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Convolution interpolation explanation addition #2 
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‎colab_notebooks/Notebook06_Convolutions_PyTorch.ipynb‎

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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/ssec/WAF_ML_Tutorial_Part2/blob/main/colab_notebooks/Notebook06_Convolutions_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"<a href=\"https://colab.research.google.com/github/ssec/WAF_ML_Tutorial_Part2/blob/2-confusion-about-interpolating/colab_notebooks/Notebook06_Convolutions_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"metadata": {
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{
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"data": {
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{
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"colab": {
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"base_uri": "https://localhost:8080/",
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{
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" radar_field: 4)\n",
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"Dimensions without coordinates: grid_row, grid_column, radar_height, radar_field\n",
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"Data variables:\n",
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" radar_image_matrix (grid_row, grid_column, radar_height, radar_field) float32 197kB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a7755d39-2484-4e97-ae1d-454290a50143' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a7755d39-2484-4e97-ae1d-454290a50143' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>grid_row</span>: 32</li><li><span>grid_column</span>: 32</li><li><span>radar_height</span>: 12</li><li><span>radar_field</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9a5d17a4-de47-407b-a07c-85494b34bafb' class='xr-section-summary-in' type='checkbox' checked><label for='section-9a5d17a4-de47-407b-a07c-85494b34bafb' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>radar_image_matrix</span></div><div class='xr-var-dims'>(grid_row, grid_column, radar_height, radar_field)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-59812b5c-822f-469f-a22f-77ed4ca76955' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59812b5c-822f-469f-a22f-77ed4ca76955' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f28124eb-5b12-4d34-ba0a-4485b53f44d0' class='xr-var-data-in' type='checkbox'><label for='data-f28124eb-5b12-4d34-ba0a-4485b53f44d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[49152 values with dtype=float32]</pre></div></li></ul></div></li></ul></div></div>"
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" radar_image_matrix (grid_row, grid_column, radar_height, radar_field) float32 197kB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4183d7c7-e4db-4160-9040-d00243e6ea7f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4183d7c7-e4db-4160-9040-d00243e6ea7f' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>grid_row</span>: 32</li><li><span>grid_column</span>: 32</li><li><span>radar_height</span>: 12</li><li><span>radar_field</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c8eb729d-53f9-4cb6-9fe8-715d4648f54f' class='xr-section-summary-in' type='checkbox' checked><label for='section-c8eb729d-53f9-4cb6-9fe8-715d4648f54f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>radar_image_matrix</span></div><div class='xr-var-dims'>(grid_row, grid_column, radar_height, radar_field)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc79c9eb-fac4-45ba-8a24-e0cf9f9b1289' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc79c9eb-fac4-45ba-8a24-e0cf9f9b1289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11a08a46-3ed6-4e67-a766-665b61804627' class='xr-var-data-in' type='checkbox'><label for='data-11a08a46-3ed6-4e67-a766-665b61804627' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[49152 values with dtype=float32]</pre></div></li></ul></div></li></ul></div></div>"
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"metadata": {
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"colab": {
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},
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{
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"cell_type": "code",
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"source": [
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"# %%\n",
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"# Convert xarray data to numpy, then to a torch tensor\n",
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"input_data = ds_sample.radar_image_matrix.isel(radar_height=slice(0,1),radar_field=0).values\n",
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"\n",
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"# Step 1: Convert to Tensor\n",
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"# The shape of input_data is likely (32, 32, 1) or (32, 32) depending on how xarray slices it.\n",
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"# We ensure it starts as a float tensor.\n",
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"print(f\"{input_data.shape}=\") # 32, 32, 1; (H, W, C)\n",
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"input_tensor = torch.from_numpy(input_data).float()\n",
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"\n",
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"# Step 2: Ensure Correct Shape (Batch, Channel, Height, Width)\n",
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"# If the input is (Height, Width, Channel), we need to permute it to (Channel, Height, Width)\n",
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"if input_tensor.ndim == 3 and input_tensor.shape[-1] == 1:\n",
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" input_tensor = input_tensor.permute(2, 0, 1) # (H, W, C) -> (C, H, W)\n",
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"elif input_tensor.ndim == 2:\n",
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" input_tensor = input_tensor.unsqueeze(0) # Add Channel dim if missing: (H, W) -> (C, H, W)\n",
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"\n",
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"# Add Batch Dimension: (C, H, W) -> (Batch, C, H, W)\n",
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"input_tensor = input_tensor.unsqueeze(0)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "zo_JAULelGBC",
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"outputId": "313fa14f-3be3-47e5-cbc5-48e964a80505"
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},
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"execution_count": 5,
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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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"(32, 32, 1)=\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Now our data (`input_tensor`) is in the correct shape: Batch, Channel, Height, Width.\n",
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"\n",
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"One last step before applying the convolution - we will resize `input_tensor`. This is simply for the sake of demonstrating the sharpening convolution. You generally would not do this in a ML model setting. The interpolation will smooth the original image a little, and the final sharpened convolution with look a little nicer. (Feel free to re-run this interpolation later with size=(32, 32) to see the convolution result below without the interpolation.)\n",
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"\n",
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"Note that we use the pytorch functional interface to interpolate the pytorch tensor object `input_tensor`. Again, we aren't performing any machine learning here. We're simply demonstrating how convolutions work."
