Change the paths inside the notebook to relative folder paths (sony#911)

* Tutorial notebooks - change the folders paths inside the notebook to relative paths (for Jupyter execution support)
reuvenperetz · Jan 2, 2024 · fb5ab62 · fb5ab62
1 parent 8767c35
commit fb5ab62
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Showing 6 changed files with 37 additions and 37 deletions.
diff --git a/tutorials/notebooks/example_keras_effdet_lite0.ipynb b/tutorials/notebooks/example_keras_effdet_lite0.ipynb
@@ -106,8 +106,8 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "sys.path.insert(0,\"/content/local_mct\")\n",
-    "!pip install -r /content/local_mct/requirements.txt\n",
+    "sys.path.insert(0,\"./local_mct\")\n",
+    "!pip install -r ./local_mct/requirements.txt\n",
     "from tutorials.resources.efficientdet import EfficientDetKeras"
    ],
    "metadata": {
@@ -133,10 +133,10 @@
    "outputs": [],
    "source": [
     "!wget -nc http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
-    "!unzip -q -o annotations_trainval2017.zip -d /content/coco\n",
+    "!unzip -q -o annotations_trainval2017.zip -d ./coco\n",
     "!echo Done loading annotations\n",
     "!wget -nc http://images.cocodataset.org/zips/val2017.zip\n",
-    "!unzip -q -o val2017.zip -d /content/coco\n",
+    "!unzip -q -o val2017.zip -d ./coco\n",
     "!echo Done loading val2017 images"
    ],
    "metadata": {
@@ -213,7 +213,7 @@
     "        The DataLoader and evaluation object for calculating accuracy\n",
     "\n",
     "    \"\"\"\n",
-    "    root = '/content/coco'\n",
+    "    root = './coco'\n",
     "\n",
     "    args = dict(interpolation='bilinear', mean=None,\n",
     "                std=None, fill_color=None)\n",
@@ -299,7 +299,7 @@
     "\n",
     "model = EfficientDetKeras(config, pretrained_backbone=False).get_model([*config.image_size] + [3])\n",
     "\n",
-    "model.save('/content/model.keras')"
+    "model.save('./model.keras')"
    ],
    "metadata": {
     "collapsed": false
@@ -397,7 +397,7 @@
     "    target_kpi=kpi,\n",
     "    core_config=core_config,\n",
     "    target_platform_capabilities=tpc)\n",
-    "quant_model.save('/content/quant_model.keras')"
+    "quant_model.save('./quant_model.keras')"
    ],
    "metadata": {
     "collapsed": false

diff --git a/tutorials/notebooks/example_keras_imagenet.ipynb b/tutorials/notebooks/example_keras_imagenet.ipynb
@@ -176,7 +176,7 @@
    "outputs": [],
    "source": [
     "import torchvision\n",
-    "ds = torchvision.datasets.ImageNet(root='/content/imagenet', split='val')"
+    "ds = torchvision.datasets.ImageNet(root='./imagenet', split='val')"
    ],
    "metadata": {
     "collapsed": false
@@ -236,7 +236,7 @@
    "source": [
     "from typing import Generator\n",
     "\n",
-    "REPRESENTATIVE_DATASET_FOLDER = '/content/imagenet/val'\n",
+    "REPRESENTATIVE_DATASET_FOLDER = './imagenet/val'\n",
     "BATCH_SIZE = 50\n",
     "n_iter=10\n",
     "\n",
@@ -422,7 +422,7 @@
    },
    "outputs": [],
    "source": [
-    "TEST_DATASET_FOLDER = '/content/imagenet/val'\n",
+    "TEST_DATASET_FOLDER = './imagenet/val'\n",
     "def get_validation_dataset() -> tf.data.Dataset:\n",
     "    \"\"\"Load the validation dataset for evaluation.\n",
     "\n",
@@ -513,10 +513,10 @@
   {
    "cell_type": "code",
    "source": [
-    "mct.exporter.keras_export_model(model=quantized_model, save_model_path='/content/qmodel.tflite',\n",
+    "mct.exporter.keras_export_model(model=quantized_model, save_model_path='./qmodel.tflite',\n",
     "                                serialization_format=mct.exporter.KerasExportSerializationFormat.TFLITE, quantization_format=mct.exporter.QuantizationFormat.FAKELY_QUANT)\n",
     "\n",
-    "mct.exporter.keras_export_model(model=quantized_model, save_model_path='/content/qmodel.keras')"
+    "mct.exporter.keras_export_model(model=quantized_model, save_model_path='./qmodel.keras')"
    ],
    "metadata": {
     "id": "z3CA16-ojoFL"

