Computer_Vision/Chapter08/Training_SSD.ipynb

import os
if not os.path.exists('open-images-bus-trucks'):
    !pip install -q torch_snippets
    !wget --quiet https://www.dropbox.com/s/agmzwk95v96ihic/open-images-bus-trucks.tar.xz
    !tar -xf open-images-bus-trucks.tar.xz
    !rm open-images-bus-trucks.tar.xz
    !git clone https://github.com/sizhky/ssd-utils/
%cd ssd-utils

tar: Error opening archive: Failed to open 'open-images-bus-trucks.tar.xz'
'rm' is not recognized as an internal or external command,
operable program or batch file.

f:\Zajecia\books\computer_vision\Modern-Computer-Vision-with-PyTorch-master\Modern-Computer-Vision-with-PyTorch-master\Modern-Computer-Vision-with-PyTorch-master\Chapter08\ssd-utils

fatal: destination path 'ssd-utils' already exists and is not an empty directory.

from torch_snippets import *
DATA_ROOT = '../open-images-bus-trucks/'
IMAGE_ROOT = f'{DATA_ROOT}/images'
DF_RAW = df = pd.read_csv(f'{DATA_ROOT}/df.csv')

df = df[df['ImageID'].isin(df['ImageID'].unique().tolist())]

label2target = {l:t+1 for t,l in enumerate(DF_RAW['LabelName'].unique())}
label2target['background'] = 0
target2label = {t:l for l,t in label2target.items()}
background_class = label2target['background']
num_classes = len(label2target)

device = 'cuda' if torch.cuda.is_available() else 'cpu'

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_28720\2147305028.py in <module>
      2 DATA_ROOT = '../open-images-bus-trucks/'
      3 IMAGE_ROOT = f'{DATA_ROOT}/images'
----> 4 DF_RAW = df = pd.read_csv(f'{DATA_ROOT}/df.csv')
      5 
      6 df = df[df['ImageID'].isin(df['ImageID'].unique().tolist())]

~\anaconda3\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
    309                     stacklevel=stacklevel,
    310                 )
--> 311             return func(*args, **kwargs)
    312 
    313         return wrapper

~\anaconda3\lib\site-packages\pandas\io\parsers\readers.py in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
    676     kwds.update(kwds_defaults)
    677 
--> 678     return _read(filepath_or_buffer, kwds)
    679 
    680 

~\anaconda3\lib\site-packages\pandas\io\parsers\readers.py in _read(filepath_or_buffer, kwds)
    573 
    574     # Create the parser.
--> 575     parser = TextFileReader(filepath_or_buffer, **kwds)
    576 
    577     if chunksize or iterator:

~\anaconda3\lib\site-packages\pandas\io\parsers\readers.py in __init__(self, f, engine, **kwds)
    930 
    931         self.handles: IOHandles | None = None
--> 932         self._engine = self._make_engine(f, self.engine)
    933 
    934     def close(self):

~\anaconda3\lib\site-packages\pandas\io\parsers\readers.py in _make_engine(self, f, engine)
   1214             # "Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]"
   1215             # , "str", "bool", "Any", "Any", "Any", "Any", "Any"
-> 1216             self.handles = get_handle(  # type: ignore[call-overload]
   1217                 f,
   1218                 mode,

~\anaconda3\lib\site-packages\pandas\io\common.py in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    784         if ioargs.encoding and "b" not in ioargs.mode:
    785             # Encoding
--> 786             handle = open(
    787                 handle,
    788                 ioargs.mode,

FileNotFoundError: [Errno 2] No such file or directory: '../open-images-bus-trucks//df.csv'

import collections, os, torch
from PIL import Image
from torchvision import transforms
normalize = transforms.Normalize(
    mean=[0.485, 0.456, 0.406],
    std=[0.229, 0.224, 0.225]
)
denormalize = transforms.Normalize(
    mean=[-0.485/0.229, -0.456/0.224, -0.406/0.255],
    std=[1/0.229, 1/0.224, 1/0.255]
)

def preprocess_image(img):
    img = torch.tensor(img).permute(2,0,1)
    img = normalize(img)
    return img.to(device).float()
    
class OpenDataset(torch.utils.data.Dataset):
    w, h = 300, 300
    def __init__(self, df, image_dir=IMAGE_ROOT):
        self.image_dir = image_dir
        self.files = glob.glob(self.image_dir+'/*')
        self.df = df
        self.image_infos = df.ImageID.unique()
        logger.info(f'{len(self)} items loaded')
        
