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"""
AlexNet in MLX with endpoints for relu1, relu2, relu3, relu4, relu5.
Loads weights from a torchvision-exported npz.
"""
import mlx.core as mx
import mlx.nn as nn
import numpy as np
def _conv(in_ch, out_ch, kernel_size, stride=1, padding=0):
return nn.Conv2d(
in_ch,
out_ch,
kernel_size=kernel_size,
stride=stride,
padding=padding,
bias=True,
)
class AlexNet(nn.Module):
def __init__(self):
super().__init__()
self.layers = [
_conv(3, 64, kernel_size=11, stride=4, padding=2), # 0
nn.ReLU(), # 1 (relu1)
nn.MaxPool2d(kernel_size=3, stride=2), # 2
_conv(64, 192, kernel_size=5, padding=2), # 3
nn.ReLU(), # 4 (relu2)
nn.MaxPool2d(kernel_size=3, stride=2), # 5
_conv(192, 384, kernel_size=3, padding=1), # 6
nn.ReLU(), # 7 (relu3)
_conv(384, 256, kernel_size=3, padding=1), # 8
nn.ReLU(), # 9 (relu4)
_conv(256, 256, kernel_size=3, padding=1), # 10
nn.ReLU(), # 11 (relu5)
nn.MaxPool2d(kernel_size=3, stride=2), # 12
]
self.endpoint_indices = {
"relu1": 1,
"relu2": 4,
"relu3": 7,
"relu4": 9,
"relu5": 11,
}
def forward_with_endpoints(self, x):
endpoints = {}
for idx, layer in enumerate(self.layers):
x = layer(x)
for name, i in self.endpoint_indices.items():
if idx == i:
endpoints[name] = x
return x, endpoints
def __call__(self, x):
_, endpoints = self.forward_with_endpoints(x)
return endpoints
def load_npz(self, path: str):
data = np.load(path)
def load_weight(key, transpose=False):
if key in data:
w = data[key]
elif f"{key}_int8" in data:
w_int8 = data[f"{key}_int8"]
scale = data[f"{key}_scale"]
w = w_int8.astype(scale.dtype) * scale
else:
raise ValueError(f"Missing key {key} in npz")
if transpose and w.ndim == 4:
w = np.transpose(w, (0, 2, 3, 1))
return mx.array(w)
# Map layer indices to 'features.X' in standard torchvision keys
conv_indices = [0, 3, 6, 8, 10]
for idx in conv_indices:
conv = self.layers[idx]
weight_key = f"features.{idx}.weight"
bias_key = f"features.{idx}.bias"
conv.weight = load_weight(weight_key, transpose=True)
conv.bias = load_weight(bias_key)
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