Add application file

.gitattributes copy

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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Dockerfile

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+FROM python:3.8
+
+
+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
+  
+WORKDIR /code
+
+RUN useradd -m -u 1000 user
+
+# 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 pip install setuptools-rust
+RUN pip install torch==1.11.0+cu115 torchvision==0.12.0+cu115 --extra-index-url https://download.pytorch.org/whl/cu115
+RUN pip install gradio scikit-image pillow openmim
+RUN pip install --upgrade setuptools
+
+WORKDIR /home/user
+
+RUN --mount=type=secret,id=git_token,mode=0444,required=true \
+    git clone --branch mmseg-only https://$(cat /run/secrets/git_token)@github.com/NASA-IMPACT/hls-foundation-os.git
+
+
+WORKDIR hls-foundation-os 
+
+RUN git checkout 9968269915db8402bf4a6d0549df9df57d489e5a
+
+RUN pip install -e .
+
+RUN mim install mmcv-full==1.6.2 -f https://download.openmmlab.com/mmcv/dist/11.5/1.11.0/index.html
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/code/miniconda/lib"
+
+# 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 copy.md

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+---
+title: Prithvi 100M Multi Temporal Crop Classification Demo
+emoji: 📚
+colorFrom: purple
+colorTo: red
+sdk: docker
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+

app.py

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+######### 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_Prithvi_100M.pth', 
+                     token=os.environ.get("token"))
+
+config_path="multi_temporal_crop_classification_Prithvi_100M.py"
+##########
+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
+
+from skimage import exposure 
+
+import pandas as pd
+from vega_datasets import data
+
+
+cdl_color_map = [{'value': 1, 'label': 'Natural vegetation', 'rgb': (233,255,190)},
+                 {'value': 2, 'label': 'Forest', 'rgb': (149,206,147)},
+                 {'value': 3, 'label': 'Corn', 'rgb': (255,212,0)},
+                 {'value': 4, 'label': 'Soybeans', 'rgb': (38,115,0)},
+                 {'value': 5, 'label': 'Wetlands', 'rgb': (128,179,179)},
+                 {'value': 6, 'label': 'Developed/Barren', 'rgb': (156,156,156)},
+                 {'value': 7, 'label': 'Open Water', 'rgb': (77,112,163)},
+                 {'value': 8, 'label': 'Winter Wheat', 'rgb': (168,112,0)},
+                 {'value': 9, 'label': 'Alfalfa', 'rgb': (255,168,227)},
+                 {'value': 10, 'label': 'Fallow/Idle cropland', 'rgb': (191,191,122)},
+                 {'value': 11, 'label': 'Cotton', 'rgb':(255,38,38)},
+                 {'value': 12, 'label': 'Sorghum', 'rgb':(255,158,15)},
+                 {'value': 13, 'label': 'Other', 'rgb':(0,175,77)}]
+
+
+def apply_color_map(rgb, color_map=cdl_color_map):
+    
+    
+    rgb_mapped = rgb.copy()
+    
+    for map_tmp in cdl_color_map:
+        
+        for i in range(3):
+            rgb_mapped[i] = np.where((rgb[0] == map_tmp['value']) & (rgb[1] == map_tmp['value']) & (rgb[2] == map_tmp['value']), map_tmp['rgb'][i], rgb_mapped[i])
+    
+    return rgb_mapped    
+    
+
+def stretch_rgb(rgb):
+    
+    ls_pct=0
+    pLow, pHigh = np.percentile(rgb[~np.isnan(rgb)], (ls_pct,100-ls_pct))
+    img_rescale = exposure.rescale_intensity(rgb, in_range=(pLow,pHigh))
+    
+    return img_rescale
+
+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 process_rgb(input, mask, indexes):
+
+    
+    rgb = stretch_rgb((input[indexes, :, :].transpose((1,2,0))/10000*255).astype(np.uint8))
+    rgb = np.where(mask.transpose((1,2,0)) == 1, 0, rgb)
+    rgb = np.where(rgb < 0, 0, rgb)
+    rgb = np.where(rgb > 255, 255, rgb)
+
+    return rgb
+
+def inference_on_file(target_image, model, custom_test_pipeline):
+
+    target_image = target_image.name
+    time_taken=-1
+    st = time.time()
+    print('Running inference...')
+    try:
+        result = inference_segmentor(model, target_image, custom_test_pipeline)
+    except:
+        print('Error: Try different channels order.')
+        model.cfg.data.test.pipeline[0]['channels_last'] = True
+        result = inference_segmentor(model, target_image, custom_test_pipeline)
+    print("Output has shape: " + str(result[0].shape))
+
+    ##### get metadata mask
+    input = open_tiff(target_image)
+    meta = get_meta(target_image)
+    mask = np.where(input == meta['nodata'], 1, 0)
+    mask = np.max(mask, axis=0)[None]
+    
+    rgb1 = process_rgb(input, mask, [2, 1, 0])
+    rgb2 = process_rgb(input, mask, [8, 7, 6])
+    rgb3 = process_rgb(input, mask, [14, 13, 12])
+
+    result[0] = np.where(mask == 1, 0, result[0])
+
+    et = time.time()
+    time_taken = np.round(et - st, 1)
+    print(f'Inference completed in {str(time_taken)} seconds')
+    
+    output=result[0][0] + 1
+    output = np.vstack([output[None], output[None], output[None]]).astype(np.uint8)
+    
+
+    output=apply_color_map(output).transpose((1,2,0))
+        
+    return rgb1,rgb2,rgb3,output
+
+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)
+
+
+stocks = data.stocks()
+gapminder = data.gapminder()
+gapminder = gapminder.loc[
+    gapminder.country.isin(["Argentina", "Australia", "Afghanistan"])
+]
+climate = data.climate()
+seattle_weather = data.seattle_weather()
+
+simple = pd.DataFrame(
+    {
+        "a": ["A", "B", "C", "D", "E", "F", "G", "H", "I"],
+        "b": [28, 55, 43, 91, 81, 53, 19, 87, 52],
+    }
+)
+
+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.Column():
+            inp1=gr.Image(image_mode='RGB', scale=10, label='T1')
+            inp2=gr.Image(image_mode='RGB', scale=10, label='T2')
+            inp3=gr.Image(image_mode='RGB', scale=10, label='T3')
+            out = gr.Image(image_mode='RGB', scale=10, label='Model prediction')
+            # gr.Image(value='Legend.png', image_mode='RGB', scale=2, show_label=False)
+    
+    btn.click(fn=func, inputs=inp, outputs=[inp1, inp2, inp3, out])
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                gr.BarPlot(simple,
+                        x="a",
+                        y="b",
+                        title="Simple Bar Plot with made up data",
+                        tooltip=["a", "b"],
+                        y_lim=[20, 100],)
+            with gr.Row():
+                gr.LinePlot(simple,
+                            x='a',
+                            y='b')
+
+        with gr.Column():
+            gr.Markdown(value='### Model prediction legend')
+            gr.Image(value='Legend.png', image_mode='RGB', show_label=False)
+    
+
+demo.launch() 

