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| from pathlib import Path | |
| import torch | |
| import gradio as gr | |
| from torch import nn | |
| from PIL import Image | |
| import numpy as np | |
| LABELS = Path('class_names.txt').read_text().splitlines() | |
| model = nn.Sequential( | |
| nn.Conv2d(1, 32, 3, padding='same'), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(32, 64, 3, padding='same'), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, 3, padding='same'), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Flatten(), | |
| nn.Linear(1152, 256), | |
| nn.ReLU(), | |
| nn.Linear(256, len(LABELS)), | |
| ) | |
| state_dict = torch.load('pytorch_model.bin', map_location='cpu') | |
| model.load_state_dict(state_dict, strict=False) | |
| model.eval() | |
| def predict(im_dict): | |
| im_raw = im_dict['composite'][:,:,3] | |
| img = Image.fromarray(im_raw) | |
| img_small = img.resize([24,28],resample=0) | |
| im = np.array(np.uint8(img_small)) | |
| im = np.transpose(im,(1,0)) | |
| x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255. | |
| with torch.no_grad(): | |
| out = model(x) | |
| probabilities = torch.nn.functional.softmax(out[0], dim=0) | |
| values, indices = torch.topk(probabilities, 5) | |
| return {LABELS[i]: v.item() for i, v in zip(indices, values)} | |
| interface = gr.Interface( | |
| predict, | |
| inputs="sketchpad", | |
| outputs='label', | |
| title="Sketch Recognition", | |
| description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!", | |
| article = "<p style='text-align: center'>Sketch Recognition | Demo Model</p>", | |
| live=True) | |
| interface.launch(debug=True) |