def export_img_classifier_to_onnx(model,
onnx_fname,
dataset,
add_softmax=True,
**kwargs):
"""Export a PyTorch image classifier to ONNX.
Args:
add_softmax: when True, adds softmax layer to the output model.
kwargs: arguments to be passed to torch.onnx.export
"""
dummy_input = distiller.get_dummy_input(dataset,
distiller.model_device(model))
# Pytorch doesn't support exporting modules wrapped in DataParallel
non_para_model = distiller.make_non_parallel_copy(model)
try:
if add_softmax:
# Explicitly add a softmax layer, because it is needed for the ONNX inference phase.
# TorchVision models use nn.CrossEntropyLoss for computing the loss,
# instead of adding a softmax layer
non_para_model.original_forward = non_para_model.forward
softmax = torch.nn.Softmax(dim=-1)
non_para_model.forward = lambda input: softmax(
non_para_model.original_forward(input))
torch.onnx.export(non_para_model, dummy_input, onnx_fname, **kwargs)
msglogger.info("Exported the model to ONNX format at %s" %
os.path.realpath(onnx_fname))
finally:
del non_para_model
def model_performance_summary(model, dummy_input, batch_size=1):
"""Collect performance data"""
def install_perf_collector(m):
if isinstance(m, torch.nn.Conv2d):
hook_handles.append(
m.register_forward_hook(
partial(conv_visitor, df=df, model=model, memo=memo)))
elif isinstance(m, torch.nn.Linear):
hook_handles.append(
m.register_forward_hook(
partial(fc_visitor, df=df, model=model, memo=memo)))
df = pd.DataFrame(columns=[
'Name', 'Type', 'Attrs', 'IFM', 'IFM volume', 'OFM', 'OFM volume',
'Weights volume', 'MACs'
])
hook_handles = []
memo = []
model = distiller.make_non_parallel_copy(model)
model.apply(install_perf_collector)
# Now run the forward path and collect the data
model(dummy_input.cuda())
# Unregister from the forward hooks
for handle in hook_handles:
handle.remove()
return df
def export_img_classifier_to_onnx(model,
onnx_fname,
dataset,
export_params=True,
add_softmax=True):
"""Export a PyTorch image classifier to ONNX.
"""
dummy_input = dataset_dummy_input(dataset).to('cuda')
# Pytorch 0.4 doesn't support exporting modules wrapped in DataParallel
model = distiller.make_non_parallel_copy(model)
with torch.onnx.set_training(model, False):
if add_softmax:
# Explicitly add a softmax layer, because it is needed for the ONNX inference phase.
model.original_forward = model.forward
softmax = torch.nn.Softmax(dim=-1)
model.forward = lambda input: softmax(model.original_forward(input)
)
torch.onnx.export(model,
dummy_input,
onnx_fname,
verbose=False,
export_params=export_params)
msglogger.info('Exported the model to ONNX format at %s' %
os.path.realpath(onnx_fname))
def draw_img_classifier_to_file(model, png_fname, dataset, display_param_nodes=False,
rankdir='TB', styles=None):
"""Draw a PyTorch image classifier to a PNG file. This a helper function that
simplifies the interface of draw_model_to_file().
Args:
model: PyTorch model instance
png_fname (string): PNG file name
dataset (string): one of 'imagenet' or 'cifar10'. This is required in order to
create a dummy input of the correct shape.
display_param_nodes (boolean): if True, draw the parameter nodes
rankdir: diagram direction. 'TB'/'BT' is Top-to-Bottom/Bottom-to-Top
'LR'/'R/L' is Left-to-Rt/Rt-to-Left
styles: a dictionary of styles. Key is module name. Value is
a legal pydot style dictionary. For example:
styles['conv1'] = {'shape': 'oval',
'fillcolor': 'gray',
'style': 'rounded, filled'}
"""
try:
dummy_input = dataset_dummy_input(dataset)
model = distiller.make_non_parallel_copy(model)
g = SummaryGraph(model, dummy_input)
draw_model_to_file(g, png_fname, display_param_nodes, rankdir, styles)
print("Network PNG image generation completed")
except FileNotFoundError:
print("An error has occured while generating the network PNG image.")
print("Please check that you have graphviz installed.")
print("\t$ sudo apt-get install graphviz")
def quantize_and_test_model(test_loader,
model,
criterion,
args,
scheduler=None,
save_flag=True):
"""Collect stats using test_loader (when stats file is absent),
clone the model and quantize the clone, and finally, test it.
args.device is allowed to differ from the model's device.
When args.qe_calibration is set to None, uses 0.05 instead.
scheduler - pass scheduler to store it in checkpoint
save_flag - defaults to save both quantization statistics and checkpoint.
"""
if hasattr(model, 'quantizer_metadata') and \
model.quantizer_metadata['type'] == distiller.quantization.PostTrainLinearQuantizer:
raise RuntimeError(
'Trying to invoke post-training quantization on a model that has already been post-'
'train quantized. Model was likely loaded from a checkpoint. Please run again without '
'passing the --quantize-eval flag')
if not (args.qe_dynamic or args.qe_stats_file or args.qe_config_file):
args_copy = copy.deepcopy(args)
args_copy.qe_calibration = args.qe_calibration if args.qe_calibration is not None else 0.05
# set stats into args stats field
args.qe_stats_file = acts_quant_stats_collection(
model, criterion, loggers, args_copy, save_to_file=save_flag)
args_qe = copy.deepcopy(args)
if args.device == 'cpu':
# NOTE: Even though args.device is CPU, we allow here that model is not in CPU.
qe_model = distiller.make_non_parallel_copy(model).cpu()
else:
qe_model = copy.deepcopy(model).to(args.device)
quantizer = quantization.PostTrainLinearQuantizer.from_args(
qe_model, args_qe)
dummy_input = utl.get_dummy_input(
input_shape=(1, 3, 224, 224)) # should modifiled! or add to args
quantizer.prepare_model(dummy_input)
if args.qe_convert_pytorch:
qe_model = _convert_ptq_to_pytorch(qe_model, args_qe)
# should check device
test_res = test(qe_model, criterion, args.device)
if save_flag:
checkpoint_name = 'quantized'
ckpt.save_checkpoint(0,
args_qe.arch,
qe_model,
scheduler=scheduler,
name='_'.join([args_qe.name, checkpoint_name])
if args_qe.name else checkpoint_name,
dir=args.model_path,
extras={'quantized_top1': test_res[0]})
del qe_model
return test_res
def __init__(self, model, dummy_input):
model = distiller.make_non_parallel_copy(model)
with torch.onnx.set_training(model, False):
trace, _ = jit.get_trace_graph(model, dummy_input.cuda())
# Let ONNX do the heavy lifting: fusing the convolution nodes; fusing the nodes
# composing a GEMM operation; etc.
torch.onnx._optimize_trace(trace, False)
graph = trace.graph()
self.ops = {}
self.params = {}
self.edges = []
self.temp = {}
in_out = list(graph.inputs()) + list(graph.outputs())
for param in in_out:
self.__add_param(param)
for node in graph.nodes():
new_op = self.__create_op(node)
# Operators with the same name create very confusing graphs (Resnet, for example),
# so we "unroll" them.
# Sometimes operations of different types have the same name, so we differentiate
# using both name and type
# (this happens, for example, when an operator is called via some functional API and
# not via a module)
same = [
op for op in self.ops.values() if op['orig-name'] +
op['type'] == new_op['orig-name'] + new_op['type']
]
if len(same) > 0:
new_op['name'] += "." + str(len(same))
new_op['name'] = onnx_name_2_pytorch_name(
new_op['name'], new_op['type'])
assert len(new_op['name']) > 0
self.ops[new_op['name']] = new_op
for input_ in node.inputs():
self.__add_input(new_op, input_)
self.edges.append(
SummaryGraph.Edge(input_.uniqueName(), new_op['name']))
for output in node.outputs():
self.__add_output(new_op, output)
self.edges.append(
SummaryGraph.Edge(new_op['name'], output.uniqueName()))
new_op['attrs'] = {
attr_name: node[attr_name]
for attr_name in node.attributeNames()
}
self.add_macs_attr()
self.add_footprint_attr()
self.add_arithmetic_intensity_attr()
del model
def quantize_and_test_model(test_loader, model, criterion, args, loggers=None, scheduler=None, save_flag=True):
"""Collect stats using test_loader (when stats file is absent),
clone the model and quantize the clone, and finally, test it.
args.device is allowed to differ from the model's device.
