diff --git a/README.md b/README.md
index 74124c41a7cca1df66f14fdd8ab33b55d6817f86..f963d2891c8dd976361d15366515b3acc892d3fa 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,30 @@
-# MultiExitViT
+# Introduction
+
+This code was developed and executed using Jupyter notebooks.
+
+The following instructions assume Ubuntu 20.04 operating system with superuser access, Nvidia GPUs, GPU drivers already installed and CUDA version 10.1, 11.0 or 11.2.
+
+# Setting Up the Environment
+
+1. [Install Docker](https://docs.docker.com/engine/install/ubuntu/)
+2. [Install Nvidia Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#setting-up-nvidia-container-toolkit)
+3. `sudo docker run --gpus all -it -p 8888:8888 tensorflow/tensorflow:2.3.2-gpu-jupyter` (also tested with the `tensorflow/tensorflow:2.4.1-gpu-jupyter` image)
+4. Copy the URL provided in docker logs (including the token).
+5. <kbd>CTRL</kbd>+<kbd>P</kbd> then <kbd>CTRL</kbd>+<kbd>Q</kbd> to detach from the container without terminating the execution.
+6. Install SciPy inside the container: `sudo docker exec -it [container_name] bash` (you can find the container name from the output of `sudo docker ps`) then `pip install scipy==1.5` (use <kbd>CTRL</kbd>+<kbd>D</kbd> to terminate and detach)
+7. Paste the copied URL in your browser to open Jupyter (if you are running the docker container on a remote server, you need to replace the IP address with that of the server).
+8. Upload all of the `.ipynb` files in this repository.
+
+# Running the Experiments
+
+Note: in each of the notebooks, you can modify `SELECTED_GPUS` to specify which GPUs to use. If you only have a single GPU available, set `SELECTED_GPUS = [0]`. The distributed training may not be supported in some notebooks.
+
+1. Run the `train_cifar10_backbone`, `train_cifar100_backbone`, `train_fashion_mnist_backbone` and`train_disco_backbone` notebooks to train the backbones.
+
+2. Run the `precompute_cifar_features`, `precompute_disco_features` and `precompute_fashion_mnist_features` notebooks to precompute the intermediate representations of the backbones.
+
+3. Run the `ee` and `cw` notebooks to run the end-to-end and classifier-wise experiments, respectively. You can change the `dataset`, `head_type`, `version` and other parameters given to the `train` function.
+
+4. Run the `calculate_flops` notebook to calculate the FLOPS, the `calculate_maes` notebook to calculate MAEs for the DISCO dataset cases, and the `plots` notebook to draw the plots.
+
diff --git a/calculate_flops.ipynb b/calculate_flops.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b1d5f68d1d5fb58a0f577d369cd92bf9e22e378e
--- /dev/null
+++ b/calculate_flops.ipynb
@@ -0,0 +1,377 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [7]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import sys\n",
+ "from skimage import transform\n",
+ "from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph\n",
+ "from vit_keras import vit\n",
+ "from vit_keras.layers import ClassToken, AddPositionEmbs, MultiHeadSelfAttention, TransformerBlock\n",
+ "\n",
+ "IMAGE_SIZE = 384\n",
+ "PATCH_SIZE = 16\n",
+ "HIDDEN_DIM = 768\n",
+ "MLP_DIM = 3072\n",
+ "CHANNELS_MLP_DIM = 3072\n",
+ "TOKENS_MLP_DIM = 384"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_flops(model):\n",
+ " \"\"\"\n",
+ " from https://github.com/tensorflow/tensorflow/issues/32809#issuecomment-768977280\n",
+ " \"\"\"\n",
+ " concrete = tf.function(lambda inputs: model(inputs))\n",
+ " concrete_func = concrete.get_concrete_function(\n",
+ " [tf.TensorSpec([1, *inputs.shape[1:]]) for inputs in model.inputs])\n",
+ " frozen_func, graph_def = convert_variables_to_constants_v2_as_graph(concrete_func)\n",
+ " with tf.Graph().as_default() as graph:\n",
+ " tf.graph_util.import_graph_def(graph_def, name='')\n",
+ " run_meta = tf.compat.v1.RunMetadata()\n",
+ " opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()\n",
+ " flops = tf.compat.v1.profiler.profile(graph=graph, run_meta=run_meta, cmd=\"op\", options=opts)\n",
+ " return flops.total_float_ops"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from https://github.com/leondgarse/Keras_mlp/blob/main/res_mlp.py\n",
+ "\n",
+ "def channel_affine(inputs, use_bias=True, weight_init_value=1, name=''):\n",
+ " ww_init = tfkeras.initializers.Constant(weight_init_value) if weight_init_value != 1 else 'ones'\n",
+ " nn = tf.keras.backend.expand_dims(inputs, 1)\n",
+ " nn = tf.keras.layers.DepthwiseConv2D(1, depthwise_initializer=ww_init, use_bias=use_bias, name=name + 'affine')(nn)\n",
+ " return tf.keras.backend.squeeze(nn, 1)\n",
+ "\n",
+ "def mlp_block(inputs, mlp_dim, activation='gelu', name=''):\n",
+ " affine_inputs = channel_affine(inputs, use_bias=True, name=name + '1_')\n",
+ " nn = tf.keras.layers.Permute((2, 1), name=name + 'permute_1')(affine_inputs)\n",
+ " nn = tf.keras.layers.Dense(nn.shape[-1], name=name + 'dense_1')(nn)\n",
+ " nn = tf.keras.layers.Permute((2, 1), name=name + 'permute_2')(nn)\n",
+ " nn = channel_affine(nn, use_bias=False, name=name + '1_gamma_')\n",
+ " skip_conn = tf.keras.layers.Add(name=name + 'add_1')([nn, affine_inputs])\n",
+ "\n",
+ " affine_skip = channel_affine(skip_conn, use_bias=True, name=name + '2_')\n",
+ " nn = tf.keras.layers.Dense(mlp_dim, name=name + 'dense_2_1')(affine_skip)\n",
+ " nn = tf.keras.layers.Activation(activation, name=name + 'gelu')(nn)\n",
+ " nn = tf.keras.layers.Dense(inputs.shape[-1], name=name + 'dense_2_2')(nn)\n",
+ " nn = channel_affine(nn, use_bias=False, name=name + '2_gamma_')\n",
+ " nn = tf.keras.layers.Add(name=name + 'add_2')([nn, affine_skip])\n",
+ " return nn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from https://github.com/Benjamin-Etheredge/mlp-mixer-keras/blob/main/mlp_mixer_keras/mlp_mixer.py\n",
+ "\n",
+ "class MlpBlock(tf.keras.layers.Layer):\n",
+ " def __init__(self, dim, hidden_dim, activation=None, **kwargs):\n",
+ " super(MlpBlock, self).__init__(**kwargs)\n",
+ "\n",
+ " if activation is None:\n",
+ " activation = tf.keras.activations.gelu\n",
+ "\n",
+ " self.dim = dim\n",
+ " self.dense1 = tf.keras.layers.Dense(hidden_dim)\n",
+ " self.activation = tf.keras.layers.Activation(activation)\n",
+ " self.dense2 = tf.keras.layers.Dense(dim)\n",
+ "\n",
+ " def call(self, inputs):\n",
+ " x = inputs\n",
+ " x = self.dense1(x)\n",
+ " x = self.activation(x)\n",
+ " x = self.dense2(x)\n",
+ " return x\n",
+ "\n",
+ " def compute_output_shape(self, input_signature):\n",
+ " return (input_signature[0], self.dim)\n",
+ "\n",
+ "class MixerBlock(tf.keras.layers.Layer):\n",
+ " def __init__(\n",
+ " self,\n",
+ " num_patches,\n",
+ " channel_dim,\n",
+ " token_mixer_hidden_dim,\n",
+ " channel_mixer_hidden_dim=None,\n",
+ " activation=None,\n",
+ " **kwargs\n",
+ " ):\n",
+ " super(MixerBlock, self).__init__(**kwargs)\n",
+ "\n",
+ " if activation is None:\n",
+ " activation = tf.keras.activations.gelu\n",
+ "\n",
+ " if channel_mixer_hidden_dim is None:\n",
+ " channel_mixer_hidden_dim = token_mixer_hidden_dim\n",
+ "\n",
+ " self.norm1 = tf.keras.layers.LayerNormalization(axis=1)\n",
+ " self.permute1 = tf.keras.layers.Permute((2, 1))\n",
+ " self.token_mixer = MlpBlock(num_patches, token_mixer_hidden_dim, name='token_mixer')\n",
+ "\n",
+ " self.permute2 = tf.keras.layers.Permute((2, 1))\n",
+ " self.norm2 = tf.keras.layers.LayerNormalization(axis=1)\n",
+ " self.channel_mixer = MlpBlock(channel_dim, channel_mixer_hidden_dim, name='channel_mixer')\n",
+ "\n",
+ " self.skip_connection1 = tf.keras.layers.Add()\n",
+ " self.skip_connection2 = tf.keras.layers.Add()\n",
+ "\n",
+ " def call(self, inputs):\n",
+ " x = inputs\n",
+ " skip_x = x\n",
+ " x = self.norm1(x)\n",
+ " x = self.permute1(x)\n",
+ " x = self.token_mixer(x)\n",
+ "\n",
+ " x = self.permute2(x)\n",
+ "\n",
+ " x = self.skip_connection1([x, skip_x])\n",
+ " skip_x = x\n",
+ "\n",
+ " x = self.norm2(x)\n",
+ " x = self.channel_mixer(x)\n",
+ "\n",
+ " x = self.skip_connection2([x, skip_x])\n",
+ "\n",
+ " return x\n",
+ "\n",
+ " def compute_output_shape(self, input_shape):\n",
+ " return input_shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_branch_id(branch_number):\n",
+ " if branch_number == 1:\n",
+ " return 'transformer_block'\n",
+ " else:\n",
+ " return 'transformer_block_%d' % (branch_number - 1)\n",
+ "\n",
+ "def get_model(dataset_name, branch_type, branch_number):\n",
+ " if dataset_name == 'disco':\n",
+ " model_file_name = 'vit_cc_backbone_v2.h5'\n",
+ " output_units = 1\n",
+ " output_activation = None\n",
+ " elif dataset_name == 'fashion_mnist':\n",
+ " model_file_name = 'vit_fashion_mnist_v1.h5'\n",
+ " output_units = 10\n",
+ " output_activation = 'softmax'\n",
+ " elif dataset_name == 'cifar10':\n",
+ " model_file_name = 'vit_cifar10_v1.h5'\n",
+ " output_units = 10\n",
+ " output_activation = 'softmax'\n",
+ " else:\n",
+ " model_file_name = 'vit_cifar100_v1.h5'\n",
+ " output_units = 100\n",
+ " output_activation = 'softmax'\n",
+ "\n",
+ " backbone_model = tf.keras.models.load_model(model_file_name, custom_objects={\n",
+ " 'ClassToken': ClassToken,\n",
+ " 'AddPositionEmbs': AddPositionEmbs,\n",
+ " 'MultiHeadSelfAttention': MultiHeadSelfAttention,\n",
+ " 'TransformerBlock': TransformerBlock,\n",
+ " })\n",
+ " \n",
+ " # freeze\n",
+ " for layer in backbone_model.layers:\n",
+ " layer.trainable = False\n",
+ " \n",
+ " if branch_type == 'mlp':\n",
+ " y, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " y = tf.keras.layers.LayerNormalization(\n",
+ " epsilon=1e-6, name=\"Transformer/encoder_norm\"\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Lambda(lambda v: v[:, 0], name=\"ExtractToken\")(y)\n",
+ "\n",
+ " elif branch_type == 'vit':\n",
+ " y, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " y, _ = TransformerBlock(\n",
+ " num_heads=12,\n",
+ " mlp_dim=3072,\n",
+ " dropout=0.1,\n",
+ " name=f\"Transformer/encoderblock_x\",\n",
+ " )(y)\n",
+ " y = tf.keras.layers.LayerNormalization(\n",
+ " epsilon=1e-6, name=\"Transformer/encoder_norm\"\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Lambda(lambda v: v[:, 0], name=\"ExtractToken\")(y)\n",
+ "\n",
+ " elif branch_type.startswith('cnn_'):\n",
+ " y0, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " channels = HIDDEN_DIM\n",
+ " width = height = IMAGE_SIZE // PATCH_SIZE\n",
+ " y1 = tf.keras.layers.Lambda(lambda v: v[:, 1:], name='RemoveToken')(y0)\n",
+ " y1 = tf.keras.layers.Reshape((width, height, channels), name='cnn_reshape')(y1)\n",
+ " y2 = tf.keras.layers.Lambda(lambda v: v[:, 0], name='ExtractToken')(y0)\n",
+ " y2 = tf.keras.layers.RepeatVector(width * height)(y2)\n",
+ " y2 = tf.keras.layers.Reshape((width, height, channels), name='cls_reshape')(y2)\n",
+ " if branch_type == 'cnn_ignore':\n",
+ " y = y1\n",
+ " elif branch_type == 'cnn_add':\n",
+ " y = tf.keras.layers.Add()([y1, y2])\n",
+ " elif branch_type == 'cnn_project':\n",
+ " y = tf.keras.layers.Concatenate()([y1, y2])\n",
+ " y = tf.keras.layers.Conv2D(\n",
+ " filters=16,\n",
+ " kernel_size=(3, 3),\n",
+ " activation='elu',\n",
+ " padding='same'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.MaxPool2D(pool_size=(2, 2))(y)\n",
+ " y = tf.keras.layers.Flatten()(y)\n",
+ "\n",
+ " elif branch_type == 'resmlp':\n",
+ " y, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " y = mlp_block(y, mlp_dim=MLP_DIM, name='mlp_mixer')\n",
+ " y = tf.keras.layers.GlobalAveragePooling1D()(y)\n",
+ "\n",
+ " elif branch_type == 'mlp_mixer':\n",
+ " y, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " num_patches = (IMAGE_SIZE // PATCH_SIZE) ** 2 + 1\n",
+ " y = MixerBlock(\n",
+ " num_patches=num_patches,\n",
+ " channel_dim=HIDDEN_DIM,\n",
+ " token_mixer_hidden_dim=TOKENS_MLP_DIM,\n",
+ " channel_mixer_hidden_dim=CHANNELS_MLP_DIM\n",
+ " )(y)\n",
+ " y = tf.keras.layers.GlobalAveragePooling1D()(y)\n",
+ "\n",
+ " else:\n",
+ " raise Exception('Unknown branch type: %s' % branch_type)\n",
+ " \n",
+ " # MLP head\n",
+ " initializer = tf.keras.initializers.he_normal()\n",
+ " regularizer = tf.keras.regularizers.l2()\n",
+ " y = tf.keras.layers.Dense(\n",
+ " units=256,\n",
+ " activation='elu',\n",
+ " kernel_initializer=initializer,\n",
+ " kernel_regularizer=regularizer\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Dropout(0.5)(y)\n",
+ " y = tf.keras.layers.Dense(\n",
+ " units=256,\n",
+ " activation='elu',\n",
+ " kernel_initializer=initializer,\n",
+ " kernel_regularizer=regularizer\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Dropout(0.5)(y)\n",
+ " y = tf.keras.layers.Dense(\n",
+ " units=output_units,\n",
+ " activation=output_activation,\n",
+ " kernel_initializer=initializer,\n",
+ " kernel_regularizer=regularizer\n",
+ " )(y)\n",
+ "\n",
+ " model = tf.keras.models.Model(\n",
+ " inputs=backbone_model.get_layer(index=0).input,\n",
+ " outputs=y\n",
+ " )\n",
+ "\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "branch_types = [\n",
+ " 'mlp',\n",
+ " 'vit',\n",
+ " 'cnn_ignore',\n",
+ " 'cnn_add',\n",
+ " 'cnn_project',\n",
+ " 'resmlp',\n",
+ " 'mlp_mixer',\n",
+ "]\n",
+ "\n",
+ "dataset_names = [\n",
+ " 'cifar10',\n",
+ " 'cifar100',\n",
+ " 'disco',\n",
+ " 'fashion_mnist',\n",
+ "]\n",
+ "\n",
+ "for dataset_name in dataset_names:\n",
+ " for branch_type in branch_types:\n",
+ " flops = []\n",
+ " for branch_number in range(1, 12):\n",
+ " tf.keras.backend.clear_session()\n",
+ " flops.append(get_flops(get_model(dataset_name, branch_type, branch_number)) / 10 ** 9)\n",
+ " print('###', dataset_name, branch_type)\n",
+ " print(flops)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/calculate_maes.ipynb b/calculate_maes.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e02b37bd2b0775aefd8871ad72178a6f5f1eb8c7
--- /dev/null
+++ b/calculate_maes.ipynb
@@ -0,0 +1,346 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [7]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import random\n",
+ "import string\n",
+ "import sys\n",
+ "from skimage import transform\n",
+ "from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph\n",
+ "from vit_keras import vit\n",
+ "from vit_keras.layers import ClassToken, AddPositionEmbs, MultiHeadSelfAttention, TransformerBlock\n",
+ "\n",
+ "VIDEO_PATCHES = (2, 3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from https://github.com/Benjamin-Etheredge/mlp-mixer-keras/blob/main/mlp_mixer_keras/mlp_mixer.py\n",
+ "\n",
+ "class MlpBlock(tf.keras.layers.Layer):\n",
+ " def __init__(self, dim, hidden_dim, activation=None, **kwargs):\n",
+ " super(MlpBlock, self).__init__(**kwargs)\n",
+ "\n",
+ " if activation is None:\n",
+ " activation = tf.keras.activations.gelu\n",
+ "\n",
+ " self.dim = dim\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.activation = activation\n",
