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| # -*- coding: utf-8 -*- | |
| """With os FASHION-EYE_VITON-HD Integrated Full Model Final.ipynb | |
| Automatically generated by Colaboratory. | |
| """ | |
| # !rm -rf sample_data | |
| # !rm -rf fashion-eye-try-on/ | |
| BASE_DIR = "/home/user/app/fashion-eye-try-on" | |
| import os | |
| os.system(f"git clone https://huggingface.co/spaces/sidharthism/fashion-eye-try-on {BASE_DIR}") | |
| # !pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 | |
| # !pip install -r /content/fashion-eye-try-on/requirements.txt | |
| os.system("pip install torch>=1.6.0 torchvision -f https://download.pytorch.org/whl/cu92/torch_stable.html") | |
| os.system("pip install opencv-python torchgeometry gdown Pillow") | |
| os.system(f"cd {BASE_DIR}") | |
| # Download and save checkpoints for cloth mask generation | |
| os.system(f"rm -rf {BASE_DIR}/cloth_segmentation/checkpoints/") | |
| os.system(f"gdown --id 1mhF3yqd7R-Uje092eypktNl-RoZNuiCJ -O {BASE_DIR}/cloth_segmentation/checkpoints/") | |
| os.system(f"git clone https://github.com/shadow2496/VITON-HD {BASE_DIR}/VITON-HD") | |
| #checkpoints | |
| os.system(f"gdown 1RM4OthSM6V4r7kWCu8SbPIPY14Oz8B2u -O {BASE_DIR}/VITON-HD/checkpoints/alias_final.pth") | |
| os.system(f"gdown 1MBHBddaAs7sy8W40jzLmNL83AUh035F1 -O {BASE_DIR}/VITON-HD/checkpoints/gmm_final.pth") | |
| os.system(f"gdown 1MBHBddaAs7sy8W40jzLmNL83AUh035F1 -O {BASE_DIR}/VITON-HD/checkpoints/gmm_final.pth") | |
| os.system(f"gdown 17U1sooR3mVIbe8a7rZuFIF3kukPchHfZ -O {BASE_DIR}/VITON-HD/checkpoints/seg_final.pth") | |
| #test data | |
| os.system(f"gdown 1ncEHn_6liOot8sgt3A2DOFJBffvx8tW8 -O {BASE_DIR}/VITON-HD/datasets/test_pairs.txt") | |
| os.system(f"gdown 1ZA2C8yMOprwc0TV4hvrt0X-ljZugrClq -O {BASE_DIR}/VITON-HD/datasets/test.zip") | |
| os.system(f"unzip {BASE_DIR}/VITON-HD/datasets/test.zip -d {BASE_DIR}/VITON-HD/datasets/") | |
| #@title To clear all the already existing test data | |
| # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/image | |
| # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/image-parse | |
| # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/cloth | |
| # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/cloth-mask | |
| # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/openpose-img | |
| # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/openpose-json | |
| """Paddle | |
| """ | |
| os.system(f"git clone https://huggingface.co/spaces/sidharthism/pipeline_paddle {BASE_DIR}/pipeline_paddle") | |
| # Required for paddle and gradio (Jinja2 dependency) | |
| os.system("pip install paddlepaddle-gpu pymatting") | |
| os.system(f"pip install -r {BASE_DIR}/pipeline_paddle/requirements.txt") | |
| os.system(f"rm -rf {BASE_DIR}/pipeline_paddle/models") | |
| if not os.path.exists(f"{BASE_DIR}/pipeline_paddle/models/ppmatting-hrnet_w18-human_1024.pdparams"): | |
| if not os.path.exists(f"{BASE_DIR}/pipeline_paddle/models"): | |
| os.mkdir(f"{BASE_DIR}/pipeline_paddle/models") | |
| os.system(f"wget https://paddleseg.bj.bcebos.com/matting/models/ppmatting-hrnet_w18-human_1024.pdparams -O {BASE_DIR}/pipeline_paddle/models/ppmatting-hrnet_w18-human_1024.pdparams") | |
