| # ================================================================================================== | |
| # ZERO-SHOT-VIDEO-GENERATION - config.py (Architectural Configuration) | |
| # ================================================================================================== | |
| # | |
| # 📝 DESCRIPTION | |
| # This configuration file defines the hyperparameter blueprints and architectural structure for | |
| # the auxiliary neural networks, specifically targeting the UniFormer backbone utilized in semantic | |
| # segmentation tasks (e.g., ADE20K dataset). It orchestrates the model's layers, dimensions, | |
| # and the optimization strategies required for precise background/foreground detection. | |
| # | |
| # 👤 AUTHORS | |
| # - Amey Thakur (https://github.com/Amey-Thakur) | |
| # | |
| # 🤝🏻 CREDITS | |
| # Based directly on the foundational logic of Text2Video-Zero. | |
| # Source Authors: Picsart AI Research (PAIR), UT Austin, U of Oregon, UIUC | |
| # Reference: https://arxiv.org/abs/2303.13439 | |
| # | |
| # 🔗 PROJECT LINKS | |
| # Repository: https://github.com/Amey-Thakur/ZERO-SHOT-VIDEO-GENERATION | |
| # Live Demo: https://huggingface.co/spaces/ameythakur/Zero-Shot-Video-Generation | |
| # Video Demo: https://youtu.be/za9hId6UPoY | |
| # | |
| # 📅 RELEASE DATE | |
| # November 22, 2023 | |
| # | |
| # 📜 LICENSE | |
| # Released under the MIT License | |
| # ================================================================================================== | |
| # Base configurations anchoring the runtime environment, datasets, and scheduling parameters. | |
| _base_ = [ | |
| '../../configs/_base_/models/upernet_uniformer.py', | |
| '../../configs/_base_/datasets/ade20k.py', | |
| '../../configs/_base_/default_runtime.py', | |
| '../../configs/_base_/schedules/schedule_160k.py' | |
| ] | |
| # Structural definition of the UniFormer backbone and its associated classification heads. | |
| model = dict( | |
| backbone=dict( | |
| type='UniFormer', | |
| embed_dim=[64, 128, 320, 512], | |
| layers=[3, 4, 8, 3], | |
| head_dim=64, | |
| drop_path_rate=0.25, | |
| windows=False, | |
| hybrid=False | |
| ), | |
| decode_head=dict( | |
| in_channels=[64, 128, 320, 512], | |
| num_classes=150 | |
| ), | |
| auxiliary_head=dict( | |
| in_channels=320, | |
| num_classes=150 | |
| )) | |
| # Optimization strategy: AdamW optimizer implementing specific constraints like omitting | |
| # weight decay for position embeddings and layer normalization to maintain spatial integrity. | |
| optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01, | |
| paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.), | |
| 'relative_position_bias_table': dict(decay_mult=0.), | |
| 'norm': dict(decay_mult=0.)})) | |
| # Learning rate scheduler leveraging a polynomial decay policy to stabilize convergence | |
| # across extensive iterations. | |
| lr_config = dict(_delete_=True, policy='poly', | |
| warmup='linear', | |
| warmup_iters=1500, | |
| warmup_ratio=1e-6, | |
| power=1.0, min_lr=0.0, by_epoch=False) | |
| # Batch definitions establishing hardware-bound concurrency limits. | |
| data=dict(samples_per_gpu=2) |