+ " self.dense1 = tf.keras.layers.Dense(hidden_dim)\n",
+ " self.activation = tf.keras.layers.Activation(activation)\n",
+ " self.dense2 = tf.keras.layers.Dense(dim)\n",
+ "\n",
+ " def call(self, inputs):\n",
+ " x = inputs\n",
+ " x = self.dense1(x)\n",
+ " x = self.activation(x)\n",
+ " x = self.dense2(x)\n",
+ " return x\n",
+ "\n",
+ " def compute_output_shape(self, input_signature):\n",
+ " return (input_signature[0], self.dim)\n",
+ "\n",
+ " def get_config(self):\n",
+ " config = super(MlpBlock, self).get_config().copy()\n",
+ " config.update({\n",
+ " 'dim': self.dim,\n",
+ " 'hidden_dim': self.hidden_dim,\n",
+ " 'activation': self.activation,\n",
+ " })\n",
+ " return config\n",
+ "\n",
+ "class MixerBlock(tf.keras.layers.Layer):\n",
+ " def __init__(\n",
+ " self,\n",
+ " num_patches,\n",
+ " channel_dim,\n",
+ " token_mixer_hidden_dim,\n",
+ " channel_mixer_hidden_dim=None,\n",
+ " activation=None,\n",
+ " **kwargs\n",
+ " ):\n",
+ " super(MixerBlock, self).__init__(**kwargs)\n",
+ "\n",
+ " if activation is None:\n",
+ " activation = tf.keras.activations.gelu\n",
+ "\n",
+ " if channel_mixer_hidden_dim is None:\n",
+ " channel_mixer_hidden_dim = token_mixer_hidden_dim\n",
+ "\n",
+ " self.num_patches = num_patches\n",
+ " self.channel_dim = channel_dim\n",
+ " self.token_mixer_hidden_dim = token_mixer_hidden_dim\n",
+ " self.channel_mixer_hidden_dim = channel_mixer_hidden_dim\n",
+ " self.activation = activation\n",
+ " \n",
+ " self.norm1 = tf.keras.layers.LayerNormalization(axis=1)\n",
+ " self.permute1 = tf.keras.layers.Permute((2, 1))\n",
+ " self.token_mixer = MlpBlock(num_patches, token_mixer_hidden_dim, name='token_mixer')\n",
+ "\n",
+ " self.permute2 = tf.keras.layers.Permute((2, 1))\n",
+ " self.norm2 = tf.keras.layers.LayerNormalization(axis=1)\n",
+ " self.channel_mixer = MlpBlock(channel_dim, channel_mixer_hidden_dim, name='channel_mixer')\n",
+ "\n",
+ " self.skip_connection1 = tf.keras.layers.Add()\n",
+ " self.skip_connection2 = tf.keras.layers.Add()\n",
+ "\n",
+ " def get_config(self):\n",
+ " config = super(MixerBlock, self).get_config().copy()\n",
+ " config.update({\n",
+ " 'num_patches': self.num_patches,\n",
+ " 'channel_dim': self.channel_dim,\n",
+ " 'token_mixer_hidden_dim': self.token_mixer_hidden_dim,\n",
+ " 'channel_mixer_hidden_dim': self.channel_mixer_hidden_dim,\n",
+ " 'activation': self.activation,\n",
+ " })\n",
+ " return config\n",
+ "\n",
+ " def call(self, inputs):\n",
+ " x = inputs\n",
+ " skip_x = x\n",
+ " x = self.norm1(x)\n",
+ " x = self.permute1(x)\n",
+ " x = self.token_mixer(x)\n",
+ "\n",
+ " x = self.permute2(x)\n",
+ "\n",
+ " x = self.skip_connection1([x, skip_x])\n",
+ " skip_x = x\n",
+ "\n",
+ " x = self.norm2(x)\n",
+ " x = self.channel_mixer(x)\n",
+ "\n",
+ " x = self.skip_connection2([x, skip_x])\n",
+ "\n",
+ " return x\n",
+ "\n",
+ " def compute_output_shape(self, input_shape):\n",
+ " return input_shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_branch_id(branch_number):\n",
+ " if branch_number == 1:\n",
+ " return 'transformer_block'\n",
+ " else:\n",
+ " return 'transformer_block_%d' % (branch_number - 1)\n",
+ "\n",
+ "def get_model(branch_type, branch_number, version):\n",
+ " backbone_model = tf.keras.models.load_model('vit_cc_backbone_v2.h5', custom_objects={\n",
+ " 'ClassToken': ClassToken,\n",
+ " 'AddPositionEmbs': AddPositionEmbs,\n",
+ " 'MultiHeadSelfAttention': MultiHeadSelfAttention,\n",
+ " 'TransformerBlock': TransformerBlock,\n",
+ " })\n",
+ " y, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " backend_model = tf.keras.models.Model(\n",
+ " inputs=backbone_model.get_layer(index=0).input,\n",
+ " outputs=y\n",
+ " )\n",
+ " backend_model._name='backend_model'\n",
+ " frontend_model = tf.keras.models.load_model(\n",
+ " 'vit_disco_cw_%d_%s_head_precomputed_%s.h5' % (branch_number, branch_type, version),\n",
+ " custom_objects={\n",
+ " 'ClassToken': ClassToken,\n",
+ " 'AddPositionEmbs': AddPositionEmbs,\n",
+ " 'MultiHeadSelfAttention': MultiHeadSelfAttention,\n",
+ " 'TransformerBlock': TransformerBlock,\n",
+ " 'MlpBlock': MlpBlock,\n",
+ " 'MixerBlock': MixerBlock,\n",
+ " }\n",
+ " )\n",
+ " frontend_model._name = 'frontend_model'\n",
+ " model = tf.keras.Sequential([\n",
+ " backend_model,\n",
+ " frontend_model\n",
+ " ])\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DISCO_PATH = 'disco'\n",
+ "CACHE_DIR = os.path.join(DISCO_PATH, 'vit_cache')\n",
+ "\n",
+ "def horizontal_flip(image):\n",
+ " return np.flip(image, axis=1)\n",
+ "\n",
+ "class CCSequence(tf.keras.utils.Sequence):\n",
+ " def __init__(self, split, batch_size):\n",
+ " self.split = split\n",
+ " self.split_len = sum([\n",
+ " 1 if file_name.startswith(self.split) else 0 for file_name in os.listdir(CACHE_DIR)\n",
+ " ])\n",
+ " self.batch_size = batch_size\n",
+ " self.random_permutation = np.random.permutation(self.split_len)\n",
+ "\n",
+ " def __len__(self):\n",
+ " return math.ceil(self.split_len / self.batch_size)\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " self.random_permutation = np.random.permutation(self.split_len)\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " spectrograms = []\n",
+ " images = []\n",
+ " density_maps = []\n",
+ " if self.split == 'test':\n",
+ " index_generator = range(\n",
+ " index * self.batch_size,\n",
+ " min((index + 1) * self.batch_size, self.split_len)\n",
+ " )\n",
+ " else:\n",
+ " index_generator = self.random_permutation[index * self.batch_size:(index + 1) * self.batch_size]\n",
+ " for random_index in index_generator:\n",
+ " all_path = os.path.join(\n",
+ " CACHE_DIR,\n",
+ " '%s_%d.pkl' % (self.split, random_index)\n",
+ " )\n",
+ " with open(all_path, 'rb') as all_file:\n",
+ " data = pickle.load(all_file)\n",
+ " if self.split == 'train' and random.random() < 0.5: # flip augmentation\n",
+ " images.append(horizontal_flip(data['image']))\n",
+ " else:\n",
+ " images.append(data['image'])\n",
+ " density_maps.append(np.sum(data['density_map']))\n",
+ "\n",
+ " return np.array(images), np.array(density_maps)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_mae(branch_type, branch_number, version):\n",
+ " tf.keras.backend.clear_session()\n",
+ " test_sequence = CCSequence('test', 32)\n",
+ " model = get_model(branch_type, branch_number, version)\n",
+ " gt = None\n",
+ " out = None\n",
+ " for i, (images, density_maps) in enumerate(test_sequence):\n",
+ " sys.stdout.write('\\r%d' % (i + 1))\n",
+ " sys.stdout.flush()\n",
+ " if gt is not None:\n",
+ " gt = np.concatenate((gt, density_maps))\n",
+ " else:\n",
+ " gt = density_maps\n",
+ " if out is not None:\n",
+ " out = np.concatenate((out, model(images).numpy().flatten()))\n",
+ " else:\n",
+ " out = model(images).numpy().flatten()\n",
+ " print() # newline\n",
+ " mae = []\n",
+ " img_patches = VIDEO_PATCHES[0] * VIDEO_PATCHES[1]\n",
+ " for i in range(0, gt.shape[0], img_patches):\n",
+ " gt_subset = gt[i:i + img_patches]\n",
+ " out_subset = out[i:i + img_patches]\n",
+ " mae.append(np.abs(np.sum(gt_subset) - np.sum(out_subset)))\n",
+ " return np.mean(np.array(mae))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_maes():\n",
+ " for branch_type in [\n",
+ " 'vit',\n",
+ " 'mlp',\n",
+ " 'cnn_ignore',\n",
+ " 'cnn_add',\n",
+ " 'cnn_project',\n",
+ " 'resmlp',\n",
+ " 'mlp_mixer',\n",
+ " ]:\n",
+ " maes = []\n",
+ " for branch_number in range(1, 12):\n",
+ " mae = get_mae(branch_type, branch_number, 'v1')\n",
+ " print(mae)\n",
+ " maes.append(mae)\n",
+ " print('###', branch_type, maes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "get_maes()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/cw.ipynb b/cw.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bfd3b2164607f36b0232bc3f0f3babb8d2ff99c5
--- /dev/null
+++ b/cw.ipynb
@@ -0,0 +1,579 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [4, 5]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import json\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import sys\n",
+ "from skimage import transform\n",
+ "from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph\n",
+ "from vit_keras import vit\n",
+ "from vit_keras.layers import ClassToken, AddPositionEmbs, MultiHeadSelfAttention, TransformerBlock\n",
+ "\n",
+ "IMAGE_SIZE = 384\n",
+ "PATCH_SIZE = 16\n",
+ "NUM_PATCHES = (384 // PATCH_SIZE) ** 2 + 1\n",
+ "HIDDEN_DIM = 768\n",
+ "VIDEO_PATCHES = (2, 3)\n",
+ "VIDEO_SIZE = (VIDEO_PATCHES[0] * IMAGE_SIZE, VIDEO_PATCHES[1] * IMAGE_SIZE)\n",
+ "MLP_DIM = 3072 # ResMLP\n",
+ "CHANNELS_MLP_DIM = 3072 # MLP-Mixer\n",
+ "TOKENS_MLP_DIM = 384 # MLP-Mixer\n",
+ "PRECOMPUTE_DIR = 'precompute'\n",
+ "PRECOMPUTE_FASHION_MNIST_DIR = os.path.join(PRECOMPUTE_DIR, 'fashion_mnist')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_params(model):\n",
+ " string_list = []\n",
+ " model.summary(print_fn=lambda x: string_list.append(x))\n",
+ " for string in string_list:\n",
+ " if string.startswith('Trainable params:'):\n",
+ " return int(string.split()[-1].replace(',', ''))\n",
+ " return None\n",
+ "\n",
+ "def get_flops(model):\n",
+ " \"\"\"\n",
+ " from https://github.com/tensorflow/tensorflow/issues/32809#issuecomment-768977280\n",
+ " \"\"\"\n",
+ " concrete = tf.function(lambda inputs: model(inputs))\n",
+ " concrete_func = concrete.get_concrete_function(\n",
+ " [tf.TensorSpec([1, *inputs.shape[1:]]) for inputs in model.inputs])\n",
+ " frozen_func, graph_def = convert_variables_to_constants_v2_as_graph(concrete_func)\n",
+ " with tf.Graph().as_default() as graph:\n",
+ " tf.graph_util.import_graph_def(graph_def, name='')\n",
+ " run_meta = tf.compat.v1.RunMetadata()\n",
+ " opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()\n",
+ " flops = tf.compat.v1.profiler.profile(graph=graph, run_meta=run_meta, cmd=\"op\", options=opts)\n",
+ " return flops.total_float_ops"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from https://github.com/leondgarse/Keras_mlp/blob/main/res_mlp.py\n",
+ "\n",
+ "def channel_affine(inputs, use_bias=True, weight_init_value=1, name=''):\n",
+ " ww_init = tfkeras.initializers.Constant(weight_init_value) if weight_init_value != 1 else 'ones'\n",
+ " nn = tf.keras.backend.expand_dims(inputs, 1)\n",
+ " nn = tf.keras.layers.DepthwiseConv2D(1, depthwise_initializer=ww_init, use_bias=use_bias, name=name + 'affine')(nn)\n",
+ " return tf.keras.backend.squeeze(nn, 1)\n",
+ "\n",
+ "def mlp_block(inputs, mlp_dim, activation='gelu', name=''):\n",
+ " affine_inputs = channel_affine(inputs, use_bias=True, name=name + '1_')\n",
+ " nn = tf.keras.layers.Permute((2, 1), name=name + 'permute_1')(affine_inputs)\n",
+ " nn = tf.keras.layers.Dense(nn.shape[-1], name=name + 'dense_1')(nn)\n",
+ " nn = tf.keras.layers.Permute((2, 1), name=name + 'permute_2')(nn)\n",
+ " nn = channel_affine(nn, use_bias=False, name=name + '1_gamma_')\n",
+ " skip_conn = tf.keras.layers.Add(name=name + 'add_1')([nn, affine_inputs])\n",
+ "\n",
+ " affine_skip = channel_affine(skip_conn, use_bias=True, name=name + '2_')\n",
+ " nn = tf.keras.layers.Dense(mlp_dim, name=name + 'dense_2_1')(affine_skip)\n",
+ " nn = tf.keras.layers.Activation(activation, name=name + 'gelu')(nn)\n",
+ " nn = tf.keras.layers.Dense(inputs.shape[-1], name=name + 'dense_2_2')(nn)\n",
+ " nn = channel_affine(nn, use_bias=False, name=name + '2_gamma_')\n",
+ " nn = tf.keras.layers.Add(name=name + 'add_2')([nn, affine_skip])\n",
+ " return nn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from https://github.com/Benjamin-Etheredge/mlp-mixer-keras/blob/main/mlp_mixer_keras/mlp_mixer.py\n",
+ "\n",
+ "class MlpBlock(tf.keras.layers.Layer):\n",
+ " def __init__(self, dim, hidden_dim, activation=None, **kwargs):\n",
+ " super(MlpBlock, self).__init__(**kwargs)\n",
+ "\n",
+ " if activation is None:\n",
+ " activation = tf.keras.activations.gelu\n",
+ "\n",
+ " self.dim = dim\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.activation = activation\n",
+ " self.dense1 = tf.keras.layers.Dense(hidden_dim)\n",
+ " self.activation = tf.keras.layers.Activation(activation)\n",
+ " self.dense2 = tf.keras.layers.Dense(dim)\n",
+ "\n",
+ " def call(self, inputs):\n",
+ " x = inputs\n",
+ " x = self.dense1(x)\n",
+ " x = self.activation(x)\n",
+ " x = self.dense2(x)\n",
+ " return x\n",
+ "\n",
+ " def compute_output_shape(self, input_signature):\n",
+ " return (input_signature[0], self.dim)\n",
+ "\n",
+ " def get_config(self):\n",
+ " config = super(MlpBlock, self).get_config().copy()\n",
+ " config.update({\n",
+ " 'dim': self.dim,\n",
+ " 'hidden_dim': self.hidden_dim,\n",
+ " 'activation': self.activation,\n",
+ " })\n",
+ " return config\n",
+ "\n",
+ "class MixerBlock(tf.keras.layers.Layer):\n",
+ " def __init__(\n",
+ " self,\n",
+ " num_patches,\n",
+ " channel_dim,\n",
+ " token_mixer_hidden_dim,\n",
+ " channel_mixer_hidden_dim=None,\n",
+ " activation=None,\n",
+ " **kwargs\n",
+ " ):\n",
+ " super(MixerBlock, self).__init__(**kwargs)\n",
+ "\n",
+ " if activation is None:\n",
+ " activation = tf.keras.activations.gelu\n",
+ "\n",
+ " if channel_mixer_hidden_dim is None:\n",
+ " channel_mixer_hidden_dim = token_mixer_hidden_dim\n",
+ "\n",
+ " self.num_patches = num_patches\n",
+ " self.channel_dim = channel_dim\n",
+ " self.token_mixer_hidden_dim = token_mixer_hidden_dim\n",
+ " self.channel_mixer_hidden_dim = channel_mixer_hidden_dim\n",
+ " self.activation = activation\n",
+ " \n",
+ " self.norm1 = tf.keras.layers.LayerNormalization(axis=1)\n",
+ " self.permute1 = tf.keras.layers.Permute((2, 1))\n",
+ " self.token_mixer = MlpBlock(num_patches, token_mixer_hidden_dim, name='token_mixer')\n",
+ "\n",
+ " self.permute2 = tf.keras.layers.Permute((2, 1))\n",
+ " self.norm2 = tf.keras.layers.LayerNormalization(axis=1)\n",
+ " self.channel_mixer = MlpBlock(channel_dim, channel_mixer_hidden_dim, name='channel_mixer')\n",
+ "\n",
+ " self.skip_connection1 = tf.keras.layers.Add()\n",
+ " self.skip_connection2 = tf.keras.layers.Add()\n",
+ "\n",
+ " def get_config(self):\n",
+ " config = super(MixerBlock, self).get_config().copy()\n",
+ " config.update({\n",
+ " 'num_patches': self.num_patches,\n",
+ " 'channel_dim': self.channel_dim,\n",
+ " 'token_mixer_hidden_dim': self.token_mixer_hidden_dim,\n",
+ " 'channel_mixer_hidden_dim': self.channel_mixer_hidden_dim,\n",
+ " 'activation': self.activation,\n",
+ " })\n",
+ " return config\n",
+ "\n",
+ " def call(self, inputs):\n",
+ " x = inputs\n",
+ " skip_x = x\n",
+ " x = self.norm1(x)\n",
+ " x = self.permute1(x)\n",
+ " x = self.token_mixer(x)\n",
+ "\n",
+ " x = self.permute2(x)\n",
+ "\n",
+ " x = self.skip_connection1([x, skip_x])\n",
+ " skip_x = x\n",
+ "\n",
+ " x = self.norm2(x)\n",
+ " x = self.channel_mixer(x)\n",
+ "\n",
