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OldLipeOldLipe
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Merge pull request #1131 from M3nin0/hotfix/args-input-pixels
hotfix: update input_pixels argument for compatibility with old models
2 parents 0fb9f4a + c67fadb commit ac3b16c

15 files changed

+42
-42
lines changed

‎R/api_check.R

+3-3
Original file line numberDiff line numberDiff line change
@@ -1364,14 +1364,14 @@
13641364
#' @title Does the result have the same number of pixels as the input values?
13651365
#' @name .check_processed_values
13661366
#' @param values a matrix of processed values
1367-
#' @param n_input_pixels number of pixels in input matrix
1367+
#' @param input_pixels number of pixels in input matrix
13681368
#' @return Called for side effects.
13691369
#' @keywords internal
13701370
#' @noRd
1371-
.check_processed_values <- function(values, n_input_pixels) {
1371+
.check_processed_values <- function(values, input_pixels) {
13721372
.check_set_caller(".check_processed_values")
13731373
.check_that(
1374-
!(is.null(nrow(values))) && nrow(values) == n_input_pixels
1374+
!(is.null(nrow(values))) && nrow(values) == input_pixels
13751375
)
13761376
return(invisible(values))
13771377
}

‎R/api_classify.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -115,7 +115,7 @@
115115
# Fill with zeros remaining NA pixels
116116
values <- C_fill_na(values, 0)
117117
# Used to check values (below)
118-
n_input_pixels <- nrow(values)
118+
input_pixels <- nrow(values)
119119
# Log here
120120
.debug_log(
121121
event = "start_block_data_classification",
@@ -127,7 +127,7 @@
127127
# Are the results consistent with the data input?
128128
.check_processed_values(
129129
values = values,
130-
n_input_pixels = n_input_pixels
130+
input_pixels = input_pixels
131131
)
132132
# Log
133133
.debug_log(

‎R/api_combine_predictions.R

+4-4
Original file line numberDiff line numberDiff line change
@@ -219,13 +219,13 @@
219219
# Average probability calculation
220220
comb_fn <- function(values, uncert_values = NULL) {
221221
# Check values length
222-
n_input_pixels <- nrow(values[[1]])
222+
input_pixels <- nrow(values[[1]])
223223
# Combine by average
224224
values <- weighted_probs(values, weights)
225225
# get the number of labels
226226
n_labels <- length(sits_labels(cubes[[1]]))
227227
# Are the results consistent with the data input?
228-
.check_processed_values(values, n_input_pixels)
228+
.check_processed_values(values, input_pixels)
229229
.check_processed_labels(values, n_labels)
230230
# Return values
231231
values
@@ -244,13 +244,13 @@
244244
# Average probability calculation
245245
comb_fn <- function(values, uncert_values) {
246246
# Check values length
247-
n_input_pixels <- nrow(values[[1]])
247+
input_pixels <- nrow(values[[1]])
248248
# Combine by average
249249
values <- weighted_uncert_probs(values, uncert_values)
250250
# get the number of labels
251251
n_labels <- length(sits_labels(cubes[[1]]))
252252
# Are the results consistent with the data input?
253-
.check_processed_values(values, n_input_pixels)
253+
.check_processed_values(values, input_pixels)
254254
.check_processed_labels(values, n_labels)
255255
# Return values
256256
values

‎R/api_label_class.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -148,10 +148,10 @@
148148
.label_fn_majority <- function() {
149149
label_fn <- function(values) {
150150
# Used to check values (below)
151-
n_input_pixels <- nrow(values)
151+
input_pixels <- nrow(values)
152152
values <- C_label_max_prob(values)
153153
# Are the results consistent with the data input?
154-
.check_processed_values(values, n_input_pixels)
154+
.check_processed_values(values, input_pixels)
155155
# Return values
156156
values
157157
}

‎R/api_mixture_model.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -160,15 +160,15 @@
160160
em_mtx <- .endmembers_as_matrix(em)
161161
mixture_fn <- function(values) {
162162
# Check values length
163-
n_input_pixels <- nrow(values)
163+
input_pixels <- nrow(values)
164164
# Process NNLS solver and return
165165
values <- C_nnls_solver_batch(
166166
x = as.matrix(values),
167167
em = em_mtx,
168168
rmse = rmse
169169
)
170170
# Are the results consistent with the data input?
171-
.check_processed_values(values, n_input_pixels)
171+
.check_processed_values(values, input_pixels)
172172
# Return values
173173
values
174174
}

‎R/api_reclassify.R

+3-3
Original file line numberDiff line numberDiff line change
@@ -158,7 +158,7 @@
158158
stop(.conf("messages", ".reclassify_fn_cube_mask"))
159159
}
160160
# Used to check values (below)
161-
n_input_pixels <- nrow(values)
161+
input_pixels <- nrow(values)
162162
# Convert to character vector
163163
values <- as.character(values)
164164
mask_values <- as.character(mask_values)
@@ -185,12 +185,12 @@
185185
# Get values as numeric
186186
values <- matrix(
187187
data = labels_code[match(values, labels)],
188-
nrow = n_input_pixels
188+
nrow = input_pixels
189189
)
190190
# Mask NA values
191191
values[is.na(env[["mask"]])] <- NA
192192
# Are the results consistent with the data input?
193-
.check_processed_values(values, n_input_pixels)
193+
.check_processed_values(values, input_pixels)
194194
# Return values
195195
values
196196
}