When args.qe_calibration is set to None, uses 0.05 instead.
scheduler - pass scheduler to store it in checkpoint
save_flag - defaults to save both quantization statistics and checkpoint.
"""
if not (args.qe_dynamic or args.qe_stats_file or args.qe_config_file):
args_copy = copy.deepcopy(args)
args_copy.qe_calibration = args.qe_calibration if args.qe_calibration is not None else 0.05
# set stats into args stats field
args.qe_stats_file = acts_quant_stats_collection(
model, criterion, loggers, args_copy, save_to_file=save_flag)
args_qe = copy.deepcopy(args)
if args.device == 'cpu':
# NOTE: Even though args.device is CPU, we allow here that model is not in CPU.
qe_model = distiller.make_non_parallel_copy(model).cpu()
else:
qe_model = copy.deepcopy(model).to(args.device)
quantizer = quantization.PostTrainLinearQuantizer.from_args(qe_model, args_qe)
quantizer.prepare_model(distiller.get_dummy_input(input_shape=model.input_shape))
test_res = test(test_loader, qe_model, criterion, loggers, args=args_qe)
if save_flag:
checkpoint_name = 'quantized'
apputils.save_checkpoint(0, args_qe.arch, qe_model, scheduler=scheduler,
name='_'.join([args_qe.name, checkpoint_name]) if args_qe.name else checkpoint_name,
dir=msglogger.logdir, extras={'quantized_top1': test_res[0]})
del qe_model
return test_res
def __init__(self, model, dummy_input, apply_scope_name_workarounds=True):
self._src_model = model
model_clone = distiller.make_non_parallel_copy(model)
# Switch all instances of torch.nn.ModuleList in the model to our DistillerModuleList
# See documentation of _DistillerModuleList class for details on why this is done
model_clone, converted_module_names_map = _to_distiller_modulelist(model_clone)
with torch.onnx.set_training(model_clone, False):
device = distiller.model_device(model_clone)
dummy_input = distiller.convert_tensors_recursively_to(dummy_input, device=device)
trace, _ = jit.get_trace_graph(model_clone, dummy_input, _force_outplace=True)
# As of PyTorch 1.1.0, ONNX trace optimization has two issues that result in incorrect scope names
# of nodes in the trace graph.
# These can make it impossible, in some cases, to derive the connectivity of the model using the original
# module names. So we try to detect these cases and apply workarounds
# Issue #1:
# Gemm ops (aka "Linear" / "addmm" / "FC") get the scope name of the last non-Gemm node
# that came before them.
# Note that if the node prior to the Gemm node isn't the result of a dedicated module call,
# then this issue doesn't occur. For simplicity we just track all Gemms.
# TODO: This should be fixed in PyTorch 1.2.0, revisit when it's released
aten_addmm_nodes_scope_names = []
onnx_gemm_count = 0
# Issue #2:
# Dropout ops are removed by ONNX trace optimization. However, the op BEFORE the original dropout op
# gets the scope name of the dropout op
pre_dropout_nodes_scope_names = OrderedDict()
prev_non_dropout_op = None
for node in trace.graph().nodes():
kind = node.kind()
if 'aten' not in kind:
continue
if kind == 'aten::dropout':
if prev_non_dropout_op:
pre_dropout_nodes_scope_names[node.scopeName()] = prev_non_dropout_op.scopeName()
else:
prev_non_dropout_op = node
if kind == 'aten::addmm':
aten_addmm_nodes_scope_names.append(node.scopeName())
# Let ONNX do the heavy lifting: fusing the convolution nodes; fusing the nodes
# composing a GEMM operation; etc.
torch.onnx._optimize_trace(trace, torch.onnx.OperatorExportTypes.ONNX)
graph = trace.graph()
self.ops = OrderedDict()
self.module_ops_map = defaultdict(list)
self.params = OrderedDict()
self.edges = []
self.temp = OrderedDict()
in_out = list(graph.inputs()) + list(graph.outputs())
for param in in_out:
self.__add_param(param)
for node in graph.nodes():
new_op = self.__create_op(node)
if apply_scope_name_workarounds:
# Here we apply the workaround to the Gemm nodes scope name issue mentioned above
if new_op['type'] == 'Gemm':
new_op['orig-name'] = aten_addmm_nodes_scope_names[onnx_gemm_count]
new_op['name'] = new_op['orig-name']
onnx_gemm_count += 1
# Here we apply the workaround to the issue of dropout op scope name overriding previous op's
# scope name
if new_op['name'] in pre_dropout_nodes_scope_names:
new_op['orig-name'] = pre_dropout_nodes_scope_names[new_op['name']]
new_op['name'] = new_op['orig-name']
# Convert the graph node's scope name to a PyTorch module name
module_name = onnx_name_2_pytorch_name(new_op['orig-name'])
# Get name from before conversion to DistillerModuleList
module_name = converted_module_names_map[module_name]
if len(module_name) == 0:
# Special case where the module name is an empty string - this happens
# when the op is called from the "top-level" of the model
new_op['name'] = 'top_level_op'
else:
new_op['name'] = module_name
# Save the calling module name in the op dict. Denormalize it so it can
# be directly matched with the actual model
module_name = distiller.denormalize_module_name(self._src_model, module_name)
new_op['module-name'] = module_name
# The node's scope name in the graph corresponds to the module from which the op was called.
# This means that when ops are invoked from the same module via functional calls or direct
# operations on tensors, these ops will have the SAME MODEL NAME associated with them.
# For example:
# t = t1 + t2
# t = F.relu(t)
# In this case the add operation and the ReLU operation will have the same name, which is
# derived from the module they're contained in.
#
# Another case where different ops will have the same module name is when a module is reused:
# out = self.conv1(x)
# out = self.relu(out) <=== First use of self.relu
# out = self.conv2(out)
# out = self.relu(out) <=== Second use of self.relu
# In this case the graph will have 2 distinct ReLU nodes, with the same scope name.
#
# Operators with the same name create very confusing graphs (in ResNet, for example),
# so we "unroll" them.
same_module_cnt = len(self.module_ops_map[module_name])
if same_module_cnt:
new_op['name'] += "__" + str(same_module_cnt)
self.module_ops_map[module_name].append(new_op['name'])
# Finally we register the new op in the ops collection
msglogger.debug("new sgraph node - Scope name: {} ; Type: {} ; Display name {}".format(
new_op['orig-name'], new_op['type'], new_op['name']))
self.ops[new_op['name']] = new_op
for input_ in node.inputs():
self.__add_input(new_op, input_)
self.edges.append(SummaryGraph.Edge(input_.uniqueName(), new_op['name']))
for output in node.outputs():
self.__add_output(new_op, output)
self.edges.append(SummaryGraph.Edge(new_op['name'], output.uniqueName()))
new_op['attrs'] = OrderedDict([(attr_name, node[attr_name]) for attr_name in node.attributeNames()])
self.__merge_pad_avgpool()
self.add_macs_attr()
self.add_footprint_attr()
self.add_arithmetic_intensity_attr()
del model_clone
def __init__(self, model, dummy_input):
self._src_model = model
model_clone = distiller.make_non_parallel_copy(model)
with torch.onnx.set_training(model_clone, False):
device = next(model_clone.parameters()).device
dummy_input = distiller.convert_tensors_recursively_to(
dummy_input, device=device
)
trace, _ = jit.get_trace_graph(
model_clone, dummy_input, _force_outplace=True
)
# Let ONNX do the heavy lifting: fusing the convolution nodes; fusing the nodes
# composing a GEMM operation; etc.
torch.onnx._optimize_trace(trace, torch.onnx.OperatorExportTypes.ONNX)
graph = trace.graph()
self.ops = OrderedDict()
self.params = OrderedDict()
self.edges = []
self.temp = OrderedDict()
in_out = list(graph.inputs()) + list(graph.outputs())
for param in in_out:
self.__add_param(param)
for node in graph.nodes():
new_op = self.__create_op(node)
