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finished helper functions #44

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finished helper functions #44

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228 changes: 126 additions & 102 deletions tutorials/W1D1_Generalization/W1D1_Tutorial1.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -8,21 +8,130 @@
"outputs": [],
"source": [
"# Install \n",
"!pip install transformers gradio sentencepiece\n",
"!pip install numpy \n",
"\n"
"#!pip install transformers gradio sentencepiece numpy torch torchvision trdg Pillow==9.5.0\n",
"\n",
"# Core Python Data Science and Image Processing Libraries\n",
"import numpy as np\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Deep Learning and Model Specific Libraries\n",
"import torch\n",
"from torchvision import transforms\n",
"from transformers import TrOCRProcessor, VisionEncoderDecoderModel\n",
"\n",
"# Utility and Interface Libraries\n",
"import gradio as gr\n",
"from IPython.display import IFrame\n",
"from trdg.generators import GeneratorFromStrings\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b00326ca-807d-460f-adf4-767e94bc0ccc",
"id": "1bf34b9a-1dd5-458a-b390-0fa12609d532",
"metadata": {},
"outputs": [],
"source": [
"from transformers import TrOCRProcessor, VisionEncoderDecoderModel\n",
"import gradio as gr\n",
"# @title Plotting functions\n",
"\n",
"def display_image(image_path):\n",
" \"\"\"Display an image from a given file path.\n",
"\n",
" Args:\n",
" image_path (str): The path to the image file.\n",
" \"\"\"\n",
" # Open the image\n",
" image = Image.open(image_path)\n",
" if image.mode != 'RGB':\n",
" image = image.convert('RGB')\n",
"\n",
" # Display the image\n",
" plt.imshow(image)\n",
" plt.axis('off') # Turn off the axis\n",
" plt.show()\n",
"\n",
"def display_transformed_images(image, transformations):\n",
" \"\"\"\n",
" Apply a list of transformations to an image and display them.\n",
"\n",
" Args:\n",
" image (Tensor): The input image as a tensor.\n",
" transformations (list): A list of torchvision transformations to apply.\n",
" \"\"\"\n",
" # Convert tensor image to PIL Image for display\n",
" pil_image = transforms.ToPILImage()(image)\n",
"\n",
" fig, axs = plt.subplots(len(transformations) + 1, 1, figsize=(5, 15))\n",
" axs[0].imshow(pil_image)\n",
" axs[0].set_title('Original')\n",
" axs[0].axis('off')\n",
"\n",
" for i, transform in enumerate(transformations):\n",
" # Apply transformation if it's not the placeholder\n",
" if transform != \"Custom ElasticTransform Placeholder\":\n",
" transformed_image = transform(image)\n",
" # Convert transformed tensor image to PIL Image for display\n",
" display_image = transforms.ToPILImage()(transformed_image)\n",
" axs[i+1].imshow(display_image)\n",
" axs[i+1].set_title(transform.__class__.__name__)\n",
" axs[i+1].axis('off')\n",
" else:\n",
" axs[i+1].text(0.5, 0.5, 'ElasticTransform Placeholder', ha='center')\n",
" axs[i+1].axis('off')\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"def display_original_and_transformed_images(original_tensor, transformed_tensor):\n",
" \"\"\"\n",
" Display the original and transformed images side by side.\n",
"\n",
" Args:\n",
" original_tensor (Tensor): The original image as a tensor.\n",
" transformed_tensor (Tensor): The transformed image as a tensor.\n",
" \"\"\"\n",
" fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
"\n",
" # Display original image\n",
" original_image = original_tensor.permute(1, 2, 0) # Convert from (C, H, W) to (H, W, C)\n",
" axs[0].imshow(original_image)\n",
" axs[0].set_title('Original')\n",
" axs[0].axis('off')\n",
"\n",
" # Display transformed image\n",
" transformed_image = transformed_tensor.permute(1, 2, 0) # Convert from (C, H, W) to (H, W, C)\n",
" axs[1].imshow(transformed_image)\n",
" axs[1].set_title('Transformed')\n",
" axs[1].axis('off')\n",
"\n",
" plt.show()\n",
"\n",
"def display_generated_images(generator):\n",
" \"\"\"\n",
" Display images generated from strings.\n",
"\n",
" Args:\n",
" generator (GeneratorFromStrings): A generator that produces images from strings.\n",
" \"\"\"\n",
" plt.figure(figsize=(15, 3))\n",
" for i, (text_img, lbl) in enumerate(generator, 1):\n",
" ax = plt.subplot(1, len(generator.strings) * generator.count // len(generator.strings), i)\n",
" plt.imshow(text_img)\n",
" plt.title(f\"Example {i}\")\n",
" plt.axis('off')\n",
" \n",
" plt.tight_layout()\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b00326ca-807d-460f-adf4-767e94bc0ccc",
"metadata": {},
"outputs": [],
"source": [
"# Load the pre-trained TrOCR model and processor\n",
"model = VisionEncoderDecoderModel.from_pretrained(\"microsoft/trocr-small-handwritten\")\n",
"processor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-small-handwritten\")\n",
Expand All @@ -43,7 +152,6 @@
" description=\"Demo for Microsoft’s TrOCR, an encoder-decoder model for OCR on single-text line images.\",\n",
")\n",
"\n",
"\n",
"# Launch the interface\n",
"interface.launch()\n"
]
Expand All @@ -55,9 +163,6 @@
"metadata": {},
"outputs": [],
"source": [
"#Imports\n",