‎R/api_smooth.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -172,7 +172,7 @@
172172
# Define smooth function
173173
smooth_fn <- function(values, block) {
174174
# Check values length
175-
n_input_pixels <- nrow(values)
175+
input_pixels <- nrow(values)
176176
# Compute logit
177177
values <- log(values / (rowSums(values) - values))
178178
# Process Bayesian
@@ -187,7 +187,7 @@
187187
# Compute inverse logit
188188
values <- exp(values) / (exp(values) + 1)
189189
# Are the results consistent with the data input?
190-
.check_processed_values(values, n_input_pixels)
190+
.check_processed_values(values, input_pixels)
191191
# Return values
192192
values
193193
}

‎R/api_uncertainty.R

+6-6
Original file line numberDiff line numberDiff line change
@@ -239,12 +239,12 @@
239239
# Define uncertainty function
240240
uncert_fn <- function(values) {
241241
# Used in check (below)
242-
n_input_pixels <- nrow(values)
242+
input_pixels <- nrow(values)
243243
# Process least confidence
244244
# return a matrix[rows(values),1]
245245
values <- C_least_probs(values)
246246
# Are the results consistent with the data input?
247-
.check_processed_values(values, n_input_pixels)
247+
.check_processed_values(values, input_pixels)
248248
# Return data
249249
values
250250
}
@@ -260,11 +260,11 @@
260260
# Define uncertainty function
261261
uncert_fn <- function(values) {
262262
# Used in check (below)
263-
n_input_pixels <- nrow(values)
263+
input_pixels <- nrow(values)
264264
# Process least confidence
265265
values <- C_entropy_probs(values) # return a matrix[rows(values),1]
266266
# Are the results consistent with the data input?
267-
.check_processed_values(values, n_input_pixels)
267+
.check_processed_values(values, input_pixels)
268268
# Return data
269269
values
270270
}
@@ -280,11 +280,11 @@
280280
# Define uncertainty function
281281
uncert_fn <- function(values) {
282282
# Used in check (below)
283-
n_input_pixels <- nrow(values)
283+
input_pixels <- nrow(values)
284284
# Process margin
285285
values <- C_margin_probs(values) # return a matrix[rows(data),1]
286286
# Are the results consistent with the data input?
287-
.check_processed_values(values, n_input_pixels)
287+
.check_processed_values(values, input_pixels)
288288
# Return data
289289
values
290290
}

‎R/api_variance.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -176,7 +176,7 @@
176176
# Define smooth function
177177
smooth_fn <- function(values, block) {
178178
# Check values length
179-
n_input_pixels <- nrow(values)
179+
input_pixels <- nrow(values)
180180
# Compute logit
181181
values <- log(values / (rowSums(values) - values))
182182
# Process variance
@@ -188,7 +188,7 @@
188188
neigh_fraction = neigh_fraction
189189
)
190190
# Are the results consistent with the data input?
191-
.check_processed_values(values, n_input_pixels)
191+
.check_processed_values(values, input_pixels)
192192
# Return values
193193
values
194194
}

‎R/sits_lighttae.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -328,7 +328,7 @@ sits_lighttae <- function(samples = NULL,
328328
# Unserialize model
329329
torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
330330
# Used to check values (below)
331-
n_input_pixels <- nrow(values)
331+
input_pixels <- nrow(values)
332332
# Transform input into a 3D tensor
333333
# Reshape the 2D matrix into a 3D array
334334
n_samples <- nrow(values)
@@ -362,7 +362,7 @@ sits_lighttae <- function(samples = NULL,
362362
)
363363
# Are the results consistent with the data input?
364364
.check_processed_values(
365-
values = values, n_input_pixels = n_input_pixels
365+
values = values, input_pixels = input_pixels
366366
)
367367
# Update the columns names to labels
368368
colnames(values) <- labels