# Operators with the same name create very confusing graphs (Resnet, for example),
# so we "unroll" them.
# Sometimes operations of different types have the same name, so we differentiate
# using both name and type
# (this happens, for example, when an operator is called via some functional API and
# not via a module)
same = [
op
for op in self.ops.values()
if op["orig-name"] + op["type"]
== new_op["orig-name"] + new_op["type"]
]
if len(same) > 0:
new_op["name"] += "." + str(len(same))
new_op["name"] = onnx_name_2_pytorch_name(
new_op["name"], new_op["type"]
)
assert len(new_op["name"]) > 0
if new_op["name"] in self.ops:
# This is a patch.
# ONNX names integrate the node type, while we don't (design bug).
# This means that while parsing the ONNX graph we might find two nodes with the "same" name.
# This patch increments the instance name, but this may break in the future.
new_op["name"] = increment_instance(new_op["name"])
self.ops[new_op["name"]] = new_op
for input_ in node.inputs():
self.__add_input(new_op, input_)
self.edges.append(
SummaryGraph.Edge(input_.uniqueName(), new_op["name"])
)
for output in node.outputs():
self.__add_output(new_op, output)
self.edges.append(
SummaryGraph.Edge(new_op["name"], output.uniqueName())
)
new_op["attrs"] = OrderedDict(
[
(attr_name, node[attr_name])
for attr_name in node.attributeNames()
]
)
self.add_macs_attr()
self.add_footprint_attr()
self.add_arithmetic_intensity_attr()
del model_clone
def convert_distiller_ptq_model_to_pytorch(model, dummy_input, backend='fbgemm'):
"""
Convert a model quantized using distiller.quantization.PostTrainLinearQuantizer to model comprised solely of
native PyTorch static post-training quantization modules and operators.
In the current implementation this conversion CANNOT be done in-place.
Conversion is done in 2 passes:
* First pass: Replace all RangeLinearQuantWrapper modules with a quantize operation followed by the respective
native PyTorch module. Modules that weren't quantized by Distiller are wrapped with a de-quantize operation.
* Second pass: Perform dummy forward pass over the model and remove redundant de-quant --> quant sequences.
The converted model returns a de-quantized output. If the last layer of the model is quantized, then an extra
dequantize module will be added to the model. This extra module is named 'output_dequant', and the model's
forward method is patched to execute this module after the main model.
NOTE: This assumes the model produces a single output tensor. In other cases the results are unexpected.
NOTE: The converted model will be on the CPU, and non-parallel (that is - without nn.DataParallel modules)
Args:
model (torch.nn.Module): The model to be converted
dummy_input (torch.nn.Tensor): A tensor in the shape expected by the model, required for the second pass
of the conversion
backend (str): The PyTorch quantization backend to use. Currently supported values: 'fbgemm', 'qnnpack'
Returns:
The converted model
"""
# Hacky deferred import for now to workaround circular dependency
# TODO: Proper fix
from distiller.quantization import PostTrainLinearQuantizer
if not hasattr(model, 'quantizer_metadata') or model.quantizer_metadata['type'] != PostTrainLinearQuantizer:
raise ValueError('Conversion to PyTorch native quantization supported only for models quantized '
'using distiller.quantization.PostTrainLinearQuantizer')
if dummy_input is None:
raise ValueError('Valid dummy input required for converting PTQ model to PyTorch')
backends = ('fbgemm', 'qnnpack')
if backend not in backends:
raise ValueError('{} is not a supported PyTorch quantization backend. Supported: {}'.format(backend, backends))
torch.backends.quantized.engine = backend
# TODO: Add in-place option. Not totally straight-forward because of the output dequantization
# Can monkey-patch instead of creating a Sequential, then it can really be in-place
# Save quantizer metadata so we can re-attach it to the model after conversion, which enables loading the
# converted model from a checkpoint
quantizer_metadata = deepcopy(model.quantizer_metadata)
model = distiller.make_non_parallel_copy(model).cpu()
# First pass
model = _ptq_convert_pass_replace_range_linear_wrappers(model)
# Second pass
model = _ptq_convert_pass_remove_redundant_quant_dequant(model, dummy_input)
# This is used when loading the model from a checkpoint, to indicate that conversion needs to be applied
quantizer_metadata['pytorch_convert'] = True
quantizer_metadata['pytorch_convert_backend'] = backend
model.quantizer_metadata = quantizer_metadata
return model
def main():
script_dir = os.path.dirname(__file__)
module_path = os.path.abspath(os.path.join(script_dir, '..', '..'))
global msglogger
# Parse arguments
args = parser.get_parser().parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(
os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
apputils.log_execution_env_state(args.compress,
msglogger.logdir,
gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
best_epochs = [
distiller.MutableNamedTuple({
'epoch': 0,
'top1': 0,
'sparsity': 0
}) for i in range(args.num_best_scores)
]
if args.deterministic:
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
# In Pytorch, support for deterministic execution is still a bit clunky.
if args.workers > 1:
msglogger.error(
'ERROR: Setting --deterministic requires setting --workers/-j to 0 or 1'
)
exit(1)
# Use a well-known seed, for repeatability of experiments
distiller.set_deterministic()
else:
# This issue: https://github.com/pytorch/pytorch/issues/3659
# Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
# results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
cudnn.benchmark = True
if args.cpu or not torch.cuda.is_available():
# Set GPU index to -1 if using CPU
args.device = 'cpu'
args.gpus = -1
else:
args.device = 'cuda'
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
msglogger.error(
'ERROR: Argument --gpus must be a comma-separated list of integers only'
)
exit(1)
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
msglogger.error(
'ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
exit(1)
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
args.num_classes = 10 if args.dataset == 'cifar10' else 1000
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
args.loss_exits = [0] * args.num_exits
args.losses_exits = []
args.exiterrors = []
# Create the model
model = create_model(args.pretrained,
args.dataset,
args.arch,
parallel=not args.load_serialized,
device_ids=args.gpus)
compression_scheduler = None
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# capture thresholds for early-exit training
if args.earlyexit_thresholds:
msglogger.info('=> using early-exit threshold values of %s',
args.earlyexit_thresholds)
# We can optionally resume from a checkpoint
if args.resume:
model, compression_scheduler, start_epoch = apputils.load_checkpoint(
model, chkpt_file=args.resume)
model.to(args.device)
# Define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().to(args.device)
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
if args.AMC:
return automated_deep_compression(model, criterion, optimizer,
pylogger, args)
if args.greedy:
return greedy(model, criterion, optimizer, pylogger, args)
