fix(state): fix model state

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023 XiMing Xing
+Copyright (c) 2024 XiMing Xing
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -201,6 +201,8 @@ python svgdreamer.py x=ink "prompt='Big Wild Goose Pagoda. ink style. Minimalist
 #### More Cases
 
 ````shell
+python svgdreamer.py x=iconography "prompt='illustration of an New York City, in the style of propaganda poster, vivid colours, detailed, sunny day, attention to detail, 8k.'" result_path='./logs/NewYorkCity'
+python svgdreamer.py x=iconography "prompt='A colorful German shepherd in vector art. tending on artstation.'" result_path='./logs/GermanShepherd'
 # Style: painting
 python svgdreamer.py x=painting "prompt='self portrait of Van Gogh. oil painting. cmyk portrait. multi colored. defiant and beautiful. cmyk. expressive eyes.'" result_path='./logs/VanGogh-Portrait'
 ````

conf/config.yaml

@@ -11,7 +11,7 @@ skip_sive: True # optimize from scratch without SIVE init
 # Accelerate config
 state:
   cpu: False # use cpu
-  mprec: no # mixed precision, choices: 'no', 'fp16', 'bf16'
+  mprec: 'no' # mixed precision, choices: 'no', 'fp16', 'bf16'
 
 # Diffusers config
 diffuser:
@@ -46,9 +46,10 @@ hydra:
   run:
     # output directory for normal runs
     # warning: make sure that the L53-55 of './libs/model_state.py' and 'dir' are modified together
-    dir: ./${result_path}/SVGDreamer-${now:%Y-%m-%d-%H-%M}
+    dir: ./${result_path}/SVGDreamer-${now:%Y-%m-%d_%H-%M-%S}
 
 # default settings
 defaults:
   - _self_
-  - x: ~
+  - x: ~
+  - override hydra/job_logging: disabled # Outputs only to stdout (no log file)

script/install.sh

@@ -1,12 +1,12 @@
 #!/bin/bash
 eval "$(conda shell.bash hook)"
 
-conda create --name svgrender python=3.10
+conda create --name svgrender python=3.10 --yes
 conda activate svgrender
 
 echo "The conda environment was successfully created"
 
-conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
+conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch --yes
 
 echo "Pytorch installation is complete. version: 1.12.1"
 
@@ -30,7 +30,7 @@ pip install diffusers==0.20.2
 echo "Diffusers installation is complete. version: 0.20.2"
 # if xformers doesnt install properly with conda try installing with pip using the code below
 # pip install --pre -U xformers
-conda install xformers -c xformers
+conda install xformers -c xformers --yes
 
 echo "xformers installation is complete."
 

svgdreamer/libs/logging.py

@@ -8,7 +8,7 @@ import sys
 import errno
 
 
-def get_logger(logs_dir: str, file_name: str = "log.txt"):
+def build_sysout_print_logger(logs_dir: str, file_name: str = "log.txt"):
     logger = PrintLogger(os.path.join(logs_dir, file_name))
     sys.stdout = logger  # record all python print
     return logger

svgdreamer/libs/model_state.py

@@ -5,15 +5,14 @@
 
 from typing import Union, List
 from pathlib import Path
-from datetime import datetime
-import logging
 
-from omegaconf import OmegaConf, DictConfig
+import hydra
+from omegaconf import OmegaConf, DictConfig, open_dict
 from pprint import pprint
 import torch
 from accelerate import Accelerator
 
-from .logging import get_logger
+from .logging import build_sysout_print_logger
 
 
 class ModelState:
@@ -31,35 +30,21 @@ class ModelState:
     def __init__(
             self,
             args: DictConfig,
-            log_path_suffix: str = None,
-            ignore_log=False,  # whether to create log file or not
+            log_path_suffix: str,
     ) -> None:
         self.args: DictConfig = args
+
+        # runtime output directory
+        with open_dict(args):
+            args.output_dir = hydra.core.hydra_config.HydraConfig.get().runtime.output_dir
+
         # set cfg
-        self.state_cfg = args.state
         self.x_cfg = args.x
 
