def collect_conv_details(model, dataset):
dummy_input = get_dummy_input(dataset)
g = SummaryGraph(model.cuda(), dummy_input.cuda())
conv_layers = OrderedDict()
total_macs = 0
total_nnz = 0
for id, (name, m) in enumerate(model.named_modules()):
if isinstance(m, torch.nn.Conv2d):
conv = SimpleNamespace()
conv.t = len(conv_layers)
conv.k = m.kernel_size[0]
conv.stride = m.stride
# Use the SummaryGraph to obtain some other details of the models
conv_op = g.find_op(normalize_module_name(name))
assert conv_op is not None
conv.weights_vol = conv_op['attrs']['weights_vol']
total_nnz += conv.weights_vol
conv.macs = conv_op['attrs']['MACs']
conv_pname = name + ".weight"
conv_p = distiller.model_find_param(model, conv_pname)
conv.macs *= distiller.density_ch(conv_p)
total_macs += conv.macs
conv.ofm_h = g.param_shape(conv_op['outputs'][0])[2]
conv.ofm_w = g.param_shape(conv_op['outputs'][0])[3]
conv.ifm_h = g.param_shape(conv_op['inputs'][0])[2]
conv.ifm_w = g.param_shape(conv_op['inputs'][0])[3]
conv.name = name
conv.id = id
conv_layers[len(conv_layers)] = conv
return conv_layers, total_macs, total_nnz
def collect_conv_details(model, dataset):
if dataset == 'imagenet':
dummy_input = torch.randn(1, 3, 224, 224)
elif dataset == 'cifar10':
dummy_input = torch.randn(1, 3, 32, 32)
else:
raise ValueError("dataset %s is not supported" % dataset)
g = SummaryGraph(model.cuda(), dummy_input.cuda())
conv_layers = OrderedDict()
total_macs = 0
for id, (name, m) in enumerate(model.named_modules()):
if isinstance(m, torch.nn.Conv2d):
conv = SimpleNamespace()
conv.t = len(conv_layers)
conv.k = m.kernel_size[0]
conv.stride = m.stride
# Use the SummaryGraph to obtain some other details of the models
conv_op = g.find_op(normalize_module_name(name))
assert conv_op is not None
conv.macs = conv_op['attrs']['MACs']
total_macs += conv.macs
conv.ofm_h = g.param_shape(conv_op['outputs'][0])[2]
conv.ofm_w = g.param_shape(conv_op['outputs'][0])[3]
conv.ifm_h = g.param_shape(conv_op['inputs'][0])[2]
conv.ifm_w = g.param_shape(conv_op['inputs'][0])[3]
conv.name = name
conv.id = id
conv_layers[len(conv_layers)] = conv
return conv_layers, total_macs
def get_model_compute_budget(model, dataset, layers_to_prune=None):
"""Return the compute budget of the Convolution layers in an image-classifier.
"""
dummy_input = distiller.get_dummy_input(dataset)
g = SummaryGraph(model, dummy_input)
total_macs = 0
for name, m in model.named_modules():
if isinstance(m, torch.nn.Conv2d):
# Use the SummaryGraph to obtain some other details of the models
conv_op = g.find_op(normalize_module_name(name))
assert conv_op is not None
total_macs += conv_op['attrs']['MACs']
del g
return total_macs
def create_graph(dataset, arch):
dummy_input = get_input(dataset)
assert dummy_input is not None, "Unsupported dataset ({}) - aborting draw operation".format(dataset)
model = create_model(False, dataset, arch, parallel=False)
assert model is not None
return SummaryGraph(model, dummy_input)
def create_graph(dataset, arch):
if dataset == 'imagenet':
dummy_input = torch.randn((1, 3, 224, 224), requires_grad=False)
elif dataset == 'cifar10':
dummy_input = torch.randn((1, 3, 32, 32))
assert dummy_input is not None, "Unsupported dataset ({}) - aborting draw operation".format(dataset)
model = create_model(False, dataset, arch, parallel=False)
assert model is not None
return SummaryGraph(model, dummy_input)
def collect_conv_details(model,
dataset,
perform_thinning,
layers_to_prune=None):
dummy_input = distiller.get_dummy_input(dataset)
g = SummaryGraph(model, dummy_input)
conv_layers = OrderedDict()
total_macs = 0
total_params = 0
for id, (name, m) in enumerate(model.named_modules()):
if isinstance(m, torch.nn.Conv2d):
conv = SimpleNamespace()
conv.t = len(conv_layers)
conv.k = m.kernel_size[0]
conv.stride = m.stride
# Use the SummaryGraph to obtain some other details of the models
conv_op = g.find_op(normalize_module_name(name))
assert conv_op is not None
conv.weights_vol = conv_op['attrs']['weights_vol']
total_params += conv.weights_vol
conv.macs = conv_op['attrs']['MACs']
conv_pname = name + ".weight"
conv_p = distiller.model_find_param(model, conv_pname)
if not perform_thinning:
#conv.macs *= distiller.density_ch(conv_p) # Channel pruning
conv.macs *= distiller.density_3D(conv_p) # Filter pruning
total_macs += conv.macs
conv.ofm_h = g.param_shape(conv_op['outputs'][0])[2]
conv.ofm_w = g.param_shape(conv_op['outputs'][0])[3]
conv.ifm_h = g.param_shape(conv_op['inputs'][0])[2]
conv.ifm_w = g.param_shape(conv_op['inputs'][0])[3]
conv.name = name
conv.id = id
if layers_to_prune is None or name in layers_to_prune:
conv_layers[len(conv_layers)] = conv
return conv_layers, total_macs, total_params
def remove_filters(model, zeros_mask_dict, arch, dataset, optimizer):
# sgraph = create_graph(dataset, arch)
# Add our model in remove filters 2018-09-19 CKH
# set dummy_input base on arch from compression scheduler 2018-10-19 13:36:53 by CKH
if arch == 'deblurGAN':
dummy_input = torch.randn((1, 3, 224, 224))
else:
dummy_input = torch.randn((1, 1, 224, 224))
sgraph = SummaryGraph(model, dummy_input.cuda())
thinning_recipe = create_thinning_recipe_filters(sgraph, model,
zeros_mask_dict)
apply_and_save_recipe(model, zeros_mask_dict, thinning_recipe, optimizer)
return model