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6b2e578 | 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 | import torch
import torch.nn as nn
import torchvision.models as models
from transformers import ConvNextModel
from huggingface_hub import hf_hub_download
# ==========================================
# 1. MODEL ARCHITECTURE DEFINITION
# ==========================================
class FusionClassifier(nn.Module):
def __init__(self, num_classes, convnext_model_name="facebook/convnext-small-224"):
super().__init__()
# EfficientNet-V2-S backbone
eff = models.efficientnet_v2_s(weights=models.EfficientNet_V2_S_Weights.IMAGENET1K_V1)
for param in eff.parameters():
param.requires_grad = False
for param in eff.features[5].parameters():
param.requires_grad = True
for param in eff.features[6].parameters():
param.requires_grad = True
for param in eff.features[7].parameters():
param.requires_grad = True
self.eff_features = eff.features
self.eff_avgpool = eff.avgpool
self.eff_out_dim = eff.classifier[1].in_features
# ConvNeXt-Small backbone
cnx = ConvNextModel.from_pretrained(convnext_model_name)
for param in cnx.parameters():
param.requires_grad = False
for param in cnx.encoder.stages[2].parameters():
param.requires_grad = True
for param in cnx.encoder.stages[3].parameters():
param.requires_grad = True
for param in cnx.layernorm.parameters():
param.requires_grad = True
self.cnx_backbone = cnx
self.cnx_out_dim = 768
# Fusion head
fused_dim = self.eff_out_dim + self.cnx_out_dim
self.fusion_head = nn.Sequential(
nn.Dropout(p=0.4),
nn.Linear(fused_dim, 512),
nn.LayerNorm(512),
nn.GELU(),
nn.Dropout(p=0.3),
nn.Linear(512, 256),
nn.LayerNorm(256),
nn.GELU(),
nn.Dropout(p=0.2),
nn.Linear(256, num_classes),
)
def forward(self, pixel_values_eff, pixel_values_cnx):
x_eff = self.eff_features(pixel_values_eff)
x_eff = self.eff_avgpool(x_eff)
x_eff = torch.flatten(x_eff, 1)
cnx_out = self.cnx_backbone(pixel_values=pixel_values_cnx, return_dict=True)
x_cnx = cnx_out.pooler_output
fused = torch.cat([x_eff, x_cnx], dim=1)
logits = self.fusion_head(fused)
return logits
# ==========================================
# 2. HUGGING FACE DOWNLOAD & LOAD FUNCTION
# ==========================================
def load_fusion_model_from_hf(
repo_id: str = "junaid17/best_fusion_model_fp16",
filename: str = "best_fusion_model_fp16.pt",
num_classes: int = 10, # Replace with your actual number of classes
device: str = "cpu",
) -> nn.Module:
"""Downloads weights from Hugging Face Hub and loads into FusionClassifier."""
print(f"Downloading checkpoint from Hugging Face Hub: '{repo_id}/{filename}'...")
# Download weights file from Hugging Face Hub
checkpoint_path = hf_hub_download(repo_id=repo_id, filename=filename)
# Instantiate Model
model = FusionClassifier(num_classes=num_classes)
# Load weights
checkpoint = torch.load(checkpoint_path, map_location=device)
if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
model.load_state_dict(checkpoint["state_dict"])
elif isinstance(checkpoint, dict):
model.load_state_dict(checkpoint)
else:
model = checkpoint
# Ensure model is float32 for stable ONNX export
model = model.float().to(device)
model.eval()
print("✅ Model loaded successfully from Hugging Face.")
return model |