6
     target_width = 4
     expected_data = {}
     expected_data[image_ops.ResizeMethod.BILINEAR] = [
-        128.0, 96.0, 64.0, 64.0,
-        96.0, 96.0, 96.0, 96.0,
-        64.0, 96.0, 128.0, 128.0,
-        57.0, 85.5, 114.0, 114.0,
+        64.0, 48.0, 32.0, 32.0,
+        48.0, 48.0, 48.0, 48.0,
+        32.0, 48.0, 64.0, 64.0,
+        41.0, 61.5, 82.0, 82.0,
         50.0, 75.0, 100.0, 100.0,
         50.0, 75.0, 100.0, 100.0]
     expected_data[image_ops.ResizeMethod.NEAREST_NEIGHBOR] = [
-        128.0, 128.0, 64.0, 64.0,
-        128.0, 128.0, 64.0, 64.0,
-        64.0, 64.0, 128.0, 128.0,
-        64.0, 64.0, 128.0, 128.0,
+        64.0, 64.0, 32.0, 32.0,
+        64.0, 64.0, 32.0, 32.0,
+        32.0, 32.0, 64.0, 64.0,
+        32.0, 32.0, 64.0, 64.0,
         50.0, 50.0, 100.0, 100.0,
         50.0, 50.0, 100.0, 100.0]
     expected_data[image_ops.ResizeMethod.AREA] = [
-        128.0, 128.0, 64.0, 64.0,
-        128.0, 128.0, 64.0, 64.0,
-        64.0, 64.0, 128.0, 128.0,
-        64.0, 64.0, 128.0, 128.0,
+        64.0, 64.0, 32.0, 32.0,
+        64.0, 64.0, 32.0, 32.0,
+        32.0, 32.0, 64.0, 64.0,
+        32.0, 32.0, 64.0, 64.0,
         50.0, 50.0, 100.0, 100.0,
         50.0, 50.0, 100.0, 100.0]
 
-    for opt in [
-        image_ops.ResizeMethod.BILINEAR,
-        image_ops.ResizeMethod.NEAREST_NEIGHBOR,
-        image_ops.ResizeMethod.AREA]:
-      with self.test_session():
-        image = constant_op.constant(img_np, shape=img_shape)
-        y = image_ops.resize_images(image, target_height, target_width, opt)
-        resized = y.eval()
-        expected = np.array(expected_data[opt]).reshape(
-            [1, target_height, target_width, 1])
-        self.assertAllClose(resized, expected, atol=1e-05)
+    for nptype in self.TYPES:
+      for opt in [
+          image_ops.ResizeMethod.BILINEAR,
+          image_ops.ResizeMethod.NEAREST_NEIGHBOR,
+          image_ops.ResizeMethod.AREA]:
+        with self.test_session():
+          img_np = np.array(data, dtype=nptype).reshape(img_shape)
+          image = constant_op.constant(img_np, shape=img_shape)
+          y = image_ops.resize_images(image, target_height, target_width, opt)
+          resized = y.eval()
+          expected = np.array(expected_data[opt]).reshape(
+              [1, target_height, target_width, 1])
+          self.assertAllClose(resized, expected, atol=1e-05)
 
   def testResizeUpBicubic(self):
     img_shape = [1, 6, 6, 1]

tensorflow/python/ops/template.py

@@ -187,7 +187,7 @@ class Template(object):
                        "meant tf.get_variable: %s",
                        variables[vars_at_start:])
       return result
-    except Exception, exc:
+    except Exception as exc:
       # Reraise the exception, but append the original definition to the
       # trace.
       args = exc.args

tensorflow/python/training/coordinator.py

@@ -70,7 +70,7 @@ class Coordinator(object):
   try:
     while not coord.should_stop():
       ...do some work...
-  except Exception, e:
+  except Exception as e:
     coord.request_stop(e)
   ```
 
@@ -85,7 +85,7 @@ class Coordinator(object):
     ...start thread N...(coord, ...)
     # Wait for all the threads to terminate.
     coord.join(threads)
-  except Exception, e:
+  except Exception as e:
     ...exception that was passed to coord.request_stop()
   ```
 
@@ -188,7 +188,7 @@ class Coordinator(object):
     ```python
     try:
       ...body...
-    exception Exception, ex:
+    exception Exception as ex:
       coord.request_stop(ex)
     ```
 
@@ -198,7 +198,7 @@ class Coordinator(object):
     # pylint: disable=broad-except
     try:
       yield
-    except Exception, ex:
+    except Exception as ex:
       self.request_stop(ex)
     # pylint: enable=broad-except
 

tensorflow/stream_executor/BUILD

@@ -19,6 +19,7 @@ cc_library(
     ),
     hdrs = glob([
         "*.h",
+        "cuda/*.h",
         "lib/*.h",
         "platform/**/*.h",
     ]),