Prototxts + script for training COCO caption language model

BVLC · Mar 25, 2015 · 87d9038 · 87d9038
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diff --git a/examples/coco_caption/lstm_language_model.prototxt b/examples/coco_caption/lstm_language_model.prototxt
@@ -0,0 +1,150 @@
+name: "lstm_language_model"
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "cont_sentence"
+  top: "input_sentence"
+  top: "target_sentence"
+  include { phase: TRAIN }
+  hdf5_data_param {
+    source: "./examples/coco_caption/h5_data/buffer_100/train_unaligned_batches/hdf5_chunk_list.txt"
+    batch_size: 20
+  }
+}
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "cont_sentence"
+  top: "input_sentence"
+  top: "target_sentence"
+  include {
+    phase: TEST
+    stage: "test-on-train"
+  }
+  hdf5_data_param {
+    source: "./examples/coco_caption/h5_data/buffer_100/train_unaligned_batches/hdf5_chunk_list.txt"
+    batch_size: 20
+  }
+}
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "cont_sentence"
+  top: "input_sentence"
+  top: "target_sentence"
+  include {
+    phase: TEST
+    stage: "test-on-val"
+  }
+  hdf5_data_param {
+    source: "./examples/coco_caption/h5_data/buffer_100/val_unaligned_batches/hdf5_chunk_list.txt"
+    batch_size: 20
+  }
+}
+layer {
+  name: "embedding"
+  type: "Embed"
+  bottom: "input_sentence"
+  top: "embedded_input_sentence"
+  param {
+    lr_mult: 1
+  }
+  embed_param {
+    bias_term: false
+    input_dim: 8801  # = vocab_size + 1 (for EOS)
+    num_output: 1000
+    weight_filler {
+      type: "uniform"
+      min: -0.08
+      max: 0.08
+    }
+  }
+}
+layer {
+  name: "embed-drop"
+  type: "Dropout"
+  bottom: "embedded_input_sentence"
+  top: "embedded_input_sentence"
+  dropout_param { dropout_ratio: 0.5 }
+  include { stage: "embed-drop" }
+}
+layer {
+  name: "lstm1"
+  type: "LSTM"
+  bottom: "embedded_input_sentence"
+  bottom: "cont_sentence"
+  top: "lstm1"
+  recurrent_param {
+    num_output: 1000
+    weight_filler {
+      type: "uniform"
+      min: -0.08
+      max: 0.08
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "lstm-drop"
+  type: "Dropout"
+  bottom: "lstm1"
+  top: "lstm1"
+  dropout_param { dropout_ratio: 0.5 }
+  include { stage: "lstm-drop" }
+}
+layer {
+  name: "predict"
+  type: "InnerProduct"
+  bottom: "lstm1"
+  top: "predict"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 8801  # = vocab_size + 1 (+1 for EOS)
+    weight_filler {
+      type: "uniform"
+      min: -0.08
+      max: 0.08
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+    axis: 2
+  }
+}
+layer {
+  name: "cross_entropy_loss"
+  type: "SoftmaxWithLoss"
+  bottom: "predict"
+  bottom: "target_sentence"
+  top: "cross_entropy_loss"
+  loss_weight: 20
+  loss_param {
+    ignore_label: -1
+  }
+  softmax_param {
+    axis: 2
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "predict"
+  bottom: "target_sentence"
+  top: "accuracy"
+  include { phase: TEST }
+  accuracy_param {
+    axis: 2
+    ignore_label: -1
+  }
+}
diff --git a/examples/coco_caption/lstm_lm_solver.prototxt b/examples/coco_caption/lstm_lm_solver.prototxt
@@ -0,0 +1,21 @@
+net: "./examples/coco_caption/lstm_language_model.prototxt"
+train_state: { stage: 'embed-drop' stage: 'lstm-drop' }
+test_iter: 25
+test_state: { stage: 'test-on-train' }
+test_iter: 25
+test_state: { stage: 'test-on-val' }
+test_interval: 1000
+base_lr: 0.1
+lr_policy: "step"
+gamma: 0.5
+stepsize: 20000
+display: 1
+max_iter: 110000
+momentum: 0.9
+weight_decay: 0.0000
+snapshot: 5000
+snapshot_prefix: "./examples/coco_caption/lstm_lm"
+solver_mode: GPU
+random_seed: 1701
+average_loss: 100
+clip_gradients: 10
diff --git a/examples/coco_caption/train_language_model.sh b/examples/coco_caption/train_language_model.sh
@@ -0,0 +1,14 @@
+#!/usr/bin/env bash
+
+GPU_ID=0
+DATA_DIR=./examples/coco_caption/h5_data/
+if [ ! -d $DATA_DIR ]; then
+    echo "Data directory not found: $DATA_DIR"
+    echo "First, download the COCO dataset (follow instructions in data/coco)"
+    echo "Then, run ./examples/coco_caption/coco_to_hdf5_data.py to create the Caffe input data"
+    exit 1
+fi
+
+./build/tools/caffe train \
+    -solver ./examples/coco_caption/lstm_lm_solver.prototxt \
+    -gpu $GPU_ID