| # !wget "https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz" -O "/content/fashion-eye-try-on/pipeline_paddle/models/hrnet_w18_ssld.tar.gz" | |
| """Initialization | |
| Pose estimator - open pose | |
| """ | |
| # Clone openpose model repo | |
| # os.system(f"git clone https://github.com/CMU-Perceptual-Computing-Lab/openpose.git {BASE_DIR}/openpose") | |
| #@ Building and Installation of openpose model | |
| import os | |
| import subprocess | |
| from os.path import exists, join, basename, splitext | |
| project_name = f"{BASE_DIR}/openpose" | |
| print(project_name) | |
| if not exists(project_name): | |
| # see: https://github.com/CMU-Perceptual-Computing-Lab/openpose/issues/949 | |
| # install new CMake becaue of CUDA10 | |
| os.system(f"wget -q https://cmake.org/files/v3.13/cmake-3.13.0-Linux-x86_64.tar.gz") | |
| os.system(f"sudo tar xfz cmake-3.13.0-Linux-x86_64.tar.gz --strip-components=1 -C /usr/local") | |
| # clone openpose | |
| os.system(f"cd {BASE_DIR} && git clone -q --depth 1 https://github.com/CMU-Perceptual-Computing-Lab/openpose.git") | |
| os.system("sudo sed -i 's/execute_process(COMMAND git checkout master WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}\/3rdparty\/caffe)/execute_process(COMMAND git checkout f019d0dfe86f49d1140961f8c7dec22130c83154 WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}\/3rdparty\/caffe)/g' %s/openpose/CMakeLists.txt" % (BASE_DIR, )) | |
| # install system dependencies | |
| os.system("sudo apt-get -qq install -y libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler libgflags-dev libgoogle-glog-dev liblmdb-dev opencl-headers ocl-icd-opencl-dev libviennacl-dev") | |
| # build openpose | |
| print("Building openpose ... May take nearly 15 mins to build ...") | |
| os.system(f"sudo cd {BASE_DIR}/openpose && rm -rf {BASE_DIR}/openpose/build || true && mkdir {BASE_DIR}/openpose/build && cd {BASE_DIR}/openpose/build && cmake .. && make -j`nproc`") | |
| print("Openpose successfully build and installed.") | |
| # subprocess.Popen(f"cd {BASE_DIR}/openpose && rm -rf {BASE_DIR}/openpose/build || true && mkdir {BASE_DIR}/openpose/build && cd {BASE_DIR}/openpose/build && cmake .. && make -j`nproc`") | |
| # subprocess.call(["cd", f"{BASE_DIR}/openpose"]) | |
| # subprocess.check_output(["rm", "-rf", f"{BASE_DIR}/openpose/build || true"]) | |
| # subprocess.check_output(["mkdir", f"{BASE_DIR}/openpose/build"]) | |
| # subprocess.check_output(["cd", f"{BASE_DIR}/openpose/build"]) | |
| # subprocess.check_output(["cmake", ".."]) | |
| # subprocess.check_output(["make","-j`nproc`"]) | |
| # !cd {BASE_DIR}/openpose && rm -rf {BASE_DIR}/openpose/build || true && mkdir {BASE_DIR}/openpose/build && cd {BASE_DIR}/openpose/build && cmake .. && make -j`nproc` | |
| """Self correction human parsing""" | |
| os.system(f"git clone https://github.com/PeikeLi/Self-Correction-Human-Parsing.git {BASE_DIR}/human_parse") | |
| os.system(f"cd {BASE_DIR}/human_parse") | |
| os.system(f"mkdir {BASE_DIR}/human_parse/checkpoints") | |
| # !mkdir inputs | |
| # !mkdir outputs | |
| dataset = 'lip' | |
| import gdown | |
| dataset_url = 'https://drive.google.com/uc?id=1k4dllHpu0bdx38J7H28rVVLpU-kOHmnH' | |
| output = f'{BASE_DIR}/human_parse/checkpoints/final.pth' | |
| gdown.download(dataset_url, output, quiet=False) | |
| # For human parse | |
