Ubuntu 16, mxnet 0.9.4 (compiled with GPU support), imager for image processind, abind for manipulations with arrays. It is almost end-to-end R solution for Kaggle competition https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/, we will use Python only for creating .rec-files.
Thanks to jeremiedb, my code for fine-tuning is largely based on his answers.
knitr::opts_chunk$set(eval = FALSE)library(imager)
library(mxnet)
library(abind)files <- list.files("train")
old_names <- sapply(files, strsplit, split = ".", fixed = TRUE)
max_length <- max(sapply(old_names, function(x) nchar(x[[2]])))
zeros <- max_length - sapply(old_names, function(x) nchar(x[[2]]))
zeros <- sapply(zeros, function(x) paste(rep(0, x), collapse = ""))
new_names <- Map(function(x, y) {paste0("./train/",
x[1],
"/",
y,
x[2],
".jpg")},
x = old_names, y = zeros
)
# Full names
files <- paste0("./train/", files)
dir.create("./train/cat")
dir.create("./train/dog")
# New names will be in 00001.jpg format
Map(function(x, y) file.rename(from = x, to = y), files, new_names)files <- list.files("train", recursive = TRUE)
new_names <- paste0("train_pad_224x224/", files)
files <- paste0("./train/", files)
dir.create("./train_pad_224x224/")
dir.create("./train_pad_224x224/cat")
dir.create("./train_pad_224x224/dog")
padImage <- function(x) {
long_side <- max(dim(x)[1:2])
short_side <- min(dim(x)[1:2])
pad_img <- pad(x,
nPix = long_side - short_side,
axes = ifelse(dim(x)[1] < dim(x)[2], "x", "y"))
return(pad_img)
}
Map(function(x, y) {
pad_img <- padImage(load.image(x))
res_img <- resize(pad_img, size_x = 224, size_y = 224)
imager::save.image(res_img, y)
}, x = files, y = new_names)files <- list.files("test")
max_length <- max(sapply(files, nchar))
zeros <- max_length - sapply(files, nchar)
zeros <- sapply(zeros, function(x) paste(rep(0, x), collapse = ""))
newnames <- paste0("./test/", zeros, files)
files <- paste0("./test/", files)
Map(function(x, y) file.rename(from = x, to = y), files, newnames)files <- list.files("test")
new_names <- paste0("test_pad_224x224/", files)
files <- paste0("./test/", files)
dir.create("./test_pad_224x224/")
Map(function(x, y) {
pad_img <- padImage(load.image(x))
res_img <- resize(pad_img, size_x = 224, size_y = 224)
imager::save.image(res_img, y)
}, x = files, y = new_names)python ~/mxnet/tools/im2rec.py --list=1 --recursive=1 --train-ratio=0.8 cats_dogs train_pad_224x224
python ~/mxnet/tools/im2rec.py --num-thread=4 --pass-through=1 cats_dogs_train.lst train_pad_224x224
python ~/mxnet/tools/im2rec.py --num-thread=4 --pass-through=1 cats_dogs_val.lst train_pad_224x224get_iterator <- function(data_shape,
train_data,
val_data,
batch_size = 128) {
train <- mx.io.ImageRecordIter(
path.imgrec = train_data,
batch.size = batch_size,
data.shape = data_shape,
rand.crop = TRUE,
rand.mirror = TRUE)
val <- mx.io.ImageRecordIter(
path.imgrec = val_data,
batch.size = batch_size,
data.shape = data_shape,
rand.crop = FALSE,
rand.mirror = FALSE
)
return(list(train = train, val = val))
}data <- get_iterator(data_shape = c(224, 224, 3),
train_data = "/media/andrey/Data/KAGGLE/cats_dogs/cats_dogs_train.rec",
val_data = "/media/andrey/Data/KAGGLE/cats_dogs/cats_dogs_val.rec",
batch_size = 8)
train <- data$train
val <- data$valModel from http://data.dmlc.ml/models/imagenet/ Last fully connected layes for 1000 classes replaced with new layer for 2 classes.