# This sample application can be invoked to produce various summary reports.
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
activations_collectors = create_activation_stats_collectors(
model, *args.activation_stats)
if args.qe_calibration:
msglogger.info('Quantization calibration stats collection enabled:')
msglogger.info(
'\tStats will be collected for {:.1%} of test dataset'.format(
args.qe_calibration))
msglogger.info(
'\tSetting constant seeds and converting model to serialized execution'
)
distiller.set_deterministic()
model = distiller.make_non_parallel_copy(model)
activations_collectors.update(
create_quantization_stats_collector(model))
args.evaluate = True
args.effective_test_size = args.qe_calibration
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.workers, args.validation_split, args.deterministic,
args.effective_train_size, args.effective_valid_size,
args.effective_test_size)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler),
len(test_loader.sampler))
if args.sensitivity is not None:
sensitivities = np.arange(args.sensitivity_range[0],
args.sensitivity_range[1],
args.sensitivity_range[2])
return sensitivity_analysis(model, criterion, test_loader, pylogger,
args, sensitivities)
if args.evaluate:
return evaluate_model(model, criterion, test_loader, pylogger,
activations_collectors, args,
compression_scheduler)
if args.compress:
# The main use-case for this sample application is CNN compression. Compression
# requires a compression schedule configuration file in YAML.
compression_scheduler = distiller.file_config(model, optimizer,
args.compress,
compression_scheduler)
# Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
model.to(args.device)
elif compression_scheduler is None:
compression_scheduler = distiller.CompressionScheduler(model)
if args.thinnify:
#zeros_mask_dict = distiller.create_model_masks_dict(model)
assert args.resume is not None, "You must use --resume to provide a checkpoint file to thinnify"
distiller.remove_filters(model,
compression_scheduler.zeros_mask_dict,
args.arch,
args.dataset,
optimizer=None)
apputils.save_checkpoint(0,
args.arch,
model,
optimizer=None,
scheduler=compression_scheduler,
name="{}_thinned".format(
args.resume.replace(".pth.tar", "")),
dir=msglogger.logdir)
print(
"Note: your model may have collapsed to random inference, so you may want to fine-tune"
)
return
args.kd_policy = None
if args.kd_teacher:
teacher = create_model(args.kd_pretrained,
args.dataset,
args.kd_teacher,
device_ids=args.gpus)
if args.kd_resume:
teacher, _, _ = apputils.load_checkpoint(teacher,
chkpt_file=args.kd_resume)
dlw = distiller.DistillationLossWeights(args.kd_distill_wt,
args.kd_student_wt,
args.kd_teacher_wt)
args.kd_policy = distiller.KnowledgeDistillationPolicy(
model, teacher, args.kd_temp, dlw)
compression_scheduler.add_policy(args.kd_policy,
starting_epoch=args.kd_start_epoch,
ending_epoch=args.epochs,
frequency=1)
msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
msglogger.info('\tTeacher Model: %s', args.kd_teacher)
msglogger.info('\tTemperature: %s', args.kd_temp)
msglogger.info('\tLoss Weights (distillation | student | teacher): %s',
' | '.join(['{:.2f}'.format(val) for val in dlw]))
msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)
for epoch in range(start_epoch, start_epoch + args.epochs):
# This is the main training loop.
msglogger.info('\n')
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
# Train for one epoch
with collectors_context(activations_collectors["train"]) as collectors:
train(train_loader,
model,
criterion,
optimizer,
epoch,
compression_scheduler,
loggers=[tflogger, pylogger],
args=args)
distiller.log_weights_sparsity(model,
epoch,
loggers=[tflogger, pylogger])
distiller.log_activation_statsitics(
epoch,
"train",
loggers=[tflogger],
collector=collectors["sparsity"])
if args.masks_sparsity:
msglogger.info(
distiller.masks_sparsity_tbl_summary(
model, compression_scheduler))
# evaluate on validation set
with collectors_context(activations_collectors["valid"]) as collectors:
top1, top5, vloss = validate(val_loader, model, criterion,
[pylogger], args, epoch)
distiller.log_activation_statsitics(
epoch,
"valid",
loggers=[tflogger],
collector=collectors["sparsity"])
save_collectors_data(collectors, msglogger.logdir)
stats = ('Peformance/Validation/',
OrderedDict([('Loss', vloss), ('Top1', top1),
('Top5', top5)]))
distiller.log_training_progress(stats,
None,
epoch,
steps_completed=0,
total_steps=1,
log_freq=1,
loggers=[tflogger])
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch, optimizer)
# Update the list of top scores achieved so far, and save the checkpoint
is_best = top1 > best_epochs[-1].top1
if top1 > best_epochs[0].top1:
best_epochs[0].epoch = epoch
best_epochs[0].top1 = top1
# Keep best_epochs sorted such that best_epochs[0] is the lowest top1 in the best_epochs list
best_epochs = sorted(best_epochs, key=lambda score: score.top1)
for score in reversed(best_epochs):
if score.top1 > 0:
msglogger.info('==> Best Top1: %.3f on Epoch: %d', score.top1,
score.epoch)
apputils.save_checkpoint(epoch, args.arch, model, optimizer,
compression_scheduler, best_epochs[-1].top1,
is_best, args.name, msglogger.logdir)
# Finally run results on the test set
test(test_loader,
model,
criterion, [pylogger],
activations_collectors,
args=args)
def main():
script_dir = os.path.dirname(__file__)
module_path = os.path.abspath(os.path.join(script_dir, '..', '..'))
global msglogger
# Parse arguments
args = parser.get_parser().parse_args()
if args.epochs is None:
args.epochs = 90
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(
os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
apputils.log_execution_env_state(args.compress,
msglogger.logdir,
gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
ending_epoch = args.epochs
perf_scores_history = []
if args.evaluate:
args.deterministic = True
if args.deterministic:
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
distiller.set_deterministic(
) # Use a well-known seed, for repeatability of experiments
else:
# Turn on CUDNN benchmark mode for best performance. This is usually "safe" for image
# classification models, as the input sizes don't change during the run
# See here: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3
cudnn.benchmark = True
if args.cpu or not torch.cuda.is_available():
# Set GPU index to -1 if using CPU
args.device = 'cpu'
args.gpus = -1
else:
args.device = 'cuda'
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
raise ValueError(
'ERROR: Argument --gpus must be a comma-separated list of integers only'
)
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
raise ValueError(
'ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
args.num_classes = 10 if args.dataset == 'cifar10' else 1000
# Create the model
model = create_model(args.pretrained,
args.dataset,
args.arch,
parallel=not args.load_serialized,
device_ids=args.gpus)
compression_scheduler = None
optimizer = None
if args.resumed_checkpoint_path:
model, compression_scheduler, optimizer, start_epoch = apputils.load_checkpoint(
model,
args.resumed_checkpoint_path,
use_swa_model=args.use_swa_model,
model_device=args.device)
elif args.load_model_path:
model = apputils.load_lean_checkpoint(model,
args.load_model_path,
model_device=args.device)
if args.reset_optimizer:
start_epoch = 0
if optimizer is not None:
optimizer = None
msglogger.info(
'\nreset_optimizer flag set: Overriding resumed optimizer and resetting epoch count to 0'
)
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# Define loss function (criterion)
criterion = nn.CrossEntropyLoss().to(args.device)
if optimizer is None:
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
# This sample application can be invoked to produce various summary reports.
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
activations_collectors = create_activation_stats_collectors(
model, *args.activation_stats)
if args.qe_calibration:
msglogger.info('Quantization calibration stats collection enabled:')
msglogger.info(
'\tStats will be collected for {:.1%} of test dataset'.format(
args.qe_calibration))
msglogger.info(
'\tSetting constant seeds and converting model to serialized execution'
)
distiller.set_deterministic()
model = distiller.make_non_parallel_copy(model)
activations_collectors.update(
create_quantization_stats_collector(model))
args.evaluate = True
args.effective_test_size = args.qe_calibration
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.workers, args.validation_split, args.deterministic,
args.effective_train_size, args.effective_valid_size,
args.effective_test_size)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler),
len(test_loader.sampler))
if args.evaluate:
return evaluate_model(model, criterion, test_loader, pylogger,
activations_collectors, args,
compression_scheduler)
def main():
script_dir = os.path.dirname(__file__)
module_path = os.path.abspath(os.path.join(script_dir, '..', '..'))