| os.system("pip install ninja") | |
| """Preprocessing | |
| """ | |
| # png to jpg | |
| def convert_to_jpg(path): | |
| from PIL import Image | |
| import os | |
| if os.path.exists(path): | |
| cl = Image.open(path) | |
| jpg_path = path[:-4] + ".jpg" | |
| cl.save(jpg_path) | |
| def resize_img(path): | |
| from PIL import Image | |
| print(path) | |
| im = Image.open(path) | |
| im = im.resize((768, 1024), Image.BICUBIC) | |
| im.save(path) | |
| def remove_ipynb_checkpoints(): | |
| import os | |
| os.system(f"rm -rf {BASE_DIR}/VITON-HD/datasets/test/image/.ipynb_checkpoints") | |
| os.system(f"rm -rf {BASE_DIR}/VITON-HD/datasets/test/cloth/.ipynb_checkpoints") | |
| os.system(f"rm -rf {BASE_DIR}/VITON-HD/datasets/test/cloth-mask/.ipynb_checkpoints") | |
| # os.chdir('/content/fashion-eye-try-on') | |
| def preprocess(): | |
| remove_ipynb_checkpoints() | |
| for path in os.listdir(f'{BASE_DIR}/VITON-HD/datasets/test/image/'): | |
| resize_img(f'{BASE_DIR}/VITON-HD/datasets/test/image/{path}') | |
| for path in os.listdir(f'{BASE_DIR}/VITON-HD/datasets/test/cloth/'): | |
| resize_img(f'{BASE_DIR}/VITON-HD/datasets/test/cloth/{path}') | |
| # for path in os.listdir('/content/fashion-eye-try-on/VITON-HD/datasets/test/cloth-mask/'): | |
| # resize_img(f'/content/fashion-eye-try-on/VITON-HD/datasets/test/cloth-mask/{path}') | |
| """Paddle - removing background | |
| """ | |
| # PPMatting hrnet 1024 | |
| # --fg_estimate True - for higher quality output but slower prediction | |
| def upload_remove_background_and_save_person_image(person_img): | |
| # !export CUDA_VISIBLE_DEVICES=0 | |
| person_img = person_img.resize((768, 1024), Image.BICUBIC) | |
| if os.path.exists(f"{BASE_DIR}/pipeline_paddle/image/person.jpg"): | |
| os.remove(f"{BASE_DIR}/pipeline_paddle/image/person.jpg") | |
| person_img.save(f"{BASE_DIR}/pipeline_paddle/image/person.jpg") | |
| # resize_img(f'/content/fashion-eye-try-on/pipeline_paddle/image/person.jpg') | |
| os.system(f"cd {BASE_DIR}/pipeline_paddle/") | |
| os.system(f"python {BASE_DIR}/pipeline_paddle/bg_replace.py \ | |
| --config {BASE_DIR}/pipeline_paddle/configs/ppmatting/ppmatting-hrnet_w18-human_1024.yml \ | |
| --model_path {BASE_DIR}/pipeline_paddle/models/ppmatting-hrnet_w18-human_1024.pdparams \ | |
| --image_path {BASE_DIR}/pipeline_paddle/image/person.jpg \ | |
| --background 'w' \ | |
| --save_dir {BASE_DIR}/VITON-HD/datasets/test/image \ | |
| --fg_estimate True") | |
| # --save_dir /content/fashion-eye-try-on/pipeline_paddle/output \ | |
| try: | |
| convert_to_jpg(f"{BASE_DIR}/VITON-HD/datasets/test/image/person.png") | |
| # os.remove("/content/fashion-eye-try-on/pipeline_paddle/output/person_alpha.png") | |
| os.remove(f"{BASE_DIR}/VITON-HD/datasets/test/image/person_alpha.png") | |
| # os.remove("/content/fashion-eye-try-on/pipeline_paddle/output/person_rgba.png") | |
| os.remove(f"{BASE_DIR}/VITON-HD/datasets/test/image/person_rgba.png") | |
| os.system(f"cd {BASE_DIR}") | |
| except Exception as e: | |
| print(e) | |
| os.system(f"cd {BASE_DIR}") | |
| #@title If multiple GPU available,uncomment and try this code | |
| os.system("export CUDA_VISIBLE_DEVICES=0") | |
| # Openpose pose estimation | |
| # Ubuntu and Mac | |
| def estimate_pose(): | |