    def __getitem__(self, ix):
        # load images and masks
        image_id = self.image_infos[ix]
        img_path = find(image_id, self.files)
        img = Image.open(img_path).convert("RGB")
        img = np.array(img.resize((self.w, self.h), resample=Image.BILINEAR))/255.
        data = df[df['ImageID'] == image_id]
        labels = data['LabelName'].values.tolist()
        data = data[['XMin','YMin','XMax','YMax']].values
        data[:,[0,2]] *= self.w
        data[:,[1,3]] *= self.h
        boxes = data.astype(np.uint32).tolist() # convert to absolute coordinates
        return img, boxes, labels

    def collate_fn(self, batch):
        images, boxes, labels = [], [], []
        for item in batch:
            img, image_boxes, image_labels = item
            img = preprocess_image(img)[None]
            images.append(img)
            boxes.append(torch.tensor(image_boxes).float().to(device)/300.)
            labels.append(torch.tensor([label2target[c] for c in image_labels]).long().to(device))
        images = torch.cat(images).to(device)
        return images, boxes, labels
    def __len__(self):
        return len(self.image_infos)

from sklearn.model_selection import train_test_split
trn_ids, val_ids = train_test_split(df.ImageID.unique(), test_size=0.1, random_state=99)
trn_df, val_df = df[df['ImageID'].isin(trn_ids)], df[df['ImageID'].isin(val_ids)]
len(trn_df), len(val_df)

train_ds = OpenDataset(trn_df)
test_ds = OpenDataset(val_df)

train_loader = DataLoader(train_ds, batch_size=4, collate_fn=train_ds.collate_fn, drop_last=True)
test_loader = DataLoader(test_ds, batch_size=4, collate_fn=test_ds.collate_fn, drop_last=True)

2020-10-13 10:38:19.093 | INFO     | __main__:__init__:25 - 13702 items loaded
2020-10-13 10:38:19.138 | INFO     | __main__:__init__:25 - 1523 items loaded

def train_batch(inputs, model, criterion, optimizer):
    model.train()
    N = len(train_loader)
    images, boxes, labels = inputs
    _regr, _clss = model(images)
    loss = criterion(_regr, _clss, boxes, labels)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    return loss
    
@torch.no_grad()
def validate_batch(inputs, model, criterion):
    model.eval()
    images, boxes, labels = inputs
    _regr, _clss = model(images)
    loss = criterion(_regr, _clss, boxes, labels)
    return loss

from model import SSD300, MultiBoxLoss
from detect import *

n_epochs = 3

model = SSD300(num_classes, device)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-5)
criterion = MultiBoxLoss(priors_cxcy=model.priors_cxcy, device=device)

log = Report(n_epochs=n_epochs)
logs_to_print = 5

Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth

HBox(children=(FloatProgress(value=0.0, max=553433881.0), HTML(value='')))

Loaded base model.

/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:44: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
  warnings.warn(warning.format(ret))

for epoch in range(n_epochs):
    _n = len(train_loader)
    for ix, inputs in enumerate(train_loader):
        loss = train_batch(inputs, model, criterion, optimizer)
        pos = (epoch + (ix+1)/_n)
        log.record(pos, trn_loss=loss.item(), end='\r')

    _n = len(test_loader)
    for ix,inputs in enumerate(test_loader):
        loss = validate_batch(inputs, model, criterion)
        pos = (epoch + (ix+1)/_n)
        log.record(pos, val_loss=loss.item(), end='\r')

image_paths = Glob(f'{DATA_ROOT}/images/*')
image_id = choose(test_ds.image_infos)
img_path = find(image_id, test_ds.files)
original_image = Image.open(img_path, mode='r')
original_image = original_image.convert('RGB')

2020-10-13 10:39:28.949 | INFO     | torch_snippets.loader:Glob:178 - 15225 files found at ../open-images-bus-trucks//images/*

image_paths = Glob(f'{DATA_ROOT}/images/*')
for _ in range(3):
    image_id = choose(test_ds.image_infos)
    img_path = find(image_id, test_ds.files)
    original_image = Image.open(img_path, mode='r')
    bbs, labels, scores = detect(original_image, model, min_score=0.9, max_overlap=0.5,top_k=200, device=device)
    labels = [target2label[c.item()] for c in labels]
    label_with_conf = [f'{l} @ {s:.2f}' for l,s in zip(labels,scores)]
    print(bbs, label_with_conf)
    show(original_image, bbs=bbs, texts=label_with_conf, text_sz=10)

[[35, 34, 212, 123]] ['Truck @ 1.00']

[[6, 1, 250, 215]] ['Bus @ 1.00']

[[58, 22, 194, 170]] ['Bus @ 1.00']