File size: 8,923 Bytes
22889e1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
import os 
import json
import shutil
from pathlib import Path
from typing import Dict

from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

LOCAL_DATADIR = None

def setup(local_dir='./data/usm-training-data/data'):
    
    # If we are in the test environment, we need to link the data directory to the correct location
    tmp_datadir = Path('/tmp/data/data')
    local_test_datadir = Path('./data/usm-test-data-x/data')
    local_val_datadir = Path(local_dir)
    
    os.system('pwd')
    os.system('ls -lahtr .')
    
    if tmp_datadir.exists() and not local_test_datadir.exists():
        global LOCAL_DATADIR
        LOCAL_DATADIR = local_test_datadir
        # shutil.move(datadir, './usm-test-data-x/data')
        print(f"Linking {tmp_datadir} to {LOCAL_DATADIR} (we are in the test environment)")
        LOCAL_DATADIR.parent.mkdir(parents=True, exist_ok=True)
        LOCAL_DATADIR.symlink_to(tmp_datadir)
    else:
        LOCAL_DATADIR = local_val_datadir
        print(f"Using {LOCAL_DATADIR} as the data directory (we are running locally)")
        
    # os.system("ls -lahtr")
    
    assert LOCAL_DATADIR.exists(), f"Data directory {LOCAL_DATADIR} does not exist"
    return LOCAL_DATADIR
    
    
    
    
import importlib
from pathlib import Path
import subprocess

def download_package(package_name, path_to_save='packages'):
    """
    Downloads a package using pip and saves it to a specified directory.

    Parameters:
    package_name (str): The name of the package to download.
    path_to_save (str): The path to the directory where the package will be saved.
    """
    try:
        # pip download webdataset -d packages/webdataset --platform manylinux1_x86_64 --python-version 38 --only-binary=:all:
        subprocess.check_call([subprocess.sys.executable, "-m", "pip", "download", package_name, 
                               "-d", str(Path(path_to_save)/package_name),  # Download the package to the specified directory
                               "--platform", "manylinux1_x86_64",  # Specify the platform
                               "--python-version", "38",  # Specify the Python version
                               "--only-binary=:all:"])  # Download only binary packages
        print(f'Package "{package_name}" downloaded successfully')
    except subprocess.CalledProcessError as e:
        print(f'Failed to downloaded package "{package_name}". Error: {e}')
              
              
def install_package_from_local_file(package_name, folder='packages'):
    """
    Installs a package from a local .whl file or a directory containing .whl files using pip.

    Parameters:
    path_to_file_or_directory (str): The path to the .whl file or the directory containing .whl files.
    """
    try:
        pth = str(Path(folder) / package_name)
        subprocess.check_call([subprocess.sys.executable, "-m", "pip", "install", 
                               "--no-index",  # Do not use package index
                               "--find-links", pth,  # Look for packages in the specified directory or at the file
                               package_name])  # Specify the package to install
        print(f"Package installed successfully from {pth}")
    except subprocess.CalledProcessError as e:
        print(f"Failed to install package from {pth}. Error: {e}")
        
        
def importt(module_name, as_name=None):
    """
    Imports a module and returns it.

    Parameters:
    module_name (str): The name of the module to import.
    as_name (str): The name to use for the imported module. If None, the original module name will be used.

    Returns:
    The imported module.
    """
    for _ in range(2):
        try:
            if as_name is None:
                print(f'imported {module_name}')
                return importlib.import_module(module_name)
            else:
                print(f'imported {module_name} as {as_name}')
                return importlib.import_module(module_name, as_name)
        except ModuleNotFoundError as e:
            install_package_from_local_file(module_name)
            print(f"Failed to import module {module_name}. Error: {e}")
            
    
def prepare_submission():
    # Download packages from requirements.txt 
    if Path('requirements.txt').exists():
        print('downloading packages from requirements.txt')
        Path('packages').mkdir(exist_ok=True)
        with open('requirements.txt') as f:
            packages = f.readlines()
            for p in packages:
                download_package(p.strip())
                
                
    print('all packages downloaded. Don\'t foget to include the packages in the submission by adding them with git lfs.')
        

