File size: 4,862 Bytes
33c2790 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
# modeling_resnet.py
import torch
import torch.nn as nn
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import ImageClassifierOutput
class PrunedResNetConfig(PretrainedConfig):
model_type = "resnet"
def __init__(
self, channel_config: dict[str, int] | None = None, num_classes=1000, **kwargs
):
super().__init__(**kwargs)
self.channel_config = channel_config
self.num_classes = num_classes
class PrunedResNet50(PreTrainedModel):
config_class = PrunedResNetConfig
_tied_weights_keys = []
def __init__(self, config: PrunedResNetConfig):
super().__init__(config)
self.config = config
c = config.channel_config
self.conv1 = nn.Conv2d(
3, c["conv1"], kernel_size=7, stride=2, padding=3, bias=False
)
self.bn1 = nn.BatchNorm2d(c["conv1"])
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(c, stage_idx=1, layers=3, stride=1)
self.layer2 = self._make_layer(c, stage_idx=2, layers=4, stride=2)
self.layer3 = self._make_layer(c, stage_idx=3, layers=6, stride=2)
self.layer4 = self._make_layer(c, stage_idx=4, layers=3, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
last_channel = c["layer4.2.conv3"]
self.fc = nn.Linear(last_channel, config.num_classes)
self.post_init()
def _make_layer(self, c, stage_idx, layers, stride):
# Builds a ResNet layer (e.g., layer1) containing multiple Bottleneck blocks
blocks = []
# The first block in a layer often handles stride and downsampling
blocks.append(
Bottleneck(
inplanes=c[f"layer{stage_idx}.0.in"],
planes=[
c[f"layer{stage_idx}.0.conv1"],
c[f"layer{stage_idx}.0.conv2"],
c[f"layer{stage_idx}.0.conv3"],
],
stride=stride,
downsample_planes=c.get(f"layer{stage_idx}.0.downsample.0", None),
)
)
# Subsequent blocks
for i in range(1, layers):
blocks.append(
Bottleneck(
inplanes=c[f"layer{stage_idx}.{i}.in"],
planes=[
c[f"layer{stage_idx}.{i}.conv1"],
c[f"layer{stage_idx}.{i}.conv2"],
c[f"layer{stage_idx}.{i}.conv3"],
],
)
)
return nn.Sequential(*blocks)
def forward(self, pixel_values=None, labels=None, **kwargs):
x = pixel_values
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
logits = self.fc(x)
loss = None
if labels is not None:
# CrossEntropyLoss handles the Softmax internally
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_classes), labels.view(-1))
return ImageClassifierOutput(logits=logits, loss=loss)
class Bottleneck(nn.Module):
# Standard Bottleneck but with dynamic channel sizes
def __init__(self, inplanes, planes, stride=1, downsample_planes=None):
super().__init__()
c1, c2, c3 = planes # The 3 conv widths inside the bottleneck
self.conv1 = nn.Conv2d(inplanes, c1, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(c1)
self.conv2 = nn.Conv2d(
c1, c2, kernel_size=3, stride=stride, padding=1, bias=False
)
self.bn2 = nn.BatchNorm2d(c2)
self.conv3 = nn.Conv2d(c2, c3, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(c3)
self.relu = nn.ReLU(inplace=True)
self.downsample = None
if downsample_planes is not None:
self.downsample = nn.Sequential(
nn.Conv2d(
inplanes,
downsample_planes,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm2d(downsample_planes),
)
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
|