diff --git a/tutorials/notebooks/example_keras_mobilenet_gptq.ipynb b/tutorials/notebooks/example_keras_mobilenet_gptq.ipynb
@@ -108,7 +108,7 @@
    "outputs": [],
    "source": [
     "import torchvision\n",
-    "torchvision.datasets.ImageNet(root='/content/imagenet', split='val')"
+    "torchvision.datasets.ImageNet(root='./imagenet', split='val')"
    ],
    "metadata": {
     "collapsed": false
@@ -172,7 +172,7 @@
     "    \"\"\"\n",
     "    print('loading dataset, this may take a few minutes ...')\n",
     "    dataset = tf.keras.utils.image_dataset_from_directory(\n",
-    "        directory='/content/imagenet/val',\n",
+    "        directory='./imagenet/val',\n",
     "        batch_size=batch_size,\n",
     "        image_size=[224, 224],\n",
     "        shuffle=True,\n",
@@ -321,7 +321,7 @@
     "         the validation dataset\n",
     "    \"\"\"\n",
     "    dataset = tf.keras.utils.image_dataset_from_directory(\n",
-    "        directory='/content/imagenet/val',\n",
+    "        directory='./imagenet/val',\n",
     "        batch_size=50,\n",
     "        image_size=[224, 224],\n",
     "        shuffle=False,\n",

diff --git a/tutorials/notebooks/example_keras_mobilenet_mixed_precision.ipynb b/tutorials/notebooks/example_keras_mobilenet_mixed_precision.ipynb
@@ -110,7 +110,7 @@
    "outputs": [],
    "source": [
     "import torchvision\n",
-    "torchvision.datasets.ImageNet(root='/content/imagenet', split='val')"
+    "torchvision.datasets.ImageNet(root='./imagenet', split='val')"
    ],
    "metadata": {
     "collapsed": false
@@ -170,7 +170,7 @@
     "    \"\"\"\n",
     "    print('loading dataset, this may take a few minutes ...')\n",
     "    dataset = tf.keras.utils.image_dataset_from_directory(\n",
-    "        directory='/content/imagenet/val',\n",
+    "        directory='./imagenet/val',\n",
     "        batch_size=batch_size,\n",
     "        image_size=[224, 224],\n",
     "        shuffle=True,\n",
@@ -337,7 +337,7 @@
     "         the validation dataset\n",
     "    \"\"\"\n",
     "    dataset = tf.keras.utils.image_dataset_from_directory(\n",
-    "        directory='/content/imagenet/val',\n",
+    "        directory='./imagenet/val',\n",
     "        batch_size=50,\n",
     "        image_size=[224, 224],\n",
     "        shuffle=False,\n",

diff --git a/tutorials/notebooks/example_keras_nanodet_plus.ipynb b/tutorials/notebooks/example_keras_nanodet_plus.ipynb
@@ -68,9 +68,9 @@
    "outputs": [],
    "source": [
     "!git clone https://github.com/sony/model_optimization.git local_mct\n",
-    "!pip install -r /content/local_mct/requirements.txt\n",
+    "!pip install -r ./local_mct/requirements.txt\n",
     "import sys\n",
-    "sys.path.insert(0,\"/content/local_mct\")"
+    "sys.path.insert(0,\"./local_mct\")"
    ],
    "metadata": {
     "collapsed": false
@@ -93,10 +93,10 @@
    "outputs": [],
    "source": [
     "!wget -nc http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
-    "!unzip -q -o annotations_trainval2017.zip -d /content/coco\n",
+    "!unzip -q -o annotations_trainval2017.zip -d ./coco\n",
     "!echo Done loading annotations\n",
     "!wget -nc http://images.cocodataset.org/zips/val2017.zip\n",
-    "!unzip -q -o val2017.zip -d /content/coco\n",
+    "!unzip -q -o val2017.zip -d ./coco\n",
     "!echo Done loading val2017 images"
    ],
    "metadata": {
@@ -124,7 +124,7 @@
    "source": [
     "import torch\n",
     "\n",
-    "PRETRAINED_WEIGHTS_FILE = '/content/nanodet-plus-m-1.5x_416.pth'\n",
+    "PRETRAINED_WEIGHTS_FILE = './nanodet-plus-m-1.5x_416.pth'\n",
     "pretrained_weights = torch.load(PRETRAINED_WEIGHTS_FILE, map_location=torch.device('cpu'))['state_dict']"
    ]
   },
@@ -210,8 +210,8 @@
     "import cv2\n",
     "from tutorials.resources.utils.coco_evaluation import coco_dataset_generator, CocoEval\n",
     "\n",
-    "EVAL_DATASET_FOLDER = '/content/coco/val2017'\n",
-    "EVAL_DATASET_ANNOTATION_FILE = '/content/coco/annotations/instances_val2017.json'\n",
+    "EVAL_DATASET_FOLDER = './coco/val2017'\n",
+    "EVAL_DATASET_ANNOTATION_FILE = './coco/annotations/instances_val2017.json'\n",
     "\n",
     "BATCH_SIZE = 5\n",
     "\n",
@@ -269,8 +269,8 @@
     "import model_compression_toolkit as mct\n",
     "from typing import Iterator, Tuple, List\n",
     "\n",
-    "REPRESENTATIVE_DATASET_FOLDER = '/content/coco/val2017'\n",
-    "REPRESENTATIVE_DATASET_ANNOTATION_FILE = '/content/coco/annotations/instances_val2017.json'\n",
+    "REPRESENTATIVE_DATASET_FOLDER = './coco/val2017'\n",
+    "REPRESENTATIVE_DATASET_ANNOTATION_FILE = './coco/annotations/instances_val2017.json'\n",
     "n_iters = 20\n",
     "\n",
     "# Load representative dataset\n",

