delete app.py

app.py

@@ -1,154 +0,0 @@
-import os
-import gc
-import PIL
-import glob
-import timm
-import torch
-import nopdb
-import pickle
-import torchvision
-
-import numpy as np
-import gradio as gr
-from torch import nn
-from PIL import Image
-import matplotlib.pyplot as plt
-import IPython.display as ipd
-from typing import Tuple, Dict
-from timeit import default_timer as timer
-from timm.data import resolve_data_config, create_transform
-
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-vision_transformer_weights = torch.load('pytorch_vit_b_16_timm.pth',
-                                        map_location=torch.device('cpu'))
-
-vision_transformer = timm.create_model('vit_base_patch16_224', pretrained=False)
-
-vision_transformer.head = nn.Linear(in_features=768,
-                                    out_features=38)
-
-vision_transformer.load_state_dict(vision_transformer_weights)
-
-from torchvision import datasets, transforms
-
-data_transforms = transforms.Compose([
-    transforms.Resize(size=(256, 256)),
-    transforms.CenterCrop(size=224),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                         std=[0.229, 0.224, 0.225],)
-])
-
-def inv_normalize(tensor):
-    """Normalize an image tensor back to the 0-255 range."""
-    tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min()) * (256 - 1e-5)
-    return tensor
-
-def inv_transform(tensor, normalize=True):
-    """Convert a tensor back to an image."""
-    tensor = inv_normalize(tensor)
-    array = tensor.detach().cpu().numpy()
-    array = array.transpose(1, 2, 0).astype(np.uint8)
-    return PIL.Image.fromarray(array)
-
-with open('class_names.ob', 'rb') as fp:
-    class_names = pickle.load(fp)
-
-img = PIL.Image.open('examples/TomatoYellowCurlVirus6.JPG').convert('RGB')
-img_transformed = data_transforms(img)
-
-def predict_disease(image) -> Tuple[Dict, float]:
-    """Return prediction classes with probabilities for an input image."""
-    input = data_transforms(image)
-    start_time = timer()
-    prediction_dict = {}
-    with torch.inference_mode():
-        [logits] = vision_transformer(input[None])
-        probs = torch.softmax(logits, dim=0)
-        topk_prob, topk_id = torch.topk(probs, 3)
-        for i in range(topk_prob.size(0)):
-            prediction_dict[class_names[topk_id[i]]] = topk_prob[i].item()
-    prediction_time = round(timer() - start_time, 5)
-    return prediction_dict, prediction_time
-
-def predict_tensor(img_tensor):
-    """Return prediction classes with probabilities for an input image."""
-    with torch.inference_mode():
-        [logits] = vision_transformer(img_tensor[None])
-        probs = torch.softmax(logits, dim=0)
-        topk_prob, topk_id = torch.topk(probs, 3)
-
-with nopdb.capture_call(vision_transformer.blocks[5].attn.forward) as attn_call:
-    predict_tensor(img_transformed)
-
-def plot_attention(image, layer_num):
-    """Given an input image, plot the average attention weight given to each image patch by each attention head."""
-    input_data = data_transforms(image)
-    with nopdb.capture_call(vision_transformer.blocks[int(layer_num)-1].attn.forward) as attn_call:
-        predict_tensor(img_transformed)
-    attn = attn_call.locals['attn'][0]
-    with torch.inference_mode():
-        # loop over attention heads
-        attention_block_num = 0
-        for h_weights in attn:
-            h_weights = h_weights.mean(axis=-2)  # average over all attention keys
-            h_weights = h_weights[1:]  # skip the [class] token
-            attention_block_num += 1
-            plot_weights(input_data, h_weights, attention_block_num)
-    attention_maps = glob.glob('storage/*.png')
-    return attention_maps
-
-def plot_weights(input_data, patch_weights, num_attention_block):
-    """Display the image: Brighter the patch, higher is the attention."""
-    # multiply each patch of the input image by the corresponding weight
-    plot = inv_normalize(input_data.clone())
-    for i in range(patch_weights.shape[0]):
-        x = i * 16 % 224
-        y = i // (224 // 16) * 16
-        plot[:, y:y + 16, x:x + 16] *= patch_weights[i]
-    attn_map_img = inv_transform(plot, normalize=False)
-    attn_map_img = attn_map_img.resize((224, 224), Image.ANTIALIAS)
-    attn_map_img.save(f"storage/attention_map_{num_attention_block}.png", "PNG")
-
-title_classify = "Image Based Plant Disease Identification 🍃🤓"
-
-description_classify = """Finetuned a Vision Transformer Base (Patch Size: 16 | Image Size: 224) architecture to 
-                  identify the plant disease."""
-
-article_classify = """Upload an image from the example list or choose one of your own. [Dataset Classes](https://data.mendeley.com/datasets/tywbtsjrjv/1)"""
-
-title_attention = "Visualize Attention Weights 🧊🔍"
-
-description_attention = """The Vision Transformer Base architecture has 12 transformer Encoder layers (12 attention heads in each)."""
-
-article_attention = """From the dropdown menu, choose the Encoder layer whose attention weights you would like to visualize."""
-
-classify_interface = gr.Interface(
-    fn=predict_disease,
-    inputs=gr.Image(type="pil", label="Image"),
-    outputs=[gr.Label(num_top_classes=3, label="Predictions"),
-              gr.Number(label="Prediction time (secs)")],
-    examples=example_list,
-    title=title_classify,
-    description=description_classify,
-    article=article_classify,
-    thumbnail="https://images.unsplash.com/photo-1470058869958-2a77ade41c02?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80"
-)
-
-attention_interface = gr.Interface(
-    fn=plot_attention,
-    inputs=[gr.Image(type="pil", label="Image"),
-            gr.Dropdown(choices=["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"],
-                        label="Attention Layer", value="6")],
-    outputs=gr.Gallery(value=attention_maps, label="Attention Maps").style(grid=(3, 4)),
-    examples=example_list,
-    title=title_attention,
-    description=description_attention,
-    article=article_attention,
-    thumbnail="https://images.unsplash.com/photo-1470058869958-2a77ade41c02?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80"
-)
-
-demo = gr.TabbedInterface([classify_interface, attention_interface], 
-                          ["Identify Disease", "Visualize Attention Map"], 
-                          title="NatureAI Diagnostics🧑🩺").launch(debug=False, share=True)