[Release] v0.0.4

.flake8

@@ -0,0 +1,18 @@
+[flake8]
+select = B,C,E,F,P,T4,W,B9
+max-line-length = 120
+# C408 ignored because we like the dict keyword argument syntax
+# E501 is not flexible enough, we're using B950 instead
+ignore =
+    E203,E305,E402,E501,E721,E741,F403,F405,F821,F841,F999,W503,W504,C408,E302,W291,E303,
+    # shebang has extra meaning in fbcode lints, so I think it's not worth trying
+    # to line this up with executable bit
+    EXE001,
+    # these ignores are from flake8-bugbear; please fix!
+    B007,B008,
+    # these ignores are from flake8-comprehensions; please fix!
+    C400,C401,C402,C403,C404,C405,C407,C411,C413,C414,C415,
+    W292 
+per-file-ignores = __init__.py: F401
+
+exclude = application, test, mqbench/tools/proto/ppl_caffe_pb2.py

.gitignore

@@ -0,0 +1,3 @@
+*.pyc
+*.idea
+__pycache__

LICENSE

@@ -0,0 +1,202 @@
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README.md

@@ -0,0 +1,51 @@
+<div align="center">
+  <img src="resources/logo.png" width="700"/>
+
+</div>
+
+------------
+
+
+## Introduction
+
+MQBench is an open-source model quantization toolkit based on PyTorch fx.
+
+The envision of MQBench is to provide:
+- **SOTA Algorithms**. With MQBench, the hardware vendors and researchers can benefit from the latest research progress in academia.
+- **Powerful Toolkits**. With the toolkit, quantization node can be inserted to the original PyTorch module automatically with respect to the specific hardware. After training, the quantized model can be smoothly converted to the format that can inference on the real device.
+
+
+## Installation
+```
+
+git clone [email protected]:ModelTC/MQBench.git
+cd MQBench
+python setup.py install
+```
+
+
+## Documentation
+
+MQBench aims to support (1) various deployable quantization algorithms and (2) hardware backend libraries to facilitate the development of the community.
+
+For the detailed information, please refer to [mqbench documentation](http://mqbench.tech/assets/docs/html/).
+
+
+## Citation
+
+If you use this toolkit or benchmark in your research, please cite this project.
+
+```
+@article{MQBench,
+  title   = {MQBench: Towards Reproducible and Deployable Model Quantization Benchmark},
+  author  = {Yuhang Li* and Mingzhu Shen* and Jian Ma* and Yan Ren* and Mingxin Zhao* and
+             Qi Zhang* and Ruihao Gong* and Fengwei Yu and Junjie Yan},
+  journal= {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
+  year={2021}
+}
+```
+
+
+## License
+
+This project is released under the [Apache 2.0 license](LICENSE).

application/imagenet_example/calibrator.py

@@ -0,0 +1,57 @@
+import tensorrt as trt
+import os
+import pycuda.driver as cuda
+import pycuda.autoinit
+from PIL import Image
+import numpy as np
+
+def load_imagenet_data(cali_data_loader):
+    dataset = []
+    for i, (data, label) in enumerate(cali_data_loader):
+        data = data.numpy().astype(np.float32)
+        dataset.append(data)
+    return dataset
+
+class ImagenetCalibrator(trt.IInt8EntropyCalibrator2):
+    def __init__(self, cali_data_loader, cache_file):
+        # Whenever you specify a custom constructor for a TensorRT class,
+        # you MUST call the constructor of the parent explicitly.
+        trt.IInt8EntropyCalibrator2.__init__(self)
+
+        self.cache_file = cache_file
+
+        # Every time get_batch is called, the next batch of size batch_size will be copied to the device and returned.
+        self.data = load_imagenet_data(cali_data_loader)
+        self.batch_size = self.data[0].shape[0]
+        self.current_index = 0
+
+        # Allocate enough memory for a whole batch.
+        self.device_input = cuda.mem_alloc(self.data[0].nbytes)
+
+    def get_batch_size(self):
+        return self.batch_size
+
+    # TensorRT passes along the names of the engine bindings to the get_batch function.
+    # You don't necessarily have to use them, but they can be useful to understand the order of
+    # the inputs. The bindings list is expected to have the same ordering as 'names'.
+    def get_batch(self, names):
+        if self.current_index == len(self.data):
+            return None
+
+        batch = self.data[self.current_index].ravel()
+        cuda.memcpy_htod(self.device_input, batch)
+        self.current_index += 1
+        print('Calibrate batch = {} / {}'.format(self.current_index, len(self.data)))
+        return [self.device_input]
+
+
+    def read_calibration_cache(self):
+        # If there is a cache, use it instead of calibrating again. Otherwise, implicitly return None.
+        # if os.path.exists(self.cache_file):
+        #     with open(self.cache_file, "rb") as f:
+        #         return f.read()
+        return None
+
+    def write_calibration_cache(self, cache):
+        with open(self.cache_file, "wb") as f:
+            f.write(cache)