" -p \"encode\" (debugging encode pass, default case):\n"
" --image can be:\n"
" \"white\", \"black\", \"gray\": filled 1.0f, 0.0f and 0.5f respectively\n"
+ " \"red\", \"green\", \"blue\": filled with respective colors\n"
" \"cb\": checkerboard pattern, alternate 1.0f and 0.0f\n"
+ " \"rainbow\": raspberry-pi-like rainbow pattern\n"
" --audio can be:\n"
" \"one\", \"zero\", \"half\": filled 1.0f, 0.0f and 0.5f respectively\n"
" \"1010\": checkerboard pattern, alternate 1.0f and 0.0f\n"
image[y][x * 3 + 2] = v;
}
}
+ } else if (input == "red") {
+ for (int i = 0; i < inp_size; ++i) {
+ auto row = std::vector<float>(inp_size * 3, 0.0f);
+ for (int j = 0; j < inp_size; ++j) {
+ row[j * 3 + 0] = 1.0f; // R channel
+ }
+ image.push_back(row);
+ }
+ } else if (input == "green") {
+ for (int i = 0; i < inp_size; ++i) {
+ auto row = std::vector<float>(inp_size * 3, 0.0f);
+ for (int j = 0; j < inp_size; ++j) {
+ row[j * 3 + 1] = 1.0f; // G channel
+ }
+ image.push_back(row);
+ }
+ } else if (input == "blue") {
+ for (int i = 0; i < inp_size; ++i) {
+ auto row = std::vector<float>(inp_size * 3, 0.0f);
+ for (int j = 0; j < inp_size; ++j) {
+ row[j * 3 + 2] = 1.0f; // B channel
+ }
+ image.push_back(row);
+ }
+ } else if (input == "rainbow") {
+ for (int i = 0; i < inp_size; ++i) {
+ image.push_back(std::vector<float>(inp_size * 3, 0.0f));
+ }
+ float cx = inp_size / 2.0f;
+ float cy = inp_size / 2.0f;
+ float max_dist = std::sqrt(cx * cx + cy * cy);
+ for (int y = 0; y < inp_size; ++y) {
+ for (int x = 0; x < inp_size; ++x) {
+ float dx = x - cx;
+ float dy = y - cy;
+ float hue = std::atan2(dy, dx) / (2.0f * 3.14159265f);
+ if (hue < 0) hue += 1.0f;
+ float sat = std::sqrt(dx * dx + dy * dy) / max_dist;
+ if (sat > 1.0f) sat = 1.0f;
+ float h6 = hue * 6.0f;
+ int i6 = (int)h6;
+ float f = h6 - i6;
+ float p = 1.0f - sat;
+ float q = 1.0f - sat * f;
+ float t = 1.0f - sat * (1.0f - f);
+ float r, g, b;
+ switch (i6 % 6) {
+ case 0: r=1; g=t; b=p; break;
+ case 1: r=q; g=1; b=p; break;
+ case 2: r=p; g=1; b=t; break;
+ case 3: r=p; g=q; b=1; break;
+ case 4: r=t; g=p; b=1; break;
+ default: r=1; g=p; b=q; break;
+ }
+ image[y][x * 3 + 0] = r;
+ image[y][x * 3 + 1] = g;
+ image[y][x * 3 + 2] = b;
+ }
+ }
} else if (input == "one") {
samples = std::vector<float>(inp_size, 1.0f);
} else if (input == "zero") {
test_vision()
```
+Example of debugging a rainbow image:
+
+```py
+import torch
+import math
+
+def make_rainbow(img_size):
+ cx, cy = img_size / 2.0, img_size / 2.0
+ max_dist = math.sqrt(cx * cx + cy * cy)
+ img = torch.zeros(1, 3, img_size, img_size)
+ for y in range(img_size):
+ for x in range(img_size):
+ dx, dy = x - cx, y - cy
+ hue = math.atan2(dy, dx) / (2 * math.pi)
+ if hue < 0:
+ hue += 1
+ sat = math.sqrt(dx * dx + dy * dy) / max_dist
+ sat = min(sat, 1.0)
+ h6 = hue * 6
+ i6 = int(h6)
+ f = h6 - i6
+ p = 1 - sat
+ q = 1 - sat * f
+ t = 1 - sat * (1 - f)
+ rgb = [(1,t,p),(q,1,p),(p,1,t),(p,q,1),(t,p,1),(1,p,q)][i6 % 6]
+ img[0, 0, y, x] = rgb[0]
+ img[0, 1, y, x] = rgb[1]
+ img[0, 2, y, x] = rgb[2]
+ return img
+
+img_size = 896
+pixel_values = make_rainbow(img_size)
+with torch.no_grad():
+ outputs = model.model.get_image_features(pixel_values=pixel_values)
+print("last_hidden_state:", outputs.last_hidden_state)
+```
+
## Debugging preprocess pass
(TODO)