multi_temporal_crop_classification_Prithvi_100M.py

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+import os
+
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+cudnn_benchmark = True
+custom_imports = dict(imports=['geospatial_fm'])
+num_frames = 3
+img_size = 224
+num_workers = 2
+
+# model
+# TO BE DEFINED BY USER: model path
+pretrained_weights_path = '<path to pretrained weights>'
+num_layers = 6
+patch_size = 16
+embed_dim = 768
+num_heads = 8
+tubelet_size = 1
+max_epochs = 80
+eval_epoch_interval = 5
+
+loss_weights_multi = [
+    0.386375, 0.661126, 0.548184, 0.640482, 0.876862, 0.925186, 3.249462,
+    1.542289, 2.175141, 2.272419, 3.062762, 3.626097, 1.198702
+]
+loss_func = dict(
+    type='CrossEntropyLoss',
+    use_sigmoid=False,
+    class_weight=loss_weights_multi,
+    avg_non_ignore=True)
+output_embed_dim = embed_dim*num_frames
+
+
+# TO BE DEFINED BY USER: Save directory
+experiment = '<experiment name>'
+project_dir = '<project directory name>'
+work_dir = os.path.join(project_dir, experiment)
+save_path = work_dir
+
+
+gpu_ids = range(0, 1)
+dataset_type = 'GeospatialDataset'
+
+# TO BE DEFINED BY USER: data directory
+data_root = '<path to data root>'
+
+splits = dict(
+    train='<path to train split>',
+    val= '<path to val split>',
+    test=  '<path to test split>'
+)
+
+
+img_norm_cfg = dict(
+    means=[
+        494.905781, 815.239594, 924.335066, 2968.881459, 2634.621962,
+        1739.579917, 494.905781, 815.239594, 924.335066, 2968.881459,
+        2634.621962, 1739.579917, 494.905781, 815.239594, 924.335066,
+        2968.881459, 2634.621962, 1739.579917
+    ],
+    stds=[
+        284.925432, 357.84876, 575.566823, 896.601013, 951.900334, 921.407808,
+        284.925432, 357.84876, 575.566823, 896.601013, 951.900334, 921.407808,
+        284.925432, 357.84876, 575.566823, 896.601013, 951.900334, 921.407808
+    ])
+
+bands = [0, 1, 2, 3, 4, 5]
+
+tile_size = 224
+orig_nsize = 512
+crop_size = (tile_size, tile_size)
+train_pipeline = [
+    dict(type='LoadGeospatialImageFromFile', to_float32=True),
+    dict(type='LoadGeospatialAnnotations', reduce_zero_label=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),
+     # to channels first
+    dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)),
+    dict(type='TorchNormalize', **img_norm_cfg),
+    dict(type='TorchRandomCrop', crop_size=crop_size),
+    dict(type='Reshape', keys=['img'], new_shape=(len(bands), num_frames, tile_size, tile_size)),
+    dict(type='Reshape', keys=['gt_semantic_seg'], new_shape=(1, tile_size, tile_size)),
+    dict(type='CastTensor', keys=['gt_semantic_seg'], new_type="torch.LongTensor"),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+
+test_pipeline = [
+    dict(type='LoadGeospatialImageFromFile', to_float32=True),
+    dict(type='ToTensor', keys=['img']),
+     # to channels first
+    dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)),
+    dict(type='TorchNormalize', **img_norm_cfg),
+    dict(type='Reshape', keys=['img'], new_shape=(len(bands), num_frames, -1, -1), look_up = {'2': 1, '3': 2}),
+    dict(type='CastTensor', keys=['img'], new_type="torch.FloatTensor"),
+    dict(type='CollectTestList', keys=['img'],
+         meta_keys=['img_info', 'seg_fields', 'img_prefix', 'seg_prefix', 'filename', 'ori_filename', 'img',
+                    'img_shape', 'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg']),
+]
+
+CLASSES = ('Natural Vegetation', 
+           'Forest', 
+           'Corn', 
+           'Soybeans', 
+           'Wetlands', 
+           'Developed/Barren', 
+           'Open Water', 