"from transformers import TrOCRProcessor, VisionEncoderDecoderModel\n",
"\n",
"# Load the model\n",
"model = VisionEncoderDecoderModel.from_pretrained(\"microsoft/trocr-small-handwritten\")\n"
]
Expand Down Expand Up @@ -101,18 +206,7 @@
"# Count parameters in the decoder\n",
"decoder_params = count_parameters(model.decoder)\n",
"\n",
"encoder_params, decoder_params\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf37c12f-77c3-4b90-841d-901b0ee4bb8c",
"metadata": {},
"outputs": [],
"source": [
"# Imports\n",
"import numpy as np"
"encoder_params, decoder_params"
]
},
{
Expand Down Expand Up @@ -253,41 +347,16 @@
"time_to_write_lifetimes_llama = calculate_writing_time(total_words_llama2, words_per_day, days_per_week, weeks_per_year, average_human_lifespan)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76cafcd3-4c19-4a5d-a9b5-c0c65e1c777a",
"metadata": {},
"outputs": [],
"source": [
"#Install and imports\n",
"!pip install torch torchvision trdg\n",
"!pip install Pillow==9.5.0\n",
"import torch\n",
"from torchvision import transforms\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78127c97-955e-46e0-869d-7f7285449f70",
"metadata": {},
"outputs": [],
"source": [
"# Path to the image\n",
"# Usage\n",
"image_path = '../static/img_1235.jpg'\n",
"\n",
"# Open the image\n",
"image = Image.open(image_path)\n",
"if image.mode != 'RGB':\n",
" image = image.convert('RGB')\n",
"\n",
"# Display the image\n",
"plt.imshow(image)\n",
"plt.axis('off') # Turn off the axis\n",
"plt.show()"
"display_image(image_path)"
]
},
{
Expand All @@ -298,8 +367,8 @@
"outputs": [],
"source": [
"# Convert PIL Image to Tensor\n",
"to_tensor = transforms.ToTensor()\n",
"image = to_tensor(image)\n",
"image = Image.open(image_path)\n",
"image = transforms.ToTensor()(image)\n",
"\n",
"# Define each transformation separately\n",
"# RandomAffine: applies rotations, translations, scaling. Here, rotates by up to ±15 degrees,\n",
Expand All @@ -321,26 +390,7 @@
"# A list of all transformations for iteration\n",
"transformations = [affine, elastic, perspective, erasing, gaussian_blur]\n",
"\n",
"# Vertical stacking of transformations\n",
"fig, axs = plt.subplots(len(transformations) + 1, 1, figsize=(5, 15)) \n",
"\n",
"# Permute the image dimensions from (C, H, W) to (H, W, C) for display\n",
"display_image = image.permute(1, 2, 0)\n",
"\n",
"axs[0].imshow(display_image)\n",
"axs[0].set_title('Original')\n",
"axs[0].axis('off')\n",
"\n",
"for i, transform in enumerate(transformations, 1):\n",
" augmented_image = transforms.Compose([transform])(image)\n",
" # Permute the augmented image dimensions from (C, H, W) to (H, W, C) for display\n",
" display_augmented = augmented_image.permute(1, 2, 0)\n",
"\n",
" axs[i].imshow(display_augmented)\n",
" axs[i].set_title(transform.__class__.__name__)\n",
" axs[i].axis('off')\n",
"\n",
"plt.show()"
"display_transformed_images(image, transformations)"
]
},
{
Expand All @@ -357,8 +407,7 @@
"image = Image.open(image_path)\n",
"\n",
"# Convert PIL Image to Tensor\n",
"to_tensor = transforms.ToTensor()\n",
"image_tensor = to_tensor(image)\n",
"image_tensor = transforms.ToTensor()(image)\n",
"\n",
"# Define your transformations here\n",
"affine = transforms.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1))\n",
Expand All @@ -379,20 +428,8 @@
"# Apply combined transformation\n",
"augmented_image_tensor = all_transforms(image_tensor)\n",
"\n",
"# Set up matplotlib subplots\n",
"fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
"\n",
"# Display original image\n",
"axs[0].imshow(image_tensor.permute(1, 2, 0)) # Change to (H, W, C) for display\n",
"axs[0].set_title('Original')\n",
"axs[0].axis('off')\n",
"\n",
"# Display augmented image\n",
"axs[1].imshow(augmented_image_tensor.permute(1, 2, 0)) # Change to (H, W, C) for display\n",
"axs[1].set_title('Transformed')\n",
"axs[1].axis('off')\n",
"\n",
"plt.show()\n"
"# Assuming 'image_tensor' and 'augmented_image_tensor' are defined as in your snippet\n",
"display_original_and_transformed_images(image_tensor, augmented_image_tensor)\n"
]
},
{
Expand All @@ -402,9 +439,6 @@
"metadata": {},
"outputs": [],
"source": [
"from trdg.generators import GeneratorFromStrings\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Define your strings\n",
"strings = ['Hello', 'This is Patrick', 'From NMA'] # Update this list as needed\n",
"\n",
Expand All @@ -417,16 +451,8 @@
" fonts=['Purisa'] # Update or add more fonts as needed\n",
")\n",
"\n",
"# Setup matplotlib figure and display images\n",
"plt.figure(figsize=(15, 3))\n",
"for i, (text_img, lbl) in enumerate(generator, 1):\n",
" ax = plt.subplot(1, 5, i) # Adjust the number of subplots if needed\n",
" plt.imshow(text_img)\n",
" plt.title(f\"Example {i}\")\n",
" plt.axis('off')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n"
"# Call the function with your generator\n",
"display_generated_images(generator)"
]
},
{
Expand All @@ -436,8 +462,6 @@
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import IFrame\n",
"\n",
"IFrame(\"https://www.calligrapher.ai/\", width=800, height=600)"
]
}
Expand Down
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