def get_results(file, logger):
"""Grab all the results according to the hyperparameter file."""
results = []
params = []
num_epochs_retraining = []
# Loop through all experiments
for param in util.file.get_parameters(file, 1, 0):
# get number of retraining epochs
n_e = param["generated"]["retraining"]["numEpochs"]
n_e -= param["generated"]["retraining"]["startEpoch"]
# initialize logger and setup parameters
logger.initialize_from_param(param)
# run the experiment (only if necessary)
try:
state = logger.get_global_state()
except ValueError:
experiment.Evaluator(logger).run()
state = logger.get_global_state()
# extract the results
results.append(copy.deepcopy(state))
params.append(copy.deepcopy(param))
num_epochs_retraining.append(n_e)
return (
OrderedDict(zip(num_epochs_retraining, results)),
OrderedDict(zip(num_epochs_retraining, params)),
)
def get_results(file, logger):
"""Grab all the results according to the hyperparameter file."""
results = []
params = []
labels = []
# Loop through all experiments
for param in get_parameters(file, 1, 0):
# initialize logger and setup parameters
logger.initialize_from_param(param)
# run the experiment (only if necessary)
try:
state = logger.get_global_state()
except ValueError:
experiment.Evaluator(logger).run()
state = logger.get_global_state()
# extract the results
results.append(copy.deepcopy(state))
params.append(copy.deepcopy(param))
# extract the legend (based on heuristic)
label = param["generated"]["datasetTest"].split("_")
if len(label) > 2:
label = label[2:]
labels.append("_".join(label))
# extract the plots
graphers = logger.generate_plots(store_figs=False)
if "imagenet" in file:
graphers[0].graph(
percentage_x=True,
percentage_y=True,
store=False,
remove_outlier=False,
)
if "_rand" in file or "retraininit" in file:
for i, grapher in enumerate(graphers[:6]):
percentage_y = bool((i + 1) % 3)
grapher.graph(
percentage_x=True,
percentage_y=percentage_y,
store=False,
show_ref=False,
show_delta=False,
remove_outlier=False,
)
if percentage_y:
# grapher._figure.gca().set_xlim([50, 99])
grapher._figure.gca().set_ylim([80, 95])
return results, params, labels, graphers
def get_results(file, logger):
"""Grab all the results according to the parameter file."""
# Loop through all experiments
for param in get_parameters(file, 1, 0):
# initialize logger and setup parameters
logger.initialize_from_param(param)
# get evaluator
evaluator = experiment.Evaluator(logger)
# run the experiment (only if necessary)
try:
state = logger.get_global_state()
except ValueError:
evaluator.run()
state = logger.get_global_state()
# extract the results
return evaluator, state
def get_results(file, logger, matches_exact, matches_partial):
"""Grab all the results according to the hyperparameter file."""
results = []
params = []
labels = []
# Loop through all experiments
for param in util.file.get_parameters(file, 1, 0):
# extract the legend (based on heuristic)
label = param["generated"]["datasetTest"].split("_")
if len(label) > 2:
label = label[2:]
label = "_".join(label)
# check if label conforms to any of the provided filters
if not (
np.any([str(mtc) == label for mtc in matches_exact]).item()
or np.any([str(mtc) in label for mtc in matches_partial]).item()
):
continue
# remove severity from label once identified
label = label if "CIFAR10" in label else label.split("_")[0]
# initialize logger and setup parameters
logger.initialize_from_param(param)
# run the experiment (only if necessary)
try:
state = logger.get_global_state()
except ValueError:
experiment.Evaluator(logger).run()
state = logger.get_global_state()
# extract the results
results.append(copy.deepcopy(state))
params.append(copy.deepcopy(param))
labels.append(label)
return OrderedDict(zip(labels, results)), OrderedDict(zip(labels, params))
MEAN_C = [0.485, 0.456, 0.406]
STD_C = [0.229, 0.224, 0.225]
elif "CIFAR" in PARAM["network"]["dataset"]:
MEAN_C = [0.4914, 0.4822, 0.4465]
STD_C = [0.2023, 0.1994, 0.2010]
else:
raise ValueError("Please adapt script to provide normalization of dset!")