+ " x = self.skip_connection2([x, skip_x])\n",
+ "\n",
+ " return x\n",
+ "\n",
+ " def compute_output_shape(self, input_shape):\n",
+ " return input_shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_model(branch_number, head_type):\n",
+ " model_input = tf.keras.Input(shape=(NUM_PATCHES, HIDDEN_DIM))\n",
+ " y = model_input\n",
+ " if head_type == 'resmlp':\n",
+ " y = mlp_block(y, mlp_dim=MLP_DIM, name='mlp_mixer')\n",
+ " y = tf.keras.layers.GlobalAveragePooling1D()(y)\n",
+ " elif head_type == 'mlp':\n",
+ " y = tf.keras.layers.LayerNormalization(\n",
+ " epsilon=1e-6,\n",
+ " name='Transformer/encoder_norm_x'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Lambda(lambda v: v[:, 0], name='ExtractToken_x')(y)\n",
+ " elif head_type == 'vit':\n",
+ " y, _ = TransformerBlock(\n",
+ " num_heads=12,\n",
+ " mlp_dim=3072,\n",
+ " dropout=0.1,\n",
+ " name='Transformer/encoderblock_x'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.LayerNormalization(\n",
+ " epsilon=1e-6,\n",
+ " name='Transformer/encoder_norm_x'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Lambda(lambda v: v[:, 0], name='ExtractToken_x')(y)\n",
+ " elif head_type == 'cnn_ignore':\n",
+ " channels = HIDDEN_DIM\n",
+ " width = height = IMAGE_SIZE // PATCH_SIZE\n",
+ " y = tf.keras.layers.Lambda(lambda v: v[:, 1:], name='RemoveToken')(y)\n",
+ " y = tf.keras.layers.Reshape((width, height, channels), name='cnn_reshape')(y)\n",
+ " y = tf.keras.layers.Conv2D(\n",
+ " filters=16,\n",
+ " kernel_size=(3, 3),\n",
+ " activation='elu',\n",
+ " padding='same'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.MaxPool2D(pool_size=(2, 2))(y)\n",
+ " y = tf.keras.layers.Flatten()(y)\n",
+ " elif head_type == 'cnn_add': \n",
+ " channels = HIDDEN_DIM\n",
+ " width = height = IMAGE_SIZE // PATCH_SIZE\n",
+ "\n",
+ " y1 = tf.keras.layers.Lambda(lambda v: v[:, 1:], name='RemoveToken_x')(y)\n",
+ " y1 = tf.keras.layers.Reshape((width, height, channels), name='cnn_reshape')(y1)\n",
+ "\n",
+ " y2 = tf.keras.layers.Lambda(lambda v: v[:, 0], name='ExtractToken_x')(y)\n",
+ " y2 = tf.keras.layers.RepeatVector(width * height)(y2)\n",
+ " y2 = tf.keras.layers.Reshape((width, height, channels), name='cls_reshape')(y2)\n",
+ "\n",
+ " y = tf.keras.layers.Add()([y1, y2])\n",
+ "\n",
+ " y = tf.keras.layers.Conv2D(\n",
+ " filters=16,\n",
+ " kernel_size=(3, 3),\n",
+ " activation='elu',\n",
+ " padding='same'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.MaxPool2D(pool_size=(2, 2))(y)\n",
+ " y = tf.keras.layers.Flatten()(y)\n",
+ " elif head_type == 'cnn_project':\n",
+ " channels = HIDDEN_DIM\n",
+ " width = height = IMAGE_SIZE // PATCH_SIZE\n",
+ "\n",
+ " y1 = tf.keras.layers.Lambda(lambda v: v[:, 1:], name='RemoveToken_x')(y)\n",
+ " y1 = tf.keras.layers.Reshape((width, height, channels), name='cnn_reshape')(y1)\n",
+ "\n",
+ " y2 = tf.keras.layers.Lambda(lambda v: v[:, 0], name='ExtractToken_x')(y)\n",
+ " y2 = tf.keras.layers.RepeatVector(width * height)(y2)\n",
+ " y2 = tf.keras.layers.Reshape((width, height, channels), name='cls_reshape')(y2)\n",
+ "\n",
+ " y = tf.keras.layers.Concatenate()([y1, y2])\n",
+ "\n",
+ " y = tf.keras.layers.Conv2D(\n",
+ " filters=16,\n",
+ " kernel_size=(3, 3),\n",
+ " activation='elu',\n",
+ " padding='same'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.MaxPool2D(pool_size=(2, 2))(y)\n",
+ " y = tf.keras.layers.Flatten()(y)\n",
+ " elif head_type == 'mlp_mixer':\n",
+ " num_patches = (IMAGE_SIZE // PATCH_SIZE) ** 2 + 1\n",
+ " y = MixerBlock(\n",
+ " num_patches=num_patches,\n",
+ " channel_dim=HIDDEN_DIM,\n",
+ " token_mixer_hidden_dim=TOKENS_MLP_DIM,\n",
+ " channel_mixer_hidden_dim=CHANNELS_MLP_DIM\n",
+ " )(y)\n",
+ " y = tf.keras.layers.GlobalAveragePooling1D()(y)\n",
+ "\n",
+ " # MLP head\n",
+ " initializer = tf.keras.initializers.he_normal()\n",
+ " regularizer = tf.keras.regularizers.l2()\n",
+ " y = tf.keras.layers.Dense(\n",
+ " units=256,\n",
+ " activation='elu',\n",
+ " kernel_initializer=initializer,\n",
+ " kernel_regularizer=regularizer\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Dropout(0.5)(y)\n",
+ " y = tf.keras.layers.Dense(\n",
+ " units=256,\n",
+ " activation='elu',\n",
+ " kernel_initializer=initializer,\n",
+ " kernel_regularizer=regularizer\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Dropout(0.5)(y)\n",
+ " y = tf.keras.layers.Dense(\n",
+ " units=10,\n",
+ " activation='softmax',\n",
+ " kernel_initializer=initializer,\n",
+ " kernel_regularizer=regularizer\n",
+ " )(y)\n",
+ "\n",
+ " model = tf.keras.models.Model(\n",
+ " inputs=model_input,\n",
+ " outputs=y\n",
+ " )\n",
+ "\n",
+ " model.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
+ " loss='categorical_crossentropy',\n",
+ " metrics=['accuracy']\n",
+ " )\n",
+ "\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class FashionMNISTSequence(tf.keras.utils.Sequence):\n",
+ " def __init__(self, split, branch_number, batch_size):\n",
+ " self.split = split\n",
+ " self.branch_number = branch_number\n",
+ " self.batch_size = batch_size * NUM_GPUS\n",
+ " self.dir = PRECOMPUTE_FASHION_MNIST_DIR\n",
+ " self.count = sum([\n",
+ " 1 if file_name.startswith('%s_branch%d_sample' % (\n",
+ " self.split,\n",
+ " self.branch_number\n",
+ " )) else 0 for file_name in os.listdir(self.dir)\n",
+ " ])\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __len__(self):\n",
+ " return math.ceil(self.count / self.batch_size)\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " features = []\n",
+ " labels = []\n",
+ " for i in self.random_permutation[index * self.batch_size:(index + 1) * self.batch_size]:\n",
+ " cache_file_path = os.path.join(\n",
+ " self.dir,\n",
+ " '%s_branch%d_sample%d.pkl' % (self.split, self.branch_number, i)\n",
+ " )\n",
+ " with open(cache_file_path, 'rb') as cache_file:\n",
+ " contents = pickle.load(cache_file)\n",
+ " features.append(contents['features'])\n",
+ " labels.append(contents['label'])\n",
+ " return np.array(features), np.array(labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train(max_epochs, branch_number, head_type, batch_size=64):\n",
+ " tf.keras.backend.clear_session()\n",
+ "\n",
+ " with DISTRIBUTED_STRATEGY.scope():\n",
+ " model = get_model(branch_number, head_type)\n",
+ " branch_params = get_params(model) / 10 ** 6\n",
+ " total_flops = get_flops(model) / 10 ** 9\n",
+ "\n",
+ " lr_reduce = tf.keras.callbacks.ReduceLROnPlateau(\n",
+ " monitor='val_accuracy',\n",
+ " factor=0.6,\n",
+ " patience=2,\n",
+ " verbose=1,\n",
+ " mode='max',\n",
+ " min_lr=1e-7\n",
+ " )\n",
+ "\n",
+ " early_stop = tf.keras.callbacks.EarlyStopping(\n",
+ " monitor='val_accuracy',\n",
+ " patience=5,\n",
+ " verbose=1,\n",
+ " mode='max'\n",
+ " )\n",
+ "\n",
+ " save_model_checkpoint_file = 'vit_shtb_cw_%d_%s_head_precomputed_v1.h5' % (branch_number, head_type)\n",
+ "\n",
+ " checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
+ " save_model_checkpoint_file,\n",
+ " monitor='val_accuracy',\n",
+ " verbose=1,\n",
+ " save_weights_only=False,\n",
+ " save_best_only=True,\n",
+ " mode='max',\n",
+ " save_freq='epoch'\n",
+ " )\n",
+ "\n",
+ " history = model.fit(\n",
+ " FashionMNISTSequence('train', branch_number, batch_size),\n",
+ " validation_data=FashionMNISTSequence('val', branch_number, batch_size),\n",
+ " epochs=max_epochs,\n",
+ " shuffle=True,\n",
+ " callbacks=[\n",
+ " lr_reduce,\n",
+ " early_stop,\n",
+ " checkpoint\n",
+ " ],\n",
+ " verbose=1\n",
+ " )\n",
+ "\n",
+ " test_accuracy = model.evaluate(FashionMNISTSequence('test', branch_number, batch_size))\n",
+ "\n",
+ " return model, test_accuracy, branch_params, total_flops"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def save_results(results, results_path):\n",
+ " with open(results_path, 'w') as results_file:\n",
+ " results_file.write(json.dumps(results))\n",
+ "\n",
+ "def print_results(results_path):\n",
+ " with open(results_path, 'r') as results_file:\n",
+ " print(json.loads(results_file.read()))\n",
+ "\n",
+ "def get_results_path(head_type):\n",
+ " return 'shtb_%s.json' % head_type\n",
+ "\n",
+ "def run_experiment(head_type):\n",
+ " results = []\n",
+ " for i in reversed(range(1, 12)):\n",
+ " model, test_accuracy, branch_params, total_flops = train(100, i, head_type) \n",
+ " results.append({\n",
+ " 'exit': i,\n",
+ " 'test_accuracy': test_accuracy,\n",
+ " 'branch_params': branch_params,\n",
+ " 'total_flops': total_flops,\n",
+ " })\n",
+ " results_path = get_results_path(head_type)\n",
+ " save_results(results, results_path)\n",
+ " print_results(results_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_experiment('vit')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_experiment('resmlp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_experiment('mlp_mixer')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_experiment('mlp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_experiment('cnn_ignore')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_experiment('cnn_add')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_experiment('cnn_project')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ee.ipynb b/ee.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ea8125408d211d17d2dfbc1bd33f3889699f2af9
--- /dev/null
+++ b/ee.ipynb
@@ -0,0 +1,677 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [4, 5, 6, 7]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import random\n",
+ "import sys\n",
+ "from skimage import transform\n",
+ "from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph\n",
+ "from vit_keras import vit\n",
+ "from vit_keras.layers import ClassToken, AddPositionEmbs, MultiHeadSelfAttention, TransformerBlock\n",
+ "\n",
+ "IMAGE_SIZE = 384\n",
+ "HIDDEN_DIM = 768\n",
+ "PATCH_SIZE = 16\n",
+ "MLP_DIM = 3072 # ResMLP\n",
+ "CHANNELS_MLP_DIM = 3072 # MLP-Mixer\n",
+ "TOKENS_MLP_DIM = 384 # MLP-Mixer\n",
+ "VIDEO_PATCHES = (2, 3) # how many sub-images there are in each image for crowd counting\n",
+ "VIDEO_SIZE = (VIDEO_PATCHES[0] * IMAGE_SIZE, VIDEO_PATCHES[1] * IMAGE_SIZE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_params(model):\n",
+ " string_list = []\n",
+ " model.summary(print_fn=lambda x: string_list.append(x))\n",
+ " for string in string_list:\n",
+ " if string.startswith('Trainable params:'):\n",
+ " return int(string.split()[-1].replace(',', ''))\n",
+ " return None\n",
+ "\n",
+ "def get_flops(model):\n",
+ " \"\"\"\n",
+ " from https://github.com/tensorflow/tensorflow/issues/32809#issuecomment-768977280\n",
+ " \"\"\"\n",
+ " concrete = tf.function(lambda inputs: model(inputs))\n",
+ " concrete_func = concrete.get_concrete_function(\n",
+ " [tf.TensorSpec([1, *inputs.shape[1:]]) for inputs in model.inputs])\n",
+ " frozen_func, graph_def = convert_variables_to_constants_v2_as_graph(concrete_func)\n",
+ " with tf.Graph().as_default() as graph:\n",
+ " tf.graph_util.import_graph_def(graph_def, name='')\n",
+ " run_meta = tf.compat.v1.RunMetadata()\n",
+ " opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()\n",
+ " flops = tf.compat.v1.profiler.profile(graph=graph, run_meta=run_meta, cmd=\"op\", options=opts)\n",
+ " return flops.total_float_ops"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from https://github.com/leondgarse/Keras_mlp/blob/main/res_mlp.py\n",
+ "\n",
+ "def channel_affine(inputs, use_bias=True, weight_init_value=1, name=''):\n",
+ " ww_init = tfkeras.initializers.Constant(weight_init_value) if weight_init_value != 1 else 'ones'\n",
+ " nn = tf.keras.backend.expand_dims(inputs, 1)\n",
+ " nn = tf.keras.layers.DepthwiseConv2D(1, depthwise_initializer=ww_init, use_bias=use_bias, name=name + 'affine')(nn)\n",
+ " return tf.keras.backend.squeeze(nn, 1)\n",
+ "\n",
+ "def mlp_block(inputs, mlp_dim, activation='gelu', name=''):\n",
+ " affine_inputs = channel_affine(inputs, use_bias=True, name=name + '1_')\n",
+ " nn = tf.keras.layers.Permute((2, 1), name=name + 'permute_1')(affine_inputs)\n",
+ " nn = tf.keras.layers.Dense(nn.shape[-1], name=name + 'dense_1')(nn)\n",
+ " nn = tf.keras.layers.Permute((2, 1), name=name + 'permute_2')(nn)\n",
+ " nn = channel_affine(nn, use_bias=False, name=name + '1_gamma_')\n",
+ " skip_conn = tf.keras.layers.Add(name=name + 'add_1')([nn, affine_inputs])\n",
+ "\n",
+ " affine_skip = channel_affine(skip_conn, use_bias=True, name=name + '2_')\n",
+ " nn = tf.keras.layers.Dense(mlp_dim, name=name + 'dense_2_1')(affine_skip)\n",
+ " nn = tf.keras.layers.Activation(activation, name=name + 'gelu')(nn)\n",
+ " nn = tf.keras.layers.Dense(inputs.shape[-1], name=name + 'dense_2_2')(nn)\n",
+ " nn = channel_affine(nn, use_bias=False, name=name + '2_gamma_')\n",
+ " nn = tf.keras.layers.Add(name=name + 'add_2')([nn, affine_skip])\n",
+ " return nn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from https://github.com/Benjamin-Etheredge/mlp-mixer-keras/blob/main/mlp_mixer_keras/mlp_mixer.py\n",
+ "\n",
+ "class MlpBlock(tf.keras.layers.Layer):\n",
+ " def __init__(self, dim, hidden_dim, activation=None, **kwargs):\n",
+ " super(MlpBlock, self).__init__(**kwargs)\n",
+ "\n",
+ " if activation is None:\n",
+ " activation = tf.keras.activations.gelu\n",
+ "\n",
+ " self.dim = dim\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.activation = activation\n",
+ " self.dense1 = tf.keras.layers.Dense(hidden_dim)\n",
+ " self.activation = tf.keras.layers.Activation(activation)\n",
+ " self.dense2 = tf.keras.layers.Dense(dim)\n",
+ "\n",
+ " def call(self, inputs):\n",
+ " x = inputs\n",
+ " x = self.dense1(x)\n",
+ " x = self.activation(x)\n",
+ " x = self.dense2(x)\n",
+ " return x\n",
+ "\n",
+ " def compute_output_shape(self, input_signature):\n",
+ " return (input_signature[0], self.dim)\n",
+ "\n",
+ " def get_config(self):\n",
+ " config = super(MlpBlock, self).get_config().copy()\n",
+ " config.update({\n",
+ " 'dim': self.dim,\n",
+ " 'hidden_dim': self.hidden_dim,\n",
+ " 'activation': self.activation,\n",
+ " })\n",
+ " return config\n",
+ "\n",
+ "class MixerBlock(tf.keras.layers.Layer):\n",
+ " def __init__(\n",
+ " self,\n",
+ " num_patches,\n",
+ " channel_dim,\n",
+ " token_mixer_hidden_dim,\n",
+ " channel_mixer_hidden_dim=None,\n",
+ " activation=None,\n",
+ " **kwargs\n",
+ " ):\n",
+ " super(MixerBlock, self).__init__(**kwargs)\n",
+ "\n",
+ " if activation is None:\n",
+ " activation = tf.keras.activations.gelu\n",
+ "\n",
+ " if channel_mixer_hidden_dim is None:\n",
+ " channel_mixer_hidden_dim = token_mixer_hidden_dim\n",
+ "\n",
+ " self.num_patches = num_patches\n",
+ " self.channel_dim = channel_dim\n",
+ " self.token_mixer_hidden_dim = token_mixer_hidden_dim\n",
+ " self.channel_mixer_hidden_dim = channel_mixer_hidden_dim\n",
+ " self.activation = activation\n",
+ " \n",
