merge branches

Dockerfile

@@ -0,0 +1,97 @@
+FROM ubuntu:18.04
+
+
+RUN apt-get update && apt-get install --no-install-recommends -y \
+  build-essential \
+  python3.8 \
+  python3-pip \
+  python3-setuptools \
+  git \
+  wget \
+  && apt-get clean && rm -rf /var/lib/apt/lists/*
+  
+RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
+  
+# RUN echo $(ls /run/secrets/)
+
+
+WORKDIR /code
+
+# COPY ./requirements.txt /code/requirements.txt
+
+# add conda
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -P /code/
+RUN chmod 777 /code/Miniconda3-latest-Linux-x86_64.sh
+RUN /code/Miniconda3-latest-Linux-x86_64.sh -b -p /code/miniconda
+ENV PATH="/code/miniconda/bin:${PATH}"
+
+RUN groupadd miniconda
+RUN chgrp -R miniconda /code/miniconda/ 
+RUN chmod 770 -R /code/miniconda/ 
+
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+RUN adduser user miniconda
+
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH \
+    PYTHONPATH=$HOME/app \
+	PYTHONUNBUFFERED=1 \
+	GRADIO_ALLOW_FLAGGING=never \
+	GRADIO_NUM_PORTS=1 \
+	GRADIO_SERVER_NAME=0.0.0.0 \
+	GRADIO_THEME=huggingface \
+	SYSTEM=spaces
+
+RUN conda install python=3.8
+
+RUN pip3 install setuptools-rust
+
+RUN conda install pillow -y
+
+# RUN pip3 install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN conda install -c pytorch pytorch==1.7.1 torchvision==0.8.2 
+# RUN pip install torchvision-cpu==0.8.2
+
+# WORKDIR /home/user
+
+# RUN git clone https://github.com/open-mmlab/mim.git
+
+RUN pip3 install openmim
+
+
+RUN conda install -c conda-forge gradio -y 
+
+ 
+# RUN --mount=type=secret,id=git_token,mode=0444,required=true \
+#     echo $(https://$(cat /run/secrets/git_token)@github.com/NASA-IMPACT/hls-foundation-os.git) 
+#     # git clone https://$(cat /run/secrets/git_token)@github.com/NASA-IMPACT/hls-foundation-os.git
+
+WORKDIR /home/user
+
+RUN --mount=type=secret,id=git_token,mode=0444,required=true \
+    git clone https://$(cat /run/secrets/git_token)@github.com/NASA-IMPACT/hls-foundation-os.git
+
+
+WORKDIR hls-foundation-os 
+
+RUN pip3 install fine-tuning-examples/
+
+
+# RUN --mount=type=secret,id=git_token,mode=0444,required=true \
+#     pip3 install git+https://$(cat /run/secrets/git_token)@github.com/NASA-IMPACT/hls-foundation-os.git@mmseg-only
+
+RUN mim install mmcv-full==1.5.0
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+CMD ["python3", "app.py"]

