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detr/main.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+import argparse
+from pathlib import Path
+
+import numpy as np
+import torch
+from .models import build_ACT_model, build_CNNMLP_model
+
+import IPython
+e = IPython.embed
+
+def get_args_parser():
+    parser = argparse.ArgumentParser('Set transformer detector', add_help=False)
+    parser.add_argument('--lr', default=1e-4, type=float) # will be overridden
+    parser.add_argument('--lr_backbone', default=1e-5, type=float) # will be overridden
+    parser.add_argument('--batch_size', default=2, type=int) # not used
+    parser.add_argument('--weight_decay', default=1e-4, type=float)
+    parser.add_argument('--epochs', default=300, type=int) # not used
+    parser.add_argument('--lr_drop', default=200, type=int) # not used
+    parser.add_argument('--clip_max_norm', default=0.1, type=float, # not used
+                        help='gradient clipping max norm')
+
+    # Model parameters
+    # * Backbone
+    parser.add_argument('--backbone', default='resnet18', type=str, # will be overridden
+                        help="Name of the convolutional backbone to use")
+    parser.add_argument('--dilation', action='store_true',
+                        help="If true, we replace stride with dilation in the last convolutional block (DC5)")
+    parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
+                        help="Type of positional embedding to use on top of the image features")
+    parser.add_argument('--camera_names', default=[], type=list, # will be overridden
+                        help="A list of camera names")
+
+    # * Transformer
+    parser.add_argument('--enc_layers', default=4, type=int, # will be overridden
+                        help="Number of encoding layers in the transformer")
+    parser.add_argument('--dec_layers', default=6, type=int, # will be overridden
+                        help="Number of decoding layers in the transformer")
+    parser.add_argument('--dim_feedforward', default=2048, type=int, # will be overridden
+                        help="Intermediate size of the feedforward layers in the transformer blocks")
+    parser.add_argument('--hidden_dim', default=256, type=int, # will be overridden
+                        help="Size of the embeddings (dimension of the transformer)")
+    parser.add_argument('--dropout', default=0.1, type=float,
+                        help="Dropout applied in the transformer")
+    parser.add_argument('--nheads', default=8, type=int, # will be overridden
+                        help="Number of attention heads inside the transformer's attentions")
+    parser.add_argument('--num_queries', default=400, type=int, # will be overridden
+                        help="Number of query slots")
+    parser.add_argument('--pre_norm', action='store_true')
+
+    # * Segmentation
+    parser.add_argument('--masks', action='store_true',
+                        help="Train segmentation head if the flag is provided")
+
+    # repeat args in imitate_episodes just to avoid error. Will not be used
+    parser.add_argument('--eval', action='store_true')
+    parser.add_argument('--onscreen_render', action='store_true')
+    parser.add_argument('--dataset_dir', action='store', type=str, help='dataset_dir', required=True)
+    parser.add_argument('--ckpt_dir', action='store', type=str, help='ckpt_dir', required=True)
+    parser.add_argument('--policy_class', action='store', type=str, help='policy_class, capitalize', required=True)
+    parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True)
+    parser.add_argument('--seed', action='store', type=int, help='seed', required=True)
+    parser.add_argument('--num_epochs', action='store', type=int, help='num_epochs', required=True)
+    parser.add_argument('--kl_weight', action='store', type=int, help='KL Weight', required=False)
+    parser.add_argument('--chunk_size', action='store', type=int, help='chunk_size', required=False)
+    parser.add_argument('--temporal_agg', action='store_true')
+
+    return parser
+
+
+def build_ACT_model_and_optimizer(args_override):
+    parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
+    args = parser.parse_args()
+
+    for k, v in args_override.items():
+        setattr(args, k, v)
+
+    model = build_ACT_model(args)
+    model.cuda()
+
+    param_dicts = [
+        {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]},
+        {
+            "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad],
+            "lr": args.lr_backbone,
+        },
+    ]
+    optimizer = torch.optim.AdamW(param_dicts, lr=args.lr,
+                                  weight_decay=args.weight_decay)
+
+    return model, optimizer
+
+
+def build_CNNMLP_model_and_optimizer(args_override):
+    parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
+    args = parser.parse_args()
+
+    for k, v in args_override.items():
+        setattr(args, k, v)
+
+    model = build_CNNMLP_model(args)
+    model.cuda()
+
+    param_dicts = [
+        {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]},
+        {
+            "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad],
+            "lr": args.lr_backbone,
+        },
+    ]
+    optimizer = torch.optim.AdamW(param_dicts, lr=args.lr,
+                                  weight_decay=args.weight_decay)
+
+    return model, optimizer
+