def archExperiments(ds):
group = "arch384_total_color2"
tags = TAG_SETS["color2"]
#outputs = [ ('baselines_7', 1.), ('boundary_mask', 0.5), ('comment_mask', 1.),
# ('inverted_background_mask', 1.), ('decoration_mask', 1.), ('text_mask', 1.) ]
outputs = [('baselines_7', 1.)]
round_weights = [0.5, 1]
for depth in [7]:
for num_filters in [48]:
for kernel_size in [7]:
for scale in [4]:
for mse_lambda in [0.1]:
name = "arch_%d_%d_%d_%d_%f" % (
depth, num_filters, kernel_size, scale, mse_lambda)
print "createExperiment(%r, %r, %r, %r)" % (
ds, tags, group, name)
createNetwork.createExperiment(
ds,
ds,
tags,
outputs,
group,
name,
num_experiments=3,
kernel_size=kernel_size,
depth=depth,
num_filters=num_filters,
num_scales=scale,
train_batch_size=5,
input_size=384,
num_upsample_filters=num_filters / 2,
mse_lambda=mse_lambda,
avg_fm=False,
round_weights=round_weights)
def augmentation3Experiments(): group = "aug_combined" experiments = EXPERIMENTS['combined'] for name, (tags, tr) in experiments.items(): print "createNetwork.createExperiment(%r, %r, %r, %r, %r)" % (ds, tags, group, name, tr) createNetwork.createExperiment(ds, tags, group, name, **tr)
def hdibcoRoundExperiments(ds):
group = "round_back"
tags = TAG_SETS["original"] + TAG_SETS["relative_darkness2"] + TAG_SETS[
"howe"]
outputs = [('processed_gt', 1., 'recall_weights', 'precision_weights')]
for num_rounds in [9]:
name = "round_%d" % num_rounds
round_weights = [
2**-(num_rounds - idx - 1) for idx in xrange(num_rounds)
]
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags,
outputs,
group,
name,
num_experiments=5,
kernel_size=9,
depth=7,
num_filters=64,
num_scales=4,
train_batch_size=5,
iter_size=2,
input_size=256,
num_upsample_filters=32,
lr=0.001,
mse_lambda=0,
avg_fm=False,
round_weights=round_weights,
backprop_probs=True,
recurrent_rounds=False,
skip_zero_loss=False)
def hdibcoExperiments(ds):
group = "output2"
tags = TAG_SETS["original"] + TAG_SETS["relative_darkness2"] + TAG_SETS[
"howe"]
base_outputs = [('processed_gt', 1., 'recall_weights', 'precision_weights')
]
outputs = [ #('processed_gt', 1., 'recall_weights', 'precision_weights'),
('left_edges', 0.05), ('right_edges', 0.05), ('top_edges', 0.05),
('bottom_edges', 0.05), ('convex_hull', 0.05), ('skeleton', 0.05),
('dilated_skeleton', 0.05), ('holes', 0.05)
]
for output in outputs:
name = "output_%s" % output[0]
output = base_outputs + [output]
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags,
output,
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=64,
num_scales=4,
train_batch_size=10,
input_size=256,
num_upsample_filters=32,
lr=0.001)
def roundBaselineExperiments(ds):
group = "base_weights_back"
tags = TAG_SETS["color2"]
outputs = [('baselines_7', 1., 'baselines_7_recall_weights',
'baselines_7_precision_weights')]
for num_rounds in xrange(1, 4):
name = "round_weighted_%d" % num_rounds
round_weights = [
2**-(num_rounds - idx - 1) for idx in xrange(num_rounds)
]
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags,
outputs,
group,
name,
num_experiments=3,
kernel_size=7,
depth=7,
num_filters=32,
num_scales=5,
train_batch_size=5,
iter_size=1,
input_size=384,
num_upsample_filters=32,
lr=0.001,
mse_lambda=0,
avg_fm=False,
round_weights=round_weights,
backprop_probs=True,
recurrent_rounds=False)
def augmentationBaselineExperiments(ds):
group = "augmentation"
tags = TAG_SETS["color2"]
outputs = [('baselines_7', 1., 'baselines_7_recall_weights',
'baselines_7_precision_weights')]
for aug in [
'shear', 'perspective', 'elastic', 'blur', 'noise', 'rotate',
'color_jitter'
]:
name = "aug_%s" % aug
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
d = {aug: True}
createNetwork.createExperiment(ds,
ds,
tags,
outputs,
group,
name,
num_experiments=3,
kernel_size=7,
depth=7,
num_filters=32,
num_scales=5,
train_batch_size=5,
iter_size=1,
input_size=384,
num_upsample_filters=32,
lr=0.001,
mse_lambda=0,
avg_fm=False,
**d)
def nwayExperiments(ds):
group = "nway_round"
tags = TAG_SETS["color2"]
output = [('original_classes', 1., 10)]
for num_rounds in xrange(2, 4):
name = "nway_round_%d" % num_rounds
round_weights = [
2**-(num_rounds - idx - 1) for idx in xrange(num_rounds)
]
createNetwork.createExperiment(ds,
ds,
tags,
output,
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=64,
