Upload deepseek_python_20250816_b5cee4.py

pyhton file for model creation created by Deepseek AI for guidance and developments

deepseek_python_20250816_b5cee4.py

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+import torch
+import torch.nn as nn
+from transformers import DistilBertModel, DistilBertConfig
+from diffusers import UNet2DConditionModel
+
+class VideoJEPA(nn.Module):
+    def __init__(self, text_dim=768, video_dim=512, latent_dim=1024):
+        super().__init__()
+        
+        # Video Encoder (Hierarchical 3D CNN)
+        self.video_encoder = nn.Sequential(
+            nn.Conv3d(3, 64, kernel_size=(3, 5, 5), stride=(1, 2, 2)),
+            nn.ReLU(),
+            nn.MaxPool3d((1, 2, 2)),
+            nn.Conv3d(64, 128, kernel_size=(3, 3, 3)),
+            nn.ReLU(),
+            nn.AdaptiveAvgPool3d((None, 8, 8))
+        )
+        self.video_proj = nn.Linear(128*8*8, video_dim)
+        
+        # Text Encoder (DistilBERT)
+        self.text_encoder = DistilBertModel.from_pretrained("distilbert-base-uncased")
+        self.text_proj = nn.Linear(text_dim, latent_dim)
+        
+        # Joint Fusion Transformer
+        self.fusion_transformer = nn.TransformerEncoder(
+            nn.TransformerEncoderLayer(d_model=latent_dim, nhead=8),
+            num_layers=4
+        )
+        
+        # Diffusion Decoder (Conditional UNet)
+        self.diffusion_decoder = UNet2DConditionModel(
+            sample_size=64,
+            in_channels=3,
+            out_channels=3,
+            cross_attention_dim=latent_dim
+        )
+
+    def forward(self, video, text_input):
+        # Video encoding
+        B, C, T, H, W = video.shape
+        video_features = self.video_encoder(video)
+        video_features = video_features.permute(0, 2, 1, 3, 4).contiguous()
+        video_features = video_features.view(B*T, -1)
+        video_emb = self.video_proj(video_features).view(B, T, -1)
+        
+        # Text encoding
+        text_emb = self.text_encoder(**text_input).last_hidden_state
+        text_emb = self.text_proj(text_emb[:, 0])  # [CLS] token
+        
+        # Contextual fusion
+        fused_emb = torch.cat([video_emb, text_emb.unsqueeze(1)], dim=1)
+        context_emb = self.fusion_transformer(fused_emb)
+        
+        return context_emb