a formula for bin size for images over 64x64 (#90)

Summary:
Signed-off-by: Michele Sanna <sanna@arrival.com>

fixes the bin_size calculation with a formula for any image_size > 64. Matches the values chosen so far.

simple test:

```
import numpy as np
import matplotlib.pyplot as plt

image_size = np.arange(64, 2048)
bin_size = np.where(image_size <= 64, 8, (2 ** np.maximum(np.ceil(np.log2(image_size)) - 4, 4)).astype(int))

print(image_size)
print(bin_size)

for ims, bins in zip(image_size, bin_size):
    if ims <= 64:
        assert bins == 8
    elif ims <= 256:
        assert bins == 16
    elif ims <= 512:
        assert bins == 32
    elif ims <= 1024:
        assert bins == 64
    elif ims <= 2048:
        assert bins == 128

    assert (ims + bins - 1) // bins < 22

plt.plot(image_size, bin_size)
plt.grid()
plt.show()
```

![img](https://user-images.githubusercontent.com/54891577/75464693-795bcf00-597f-11ea-9061-26440211691c.png)
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/90

Reviewed By: jcjohnson

Differential Revision: D21160372

Pulled By: nikhilaravi

fbshipit-source-id: 660cf5832f4ca5be243c435a6bed969596fc0188

pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu

@@ -696,7 +696,7 @@ at::Tensor RasterizeMeshesCoarseCuda(
   const int num_bins = 1 + (image_size - 1) / bin_size; // Divide round up.
   const int M = max_faces_per_bin;
 
-  if (num_bins >= 22) {
+  if (num_bins >= kMaxFacesPerBin) {
     std::stringstream ss;
     ss << "Got " << num_bins << "; that's too many!";
     AT_ERROR(ss.str());

pytorch3d/csrc/rasterize_points/rasterization_utils.cuh

@@ -17,6 +17,8 @@ __device__ inline float PixToNdc(int i, int S) {
 // TODO: is 8 enough? Would increasing have performance considerations?
 const int32_t kMaxPointsPerPixel = 150;
 
+const int32_t kMaxFacesPerBin = 22;
+
 template <typename T>
 __device__ inline void BubbleSort(T* arr, int n) {
   // Bubble sort. We only use it for tiny thread-local arrays (n < 8); in this