From 957e6777885d3a1a4fe3edbfe42f892f5a062188 Mon Sep 17 00:00:00 2001
From: Neta Zmora <neta.zmora@intel.com>
Date: Thu, 10 May 2018 18:25:03 +0300
Subject: [PATCH] pytorch 0.4: adjustments to API changes
Various small changes due to the chamnges in the semantics and syntax of the
PyTorch 0.4 API.
Note that currently distiller.model_performance_summary() returns wrong results
on graphs containing torch.nn.DataParallel layers.
---
distiller/__init__.py | 12 ++++++------
distiller/model_summaries.py | 17 +++++++++++------
jupyter/model_summary.ipynb | 2 +-
3 files changed, 18 insertions(+), 13 deletions(-)
diff --git a/distiller/__init__.py b/distiller/__init__.py
index c48eaea..c63517d 100755
--- a/distiller/__init__.py
+++ b/distiller/__init__.py
@@ -37,18 +37,18 @@ del thinning
# Distiller version
__version__ = "0.1.0"
-def model_find_param_name(model, tensor_to_find):
- """Look up the name of a model tensor.
+def model_find_param_name(model, param_to_find):
+ """Look up the name of a model parameter.
Arguments:
model: the model to search
- tensor_to_find: the tensors who's name we want to look up
+ param_to_find: the parameter whose name we want to look up
Returns:
- The parameter name (string) or None, if the paramter was not found.
+ The parameter name (string) or None, if the parameter was not found.
"""
- for name, tensor in model.state_dict().items():
- if tensor is tensor_to_find:
+ for name, param in model.named_parameters():
+ if param is param_to_find:
return name
return None
diff --git a/distiller/model_summaries.py b/distiller/model_summaries.py
index 943aed5..376e91e 100755
--- a/distiller/model_summaries.py
+++ b/distiller/model_summaries.py
@@ -43,7 +43,7 @@ def model_summary(model, optimizer, what, dataset=None):
distiller.log_weights_sparsity(model, -1, loggers=[pylogger, csvlogger])
elif what == 'compute':
if dataset == 'imagenet':
- dummy_input = Variable(torch.randn(1, 3, 224, 224), requires_grad=False)
+ dummy_input = Variable(torch.randn(1, 3, 224, 224))
elif dataset == 'cifar10':
dummy_input = Variable(torch.randn(1, 3, 32, 32))
else:
@@ -101,9 +101,9 @@ def weights_sparsity_summary(model, return_total_sparsity=False, param_dims=[2,4
distiller.sparsity_2D(param)*100,
distiller.sparsity_3D(param)*100,
(1-_density)*100,
- param.std(),
- param.mean(),
- param.abs().mean()
+ param.std().item(),
+ param.mean().item(),
+ param.abs().mean().item()
])
total_sparsity = (1 - sparse_params_size/params_size)*100
@@ -158,7 +158,7 @@ def module_visitor(self, input, output, df, model, weights_vol, macs, attrs=None
in_features_shape = input[0].size()
out_features_shape = output.size()
- param_name = distiller.model_find_param_name(model, self.weight.data)
+ param_name = distiller.model_find_param_name(model, self.weight)
if param_name is None:
return
mod_name = param_name[:param_name.find(".weight")]
@@ -168,8 +168,13 @@ def module_visitor(self, input, output, df, model, weights_vol, macs, attrs=None
distiller.size_to_str(out_features_shape), distiller.volume(output),
weights_vol, int(macs)])
+
def model_performance_summary(model, dummy_input, batch_size=1):
- """Collect performance data"""
+ """Collect performance data
+
+ warning: in PyTorch 0.4 this function does not return correct values when
+ the graph contains torch.nn.DataParallel layers.
+ """
def install_perf_collector(m):
if isinstance(m, torch.nn.Conv2d):
hook_handles.append(m.register_forward_hook(
diff --git a/jupyter/model_summary.ipynb b/jupyter/model_summary.ipynb
index 179437d..c15da35 100644
--- a/jupyter/model_summary.ipynb
+++ b/jupyter/model_summary.ipynb
@@ -78,7 +78,7 @@
"outputs": [],
"source": [
"dataset = 'imagenet'\n",
- "dummy_input = Variable(torch.randn(1, 3, 224, 224), requires_grad=False)\n",
+ "dummy_input = torch.randn(1, 3, 224, 224)\n",
"arch = 'resnet18'\n",
"#arch = 'alexnet'\n",
"checkpoint_file = None \n",
--
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