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github-python

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# PRED_DOWNSCALE_FACTORS is the set of integer factors indicating how much to
# downscale the dimensions of the ground truth prediction for each scale output.
# Note that the data reader under default settings creates prediction maps at
# one-half resolution (wrt input sizes) and hence PRED_DOWNSCALE_FACTORS =
# (8, 4, 2, 1) translates to 1/16, 1/8, 1/4 and 1/2 prediction sizes (s={0,1,2,3}).
PRED_DOWNSCALE_FACTORS = (8, 4, 2, 1)
# Size increments for the box sizes (\gamma) as mentioned in the paper.
GAMMA = [1, 1, 2, 4]
# Number of predefined boxes per scales (n_{mathcal{B}}).
NUM_BOXES_PER_SCALE = 3
###############################################################
# ---- Computing predefined box sizes and global variables
BOX_SIZE_BINS = [1]
BOX_IDX = [0]
g_idx = 0
while len(BOX_SIZE_BINS) < NUM_BOXES_PER_SCALE * len(PRED_DOWNSCALE_FACTORS):
gamma_idx = len(BOX_SIZE_BINS) // (len(GAMMA)-1)
box_size = BOX_SIZE_BINS[g_idx] + GAMMA[gamma_idx]
box_idx = gamma_idx*(NUM_BOXES_PER_SCALE+1) + (len(BOX_SIZE_BINS) % (len(GAMMA)-1))
BOX_IDX.append(box_idx)
BOX_SIZE_BINS.append(box_size)
g_idx += 1
BOX_INDEX = dict(zip(BOX_SIZE_BINS, BOX_IDX))
SCALE_BINS_ON_BOX_SIZE_BINS = [NUM_BOXES_PER_SCALE * (s + 1) \
for s in range(len(GAMMA))]
BOX_SIZE_BINS_NPY = np.array(BOX_SIZE_BINS)
BOXES = np.reshape(BOX_SIZE_BINS_NPY, (4, 3))
BOXES = BOXES[::-1]
metrics = ['loss1', 'new_mae']
# Loss Weights (to be read from .npy file while training)
loss_weights = None
matplotlib.use('Agg')
parser = argparse.ArgumentParser(description='PyTorch LSC-CNN Training')
parser.add_argument('--epochs', default=200, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--gpu', default=1, type=int,
help='GPU number')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts),\
0-indexed - so equal to the number of epochs completed \
in the last save-file')
parser.add_argument('-b', '--batch-size', default=4, type=int, metavar='N',
help='mini-batch size (default: 4),only used for train')
parser.add_argument('--patches', default=100, type=int, metavar='N',
help='number of patches per image')
parser.add_argument('--dataset', default="parta", type=str,
help='dataset to train on')
parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float,
metavar='M', help='momentum')
parser.add_argument('--threshold', default=-1.0, type=float,
metavar='M', help='fixed threshold to do NMS')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W',
help='weight decay (default: 1e-4)')
parser.add_argument('--mle', action='store_true',
help='calculate mle')
parser.add_argument('--lsccnn', action='store_true',
help='use the vgg_modified network')
parser.add_argument('--trained-model', default='', type=str, metavar='PATH', help='filename of model to load', nargs='+')
dataset_paths, model_save_dir, batch_size, crop_size, dataset = None, None, None, None, None
class networkFunctions():
def __init__(self):
self.train_funcs = []
self.test_funcs = None
self.optimizers = None
'''
Get N channel ground truth for each scale. (Here N = 4 except for WIDERFACE)
B1, B2, B3, Z - Bi's are Box GT and Z is the background i.e
if there is not GT in any of the scales.
Parameters
-----------
Yss (list of torch cuda tensor)
bool_masks (list of torch cuda tensor) - Used only while training
mode (string) - To specify if the fn. is called at test/train time.
Returns
-------
Yss_out (list of torch cuda tensor)
'''
def get_box_gt(self, Yss):
Yss_out = []
for yss in Yss: # iterate over all scales!
# Make empty maps of shape gt_pred_map.shape for x, y, w, h
w_map = np.zeros((yss.shape[0], 4) + yss.shape[2:]) # (B,4,h,w)
w_map[:, 3] = 1 # Making Z initialized as 1's since they are in majority!
Yss_out.append(w_map)
assert(len(Yss_out) == 4)
# Get largest spatial gt