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import time |
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from options.test_options import TestOptions |
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from data.data_loader_test import CreateDataLoader |
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from models.networks import ResUnetGenerator, load_checkpoint |
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from models.afwm import AFWM |
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import torch.nn as nn |
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import os |
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import numpy as np |
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import torch |
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import cv2 |
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import torch.nn.functional as F |
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import io |
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from PIL import Image |
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from flask import Flask, jsonify, request |
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from tqdm.auto import tqdm |
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app = Flask(__name__) |
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opt = TestOptions().parse() |
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with open('demo.txt', 'w') as file: |
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lines = [f'input.png {cloth_img_fn}\n' for cloth_img_fn in os.listdir('dataset/test_clothes')] |
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file.writelines(lines) |
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warp_model = AFWM("", 3) |
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warp_model.eval() |
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warp_model.cuda() |
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load_checkpoint(warp_model, 'checkpoints/PFAFN/warp_model_final.pth') |
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gen_model = ResUnetGenerator(7, 4, 5, ngf=64, norm_layer=nn.BatchNorm2d) |
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gen_model.eval() |
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gen_model.cuda() |
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load_checkpoint(gen_model, 'checkpoints/PFAFN/gen_model_final.pth') |
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def save_cloth_transfers(image_bytes): |
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opt_name = 'demo' |
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opt_batchSize = 1 |
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image = Image.open(io.BytesIO(image_bytes)) |
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image.save('dataset/test_img/input.png') |
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data_loader = CreateDataLoader(opt) |
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dataset = data_loader.load_data() |
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dataset_size = len(data_loader) |
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start_epoch, epoch_iter = 1, 0 |
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total_steps = (start_epoch - 1) * dataset_size + epoch_iter |
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step = 0 |
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step_per_batch = dataset_size / opt_batchSize |
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for epoch in range(1, 2): |
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for i, data in tqdm(enumerate(dataset, start=epoch_iter)): |
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iter_start_time = time.time() |
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total_steps += opt_batchSize |
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epoch_iter += opt_batchSize |
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real_image = data['image'] |
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clothes = data['clothes'] |
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edge = data['edge'] |
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edge = torch.FloatTensor((edge.detach().numpy() > 0.5).astype(np.int)) |
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clothes = clothes * edge |
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flow_out = warp_model(real_image.cuda(), clothes.cuda()) |
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warped_cloth, last_flow, = flow_out |
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warped_edge = F.grid_sample(edge.cuda(), last_flow.permute(0, 2, 3, 1), |
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mode='bilinear', padding_mode='zeros') |
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gen_inputs = torch.cat([real_image.cuda(), warped_cloth, warped_edge], 1) |
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gen_outputs = gen_model(gen_inputs) |
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p_rendered, m_composite = torch.split(gen_outputs, [3, 1], 1) |
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p_rendered = torch.tanh(p_rendered) |
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m_composite = torch.sigmoid(m_composite) |
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m_composite = m_composite * warped_edge |
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p_tryon = warped_cloth * m_composite + p_rendered * (1 - m_composite) |
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path = 'results/' + opt_name |
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os.makedirs(path, exist_ok=True) |
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sub_path = path + '/PFAFN' |
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os.makedirs(sub_path, exist_ok=True) |
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if step % 1 == 0: |
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a = real_image.float().cuda() |
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b = clothes.cuda() |
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c = p_tryon |
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combine = torch.cat([a[0], b[0], c[0]], 2).squeeze() |
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cv_img = (combine.permute(1, 2, 0).detach().cpu().numpy() + 1) / 2 |
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rgb = (cv_img * 255).astype(np.uint8) |
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bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR) |
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cv2.imwrite(sub_path + '/' + str(step) + '.jpg', bgr) |
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step += 1 |
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if epoch_iter >= dataset_size: |
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break |
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return True |
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@app.route('/predict') |
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def predict(): |
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if request.method == 'POST': |
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print('#'*100) |
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file = request.files['file'] |
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image_bytes = file.read() |
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save_cloth_transfers(image_bytes=image_bytes) |
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return jsonify({'status': True}) |
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else: |
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return jsonify({'message': "Only accept POST requests"}) |
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if __name__ == '__main__': |
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app.run() |
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