num_scales=4,
train_batch_size=5,
input_size=256,
num_upsample_filters=32,
round_weights=round_weights,
backprop_probs=False,
recurrent_rounds=False)
def bn2Experiments(): group = "bn2" experiments = EXPERIMENTS["bn2"] for name, (tags, tr) in experiments.items(): print "createNetwork.createExperiment(%r, %r, %r, %r, %r)" % (ds, tags, group, name, tr) createNetwork.createExperiment(ds, tags, group, name, **tr)
def variantExperiments():
group = "variants_2"
experiment = {"combo_gbGB": (generateTag('gbGB'), dict(num_experiments=10, **default)),
"combo_gG": (generateTag('gG'), dict(num_experiments=10, **default))}
for name, (t, tr) in experiment.items():
createNetwork.createExperiment(ds, t, group, name, **tr)
def equiArchExperiments(): group = "equi_arch" experiments = EXPERIMENTS["equi_arch"] for name, (tags, tr) in experiments.items(): print "createNetwork.createExperiment(%r, %r, %r, %r, %r)" % (ds, tags, group, name, tr) createNetwork.createExperiment(ds, tags, group, name, **tr)
def sizeExperiments(): group = "size" experiments = EXPERIMENTS["size"] experiments.update(EXPERIMENTS["multiscale"]) for name, (tags, tr) in experiments.items(): print "createNetwork.createExperiment(%r, %r, %r, %r, %r)" % (ds, tags, group, name, tr) createNetwork.createExperiment(ds, tags, group, name, **tr)
def dsurfExperiments(): group = "dsurf" experiments = EXPERIMENTS["dsurf"] experiments.update(EXPERIMENTS["dsurf_gray"]) for name, (tags, tr) in experiments.items(): print "createNetwork.createExperiment(%r, %r, %r, %r, %r)" % (ds, tags, group, name, tr) createNetwork.createExperiment(ds, tags, group, name, **tr)
def widthExperiments(): group = "width" experiments = EXPERIMENTS["width"] experiments.update(EXPERIMENTS["width_2"]) for name, (tags, tr) in experiments.items(): print "createNetwork.createExperiment(%r, %r, %r, %r, %r)" % (ds, tags, group, name, tr) createNetwork.createExperiment(ds, tags, group, name, **tr)
def paddingExperiments(): group = "padding" experiments = EXPERIMENTS["padding"] #experiments = EXPERIMENTS["multiple2"] experiments.update(EXPERIMENTS["multiple2"]) experiments.update(EXPERIMENTS["multiple2_crop"]) for name, (tags, tr) in experiments.items(): print "createNetwork.createExperiment(%r, %r, %r, %r, %r)" % (ds, tags, group, name, tr) createNetwork.createExperiment(ds, tags, group, name, **tr)
def channelExperiments():
group = "variants"
#tags = ["gray_227","gray_227_invert", "binary_227", "binary_227_invert", "color_227", "color_227_invert"]
#tags = {"combo_gG": [TAGS['g'], TAGS['G']], "combo_bB": [TAGS['b'], TAGS['B']], "combo_cC": [TAGS['c'], TAGS['C']],
# "combo_gb": [TAGS['g'], TAGS['b']], "combo_GB": [TAGS['G'], TAGS['B']], "combo_gbGB": [TAGS['g'], TAGS['b'], TAGS['B'], TAGS['G']],
# "combo_cg": [TAGS['c'], TAGS['g']], "combo_cb": [TAGS['c'], TAGS['b']], "combo_cgb": [TAGS['c'], TAGS['g'], TAGS['b']],
# "combo_CG": [TAGS['C'], TAGS['G']], "combo_CB": [TAGS['C'], TAGS['B']], "combo_CGB": [TAGS['C'], TAGS['G'], TAGS['B']],
# "combo_cgbCGB": [TAGS['c'], TAGS['g'], TAGS['b'], TAGS['C'], TAGS['G'], TAGS['B']]}
experiments = {"combo_cgbcgb" : (generateTag('cgbcgb'), dict(num_experiments=10, **default))}
for name, (t, tr) in experiments.items():
createNetwork.createExperiment(ds, t, group, name, **tr)
def augmentationExperiments(): group = "augmentation" #experiments = EXPERIMENTS["standard"] experiments = EXPERIMENTS['baseline'] experiments.update(EXPERIMENTS["standard"]) experiments.update(EXPERIMENTS["shear"]) experiments.update(EXPERIMENTS["blur_sharp"]) experiments.update(EXPERIMENTS["rotate"]) experiments.update(EXPERIMENTS["shear"]) experiments.update(EXPERIMENTS["perspective"]) experiments.update(EXPERIMENTS["color_jitter"]) experiments.update(EXPERIMENTS["elastic"]) #experiments = EXPERIMENTS["combined"] for name, (tags, tr) in experiments.items(): print "createNetwork.createExperiment(%r, %r, %r, %r, %r)" % (ds, tags, group, name, tr) createNetwork.createExperiment(ds, tags, group, name, **tr)
def outputSingleExperiments(ds):
group = "output_single_256"
tags = TAG_SETS["gray"]
outputs = [('baselines_7', 1.), ('boundary_mask', 0.5),
('comment_mask', 1.), ('inverted_background_mask', 1.),
('decoration_mask', 1.), ('text_mask', 1.), ('task3_gt', 1.)]