inception_bn <- mx.model.load("models/inception_bn/Inception-BN",
iteration = 126)
symbol <- inception_bn$symbol
# check symbol$arguments for layer names
internals <- symbol$get.internals()
outputs <- internals$outputs
flatten <- internals$get.output(which(outputs == "flatten_output"))
new_fc <- mx.symbol.FullyConnected(data = flatten,
num_hidden = 2,
name = "fc1")
# set name to original name in symbol$arguments
new_soft <- mx.symbol.SoftmaxOutput(data = new_fc,
name = "softmax")
# set name to original name in symbol$arguments
arg_params_new <- mxnet:::mx.model.init.params(
symbol = new_soft,
input.shape = c(224, 224, 3, 8),
initializer = mxnet:::mx.init.uniform(0.1),
ctx = mx.gpu(0)
)$arg.params
fc1_weights_new <- arg_params_new[["fc1_weight"]]
fc1_bias_new <- arg_params_new[["fc1_bias"]]
arg_params_new <- inception_bn$arg.params
arg_params_new[["fc1_weight"]] <- fc1_weights_new
arg_params_new[["fc1_bias"]] <- fc1_bias_new model <- mx.model.FeedForward.create(
symbol = new_soft,
X = train,
eval.data = val,
ctx = mx.gpu(0),
eval.metric = mx.metric.accuracy,
num.round = 1,
learning.rate = 0.05,
momentum = 0.9,
wd = 0.00001,
kvstore = "local",
array.batch.size = 128,
epoch.end.callback = mx.callback.save.checkpoint("inception_bn"),
batch.end.callback = mx.callback.log.train.metric(150),
initializer = mx.init.Xavier(factor_type = "in", magnitude = 2.34),
optimizer = "sgd",
arg.params = arg_params_new,
aux.params = inception_bn$aux.params
)model <- mx.model.load("inception_bn", 1)Continue training with decreased speed (learning.rate = 0.03):
model <- mx.model.FeedForward.create(
symbol = model$symbol,
X = train,
eval.data = val,
ctx = mx.gpu(0),
eval.metric = mx.metric.accuracy,
num.round = 5,
learning.rate = 0.03,
momentum = 0.9,
wd = 0.00001,
kvstore = "local",
array.batch.size = 100,
epoch.end.callback = mx.callback.save.checkpoint("inception_bn"),
batch.end.callback = mx.callback.log.train.metric(150),
initializer = mx.init.Xavier(factor_type = "in", magnitude = 2.34),
optimizer = "sgd",
arg.params = model$arg.params,
aux.params = model$aux.params
)model <- mx.model.load("inception_bn", 1)My R session crashed after each iteration, so I made some iterations manually.
preprocImage<- function(src, # URL or file location
height = 224,
width = 224,
num_channels = 3, # 3 for RGB, 1 for grayscale
mult_by = 1, # set to 255 for normalized image
crop = FALSE) { # no crop by default
im <- load.image(src)
if (crop) {
shape <- dim(im)
short_edge <- min(shape[1:2])
xx <- floor((shape[1] - short_edge) / 2)
yy <- floor((shape[2] - short_edge) / 2)
im <- crop.borders(im, xx, yy)
}
resized <- resize(im, size_x = width, size_y = height)
arr <- as.array(resized) * mult_by
dim(arr) <- c(width, height, num_channels, 1)
return(arr)
} files <- list.files("test_pad_224x224/")
files <- paste0("./test_pad_224x224/", files)
# ind <- seq(1, 12500, 1250)
# probs <- numeric()
# for (i in ind) {
# images <- lapply(files[i:i+1249], preprocImage, mult_by = 255)
# images <- do.call(abind, images)
# probs[i:i+1249] <- predict(model, X = images, ctx = mx.gpu(0))
# }
files <- split(files, rep(1:1250, each = 10))
probs <- lapply(files, function(x) {
images <- lapply(x, preprocImage, mult_by = 255)
images <- do.call(abind, images)
probs <- predict(model, X = images, ctx = mx.gpu(0))
})
saveRDS(probs, "probs.rds")
probs <- t(do.call(cbind, probs))
preds <- data.frame(id = 1:12500, label = probs[, 2])
write.csv(preds, "subm.csv", row.names = FALSE, quote = FALSE)