+ " self.norm1 = tf.keras.layers.LayerNormalization(axis=1)\n",
+ " self.permute1 = tf.keras.layers.Permute((2, 1))\n",
+ " self.token_mixer = MlpBlock(num_patches, token_mixer_hidden_dim, name='token_mixer')\n",
+ "\n",
+ " self.permute2 = tf.keras.layers.Permute((2, 1))\n",
+ " self.norm2 = tf.keras.layers.LayerNormalization(axis=1)\n",
+ " self.channel_mixer = MlpBlock(channel_dim, channel_mixer_hidden_dim, name='channel_mixer')\n",
+ "\n",
+ " self.skip_connection1 = tf.keras.layers.Add()\n",
+ " self.skip_connection2 = tf.keras.layers.Add()\n",
+ "\n",
+ " def get_config(self):\n",
+ " config = super(MixerBlock, self).get_config().copy()\n",
+ " config.update({\n",
+ " 'num_patches': self.num_patches,\n",
+ " 'channel_dim': self.channel_dim,\n",
+ " 'token_mixer_hidden_dim': self.token_mixer_hidden_dim,\n",
+ " 'channel_mixer_hidden_dim': self.channel_mixer_hidden_dim,\n",
+ " 'activation': self.activation,\n",
+ " })\n",
+ " return config\n",
+ "\n",
+ " def call(self, inputs):\n",
+ " x = inputs\n",
+ " skip_x = x\n",
+ " x = self.norm1(x)\n",
+ " x = self.permute1(x)\n",
+ " x = self.token_mixer(x)\n",
+ "\n",
+ " x = self.permute2(x)\n",
+ "\n",
+ " x = self.skip_connection1([x, skip_x])\n",
+ " skip_x = x\n",
+ "\n",
+ " x = self.norm2(x)\n",
+ " x = self.channel_mixer(x)\n",
+ "\n",
+ " x = self.skip_connection2([x, skip_x])\n",
+ "\n",
+ " return x\n",
+ "\n",
+ " def compute_output_shape(self, input_shape):\n",
+ " return input_shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_branch_id(branch_number):\n",
+ " if branch_number == 1:\n",
+ " return 'transformer_block'\n",
+ " else:\n",
+ " return 'transformer_block_%d' % (branch_number - 1)\n",
+ "\n",
+ "def get_model(branch_numbers, head_type, dataset):\n",
+ " if dataset == 'cifar10':\n",
+ " model_file_name = 'vit_cifar10_v1.h5'\n",
+ " elif dataset == 'cifar100':\n",
+ " model_file_name = 'vit_cifar100_v1.h5'\n",
+ " elif dataset == 'disco':\n",
+ " model_file_name = 'vit_cc_backbone_v2.h5'\n",
+ " else:\n",
+ " model_file_name = None\n",
+ " \n",
+ " backbone_model = tf.keras.models.load_model(model_file_name, custom_objects={\n",
+ " 'ClassToken': ClassToken,\n",
+ " 'AddPositionEmbs': AddPositionEmbs,\n",
+ " 'MultiHeadSelfAttention': MultiHeadSelfAttention,\n",
+ " 'TransformerBlock': TransformerBlock,\n",
+ " })\n",
+ "\n",
+ " outputs = []\n",
+ " for i, branch_number in enumerate(branch_numbers):\n",
+ " y, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " if head_type == 'resmlp':\n",
+ " y = mlp_block(y, mlp_dim=MLP_DIM, name='mlp_mixer_%d' % i)\n",
+ " y = tf.keras.layers.GlobalAveragePooling1D()(y)\n",
+ " elif head_type == 'mlp':\n",
+ " y = tf.keras.layers.LayerNormalization(\n",
+ " epsilon=1e-6,\n",
+ " name='Transformer/encoder_norm_x_%d' % i\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Lambda(lambda v: v[:, 0], name='ExtractToken_x_%d' % i)(y)\n",
+ " elif head_type == 'vit':\n",
+ " y, _ = TransformerBlock(\n",
+ " num_heads=12,\n",
+ " mlp_dim=3072,\n",
+ " dropout=0.1,\n",
+ " name='Transformer/encoderblock_x_%d' % i\n",
+ " )(y)\n",
+ " y = tf.keras.layers.LayerNormalization(\n",
+ " epsilon=1e-6,\n",
+ " name='Transformer/encoder_norm_x_%d' % i\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Lambda(lambda v: v[:, 0], name='ExtractToken_x_%d' % i)(y)\n",
+ " elif head_type == 'cnn_ignore':\n",
+ " channels = HIDDEN_DIM\n",
+ " width = height = IMAGE_SIZE // PATCH_SIZE\n",
+ " y = tf.keras.layers.Lambda(lambda v: v[:, 1:], name='RemoveToken_%d' % i)(y)\n",
+ " y = tf.keras.layers.Reshape((width, height, channels), name='cnn_reshape_%d' % i)(y)\n",
+ " y = tf.keras.layers.Conv2D(\n",
+ " filters=16,\n",
+ " kernel_size=(3, 3),\n",
+ " activation='elu',\n",
+ " padding='same'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.MaxPool2D(pool_size=(2, 2))(y)\n",
+ " y = tf.keras.layers.Flatten()(y)\n",
+ " elif head_type == 'cnn_add': \n",
+ " channels = HIDDEN_DIM\n",
+ " width = height = IMAGE_SIZE // PATCH_SIZE\n",
+ "\n",
+ " y1 = tf.keras.layers.Lambda(lambda v: v[:, 1:], name='RemoveToken_x_%d' % i)(y)\n",
+ " y1 = tf.keras.layers.Reshape((width, height, channels), name='cnn_reshape_%d' % i)(y1)\n",
+ "\n",
+ " y2 = tf.keras.layers.Lambda(lambda v: v[:, 0], name='ExtractToken_x_%d' % i)(y)\n",
+ " y2 = tf.keras.layers.RepeatVector(width * height)(y2)\n",
+ " y2 = tf.keras.layers.Reshape((width, height, channels), name='cls_reshape_%d' % i)(y2)\n",
+ "\n",
+ " y = tf.keras.layers.Add()([y1, y2])\n",
+ "\n",
+ " y = tf.keras.layers.Conv2D(\n",
+ " filters=16,\n",
+ " kernel_size=(3, 3),\n",
+ " activation='elu',\n",
+ " padding='same'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.MaxPool2D(pool_size=(2, 2))(y)\n",
+ " y = tf.keras.layers.Flatten()(y)\n",
+ " elif head_type == 'cnn_project':\n",
+ " channels = HIDDEN_DIM\n",
+ " width = height = IMAGE_SIZE // PATCH_SIZE\n",
+ "\n",
+ " y1 = tf.keras.layers.Lambda(lambda v: v[:, 1:], name='RemoveToken_x_%d' % i)(y)\n",
+ " y1 = tf.keras.layers.Reshape((width, height, channels), name='cnn_reshape_%d' % i)(y1)\n",
+ "\n",
+ " y2 = tf.keras.layers.Lambda(lambda v: v[:, 0], name='ExtractToken_x_%d' % i)(y)\n",
+ " y2 = tf.keras.layers.RepeatVector(width * height)(y2)\n",
+ " y2 = tf.keras.layers.Reshape((width, height, channels), name='cls_reshape_%d' % i)(y2)\n",
+ "\n",
+ " y = tf.keras.layers.Concatenate()([y1, y2])\n",
+ "\n",
+ " y = tf.keras.layers.Conv2D(\n",
+ " filters=16,\n",
+ " kernel_size=(3, 3),\n",
+ " activation='elu',\n",
+ " padding='same'\n",
+ " )(y)\n",
+ " y = tf.keras.layers.MaxPool2D(pool_size=(2, 2))(y)\n",
+ " y = tf.keras.layers.Flatten()(y)\n",
+ " elif head_type == 'mlp_mixer':\n",
+ " num_patches = (IMAGE_SIZE // PATCH_SIZE) ** 2 + 1\n",
+ " y = MixerBlock(\n",
+ " num_patches=num_patches,\n",
+ " channel_dim=HIDDEN_DIM,\n",
+ " token_mixer_hidden_dim=TOKENS_MLP_DIM,\n",
+ " channel_mixer_hidden_dim=CHANNELS_MLP_DIM\n",
+ " )(y)\n",
+ " y = tf.keras.layers.GlobalAveragePooling1D()(y)\n",
+ "\n",
+ " if dataset == 'cifar10':\n",
+ " output_units = 10\n",
+ " output_activation = 'softmax'\n",
+ " elif dataset == 'cifar100':\n",
+ " output_units = 100\n",
+ " output_activation = 'softmax'\n",
+ " elif dataset == 'disco':\n",
+ " output_units = 1\n",
+ " output_activation = None\n",
+ " else:\n",
+ " output_units = None\n",
+ " output_activation = None\n",
+ "\n",
+ " # MLP head\n",
+ " initializer = tf.keras.initializers.he_normal()\n",
+ " regularizer = tf.keras.regularizers.l2()\n",
+ " y = tf.keras.layers.Dense(\n",
+ " units=256,\n",
+ " activation='elu',\n",
+ " kernel_initializer=initializer,\n",
+ " kernel_regularizer=regularizer\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Dropout(0.5)(y)\n",
+ " y = tf.keras.layers.Dense(\n",
+ " units=256,\n",
+ " activation='elu',\n",
+ " kernel_initializer=initializer,\n",
+ " kernel_regularizer=regularizer\n",
+ " )(y)\n",
+ " y = tf.keras.layers.Dropout(0.5)(y)\n",
+ " y = tf.keras.layers.Dense(\n",
+ " units=output_units,\n",
+ " activation=output_activation,\n",
+ " kernel_initializer=initializer,\n",
+ " kernel_regularizer=regularizer\n",
+ " )(y)\n",
+ " outputs.append(y)\n",
+ "\n",
+ " outputs.append(backbone_model.get_layer(index=-1).output)\n",
+ " model = tf.keras.models.Model(\n",
+ " inputs=backbone_model.get_layer(index=0).input,\n",
+ " outputs=outputs\n",
+ " )\n",
+ "\n",
+ " if dataset == 'cifar10' or dataset == 'cifar100':\n",
+ " loss_type = 'categorical_crossentropy'\n",
+ " metric_type = 'accuracy'\n",
+ " elif dataset == 'disco':\n",
+ " loss_type = 'mean_absolute_error'\n",
+ " metric_type = 'mean_absolute_error'\n",
+ " \n",
+ " model.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
+ " loss=[loss_type] * (len(branch_numbers) + 1),\n",
+ " loss_weights=[1] * len(branch_numbers) + [2],\n",
+ " metrics=[metric_type]\n",
+ " )\n",
+ "\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cache_split(cache_dir, images, labels, split):\n",
+ " for i in range(images.shape[0]):\n",
+ " if (i + 1) % 100 == 0:\n",
+ " sys.stdout.write('\\r%d' % (i + 1))\n",
+ " sys.stdout.flush()\n",
+ " with open(os.path.join(cache_dir, '%s_%d.pkl' % (split, i)), 'wb') as cache_file:\n",
+ " pickle.dump({\n",
+ " 'image': transform.resize(images[i], (IMAGE_SIZE, IMAGE_SIZE)),\n",
+ " 'label': labels[i],\n",
+ " }, cache_file)\n",
+ " print() # newline\n",
+ "\n",
+ "def cache_all(dataset):\n",
+ " if dataset == 'cifar10':\n",
+ " (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()\n",
+ " elif dataset == 'cifar100':\n",
+ " (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar100.load_data()\n",
+ " else:\n",
+ " raise Exception('Unknown dataset: %s' % dataset)\n",
+ "\n",
+ " train_labels = tf.keras.utils.to_categorical(train_labels)\n",
+ " test_labels = tf.keras.utils.to_categorical(test_labels)\n",
+ "\n",
+ " val_index = int(len(train_images) * 0.8)\n",
+ " val_images = train_images[val_index:]\n",
+ " val_labels = train_labels[val_index:]\n",
+ " train_images = train_images[:val_index]\n",
+ " train_labels = train_labels[:val_index]\n",
+ "\n",
+ " cache_split(dataset, train_images, train_labels, 'train')\n",
+ " cache_split(dataset, val_images, val_labels, 'val')\n",
+ " cache_split(dataset, test_images, test_labels, 'test')\n",
+ "\n",
+ "class CIFARSequence(tf.keras.utils.Sequence):\n",
+ " def __init__(self, split, batch_size, dataset):\n",
+ " self.split = split\n",
+ " self.batch_size = batch_size * NUM_GPUS\n",
+ " self.cache_dir = dataset\n",
+ " self.count = sum([1 if file_name.startswith(split) else 0 for file_name in os.listdir(self.cache_dir)])\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __len__(self):\n",
+ " return math.ceil(self.count / self.batch_size)\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " images = []\n",
+ " labels = []\n",
+ " for i in self.random_permutation[index * self.batch_size:(index + 1) * self.batch_size]:\n",
+ " with open(os.path.join(self.cache_dir, '%s_%d.pkl' % (self.split, i)), 'rb') as cache_file:\n",
+ " contents = pickle.load(cache_file)\n",
+ " images.append(contents['image'])\n",
+ " labels.append(contents['label'])\n",
+ " return np.array(images), np.array(labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def horizontal_flip(image):\n",
+ " return np.flip(image, axis=1)\n",
+ "\n",
+ "class DISCOSequence(tf.keras.utils.Sequence):\n",
+ " def __init__(self, split, batch_size):\n",
+ " self.split = split\n",
+ " self.cache_dir = os.path.join('disco', 'vit_cache')\n",
+ " self.split_len = sum([\n",
+ " 1 if file_name.startswith(self.split) else 0 for file_name in os.listdir(self.cache_dir)\n",
+ " ])\n",
+ " self.batch_size = batch_size * NUM_GPUS\n",
+ " self.random_permutation = np.random.permutation(self.split_len)\n",
+ "\n",
+ " def __len__(self):\n",
+ " return math.ceil(self.split_len / self.batch_size)\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " self.random_permutation = np.random.permutation(self.split_len)\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " spectrograms = []\n",
+ " images = []\n",
+ " density_maps = []\n",
+ " if self.split == 'test':\n",
+ " index_generator = range(\n",
+ " index * self.batch_size,\n",
+ " min((index + 1) * self.batch_size, self.split_len - 1)\n",
+ " )\n",
+ " else:\n",
+ " index_generator = self.random_permutation[index * self.batch_size:(index + 1) * self.batch_size]\n",
+ " for random_index in index_generator:\n",
+ " all_path = os.path.join(\n",
+ " self.cache_dir,\n",
+ " '%s_%d.pkl' % (self.split, random_index)\n",
+ " )\n",
+ " with open(all_path, 'rb') as all_file:\n",
+ " data = pickle.load(all_file)\n",
+ " if self.split == 'train' and random.random() < 0.5: # flip augmentation\n",
+ " images.append(horizontal_flip(data['image']))\n",
+ " else:\n",
+ " images.append(data['image'])\n",
+ " density_maps.append(np.sum(data['density_map']))\n",
+ "\n",
+ " return np.array(images), np.array(density_maps)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def test_cc(model, test_sequence, total_branches):\n",
+ " gt = None\n",
+ " outs = []\n",
+ " for i, (images, density_maps) in enumerate(test_sequence):\n",
+ " sys.stdout.write('\\r%d' % (i + 1))\n",
+ " sys.stdout.flush()\n",
+ " if gt is not None:\n",
+ " gt = np.concatenate((gt, density_maps))\n",
+ " else:\n",
+ " gt = density_maps\n",
+ " output = model(images)\n",
+ " for j in range(total_branches):\n",
+ " if i == 0:\n",
+ " outs.append(output[j].numpy().flatten())\n",
+ " else:\n",
+ " outs[j] = np.concatenate((outs[j], output[j].numpy().flatten()))\n",
+ " print() # newline\n",
+ " maes = []\n",
+ " img_patches = VIDEO_PATCHES[0] * VIDEO_PATCHES[1]\n",
+ " for i in range(0, gt.shape[0], img_patches):\n",
+ " gt_subset = gt[i:i + img_patches]\n",
+ " for j in range(total_branches):\n",
+ " if i == 0:\n",
+ " maes.append([np.abs(np.sum(gt_subset) - np.sum(outs[j][i:i + img_patches]))])\n",
+ " else:\n",
+ " maes[j].append(np.abs(np.sum(gt_subset) - np.sum(outs[j][i:i + img_patches])))\n",
+ " return [np.mean(np.array(item)) for item in maes]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train(max_epochs, branch_numbers, head_type, dataset, version, temporary):\n",
+ " tf.keras.backend.clear_session()\n",
+ "\n",
+ " with DISTRIBUTED_STRATEGY.scope():\n",
+ " model = get_model(branch_numbers, head_type, dataset)\n",
+ "\n",
+ " lr_reduce = tf.keras.callbacks.ReduceLROnPlateau(\n",
+ " monitor='val_loss',\n",
+ " factor=0.6,\n",
+ " patience=2,\n",
+ " verbose=1,\n",
+ " mode='min',\n",
+ " min_lr=1e-7\n",
+ " )\n",
+ "\n",
+ " early_stop = tf.keras.callbacks.EarlyStopping(\n",
+ " monitor='val_loss',\n",
+ " patience=5,\n",
+ " verbose=1,\n",
+ " mode='min'\n",
+ " )\n",
+ "\n",
+ " save_model_checkpoint_file = 'bmvc_rebuttal_ee_v%d_%s_%s_%s.h5' % (\n",
+ " version,\n",
+ " head_type,\n",
+ " dataset,\n",
+ " '-'.join([str(branch_number) for branch_number in branch_numbers])\n",
+ " )\n",
+ "\n",
+ " checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
+ " save_model_checkpoint_file,\n",
+ " monitor='val_loss',\n",
+ " verbose=1,\n",
+ " save_weights_only=False,\n",
+ " save_best_only=True,\n",
+ " mode='min',\n",
+ " save_freq='epoch'\n",
+ " )\n",
+ "\n",
+ " callbacks = [lr_reduce, early_stop]\n",
+ " if not temporary:\n",
+ " callbacks.append(checkpoint)\n",
+ "\n",
+ " batch_size = 4\n",
+ " if dataset == 'cifar10' or dataset == 'cifar100':\n",
+ " train_sequence = CIFARSequence('train', batch_size, dataset)\n",
+ " val_sequence = CIFARSequence('val', batch_size, dataset)\n",
+ " test_sequence = CIFARSequence('test', batch_size, dataset)\n",
+ " elif dataset == 'disco':\n",
+ " train_sequence = DISCOSequence('train', batch_size)\n",
+ " val_sequence = DISCOSequence('val', batch_size)\n",