| os.system(f"cd {BASE_DIR}/openpose && ./build/examples/openpose/openpose.bin --image_dir {BASE_DIR}/VITON-HD/datasets/test/image --write_json {BASE_DIR}/VITON-HD/datasets/test/openpose-json/ --display 0 --face --hand --render_pose 0") | |
| os.system(f"cd {BASE_DIR}/openpose && ./build/examples/openpose/openpose.bin --image_dir {BASE_DIR}/VITON-HD/datasets/test/image --write_images {BASE_DIR}/VITON-HD/datasets/test/openpose-img/ --display 0 --hand --render_pose 1 --disable_blending true") | |
| os.system(f"cd {BASE_DIR}") | |
| # !cd /content/fashion-eye-try-on/openpose && ./build/examples/openpose/openpose.bin --image_dir /content/fashion-eye-try-on/pipeline_paddle/output/ --write_images /content/fashion-eye-try-on/openpose_img/ --display 0 --hand --render_pose 1 --disable_blending true | |
| # Run self correction human parser | |
| # !python3 /content/fashion-eye-try-on/human_parse/simple_extractor.py --dataset 'lip' --model-restore '/content/fashion-eye-try-on/human_parse/checkpoints/final.pth' --input-dir '/content/fashion-eye-try-on/image' --output-dir '/content/fashion-eye-try-on/VITON-HD/datasets/test/image-parse' | |
| def generate_human_segmentation_map(): | |
| # remove_ipynb_checkpoints() | |
| os.system(f"python3 {BASE_DIR}/human_parse/simple_extractor.py --dataset 'lip' --model-restore '{BASE_DIR}/human_parse/checkpoints/final.pth' --input-dir '{BASE_DIR}/VITON-HD/datasets/test/image' --output-dir '{BASE_DIR}/VITON-HD/datasets/test/image-parse'") | |
| # model_image = os.listdir('/content/fashion-eye-try-on/VITON-HD/datasets/test/image') | |
| # cloth_image = os.listdir('/content/fashion-eye-try-on/VITON-HD/datasets/test/cloth') | |
| # pairs = zip(model_image, cloth_image) | |
| # with open('/content/fashion-eye-try-on/VITON-HD/datasets/test_pairs.txt', 'w') as file: | |
| # for model, cloth in pairs: | |
| # file.write(f"{model} {cloth}\n") | |
| def generate_test_pairs_txt(): | |
| with open(f"{BASE_DIR}/VITON-HD/datasets/test_pairs.txt", 'w') as file: | |
| file.write(f"person.jpg cloth.jpg\n") | |
| # VITON-HD | |
| # Transfer the cloth to the model | |
| def generate_viton_hd(): | |
| os.system(f"python {BASE_DIR}/VITON-HD/test.py --name output --dataset_list {BASE_DIR}/VITON-HD/datasets/test_pairs.txt --dataset_dir {BASE_DIR}/VITON-HD/datasets/ --checkpoint_dir {BASE_DIR}/VITON-HD/checkpoints --save_dir {BASE_DIR}/") | |
| import sys | |
| # To resolve ModuleNotFoundError during imports | |
| if BASE_DIR not in sys.path: | |
| sys.path.append(BASE_DIR) | |
| sys.path.append(f"{BASE_DIR}/cloth_segmentation") | |
| from cloth_segmentation.networks import U2NET | |
| import torchvision.transforms as transforms | |
| import torch.nn.functional as F | |
| import os | |
| from PIL import Image | |
| from collections import OrderedDict | |
| import torch | |
| device = 'cuda' if torch.cuda.is_available() else "cpu" | |
| if device == 'cuda': | |
| torch.cuda.empty_cache() | |
| # for hugging face | |
| # BASE_DIR = "/home/path/app" | |
| image_dir = 'cloth' | |
| result_dir = 'cloth_mask' | |
| checkpoint_path = 'cloth_segmentation/checkpoints/cloth_segm_u2net_latest.pth' | |
| def load_checkpoint_mgpu(model, checkpoint_path): | |
| if not os.path.exists(checkpoint_path): | |
| print("----No checkpoints at given path----") | |
| return | |
