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syntax = "proto2";
package caffe2;
// Original Caffe1 Datum copy: this is used in image input op to allow us to
// load caffe1 serialized datum without having to regenerate the database.
message CaffeDatum {
optional int32 channels = 1;
optional int32 height = 2;
optional int32 width = 3;
// the actual image data, in bytes
optional bytes data = 4;
optional int32 label = 5;
// Optionally, the datum could also hold float data.
repeated float float_data = 6;
// If true data contains an encoded image that need to be decoded
optional bool encoded = 7 [ default = false ];
}
enum LegacyPadding {
NOTSET = 0; // Do not use old-stype padding strategies.
// VALID and SAME are two strategies adopted in Google DistBelief: it forces
// the input shape as follows. For SAME, the output is:
// R_out = ceil(float(R) / float(S))
// C_out = ceil(float(C) / float(S))
// where R and C are row and column, S is the stride, and K is the kernel.
// The number of padded pixels is then computed as
// Pr = ((R_out - 1) * S + K - R)
// Pc = ((C_out - 1) * S + K - C)
// When Pr and Pc are even numbers, both sides (left and right, or top and
// bottom) get half each. When Pr and Pc are odd numbers, the right and the
// bottom gets the one additional padding pixel.
// For VALID, padding values of 0 are always used.
VALID = 1;
SAME = 2;
// CAFFE_LEGACY_POOLING is a flag that notifies the code to use the old Caffe
// padding strategy.
// Basically, in caffe2, after padding the convolution and pooling use the
// same computation strategy: half-windows at the right and bottom are
// discarded. In Caffe, convolution follows this strategy but if there are
// some pixels in the half-windows, the pooling layer will actually put one
// additional output. If you set LegacyPadding to this, we will compute the
// equivalent padding strategy in caffe2 so that the output size is
// backward compatible with Caffe.
// THIS IS NOW DEPRECATED. ANY non-conventional use has to be manually
// converted.
CAFFE_LEGACY_POOLING = 3;
}
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