+ " test_sequence = DISCOSequence('test', 2 * batch_size)\n",
+ " else:\n",
+ " raise Exception('Unknown dataset: %s' % dataset)\n",
+ "\n",
+ " history = model.fit(\n",
+ " train_sequence,\n",
+ " validation_data=val_sequence,\n",
+ " epochs=max_epochs,\n",
+ " shuffle=True,\n",
+ " callbacks=callbacks,\n",
+ " verbose=1\n",
+ " )\n",
+ "\n",
+ " if dataset == 'cifar10' or dataset == 'cifar100':\n",
+ " test_accuracy = model.evaluate(test_sequence)[1]\n",
+ " elif dataset == 'disco':\n",
+ " test_accuracy = test_cc(model, test_sequence, len(branch_numbers) + 1)\n",
+ "\n",
+ " model_params = get_params(model) / 10 ** 6\n",
+ " model_flops = get_flops(model) / 10 ** 9\n",
+ "\n",
+ " return model, test_accuracy, model_params, model_flops"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cache_all('cifar10')\n",
+ "cache_all('cifar100')\n",
+ "model, test_accuracy, model_params, model_flops = train(\n",
+ " max_epochs=100,\n",
+ " branch_numbers=[3, 6, 9],\n",
+ " head_type='resmlp',\n",
+ " dataset='disco',\n",
+ " version=5,\n",
+ " temporary=False\n",
+ ")\n",
+ "print(test_accuracy)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/plots.ipynb b/plots.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..78100b2678bf6c4ec1268235aeeec96cae28b98b
--- /dev/null
+++ b/plots.ipynb
@@ -0,0 +1,776 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.patches import Circle\n",
+ "from operator import sub\n",
+ "\n",
+ "matplotlib.rc('font',**{'size': 20})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "EPSILON = 0.1\n",
+ "\n",
+ "def practical_points_generator(flops, accuracies, mode):\n",
+ " all_data_points = []\n",
+ " for i in range(len(flops)):\n",
+ " for j in range(len(flops[i])):\n",
+ " all_data_points.append((flops[i][j], accuracies[i][j]))\n",
+ " if mode == 'max':\n",
+ " sorted_data_points = sorted(all_data_points, key=lambda x: 10 ** 6 * x[0] - x[1])\n",
+ " max_accuracy = 0\n",
+ " for point in sorted_data_points:\n",
+ " if point[1] - max_accuracy > EPSILON:\n",
+ " max_accuracy = point[1]\n",
+ " yield point\n",
+ " else:\n",
+ " sorted_data_points = sorted(all_data_points, key=lambda x: 10 ** 6 * x[0] + x[1])\n",
+ " min_mae = 10 ** 6\n",
+ " for point in sorted_data_points:\n",
+ " if min_mae - point[1] > EPSILON:\n",
+ " min_mae = point[1]\n",
+ " yield point"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_marker_linestyle_color(label):\n",
+ " if label == 'CNN-Ignore-EE':\n",
+ " return 'o', '-', 'b'\n",
+ " elif label == 'MLP-Mixer-EE':\n",
+ " return 'v', '--', 'g'\n",
+ " elif label == 'MLP-EE':\n",
+ " return 'P', '-.', 'r'\n",
+ " elif label == 'ViT-EE':\n",
+ " return 'X', ':', 'c'\n",
+ " elif label == 'CNN-Add-EE':\n",
+ " return 'D', '-', 'm'\n",
+ " elif label == 'ResMLP-EE':\n",
+ " return '^', '--', 'y'\n",
+ " elif label == 'CNN-Project-EE':\n",
+ " return 's', '-.', 'k'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def correct_flops(flops):\n",
+ " new_flops = []\n",
+ " for exit_type_flops in flops:\n",
+ " new_exit_type_flops = []\n",
+ " for flops in exit_type_flops:\n",
+ " new_exit_type_flops.append(flops / 2)\n",
+ " new_flops.append(new_exit_type_flops)\n",
+ " return new_flops\n",
+ "\n",
+ "def draw_plots(\n",
+ " flops,\n",
+ " accuracies,\n",
+ " labels,\n",
+ " ranges,\n",
+ " name,\n",
+ " backbone_flops,\n",
+ " backbone_accuracy,\n",
+ " mode='max',\n",
+ " include_mlp=True,\n",
+ " y_axis_limit=None\n",
+ "):\n",
+ " flops = correct_flops(flops)\n",
+ " backbone_flops /= 2\n",
+ " fig, axes = plt.subplots(1, len(ranges), figsize=(30, 10))\n",
+ " for range_index in range(len(ranges)):\n",
+ " start = ranges[range_index][0]\n",
+ " end = ranges[range_index][1]\n",
+ " if len(ranges) == 1:\n",
+ " ax = axes\n",
+ " else:\n",
+ " ax = axes[range_index]\n",
+ " ax.set_title('%s, Exits %d to %d' % (name, start + 1, end))\n",
+ " ax.set_xlabel('FLOPS (B)')\n",
+ " ax.set_ylabel('Accuracy (%)' if mode == 'max' else 'MAE')\n",
+ " used_flops = []\n",
+ " used_accuracies = []\n",
+ " for i in range(len(flops)):\n",
+ " if include_mlp or labels[i] != 'MLP-EE':\n",
+ " used_flops += flops[i][start:end]\n",
+ " used_accuracies += accuracies[i][start:end]\n",
+ " marker, linestyle, color = get_marker_linestyle_color(labels[i])\n",
+ " ax.plot(\n",
+ " flops[i][start:end],\n",
+ " accuracies[i][start:end],\n",
+ " marker=marker,\n",
+ " linestyle=linestyle,\n",
+ " color=color,\n",
+ " label=labels[i],\n",
+ " markersize=8\n",
+ " )\n",
+ " if y_axis_limit is not None:\n",
+ " ax.set_ylim(y_axis_limit)\n",
+ " ax.legend()\n",
+ " for point in practical_points_generator(flops, accuracies, mode):\n",
+ " if point in zip(used_flops, used_accuracies):\n",
+ " ax.plot(point[0], point[1], color='grey', marker='o', fillstyle='none', markersize=20)\n",
+ " if len(ranges) == 1:\n",
+ " last_ax = axes\n",
+ " else:\n",
+ " last_ax = axes[-1]\n",
+ " last_ax.scatter([backbone_flops], [backbone_accuracy])\n",
+ " last_ax.annotate(\n",
+ " 'Final',\n",
+ " xy=(backbone_flops, backbone_accuracy),\n",
+ " xytext=(-24, 8),\n",
+ " textcoords='offset pixels'\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cnn_ignore_accuracies = [\n",
+ " 74.86,\n",
+ " 80.47,\n",
+ " 85.10,\n",
+ " 89.95,\n",
+ " 92.51,\n",
+ " 94.02,\n",
+ " 95.51,\n",
+ " 96.63,\n",
+ " 97.62,\n",
+ " 97.93,\n",
+ " 98.00,\n",
+ "]\n",
+ "\n",
+ "cnn_ignore_flops = [10.039666312, 19.27067618, 28.501686048, 37.732695916, 46.963705784, 56.194715652, 65.42572552, 74.656735388, 83.887745256, 93.118755124, 102.349764992]\n",
+ "\n",
+ "mlp_mixer_accuracies = [\n",
+ " 74.53,\n",
+ " 81.40,\n",
+ " 85.49,\n",
+ " 88.33,\n",
+ " 91.84,\n",
+ " 93.79,\n",
+ " 94.97,\n",
+ " 95.99,\n",
+ " 97.08,\n",
+ " 97.75,\n",
+ " 97.86,\n",
+ "]\n",
+ "\n",
+ "mlp_mixer_flops = [16.054064008, 25.285073876, 34.516083744, 43.747093612, 52.97810348, 62.209113348, 71.440123216, 80.671133084, 89.902142952, 99.13315282, 108.364162688]\n",
+ "\n",
+ "mlp_accuracies = [\n",
+ " 26.19,\n",
+ " 42.07,\n",
+ " 58.42,\n",
+ " 72.60,\n",
+ " 81.46,\n",
+ " 87.19,\n",
+ " 90.81,\n",
+ " 93.11,\n",
+ " 96.07,\n",
+ " 97.06,\n",
+ " 97.92,\n",
+ "]\n",
+ "\n",
+ "mlp_flops = [9.915005706, 19.146015574, 28.377025442, 37.60803531, 46.839045178, 56.070055046, 65.301064914, 74.532074782, 83.76308465, 92.994094518, 102.225104386]\n",
+ "\n",
+ "vit_accuracies = [\n",
+ " 79.14,\n",
+ " 84.91,\n",
+ " 89.83,\n",
+ " 92.60,\n",
+ " 94.61,\n",
+ " 96.04,\n",
+ " 96.65,\n",
+ " 97.39,\n",
+ " 97.67,\n",
+ " 98.02,\n",
+ " 98.09,\n",
+ "]\n",
+ "\n",
+ "vit_flops = [19.146015574, 28.377025442, 37.60803531, 46.839045178, 56.070055046, 65.301064914, 74.532074782, 83.76308465, 92.994094518, 102.225104386, 111.456114254]\n",
+ "\n",
+ "cnn_add_accuracies = [\n",
+ " 77.19,\n",
+ " 81.22,\n",
+ " 86.25,\n",
+ " 89.50,\n",
+ " 92.03,\n",
+ " 93.94,\n",
+ " 95.19,\n",
+ " 95.98,\n",
+ " 97.43,\n",
+ " 97.72,\n",
+ " 98.13,\n",
+ "]\n",
+ "\n",
+ "cnn_add_flops = [10.039666312, 19.27067618, 28.501686048, 37.732695916, 46.963705784, 56.194715652, 65.42572552, 74.656735388, 83.887745256, 93.118755124, 102.349764992]\n",
+ "\n",
+ "resmlp_accuracies = [\n",
+ " 74.99,\n",
+ " 85.49,\n",
+ " 90.45,\n",
+ " 92.44,\n",
+ " 94.14,\n",
+ " 94.76,\n",
+ " 95.93,\n",
+ " 96.60,\n",
+ " 97.63,\n",
+ " 97.94,\n",
+ " 98.12,\n",
+ "]\n",
+ "\n",
+ "resmlp_flops = [15.88094452, 25.111954388, 34.342964256, 43.573974124, 52.804983992, 62.03599386, 71.267003728, 80.498013596, 89.729023464, 98.960033332, 108.1910432]\n",
+ "\n",
+ "cnn_project_accuracies = [\n",
+ " 76.60,\n",
+ " 81.07,\n",
+ " 86.76,\n",
+ " 90.28,\n",
+ " 92.17,\n",
+ " 94.24,\n",
+ " 95.67,\n",
+ " 96.73,\n",
+ " 97.37,\n",
+ " 97.82,\n",
+ " 97.90,\n",
+ "]\n",
+ "\n",
+ "cnn_project_flops = [10.167068296, 19.398078164, 28.629088032, 37.8600979, 47.091107768, 56.322117636, 65.553127504, 74.784137372, 84.01514724, 93.246157108, 102.477166976]\n",
+ "\n",
+ "backbone_accuracy = 98.31\n",
+ "backbone_flops = 111.46"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "flops = [\n",
+ " cnn_ignore_flops,\n",
+ " mlp_mixer_flops,\n",
+ " mlp_flops,\n",
+ " vit_flops,\n",
+ " cnn_add_flops,\n",
+ " resmlp_flops,\n",
+ " cnn_project_flops,\n",
+ "]\n",
+ "accuracies = [\n",
+ " cnn_ignore_accuracies,\n",
+ " mlp_mixer_accuracies,\n",
+ " mlp_accuracies,\n",
+ " vit_accuracies,\n",
+ " cnn_add_accuracies,\n",
+ " resmlp_accuracies,\n",
+ " cnn_project_accuracies,\n",
+ "]\n",
+ "labels = [\n",
+ " 'CNN-Ignore-EE',\n",
+ " 'MLP-Mixer-EE',\n",
+ " 'MLP-EE',\n",
+ " 'ViT-EE',\n",
+ " 'CNN-Add-EE',\n",
+ " 'ResMLP-EE',\n",
+ " 'CNN-Project-EE',\n",
+ "]\n",
+ "ranges = [(0, 6), (5, 11)]\n",
+ "full_range = [(0, 11)]\n",
+ "name = 'CIFAR-10'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 2160x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "draw_plots(flops, accuracies, labels, full_range, name, backbone_flops, backbone_accuracy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 2160x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "draw_plots(flops, accuracies, labels, ranges, name, backbone_flops, backbone_accuracy, include_mlp=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cnn_ignore_accuracies = [\n",
+ " 45.23,\n",
+ " 50.60,\n",
+ " 56.41,\n",
+ " 61.53,\n",
+ " 67.16,\n",
+ " 70.39,\n",
+ " 75.41,\n",
+ " 79.47,\n",
+ " 83.56,\n",
+ " 87.76,\n",
+ " 89.33,\n",
+ "]\n",
+ "\n",
+ "cnn_ignore_flops = [10.039666312, 19.27067618, 28.501686048, 37.732695916, 46.963705784, 56.194715652, 65.42572552, 74.656735388, 83.887745256, 93.118755124, 102.349764992]\n",
+ "\n",
+ "mlp_accuracies = [\n",
+ " 7.07,\n",
+ " 16.26,\n",
+ " 21.63,\n",
+ " 40.28,\n",
+ " 49.04,\n",
+ " 55.39,\n",
+ " 59.79,\n",
+ " 65.58,\n",
+ " 71.40,\n",
+ " 81.64,\n",
+ " 88.69,\n",
+ "]\n",
+ "\n",
+ "mlp_flops = [9.915005706, 19.146015574, 28.377025442, 37.60803531, 46.839045178, 56.070055046, 65.301064914, 74.532074782, 83.76308465, 92.994094518, 102.225104386]\n",
+ "\n",
+ "vit_accuracies = [\n",
+ " 31.08,\n",
+ " 45.04,\n",
+ " 58.59,\n",
+ " 66.54,\n",
+ " 73.44,\n",
+ " 79.18,\n",
+ " 83.10,\n",
+ " 86.26,\n",
+ " 88.38,\n",
+ " 90.12,\n",
+ " 90.92,\n",
+ "]\n",
+ "\n",
+ "vit_flops = [19.146015574, 28.377025442, 37.60803531, 46.839045178, 56.070055046, 65.301064914, 74.532074782, 83.76308465, 92.994094518, 102.225104386, 111.456114254]\n",
+ "\n",
+ "mlp_mixer_accuracies = [\n",
+ " 34.31,\n",
+ " 47.03,\n",
+ " 59.18,\n",
+ " 66.32,\n",
+ " 73.13,\n",
+ " 78.11,\n",
+ " 81.64,\n",
+ " 84.31,\n",
+ " 87.33,\n",
+ " 88.50,\n",
+ " 89.98,\n",
+ "]\n",
+ "\n",
+ "mlp_mixer_flops = [16.054064008, 25.285073876, 34.516083744, 43.747093612, 52.97810348, 62.209113348, 71.440123216, 80.671133084, 89.902142952, 99.13315282, 108.364162688]\n",
+ "\n",
+ "resmlp_accuracies = [\n",
+ " 34.65,\n",
+ " 58.73,\n",
+ " 66.71,\n",
+ " 72.44,\n",
+ " 76.88,\n",
+ " 80.94,\n",
+ " 84.51,\n",
+ " 86.83,\n",
+ " 88.51,\n",
+ " 90.20,\n",
+ " 91.13,\n",
+ "]\n",
+ "\n",
+ "resmlp_flops = [15.88094452, 25.111954388, 34.342964256, 43.573974124, 52.804983992, 62.03599386, 71.267003728, 80.498013596, 89.729023464, 98.960033332, 108.1910432]\n",
+ "\n",
+ "cnn_project_accuracies = [\n",
+ " 43.46,\n",
+ " 47.42,\n",
+ " 50.43,\n",
+ " 59.26,\n",
+ " 60.92,\n",
+ " 64.07,\n",
+ " 68.61,\n",
+ " 70.97,\n",
+ " 65.69,\n",
+ " 70.57,\n",
+ " 80.95,\n",
+ "]\n",
+ "\n",
+ "cnn_project_flops = [10.167068296, 19.398078164, 28.629088032, 37.8600979, 47.091107768, 56.322117636, 65.553127504, 74.784137372, 84.01514724, 93.246157108, 102.477166976]\n",
+ "\n",
+ "cnn_add_accuracies = [\n",
+ " 44.36,\n",
+ " 47.66,\n",
+ " 52.82,\n",
+ " 58.94,\n",
+ " 62.96,\n",
+ " 65.25,\n",
+ " 70.61,\n",
+ " 69.48,\n",
+ " 73.92,\n",
+ " 78.11,\n",
+ " 80.88,\n",
+ "]\n",
+ "\n",
+ "cnn_add_flops = [10.039666312, 19.27067618, 28.501686048, 37.732695916, 46.963705784, 56.194715652, 65.42572552, 74.656735388, 83.887745256, 93.118755124, 102.349764992]\n",
+ "\n",
+ "backbone_accuracy = 91.24\n",
+ "backbone_flops = 111.46"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "flops = [\n",
+ " cnn_ignore_flops,\n",
+ " mlp_mixer_flops,\n",
+ " mlp_flops,\n",
+ " vit_flops,\n",
+ " cnn_add_flops,\n",
+ " resmlp_flops,\n",
+ " cnn_project_flops,\n",
+ "]\n",
+ "accuracies = [\n",
+ " cnn_ignore_accuracies,\n",
+ " mlp_mixer_accuracies,\n",
+ " mlp_accuracies,\n",
+ " vit_accuracies,\n",
+ " cnn_add_accuracies,\n",
+ " resmlp_accuracies,\n",
+ " cnn_project_accuracies,\n",
+ "]\n",
+ "markers = ['o', 'v', 'P', 'X', 'D', '^', 's'] # https://matplotlib.org/2.0.2/api/lines_api.html\n",
+ "linestyles = ['-', '--', '-.', ':']\n",
+ "labels = [\n",
+ " 'CNN-Ignore-EE',\n",
+ " 'MLP-Mixer-EE',\n",
+ " 'MLP-EE',\n",
+ " 'ViT-EE',\n",
+ " 'CNN-Add-EE',\n",
+ " 'ResMLP-EE',\n",
+ " 'CNN-Project-EE',\n",
+ "]\n",
+ "ranges = [(0, 6), (5, 11)]\n",
+ "name = 'CIFAR-100'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 2160x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "draw_plots(flops, accuracies, labels, full_range, name, backbone_flops, backbone_accuracy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "draw_plots(flops, accuracies, labels, ranges, name, backbone_flops, backbone_accuracy, include_mlp=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mlp_maes_5 = [66.95597652493295, 55.6043279293333, 40.01493319068866, 27.534241703863543, 20.099828833925915, 16.6587248431277, 14.207798832063078, 13.643488861268288, 12.692167538868029, 11.745789087804178, 11.116295196966094]\n",
+ "\n",
+ "mlp_maes_4 = [63.81760612188089, 48.84459626358478, 31.38019274014999, 21.35408311672693, 17.32409016078545, 15.096452528379604, 13.05824572280687, 12.952922174428489, 12.154847678503849, 11.278382839842044, 10.903999793224612]\n",
+ "\n",
+ "mlp_maes = [min(mlp_maes_4[i], mlp_maes_5[i]) for i in range(len(mlp_maes_4))]\n",
+ "\n",
+ "mlp_flops = [9.915005706, 19.146015574, 28.377025442, 37.60803531, 46.839045178, 56.070055046, 65.301064914, 74.532074782, 83.76308465, 92.994094518, 102.225104386]\n",
+ "\n",
+ "resmlp_maes_4 = [17.60861961755429, 15.780182688240421, 14.967578141838581, 12.967585373219885, 13.565632146464361, 17.838170967843872, 25.29613745927154, 18.395887481893116, 22.84772446918155, 21.12996455613271, 19.378696329954778]\n",
+ "\n",
+ "resmlp_maes_5 = [37.567863819194585, 16.379686318456177, 15.91074247730594, 14.830663657496425, 14.105883239636947, 13.518657890148038, 14.987332331531313, 13.092971086047813, 12.99657056636505, 11.822814100180334, 13.347536167698324]\n",
+ "\n",
+ "resmlp_maes = [min(resmlp_maes_4[i], resmlp_maes_5[i]) for i in range(len(resmlp_maes_4))]\n",
+ "\n",
+ "resmlp_flops = [15.88094452, 25.111954388, 34.342964256, 43.573974124, 52.804983992, 62.03599386, 71.267003728, 80.498013596, 89.729023464, 98.960033332, 108.1910432]\n",
