First Commit

README.md

@@ -0,0 +1,15 @@
+# FSS
+## Few Shot Learning in Semantic Segmentation
+
+# How to download data
+
+> cd <target folder>
+> ../data/download_voc.sh
+  
+# How to run the training
+
+> python -m torch.distributed.launch --nproc_per_node="total GPUs" train.py --data_root "folder where you downloaded the data" --name "name of exp" --batch_size=4 --num_workers=1 --other_args
+
+The default folder for the logs is logs/"name of exp". The log is in the format of tensorboard.
+
+The default is to use a pretraining for the backbone used, that is searched in the pretrained folder of the project. If you don't want to use pretrained, please use --no-pretrained.

argparser.py

@@ -0,0 +1,126 @@
+import argparse
+import task
+
+
+def modify_command_options(opts):
+    if opts.dataset == 'voc':
+        opts.num_classes = 21
+    if opts.dataset == 'ade':
+        opts.num_classes = 150
+
+    if not opts.visualize:
+        opts.sample_num = 0
+
+    opts.no_cross_val = not opts.cross_val
+    opts.pooling = round(opts.crop_size / opts.output_stride)
+
+    return opts
+
+
+def get_argparser():
+    parser = argparse.ArgumentParser()
+
+    # Performance Options
+    parser.add_argument("--local_rank", type=int, default=0)
+    parser.add_argument("--random_seed", type=int, default=42,
+                        help="random seed (default: 42)")
+    parser.add_argument("--num_workers", type=int, default=1,
+                        help='number of workers (default: 1)')
+    parser.add_argument('--opt_level', type=str, choices=['O0', 'O1', 'O2', 'O3'], default='O0')
+
+    # Dataset Options
+    parser.add_argument("--data_root", type=str, default="data",
+                        help="path to Dataset")
+    parser.add_argument("--dataset", type=str, default='voc',
+                        choices=['voc'], help='Name of dataset')
+    parser.add_argument("--num_classes", type=int, default=None,
+                        help="num classes (default: None)")
+
+    # Task Options
+    parser.add_argument("--step", type=int, default=0,
+                        help="Step (0 is base)")
+    parser.add_argument("--task", type=str, default="15-5", choices=task.get_task_list(),
+                        help="Task to be executed (default: 15-5)")
+    parser.add_argument("--nshot", type=int, default=5,
+                        help="If step>0, the shot to use for FSL (Def=5)")
+    parser.add_argument("--ishot", type=int, default=0,
+                        help="First index where to sample shots")
+    parser.add_argument("--use_bkg", default=False, action='store_true',
+                        help="Whether to use or not the background as a class (def is not)")
+    parser.add_argument("--input_mix", default="novel", choices=['novel', 'both'],
+                        help="Which class to use for FSL")
+
+    # Train Options
+    parser.add_argument("--epochs", type=int, default=30,
+                        help="epoch number (default: 30)")
+
+    parser.add_argument("--fix_bn", action='store_true', default=False,
+                        help='fix batch normalization during training (default: False)')
+    parser.add_argument("--batch_size", type=int, default=4,
+                        help='batch size (default: 4)')
+    parser.add_argument("--crop_size", type=int, default=512,
+                        help="crop size (default: 512)")
+
+    parser.add_argument("--lr", type=float, default=0.007,
+                        help="learning rate (default: 0.007)")
+    parser.add_argument("--momentum", type=float, default=0.9,
+                        help='momentum for SGD (default: 0.9)')
+    parser.add_argument("--weight_decay", type=float, default=1e-4,
+                        help='weight decay (default: 1e-4)')
+
+    parser.add_argument("--lr_policy", type=str, default='poly',
+                        choices=['poly', 'step'], help="lr schedule policy (default: poly)")
+    parser.add_argument("--lr_decay_step", type=int, default=5000,
+                        help="decay step for stepLR (default: 5000)")
+    parser.add_argument("--lr_decay_factor", type=float, default=0.1,
+                        help="decay factor for stepLR (default: 0.1)")
+    parser.add_argument("--lr_power", type=float, default=0.9,
+                        help="power for polyLR (default: 0.9)")
+
+    # Logging Options
+    parser.add_argument("--logdir", type=str, default='./logs',
+                        help="path to Log directory (default: ./logs)")
+    parser.add_argument("--name", type=str, default='Experiment',
+                        help="name of the experiment - to append to log directory (default: Experiment)")
+    parser.add_argument("--sample_num", type=int, default=4,
+                        help='number of samples for visualization (default: 0)')
+    parser.add_argument("--debug",  action='store_true', default=False,
+                        help="verbose option")
+    parser.add_argument("--visualize",  action='store_false', default=True,
+                        help="visualization on tensorboard (def: Yes)")
+    parser.add_argument("--print_interval", type=int, default=10,
+                        help="print interval of loss (default: 10)")
+    parser.add_argument("--val_interval", type=int, default=1,
+                        help="epoch interval for eval (default: 1)")
+
+    # Segmentation Architecture Options
+    parser.add_argument("--backbone", type=str, default='resnet101',
+                        choices=['resnet50', 'resnet101'], help='backbone for the body (def: resnet50)')
+    parser.add_argument("--output_stride", type=int, default=16,
+                        choices=[8, 16], help='stride for the backbone (def: 16)')
+    parser.add_argument("--no_pretrained", action='store_true', default=False,
+                        help='Wheather to use pretrained or not (def: True)')
+    parser.add_argument("--norm_act", type=str, default="iabn_sync",
+                        choices=['iabn_sync', 'iabn', 'abn', 'std'], help='Which BN to use (def: abn_sync')
+    parser.add_argument("--fusion-mode", metavar="NAME", type=str, choices=["mean", "voting", "max"], default="mean",
+                        help="How to fuse the outputs. Options: 'mean', 'voting', 'max'")
+
+    # Test and Checkpoint options
+    parser.add_argument("--test",  action='store_true', default=False,
+                        help="Whether to train or test only (def: train and test)")
+    parser.add_argument("--ckpt", default=None, type=str,
+                        help="path to trained model. Leave it None if you want to retrain your model")
+    parser.add_argument("--continue_ckpt", default=False, action='store_true',
+                        help="Restart from the ckpt. Named taken automatically from method name.")
+    parser.add_argument("--ckpt_interval", type=int, default=1,
+                        help="epoch interval for saving model (default: 1)")
+    parser.add_argument("--cross_val", action='store_true', default=False,
+                        help="If validate on training or on validation (default: Train)")
+
+    # Method
+    parser.add_argument("--method", type=str, default='FT',
+                        choices=['FT', 'SPN'],
+                        help="The method you want to use.")
+    parser.add_argument("--embedding", type=str, default="fastnvec", choices=['word2vec', 'fasttext', 'fastnvec'])
+
+    return parser