global msglogger
# Parse arguments
args = parser.get_parser().parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(
os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
apputils.log_execution_env_state(args.compress,
msglogger.logdir,
gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
best_epochs = list()
if args.deterministic:
if args.loaders is None:
args.loaders = 1
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
# In Pytorch, support for deterministic execution is still a bit clunky.
if args.loaders > 1:
msglogger.error(
'ERROR: Setting --deterministic requires setting --loaders to 0 or 1'
)
exit(1)
# Use a well-known seed, for repeatability of experiments
distiller.set_deterministic()
else:
# This issue: https://github.com/pytorch/pytorch/issues/3659
# Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
# results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
cudnn.benchmark = True
if args.use_cpu or (args.gpus is None
and not torch.cuda.is_available()) or (args.gpus
== ''):
# Set GPU index to -1 if using CPU
args.device = 'cpu'
args.gpus = -1
else:
args.device = 'cuda'
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
msglogger.error(
'ERROR: Argument --gpus must be a comma-separated list of integers only'
)
exit(1)
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
msglogger.error(
'ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
exit(1)
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
if args.loaders is None:
active_gpus = args.gpus if args.gpus is not None else torch.cuda.device_count(
)
args.loaders = max(parser.DEFAULT_LOADERS_COUNT,
parser.DEFAULT_LOADERS_COUNT * active_gpus)
msglogger.debug('Number of data loaders set to: {}'.format(args.loaders))
# Infer the dataset from the model name
args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
args.num_classes = 10 if args.dataset == 'cifar10' else 1000
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
args.loss_exits = [0] * args.num_exits
args.losses_exits = []
args.exiterrors = []
# Create the model
model = create_model(args.pretrained,
args.dataset,
args.arch,
parallel=not args.load_serialized,
device_ids=args.gpus)
compression_scheduler = None
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# capture thresholds for early-exit training
if args.earlyexit_thresholds:
msglogger.info('=> using early-exit threshold values of %s',
args.earlyexit_thresholds)
# We can optionally resume from a checkpoint
optimizer = None
resumed_training_steps = None
if args.resume or args.load_state_dict:
if args.resume and not args.reset_optimizer:
# initiate SGD with dummy lr
optimizer = torch.optim.SGD(model.parameters(), lr=0.36787944117)
model, compression_scheduler, optimizer, start_epoch, resumed_training_steps = apputils.load_checkpoint(
model, args.resume or args.load_state_dict, optimizer=optimizer)
model.to(args.device)
# Define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().to(args.device)
if optimizer is not None:
# optimizer was resumed from checkpoint
# check if user has tried to set optimizer arguments
# if so, ignore arguments with a warning.
optimizer_group_args = [
'lr', 'learning-rate', 'momentum', 'weight-decay', 'wd'
]
user_optim_args = [
x for x in optimizer_group_args for arg in sys.argv
if arg.startswith('--' + x)
]
if user_optim_args:
msglogger.warning(
'{} optimizer arguments are ignored.'.format(user_optim_args))
msglogger.info(
'setting optimizer arguments when optimizer is resumed '
'from checkpoint is forbidden.')
else:
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
if args.AMC:
return automated_deep_compression(model, criterion, optimizer,
pylogger, args)
if args.greedy:
return greedy(model, criterion, optimizer, pylogger, args)
# This sample application can be invoked to produce various summary reports.
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
activations_collectors = create_activation_stats_collectors(
model, *args.activation_stats)
if args.qe_calibration:
msglogger.info('Quantization calibration stats collection enabled:')
msglogger.info(
'\tStats will be collected for {:.1%} of test dataset'.format(
args.qe_calibration))
msglogger.info(
'\tSetting constant seeds and converting model to serialized execution'
)
distiller.set_deterministic()
model = distiller.make_non_parallel_copy(model)
activations_collectors.update(
create_quantization_stats_collector(model))
args.evaluate = True
args.effective_test_size = args.qe_calibration
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.loaders, args.validation_split, args.deterministic,
args.effective_train_size, args.effective_valid_size,
args.effective_test_size)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler),
len(test_loader.sampler))
args.trainset_print_period = parser.getPrintPeriod(
args, len(train_loader.sampler), args.batch_size)
args.validset_print_period = parser.getPrintPeriod(args,
len(val_loader.sampler),
args.batch_size)
args.testset_print_period = parser.getPrintPeriod(args,
len(test_loader.sampler),
args.batch_size)
if args.sensitivity is not None:
sensitivities = np.arange(args.sensitivity_range[0],
args.sensitivity_range[1],
args.sensitivity_range[2])
return sensitivity_analysis(model, criterion, test_loader, pylogger,
args, sensitivities)
if args.evaluate:
return evaluate_model(model, criterion, test_loader, pylogger,
activations_collectors, args,
compression_scheduler)
if args.compress:
# The main use-case for this sample application is CNN compression. Compression
# requires a compression schedule configuration file in YAML.
compression_scheduler = distiller.file_config(
model, optimizer, args.compress, compression_scheduler,
(start_epoch - 1) if
(args.resume and not args.reset_optimizer) else None)
# Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
model.to(args.device)
elif compression_scheduler is None:
compression_scheduler = distiller.CompressionScheduler(model)
if args.thinnify:
#zeros_mask_dict = distiller.create_model_masks_dict(model)
assert args.resume is not None, "You must use --resume to provide a checkpoint file to thinnify"
distiller.remove_filters(model,
compression_scheduler.zeros_mask_dict,
args.arch,
args.dataset,
optimizer=None)
apputils.save_checkpoint(0,
args.arch,
model,
optimizer=None,
scheduler=compression_scheduler,
name="{}_thinned".format(
args.resume.replace(".pth.tar", "")),
dir=msglogger.logdir)
print(
"Note: your model may have collapsed to random inference, so you may want to fine-tune"
)
return
args.kd_policy = None
if args.kd_teacher:
teacher = create_model(args.kd_pretrained,
args.dataset,
args.kd_teacher,
device_ids=args.gpus)
if args.kd_resume:
teacher = apputils.load_checkpoint(teacher,
chkpt_file=args.kd_resume)[0]
dlw = distiller.DistillationLossWeights(args.kd_distill_wt,
args.kd_student_wt,
args.kd_teacher_wt)
args.kd_policy = distiller.KnowledgeDistillationPolicy(
model, teacher, args.kd_temp, dlw)
compression_scheduler.add_policy(
args.kd_policy, range(args.kd_start_epoch, args.epochs, 1))
msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
msglogger.info('\tTeacher Model: %s', args.kd_teacher)
msglogger.info('\tTemperature: %s', args.kd_temp)
msglogger.info('\tLoss Weights (distillation | student | teacher): %s',
' | '.join(['{:.2f}'.format(val) for val in dlw]))
msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)
if getattr(compression_scheduler, 'global_policy_end_epoch',
None) is not None:
if compression_scheduler.global_policy_end_epoch >= (start_epoch +
args.epochs):
msglogger.warning(
'scheduler requires at least {} epochs, but only {} are sanctioned'
.format(compression_scheduler.global_policy_end_epoch,
args.epochs))
accumulated_training_steps = resumed_training_steps if resumed_training_steps is not None else 0
for epoch in range(start_epoch, start_epoch + args.epochs):