README.md

@@ -8,4 +8,4 @@ pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,234 @@
+######### pull files
+import os
+from huggingface_hub import hf_hub_download
+config_path=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification", 
+                            filename="multi_temporal_crop_classification_Prithvi_100M.py", 
+                            token=os.environ.get("token"))
+ckpt=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification", 
+                     filename='multi_temporal_crop_classification_best_mIoU_epoch_66.pth', 
+                     token=os.environ.get("token"))
+##########
+import argparse
+from mmcv import Config
+
+from mmseg.models import build_segmentor
+
+from mmseg.datasets.pipelines import Compose, LoadImageFromFile
+
+import rasterio
+import torch
+
+from mmseg.apis import init_segmentor
+
+from mmcv.parallel import collate, scatter
+
+import numpy as np
+import glob
+import os
+
+import time
+
+import numpy as np
+import gradio as gr
+from functools import partial
+
+import pdb
+
+import matplotlib.pyplot as plt
+
+
+def open_tiff(fname):
+    
+    with rasterio.open(fname, "r") as src:
+        
+        data = src.read()
+        
+    return data
+
+def write_tiff(img_wrt, filename, metadata):
+
+    """
+    It writes a raster image to file.
+
+    :param img_wrt: numpy array containing the data (can be 2D for single band or 3D for multiple bands)
+    :param filename: file path to the output file
+    :param metadata: metadata to use to write the raster to disk
+    :return:
+    """
+
+    with rasterio.open(filename, "w", **metadata) as dest:
+
+        if len(img_wrt.shape) == 2:
+            
+            img_wrt = img_wrt[None]
+
+        for i in range(img_wrt.shape[0]):
+            dest.write(img_wrt[i, :, :], i + 1)
+    
+    return filename
+            
+
+def get_meta(fname):
+    
+    with rasterio.open(fname, "r") as src:
+        
+        meta = src.meta
+        
+    return meta
+
+def preprocess_example(example_list):
+    
+    example_list = [os.path.join(os.path.abspath(''), x) for x in example_list]
+    
+    return example_list
+
+
+def inference_segmentor(model, imgs, custom_test_pipeline=None):
+    """Inference image(s) with the segmentor.
+
+    Args:
+        model (nn.Module): The loaded segmentor.
+        imgs (str/ndarray or list[str/ndarray]): Either image files or loaded
+            images.
+
+    Returns:
+        (list[Tensor]): The segmentation result.
+    """
+    cfg = model.cfg
+    device = next(model.parameters()).device  # model device
+    # build the data pipeline
+    test_pipeline = [LoadImageFromFile()] + cfg.data.test.pipeline[1:] if custom_test_pipeline == None else custom_test_pipeline
+    test_pipeline = Compose(test_pipeline)
+    # prepare data
+    data = []
+    imgs = imgs if isinstance(imgs, list) else [imgs]
+    for img in imgs:
+        img_data = {'img_info': {'filename': img}}
+        img_data = test_pipeline(img_data)
+        data.append(img_data)
+    # print(data.shape)
+    
+    data = collate(data, samples_per_gpu=len(imgs))
+    if next(model.parameters()).is_cuda:
+        # data = collate(data, samples_per_gpu=len(imgs))
+        # scatter to specified GPU
+        data = scatter(data, [device])[0]
+    else:
+        # img_metas = scatter(data['img_metas'],'cpu')
+        # data['img_metas'] = [i.data[0] for i in data['img_metas']]
+        
+        img_metas = data['img_metas'].data[0]
+        img = data['img']
+        data = {'img': img, 'img_metas':img_metas}
+    
+    with torch.no_grad():
+        result = model(return_loss=False, rescale=True, **data)
+    return result
+
+
+def inference_on_file(target_image, model, custom_test_pipeline):
+
+    target_image = target_image.name
+    # print(type(target_image))
+
+    # output_image = target_image.replace('.tif', '_pred.tif')
+    time_taken=-1
+    try:
+        st = time.time()
+        print('Running inference...')
+        result = inference_segmentor(model, target_image, custom_test_pipeline)
+        print("Output has shape: " + str(result[0].shape))
+        
+        ##### get metadata mask
+        mask = open_tiff(target_image)
+        # rgb = mask[[2, 1, 0], :, :].transpose((1,2,0))
+        rgb1 = mask[[2, 1, 0], :, :].transpose((1,2,0))
+        rgb2 = mask[[8, 7, 6], :, :].transpose((1,2,0))
+        rgb3 = mask[[14, 13, 12], :, :].transpose((1,2,0))
+        meta = get_meta(target_image)
+        mask = np.where(mask == meta['nodata'], 1, 0)
+        mask = np.max(mask, axis=0)[None]
+        
+        result[0] = np.where(mask == 1, -1, result[0])
+        
+        ##### Save file to disk
+        meta["count"] = 1
+        meta["dtype"] = "int16"
+        meta["compress"] = "lzw"
+        meta["nodata"] = -1
+        print('Saving output...')
+        # write_tiff(result[0], output_image, meta)
+        et = time.time()
+        time_taken = np.round(et - st, 1)
+        print(f'Inference completed in {str(time_taken)} seconds')
+        
+    except:
+        print(f'Error on image {target_image} \nContinue to next input')
+        
+    return rgb, result[0][0]*255
+
+def process_test_pipeline(custom_test_pipeline, bands=None):
+    
+    # change extracted bands if necessary
+    if bands is not None:
+        
+        extract_index = [i for i, x in enumerate(custom_test_pipeline) if x['type'] == 'BandsExtract' ]
+        
+        if len(extract_index) > 0:
+            
+            custom_test_pipeline[extract_index[0]]['bands'] = eval(bands)
+            
+    collect_index = [i for i, x in enumerate(custom_test_pipeline) if x['type'].find('Collect') > -1]
+    
+    # adapt collected keys if necessary
+    if len(collect_index) > 0:
+        
+        keys = ['img_info', 'filename', 'ori_filename', 'img', 'img_shape', 'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg']
+        custom_test_pipeline[collect_index[0]]['meta_keys'] = keys
+    
+    return custom_test_pipeline
+
+config = Config.fromfile(config_path)
+config.model.backbone.pretrained=None
+model = init_segmentor(config, ckpt, device='cpu')
+custom_test_pipeline=process_test_pipeline(model.cfg.data.test.pipeline, None)
+
+func = partial(inference_on_file, model=model, custom_test_pipeline=custom_test_pipeline)
+
+with gr.Blocks() as demo:
+   
+    gr.Markdown(value='# Prithvi multi temporal crop classification')
+    gr.Markdown(value='''Prithvi is a first-of-its-kind temporal Vision transformer pretrained by the IBM and NASA team on continental US Harmonised Landsat Sentinel 2 (HLS) data. This demo showcases how the model was finetuned to classify crop and other land use categories using multi temporal data. More detailes can be found [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification).\n
+    The user needs to provide an HLS geotiff image, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order.
+    ''')
+    with gr.Row():
+        with gr.Column():
+            inp = gr.File()
+            btn = gr.Button("Submit")
+    
+    with gr.Row():
+        gr.Markdown(value='### T1')
+        gr.Markdown(value='### T2')
+        gr.Markdown(value='### T3')
+        gr.Markdown(value='### Model prediction')
+        
+    with gr.Row():
+        inp1=gr.Image(image_mode='RGB')
+        inp2=gr.Image(image_mode='RGB')
+        inp3=gr.Image(image_mode='RGB')
+        out = gr.Image(image_mode='L')
+    
+    btn.click(fn=func, inputs=inp, outputs=[inp1, inp2, inp3, out])
+    
+    with gr.Row():
+        gr.Examples(examples=["chip_102_345_merged.tif",
+                     "chip_104_104_merged.tif",
+                     "chip_109_421_merged.tif"],
+                    inputs=inp,
+                    outputs=[inp1, inp2, inp3, out],
+                    preprocess=preprocess_example,
+                    fn=func,
+                    cache_examples=True,
+    )
+
+demo.launch() 

requirements.txt

@@ -0,0 +1,3 @@
+pytorch==1.7.1
+torchvision==0.8.2
+openmim