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caffe_traininglayers.py
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caffe_traininglayers.py
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import numpy as np
import warnings
import os
import caffe
from skimage import color
import color_quantization as cq
# ***************************************
# ***** LAYERS FOR GLOBAL HISTOGRAM *****
# ***************************************
class SpatialRepLayer(caffe.Layer):
'''
INPUTS
bottom[0].data NxCx1x1
bottom[1].data NxCxXxY
OUTPUTS
top[0].data NxCxXxY repeat 0th input spatially '''
def setup(self, bottom, top):
if(len(bottom) != 2):
raise Exception("Layer needs 2 inputs")
self.param_str_split = self.param_str.split(' ')
# self.keep_ratio = float(self.param_str_split[0]) # frequency keep whole input
self.N = bottom[0].data.shape[0]
self.C = bottom[0].data.shape[1]
self.X = bottom[0].data.shape[2]
self.Y = bottom[0].data.shape[3]
if(self.X != 1 or self.Y != 1):
raise Exception("bottom[0] should have spatial dimensions 1x1")
# self.Nref = bottom[1].data.shape[0]
# self.Cref = bottom[1].data.shape[1]
self.Xref = bottom[1].data.shape[2]
self.Yref = bottom[1].data.shape[3]
def reshape(self, bottom, top):
top[0].reshape(self.N, self.C, self.Xref, self.Yref) # output shape
def forward(self, bottom, top):
top[0].data[...] = bottom[0].data[:, :, :, :] # will do singleton expansion
def backward(self, top, propagate_down, bottom):
bottom[0].diff[:, :, 0, 0] = np.sum(np.sum(top[0].diff, axis=2), axis=2)
bottom[1].diff[...] = 0
class BGR2HSVLayer(caffe.Layer):
''' Layer converts BGR to HSV
INPUTS
bottom[0] Nx3xXxY
OUTPUTS
top[0].data Nx3xXxY
'''
def setup(self, bottom, top):
warnings.filterwarnings("ignore")
if(len(bottom) != 1):
raise Exception("Layer should a single input")
if(bottom[0].data.shape[1] != 3):
raise Exception("Input should be 3-channel BGR image")
self.N = bottom[0].data.shape[0]
self.X = bottom[0].data.shape[2]
self.Y = bottom[0].data.shape[3]
def reshape(self, bottom, top):
top[0].reshape(self.N, 3, self.X, self.Y)
def forward(self, bottom, top):
for nn in range(self.N):
top[0].data[nn, :, :, :] = color.rgb2hsv(bottom[0].data[nn, ::-1, :, :].astype('uint8').transpose((1, 2, 0))).transpose((2, 0, 1))
def backward(self, top, propagate_down, bottom):
# no back-prop
for i in range(len(bottom)):
if not propagate_down[i]:
continue
# bottom[i].diff[...] = np.zeros_like(bottom[i].data)
class BGR2LabLayer(caffe.Layer):
''' Layer converts BGR to Lab
INPUTS
bottom[0] Nx3xXxY
OUTPUTS
top[0].data Nx3xXxY
'''
def setup(self, bottom, top):
warnings.filterwarnings("ignore")
if(len(bottom) != 1):
raise Exception("Layer should a single input")
if(bottom[0].data.shape[1] != 3):
raise Exception("Input should be 3-channel BGR image")
self.N = bottom[0].data.shape[0]
self.X = bottom[0].data.shape[2]
self.Y = bottom[0].data.shape[3]
def reshape(self, bottom, top):
top[0].reshape(self.N, 3, self.X, self.Y)
def forward(self, bottom, top):
top[0].data[...] = color.rgb2lab(bottom[0].data[:, ::-1, :, :].astype('uint8').transpose((2, 3, 0, 1))).transpose((2, 3, 0, 1))
def backward(self, top, propagate_down, bottom):
# no back-prop
for i in range(len(bottom)):
if not propagate_down[i]:
continue
# bottom[i].diff[...] = np.zeros_like(bottom[i].data)
class ColorGlobalDropoutLayer(caffe.Layer):
'''
Inputs
bottom[0].data NxCx1x1
Outputs
top[0].data Nx(C+1)x1x1 last channel is whether or not to keep input
first C channels are copied from bottom (if kept)
'''
def setup(self, bottom, top):
if(len(bottom) == 0):
raise Exception("Layer needs inputs")
self.param_str_split = self.param_str.split(' ')
self.keep_ratio = float(self.param_str_split[0]) # frequency keep whole input
self.cnt = 0
self.N = bottom[0].data.shape[0]
self.C = bottom[0].data.shape[1]
self.X = bottom[0].data.shape[2]
self.Y = bottom[0].data.shape[3]
def reshape(self, bottom, top):
top[0].reshape(self.N, self.C + 1, self.X, self.Y) # output mask
def forward(self, bottom, top):
top[0].data[...] = 0
# top[0].data[:,:self.C,:,:] = bottom[0].data[...]
# determine which ones are kept
keeps = np.random.binomial(1, self.keep_ratio, size=self.N)
top[0].data[:, -1, :, :] = keeps[:, np.newaxis, np.newaxis]
top[0].data[:, :-1, :, :] = bottom[0].data[...] * keeps[:, np.newaxis, np.newaxis, np.newaxis]
def backward(self, top, propagate_down, bottom):
0 # backward not implemented
class NNEncLayer(caffe.Layer):
''' Layer which encodes ab map into Q colors
INPUTS
bottom[0] Nx2xXxY
OUTPUTS
top[0].data NxQ
'''
def setup(self, bottom, top):
warnings.filterwarnings("ignore")
if len(bottom) == 0:
raise Exception("Layer should have inputs")
# self.NN = 10.
self.NN = 1.
self.sigma = 5.
self.ENC_DIR = './data/color_bins'
self.nnenc = cq.NNEncode(self.NN, self.sigma, km_filepath=os.path.join(self.ENC_DIR, 'pts_in_hull.npy'))
self.N = bottom[0].data.shape[0]
self.X = bottom[0].data.shape[2]
self.Y = bottom[0].data.shape[3]
self.Q = self.nnenc.K
def reshape(self, bottom, top):
top[0].reshape(self.N, self.Q, self.X, self.Y)
def forward(self, bottom, top):
top[0].data[...] = self.nnenc.encode_points_mtx_nd(bottom[0].data[...], axis=1)
def backward(self, top, propagate_down, bottom):
# no back-prop
for i in range(len(bottom)):
if not propagate_down[i]:
continue
bottom[i].diff[...] = np.zeros_like(bottom[i].data)