initial commit

Author: dorarad

Diff for: config.py

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+--parallel
+--evalTrain
+--retainVal
+--useEMA
+--lrReduce
+--adam
+--clip
+--memoryVariationalDropout
+--relu=ELU
+--encBi
+--wrdEmbRandom
+--wrdEmbUniform
+--outQuestion
+--initCtrl=Q
+--controlContextual
+--controlInputUnshared
+--readProjInputs
+--readMemConcatKB
+--readMemConcatProj
+--readMemProj
+--readCtrl
+--writeMemProj

Diff for: configs/args.txt

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+--netLength=8
+--parallel
+--evalTrain
+--retainVal
+--useEMA
+--lrReduce
+--adam
+--clip
+--memoryVariationalDropout
+--relu=ELU
+--encBi
+--wrdEmbRandom
+--wrdEmbUniform
+--outQuestion
+--controlContextual
+--readProjInputs
+--readMemConcatKB
+--readMemConcatProj
+--readMemProj
+--readCtrl
+--writeMemProj
+--initCtrl=PRM
+--controlFeedPrev
+--controlFeedPrevAtt
+--controlFeedInputs
+--controlContAct=TANH

Diff for: configs/args1.txt

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+--netLength=12
+--parallel
+--evalTrain
+--retainVal
+--useEMA
+--lrReduce
+--adam
+--clip
+--memoryVariationalDropout
+--relu=ELU
+--encBi
+--wrdEmbRandom
+--wrdEmbUniform
+--outQuestion
+--initCtrl=Q
+--controlContextual
+--controlInputUnshared
+--readProjInputs
+--readMemConcatKB
+--readMemConcatProj
+--readMemProj
+--readCtrl
+--writeMemProj
+--qDropout=0.85
+--stemDropout=0.85
+--noBucket
+--noRebucket

Diff for: configs/args2.txt

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+--netLength=8
+--parallel
+--evalTrain
+--retainVal
+--useEMA
+--lrReduce
+--adam
+--clip
+--memoryVariationalDropout
+--relu=ELU
+--encBi
+--wrdEmbRandom
+--wrdEmbUniform
+--outQuestion
+--initCtrl=Q
+--controlContextual
+--controlInputUnshared
+--readProjInputs
+--readMemConcatKB
+--readMemConcatProj
+--readMemProj
+--readCtrl
+--writeMemProj
+--writeSelfAtt
+--writeSelfAttMod CONT

Diff for: configs/args3.txt

@@ -0,0 +1,24 @@
+--netLength=8
+--parallel
+--evalTrain
+--retainVal
+--useEMA
+--lrReduce
+--adam
+--clip
+--memoryVariationalDropout
+--relu=ELU
+--encBi
+--wrdEmbRandom
+--wrdEmbUniform
+--outQuestion
+--initCtrl=Q
+--controlContextual
+--controlInputUnshared
+--readProjInputs
+--readMemConcatKB
+--readMemConcatProj
+--readMemProj
+--readCtrl
+--writeMemProj
+--writeGate

Diff for: configs/args4.txt

@@ -0,0 +1,114 @@
+# Copyright 2017-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse, os, json
+import h5py
+import numpy as np
+from scipy.misc import imread, imresize
+
+import torch
+import torchvision
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_image_dir', required=True)
+parser.add_argument('--max_images', default=None, type=int)
+parser.add_argument('--output_h5_file', required=True)
+
+parser.add_argument('--image_height', default=224, type=int)
+parser.add_argument('--image_width', default=224, type=int)
+
+parser.add_argument('--model', default='resnet101')
+parser.add_argument('--model_stage', default=3, type=int)
+parser.add_argument('--batch_size', default=128, type=int)
+
+
+def build_model(args):
+  if not hasattr(torchvision.models, args.model):
+    raise ValueError('Invalid model "%s"' % args.model)
+  if not 'resnet' in args.model:
+    raise ValueError('Feature extraction only supports ResNets')
+  cnn = getattr(torchvision.models, args.model)(pretrained=True)
+  layers = [
+    cnn.conv1,
+    cnn.bn1,
+    cnn.relu,
+    cnn.maxpool,
+  ]
+  for i in range(args.model_stage):
+    name = 'layer%d' % (i + 1)
+    layers.append(getattr(cnn, name))
+  model = torch.nn.Sequential(*layers)
+  model.cuda()
+  model.eval()
+  return model
+
+
+def run_batch(cur_batch, model):
+  mean = np.array([0.485, 0.456, 0.406]).reshape(1, 3, 1, 1)
+  std = np.array([0.229, 0.224, 0.224]).reshape(1, 3, 1, 1)
+
+  image_batch = np.concatenate(cur_batch, 0).astype(np.float32)
+  image_batch = (image_batch / 255.0 - mean) / std
+  image_batch = torch.FloatTensor(image_batch).cuda()
+  image_batch = torch.autograd.Variable(image_batch, volatile=True)
+
+  feats = model(image_batch)
+  feats = feats.data.cpu().clone().numpy()
+
+  return feats
+
+
+def main(args):
+  input_paths = []
+  idx_set = set()
+  for fn in os.listdir(args.input_image_dir):
+    if not fn.endswith('.png'): continue
+    idx = int(os.path.splitext(fn)[0].split('_')[-1])
+    input_paths.append((os.path.join(args.input_image_dir, fn), idx))
+    idx_set.add(idx)
+  input_paths.sort(key=lambda x: x[1])
+  assert len(idx_set) == len(input_paths)
+  assert min(idx_set) == 0 and max(idx_set) == len(idx_set) - 1
+  if args.max_images is not None:
+    input_paths = input_paths[:args.max_images]
+  print(input_paths[0])
+  print(input_paths[-1])
+
+  model = build_model(args)
+
+  img_size = (args.image_height, args.image_width)
+  with h5py.File(args.output_h5_file, 'w') as f:
+    feat_dset = None
+    i0 = 0
+    cur_batch = []
+    for i, (path, idx) in enumerate(input_paths):
+      img = imread(path, mode='RGB')
+      img = imresize(img, img_size, interp='bicubic')
+      img = img.transpose(2, 0, 1)[None]
+      cur_batch.append(img)
+      if len(cur_batch) == args.batch_size:
+        feats = run_batch(cur_batch, model)
+        if feat_dset is None:
+          N = len(input_paths)
+          _, C, H, W = feats.shape
+          feat_dset = f.create_dataset('features', (N, C, H, W),
+                                       dtype=np.float32)
+        i1 = i0 + len(cur_batch)
+        feat_dset[i0:i1] = feats
+        i0 = i1
+        print('Processed %d / %d images' % (i1, len(input_paths)))
+        cur_batch = []
+    if len(cur_batch) > 0:
+      feats = run_batch(cur_batch, model)
+      i1 = i0 + len(cur_batch)
+      feat_dset[i0:i1] = feats
+      print('Processed %d / %d images' % (i1, len(input_paths)))
+
+
+if __name__ == '__main__':
+  args = parser.parse_args()
+  main(args)