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docs: clean up some spacing issues in website (#1256)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -29,33 +29,33 @@ | |
| "import urllib.request\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import PIL, io\n", | ||
| "from PIL import Image\r\n", | ||
| "\r\n", | ||
| "vec_slice = udf(lambda vec, indices: (vec.toArray())[indices].tolist(), ArrayType(FloatType()))\r\n", | ||
| "arg_top_k = udf(lambda vec, k: (-vec.toArray()).argsort()[:k].tolist(), ArrayType(IntegerType()))\r\n", | ||
| "\r\n", | ||
| "def downloadBytes(url: str):\r\n", | ||
| " with urllib.request.urlopen(url) as url:\r\n", | ||
| " barr = url.read()\r\n", | ||
| " return barr\r\n", | ||
| "\r\n", | ||
| "def rotate_color_channel(bgr_image_array, height, width, nChannels):\r\n", | ||
| " B, G, R, *_ = np.asarray(bgr_image_array).reshape(height, width, nChannels).T\r\n", | ||
| " rgb_image_array = np.array((R, G, B)).T\r\n", | ||
| " return rgb_image_array\r\n", | ||
| " \r\n", | ||
| "def plot_superpixels(image_rgb_array, sp_clusters, weights, green_threshold=99):\r\n", | ||
| " superpixels = sp_clusters\r\n", | ||
| " green_value = np.percentile(weights, green_threshold)\r\n", | ||
| " img = Image.fromarray(image_rgb_array, mode='RGB').convert(\"RGBA\")\r\n", | ||
| " image_array = np.asarray(img).copy()\r\n", | ||
| " for (sp, v) in zip(superpixels, weights):\r\n", | ||
| " if v > green_value:\r\n", | ||
| " for (x, y) in sp:\r\n", | ||
| " image_array[y, x, 1] = 255\r\n", | ||
| " image_array[y, x, 3] = 200\r\n", | ||
| " plt.clf()\r\n", | ||
| " plt.imshow(image_array)\r\n", | ||
| "from PIL import Image\n", | ||
| "\n", | ||
| "vec_slice = udf(lambda vec, indices: (vec.toArray())[indices].tolist(), ArrayType(FloatType()))\n", | ||
| "arg_top_k = udf(lambda vec, k: (-vec.toArray()).argsort()[:k].tolist(), ArrayType(IntegerType()))\n", | ||
| "\n", | ||
| "def downloadBytes(url: str):\n", | ||
| " with urllib.request.urlopen(url) as url:\n", | ||
| " barr = url.read()\n", | ||
| " return barr\n", | ||
| "\n", | ||
| "def rotate_color_channel(bgr_image_array, height, width, nChannels):\n", | ||
| " B, G, R, *_ = np.asarray(bgr_image_array).reshape(height, width, nChannels).T\n", | ||
| " rgb_image_array = np.array((R, G, B)).T\n", | ||
| " return rgb_image_array\n", | ||
| " \n", | ||
| "def plot_superpixels(image_rgb_array, sp_clusters, weights, green_threshold=99):\n", | ||
| " superpixels = sp_clusters\n", | ||
| " green_value = np.percentile(weights, green_threshold)\n", | ||
| " img = Image.fromarray(image_rgb_array, mode='RGB').convert(\"RGBA\")\n", | ||
| " image_array = np.asarray(img).copy()\n", | ||
| " for (sp, v) in zip(superpixels, weights):\n", | ||
| " if v > green_value:\n", | ||
| " for (x, y) in sp:\n", | ||
| " image_array[y, x, 1] = 255\n", | ||
| " image_array[y, x, 3] = 200\n", | ||
| " plt.clf()\n", | ||
| " plt.imshow(image_array)\n", | ||
| " display()" | ||
| ], | ||
| "outputs": [], | ||
|
|
@@ -74,36 +74,36 @@ | |
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "source": [ | ||
