summary: "Extracts a glimpse from the input tensor."
description: "Returns a set of windows called glimpses extracted at location\n`offsets` from the input tensor. If the windows only partially\noverlaps the inputs, the non overlapping areas will be filled with\nrandom noise.\n\nThe result is a 4-D tensor of shape `[batch_size, glimpse_height,\nglimpse_width, channels]`. The channels and batch dimensions are the\nsame as that of the input tensor. The height and width of the output\nwindows are specified in the `size` parameter.\n\nThe argument `normalized` and `centered` controls how the windows are"
}
op {
name: "ExtractImagePatches"
input_arg {
name: "images"
description: "4-D Tensor with shape `[batch, in_rows, in_cols, depth]`."
type_attr: "T"
}
output_arg {
name: "patches"
description: "4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *\nksize_cols * depth]` containing image patches with size\n`ksize_rows x ksize_cols x depth` vectorized in the \"depth\" dimension."
type_attr: "T"
}
attr {
name: "ksizes"
type: "list(int)"
default_value {
}
description: "The size of the sliding window for each dimension of `images`."
}
attr {
name: "strides"
type: "list(int)"
default_value {
}
description: "1-D of length 4. How far the centers of two consecutive patches are in\nthe images. Must be: `[1, stride_rows, stride_cols, 1]`."
}
attr {
name: "rates"
type: "list(int)"
default_value {
}
description: "1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the\ninput stride, specifying how far two consecutive patch samples are in the\ninput. Equivalent to extracting patches with\n`patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1), followed by\nsubsampling them spatially by a factor of `rates`."
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
type: DT_INT32
type: DT_INT64
type: DT_UINT8
type: DT_INT16
type: DT_INT8
type: DT_UINT16
type: DT_HALF
}
}
}
attr {
name: "padding"
type: "string"
description: "The type of padding algorithm to use.\n\nWe specify the size-related attributes as:\n\n ksizes = [1, ksize_rows, ksize_cols, 1]\n strides = [1, strides_rows, strides_cols, 1]\n rates = [1, rates_rows, rates_cols, 1]"
allowed_values {
list {
s: "SAME"
s: "VALID"
}
}
}
summary: "Extract `patches` from `images` and puth them in the \"depth\" output dimension."