| model_state_dict = torch.load( | |
| checkpoint_path, map_location=torch.device("cpu")) | |
| new_state_dict = OrderedDict() | |
| for k, v in model_state_dict.items(): | |
| name = k[7:] # remove `module.` | |
| new_state_dict[name] = v | |
| model.load_state_dict(new_state_dict) | |
| print("----checkpoints loaded from path: {}----".format(checkpoint_path)) | |
| return model | |
| class Normalize_image(object): | |
| """Normalize given tensor into given mean and standard dev | |
| Args: | |
| mean (float): Desired mean to substract from tensors | |
| std (float): Desired std to divide from tensors | |
| """ | |
| def __init__(self, mean, std): | |
| assert isinstance(mean, (float)) | |
| if isinstance(mean, float): | |
| self.mean = mean | |
| if isinstance(std, float): | |
| self.std = std | |
| self.normalize_1 = transforms.Normalize(self.mean, self.std) | |
| self.normalize_3 = transforms.Normalize( | |
| [self.mean] * 3, [self.std] * 3) | |
| self.normalize_18 = transforms.Normalize( | |
| [self.mean] * 18, [self.std] * 18) | |
| def __call__(self, image_tensor): | |
| if image_tensor.shape[0] == 1: | |
| return self.normalize_1(image_tensor) | |
| elif image_tensor.shape[0] == 3: | |
| return self.normalize_3(image_tensor) | |
| elif image_tensor.shape[0] == 18: | |
| return self.normalize_18(image_tensor) | |
| else: | |
| assert "Please set proper channels! Normlization implemented only for 1, 3 and 18" | |
| def get_palette(num_cls): | |
| """ Returns the color map for visualizing the segmentation mask. | |
| Args: | |
| num_cls: Number of classes | |
| Returns: | |
| The color map | |
| """ | |
| n = num_cls | |
| palette = [0] * (n * 3) | |
| for j in range(0, n): | |
| lab = j | |
| palette[j * 3 + 0] = 0 | |
| palette[j * 3 + 1] = 0 | |
| palette[j * 3 + 2] = 0 | |
| i = 0 | |
| while lab: | |
| palette[j * 3 + 0] = 255 | |
| palette[j * 3 + 1] = 255 | |
| palette[j * 3 + 2] = 255 | |
| # palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i)) | |
| # palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i)) | |
| # palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i)) | |
| i += 1 | |
| lab >>= 3 | |
| return palette | |
| def generate_cloth_mask(img_dir, output_dir, chkpt_dir): | |
| global image_dir | |
| global result_dir | |
| global checkpoint_path | |
| image_dir = img_dir | |
| result_dir = output_dir | |
| checkpoint_path = chkpt_dir | |
| transforms_list = [] | |
| transforms_list += [transforms.ToTensor()] | |
| transforms_list += [Normalize_image(0.5, 0.5)] | |
| transform_rgb = transforms.Compose(transforms_list) | |
| net = U2NET(in_ch=3, out_ch=4) | |
| with torch.no_grad(): | |
| net = load_checkpoint_mgpu(net, checkpoint_path) | |
| net = net.to(device) | |
| net = net.eval() | |
| palette = get_palette(4) | |
| images_list = sorted(os.listdir(image_dir)) | |
| for image_name in images_list: | |
| img = Image.open(os.path.join( | |
| image_dir, image_name)).convert('RGB') | |
| img_size = img.size | |
| img = img.resize((768, 768), Image.BICUBIC) | |
| image_tensor = transform_rgb(img) | |
| image_tensor = torch.unsqueeze(image_tensor, 0) | |
| output_tensor = net(image_tensor.to(device)) | |
| output_tensor = F.log_softmax(output_tensor[0], dim=1) | |
