Merge pull request #748 from hXl3s/RN50/argparse_fix

Minor fixes in ConvNets

PyTorch/Classification/ConvNets/triton/deployer.py

@@ -61,7 +61,6 @@ def initialize_model(args):
         model.load_state_dict(
             {k.replace("module.", ""): v for k, v in state_dict.items()}
         )
-        model.load_state_dict(state_dict)
     return model.half() if args.fp16 else model
 
 

TensorFlow/Classification/ConvNets/utils/cmdline_helper.py

@@ -173,51 +173,6 @@ def parse_cmdline(available_arch):
         help="Quantize weights and activations during training using symmetric quantization."
     )
 
-    p.add_argument(
-        '--finetune_checkpoint',
-        required=False,
-        default=None,
-        type=str,
-        help="Path to pre-trained checkpoint which will be used for fine-tuning"
-    )
-    
-    _add_bool_argument(
-        parser=p, name="use_final_conv", default=False, required=False, help="Use cosine learning rate schedule."
-    )
-
-    p.add_argument(
-        '--quant_delay',
-        type=int,
-        default=0,
-        required=False,
-        help="Number of steps to be run before quantization starts to happen"
-    )
-
-    _add_bool_argument(
-        parser=p,
-        name="quantize",
-        default=False,
-        required=False,
-        help="Quantize weights and activations during training. (Defaults to Assymmetric quantization)"
-    )
-
-    _add_bool_argument(
-        parser=p,
-        name="use_qdq",
-        default=False,
-        required=False,
-        help="Use QDQV3 op instead of FakeQuantWithMinMaxVars op for quantization. QDQv3 does only scaling"
-    )
-
-    _add_bool_argument(
-        parser=p,
-        name="symmetric",
-        default=False,
-        required=False,
-        help="Quantize weights and activations during training using symmetric quantization."
-    )
-
-
     p.add_argument(
         '--log_filename',
         type=str,