Python backend.resize_images方法代码示例

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import resize_images [as 别名]
def resize_image(inp,  s, data_format):

    try:

        return Lambda(lambda x: K.resize_images(x,
                                                height_factor=s[0],
                                                width_factor=s[1],
                                                data_format=data_format,
                                                interpolation='bilinear'))(inp)

    except Exception as e:
        # if keras is old, then rely on the tf function
        # Sorry theano/cntk users!!!
        assert data_format == 'channels_last'
        assert IMAGE_ORDERING == 'channels_last'

        import tensorflow as tf

        return Lambda(
            lambda x: tf.image.resize_images(
                x, (K.int_shape(x)[1]*s[0], K.int_shape(x)[2]*s[1]))
        )(inp) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import resize_images [as 别名]
def call(self, inputs):
        return K.resize_images(inputs, self.factor, self.factor, self.data_format) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import resize_images [as 别名]
def Interp(x, shape):
    from keras.backend import tf as ktf
    new_height, new_width = shape
    resized = ktf.image.resize_images(x, [int(new_height), int(new_width)], align_corners=True)
    return resized


# interpolation block 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import resize_images [as 别名]
def _resize_nearest_neighbour(self, input_tensor, size):
        """ Resize a tensor using nearest neighbor interpolation.

        Notes
        -----
        Tensorflow has a bug that resizes the image incorrectly if :attr:`align_corners` is not set
        to ``True``. Keras Backend does not set this flag, so we explicitly call the Tensorflow
        operation for non-amd backends.

        Parameters
        ----------
        input_tensor: tensor
            The tensor to be resized
        tuple: int
            The (`h`, `w`) that the tensor should be resized to (used for non-amd backends only)

        Returns
        -------
        tensor
            The input tensor resized to the given size
        """
        if get_backend() == "amd":
            retval = K.resize_images(input_tensor, self.scale, self.scale, "channels_last",
                                     interpolation="nearest")
        else:
            retval = tf.image.resize_nearest_neighbor(input_tensor, size=size, align_corners=True)
        logger.debug("Input Tensor: %s, Output Tensor: %s", input_tensor, retval)
        return retval 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import resize_images [as 别名]
def _backward_pass(self, X, target_layer, d_switch, feat_map):
        # Run deconv/maxunpooling until input pixel space
        layer_index = self.lnames.index(target_layer)
        # Get the output of the target_layer of interest
        layer_output = K.function(
            [self[self.lnames[0]].input], self[target_layer].output)
        X_outl = layer_output([X])
        # Special case for the starting layer where we may want
        # to switchoff somes maps/ activations
        print("Deconvolving %s..." % target_layer)
        if "maxpooling2d" in target_layer:
            X_maxunp = K.pool.max_pool_2d_same_size(
                self[target_layer].input, self[target_layer].pool_size)
            unpool_func = K.function([self[self.lnames[0]].input], X_maxunp)
            X_outl = unpool_func([X])
            if feat_map is not None:
                for i in range(X_outl.shape[1]):
                    if i != feat_map:
                        X_outl[:, i, :, :] = 0
                for i in range(X_outl.shape[0]):
                    iw, ih = np.unravel_index(
                        X_outl[i, feat_map, :, :].argmax(), X_outl[i, feat_map, :, :].shape)
                    m = np.max(X_outl[i, feat_map, :, :])
                    X_outl[i, feat_map, :, :] = 0
                    X_outl[i, feat_map, iw, ih] = m
        elif "conv2d" in target_layer:
            X_outl = self._deconv(X_outl, target_layer,
                                  d_switch, feat_map=feat_map)
        else:
            raise ValueError(
                "Invalid layer name: %s \n Can only handle maxpool and conv" % target_layer)
        # Iterate over layers (deepest to shallowest)
        for lname in self.lnames[:layer_index][::-1]:
            print("Deconvolving %s..." % lname)
            # Unpool, Deconv or do nothing
            if "maxpooling2d" in lname:
                p1, p2 = self[lname].pool_size
                uppool = K.function(
                    [self.x], K.resize_images(self.x, p1, p2, "th"))
                X_outl = uppool([X_outl])

            elif "conv2d" in lname:
                X_outl = self._deconv(X_outl, lname, d_switch)
            elif "padding" in lname:
                pass
            else:
                raise ValueError(
                    "Invalid layer name: %s \n Can only handle maxpool and conv" % lname)
        return X_outl 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import resize_images [as 别名]
def call(self, input_tensor, training=None):
        input_transposed = tf.transpose(input_tensor, [3, 0, 1, 2, 4])
        input_shape = K.shape(input_transposed)
        input_tensor_reshaped = K.reshape(input_transposed, [
            input_shape[1] * input_shape[0], self.input_height, self.input_width, self.input_num_atoms])
        input_tensor_reshaped.set_shape((None, self.input_height, self.input_width, self.input_num_atoms))


        if self.upsamp_type == 'resize':
            upsamp = K.resize_images(input_tensor_reshaped, self.scaling, self.scaling, 'channels_last')
            outputs = K.conv2d(upsamp, kernel=self.W, strides=(1, 1), padding=self.padding, data_format='channels_last')
        elif self.upsamp_type == 'subpix':
            conv = K.conv2d(input_tensor_reshaped, kernel=self.W, strides=(1, 1), padding='same',
                            data_format='channels_last')
            outputs = tf.depth_to_space(conv, self.scaling)
        else:
            batch_size = input_shape[1] * input_shape[0]

            # Infer the dynamic output shape:
            out_height = deconv_length(self.input_height, self.scaling, self.kernel_size, self.padding)
            out_width = deconv_length(self.input_width, self.scaling, self.kernel_size, self.padding)
            output_shape = (batch_size, out_height, out_width, self.num_capsule * self.num_atoms)

            outputs = K.conv2d_transpose(input_tensor_reshaped, self.W, output_shape, (self.scaling, self.scaling),
                                     padding=self.padding, data_format='channels_last')

        votes_shape = K.shape(outputs)
        _, conv_height, conv_width, _ = outputs.get_shape()

        votes = K.reshape(outputs, [input_shape[1], input_shape[0], votes_shape[1], votes_shape[2],
                                 self.num_capsule, self.num_atoms])
        votes.set_shape((None, self.input_num_capsule, conv_height.value, conv_width.value,
                         self.num_capsule, self.num_atoms))

        logit_shape = K.stack([
            input_shape[1], input_shape[0], votes_shape[1], votes_shape[2], self.num_capsule])
        biases_replicated = K.tile(self.b, [votes_shape[1], votes_shape[2], 1, 1])

        activations = update_routing(
            votes=votes,
            biases=biases_replicated,
            logit_shape=logit_shape,
            num_dims=6,
            input_dim=self.input_num_capsule,
            output_dim=self.num_capsule,
            num_routing=self.routings)

        return activations