+ "\n",
+ "vit_maes_4 = [17.25549441917847, 15.310706459980956, 13.595461759060662, 12.577511369468663, 12.067579316858268, 11.083785289440117, 11.188187754225888, 10.87809422029592, 11.642507894276399, 10.745084875372624, 10.597995807472484]\n",
+ "\n",
+ "vit_maes_5 = [20.92489066249554, 17.892692841223028, 15.841838350091864, 14.224327003689748, 13.197131156595411, 12.574951000025784, 12.357885581834648, 11.632370412163207, 11.68577344596502, 11.010207051810061, 10.896251262811639]\n",
+ "\n",
+ "vit_maes = [min(vit_maes_4[i], vit_maes_5[i]) for i in range(len(vit_maes_4))]\n",
+ "\n",
+ "vit_alt_maes = [18.049246351608634, 15.180274767263906, 13.628905414822626, 12.978167053696502, 11.69005954003767, 11.319822538837004, 10.915161482429214, 11.06383380322437, 10.836275275190616, 10.95410315675852, 10.759412091444661]\n",
+ "\n",
+ "vit_flops = [19.146015574, 28.377025442, 37.60803531, 46.839045178, 56.070055046, 65.301064914, 74.532074782, 83.76308465, 92.994094518, 102.225104386, 111.456114254]\n",
+ "\n",
+ "cnn_ignore_maes_4 = [16.324433793363568, 15.642264217512718, 15.203111727147673, 14.539004881089028, 14.859935972397198, 16.9033360170686, 14.710937910877693, 15.024733024942853, 13.002627540657842, 15.919211207802743, 13.1617159714426]\n",
+ "\n",
+ "cnn_ignore_maes_5 = [19.472250306927545, 16.382709773809054, 15.30624921188889, 16.640002978804905, 15.339903997527072, 14.558104298405594, 14.186327249106556, 18.8666830396562, 15.023331387561072, 16.011291563485088, 16.721815794863026]\n",
+ "\n",
+ "cnn_ignore_maes = [min(cnn_ignore_maes_4[i], cnn_ignore_maes_5[i]) for i in range(len(cnn_ignore_maes_4))]\n",
+ "\n",
+ "cnn_ignore_flops = [10.039666312, 19.27067618, 28.501686048, 37.732695916, 46.963705784, 56.194715652, 65.42572552, 74.656735388, 83.887745256, 93.118755124, 102.349764992]\n",
+ "\n",
+ "cnn_add_maes_4 = [18.90440685073256, 16.726752551982553, 13.927111037765874, 13.950359128337304, 15.485235473291226, 13.63742827597631, 15.302497663269468, 14.101453771361111, 13.903442669190742, 13.146807598164951, 11.929169691797588]\n",
+ "\n",
+ "cnn_add_maes_5 = [23.029530046001007, 17.36398403153728, 17.532784224678203, 15.0629114230157, 14.053487970473283, 14.495352183394191, 12.894946989563001, 15.040746399772036, 14.855697106636278, 13.728063926384943, 12.44930448742072]\n",
+ "\n",
+ "cnn_add_maes = [min(cnn_add_maes_4[i], cnn_add_maes_5[i]) for i in range(len(cnn_add_maes_4))]\n",
+ "\n",
+ "cnn_add_flops = [10.039666312, 19.27067618, 28.501686048, 37.732695916, 46.963705784, 56.194715652, 65.42572552, 74.656735388, 83.887745256, 93.118755124, 102.349764992]\n",
+ "\n",
+ "cnn_project_maes_4 = [17.97360331761429, 15.467450911446946, 14.959393072910776, 14.132034754839491, 16.076203802519633, 14.18988822844617, 13.815687876052461, 12.58639125568036, 11.517910521763955, 12.874515941075424, 13.256840143438424]\n",
+ "\n",
+ "cnn_project_maes_5 = [27.43422174678897, 16.961936098398724, 15.575427034930241, 14.76191664899309, 13.80299366597453, 13.76081104695114, 13.545493860247136, 12.36295470406632, 12.355132346136038, 11.9225329570658, 13.00598683830469]\n",
+ "\n",
+ "cnn_project_maes = [min(cnn_project_maes_4[i], cnn_project_maes_5[i]) for i in range(len(cnn_project_maes_4))]\n",
+ "\n",
+ "cnn_project_flops = [10.167068296, 19.398078164, 28.629088032, 37.8600979, 47.091107768, 56.322117636, 65.553127504, 74.784137372, 84.01514724, 93.246157108, 102.477166976]\n",
+ "\n",
+ "mlp_mixer_maes_4 = [17.449155579073565, 15.024769666981092, 13.918311021642689, 13.102047395174287, 13.740980489502375, 13.465846355746645, 13.171297758797463, 15.58475445045778, 14.971250198540853, 18.186729623156797, 17.877043017754772]\n",
+ "\n",
+ "mlp_mixer_maes_5 = [17.895658858100944, 15.83869162005821, 14.261989270241047, 13.886250207755124, 13.046678551942659, 12.559346749946299, 14.470972782496279, 12.07975543923034, 12.112579300768346, 13.57506639775688, 12.628674099279955]\n",
+ "\n",
+ "mlp_mixer_maes = [min(mlp_mixer_maes_4[i], mlp_mixer_maes_5[i]) for i in range(len(mlp_mixer_maes_4))]\n",
+ "\n",
+ "mlp_mixer_flops = [16.054064008, 25.285073876, 34.516083744, 43.747093612, 52.97810348, 62.209113348, 71.440123216, 80.671133084, 89.902142952, 99.13315282, 108.364162688]\n",
+ "\n",
+ "backbone_mae = 11.07\n",
+ "backbone_flops = 111.46"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "flops = [\n",
+ " mlp_flops,\n",
+ " vit_flops,\n",
+ " resmlp_flops,\n",
+ " cnn_ignore_flops,\n",
+ " cnn_add_flops,\n",
+ " cnn_project_flops,\n",
+ " mlp_mixer_flops,\n",
+ "]\n",
+ "maes = [\n",
+ " mlp_maes,\n",
+ " vit_maes,\n",
+ " resmlp_maes,\n",
+ " cnn_ignore_maes,\n",
+ " cnn_add_maes,\n",
+ " cnn_project_maes,\n",
+ " mlp_mixer_maes,\n",
+ "]\n",
+ "markers = ['o', 'v', 'P', 'X', 'D', '^', 's'] # https://matplotlib.org/2.0.2/api/lines_api.html\n",
+ "linestyles = ['-', '--', '-.', ':']\n",
+ "labels = [\n",
+ " 'MLP-EE',\n",
+ " 'ViT-EE',\n",
+ " 'ResMLP-EE',\n",
+ " 'CNN-Ignore-EE',\n",
+ " 'CNN-Add-EE',\n",
+ " 'CNN-Project-EE',\n",
+ " 'MLP-Mixer-EE'\n",
+ "]\n",
+ "ranges = [(0, 6), (5, 11)]\n",
+ "name = 'DISCO'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 2160x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "draw_plots(flops, maes, labels, full_range, name, backbone_flops, backbone_mae, mode='min', y_axis_limit=[10, 20])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mlp_flops = [9.915005706, 19.146015574, 28.377025442, 37.60803531, 46.839045178, 56.070055046, 65.301064914, 74.532074782, 83.76308465, 92.994094518, 102.225104386]\n",
+ "mlp_accuracies = [60.66, 73.76, 82.65, 87.55, 89.60, 92.23, 92.97, 94.04, 94.67, 94.83, 94.81]\n",
+ "cnn_ignore_flops = [10.039666312, 19.27067618, 28.501686048, 37.732695916, 46.963705784, 56.194715652, 65.42572552, 74.656735388, 83.887745256, 93.118755124, 102.349764992]\n",
+ "cnn_ignore_accuracies = [92.86, 93.42, 93.49, 93.74, 93.85, 94.12, 94.16, 94.49, 94.58, 94.73, 94.79]\n",
+ "cnn_add_flops = [10.039666312, 19.27067618, 28.501686048, 37.732695916, 46.963705784, 56.194715652, 65.42572552, 74.656735388, 83.887745256, 93.118755124, 102.349764992]\n",
+ "cnn_add_accuracies = [92.45, 93.08, 93.83, 93.75, 93.67, 93.54, 93.91, 94.52, 94.71, 94.68, 94.68]\n",
+ "cnn_project_flops = [10.167068296, 19.398078164, 28.629088032, 37.8600979, 47.091107768, 56.322117636, 65.553127504, 74.784137372, 84.01514724, 93.246157108, 102.477166976]\n",
+ "cnn_project_accuracies = [92.72, 92.95, 94.13, 93.68, 93.71, 93.84, 94.15, 94.52, 94.65, 94.49, 94.88]\n",
+ "vit_flops = [19.146015574, 28.377025442, 37.60803531, 46.839045178, 56.070055046, 65.301064914, 74.532074782, 83.76308465, 92.994094518, 102.225104386, 111.456114254]\n",
+ "vit_accuracies = [91.38, 92.28, 92.61, 93.48, 93.86, 94.19, 94.54, 94.65, 94.85, 94.94, 94.89]\n",
+ "resmlp_flops = [15.88094452, 25.111954388, 34.342964256, 43.573974124, 52.804983992, 62.03599386, 71.267003728, 80.498013596, 89.729023464, 98.960033332, 108.1910432]\n",
+ "resmlp_accuracies = [90.25, 92.54, 93.64, 93.76, 93.63, 93.68, 94.08, 94.40, 94.74, 94.82, 94.85]\n",
+ "mlp_mixer_flops = [16.054064008, 25.285073876, 34.516083744, 43.747093612, 52.97810348, 62.209113348, 71.440123216, 80.671133084, 89.902142952, 99.13315282, 108.364162688]\n",
+ "mlp_mixer_accuracies = [90.81, 92.52, 93.26, 93.78, 93.42, 93.94, 94.08, 94.52, 94.78, 94.77, 94.86]\n",
+ "backbone_accuracy = 95.00\n",
+ "backbone_flops = 111.46"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "flops = [\n",
+ " cnn_ignore_flops,\n",
+ " mlp_mixer_flops,\n",
+ " mlp_flops,\n",
+ " vit_flops,\n",
+ " cnn_add_flops,\n",
+ " resmlp_flops,\n",
+ " cnn_project_flops,\n",
+ "]\n",
+ "accuracies = [\n",
+ " cnn_ignore_accuracies,\n",
+ " mlp_mixer_accuracies,\n",
+ " mlp_accuracies,\n",
+ " vit_accuracies,\n",
+ " cnn_add_accuracies,\n",
+ " resmlp_accuracies,\n",
+ " cnn_project_accuracies,\n",
+ "]\n",
+ "markers = ['o', 'v', 'P', 'X', 'D', '^', 's'] # https://matplotlib.org/2.0.2/api/lines_api.html\n",
+ "linestyles = ['-', '--', '-.', ':']\n",
+ "labels = [\n",
+ " 'CNN-Ignore-EE',\n",
+ " 'MLP-Mixer-EE',\n",
+ " 'MLP-EE',\n",
+ " 'ViT-EE',\n",
+ " 'CNN-Add-EE',\n",
+ " 'ResMLP-EE',\n",
+ " 'CNN-Project-EE',\n",
+ "]\n",
+ "ranges = [(0, 6), (5, 11)]\n",
+ "name = 'Fashion MNIST'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 2160x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "draw_plots(flops, accuracies, labels, ranges, name, backbone_flops, backbone_accuracy, include_mlp=False)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/precompute_cifar_features.ipynb b/precompute_cifar_features.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b5829ca5e0e27b85c37d93b2df990df70120d88d
--- /dev/null
+++ b/precompute_cifar_features.ipynb
@@ -0,0 +1,159 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [4]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import random\n",
+ "import sys\n",
+ "import time\n",
+ "from skimage import transform\n",
+ "from vit_keras import vit\n",
+ "from vit_keras.layers import ClassToken, AddPositionEmbs, MultiHeadSelfAttention, TransformerBlock\n",
+ "\n",
+ "PRECOMPUTE_DIR = 'precompute'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_branch_id(branch_number):\n",
+ " if branch_number == 1:\n",
+ " return 'transformer_block'\n",
+ " else:\n",
+ " return 'transformer_block_%d' % (branch_number - 1)\n",
+ "\n",
+ "def get_model(dataset):\n",
+ " backbone_model = tf.keras.models.load_model('vit_%s_v1.h5' % dataset, custom_objects={\n",
+ " 'ClassToken': ClassToken,\n",
+ " 'AddPositionEmbs': AddPositionEmbs,\n",
+ " 'MultiHeadSelfAttention': MultiHeadSelfAttention,\n",
+ " 'TransformerBlock': TransformerBlock,\n",
+ " })\n",
+ "\n",
+ " # freeze\n",
+ " for layer in backbone_model.layers:\n",
+ " layer.trainable = False\n",
+ " \n",
+ " outputs = []\n",
+ " for branch_number in range(1, 12):\n",
+ " y, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " outputs.append(y)\n",
+ " \n",
+ " model = tf.keras.models.Model(\n",
+ " inputs=backbone_model.get_layer(index=0).input,\n",
+ " outputs=outputs\n",
+ " )\n",
+ "\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def precompute(dataset, batch_size=32 * NUM_GPUS):\n",
+ " with DISTRIBUTED_STRATEGY.scope():\n",
+ " model = get_model(dataset)\n",
+ " for split in ['train', 'val', 'test']:\n",
+ " print(split)\n",
+ " total_count = sum([1 if file_name.startswith(split) else 0 for file_name in os.listdir(dataset)])\n",
+ " batch_count = math.ceil(total_count / batch_size)\n",
+ " for batch_index in range(batch_count):\n",
+ " sys.stdout.write('\\r[%d/%d]' % (batch_index + 1, batch_count))\n",
+ " sys.stdout.flush()\n",
+ " images = []\n",
+ " labels = []\n",
+ " for sample_index in range(batch_index * batch_size, (batch_index + 1) * batch_size):\n",
+ " image_path = os.path.join(dataset, '%s_%d.pkl' % (split, sample_index))\n",
+ " if os.path.exists(image_path): # last batch may contain less\n",
+ " with open(image_path, 'rb') as cache_file:\n",
+ " contents = pickle.load(cache_file)\n",
+ " images.append(contents['image'])\n",
+ " labels.append(contents['label'])\n",
+ " outputs = model(np.array(images))\n",
+ " for branch_number in range(1, 12):\n",
+ " branch_outputs = outputs[branch_number - 1]\n",
+ " for i, branch_output in enumerate(branch_outputs):\n",
+ " sample_index = batch_index * batch_size + i\n",
+ " sample_path = os.path.join(\n",
+ " PRECOMPUTE_DIR,\n",
+ " dataset,\n",
+ " '%s_branch%d_sample%d.pkl' % (split, branch_number, sample_index)\n",
+ " )\n",
+ " with open(sample_path, 'wb') as sample_file:\n",
+ " pickle.dump({\n",
+ " 'features': branch_output,\n",
+ " 'label': labels[i],\n",
+ " }, sample_file)\n",
+ " print() # newline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precompute('cifar10')\n",
+ "precompute('cifar100')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/precompute_disco_features.ipynb b/precompute_disco_features.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d2b1f52fe5152d0478e6afcd33b19e015470e22a
--- /dev/null
+++ b/precompute_disco_features.ipynb
@@ -0,0 +1,160 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [4]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import random\n",
+ "import sys\n",
+ "import time\n",
+ "from skimage import transform\n",
+ "from vit_keras import vit\n",
+ "from vit_keras.layers import ClassToken, AddPositionEmbs, MultiHeadSelfAttention, TransformerBlock\n",
+ "\n",
+ "PRECOMPUTE_DIR = 'precompute'\n",
+ "DISCO_PATH = 'disco'\n",
+ "CACHE_DIR = os.path.join(DISCO_PATH, 'vit_cache')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_branch_id(branch_number):\n",
+ " if branch_number == 1:\n",
+ " return 'transformer_block'\n",
+ " else:\n",
+ " return 'transformer_block_%d' % (branch_number - 1)\n",
+ "\n",
+ "def get_model():\n",
+ " backbone_model = tf.keras.models.load_model('vit_cc_backbone_v2.h5', custom_objects={\n",
+ " 'ClassToken': ClassToken,\n",
+ " 'AddPositionEmbs': AddPositionEmbs,\n",
+ " 'MultiHeadSelfAttention': MultiHeadSelfAttention,\n",
+ " 'TransformerBlock': TransformerBlock,\n",
+ " })\n",
+ "\n",
+ " # freeze\n",
+ " for layer in backbone_model.layers:\n",
+ " layer.trainable = False\n",
+ " \n",
+ " outputs = []\n",
+ " for branch_number in range(1, 12):\n",
+ " y, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " outputs.append(y)\n",
+ " \n",
+ " model = tf.keras.models.Model(\n",
+ " inputs=backbone_model.get_layer(index=0).input,\n",
+ " outputs=outputs\n",
+ " )\n",
+ "\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def precompute(batch_size=32 * NUM_GPUS):\n",
+ " with DISTRIBUTED_STRATEGY.scope():\n",
+ " model = get_model()\n",
+ " for split in ['train', 'val', 'test']:\n",
+ " print(split)\n",
+ " total_count = sum([1 if file_name.startswith(split) else 0 for file_name in os.listdir(CACHE_DIR)])\n",
+ " batch_count = math.ceil(total_count / batch_size)\n",
+ " for batch_index in range(batch_count):\n",
+ " sys.stdout.write('\\r[%d/%d]' % (batch_index + 1, batch_count))\n",
+ " sys.stdout.flush()\n",
+ " images = []\n",
+ " labels = []\n",
+ " for sample_index in range(batch_index * batch_size, (batch_index + 1) * batch_size):\n",
+ " image_path = os.path.join(CACHE_DIR, '%s_%d.pkl' % (split, sample_index))\n",
+ " if os.path.exists(image_path): # last batch may contain less\n",
+ " with open(image_path, 'rb') as cache_file:\n",
+ " contents = pickle.load(cache_file)\n",
+ " images.append(contents['image'])\n",
+ " labels.append(np.sum(contents['density_map']))\n",
+ " outputs = model(np.array(images))\n",
+ " for branch_number in range(1, 12):\n",
+ " branch_outputs = outputs[branch_number - 1]\n",
