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IQ.Pilot Release Commit @ b6534c0

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IQ.Lvbs CI [bot]
2026-08-27 20:17:33 -05:00
commit 00f07cac48
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artifacts/package_sources/tinygrad/extra/models/efficientnet.py Normal file
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import math
from tinygrad.tensor import Tensor
from tinygrad.nn import BatchNorm2d
from tinygrad.helpers import get_child, fetch
from tinygrad.nn.state import torch_load
class MBConvBlock:
def __init__(self, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio, has_se, track_running_stats=True):
oup = expand_ratio * input_filters
if expand_ratio != 1:
self._expand_conv = Tensor.glorot_uniform(oup, input_filters, 1, 1)
self._bn0 = BatchNorm2d(oup, track_running_stats=track_running_stats)
else:
self._expand_conv = None
self.strides = strides
if strides == (2,2):
self.pad = [(kernel_size-1)//2-1, (kernel_size-1)//2]*2
else:
self.pad = [(kernel_size-1)//2]*4
self._depthwise_conv = Tensor.glorot_uniform(oup, 1, kernel_size, kernel_size)
self._bn1 = BatchNorm2d(oup, track_running_stats=track_running_stats)
self.has_se = has_se
if self.has_se:
num_squeezed_channels = max(1, int(input_filters * se_ratio))
self._se_reduce = Tensor.glorot_uniform(num_squeezed_channels, oup, 1, 1)
self._se_reduce_bias = Tensor.zeros(num_squeezed_channels)
self._se_expand = Tensor.glorot_uniform(oup, num_squeezed_channels, 1, 1)
self._se_expand_bias = Tensor.zeros(oup)
self._project_conv = Tensor.glorot_uniform(output_filters, oup, 1, 1)
self._bn2 = BatchNorm2d(output_filters, track_running_stats=track_running_stats)
def __call__(self, inputs):
x = inputs
if self._expand_conv is not None:
x = self._bn0(x.conv2d(self._expand_conv)).swish()
x = x.conv2d(self._depthwise_conv, padding=self.pad, stride=self.strides, groups=self._depthwise_conv.shape[0])
x = self._bn1(x).swish()
if self.has_se:
x_squeezed = x.avg_pool2d(kernel_size=x.shape[2:4])
x_squeezed = x_squeezed.conv2d(self._se_reduce, self._se_reduce_bias).swish()
x_squeezed = x_squeezed.conv2d(self._se_expand, self._se_expand_bias)
x = x.mul(x_squeezed.sigmoid())
x = self._bn2(x.conv2d(self._project_conv))
if x.shape == inputs.shape:
x = x.add(inputs)
return x
class EfficientNet:
def __init__(self, number=0, classes=1000, has_se=True, track_running_stats=True, input_channels=3, has_fc_output=True):
self.number = number
global_params = [
# width, depth
(1.0, 1.0), # b0
(1.0, 1.1), # b1
(1.1, 1.2), # b2
(1.2, 1.4), # b3
(1.4, 1.8), # b4
(1.6, 2.2), # b5
(1.8, 2.6), # b6
(2.0, 3.1), # b7
(2.2, 3.6), # b8
(4.3, 5.3), # l2
][max(number,0)]
def round_filters(filters):
multiplier = global_params[0]
divisor = 8
filters *= multiplier
new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)
if new_filters < 0.9 * filters: # prevent rounding by more than 10%
new_filters += divisor
return int(new_filters)
def round_repeats(repeats):
return int(math.ceil(global_params[1] * repeats))
out_channels = round_filters(32)
self._conv_stem = Tensor.glorot_uniform(out_channels, input_channels, 3, 3)
self._bn0 = BatchNorm2d(out_channels, track_running_stats=track_running_stats)
blocks_args = [
[1, 3, (1,1), 1, 32, 16, 0.25],
[2, 3, (2,2), 6, 16, 24, 0.25],
[2, 5, (2,2), 6, 24, 40, 0.25],
[3, 3, (2,2), 6, 40, 80, 0.25],
[3, 5, (1,1), 6, 80, 112, 0.25],
[4, 5, (2,2), 6, 112, 192, 0.25],
[1, 3, (1,1), 6, 192, 320, 0.25],
]
if self.number == -1:
blocks_args = [
[1, 3, (2,2), 1, 32, 40, 0.25],
[1, 3, (2,2), 1, 40, 80, 0.25],
[1, 3, (2,2), 1, 80, 192, 0.25],
[1, 3, (2,2), 1, 192, 320, 0.25],
]
elif self.number == -2:
blocks_args = [
[1, 9, (8,8), 1, 32, 320, 0.25],
]
self._blocks = []
for num_repeats, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio in blocks_args:
input_filters, output_filters = round_filters(input_filters), round_filters(output_filters)
for n in range(round_repeats(num_repeats)):
self._blocks.append(MBConvBlock(kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio, has_se=has_se, track_running_stats=track_running_stats))
input_filters = output_filters
strides = (1,1)
in_channels = round_filters(320)
out_channels = round_filters(1280)
self._conv_head = Tensor.glorot_uniform(out_channels, in_channels, 1, 1)
self._bn1 = BatchNorm2d(out_channels, track_running_stats=track_running_stats)
if has_fc_output:
self._fc = Tensor.glorot_uniform(out_channels, classes)
self._fc_bias = Tensor.zeros(classes)
else:
self._fc = None
def forward(self, x):
x = self._bn0(x.conv2d(self._conv_stem, padding=(0,1,0,1), stride=2)).swish()
x = x.sequential(self._blocks)
x = self._bn1(x.conv2d(self._conv_head)).swish()
x = x.avg_pool2d(kernel_size=x.shape[2:4])
x = x.reshape(shape=(-1, x.shape[1]))
return x.linear(self._fc, self._fc_bias) if self._fc is not None else x
def load_from_pretrained(self):
model_urls = {
0: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth",
1: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth",
2: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth",
3: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth",
4: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth",
5: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth",
6: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth",
7: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth"
}
b0 = torch_load(fetch(model_urls[self.number]))
for k,v in b0.items():
if k.endswith("num_batches_tracked"): continue
for cat in ['_conv_head', '_conv_stem', '_depthwise_conv', '_expand_conv', '_fc', '_project_conv', '_se_reduce', '_se_expand']:
if cat in k:
k = k.replace('.bias', '_bias')
k = k.replace('.weight', '')
#print(k, v.shape)
mv:Tensor = get_child(self, k)
vnp = v #.astype(np.float32)
vnp = vnp if k != '_fc' else vnp.T
#vnp = vnp if vnp.shape != () else np.array([vnp])
if mv.shape == vnp.shape:
mv.replace(vnp.to(mv.device))
else:
print("MISMATCH SHAPE IN %s, %r %r" % (k, mv.shape, vnp.shape))