first commit

.gitignore

@@ -0,0 +1,111 @@
+.DS_Store
+fx_explore/
+# *test*
+*.t7
+*.pth
+# only keep the setup.py under root folder
+./*.py
+!./setup.py
+
+.idea/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/:

LICENSE.txt

@@ -0,0 +1,7 @@
+Copyright 2021 Rex Geng
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -0,0 +1,4 @@
+This repository is written to simplify the common neural network pipeline
+
+In this repo, you will find: 1) code for training and evaluation of neural networks
+; 2) code for layerwise analysis of computational complexity

nnutils/cnn_complexity_analyzer/profile.py

@@ -0,0 +1,218 @@
+from distutils.version import LooseVersion
+
+from torch import nn
+
+from .utils import NNComputeModuleProfile, ProfilerResult, is_compute_layer
+from .vision_hooks import *
+
+logger = logging.getLogger(__name__)
+
+
+def prRed(skk): print("\033[91m{}\033[00m".format(skk))
+
+
+def prGreen(skk): print("\033[92m{}\033[00m".format(skk))
+
+
+def prYellow(skk): print("\033[93m{}\033[00m".format(skk))
+
+
+if LooseVersion(torch.__version__) < LooseVersion("1.0.0"):
+    logger.warning(
+        "You are using an old version PyTorch {version}, which THOP is not going to support in the future.".format(
+            version=torch.__version__))
+
+default_dtype = torch.float64
+
+register_hooks = {
+    nn.ZeroPad2d: zero_ops,  # padding does not involve any multiplication.
+
+    nn.Conv1d: count_convNd,
+    nn.Conv2d: count_convNd,
+    nn.Conv3d: count_convNd,
+    # Conv2dSamePadding: count_convNd,
+    nn.ConvTranspose1d: count_convNd,
+    nn.ConvTranspose2d: count_convNd,
+    nn.ConvTranspose3d: count_convNd,
+
+    nn.BatchNorm1d: count_bn,
+    nn.BatchNorm2d: count_bn,
+    nn.BatchNorm3d: count_bn,
+
+    nn.ReLU: zero_ops,
+    nn.ReLU6: zero_ops,
+    nn.LeakyReLU: count_relu,
+
+    nn.MaxPool1d: zero_ops,
+    nn.MaxPool2d: zero_ops,
+    nn.MaxPool3d: zero_ops,
+    nn.AdaptiveMaxPool1d: zero_ops,
+    nn.AdaptiveMaxPool2d: zero_ops,
+    nn.AdaptiveMaxPool3d: zero_ops,
+
+    nn.AvgPool1d: count_avgpool,
+    nn.AvgPool2d: count_avgpool,
+    nn.AvgPool3d: count_avgpool,
+    nn.AdaptiveAvgPool1d: count_adap_avgpool,
+    nn.AdaptiveAvgPool2d: count_adap_avgpool,
+    nn.AdaptiveAvgPool3d: count_adap_avgpool,
+
+    nn.Linear: count_linear,
+    nn.Dropout: zero_ops,
+
+    nn.Upsample: count_upsample,
+    nn.UpsamplingBilinear2d: count_upsample,
+    nn.UpsamplingNearest2d: count_upsample,
+
+}
+
+if LooseVersion(torch.__version__) >= LooseVersion("1.1.0"):
+    register_hooks.update({
+        nn.SyncBatchNorm: count_bn
+    })
+
+
+def profile(model: nn.Module, inputs, custom_ops=None, verbose=True):
+    handler_collection = {}
+    types_collection = set()
+    if custom_ops is None:
+        custom_ops = {}
+
+    def add_hooks(m: nn.Module):
+        m.register_buffer('total_ops', torch.zeros(1, dtype=torch.float64))
+        m.register_buffer('total_params', torch.zeros(1, dtype=torch.float64))
+        m.register_buffer('num_act', torch.zeros(1, dtype=torch.float64))
+        m.register_buffer('num_dp', torch.zeros(1, dtype=torch.float64))
+        m.register_buffer('data_reuse', torch.zeros(1, dtype=torch.float64))
+        m.register_buffer('weight_reuse', torch.zeros(1, dtype=torch.float64))
+        m.register_buffer('dim_dp', torch.zeros(1, dtype=torch.float64))
+        m.register_buffer('input_dim', torch.zeros(1, dtype=torch.float64))
+        m.register_buffer('output_dim', torch.zeros(1, dtype=torch.float64))
+
+        m_type = type(m)
+
+        fn = None
+        if m_type in custom_ops:  # if defined both op maps, use custom_ops to overwrite.
+            fn = custom_ops[m_type]
+            if m_type not in types_collection and verbose:
+                print("[INFO] Customize rule %s() %s." % (fn.__qualname__, m_type))
+        elif m_type in register_hooks:
+            fn = register_hooks[m_type]
+            if m_type not in types_collection and verbose:
+                print("[INFO] Register %s() for %s." % (fn.__qualname__, m_type))
+        else:
+            if m_type not in types_collection and verbose:
+                prRed("[WARN] Cannot find rule for %s. Treat it as zero Macs and zero Params." % m_type)
