112 lines
2.0 KiB
Plaintext
112 lines
2.0 KiB
Plaintext
{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.8.8 64-bit ('tensorflow2': conda)",
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"metadata": {
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"interpreter": {
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"hash": "ee99f7bd678359d45d92ad289bdab8f6bcfaae579cfd1bff07d2bb16d7ba024f"
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}
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import fast_depth_functional as fd"
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]
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},
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{
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"source": [
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"### Windows GPU Fix"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"1 Physical GPUs, 1 Logical GPUs\n"
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]
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}
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],
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"source": [
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"fd.fix_windows_gpu()"
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]
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},
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{
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"source": [
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"## Create and compile the model"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = fd.mobilenet_nnconv5(weights='imagenet')\n",
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"fd.compile(model)"
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]
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},
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{
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"source": [
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"## Train the model using the nyu_v2 dataset (default, huge download)"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fd.train(existing_model=model, save_file='../fast-depth-experimental')"
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]
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},
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{
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"source": [
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"## Evaluate the trained model"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fd.evaluate(model)"
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]
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}
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]
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} |