def Rt_to_eye_target(im, K, R, t):
    height = im.height
    focal_length = K[0,0]
    fov = 2.0 * np.arctan2((0.5 * height), focal_length) / (np.pi / 180.0)

    x_axis, y_axis, z_axis = R

    eye = -(R.T @ t).squeeze()
    z_axis = z_axis.squeeze()
    target = eye + z_axis
    up = -y_axis
    
    return eye, target, up, fov        


########## general utilities ##########
import contextlib
import tempfile 
from pathlib import Path

@contextlib.contextmanager
def working_directory(path):
    """Changes working directory and returns to previous on exit."""
    prev_cwd = Path.cwd()
    os.chdir(path)
    try:
        yield
    finally:
        os.chdir(prev_cwd)
        
@contextlib.contextmanager
def temp_working_directory():
    with tempfile.TemporaryDirectory(dir='.') as D:
        with working_directory(D):
            yield


############# Dataset ############# 
def proc(row, split='train'):
    # column_names_train = ['ade20k', 'depthcm', 'gestalt', 'colmap', 'KRt', 'mesh', 'wireframe']
    # column_names_test = ['ade20k', 'depthcm', 'gestalt', 'colmap', 'KRt', 'wireframe']
    # cols = column_names_train if split == 'train' else column_names_test
    out = {}
    for k, v in row.items():
        colname = k.split('.')[0]
        if colname in {'ade20k', 'depthcm', 'gestalt'}:
            if colname in out:
                out[colname].append(v)
            else:
                out[colname] = [v]
        elif colname in {'wireframe', 'mesh'}:
            # out.update({a: b.tolist() for a,b in v.items()})
            out.update({a: b for a,b in v.items()})
        elif colname in 'kr':
            out[colname.upper()] = v
        else:
            out[colname] = v
            
    return Sample(out)


class Sample(Dict):
    def __repr__(self):
        return str({k: v.shape if hasattr(v, 'shape') else [type(v[0])] if isinstance(v, list) else type(v) for k,v in self.items()})

    
        
def get_params():
    exmaple_param_dict = {
        "competition_id": "usm3d/S23DR",
        "competition_type": "script",
        "metric": "custom",
        "token": "hf_**********************************",
        "team_id": "local-test-team_id",
        "submission_id": "local-test-submission_id",
        "submission_id_col": "__key__",
        "submission_cols": [
            "__key__",
            "wf_edges",
            "wf_vertices",
            "edge_semantics"
        ],
        "submission_rows": 180,
        "output_path": ".",
        "submission_repo": "<THE HF MODEL ID of THIS REPO",
        "time_limit": 7200,
        "dataset": "usm3d/usm-test-data-x",
        "submission_filenames": [
            "submission.parquet"
        ]
    }
    
    param_path = Path('params.json')
    
    if not param_path.exists():
        print('params.json not found (this means we probably aren\'t in the test env). Using example params.')
        params = exmaple_param_dict
    else:
        print('found params.json (this means we are probably in the test env). Using params from file.')
        with param_path.open() as f:
            params = json.load(f)
    print(params)
    return params



import webdataset as wds 
import numpy as np

def get_dataset(decode='pil', proc=proc, split='train', dataset_type='webdataset'):
    if LOCAL_DATADIR is None:
        raise ValueError('LOCAL_DATADIR is not set. Please run setup() first.')
        
    local_dir = Path(LOCAL_DATADIR)
    if split != 'all':
        local_dir = local_dir / split
    
    paths = [str(p) for p in local_dir.rglob('*.tar.gz')]
    
    dataset = wds.WebDataset(paths)
    if decode is not None:
        dataset = dataset.decode(decode)
    else:
        dataset = dataset.decode()
    
    dataset = dataset.map(proc)
    
    if dataset_type == 'webdataset':
        return dataset
    
    if dataset_type == 'hf':
        import datasets
        from datasets import Features, Value, Sequence, Image, Array2D
       
        if split == 'train':
            return datasets.IterableDataset.from_generator(lambda: dataset.iterator())
        elif split == 'val':
            return datasets.IterableDataset.from_generator(lambda: dataset.iterator())