data/download_ade.sh

@@ -0,0 +1,14 @@
+#!/usr/bin/env bash
+
+# such as add cd /vandal/dataset
+if [ $# -eq 1 ]; then
+  dest=$1
+else
+  dest="."
+fi
+
+wget http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip -P $dest
+unzip $dest/ADEChallengeData2016.zip
+
+echo "Copy the files in data/ade_splits in the ade main folder."
+cp data/ade_splits/* $dest

data/download_voc.sh

@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+# use this script in the destination folder.
+# such as add cd /home/datasets
+
+wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+tar -xf VOCtrainval_11-May-2012.tar
+mkdir PascalVOC12
+mv VOCdevkit/VOC2012/* PascalVOC12
+cd PascalVOC12
+wget http://cs.jhu.edu/~cxliu/data/SegmentationClassAug.zip
+wget http://cs.jhu.edu/~cxliu/data/SegmentationClassAug_Visualization.zip
+wget http://cs.jhu.edu/~cxliu/data/list.zip
+unzip SegmentationClassAug.zip
+unzip SegmentationClassAug_Visualization.zip
+unzip list.zip
+mv list splits
+
+# then link SegmentationClassAug into annotations
+#  and link JPEGImages into images
+ln -s JPEGImages images
+ln -s SegmentationClassAug annotations

data/voc/labels.txt

@@ -0,0 +1,21 @@
+0	background
+1	aeroplane
+2	bicycle
+3	bird
+4	boat
+5	bottle
+6	bus
+7	car
+8	cat
+9	chair
+10	cow
+11	diningtable
+12	dog
+13	horse
+14	motorbike
+15	person
+16	potted_plant
+17	sheep
+18	sofa
+19	train
+20	tv

dataset/__init__.py

@@ -0,0 +1,46 @@
+from .voc import VOCFSSDataset
+from .transform import Compose, RandomScale, RandomCrop, RandomHorizontalFlip, ToTensor, Normalize, CenterCrop
+import torch
+
+
+def get_dataset(opts, task):
+    """ Dataset And Augmentation
+    """
+    train_transform = Compose([
+        RandomScale((0.75, 1.5)),
+        RandomCrop(opts.crop_size, pad_if_needed=True),
+        RandomHorizontalFlip(),
+        ToTensor(),
+        Normalize(mean=[0.485, 0.456, 0.406],
+                  std=[0.229, 0.224, 0.225]),
+    ])
+
+    val_transform = Compose([
+        CenterCrop(size=opts.crop_size),
+        ToTensor(),
+        Normalize(mean=[0.485, 0.456, 0.406],
+                  std=[0.229, 0.224, 0.225]),
+    ])
+    test_transform = Compose([
+        ToTensor(),
+        Normalize(mean=[0.485, 0.456, 0.406],
+                  std=[0.229, 0.224, 0.225]),
+    ])
+
+    if opts.dataset == 'voc':
+        dataset = VOCFSSDataset
+    else:
+        raise NotImplementedError
+
+    train_dst = dataset(root=opts.data_root, task=task, train=True, transform=train_transform)
+
+    if opts.cross_val:
+        train_len = int(0.8 * len(train_dst))
+        val_len = len(train_dst)-train_len
+        train_dst, val_dst = torch.utils.data.random_split(train_dst, [train_len, val_len])
+    else:  # don't use cross_val
+        val_dst = dataset(root=opts.data_root, task=task, train=False, transform=val_transform)
+
+    test_dst = dataset(root=opts.data_root, task=task, train=False, transform=test_transform)
+
+    return train_dst, val_dst, test_dst