update env name to minigptv, add bug report and feature request back

minigpt4/models/minigpt_base.py

@@ -178,7 +178,6 @@ class MiniGPTBase(BaseModel):
             answers = [self.llama_tokenizer(a + self.end_sym,
                                             return_tensors="pt",
                                             add_special_tokens=False).to(self.device) for a in answers]
-
             cur_id = []
             cur_target = []
             for i in range(len(questions)):
@@ -226,8 +225,6 @@ class MiniGPTBase(BaseModel):
 
             conv_q = [[self.prompt_template.format(item) for item in items] for items in conv_q]
 
-        
-
             cond_embeds, cond_atts = self.prompt_wrap(img_embeds, img_atts, [q[0] for q in conv_q])
             regress_token_ids, regress_atts, part_targets = self.tokenize_conversation(conv_q, conv_a)
 

train.py

@@ -12,6 +12,7 @@ import random
 import numpy as np
 import torch
 import torch.backends.cudnn as cudnn
+import wandb
 
 import minigpt4.tasks as tasks
 from minigpt4.common.config import Config
@@ -30,7 +31,6 @@ from minigpt4.models import *
 from minigpt4.processors import *
 from minigpt4.runners import *
 from minigpt4.tasks import *
-import wandb
 
 
 def parse_args():
@@ -44,12 +44,10 @@ def parse_args():
         "in xxx=yyy format will be merged into config file (deprecate), "
         "change to --cfg-options instead.",
     )
-    parser.add_argument("--wandb_log", default=False)
-    parser.add_argument("--job_name",default="minigpt_v2",type=str)
+    parser.add_argument("--job_name", default="minigpt_v2",type=str)
 
     args = parser.parse_args()
 
-
     return args
 
 
@@ -80,16 +78,13 @@ def main():
     # set before init_distributed_mode() to ensure the same job_id shared across all ranks.
     job_id = now()
     args = parse_args()
-
-    cfg = Config(parse_args())
+    cfg = Config(args)
 
     init_distributed_mode(cfg.run_cfg)
-
     setup_seeds(cfg)
 
     # set after init_distributed_mode() to only log on master.
     setup_logger()
-
     cfg.pretty_print()
 
     task = tasks.setup_task(cfg)
@@ -98,10 +93,9 @@ def main():
 
     if cfg.run_cfg.wandb_log:
         wandb.login()
-        wandb.init(project="minigptv2",name=args.job_name)
+        wandb.init(project="minigptv", name=cfg.run_cfg.job_name)
         wandb.watch(model)
 
-
     runner = get_runner_class(cfg)(
         cfg=cfg, job_id=job_id, task=task, model=model, datasets=datasets
     )

train_configs/minigpt4_llama2_stage1_pretrain.yaml

@@ -52,4 +52,7 @@ run:
   device: "cuda"
   world_size: 1
   dist_url: "env://"
-  distributed: True
+  distributed: True
+
+  wandb_log: True
+  job_name: minigpt4_llama2_pretrain

train_configs/minigpt4_llama2_stage2_finetune.yaml

@@ -46,4 +46,7 @@ run:
   device: "cuda"
   world_size: 1
   dist_url: "env://"
-  distributed: True
+  distributed: True
+
+  wandb_log: True
+  job_name: minigpt4_llama2_finetune

train_configs/minigpt4_stage1_pretrain.yaml

@@ -52,4 +52,7 @@ run:
   device: "cuda"
   world_size: 1
   dist_url: "env://"
-  distributed: True
+  distributed: True
+
+  wandb_log: True
+  job_name: minigpt4_pretrain

train_configs/minigpt4_stage2_finetune.yaml

@@ -46,4 +46,7 @@ run:
   device: "cuda"
   world_size: 1
   dist_url: "env://"
-  distributed: True
+  distributed: True
+
+  wandb_log: True
+  job_name: minigpt4_finetune

train_configs/minigpt_v2_finetune.yaml

@@ -276,7 +276,6 @@ run:
   init_lr: 1e-5
   min_lr: 8e-5
   warmup_lr: 1e-6
-  wandb_log: True
 
   weight_decay: 0.05
   max_epoch: 50
@@ -296,4 +295,7 @@ run:
   device: "cuda"
   world_size: 1
   dist_url: "env://"
-  distributed: True
+  distributed: True
+
+  wandb_log: True
+  job_name: minigptv2_finetune