for output in outputs:
name = "output_%s" % output[0]
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags, [output],
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=24,
num_scales=5,
train_batch_size=5,
input_size=256,
num_upsample_filters=8,
round_0_only=True,
round_0_weight=1.)
name = "output_all"
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags,
outputs,
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=24,
num_scales=5,
train_batch_size=5,
input_size=256,
num_upsample_filters=8,
round_0_only=True,
round_0_weight=1.)
def outputExperiments(ds):
group = "output"
tags = TAG_SETS["color2"]
#outputs = [ ('baselines_7', 1.), ('boundary_mask', 0.5), ('comment_mask', 1.),
# ('inverted_background_mask', 1.), ('decoration_mask', 1.), ('text_mask', 1.),
# ('task3_gt', 1.)]
for output in outputs:
name = "output_%s" % output[0]
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags, [output],
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=48,
num_scales=4,
train_batch_size=5,
input_size=256,
num_upsample_filters=16,
mse_lambda=0,
avg_fm=False)
name = "output_all"
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags,
outputs,
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=48,
num_scales=4,
train_batch_size=5,
input_size=256,
num_upsample_filters=16,
mse_lambda=0,
avg_fm=False)
def outputPairExperiments(ds):
group = "output_pair"
tags = TAG_SETS["color2"]
outputs = {
'base': ('baselines_7', 1., 4),
'bound': ('boundary_mask', 0.5),
'comment': ('comment_mask', 1.),
'back': ('inverted_background_mask', 1.),
'dec': ('decoration_mask', 1.),
'text': ('text_mask', 1.),
'task3': ('task3_gt', 1.)
}
pairs = [('text', 'comment'), ('text', 'comment', 'base'), ('dec', 'text'),
('text', 'task3'), ('text', 'bound'), ('text', 'base'),
('dec', 'comment'), ('back', 'comment'), ('bound', 'base'),
('comment', 'base'), ('task3', 'base')]
for pair in pairs:
name = "output_%s" % "_".join(pair)
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
output = [outputs[p] for p in pair]
createNetwork.createExperiment(ds,
ds,
tags,
output,
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=64,
num_scales=4,
train_batch_size=5,
input_size=256,
num_upsample_filters=32,
mse_lambda=0,
avg_fm=False)
def featuresBaselineExperiments(ds):
group = "binarization"
outputs = [('baselines_7', 1., 'baselines_7_recall_weights',
'baselines_7_precision_weights')]
tags = TAG_SETS["color2"]
name = "bin_none"
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags,
outputs,
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=32,
num_scales=5,
train_batch_size=5,
iter_size=1,
input_size=384,
num_upsample_filters=32,
lr=0.001,
mse_lambda=0,
avg_fm=False)
tags = TAG_SETS["color2"] + TAG_SETS["otsu"]
name = "bin_otsu"
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags,
outputs,
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=32,
num_scales=5,
train_batch_size=5,
iter_size=1,
input_size=384,
num_upsample_filters=32,
lr=0.001,
mse_lambda=0,
avg_fm=False)
tags = TAG_SETS["color2"] + TAG_SETS["fcn_bin"]
name = "bin_fcn"
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags,
outputs,
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=32,
num_scales=5,
train_batch_size=5,
iter_size=1,
input_size=384,
num_upsample_filters=32,
lr=0.001,
mse_lambda=0,
avg_fm=False)
tags = TAG_SETS["color2"] + TAG_SETS["otsu"] + TAG_SETS["fcn_bin"]
name = "bin_both"
print "createExperiment(%r, %r, %r, %r)" % (ds, tags, group, name)
createNetwork.createExperiment(ds,
ds,
tags,
outputs,
group,
name,
num_experiments=3,
kernel_size=9,
depth=7,
num_filters=32,
num_scales=5,
train_batch_size=5,
iter_size=1,
input_size=384,
num_upsample_filters=32,
lr=0.001,
mse_lambda=0,
avg_fm=False)
def channel2Experiments():
tr = {'num_experiments': 10, 'scale': (1./255), 'shift': 'mean'}
createNetwork.createExperiment(ds, ['binary_227'], 'channel2', 'binary', **tr)
createNetwork.createExperiment(ds, ['gray_227_invert'], 'channel2', 'gray_invert', **tr)
createNetwork.createExperiment(ds, ['color_227_invert'], 'channel2', 'color_invert', **tr)
createNetwork.createExperiment(ds, ['gray_227'], 'channel2', 'gray', **tr)
def hsvExperiments():
tr = {'num_experiments': 10, 'hsv': True, 'scale': (1./255), 'shift': 'mean'}
createNetwork.createExperiment(ds, ['color_227'], 'channel2', 'hsv', **tr)
createNetwork.createExperiment(ds, ['color_227', 'color_227'], 'channel2', 'hsv_rgb', **tr)