diff --git a/tutorials/notebooks/example_keras_yolov8n.ipynb b/tutorials/notebooks/example_keras_yolov8n.ipynb
@@ -69,9 +69,9 @@
    "outputs": [],
    "source": [
     "!git clone https://github.com/sony/model_optimization.git local_mct\n",
-    "!pip install -r /content/local_mct/requirements.txt\n",
+    "!pip install -r ./local_mct/requirements.txt\n",
     "import sys\n",
-    "sys.path.insert(0,\"/content/local_mct\")"
+    "sys.path.insert(0,\"./local_mct\")"
    ],
    "metadata": {
     "collapsed": false
@@ -94,10 +94,10 @@
    "outputs": [],
    "source": [
     "!wget -nc http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
-    "!unzip -q -o annotations_trainval2017.zip -d /content/coco\n",
+    "!unzip -q -o annotations_trainval2017.zip -d ./coco\n",
     "!echo Done loading annotations\n",
     "!wget -nc http://images.cocodataset.org/zips/val2017.zip\n",
-    "!unzip -q -o val2017.zip -d /content/coco\n",
+    "!unzip -q -o val2017.zip -d ./coco\n",
     "!echo Done loading val2017 images"
    ],
    "metadata": {
@@ -126,7 +126,7 @@
     "!wget -nc https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt\n",
     "import torch\n",
     "\n",
-    "PRETRAINED_WEIGHTS_FILE = '/content/yolov8n.pt'\n",
+    "PRETRAINED_WEIGHTS_FILE = './yolov8n.pt'\n",
     "pretrained_weights = torch.load(PRETRAINED_WEIGHTS_FILE)['model'].state_dict()"
    ]
   },
@@ -159,7 +159,7 @@
     "INPUT_RESOLUTION = 640\n",
     "\n",
     "# Generate Yolov8n model \n",
-    "model = yolov8_keras('/content/local_mct/tutorials/resources/yolov8/yolov8n.yaml', INPUT_RESOLUTION)\n",
+    "model = yolov8_keras('./local_mct/tutorials/resources/yolov8/yolov8n.yaml', INPUT_RESOLUTION)\n",
     "\n",
     "# Set the pre-trained weights\n",
     "load_state_dict(model, state_dict_torch=pretrained_weights)\n",
@@ -205,8 +205,8 @@
     "from tutorials.resources.utils.coco_evaluation import coco_dataset_generator, CocoEval\n",
     "from tutorials.resources.yolov8.yolov8_preprocess import yolov8_preprocess\n",
     "\n",
-    "EVAL_DATASET_FOLDER = '/content/coco/val2017'\n",
-    "EVAL_DATASET_ANNOTATION_FILE = '/content/coco/annotations/instances_val2017.json'\n",
+    "EVAL_DATASET_FOLDER = './coco/val2017'\n",
+    "EVAL_DATASET_ANNOTATION_FILE = './coco/annotations/instances_val2017.json'\n",
     "BATCH_SIZE = 5\n",
     "\n",
     "# Load COCO evaluation set\n",
@@ -264,8 +264,8 @@
     "import model_compression_toolkit as mct\n",
     "from typing import Iterator, Tuple, List\n",
     "\n",
-    "REPRESENTATIVE_DATASET_FOLDER = '/content/coco/val2017/'\n",
-    "REPRESENTATIVE_DATASET_ANNOTATION_FILE = '/content/coco/annotations/instances_val2017.json'\n",
+    "REPRESENTATIVE_DATASET_FOLDER = './coco/val2017/'\n",
+    "REPRESENTATIVE_DATASET_ANNOTATION_FILE = './coco/annotations/instances_val2017.json'\n",
     "n_iters = 20\n",
     "\n",
     "# Load representative dataset\n",