Spaces:
Runtime error
Runtime error
File size: 3,592 Bytes
418196b 9c43502 418196b 90f97af 418196b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
import numpy as np
import gradio as gr
from base import TrainingObjective, ModelBackbone, model_input_dict, TransformationType
import os
import json
from utils import get_model_architecture, get_transforms_to_apply_, get_transforms_to_apply
import torch
from PIL import Image
from torchvision import transforms
from matplotlib import pyplot as plt
from dataset import AcneDataset
import config
from torch.utils.data import DataLoader
from tqdm import tqdm
curr_dir = os.path.dirname(os.path.abspath(__file__))
#go up one level
# curr_dir = os.path.dirname(curr_dir)
model_dir = os.path.join(curr_dir, 'model')
model_name = 'model_0'
model_dir = os.path.join(model_dir, model_name)
# model_dir = '/Users/suyashharlalka/Documents/workspace/gabit/acne_classification/model/model_0'
model_config = os.path.join(model_dir, 'config.json')
with open(model_config, 'r') as f:
config_json = json.load(f)
model = get_model_architecture(config_json)
model_path = os.path.join(model_dir, 'model.pth')
model.load_state_dict(torch.load(model_path,map_location=torch.device('cpu')))
testing_transform = config_json['TRANSFORMS_TO_APPLY']
testing_transform = [TransformationType[transform] for transform in testing_transform if transform != 'RANDOM_HORIZONTAL_FLIP']
testing_transform = [get_transforms_to_apply_(transform) for transform in testing_transform]
testing_transform = transforms.Compose(testing_transform)
# data_dir = config.DATASET_PATH
# isLimited = config.IS_LIMITED
# dataset = AcneDataset(data_dir, limit=isLimited)
# dataset.transform = testing_transform
# dataloader = DataLoader(dataset, batch_size=32, shuffle=False, num_workers=0)
device = torch.device('cpu')
def predict_acne(img):
img = testing_transform(img)
img = img.unsqueeze(0)
img = img.to(device)
output = model(img)
predicted = torch.round(output.data)
predicted = predicted.squeeze(1)
predicted = predicted.cpu().numpy()[0]
return predicted
# img_dir = '/Users/suyash.harlalka/Desktop/personal/acne_classification/dataset/Classification/JPEGImages'
# img_names = os.listdir(img_dir)
# for name in img_names:
# if name.endswith('.jpg'):
# img_path = os.path.join(img_dir, name)
# img = Image.open(img_path)
# img = img.convert('RGB')
# print(predict_acne(img))
correct = 0
total = 0
mainLabel = []
predictedLabel = []
model.to(device)
model.eval()
# for name in tqdm(img_names):
# if name.endswith('.jpg'):
# img_path = os.path.join(img_dir, name)
# img = Image.open(img_path)
# predicted = predict_acne(img)
# label = int(name.split('_')[0][-1])
# label = int(label)
# correct += (predicted == label).sum().item()
# mainLabel.append(label)
# predictedLabel.append(predicted)
# total += 1
import gradio as gr
gr.Interface(fn=predict_acne,
inputs=gr.Image(type="pil"),
outputs="number",
).launch()
# for images, labels in tqdm(dataloader, desc="Processing", unit="batch"):
# images = images.to(device)
# labels = labels.to(device)
# outputs = model(images)
# # print('Outputs:{outputs}, label: {label}'.format(outputs=outputs, label=labels))
# predicted = torch.round(outputs.data)
# predicted = predicted.squeeze(1)
# total += labels.size(0)
# correct += (predicted == labels).sum().item()
# mainLabel.extend(labels.cpu().numpy())
# predictedLabel.extend(predicted.cpu().numpy())
# print(f'Accuracy of the network on the {total} test images: {100 * correct / total}%')
|