Python源码示例：fast.MODELS_DIR
示例1
def get_solvers(net_name):
# Faster R-CNN Alternating Optimization
n = 'faster_rcnn_alt_opt'
# Solver for each training stage
solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
[net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'],
[net_name, n, 'stage2_rpn_solver60k80k.pt'],
[net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']]
solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
# Iterations for each training stage
max_iters = [80000, 40000, 80000, 40000]
# max_iters = [100, 100, 100, 100]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
cfg.MODELS_DIR, net_name, n, 'rpn_test.pt')
return solvers, max_iters, rpn_test_prototxt
# ------------------------------------------------------------------------------
# Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded
# (e.g. "del net" in Python code). To work around this issue, each training
# stage is executed in a separate process using multiprocessing.Process.
# ------------------------------------------------------------------------------
示例2
def get_solvers(net_name):
# Faster R-CNN Alternating Optimization
n = 'faster_rcnn_alt_opt'
# Solver for each training stage
solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
[net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'],
[net_name, n, 'stage2_rpn_solver60k80k.pt'],
[net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']]
solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
# Iterations for each training stage
max_iters = [80000, 40000, 80000, 40000]
# max_iters = [100, 100, 100, 100]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
cfg.MODELS_DIR, net_name, n, 'rpn_test.pt')
return solvers, max_iters, rpn_test_prototxt
# ------------------------------------------------------------------------------
# Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded
# (e.g. "del net" in Python code). To work around this issue, each training
# stage is executed in a separate process using multiprocessing.Process.
# ------------------------------------------------------------------------------
示例3
def get_solvers(net_name):
# Faster R-CNN Alternating Optimization
n = 'faster_rcnn_alt_opt'
# Solver for each training stage
solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
[net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'],
[net_name, n, 'stage2_rpn_solver60k80k.pt'],
[net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']]
solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
# Iterations for each training stage
max_iters = [80000, 40000, 80000, 40000]
# max_iters = [100, 100, 100, 100]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
cfg.MODELS_DIR, net_name, n, 'rpn_test.pt')
return solvers, max_iters, rpn_test_prototxt
# ------------------------------------------------------------------------------
# Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded
# (e.g. "del net" in Python code). To work around this issue, each training
# stage is executed in a separate process using multiprocessing.Process.
# ------------------------------------------------------------------------------
示例4
def get_solvers(net_name):
# Faster R-CNN Alternating Optimization
n = 'faster_rcnn_alt_opt'
# Solver for each training stage
solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
[net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'],
[net_name, n, 'stage2_rpn_solver60k80k.pt'],
[net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']]
solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
# Iterations for each training stage
max_iters = [80000, 40000, 80000, 40000]
# max_iters = [100, 100, 100, 100]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
cfg.MODELS_DIR, net_name, n, 'rpn_test.pt')
return solvers, max_iters, rpn_test_prototxt
# ------------------------------------------------------------------------------
# Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded
# (e.g. "del net" in Python code). To work around this issue, each training
# stage is executed in a separate process using multiprocessing.Process.
# ------------------------------------------------------------------------------
示例5
def get_solvers(net_name):
# Faster R-CNN Alternating Optimization
n = 'faster_rcnn_alt_opt'
# Solver for each training stage
solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
[net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'],
[net_name, n, 'stage2_rpn_solver60k80k.pt'],
[net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']]
solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
# Iterations for each training stage
max_iters = [80000, 40000, 80000, 40000]
# max_iters = [100, 100, 100, 100]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
cfg.MODELS_DIR, net_name, n, 'rpn_test.pt')
return solvers, max_iters, rpn_test_prototxt
# ------------------------------------------------------------------------------
# Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded
# (e.g. "del net" in Python code). To work around this issue, each training
# stage is executed in a separate process using multiprocessing.Process.
# ------------------------------------------------------------------------------
示例6
def get_solvers(net_name):
# Faster R-CNN Alternating Optimization
n = 'faster_rcnn_alt_opt'
# Solver for each training stage
solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
[net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'],
[net_name, n, 'stage2_rpn_solver60k80k.pt'],
[net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']]
solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
# Iterations for each training stage
max_iters = [80000, 40000, 80000, 40000]
# max_iters = [100, 100, 100, 100]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
cfg.MODELS_DIR, net_name, n, 'rpn_test.pt')
return solvers, max_iters, rpn_test_prototxt
# ------------------------------------------------------------------------------
# Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded
# (e.g. "del net" in Python code). To work around this issue, each training
# stage is executed in a separate process using multiprocessing.Process.
# ------------------------------------------------------------------------------
示例7
def get_solvers(imdb_name, net_name, model_name):
# R-FCN Alternating Optimization
# Solver for each training stage
if imdb_name.startswith('coco'):
solvers = [[net_name, model_name, 'stage1_rpn_solver360k480k.pt'],
[net_name, model_name, 'stage1_rfcn_ohem_solver360k480k.pt'],
[net_name, model_name, 'stage2_rpn_solver360k480k.pt'],
[net_name, model_name, 'stage2_rfcn_ohem_solver360k480k.pt'],
[net_name, model_name, 'stage3_rpn_solver360k480k.pt']]
solvers = [os.path.join('.', 'models', 'coco', *s) for s in solvers]
# Iterations for each training stage
max_iters = [480000, 480000, 480000, 480000, 480000]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
'.', 'models', 'coco', net_name, model_name, 'rpn_test.pt')
else:
solvers = [[net_name, model_name, 'stage1_rpn_solver60k80k.pt'],
[net_name, model_name, 'stage1_rfcn_ohem_solver80k120k.pt'],
[net_name, model_name, 'stage2_rpn_solver60k80k.pt'],
[net_name, model_name, 'stage2_rfcn_ohem_solver80k120k.pt'],
[net_name, model_name, 'stage3_rpn_solver60k80k.pt']]
solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
# Iterations for each training stage
max_iters = [80000, 120000, 80000, 120000, 80000]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
cfg.MODELS_DIR, net_name, model_name, 'rpn_test.pt')
return solvers, max_iters, rpn_test_prototxt
示例8
def get_solvers(imdb_name, net_name, model_name):
# R-FCN Alternating Optimization
# Solver for each training stage
if imdb_name.startswith('coco'):
solvers = [[net_name, model_name, 'stage1_rpn_solver360k480k.pt'],
[net_name, model_name, 'stage1_rfcn_ohem_solver360k480k.pt'],
[net_name, model_name, 'stage2_rpn_solver360k480k.pt'],
[net_name, model_name, 'stage2_rfcn_ohem_solver360k480k.pt'],
[net_name, model_name, 'stage3_rpn_solver360k480k.pt']]
solvers = [os.path.join('.', 'models', 'coco', *s) for s in solvers]
# Iterations for each training stage
max_iters = [480000, 480000, 480000, 480000, 480000]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
'.', 'models', 'coco', net_name, model_name, 'rpn_test.pt')
else:
solvers = [[net_name, model_name, 'stage1_rpn_solver60k80k.pt'],
[net_name, model_name, 'stage1_rfcn_ohem_solver80k120k.pt'],
[net_name, model_name, 'stage2_rpn_solver60k80k.pt'],
[net_name, model_name, 'stage2_rfcn_ohem_solver80k120k.pt'],
[net_name, model_name, 'stage3_rpn_solver60k80k.pt']]
solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
# Iterations for each training stage
max_iters = [80000, 120000, 80000, 120000, 80000]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
cfg.MODELS_DIR, net_name, model_name, 'rpn_test.pt')
return solvers, max_iters, rpn_test_prototxt