# This is the main training loop.
msglogger.info('\n')
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
# Train for one epoch
with collectors_context(activations_collectors["train"]) as collectors:
try:
train(train_loader,
model,
criterion,
optimizer,
epoch,
accumulated_training_steps,
compression_scheduler,
loggers=[tflogger, pylogger],
args=args)
except RuntimeError as e:
if ('cuda out of memory' in str(e).lower()):
msglogger.error(
'CUDA memory failure has been detected.\n'
'Sometimes it helps to decrease batch size.\n'
'e.g. Add the following flag to your call: --batch-size={}'
.format(args.batch_size // 10))
raise
distiller.log_weights_sparsity(model,
epoch,
loggers=[tflogger, pylogger])
distiller.log_activation_statsitics(
epoch,
"train",
loggers=[tflogger],
collector=collectors["sparsity"])
if args.masks_sparsity:
msglogger.info(
distiller.masks_sparsity_tbl_summary(
model, compression_scheduler))
accumulated_training_steps += math.ceil(
len(train_loader.sampler) / train_loader.batch_size)
# evaluate on validation set
with collectors_context(activations_collectors["valid"]) as collectors:
top1, top5, vloss = validate(val_loader, model, criterion,
[pylogger], args, epoch)
distiller.log_activation_statsitics(
epoch,
"valid",
loggers=[tflogger],
collector=collectors["sparsity"])
save_collectors_data(collectors, msglogger.logdir)
stats = ('Performance/Validation/',
OrderedDict([('Loss', vloss), ('Top1', top1),
('Top5', top5)]))
tflogger.log_training_progress(stats, epoch, None)
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch, optimizer)
if getattr(compression_scheduler, 'global_policy_end_epoch',
None) is None or (
compression_scheduler.global_policy_end_epoch <= epoch):
# Update the list of top scores achieved since all policies have concluded
if top1 > 0:
best_epochs.append(
distiller.MutableNamedTuple({
'top1': top1,
'top5': top5,
'epoch': epoch
}))
# Keep best_epochs sorted from best to worst
# Sort by top1 first, secondary sort by top5, and so forth
best_epochs.sort(key=operator.attrgetter('top1', 'top5', 'epoch'),
reverse=True)
for score in best_epochs[:args.num_best_scores]:
msglogger.info('==> Best Top1: %.3f Top5: %.3f on epoch: %d',
score.top1, score.top5, score.epoch)
is_best = best_epochs and (epoch == best_epochs[0].epoch)
apputils.save_checkpoint(epoch, args.arch, model, optimizer,
compression_scheduler,
best_epochs[0].top1 if best_epochs else None,
is_best, args.name, msglogger.logdir,
accumulated_training_steps)
# Finally run results on the test set
test(test_loader,
model,
criterion, [pylogger],
activations_collectors,
args=args)
def __init__(self, model, dummy_input):
self._src_model = model
model_clone = distiller.make_non_parallel_copy(model)
with torch.onnx.set_training(model_clone, False):
device = next(model_clone.parameters()).device
dummy_input = distiller.convert_tensors_recursively_to(
dummy_input, device=device)
trace, _ = jit.get_trace_graph(model_clone,
dummy_input,
_force_outplace=True)
# ONNX trace optimization has issues with Gemm ops (aka "Linear" / "addmm" / "FC"), where
# Gemm nodes get the scope name of the last non-Gemm node that came before them. This can make
# it impossible, in some cases, to derive the connectivity of the model using the original
# module names. So we save the scope names for these nodes from the un-optimized trace.
aten_addmm_nodes_scope_names = [
n.scopeName() for n in trace.graph().nodes()
if n.kind() == 'aten::addmm'
]
onnx_gemm_count = 0
# Let ONNX do the heavy lifting: fusing the convolution nodes; fusing the nodes
# composing a GEMM operation; etc.
torch.onnx._optimize_trace(trace,
torch.onnx.OperatorExportTypes.ONNX)
graph = trace.graph()
self.ops = OrderedDict()
self.module_ops_map = defaultdict(list)
self.params = OrderedDict()
self.edges = []
self.temp = OrderedDict()
in_out = list(graph.inputs()) + list(graph.outputs())
for param in in_out:
self.__add_param(param)
for node in graph.nodes():
new_op = self.__create_op(node)
# Here we apply the workaround to the Gemm nodes scope name issue mentioned above
if new_op['type'] == 'Gemm':
new_op['orig-name'] = aten_addmm_nodes_scope_names[
onnx_gemm_count]
new_op['name'] = new_op['orig-name']
onnx_gemm_count += 1
# Convert the graph node's scope name to a PyTorch module name
module_name = onnx_name_2_pytorch_name(new_op['orig-name'])
new_op['module-name'] = module_name
if len(module_name) == 0:
# Special case where the module name is an empty string - this happens
# when the op is called from the "top-level" of the model
new_op['name'] = 'top_level_op'
else:
new_op['name'] = module_name
# The node's scope name in the graph corresponds to the module from which the op was called.
# This means that when ops are invoked from the same module via functional calls or direct
# operations on tensors, these ops will have the SAME MODEL NAME associated with them.
# For example:
# t = t1 + t2
# t = F.relu(t)
# In this case the add operation and the ReLU operation will have the same name, which is
# derived from the module they're contained in.
#
# Another case where different ops will have the same module name is when a module is reused:
# out = self.conv1(x)
# out = self.relu(out) <=== First use of self.relu
# out = self.conv2(out)
# out = self.relu(out) <=== Second use of self.relu
# In this case the graph will have 2 distinct ReLU nodes, with the same scope name.
#
# Operators with the same name create very confusing graphs (in ResNet, for example),
# so we "unroll" them.
same_module_cnt = len(self.module_ops_map[module_name])
if same_module_cnt:
new_op['name'] += "__" + str(same_module_cnt)
self.module_ops_map[module_name].append(new_op['name'])
# Finally we register the new op in the ops collection
msglogger.debug(
"new sgraph node - Scope name: {} ; Type: {} ; Display name {}"
.format(new_op['orig-name'], new_op['type'],
new_op['name']))
self.ops[new_op['name']] = new_op
for input_ in node.inputs():
self.__add_input(new_op, input_)
self.edges.append(
SummaryGraph.Edge(input_.uniqueName(), new_op['name']))
for output in node.outputs():
self.__add_output(new_op, output)
self.edges.append(
SummaryGraph.Edge(new_op['name'], output.uniqueName()))
new_op['attrs'] = OrderedDict([
(attr_name, node[attr_name])
for attr_name in node.attributeNames()
])
self.__merge_pad_avgpool()
self.add_macs_attr()
self.add_footprint_attr()
self.add_arithmetic_intensity_attr()
del model_clone
return automated_deep_compression(model, criterion, optimizer, pylogger, args)
if args.greedy:
return greedy(model, criterion, optimizer, pylogger, args)
# This sample application can be invoked to produce various summary reports.
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
activations_collectors = create_activation_stats_collectors(model, *args.activation_stats)
if args.qe_calibration:
msglogger.info('Quantization calibration stats collection enabled:')
msglogger.info('\tStats will be collected for {:.1%} of test dataset'.format(args.qe_calibration))
msglogger.info('\tSetting constant seeds and converting model to serialized execution')
distiller.set_deterministic()
model = distiller.make_non_parallel_copy(model)
activations_collectors.update(create_quantization_stats_collector(model))
args.evaluate = True
args.effective_test_size = args.qe_calibration
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.workers, args.validation_split, args.deterministic,
args.effective_train_size, args.effective_valid_size, args.effective_test_size)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler))
if args.sensitivity is not None:
def main():
script_dir = os.path.dirname(__file__)
module_path = os.path.abspath(os.path.join(script_dir, '..', '..'))
global msglogger
# Parse arguments
args = parser.get_parser().parse_args()
if args.epochs is None:
args.epochs = 90
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
apputils.log_execution_env_state(args.compress, msglogger.logdir, gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
ending_epoch = args.epochs
perf_scores_history = []
if args.evaluate:
args.deterministic = True
if args.deterministic:
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
distiller.set_deterministic() # Use a well-known seed, for repeatability of experiments
else:
# Turn on CUDNN benchmark mode for best performance. This is usually "safe" for image
# classification models, as the input sizes don't change during the run
# See here: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3
cudnn.benchmark = True
if args.cpu or not torch.cuda.is_available():
# Set GPU index to -1 if using CPU
args.device = 'cpu'
args.gpus = -1
else:
args.device = 'cuda'
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
raise ValueError('ERROR: Argument --gpus must be a comma-separated list of integers only')
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
raise ValueError('ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
args.num_classes = 10 if args.dataset == 'cifar10' else 1000
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
args.loss_exits = [0] * args.num_exits
args.losses_exits = []
args.exiterrors = []
# Create the model
model = create_model(args.pretrained, args.dataset, args.arch,
parallel=not args.load_serialized, device_ids=args.gpus)
compression_scheduler = None
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# capture thresholds for early-exit training
if args.earlyexit_thresholds:
msglogger.info('=> using early-exit threshold values of %s', args.earlyexit_thresholds)
# TODO(barrh): args.deprecated_resume is deprecated since v0.3.1
if args.deprecated_resume:
msglogger.warning('The "--resume" flag is deprecated. Please use "--resume-from=YOUR_PATH" instead.')