-        """check valid"""
-        mixed_precision = self.state_cfg.get("mprec")
-        # Bug: omegaconf convert 'no' to false
-        mixed_precision = "no" if type(mixed_precision) == bool else mixed_precision
-
         """create working space"""
-        # rule: ['./config'. 'method_name', 'exp_name.yaml']
-        # -> result_path: ./runs/{method_name}-{exp_name}, as a base folder
-        now_time = datetime.now().strftime('%Y-%m-%d-%H-%M')
-        results_folder = self.args.get("result_path", None)
-        if results_folder is None:
-            self.result_path = Path("./workdir") / f"SVGDreamer-{now_time}"
-        else:
-            self.result_path = Path(results_folder) / f"SVGDreamer-{now_time}"
-
-        # update result_path: ./runs/{method_name}-{exp_name}/{log_path_suffix}
-        # noting: can be understood as "results dir / methods / ablation study / your result"
-        if log_path_suffix is not None:
-            self.result_path = self.result_path / f"{log_path_suffix}"
-        else:
-            self.result_path = self.result_path / f"SVGDreamer"
+        self.result_path = Path(args.output_dir)  # saving path
+        self.monitor_dir = self.result_path / 'runs'  # monitor path
+        self.result_path = self.result_path / f"{log_path_suffix}"  # method results path
 
         """init visualized tracker"""
         # TODO: monitor with WANDB or TENSORBOARD
@@ -71,39 +56,30 @@ class ModelState:
 
         """HuggingFace Accelerator"""
         self.accelerator = Accelerator(
-            device_placement=True,
-            mixed_precision=mixed_precision,
-            cpu=True if self.state_cfg.cpu else False,
+            mixed_precision=args.state.get("mprec"),
+            cpu=args.state.get('cpu', False),
             log_with=None if len(self.log_with) == 0 else self.log_with,
-            project_dir=self.result_path / "vis",
+            project_dir=self.monitor_dir,
         )
 
         """logs"""
         if self.accelerator.is_local_main_process:
-            # logging
-            self.log = logging.getLogger(__name__)
-
-            # log results in a folder periodically
             self.result_path.mkdir(parents=True, exist_ok=True)
-            if not ignore_log:
-                self.logger = get_logger(
-                    logs_dir=self.result_path.as_posix(),
-                    file_name=f"{now_time}-{args.seed}-log.txt"
-                )
+            # system print recorder
+            self.logger = build_sysout_print_logger(logs_dir=self.result_path.as_posix(),
+                                                    file_name=f"stdout-print-log.txt")
 
             print("==> system args: ")
-            sys_cfg = OmegaConf.masked_copy(args, ["x"])
+            custom_cfg = OmegaConf.masked_copy(args, ["x"])
+            sys_cfg = dictconfig_diff(args, custom_cfg)
             print(sys_cfg)
             print("==> yaml config args: ")
             print(self.x_cfg)
 
             print("\n***** Model State *****")
-            print(f"-> Mixed Precision: {mixed_precision}, AMP: {self.accelerator.native_amp}")
+            print(f"-> Mixed Precision: {self.accelerator.state.mixed_precision}")
             print(f"-> Weight dtype:  {self.weight_dtype}")
 
-            if self.accelerator.scaler_handler is not None and self.accelerator.scaler_handler.enabled:
-                print(f"-> Enabled GradScaler: {self.accelerator.scaler_handler.to_kwargs()}")
-
             print(f"-> Working Space: '{self.result_path}'")
 
         """glob step"""
@@ -251,3 +227,27 @@ class ModelState:
         if len(self.log_with) > 0:
             self.close_tracker()
         self.print(msg)
+
+
+def dictconfig_diff(dict1, dict2):
+    """
+    Find the difference between two OmegaConf.DictConfig objects
+    """
+    # Convert OmegaConf.DictConfig to regular dictionaries
+    dict1 = OmegaConf.to_container(dict1, resolve=True)
+    dict2 = OmegaConf.to_container(dict2, resolve=True)
+
+    # Find the keys that are in dict1 but not in dict2
+    diff = {}
+    for key in dict1:
+        if key not in dict2:
+            diff[key] = dict1[key]
+        elif dict1[key] != dict2[key]:
+            if isinstance(dict1[key], dict) and isinstance(dict2[key], dict):
+                nested_diff = dictconfig_diff(dict1[key], dict2[key])
+                if nested_diff:
+                    diff[key] = nested_diff
+            else:
+                diff[key] = dict1[key]
+
+    return diff