+ " for i, branch_output in enumerate(branch_outputs):\n",
+ " sample_index = batch_index * batch_size + i\n",
+ " sample_path = os.path.join(\n",
+ " PRECOMPUTE_DIR,\n",
+ " 'disco',\n",
+ " '%s_branch%d_sample%d.pkl' % (split, branch_number, sample_index)\n",
+ " )\n",
+ " with open(sample_path, 'wb') as sample_file:\n",
+ " pickle.dump({\n",
+ " 'features': branch_output,\n",
+ " 'label': labels[i],\n",
+ " }, sample_file)\n",
+ " print() # newline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precompute()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/precompute_fashion_mnist_features.ipynb b/precompute_fashion_mnist_features.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4926feb885617f15806c5bf8a456f7b6751adf61
--- /dev/null
+++ b/precompute_fashion_mnist_features.ipynb
@@ -0,0 +1,164 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [7]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import random\n",
+ "import sys\n",
+ "import time\n",
+ "from skimage import transform\n",
+ "from vit_keras import vit\n",
+ "from vit_keras.layers import ClassToken, AddPositionEmbs, MultiHeadSelfAttention, TransformerBlock\n",
+ "\n",
+ "PRECOMPUTE_DIR = 'precompute'\n",
+ "PRECOMPUTE_FASHION_MNIST_DIR = os.path.join(PRECOMPUTE_DIR, 'fashion_mnist')\n",
+ "if not os.path.exists(PRECOMPUTE_FASHION_MNIST_DIR):\n",
+ " os.makedirs(PRECOMPUTE_FASHION_MNIST_DIR)\n",
+ "CACHE_DIR = 'fashion_mnist'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_branch_id(branch_number):\n",
+ " if branch_number == 1:\n",
+ " return 'transformer_block'\n",
+ " else:\n",
+ " return 'transformer_block_%d' % (branch_number - 1)\n",
+ "\n",
+ "def get_model():\n",
+ " backbone_model = tf.keras.models.load_model('vit_fashion_mnist_v1.h5', custom_objects={\n",
+ " 'ClassToken': ClassToken,\n",
+ " 'AddPositionEmbs': AddPositionEmbs,\n",
+ " 'MultiHeadSelfAttention': MultiHeadSelfAttention,\n",
+ " 'TransformerBlock': TransformerBlock,\n",
+ " })\n",
+ "\n",
+ " # freeze\n",
+ " for layer in backbone_model.layers:\n",
+ " layer.trainable = False\n",
+ " \n",
+ " outputs = []\n",
+ " for branch_number in range(1, 12):\n",
+ " y, _ = backbone_model.get_layer(get_branch_id(branch_number)).output\n",
+ " outputs.append(y)\n",
+ " \n",
+ " model = tf.keras.models.Model(\n",
+ " inputs=backbone_model.get_layer(index=0).input,\n",
+ " outputs=outputs\n",
+ " )\n",
+ "\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def precompute(batch_size=32 * NUM_GPUS):\n",
+ " with DISTRIBUTED_STRATEGY.scope():\n",
+ " model = get_model()\n",
+ " for split in ['train', 'val', 'test']:\n",
+ " print(split)\n",
+ " total_count = sum([1 if file_name.startswith(split) else 0 for file_name in os.listdir(CACHE_DIR)])\n",
+ " batch_count = math.ceil(total_count / batch_size)\n",
+ " for batch_index in range(batch_count):\n",
+ " sys.stdout.write('\\r[%d/%d]' % (batch_index + 1, batch_count))\n",
+ " sys.stdout.flush()\n",
+ " images = []\n",
+ " labels = []\n",
+ " for sample_index in range(batch_index * batch_size, (batch_index + 1) * batch_size):\n",
+ " image_path = os.path.join(CACHE_DIR, '%s_%d.pkl' % (split, sample_index))\n",
+ " if os.path.exists(image_path): # last batch may contain less\n",
+ " with open(image_path, 'rb') as cache_file:\n",
+ " contents = pickle.load(cache_file)\n",
+ " image = contents['image']\n",
+ " expanded = np.expand_dims(image, axis=-1)\n",
+ " repeated = np.repeat(expanded, 3, axis=-1)\n",
+ " images.append(repeated)\n",
+ " labels.append(contents['label'])\n",
+ " outputs = model(np.array(images))\n",
+ " for branch_number in range(1, 12):\n",
+ " branch_outputs = outputs[branch_number - 1]\n",
+ " for i, branch_output in enumerate(branch_outputs):\n",
+ " sample_index = batch_index * batch_size + i\n",
+ " sample_path = os.path.join(\n",
+ " PRECOMPUTE_FASHION_MNIST_DIR,\n",
+ " '%s_branch%d_sample%d.pkl' % (split, branch_number, sample_index)\n",
+ " )\n",
+ " with open(sample_path, 'wb') as sample_file:\n",
+ " pickle.dump({\n",
+ " 'features': branch_output,\n",
+ " 'label': labels[i],\n",
+ " }, sample_file)\n",
+ " print() # newline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precompute()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/train_cifar100_backbone.ipynb b/train_cifar100_backbone.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..435e1ea5e403f62d3f1f7664806f8378d4241ca0
--- /dev/null
+++ b/train_cifar100_backbone.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [4, 5, 6, 7]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import sys\n",
+ "from skimage import transform\n",
+ "from vit_keras import vit\n",
+ "\n",
+ "BATCH_SIZE = 8 * NUM_GPUS\n",
+ "IMAGE_SIZE = 384\n",
+ "CACHE_DIR = 'cifar100'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_model():\n",
+ " model = vit.vit_b16(\n",
+ " image_size=IMAGE_SIZE,\n",
+ " activation='sigmoid',\n",
+ " pretrained=True,\n",
+ " include_top=True,\n",
+ " pretrained_top=False,\n",
+ " classes=100\n",
+ " )\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cache_split(images, labels, split):\n",
+ " for i in range(images.shape[0]):\n",
+ " if (i + 1) % 100 == 0:\n",
+ " sys.stdout.write('\\r%d' % (i + 1))\n",
+ " sys.stdout.flush()\n",
+ " with open(os.path.join(CACHE_DIR, '%s_%d.pkl' % (split, i)), 'wb') as cache_file:\n",
+ " pickle.dump({\n",
+ " 'image': transform.resize(images[i], (IMAGE_SIZE, IMAGE_SIZE)),\n",
+ " 'label': labels[i],\n",
+ " }, cache_file)\n",
+ " print() # newline\n",
+ "\n",
+ "def cache_all():\n",
+ " (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar100.load_data()\n",
+ "\n",
+ " train_labels = tf.keras.utils.to_categorical(train_labels)\n",
+ " test_labels = tf.keras.utils.to_categorical(test_labels)\n",
+ "\n",
+ " val_index = int(len(train_images) * 0.8)\n",
+ " val_images = train_images[val_index:]\n",
+ " val_labels = train_labels[val_index:]\n",
+ " train_images = train_images[:val_index]\n",
+ " train_labels = train_labels[:val_index]\n",
+ "\n",
+ " cache_split(train_images, train_labels, 'train')\n",
+ " cache_split(val_images, val_labels, 'val')\n",
+ " cache_split(test_images, test_labels, 'test')\n",
+ "\n",
+ "class CIFAR100Sequence(tf.keras.utils.Sequence):\n",
+ " def __init__(self, split):\n",
+ " self.split = split\n",
+ " self.count = sum([1 if file_name.startswith(split) else 0 for file_name in os.listdir(CACHE_DIR)])\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __len__(self):\n",
+ " return math.ceil(self.count / BATCH_SIZE)\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " images = []\n",
+ " labels = []\n",
+ " for i in self.random_permutation[index * BATCH_SIZE:(index + 1) * BATCH_SIZE]:\n",
+ " with open(os.path.join(CACHE_DIR, '%s_%d.pkl' % (self.split, i)), 'rb') as cache_file:\n",
+ " contents = pickle.load(cache_file)\n",
+ " images.append(contents['image'])\n",
+ " labels.append(contents['label'])\n",
+ " return np.array(images), np.array(labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train(max_epochs):\n",
+ " with DISTRIBUTED_STRATEGY.scope():\n",
+ " model = get_model()\n",
+ " model.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
+ " loss='categorical_crossentropy',\n",
+ " metrics=['accuracy']\n",
+ " )\n",
+ "\n",
+ " lr_reduce = tf.keras.callbacks.ReduceLROnPlateau(\n",
+ " monitor='val_accuracy',\n",
+ " factor=0.6,\n",
+ " patience=2,\n",
+ " verbose=1,\n",
+ " mode='max',\n",
+ " min_lr=1e-7\n",
+ " )\n",
+ "\n",
+ " early_stop = tf.keras.callbacks.EarlyStopping(\n",
+ " monitor='val_accuracy',\n",
+ " patience=5,\n",
+ " verbose=1,\n",
+ " mode='max'\n",
+ " )\n",
+ "\n",
+ " model_checkpoint_file = 'vit_cifar100_v1.h5'\n",
+ "\n",
+ " checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
+ " model_checkpoint_file,\n",
+ " monitor='val_accuracy',\n",
+ " verbose=1,\n",
+ " save_weights_only=False,\n",
+ " save_best_only=True,\n",
+ " mode='max',\n",
+ " save_freq='epoch'\n",
+ " )\n",
+ "\n",
+ " history = model.fit(\n",
+ " CIFAR100Sequence('train'),\n",
+ " validation_data=CIFAR100Sequence('val'),\n",
+ " epochs=max_epochs,\n",
+ " shuffle=True,\n",
+ " callbacks=[\n",
+ " lr_reduce,\n",
+ " early_stop,\n",
+ " checkpoint\n",
+ " ],\n",
+ " verbose=1\n",
+ " )\n",
+ "\n",
+ " test_accuracy = model.evaluate(CIFAR100Sequence('test'))[1]\n",
+ "\n",
+ " return model, test_accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cache_all()\n",
+ "model, test_accuracy = train(100)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/train_cifar10_backbone.ipynb b/train_cifar10_backbone.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6cdb3097501039763eed07e3d7e1d8c13e4c4ade
--- /dev/null
+++ b/train_cifar10_backbone.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [4, 5, 6, 7]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import sys\n",
+ "from skimage import transform\n",
+ "from vit_keras import vit\n",
+ "\n",
+ "BATCH_SIZE = 8 * NUM_GPUS\n",
+ "IMAGE_SIZE = 384\n",
+ "CACHE_DIR = 'cifar10'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_model():\n",
+ " model = vit.vit_b16(\n",
+ " image_size=IMAGE_SIZE,\n",
+ " activation='sigmoid',\n",
+ " pretrained=True,\n",
+ " include_top=True,\n",
+ " pretrained_top=False,\n",
+ " classes=10\n",
+ " )\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cache_split(images, labels, split):\n",
+ " for i in range(images.shape[0]):\n",
+ " if (i + 1) % 100 == 0:\n",
+ " sys.stdout.write('\\r%d' % (i + 1))\n",
+ " sys.stdout.flush()\n",
+ " with open(os.path.join(CACHE_DIR, '%s_%d.pkl' % (split, i)), 'wb') as cache_file:\n",
+ " pickle.dump({\n",
+ " 'image': transform.resize(images[i], (IMAGE_SIZE, IMAGE_SIZE)),\n",
+ " 'label': labels[i],\n",
+ " }, cache_file)\n",
+ " print() # newline\n",
+ "\n",
+ "def cache_all():\n",
+ " (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()\n",
+ "\n",
+ " train_labels = tf.keras.utils.to_categorical(train_labels)\n",
+ " test_labels = tf.keras.utils.to_categorical(test_labels)\n",
+ "\n",
+ " val_index = int(len(train_images) * 0.8)\n",
+ " val_images = train_images[val_index:]\n",
+ " val_labels = train_labels[val_index:]\n",
+ " train_images = train_images[:val_index]\n",
+ " train_labels = train_labels[:val_index]\n",
+ "\n",
+ " cache_split(train_images, train_labels, 'train')\n",
+ " cache_split(val_images, val_labels, 'val')\n",
+ " cache_split(test_images, test_labels, 'test')\n",
+ "\n",
+ "class CIFAR10Sequence(tf.keras.utils.Sequence):\n",
+ " def __init__(self, split):\n",
+ " self.split = split\n",
+ " self.count = sum([1 if file_name.startswith(split) else 0 for file_name in os.listdir(CACHE_DIR)])\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __len__(self):\n",
+ " return math.ceil(self.count / BATCH_SIZE)\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " images = []\n",
+ " labels = []\n",
+ " for i in self.random_permutation[index * BATCH_SIZE:(index + 1) * BATCH_SIZE]:\n",
+ " with open(os.path.join(CACHE_DIR, '%s_%d.pkl' % (self.split, i)), 'rb') as cache_file:\n",
+ " contents = pickle.load(cache_file)\n",
+ " images.append(contents['image'])\n",
+ " labels.append(contents['label'])\n",
+ " return np.array(images), np.array(labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train(max_epochs):\n",
+ " with DISTRIBUTED_STRATEGY.scope():\n",
+ " model = get_model()\n",
+ " model.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
+ " loss='categorical_crossentropy',\n",
+ " metrics=['accuracy']\n",
+ " )\n",
+ "\n",
+ " lr_reduce = tf.keras.callbacks.ReduceLROnPlateau(\n",
+ " monitor='val_accuracy',\n",
+ " factor=0.6,\n",
+ " patience=2,\n",
+ " verbose=1,\n",
+ " mode='max',\n",
+ " min_lr=1e-7\n",
+ " )\n",
+ "\n",
+ " early_stop = tf.keras.callbacks.EarlyStopping(\n",
+ " monitor='val_accuracy',\n",
+ " patience=5,\n",
+ " verbose=1,\n",
+ " mode='max'\n",
+ " )\n",
+ "\n",
+ " model_checkpoint_file = 'vit_cifar10_v1.h5'\n",
+ "\n",
+ " checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
+ " model_checkpoint_file,\n",
+ " monitor='val_accuracy',\n",
+ " verbose=1,\n",
+ " save_weights_only=False,\n",
+ " save_best_only=True,\n",
+ " mode='max',\n",
+ " save_freq='epoch'\n",
+ " )\n",
+ "\n",
+ " history = model.fit(\n",
+ " CIFAR10Sequence('train'),\n",
+ " validation_data=CIFAR10Sequence('val'),\n",
+ " epochs=max_epochs,\n",
+ " shuffle=True,\n",
+ " callbacks=[\n",
+ " lr_reduce,\n",
+ " early_stop,\n",
+ " checkpoint\n",
+ " ],\n",
+ " verbose=1\n",
+ " )\n",
+ "\n",
+ " test_accuracy = model.evaluate(CIFAR10Sequence('test'))[1]\n",
+ "\n",
+ " return model, test_accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cache_all()\n",
+ "model, test_accuracy = train(100)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/train_disco_backbone.ipynb b/train_disco_backbone.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0b8cb3964740b2ac260b6498f58254ffd3f3c932
--- /dev/null
+++ b/train_disco_backbone.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [4, 5, 6, 7] # which GPUs to use\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import random\n",
+ "import scipy.io\n",
+ "import scipy.stats as st\n",
+ "import sys\n",
+ "import tensorflow_addons as tfa\n",
+ "from scipy import signal\n",
+ "from skimage.transform import resize\n",
+ "\n",
+ "DISCO_PATH = 'disco'\n",
+ "WAVEFORMS_PATH = os.path.join(DISCO_PATH, 'auds')\n",
+ "IMAGES_PATH = os.path.join(DISCO_PATH, 'imgs')\n",
+ "TRAIN_DENSITY_MAPS_PATH = os.path.join(DISCO_PATH, 'train')\n",
+ "VAL_DENSITY_MAPS_PATH = os.path.join(DISCO_PATH, 'val')\n",
+ "TEST_DENSITY_MAPS_PATH = os.path.join(DISCO_PATH, 'test')\n",
+ "CACHE_DIR = os.path.join(DISCO_PATH, 'vit_cache')\n",
+ "\n",
+ "from vit_keras import vit\n",
+ "from vit_keras.layers import ClassToken, AddPositionEmbs, MultiHeadSelfAttention, TransformerBlock\n",
+ "\n",
+ "IMAGE_SIZE = 384\n",
+ "VIDEO_PATCHES = (2, 3)\n",
+ "VIDEO_SIZE = (VIDEO_PATCHES[0] * IMAGE_SIZE, VIDEO_PATCHES[1] * IMAGE_SIZE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_model():\n",
+ " backbone_model = vit.vit_b16(\n",
+ " image_size=IMAGE_SIZE,\n",
+ " pretrained=True,\n",
+ " include_top=False,\n",
+ " pretrained_top=False\n",
+ " )\n",
+ " y = backbone_model.get_layer(index=-1).output\n",
+ " y = tf.keras.layers.Dense(1, name='regression_head')(y)\n",
+ " model = tf.keras.models.Model(\n",
+ " inputs=backbone_model.get_layer(index=0).input,\n",
+ " outputs=y\n",
+ " )\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_dataset_split(split):\n",
+ " examples = {}\n",
+ " for file_name in os.listdir(WAVEFORMS_PATH):\n",
+ " waveform_path = os.path.join(WAVEFORMS_PATH, file_name)\n",
+ " if os.path.isfile(waveform_path) and file_name.endswith('.wav'):\n",
+ " key = '.'.join(file_name.split('.')[:-1])\n",
+ " if key not in examples:\n",