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],
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"metadata": {
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"id": "cc5ABhj5nQ2e"
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}
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{
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"cell_type": "code",
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"source": [
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"# interpolate from (1, 1, 32, 32), (1, 1, 36, 36)\n",
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"more_points = F.interpolate(input_tensor, size=(36, 36), mode='bilinear', align_corners=False)\n"
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],
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"metadata": {
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"id": "rnhhUlxnnLBT"
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{
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"cell_type": "markdown",
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"source": [
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"Finally we can define, configure, and apply our pytorch convolution operation, then plot the results."
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"more_points.shape=torch.Size([1, 1, 36, 36]), convolution_result.shape=torch.Size([1, 1, 34, 34])\n"
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}
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],
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"source": [
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"# %%\n",
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"# Convert xarray data to numpy, then to a torch tensor\n",
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"input_data = ds_sample.radar_image_matrix.isel(radar_height=slice(0,1),radar_field=0).values\n",
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"\n",
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"# Step 1: Convert to Tensor\n",
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"# The shape of input_data is likely (32, 32, 1) or (32, 32) depending on how xarray slices it.\n",
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"# We ensure it starts as a float tensor.\n",
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"print(f\"{input_data.shape}=\") # 32, 32, 1; (H, W, C)\n",
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"input_tensor = torch.from_numpy(input_data).float()\n",
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"\n",
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"# Step 2: Ensure Correct Shape (Batch, Channel, Height, Width)\n",
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"# If the input is (Height, Width, Channel), we need to permute it to (Channel, Height, Width)\n",
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"if input_tensor.ndim == 3 and input_tensor.shape[-1] == 1:\n",
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" input_tensor = input_tensor.permute(2, 0, 1) # (H, W, C) -> (C, H, W)\n",
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"elif input_tensor.ndim == 2:\n",
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" input_tensor = input_tensor.unsqueeze(0) # Add Channel dim if missing: (H, W) -> (C, H, W)\n",
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"\n",
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"# Add Batch Dimension: (C, H, W) -> (Batch, C, H, W)\n",
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"input_tensor = input_tensor.unsqueeze(0)\n",
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"\n",
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"# Step 3: Resize using interpolate\n",
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"# Now input_tensor is (1, 1, 32, 32), which matches the 2D spatial size expected by interpolate\n",
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"more_points = F.interpolate(input_tensor, size=(36, 36), mode='bilinear', align_corners=False)\n",
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"\n",
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"# Define the sharpen filter\n",
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"kernel_weights = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])\n",
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"\n",
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"# Define conv with specific weights.\n",
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"# Define conv\n",
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"conv = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, bias=False)\n",
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"\n",
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"# Set the weights manually\n",
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"# Set the conv weights manually\n",
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"with torch.no_grad():\n",
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" # We reshape our 3x3 kernel to (1, 1, 3, 3)\n",
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" conv.weight.data = torch.from_numpy(kernel_weights).float().view(1, 1, 3, 3)\n",
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"\n",
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"\n",
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"fig,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5))\n",
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"text": [
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"output_type": "display_data",
@@ -1393,6 +1452,7 @@
13931452
"#run the data through\n",
13941453
"res = conv(more_points)\n",
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"\n",
1455+
"print(f\"{more_points.shape=}, {res.shape=}\")\n",
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"\n",
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"fig,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5))\n",
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"ax1.imshow(more_points.squeeze(), vmin=0, vmax=60, cmap='Spectral_r')\n",

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