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import sys
import os
from pathlib import Path
import torch
import torch.nn as nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import ImageClassifierOutputWithNoAttention
from safetensors.torch import load_file
# Embed ResNet code directly to avoid import issues when transformers caches modules
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
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)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
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
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=51, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def forward(self, x):
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)
x = self.fc(x)
return x
# Import configuration
try:
from configuration_moco import MoCoResNetConfig
except ImportError:
# Fallback: import from same directory
import importlib.util
config_path = Path(__file__).parent / "configuration_moco.py"
spec = importlib.util.spec_from_file_location("configuration_moco", config_path)
config_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(config_module)
MoCoResNetConfig = config_module.MoCoResNetConfig
class MoCoResNetForImageClassification(PreTrainedModel):
"""MoCo ResNet model for image classification or feature extraction"""
config_class = MoCoResNetConfig
def __init__(self, config):
super().__init__(config)
# Build ResNet model from config
if config.block == "Bottleneck":
block = Bottleneck
elif config.block == "BasicBlock":
block = BasicBlock
else:
raise ValueError(f"Unsupported block type: {config.block}")
# Create ResNet backbone
# For MoCo models, we typically want feature extraction (no classification head)
# But we need to initialize with some num_classes, then replace fc if needed
self.model = ResNet(
block=block,
layers=config.layers,
num_classes=2048 # Standard ResNet-50 feature dimension
)
# Replace classification head based on num_labels
if config.num_labels == 0:
# Feature extraction mode: replace fc with identity
self.model.fc = nn.Identity()
else:
# Classification mode: replace fc with new classifier
self.model.fc = nn.Linear(512 * block.expansion, config.num_labels)
def forward(self, pixel_values=None, labels=None, return_dict=None, **kwargs):
"""
Args:
pixel_values: Input images (B, C, H, W)
labels: Optional labels for loss computation (only if num_labels > 0)
return_dict: Whether to return a ModelOutput instead of a plain tuple
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("pixel_values must be provided")
# Forward through ResNet
features = self.model(pixel_values)
# If num_labels > 0, features are logits; otherwise they're feature vectors
if self.config.num_labels > 0:
logits = features
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
if not return_dict:
output = (logits,)
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=loss,
logits=logits,
hidden_states=None,
)
else:
# Feature extraction mode
if not return_dict:
return (features,)
return {"features": features}
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
"""Load model from pretrained checkpoint"""
config = kwargs.pop("config", None)
if config is None:
config = MoCoResNetConfig.from_pretrained(pretrained_model_name_or_path)
model = cls(config)
# Load weights from safetensors
model_path = Path(pretrained_model_name_or_path)
safetensors_path = model_path / "model.safetensors"
if safetensors_path.exists():
state_dict = load_file(str(safetensors_path))
# Remove 'model.' prefix if present
state_dict_clean = {}
for k, v in state_dict.items():
if k.startswith("model."):
state_dict_clean[k[6:]] = v
else:
state_dict_clean[k] = v
model.model.load_state_dict(state_dict_clean, strict=False)
else:
raise FileNotFoundError(f"Model weights not found at {safetensors_path}")
return model
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