+           'Winter Wheat', 
+           'Alfalfa', 
+           'Fallow/Idle Cropland', 
+           'Cotton', 
+           'Sorghum', 
+           'Other')
+
+dataset = 'GeospatialDataset'
+
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset,
+        CLASSES=CLASSES,
+        reduce_zero_label=True,
+        data_root=data_root,
+        img_dir='training_chips',
+        ann_dir='training_chips',
+        pipeline=train_pipeline,
+        img_suffix='_merged.tif',
+        seg_map_suffix='.mask.tif',
+        split=splits['train']),
+    val=dict(
+        type=dataset,
+        CLASSES=CLASSES,
+        reduce_zero_label=True,
+        data_root=data_root,
+        img_dir='validation_chips',
+        ann_dir='validation_chips',
+        pipeline=test_pipeline,
+        img_suffix='_merged.tif',
+        seg_map_suffix='.mask.tif',
+        split=splits['val']
+    ),
+    test=dict(
+        type=dataset,
+        CLASSES=CLASSES,
+        reduce_zero_label=True,
+        data_root=data_root,
+        img_dir='validation_chips',
+        ann_dir='validation_chips',
+        pipeline=test_pipeline,
+        img_suffix='_merged.tif',
+        seg_map_suffix='.mask.tif',
+        split=splits['val']
+    ))
+
+optimizer = dict(
+    type='Adam', lr=1.5e-05, betas=(0.9, 0.999), weight_decay=0.05)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(
+    policy='poly',
+    warmup='linear',
+    warmup_iters=1500,
+    warmup_ratio=1e-06,
+    power=1.0,
+    min_lr=0.0,
+    by_epoch=False)
+log_config = dict(
+    interval=10,
+    hooks=[dict(type='TextLoggerHook'),
+           dict(type='TensorboardLoggerHook')])
+
+checkpoint_config = dict(
+    by_epoch=True,
+    interval=100,
+    out_dir=save_path)
+
+evaluation = dict(interval=eval_epoch_interval, metric='mIoU', pre_eval=True, save_best='mIoU', by_epoch=True)
+reduce_train_set = dict(reduce_train_set=False)
+reduce_factor = dict(reduce_factor=1)
+runner = dict(type='EpochBasedRunner', max_epochs=max_epochs)
+workflow = [('train', 1)]
+norm_cfg = dict(type='BN', requires_grad=True)
+
+model = dict(
+    type='TemporalEncoderDecoder',
+    frozen_backbone=False,
+    backbone=dict(
+        type='TemporalViTEncoder',
+        pretrained=pretrained_weights_path,
+        img_size=img_size,
+        patch_size=patch_size,
+        num_frames=num_frames,
+        tubelet_size=1,
+        in_chans=len(bands),
+        embed_dim=embed_dim,
+        depth=6,
+        num_heads=num_heads,
+        mlp_ratio=4.0,
+        norm_pix_loss=False),
+    neck=dict(
+        type='ConvTransformerTokensToEmbeddingNeck',
+        embed_dim=embed_dim*num_frames,
+        output_embed_dim=output_embed_dim,
+        drop_cls_token=True,
+        Hp=14,
+        Wp=14),
+    decode_head=dict(
+        num_classes=len(CLASSES),
+        in_channels=output_embed_dim,
+        type='FCNHead',
+        in_index=-1,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        align_corners=False,
+        loss_decode=loss_func),
+    auxiliary_head=dict(
+        num_classes=len(CLASSES),
+        in_channels=output_embed_dim,
+        type='FCNHead',
+        in_index=-1,
+        channels=256,
+        num_convs=2,
+        concat_input=False,
+        dropout_ratio=0.1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        align_corners=False,
+        loss_decode=loss_func),
+    train_cfg=dict(),
+    test_cfg=dict(mode='slide', stride=(int(tile_size/2), int(tile_size/2)), crop_size=(tile_size, tile_size)))
+auto_resume = False

requirements.txt

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