MEAN_C = np.asarray(MEAN_C)[:, np.newaxis, np.newaxis]
STD_C = np.asarray(STD_C)[:, np.newaxis, np.newaxis]
# Now initialize the logger
LOGGER = experiment.Logger()
LOGGER.initialize_from_param(PARAM)
# Initialize the evaluator and run it (will return if nothing to compute)
COMPRESSOR = experiment.Evaluator(LOGGER)
COMPRESSOR.run()
# device settings
torch.cuda.set_device("cuda:0")
DEVICE = torch.device("cuda:0")
DEVICE_STORAGE = torch.device("cpu")
# Generate all the models we like.
# get a list of models
MODELS = [COMPRESSOR.get_all(**kw) for kw in MODELS_ALL_DESCRIPTION]
IDX_RANGES = []
NUM_TOTAL = 0
for model_array in MODELS:
IDX_RANGES.append(np.arange(NUM_TOTAL, NUM_TOTAL + len(model_array)))
NUM_TOTAL += len(model_array)
def get_results(file, logger, legend_on):
"""Grab all the results according to the hyperparameter file."""
results = []
params = []
labels = []
graphers_all = []
# Loop through all experiments
for param in get_parameters(file, 1, 0):
# initialize logger and setup parameters
logger.initialize_from_param(param)
# run the experiment (only if necessary)
try:
state = logger.get_global_state()
except ValueError:
experiment.Evaluator(logger).run()
state = logger.get_global_state()
# extract the results
results.append(copy.deepcopy(state))
params.append(copy.deepcopy(param))
# extract the legend (based on heuristic)
label = param["generated"]["datasetTest"].split("_")
if len(label) > 2:
label = label[2:]
labels.append("_".join(label))
# extract the plots
graphers = logger.generate_plots(store_figs=False)
# modify label of x-axis
graphers[0]._figure.gca().set_xlabel("Compression Ratio (Parameters)")
if "cifar/retraininit" in file:
for i, grapher in enumerate(graphers[:6]):
percentage_y = bool((i + 1) % 3)
grapher.graph(
percentage_x=True,
percentage_y=percentage_y,
store=False,
show_ref=False,
show_delta=False,
remove_outlier=False,
)
if percentage_y:
grapher._figure.gca().set_ylim([86, 98])
elif "cifar/prune" in file and "_plus" in file:
graphers[0]._figure.gca().set_xlim([20, 65])
graphers[0]._figure.gca().set_ylim([-61, 2])
elif "cifar/prune" in file:
graphers[0]._figure.gca().set_ylim([-87, 5])
elif "imagenet/prune" in file and "_plus" in file:
graphers[0]._figure.gca().set_xlim([39, 81])
graphers[0]._figure.gca().set_ylim([-61, 2])
elif "imagenet/prune" in file:
graphers[0]._figure.gca().set_xlim([0, 87])
graphers[0]._figure.gca().set_ylim([-87, 5])
elif "imagenet/retrain/" in file:
graphers[0]._figure.gca().set_ylim([-3.5, 1.5])
elif "imagenet/retraincascade" in file:
# graphers[0]._figure.gca().set_xlim([-11, 2])
graphers[0]._figure.gca().set_ylim([-2.5, 1])
elif "imagenet/retrain" in file:
graphers[0]._figure.gca().set_ylim([-11, 2])
elif "cifar/retrainablation/" in file:
graphers[0]._figure.gca().set_ylim([-3.2, 1.2])
elif "cifar/retrain/densenet" in file:
graphers[0]._figure.gca().set_xlim([14, 78])
graphers[0]._figure.gca().set_ylim([-3.5, 1.5])
elif "cifar/retrain/vgg" in file:
graphers[0]._figure.gca().set_xlim([70, 97.5])
graphers[0]._figure.gca().set_ylim([-3.5, 1.5])
elif "cifar/retrain/wrn" in file:
graphers[0]._figure.gca().set_xlim([85, 97.5])
graphers[0]._figure.gca().set_ylim([-2.5, 0.0])
elif "cifar/retrain/" in file:
# graphers[0]._figure.gca().set_ylim([-11, 3])
graphers[0]._figure.gca().set_ylim([-3.5, 1.5])
elif "cifar/retrainlittle/" in file:
graphers[0]._figure.gca().set_ylim([-3.5, 1.5])
elif "cifar/retrain" in file:
graphers[0]._figure.gca().set_ylim([-11, 2])
elif "voc/prune" in file:
graphers[0]._figure.gca().set_xlim([0, 90])
graphers[0]._figure.gca().set_ylim([-87, 5])
elif "voc/retrain" in file:
graphers[0]._figure.gca().set_ylim([-3, 2])
for grapher in graphers:
legend = grapher._figure.gca().get_legend()
if legend is not None:
grapher._figure.gca().get_legend().remove()
legend.set_bbox_to_anchor((1.1, 0.7))
if legend_on:
graphers[0].graph(
percentage_x=True,
percentage_y=True,
store=False,
kwargs_legend={
"loc": "upper left",
"ncol": 1,
"bbox_to_anchor": (1.1, 0.9),
},
)
graphers_all.append(graphers)
return results, params, labels, graphers_all
