fixes that a CPU-only pytorch needed

image_classifier/config.py

@@ -10,7 +10,7 @@ import time
 from dataclasses import asdict, dataclass
 from dataclasses import dataclass, field
 
-from functools import cached_property
+from functools import cached_property, cache
 from pathlib import Path
 from omegaconf import OmegaConf
 
@@ -340,6 +340,9 @@ class Config(_Config):
 	inference: Inference = field(default_factory=lambda: Inference)
 	bitsandbytes: BitsAndBytes = field(default_factory=lambda: BitsAndBytes)
 
+	def get_device(self):
+		return torch.cuda.current_device() if self.device == "cuda" else self.device
+
 	@property
 	def cache_dir(self):
 		return ".cache" / self.relpath

image_classifier/data.py

@@ -4,7 +4,7 @@ import copy
 # import h5py
 import json
 import logging
-import numpy as np
+#import numpy as np
 import os
 import random
 import torch
@@ -111,7 +111,7 @@ def collate_fn(samples: list[dict]):
 
 def _seed_worker(worker_id):
 	worker_seed = torch.initial_seed() % 2**32
-	np.random.seed(worker_seed)
+	#np.random.seed(worker_seed)
 	random.seed(worker_seed)
 
 

image_classifier/engines/base.py

@@ -45,7 +45,10 @@ from .base import TrainFeeder
 _logger = logging.getLogger(__name__)
 
 if not distributed_initialized() and cfg.trainer.backend == "local":
-	init_distributed(torch.distributed.init_process_group)
+	def _nop():
+		...
+	fn = _nop if cfg.device == "cpu" else torch.distributed.init_process_group
+	init_distributed(fn)
 
 # A very naive engine implementation using barebones PyTorch
 # to-do: implement lr_sheduling
@@ -276,7 +279,7 @@ class Engines(dict[str, Engine]):
 			stats.update(flatten_dict({ name.split("-")[0]: stat }))
 		return stats
 
-	def step(self, batch, feeder: TrainFeeder = default_feeder, device=torch.cuda.current_device()):
+	def step(self, batch, feeder: TrainFeeder = default_feeder, device=cfg.get_device()):
 		total_elapsed_time = 0
 
 		stats: Any = dict()

image_classifier/inference.py

@@ -7,13 +7,13 @@ from .export import load_models
 from .data import get_symmap, _get_symbols
 
 class Classifier():
-	def __init__( self, width=300, height=80, config=None, ckpt=None, device="cuda", dtype="float32" ):
-		self.loading = True 
-		self.device = device
-		
+	def __init__( self, width=300, height=80, config=None, ckpt=None, device=cfg.get_device(), dtype="float32" ):
 		if config:
 			cfg.load_yaml( config )
 
+		self.loading = True 
+		self.device = device
+
 		if ckpt:
 			self.load_model_from_ckpt( ckpt )
 		else:

image_classifier/models/base.py

@@ -57,7 +57,7 @@ class Model(nn.Module):
 		self,
 		
 		image,
-		text = None,
+		text = None, # 
 
 		sampling_temperature: float = 1.0,
 	):

image_classifier/utils/trainer.py

@@ -6,7 +6,6 @@ import humanize
 import json
 import os
 import logging
-import numpy as np
 import random
 import selectors
 import sys
@@ -173,7 +172,7 @@ def logger(data):
 def seed(seed):
 	# Set up random seeds, after fork()
 	random.seed(seed + global_rank())
-	np.random.seed(seed + global_rank())
+	#np.random.seed(seed + global_rank())
 	torch.manual_seed(seed + global_rank())
 
 

setup.py

@@ -24,7 +24,7 @@ def write_version(version_core, pre_release=True):
     return version
 
 
-with open("README.md", "r") as f:
+with open("README.md", "r", encoding="utf-8") as f:
     long_description = f.read()
 
 setup(
@@ -41,15 +41,12 @@ setup(
         "coloredlogs>=15.0.1",
         "diskcache>=5.4.0",
         "einops>=0.6.0",
-        "matplotlib>=3.6.0",
-        "numpy==1.23.0",
         "omegaconf==2.0.6",
         "tqdm>=4.64.1",
         "humanize>=4.4.0",
 
         "pandas>=1.5.0",
         "torch>=1.13.0",
-        "torchaudio>=0.13.0",
         "torchmetrics",
     ],
     url="https://git.ecker.tech/mrq/resnet-classifier",