+
+        if fn is not None:
+            handler_collection[m] = (
+                m.register_forward_hook(fn),
+                m.register_forward_hook(count_parameters),
+            )
+        types_collection.add(m_type)
+
+    prev_training_status = model.training
+
+    model.eval()
+    model.apply(add_hooks)
+
+    with torch.no_grad():
+        model(**inputs)
+
+    per_compute_layer_complexity = []
+
+    def dfs_count(module: nn.Module, prefix="\t") -> (int, int):
+        total_ops, total_params = 0, 0
+        total_num_act, total_num_dp = 0, 0
+        for m in module.children():
+            # if not hasattr(m, "total_ops") and not hasattr(m, "total_params"):  # and len(list(m.children())) > 0:
+            #     m_ops, m_params = dfs_count(m, prefix=prefix + "\t")
+            # else:
+            #     m_ops, m_params = m.total_ops, m.total_params
+            if m in handler_collection and not isinstance(m, (nn.Sequential, nn.ModuleList)):
+                m_ops, m_params, m_num_act, m_num_dp, m_weight_reuse, m_input_reuse, m_dim_dp \
+                    = m.total_ops.item(), m.total_params.item(), m.num_act.item(), m.num_dp.item(), \
+                      m.weight_reuse.item(), m.input_reuse.item(), m.dim_dp.item()
+            else:
+                m_ops, m_params, m_num_act, m_num_dp = dfs_count(m, prefix=prefix + "\t")
+                m_weight_reuse = 0
+                m_input_reuse = 0
+                m_dim_dp = 0
+            total_ops += m_ops
+            total_params += m_params
+            total_num_act += m_num_act
+            total_num_dp += m_num_dp
+            module_name = m._get_name()
+            # print(prefix, module_name, '(ops:', m_ops, 'params:', m_params, 'act:', m_num_act, 'dp:', m_num_dp,')')
+
+            if is_compute_layer(m):
+                per_compute_layer_complexity.append([
+                    module_name,
+                    m_ops,
+                    m_params,
+                    m_num_act,
+                    m_num_dp,
+                    m_dim_dp,
+                    NNComputeModuleProfile(m, m.input_dim, m.output_dim)
+                ])
+
+        return total_ops, total_params, total_num_act, total_num_dp
+
+    total_ops, total_params, total_num_act, total_num_dp = dfs_count(model)
+
+    # reset model to original status
+    model.train(prev_training_status)
+    for m, (op_handler, params_handler) in handler_collection.items():
+        op_handler.remove()
+        params_handler.remove()
+        m._buffers.pop("total_ops")
+        m._buffers.pop('total_params')
+        m._buffers.pop('num_act')
+        m._buffers.pop('num_dp')
+        m._buffers.pop('data_reuse')
+        m._buffers.pop('weight_reuse')
+        m._buffers.pop('dim_dp')
+        m._buffers.pop('input_dim')
+        m._buffers.pop('output_dim')
+
+    return total_ops, total_params, total_num_act, total_num_dp, per_compute_layer_complexity
+
+
+class Profiler:
+    def __init__(self, model, inputs):
+        self.model = model
+        self.inputs = inputs
+
+    def profile(self):
+        macs, params, num_act, num_dp, per_compute_layer_complexity = profile(self.model, inputs=self.inputs)
+        return ProfilerResult(macs, params, num_act, num_dp, per_compute_layer_complexity)
+
+
+def profile_compute_layers(model, inputs, custom_ops=None, verbose=False):
+    profiling_results = profile(model, inputs, custom_ops=custom_ops, verbose=verbose)
+    total_params = profiling_results[1]
+
+    input_details = [p[-1] for p in profiling_results[-1]]
+
+    ret = {}
+    count = 0
+
+    num_ones = 0
+    num_ele = 0
+    for idx, (n, m) in enumerate(model.named_modules()):
+        if idx == 0:
+            continue
+
+        if not is_compute_layer(m):
+            continue
+
+        setattr(input_details[count], 'param_proportion', input_details[count].num_param / total_params)
+        ret[n + '.weight'] = input_details[count]
+
+        num_ones += input_details[count].mask.sum()
+        num_ele += input_details[count].mask.numel()
+
+        count += 1
+
+    model_sparsity = 1 - 1.0 * num_ones.item() / num_ele
+    return ret, model_sparsity