+ " examples[key] = {}\n",
+ " examples[key]['waveform_path'] = waveform_path\n",
+ " for file_name in os.listdir(IMAGES_PATH):\n",
+ " image_path = os.path.join(IMAGES_PATH, file_name)\n",
+ " if os.path.isfile(image_path) and file_name.endswith('.jpg'):\n",
+ " key = '.'.join(file_name.split('.')[:-1])\n",
+ " if key not in examples:\n",
+ " examples[key] = {}\n",
+ " examples[key]['image_path'] = image_path\n",
+ " for file_name in os.listdir(TRAIN_DENSITY_MAPS_PATH):\n",
+ " density_map_path = os.path.join(TRAIN_DENSITY_MAPS_PATH, file_name)\n",
+ " if os.path.isfile(density_map_path) and file_name.endswith('.mat'):\n",
+ " key = '.'.join(file_name.split('.')[:-1])\n",
+ " if key not in examples:\n",
+ " examples[key] = {}\n",
+ " examples[key]['density_map_path'] = density_map_path\n",
+ " examples[key]['split'] = 'train'\n",
+ " for file_name in os.listdir(VAL_DENSITY_MAPS_PATH):\n",
+ " density_map_path = os.path.join(VAL_DENSITY_MAPS_PATH, file_name)\n",
+ " if os.path.isfile(density_map_path) and file_name.endswith('.mat'):\n",
+ " key = '.'.join(file_name.split('.')[:-1])\n",
+ " if key not in examples:\n",
+ " examples[key] = {}\n",
+ " examples[key]['density_map_path'] = density_map_path\n",
+ " examples[key]['split'] = 'val'\n",
+ " for file_name in os.listdir(TEST_DENSITY_MAPS_PATH):\n",
+ " density_map_path = os.path.join(TEST_DENSITY_MAPS_PATH, file_name)\n",
+ " if os.path.isfile(density_map_path) and file_name.endswith('.mat'):\n",
+ " key = '.'.join(file_name.split('.')[:-1])\n",
+ " if key not in examples:\n",
+ " examples[key] = {}\n",
+ " examples[key]['density_map_path'] = density_map_path\n",
+ " examples[key]['split'] = 'test'\n",
+ " final_examples = []\n",
+ " for key, info in examples.items():\n",
+ " if 'split' in info and info['split'] == split:\n",
+ " final_examples.append(info)\n",
+ " return final_examples\n",
+ "\n",
+ "def visualize_data(image, density_map):\n",
+ " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(20, 10))\n",
+ " ax1.imshow(image)\n",
+ " ax2.imshow(density_map)\n",
+ " ax1.axis('off')\n",
+ " ax2.axis('off')\n",
+ " plt.show()\n",
+ "\n",
+ "def get_gaussian_kernel(kernel_size, sigma):\n",
+ " \"\"\"\n",
+ " Returns a 2D Gaussian kernel.\n",
+ " from:\n",
+ " https://stackoverflow.com/questions/29731726/how-to-calculate-a-gaussian-kernel-matrix-efficiently-in-numpy\n",
+ " \"\"\"\n",
+ " x = np.linspace(-sigma, sigma, kernel_size + 1)\n",
+ " kern1d = np.diff(st.norm.cdf(x))\n",
+ " kern2d = np.outer(kern1d, kern1d)\n",
+ " return kern2d / kern2d.sum()\n",
+ "\n",
+ "def extract_patches(image):\n",
+ " patches = []\n",
+ " for i in range(VIDEO_PATCHES[0]):\n",
+ " for j in range(VIDEO_PATCHES[1]):\n",
+ " if len(image.shape) == 3:\n",
+ " patches.append(\n",
+ " image[i * IMAGE_SIZE:(i + 1) * IMAGE_SIZE, j * IMAGE_SIZE:(j + 1) * IMAGE_SIZE, :]\n",
+ " )\n",
+ " else:\n",
+ " patches.append(image[i * IMAGE_SIZE:(i + 1) * IMAGE_SIZE, j * IMAGE_SIZE:(j + 1) * IMAGE_SIZE])\n",
+ " return np.array(patches)\n",
+ "\n",
+ "def precompute_batches():\n",
+ " gaussian_kernel = get_gaussian_kernel(15, 4)\n",
+ " split_lens = []\n",
+ " resize_errors = []\n",
+ " for split in ['train', 'val', 'test']:\n",
+ " infos = get_dataset_split(split)\n",
+ " infos_len = len(infos)\n",
+ " split_lens.append(infos_len * VIDEO_PATCHES[0] * VIDEO_PATCHES[1])\n",
+ " for index in range(infos_len):\n",
+ " sys.stdout.write('\\r%d' % (index + 1))\n",
+ " sys.stdout.flush()\n",
+ "\n",
+ " info = infos[index]\n",
+ " crowd_image = plt.imread(info['image_path'], format='jpeg')\n",
+ " resized_crowd_image = resize(crowd_image, VIDEO_SIZE)\n",
+ " crowd_image_patches = extract_patches(resized_crowd_image)\n",
+ "\n",
+ " head_annotation = scipy.io.loadmat(info['density_map_path'])['map']\n",
+ " density_map = signal.convolve2d(head_annotation, gaussian_kernel)\n",
+ " resize_factor = density_map.shape[0] / VIDEO_SIZE[0] * density_map.shape[1] / VIDEO_SIZE[1]\n",
+ " resized_density_map = resize(density_map, VIDEO_SIZE) * resize_factor # to preserve sum\n",
+ " density_patches = extract_patches(resized_density_map)\n",
+ "\n",
+ " resize_errors.append(np.abs(np.sum(density_patches) - np.sum(resized_density_map)))\n",
+ "\n",
+ " for patch_index in range(VIDEO_PATCHES[0] * VIDEO_PATCHES[1]):\n",
+ " all_path = os.path.join(\n",
+ " CACHE_DIR,\n",
+ " '%s_%d.pkl' % (split, index * VIDEO_PATCHES[0] * VIDEO_PATCHES[1] + patch_index)\n",
+ " )\n",
+ " with open(all_path, 'wb') as all_file:\n",
+ " pickle.dump({\n",
+ " 'image': crowd_image_patches[patch_index],\n",
+ " 'density_map': density_patches[patch_index],\n",
+ " }, all_file)\n",
+ " print() # newline\n",
+ " if resize_errors:\n",
+ " print('Mean absolute resize error:', np.mean(resize_errors))\n",
+ " return split_lens\n",
+ "\n",
+ "def horizontal_flip(image):\n",
+ " return np.flip(image, axis=1)\n",
+ "\n",
+ "class CCSequence(tf.keras.utils.Sequence):\n",
+ " def __init__(self, split, batch_size):\n",
+ " self.split = split\n",
+ " self.split_len = sum([\n",
+ " 1 if file_name.startswith(self.split) else 0 for file_name in os.listdir(CACHE_DIR)\n",
+ " ])\n",
+ " self.batch_size = batch_size\n",
+ " self.random_permutation = np.random.permutation(self.split_len)\n",
+ "\n",
+ " def __len__(self):\n",
+ " return math.ceil(self.split_len / self.batch_size)\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " self.random_permutation = np.random.permutation(self.split_len)\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " spectrograms = []\n",
+ " images = []\n",
+ " density_maps = []\n",
+ " if self.split == 'test':\n",
+ " index_generator = range(\n",
+ " index * self.batch_size,\n",
+ " min((index + 1) * self.batch_size, self.split_len - 1)\n",
+ " )\n",
+ " else:\n",
+ " index_generator = self.random_permutation[index * self.batch_size:(index + 1) * self.batch_size]\n",
+ " for random_index in index_generator:\n",
+ " all_path = os.path.join(\n",
+ " CACHE_DIR,\n",
+ " '%s_%d.pkl' % (self.split, random_index)\n",
+ " )\n",
+ " with open(all_path, 'rb') as all_file:\n",
+ " data = pickle.load(all_file)\n",
+ " if self.split == 'train' and random.random() < 0.5: # flip augmentation\n",
+ " images.append(horizontal_flip(data['image']))\n",
+ " else:\n",
+ " images.append(data['image'])\n",
+ " density_maps.append(np.sum(data['density_map']))\n",
+ "\n",
+ " return np.array(images), np.array(density_maps)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train_backbone(epochs):\n",
+ " tf.keras.backend.clear_session()\n",
+ "\n",
+ " batch_size=4 * NUM_GPUS\n",
+ " train_sequence = CCSequence('train', batch_size)\n",
+ " val_sequence = CCSequence('val', batch_size)\n",
+ " test_sequence = CCSequence('test', batch_size)\n",
+ "\n",
+ " with DISTRIBUTED_STRATEGY.scope():\n",
+ " model = get_model()\n",
+ " model.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
+ " loss='mean_absolute_error',\n",
+ " metrics=['mean_absolute_error']\n",
+ " )\n",
+ "\n",
+ " lr_reduce = tf.keras.callbacks.ReduceLROnPlateau(\n",
+ " monitor='val_mean_absolute_error',\n",
+ " factor=0.6,\n",
+ " patience=2,\n",
+ " verbose=1,\n",
+ " mode='min',\n",
+ " min_lr=1e-7\n",
+ " )\n",
+ "\n",
+ " model_checkpoint_file = 'vit_cc_backbone_v2.h5'\n",
+ "\n",
+ " checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
+ " model_checkpoint_file,\n",
+ " monitor='val_mean_absolute_error',\n",
+ " verbose=1,\n",
+ " save_weights_only=False,\n",
+ " save_best_only=True,\n",
+ " mode='min',\n",
+ " save_freq='epoch'\n",
+ " )\n",
+ "\n",
+ " history = model.fit(\n",
+ " train_sequence,\n",
+ " validation_data=val_sequence,\n",
+ " epochs=epochs,\n",
+ " shuffle=True,\n",
+ " callbacks=[\n",
+ " lr_reduce,\n",
+ " checkpoint\n",
+ " ],\n",
+ " verbose=1\n",
+ " )\n",
+ "\n",
+ " model.evaluate(test_sequence)\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precompute_batches()\n",
+ "model = train_backbone(100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_sequence = CCSequence('test', 4 * NUM_GPUS)\n",
+ "model = tf.keras.models.load_model('vit_cc_backbone_v2.h5', custom_objects={\n",
+ " 'ClassToken': ClassToken,\n",
+ " 'AddPositionEmbs': AddPositionEmbs,\n",
+ " 'MultiHeadSelfAttention': MultiHeadSelfAttention,\n",
+ " 'TransformerBlock': TransformerBlock,\n",
+ "})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gt = None\n",
+ "out = None\n",
+ "for i, (images, density_maps) in enumerate(test_sequence):\n",
+ " sys.stdout.write('\\r%d' % (i + 1))\n",
+ " sys.stdout.flush()\n",
+ " if gt is not None:\n",
+ " gt = np.concatenate((gt, density_maps))\n",
+ " else:\n",
+ " gt = density_maps\n",
+ " if out is not None:\n",
+ " out = np.concatenate((out, model(images).numpy().flatten()))\n",
+ " else:\n",
+ " out = model(images).numpy().flatten()\n",
+ "print() # newline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mae = []\n",
+ "img_patches = VIDEO_PATCHES[0] * VIDEO_PATCHES[1]\n",
+ "for i in range(0, gt.shape[0], img_patches):\n",
+ " gt_subset = gt[i:i + img_patches]\n",
+ " out_subset = out[i:i + img_patches]\n",
+ " mae.append(np.abs(np.sum(gt_subset) - np.sum(out_subset)))\n",
+ "print(np.mean(np.array(mae)))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/train_fashion_mnist_backbone.ipynb b/train_fashion_mnist_backbone.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bcceff9360d2f8810c6168786864237a3180632e
--- /dev/null
+++ b/train_fashion_mnist_backbone.ipynb
@@ -0,0 +1,220 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SELECTED_GPUS = [7]\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu_number) for gpu_number in SELECTED_GPUS])\n",
+ "\n",
+ "import tensorflow as tf \n",
+ "\n",
+ "tf.get_logger().setLevel('INFO')\n",
+ "\n",
+ "assert len(tf.config.list_physical_devices('GPU')) > 0\n",
+ "\n",
+ "GPUS = tf.config.experimental.list_physical_devices('GPU')\n",
+ "for gpu in GPUS:\n",
+ " tf.config.experimental.set_memory_growth(gpu, True)\n",
+ "\n",
+ "DISTRIBUTED_STRATEGY = tf.distribute.MirroredStrategy(\n",
+ " cross_device_ops=tf.distribute.NcclAllReduce(),\n",
+ " devices=['/gpu:%d' % index for index in range(len(SELECTED_GPUS))]\n",
+ ")\n",
+ "\n",
+ "NUM_GPUS = DISTRIBUTED_STRATEGY.num_replicas_in_sync\n",
+ "\n",
+ "print('Number of devices: {}'.format(NUM_GPUS))\n",
+ "\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import sys\n",
+ "from skimage import transform\n",
+ "from vit_keras import vit\n",
+ "from vit_keras.layers import ClassToken, AddPositionEmbs, MultiHeadSelfAttention, TransformerBlock\n",
+ "\n",
+ "BATCH_SIZE = 8 * NUM_GPUS\n",
+ "IMAGE_SIZE = 384\n",
+ "CACHE_DIR = 'fashion_mnist'\n",
+ "if not os.path.exists(CACHE_DIR):\n",
+ " os.makedirs(CACHE_DIR)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_model():\n",
+ " model = vit.vit_b16(\n",
+ " image_size=IMAGE_SIZE,\n",
+ " activation='sigmoid',\n",
+ " pretrained=True,\n",
+ " include_top=True,\n",
+ " pretrained_top=False,\n",
+ " classes=10\n",
+ " )\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cache_split(images, labels, split):\n",
+ " for i in range(images.shape[0]):\n",
+ " if (i + 1) % 100 == 0:\n",
+ " sys.stdout.write('\\r%d' % (i + 1))\n",
+ " sys.stdout.flush()\n",
+ " with open(os.path.join(CACHE_DIR, '%s_%d.pkl' % (split, i)), 'wb') as cache_file:\n",
+ " pickle.dump({\n",
+ " 'image': transform.resize(images[i], (IMAGE_SIZE, IMAGE_SIZE)),\n",
+ " 'label': labels[i],\n",
+ " }, cache_file)\n",
+ " print() # newline\n",
+ "\n",
+ "def cache_all():\n",
+ " (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.fashion_mnist.load_data()\n",
+ "\n",
+ " train_labels = tf.keras.utils.to_categorical(train_labels)\n",
+ " test_labels = tf.keras.utils.to_categorical(test_labels)\n",
+ "\n",
+ " val_index = int(len(train_images) * 0.8)\n",
+ " val_images = train_images[val_index:]\n",
+ " val_labels = train_labels[val_index:]\n",
+ " train_images = train_images[:val_index]\n",
+ " train_labels = train_labels[:val_index]\n",
+ "\n",
+ " cache_split(train_images, train_labels, 'train')\n",
+ " cache_split(val_images, val_labels, 'val')\n",
+ " cache_split(test_images, test_labels, 'test')\n",
+ "\n",
+ "class FashionMNISTSequence(tf.keras.utils.Sequence):\n",
+ " def __init__(self, split):\n",
+ " self.split = split\n",
+ " self.count = sum([1 if file_name.startswith(split) else 0 for file_name in os.listdir(CACHE_DIR)])\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __len__(self):\n",
+ " return math.ceil(self.count / BATCH_SIZE)\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " self.random_permutation = np.random.permutation(self.count)\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " images = []\n",
+ " labels = []\n",
+ " for i in self.random_permutation[index * BATCH_SIZE:(index + 1) * BATCH_SIZE]:\n",
+ " with open(os.path.join(CACHE_DIR, '%s_%d.pkl' % (self.split, i)), 'rb') as cache_file:\n",
+ " contents = pickle.load(cache_file)\n",
+ " image = contents['image']\n",
+ " expanded = np.expand_dims(image, axis=-1)\n",
+ " repeated = np.repeat(expanded, 3, axis=-1)\n",
+ " images.append(repeated)\n",
+ " labels.append(contents['label'])\n",
+ " return np.array(images), np.array(labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train(max_epochs):\n",
+ " with DISTRIBUTED_STRATEGY.scope():\n",
+ " model = get_model()\n",
+ " model.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
+ " loss='categorical_crossentropy',\n",
+ " metrics=['accuracy']\n",
+ " )\n",
+ "\n",
+ " lr_reduce = tf.keras.callbacks.ReduceLROnPlateau(\n",
+ " monitor='val_accuracy',\n",
+ " factor=0.6,\n",
+ " patience=2,\n",
+ " verbose=1,\n",
+ " mode='max',\n",
+ " min_lr=1e-7\n",
+ " )\n",
+ "\n",
+ " early_stop = tf.keras.callbacks.EarlyStopping(\n",
+ " monitor='val_accuracy',\n",
+ " patience=5,\n",
+ " verbose=1,\n",
+ " mode='max'\n",
+ " )\n",
+ "\n",
+ " model_checkpoint_file = 'vit_fashion_mnist_v1.h5'\n",
+ "\n",
+ " checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
+ " model_checkpoint_file,\n",
+ " monitor='val_accuracy',\n",
+ " verbose=1,\n",
+ " save_weights_only=False,\n",
+ " save_best_only=True,\n",
+ " mode='max',\n",
+ " save_freq='epoch'\n",
+ " )\n",
+ "\n",
+ " history = model.fit(\n",
+ " FashionMNISTSequence('train'),\n",
+ " validation_data=FashionMNISTSequence('val'),\n",
+ " epochs=max_epochs,\n",
+ " shuffle=True,\n",
+ " callbacks=[\n",
+ " lr_reduce,\n",
+ " early_stop,\n",
+ " checkpoint\n",
+ " ],\n",
+ " verbose=1\n",
+ " )\n",
+ "\n",
+ " test_accuracy = model.evaluate(FashionMNISTSequence('test'))[1]\n",
+ "\n",
+ " return model, test_accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cache_all()\n",
+ "model, test_accuracy = train(100)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}