if not args.reset_optimizer:
msglogger.warning('If you wish to also reset the optimizer, call with: --reset-optimizer')
args.reset_optimizer = True
args.resumed_checkpoint_path = args.deprecated_resume
# We can optionally resume from a checkpoint
optimizer = None
if args.resumed_checkpoint_path:
model, compression_scheduler, optimizer, start_epoch = apputils.load_checkpoint(
model, args.resumed_checkpoint_path, model_device=args.device)
elif args.load_model_path:
model = apputils.load_lean_checkpoint(model, args.load_model_path,
model_device=args.device)
if args.reset_optimizer:
start_epoch = 0
if optimizer is not None:
optimizer = None
msglogger.info('\nreset_optimizer flag set: Overriding resumed optimizer and resetting epoch count to 0')
# Define loss function (criterion)
criterion = nn.CrossEntropyLoss().to(args.device)
if optimizer is None:
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
if args.AMC:
return automated_deep_compression(model, criterion, optimizer, pylogger, args)
if args.greedy:
return greedy(model, criterion, optimizer, pylogger, args)
# This sample application can be invoked to produce various summary reports.
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
activations_collectors = create_activation_stats_collectors(model, *args.activation_stats)
if args.qe_calibration:
msglogger.info('Quantization calibration stats collection enabled:')
msglogger.info('\tStats will be collected for {:.1%} of test dataset'.format(args.qe_calibration))
msglogger.info('\tSetting constant seeds and converting model to serialized execution')
distiller.set_deterministic()
model = distiller.make_non_parallel_copy(model)
activations_collectors.update(create_quantization_stats_collector(model))
args.evaluate = True
args.effective_test_size = args.qe_calibration
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.workers, args.validation_split, args.deterministic,
args.effective_train_size, args.effective_valid_size, args.effective_test_size)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler))
if args.sensitivity is not None:
sensitivities = np.arange(args.sensitivity_range[0], args.sensitivity_range[1], args.sensitivity_range[2])
return sensitivity_analysis(model, criterion, test_loader, pylogger, args, sensitivities)
if args.evaluate:
return evaluate_model(model, criterion, test_loader, pylogger, activations_collectors, args,
compression_scheduler)
if args.compress:
# The main use-case for this sample application is CNN compression. Compression
# requires a compression schedule configuration file in YAML.
compression_scheduler = distiller.file_config(model, optimizer, args.compress, compression_scheduler,
(start_epoch-1) if args.resumed_checkpoint_path else None)
# Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
model.to(args.device)
elif compression_scheduler is None:
compression_scheduler = distiller.CompressionScheduler(model)
if args.thinnify:
#zeros_mask_dict = distiller.create_model_masks_dict(model)
assert args.resumed_checkpoint_path is not None, \
"You must use --resume-from to provide a checkpoint file to thinnify"
distiller.remove_filters(model, compression_scheduler.zeros_mask_dict, args.arch, args.dataset, optimizer=None)
apputils.save_checkpoint(0, args.arch, model, optimizer=None, scheduler=compression_scheduler,
name="{}_thinned".format(args.resumed_checkpoint_path.replace(".pth.tar", "")),
dir=msglogger.logdir)
print("Note: your model may have collapsed to random inference, so you may want to fine-tune")
return
args.kd_policy = None
if args.kd_teacher:
teacher = create_model(args.kd_pretrained, args.dataset, args.kd_teacher, device_ids=args.gpus)
if args.kd_resume:
teacher = apputils.load_lean_checkpoint(teacher, args.kd_resume)
dlw = distiller.DistillationLossWeights(args.kd_distill_wt, args.kd_student_wt, args.kd_teacher_wt)
args.kd_policy = distiller.KnowledgeDistillationPolicy(model, teacher, args.kd_temp, dlw)
compression_scheduler.add_policy(args.kd_policy, starting_epoch=args.kd_start_epoch, ending_epoch=args.epochs,
frequency=1)
msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
msglogger.info('\tTeacher Model: %s', args.kd_teacher)
msglogger.info('\tTemperature: %s', args.kd_temp)
msglogger.info('\tLoss Weights (distillation | student | teacher): %s',
' | '.join(['{:.2f}'.format(val) for val in dlw]))
msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)
if start_epoch >= ending_epoch:
msglogger.error(
'epoch count is too low, starting epoch is {} but total epochs set to {}'.format(
start_epoch, ending_epoch))
raise ValueError('Epochs parameter is too low. Nothing to do.')
for epoch in range(start_epoch, ending_epoch):
# This is the main training loop.
msglogger.info('\n')
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch,
metrics=(vloss if (epoch != start_epoch) else 10**6))
# Train for one epoch
with collectors_context(activations_collectors["train"]) as collectors:
train(train_loader, model, criterion, optimizer, epoch, compression_scheduler,
loggers=[tflogger, pylogger], args=args)
distiller.log_weights_sparsity(model, epoch, loggers=[tflogger, pylogger])
distiller.log_activation_statsitics(epoch, "train", loggers=[tflogger],
collector=collectors["sparsity"])
if args.masks_sparsity:
msglogger.info(distiller.masks_sparsity_tbl_summary(model, compression_scheduler))
# evaluate on validation set
with collectors_context(activations_collectors["valid"]) as collectors:
top1, top5, vloss = validate(val_loader, model, criterion, [pylogger], args, epoch)
distiller.log_activation_statsitics(epoch, "valid", loggers=[tflogger],
collector=collectors["sparsity"])
save_collectors_data(collectors, msglogger.logdir)
stats = ('Performance/Validation/',
OrderedDict([('Loss', vloss),
('Top1', top1),
('Top5', top5)]))
distiller.log_training_progress(stats, None, epoch, steps_completed=0, total_steps=1, log_freq=1,
loggers=[tflogger])
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch, optimizer)
# Update the list of top scores achieved so far, and save the checkpoint
update_training_scores_history(perf_scores_history, model, top1, top5, epoch, args.num_best_scores)
is_best = epoch == perf_scores_history[0].epoch
checkpoint_extras = {'current_top1': top1,
'best_top1': perf_scores_history[0].top1,
'best_epoch': perf_scores_history[0].epoch}
apputils.save_checkpoint(epoch, args.arch, model, optimizer=optimizer, scheduler=compression_scheduler,
extras=checkpoint_extras, is_best=is_best, name=args.name, dir=msglogger.logdir)
# Finally run results on the test set
test(test_loader, model, criterion, [pylogger], activations_collectors, args=args)
def main():
script_dir = os.path.dirname(__file__)
module_path = os.path.abspath(os.path.join(script_dir, '..', '..'))
global msglogger
# Parse arguments
args = parser.get_parser().parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(
os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
# 记录有关执行环境的各种详细信息。有时是有用的
# 参考过去的实验执行，这些信息可能有用。
apputils.log_execution_env_state(args.compress,
msglogger.logdir,
gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
perf_scores_history = []
if args.deterministic:
