In patch_utils.py, the modules ExtractPatches and MergePatches are missing an axis swap between the reshapes, resulting in the extracted patches becoming horizontal stripes. For example, if we follow the code in ExtractPatches:

>>> inputs = jnp.arange(16).reshape(1, 4, 4, 1)
>>> inputs[0, :, :, 0]

DeviceArray([[ 0,  1,  2,  3],
             [ 4,  5,  6,  7],
             [ 8,  9, 10, 11],
             [12, 13, 14, 15]], dtype=int32)

>>> patch_size = 2
>>> batch, height, width, channels = inputs.shape
>>> height, width = height // patch_size, width // patch_size
>>> x = jnp.reshape(inputs, (batch, height, patch_size, width, patch_size, channels))
>>> x = jnp.reshape(x, (batch, height * width, patch_size ** 2 * channels))
>>> x[0, 0, :]

DeviceArray([0, 1, 2, 3], dtype=int32)

We see that the first patch extracted is not the patch containing [0, 1, 4, 5], but the horizontal stripe [0, 1, 2, 3]. To fix this problem, we should add an axis swap. For ExtractPatches, this should be:

batch, height, width, channels = inputs.shape
height, width = height // patch_size, width // patch_size
x = jnp.reshape(
    inputs, (batch, height, patch_size, width, patch_size, channels)
)
x = jnp.swapaxes(x, 2, 3)
x = jnp.reshape(x, (batch, height * width, patch_size ** 2 * channels))

For MergePatches, this should be:

batch, length, _ = inputs.shape
height = width = int(length**0.5)
x = jnp.reshape(inputs, (batch, height, width, patch_size, patch_size, -1))
x = jnp.swapaxes(x, 2, 3)
x = jnp.reshape(x, (batch, height * patch_size, width * patch_size, -1))

Hey, first of all, thanks for the nice codebase. When doing inference using the convnext model, I noticed the following issue:

Calling x.item() will call float(x), which breaks the jit tracer. We can remove the list comprehension in unnecessary conversion to make jax.jit work. Without jax.jit, the model is very slow for me, running with only ~30% GPU utilization (RTX 3090).

This issue could apply to other models as well, maybe it is a good idea to include a test for applying jax.jit to each model?