‎R/sits_machine_learning.R

+6-6
Original file line numberDiff line numberDiff line change
@@ -79,13 +79,13 @@ sits_rfor <- function(samples = NULL, num_trees = 100, mtry = NULL, ...) {
7979
# Verifies if randomForest package is installed
8080
.check_require_packages("randomForest")
8181
# Used to check values (below)
82-
n_input_pixels <- nrow(values)
82+
input_pixels <- nrow(values)
8383
# Do classification
8484
values <- stats::predict(
8585
object = model, newdata = values, type = "prob"
8686
)
8787
# Are the results consistent with the data input?
88-
.check_processed_values(values, n_input_pixels)
88+
.check_processed_values(values, input_pixels)
8989
# Reorder matrix columns if needed
9090
if (any(labels != colnames(values))) {
9191
values <- values[, labels]
@@ -193,7 +193,7 @@ sits_svm <- function(samples = NULL, formula = sits_formula_linear(),
193193
# Verifies if e1071 package is installed
194194
.check_require_packages("e1071")
195195
# Used to check values (below)
196-
n_input_pixels <- nrow(values)
196+
input_pixels <- nrow(values)
197197
# Performs data normalization
198198
values <- .pred_normalize(pred = values, stats = ml_stats)
199199
# Do classification
@@ -203,7 +203,7 @@ sits_svm <- function(samples = NULL, formula = sits_formula_linear(),
203203
# Get the predicted probabilities
204204
values <- attr(values, "probabilities")
205205
# Are the results consistent with the data input?
206-
.check_processed_values(values, n_input_pixels)
206+
.check_processed_values(values, input_pixels)
207207
# Reorder matrix columns if needed
208208
if (any(labels != colnames(values))) {
209209
values <- values[, labels]
@@ -337,14 +337,14 @@ sits_xgboost <- function(samples = NULL, learning_rate = 0.15,
337337
# Verifies if xgboost package is installed
338338
.check_require_packages("xgboost")
339339
# Used to check values (below)
340-
n_input_pixels <- nrow(values)
340+
input_pixels <- nrow(values)
341341
# Do classification
342342
values <- stats::predict(
343343
object = model, as.matrix(values), ntreelimit = ntreelimit,
344344
reshape = TRUE
345345
)
346346
# Are the results consistent with the data input?
347-
.check_processed_values(values, n_input_pixels)
347+
.check_processed_values(values, input_pixels)
348348
# Update the columns names to labels
349349
colnames(values) <- labels
350350
return(values)

‎R/sits_mlp.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -284,7 +284,7 @@ sits_mlp <- function(samples = NULL,
284284
# Unserialize model
285285
torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
286286
# Used to check values (below)
287-
n_input_pixels <- nrow(values)
287+
input_pixels <- nrow(values)
288288
# Performs data normalization
289289
values <- .pred_normalize(pred = values, stats = ml_stats)
290290
# Transform input into matrix
@@ -312,7 +312,7 @@ sits_mlp <- function(samples = NULL,
312312
)
313313
# Are the results consistent with the data input?
314314
.check_processed_values(
315-
values = values, n_input_pixels = n_input_pixels
315+
values = values, input_pixels = input_pixels
316316
)
317317
# Update the columns names to labels
318318
colnames(values) <- labels

‎R/sits_resnet.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -376,7 +376,7 @@ sits_resnet <- function(samples = NULL,
376376
# Unserialize model
377377
torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
378378
# Used to check values (below)
379-
n_input_pixels <- nrow(values)
379+
input_pixels <- nrow(values)
380380
# Transform input into a 3D tensor
381381
# Reshape the 2D matrix into a 3D array
382382
n_samples <- nrow(values)
@@ -409,7 +409,7 @@ sits_resnet <- function(samples = NULL,
409409
x = torch::torch_tensor(values, device = "cpu")
410410
)
411411
.check_processed_values(
412-
values = values, n_input_pixels = n_input_pixels
412+
values = values, input_pixels = input_pixels
413413
)
414414
# Update the columns names to labels
415415
colnames(values) <- labels

‎R/sits_tae.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -295,7 +295,7 @@ sits_tae <- function(samples = NULL,
295295
# Unserialize model
296296
torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
297297
# Used to check values (below)
298-
n_input_pixels <- nrow(values)
298+
input_pixels <- nrow(values)
299299
# Transform input into a 3D tensor
300300
# Reshape the 2D matrix into a 3D array
301301
n_samples <- nrow(values)
@@ -329,7 +329,7 @@ sits_tae <- function(samples = NULL,
329329
)
330330
# Are the results consistent with the data input?
331331
.check_processed_values(
332-
values = values, n_input_pixels = n_input_pixels
332+
values = values, input_pixels = input_pixels
333333
)
334334
# Update the columns names to labels
335335
colnames(values) <- labels

‎R/sits_tempcnn.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -346,7 +346,7 @@ sits_tempcnn <- function(samples = NULL,
346346
# Unserialize model
347347
torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
348348
# Used to check values (below)
349-
n_input_pixels <- nrow(values)
349+
input_pixels <- nrow(values)
350350
# Transform input into a 3D tensor
351351
# Reshape the 2D matrix into a 3D array
352352
n_samples <- nrow(values)
@@ -381,7 +381,7 @@ sits_tempcnn <- function(samples = NULL,
381381
)
382382
# Are the results consistent with the data input?
383383
.check_processed_values(
384-
values = values, n_input_pixels = n_input_pixels
384+
values = values, input_pixels = input_pixels
385385
)
386386
# Update the columns names to labels
387387
colnames(values) <- labels

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