| "from synapse.ml.io import *\r\n", | ||
| "\r\n", | ||
| "image_df = spark.read.image().load(\"wasbs://[email protected]/explainers/images/david-lusvardi-dWcUncxocQY-unsplash.jpg\")\r\n", | ||
| "display(image_df)\r\n", | ||
| "\r\n", | ||
| "# Rotate the image array from BGR into RGB channels for visualization later.\r\n", | ||
| "row = image_df.select(\"image.height\", \"image.width\", \"image.nChannels\", \"image.data\").head()\r\n", | ||
| "locals().update(row.asDict())\r\n", | ||
| "rgb_image_array = rotate_color_channel(data, height, width, nChannels)\r\n", | ||
| "\r\n", | ||
| "# Download the ONNX model\r\n", | ||
| "modelPayload = downloadBytes(\"https://mmlspark.blob.core.windows.net/publicwasb/ONNXModels/resnet50-v2-7.onnx\")\r\n", | ||
| "\r\n", | ||
| "featurizer = (\r\n", | ||
| " ImageTransformer(inputCol=\"image\", outputCol=\"features\")\r\n", | ||
| " .resize(224, True)\r\n", | ||
| " .centerCrop(224, 224)\r\n", | ||
| " .normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], color_scale_factor = 1/255)\r\n", | ||
| " .setTensorElementType(FloatType())\r\n", | ||
| ")\r\n", | ||
| "\r\n", | ||
| "onnx = (\r\n", | ||
| " ONNXModel()\r\n", | ||
| " .setModelPayload(modelPayload)\r\n", | ||
| " .setFeedDict({\"data\": \"features\"})\r\n", | ||
| " .setFetchDict({\"rawPrediction\": \"resnetv24_dense0_fwd\"})\r\n", | ||
| " .setSoftMaxDict({\"rawPrediction\": \"probability\"})\r\n", | ||
| " .setMiniBatchSize(1)\r\n", | ||
| ")\r\n", | ||
| "\r\n", | ||
| "from synapse.ml.io import *\n", | ||
| "\n", | ||
| "image_df = spark.read.image().load(\"wasbs://[email protected]/explainers/images/david-lusvardi-dWcUncxocQY-unsplash.jpg\")\n", | ||
| "display(image_df)\n", | ||
| "\n", | ||
| "# Rotate the image array from BGR into RGB channels for visualization later.\n", | ||
| "row = image_df.select(\"image.height\", \"image.width\", \"image.nChannels\", \"image.data\").head()\n", | ||
| "locals().update(row.asDict())\n", | ||
| "rgb_image_array = rotate_color_channel(data, height, width, nChannels)\n", | ||
| "\n", | ||
| "# Download the ONNX model\n", | ||
| "modelPayload = downloadBytes(\"https://mmlspark.blob.core.windows.net/publicwasb/ONNXModels/resnet50-v2-7.onnx\")\n", | ||
| "\n", | ||
| "featurizer = (\n", | ||
| " ImageTransformer(inputCol=\"image\", outputCol=\"features\")\n", | ||
| " .resize(224, True)\n", | ||
| " .centerCrop(224, 224)\n", | ||
| " .normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], color_scale_factor = 1/255)\n", | ||
| " .setTensorElementType(FloatType())\n", | ||
| ")\n", | ||
| "\n", | ||
| "onnx = (\n", | ||
| " ONNXModel()\n", | ||
| " .setModelPayload(modelPayload)\n", | ||
| " .setFeedDict({\"data\": \"features\"})\n", | ||
| " .setFetchDict({\"rawPrediction\": \"resnetv24_dense0_fwd\"})\n", | ||
| " .setSoftMaxDict({\"rawPrediction\": \"probability\"})\n", | ||
| " .setMiniBatchSize(1)\n", | ||
| ")\n", | ||
| "\n", | ||
| "model = Pipeline(stages=[featurizer, onnx]).fit(image_df)" | ||
| ], | ||
| "outputs": [], | ||
|
|
||
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