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
# In Pytorch, support for deterministic execution is still a bit clunky.
if args.workers > 1:
msglogger.error(
'ERROR: Setting --deterministic requires setting --workers/-j to 0 or 1'
) # 错误：设置--确定性要求将--workers/-j设置为0或1
exit(1) # 正常退出程序
# Use a well-known seed, for repeatability of experiments 使用一种众所周知的种子，用于实验的重复性。
distiller.set_deterministic()
else:
# This issue: https://github.com/pytorch/pytorch/issues/3659
# Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
# results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
cudnn.benchmark = True
if args.cpu or not torch.cuda.is_available():
# Set GPU index to -1 if using CPU
args.device = 'cpu'
args.gpus = -1
else:
args.device = 'cuda'
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
msglogger.error(
'ERROR: Argument --gpus must be a comma-separated list of integers only'
)
exit(1)
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
msglogger.error(
'ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
exit(1)
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
args.dataset = 'cousm'
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
args.loss_exits = [0] * args.num_exits
args.losses_exits = []
args.exiterrors = []
# Create the model
model = ResNet152()
# model = torch.nn.DataParallel(model, device_ids=args.gpus) # 并行GPU
model.to(args.device)
compression_scheduler = None # 压缩调度
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
# 创建两个日志后端 TensorBoardLogger以Google的Tensor板可以读取的格式写入日志文件。python logger将写入python记录器。
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# capture thresholds for early-exit training
if args.earlyexit_thresholds:
msglogger.info('=> using early-exit threshold values of %s',
args.earlyexit_thresholds)
# We can optionally resume from a checkpoint
if args.resume: # 加载训练模型
# checkpoint = torch.load(args.resume)
# model.load_state_dict(checkpoint['state_dict'])
model, compression_scheduler, start_epoch = apputils.load_checkpoint(
model, chkpt_file=args.resume)
model.to(args.device)
# Define loss function (criterion) and optimizer # 定义损失函数和优化器SGD
criterion = nn.CrossEntropyLoss().to(args.device)
# optimizer = torch.optim.SGD(model.fc.parameters(), lr=args.lr,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
optimizer = torch.optim.Adam(model.model.fc.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
if args.AMC: # 自动化的深层压缩
return automated_deep_compression(model, criterion, optimizer,
pylogger, args)
if args.greedy: # 贪婪的
return greedy(model, criterion, optimizer, pylogger, args)
# This sample application can be invoked to produce various summary reports. # 可以调用此示例应用程序来生成各种摘要报告。
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
# 激活统计收集器
activations_collectors = create_activation_stats_collectors(
model, *args.activation_stats)
if args.qe_calibration:
msglogger.info('Quantization calibration stats collection enabled:')
msglogger.info(
'\tStats will be collected for {:.1%} of test dataset'.format(
args.qe_calibration))
msglogger.info(
'\tSetting constant seeds and converting model to serialized execution'
)
distiller.set_deterministic()
model = distiller.make_non_parallel_copy(model)
activations_collectors.update(
create_quantization_stats_collector(model)) # 量化统计收集器
args.evaluate = True
args.effective_test_size = args.qe_calibration
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
# 加载数据集：从传递的模型名称推断要加载的数据集
train_loader, val_loader, test_loader, _ = get_data_loaders(
datasets_fn, r'/home/tian/Desktop/image_yasuo', args.batch_size,
args.workers, args.validation_split, args.deterministic,
args.effective_train_size, args.effective_valid_size,
args.effective_test_size)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler),
len(test_loader.sampler))
# 可以调用此示例应用程序来对模型执行敏感性分析。输出保存到csv和png。
if args.sensitivity is not None:
sensitivities = np.arange(args.sensitivity_range[0],
args.sensitivity_range[1],
args.sensitivity_range[2])
return sensitivity_analysis(model, criterion, test_loader, pylogger,
args, sensitivities)
if args.evaluate:
return evaluate_model(model, criterion, test_loader, pylogger,
activations_collectors, args,
compression_scheduler)
if args.compress:
# The main use-case for this sample application is CNN compression. Compression
# requires a compression schedule configuration file in YAML.
# #这个示例应用程序的主要用例是CNN压缩
# #需要yaml中的压缩计划配置文件。
compression_scheduler = distiller.file_config(model, optimizer,
args.compress,
compression_scheduler)
# Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
# 如果添加了参数（如PactQualifier），则模型会重新传输到GPU。
model.to(args.device)
elif compression_scheduler is None:
compression_scheduler = distiller.CompressionScheduler(model) # 压缩计划程序
if args.thinnify:
# zeros_mask_dict = distiller.create_model_masks_dict(model)
assert args.resume is not None, "You must use --resume to provide a checkpoint file to thinnify" # 必须使用--resume提供检查点文件以细化
distiller.remove_filters(model,
compression_scheduler.zeros_mask_dict,
args.arch,
args.dataset,
optimizer=None)
apputils.save_checkpoint(0,
args.arch,
model,
optimizer=None,
scheduler=compression_scheduler,
name="{}_thinned".format(
args.resume.replace(".pth.tar", "")),
dir=msglogger.logdir)
print(
"Note: your model may have collapsed to random inference, so you may want to fine-tune"
) # 注意：您的模型可能已折叠为随机推理，因此您可能需要对其进行微调。
return
args.kd_policy = None # 蒸馏
if args.kd_teacher:
teacher = create_model(args.kd_pretrained,
args.dataset,
args.kd_teacher,
device_ids=args.gpus)
if args.kd_resume:
teacher, _, _ = apputils.load_checkpoint(teacher,
chkpt_file=args.kd_resume)
dlw = distiller.DistillationLossWeights(args.kd_distill_wt,
args.kd_student_wt,
args.kd_teacher_wt)
args.kd_policy = distiller.KnowledgeDistillationPolicy(
model, teacher, args.kd_temp, dlw)
compression_scheduler.add_policy(args.kd_policy,
starting_epoch=args.kd_start_epoch,
ending_epoch=args.epochs,
frequency=1)
msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
msglogger.info('\tTeacher Model: %s', args.kd_teacher)
msglogger.info('\tTemperature: %s', args.kd_temp)
msglogger.info('\tLoss Weights (distillation | student | teacher): %s',
' | '.join(['{:.2f}'.format(val) for val in dlw]))
msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)
lr = args.lr
lr_decay = 0.5
for epoch in range(start_epoch, args.epochs):
# This is the main training loop.
msglogger.info('\n')
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
# Train for one epoch
with collectors_context(activations_collectors["train"]) as collectors:
train(train_loader,
model,
criterion,
optimizer,
epoch,
compression_scheduler,
loggers=[tflogger, pylogger],
args=args)
distiller.log_weights_sparsity(model,
epoch,
loggers=[tflogger, pylogger])
distiller.log_activation_statsitics(
epoch,
"train",
loggers=[tflogger],
collector=collectors["sparsity"])
if args.masks_sparsity: # 打印掩盖稀疏表 在end of each epoch
msglogger.info(
distiller.masks_sparsity_tbl_summary(
model, compression_scheduler))
# evaluate on validation set
with collectors_context(activations_collectors["valid"]) as collectors:
top1, top5, vloss = validate(val_loader, model, criterion,
[pylogger], args, epoch)
distiller.log_activation_statsitics(
epoch,
"valid",
loggers=[tflogger],
collector=collectors["sparsity"])
save_collectors_data(collectors, msglogger.logdir)
stats = ('Peformance/Validation/',
OrderedDict([('Loss', vloss), ('Top1', top1),
('Top5', top5)]))
distiller.log_training_progress(stats,
None,
epoch,
steps_completed=0,
total_steps=1,
log_freq=1,
loggers=[tflogger])
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch, optimizer)
# Update the list of top scores achieved so far, and save the checkpoint # 更新到目前为止获得的最高分数列表，并保存检查点
sparsity = distiller.model_sparsity(model)
perf_scores_history.append(
distiller.MutableNamedTuple({
'sparsity': sparsity,
'top1': top1,
'top5': top5,
'epoch': epoch
}))
# Keep perf_scores_history sorted from best to worst
# Sort by sparsity as main sort key, then sort by top1, top5 and epoch
# 保持绩效分数历史记录从最好到最差的排序
# 按稀疏度排序为主排序键，然后按top1、top5、epoch排序
perf_scores_history.sort(key=operator.attrgetter(
'sparsity', 'top1', 'top5', 'epoch'),
reverse=True)
for score in perf_scores_history[:args.num_best_scores]:
msglogger.info(
'==> Best [Top1: %.3f Top5: %.3f Sparsity: %.2f on epoch: %d]',
score.top1, score.top5, score.sparsity, score.epoch)
is_best = epoch == perf_scores_history[0].epoch
apputils.save_checkpoint(epoch, args.arch, model, optimizer,
compression_scheduler,
perf_scores_history[0].top1, is_best,
args.name, msglogger.logdir)
if not is_best:
lr = lr * lr_decay
# 当loss大于上一次loss,降低学习率
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# Finally run results on the test set # 最后在测试集上运行结果
test(test_loader,
model,
criterion, [pylogger],
activations_collectors,
args=args)