| output_tensor = torch.max(output_tensor, dim=1, keepdim=True)[1] | |
| output_tensor = torch.squeeze(output_tensor, dim=0) | |
| output_tensor = torch.squeeze(output_tensor, dim=0) | |
| output_arr = output_tensor.cpu().numpy() | |
| output_img = Image.fromarray(output_arr.astype('uint8'), mode='L') | |
| output_img = output_img.resize(img_size, Image.BICUBIC) | |
| output_img.putpalette(palette) | |
| output_img = output_img.convert('L') | |
| output_img.save(os.path.join(result_dir, image_name[:-4]+'.jpg')) | |
| os.system(f"cd {BASE_DIR}") | |
| from PIL import Image | |
| def upload_resize_generate_cloth_mask_and_move_to_viton_hd_test_inputs(cloth_img): | |
| os.system(f"cd {BASE_DIR}") | |
| cloth_img = cloth_img.resize((768, 1024), Image.BICUBIC) | |
| cloth_img.save(f"{BASE_DIR}/cloth/cloth.jpg") | |
| cloth_img.save(f"{BASE_DIR}/VITON-HD/datasets/test/cloth/cloth.jpg") | |
| try: | |
| generate_cloth_mask(f"{BASE_DIR}/cloth", f"{BASE_DIR}/cloth_mask", f"{BASE_DIR}/cloth_segmentation/checkpoints/cloth_segm_u2net_latest.pth") | |
| cloth_mask_img = Image.open(f"{BASE_DIR}/cloth_mask/cloth.jpg") | |
| cloth_mask_img.save(f"{BASE_DIR}/VITON-HD/datasets/test/cloth-mask/cloth.jpg") | |
| except Exception as e: | |
| print(e) | |
| # Gradio | |
| os.system("pip install gradio") | |
| import gradio as gr | |
| # import cv2 | |
| from PIL import Image | |
| IMAGEPATH='/content/fashion-eye-try-on/VITON-HD/datasets/test/image' | |
| CLOTHPATH='/content/fashion-eye-try-on/VITON-HD/datasets/test/cloth' | |
| CLOTHMASKPATH='/content/fashion-eye-try-on/VITON-HD/datasets/test/image' | |
| from threading import Thread | |
| def fashion_eye_tryon(person_img, cloth_img): | |
| result_img = person_img | |
| # img.save(IMAGEPATH + "person.jpg") | |
| # dress.save(CLOTHPATH + "cloth.jpg") | |
| # txt = open("/content/VITON-HD/datasets/test_pairs.txt", "a") | |
| # txt.write("person_img.jpg dress_img.jpg\n") | |
| # txt.close() | |
| # # result | |
| # print(person_img.info, cloth_img.info) | |
| # p_t1 = Thread(target=upload_remove_background_and_save_person_image, args=(person_img, )) | |
| # c_t2 = Thread(target=upload_resize_generate_cloth_mask_and_move_to_viton_hd_test_inputs, args=(cloth_img, )) | |
| # p_t1.start() | |
| # c_t2.start() | |
| # p_t1.join() | |
| # c_t2.join() | |
| # Estimate pose | |
| try: | |
| upload_resize_generate_cloth_mask_and_move_to_viton_hd_test_inputs(cloth_img) | |
| upload_remove_background_and_save_person_image(person_img) | |
| remove_ipynb_checkpoints() | |
| estimate_pose() | |
| # Generate human parse | |
| remove_ipynb_checkpoints() | |
| generate_human_segmentation_map() | |
| generate_test_pairs_txt() | |
| remove_ipynb_checkpoints() | |
| generate_viton_hd() | |
| for p in ["/content/fashion-eye-try-on/output/person_cloth.jpg", "/content/fashion-eye-try-on/output/person.jpg_cloth.jpg"]: | |
| if os.path.exists(p): | |
| result_img = Image.open(p) | |
| except Exception as e: | |
| print(e) | |
| return | |
| return result_img | |
| # res = fashion_eye_tryon("", "") | |
| # res.show() | |
| gr.Interface(fn=fashion_eye_tryon, | |
| inputs=[gr.Image(type = "pil", label="Your image"), gr.Image(type="pil", label="Dress")], | |
| outputs="image" | |
| ).launch(debug=True) | |
| # !pip freeze > /content/requirements_final.txt |