FM / python

/usr/local/lib64/python3.6/site-packages/caffe2/python/

/usr/local/lib64/python3.6/site-packages/caffe2/python

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docs/	-	0755	rm
examples/	-	0755	rm
fakelowp/	-	0755	rm
helpers/	-	0755	rm
ideep/	-	0755	rm
layers/	-	0755	rm
mint/	-	0755	rm
mkl/	-	0755	rm
modeling/	-	0755	rm
models/	-	0755	rm
onnx/	-	0755	rm
operator_test/	-	0755	rm
predictor/	-	0755	rm
rnn/	-	0755	rm
serialized_test/	-	0755	rm
test/	-	0755	rm
trt/	-	0755	rm
__pycache__/	-	0755	rm
allcompare_test.py	2255	0644	edit dl rm
attention.py	12359	0644	edit dl rm
benchmark_generator.py	4912	0644	edit dl rm
binarysize.py	5521	0644	edit dl rm
brew.py	4762	0644	edit dl rm
brew_test.py	11739	0644	edit dl rm
build.py	153	0644	edit dl rm
cached_reader.py	4394	0644	edit dl rm
caffe2_pybind11_state.cpython-36m-x86_64-linux-gnu.so	48299712	0755	edit dl rm
caffe2_pybind11_state_gpu.cpython-36m-x86_64-linux-gnu.so	49048144	0755	edit dl rm
caffe_translator.py	35227	0644	edit dl rm
caffe_translator_test.py	3553	0644	edit dl rm
checkpoint.py	32101	0644	edit dl rm
checkpoint_test.py	13405	0644	edit dl rm
cnn.py	7626	0644	edit dl rm
context.py	2841	0644	edit dl rm
context_test.py	1792	0644	edit dl rm
control.py	19309	0644	edit dl rm
control_ops_grad.py	28893	0644	edit dl rm
control_ops_grad_test.py	1752	0644	edit dl rm
control_ops_util.py	10863	0644	edit dl rm
control_test.py	12276	0644	edit dl rm
convert.py	55	0644	edit dl rm
convert_test.py	201	0644	edit dl rm
convnet_benchmarks.py	20533	0644	edit dl rm
convnet_benchmarks_test.py	839	0644	edit dl rm
core.py	119400	0644	edit dl rm
core_gradients_test.py	38022	0644	edit dl rm
core_test.py	47683	0644	edit dl rm
crf.py	13250	0644	edit dl rm
crf_predict.py	1159	0644	edit dl rm
crf_viterbi_test.py	1663	0644	edit dl rm
dataio.py	23532	0644	edit dl rm
dataio_test.py	17575	0644	edit dl rm
dataset.py	12886	0644	edit dl rm
data_parallel_model.py	83100	0644	edit dl rm
data_parallel_model_test.py	56145	0644	edit dl rm
data_workers.py	15941	0644	edit dl rm
data_workers_test.py	6561	0644	edit dl rm
db_file_reader.py	6608	0644	edit dl rm
db_test.py	1110	0644	edit dl rm
device_checker.py	5157	0644	edit dl rm
dyndep.py	1533	0644	edit dl rm
embedding_generation_benchmark.py	5256	0644	edit dl rm
experiment_util.py	3625	0644	edit dl rm
extension_loader.py	744	0644	edit dl rm
fakefp16_transform_lib.py	322	0644	edit dl rm
filler_test.py	748	0644	edit dl rm
functional.py	4415	0644	edit dl rm
functional_test.py	4204	0644	edit dl rm
fused_8bit_rowwise_conversion_ops_test.py	3945	0644	edit dl rm
gradient_checker.py	15377	0644	edit dl rm
gradient_check_test.py	20729	0644	edit dl rm
gru_cell.py	5129	0644	edit dl rm
hip_test_util.py	405	0644	edit dl rm
hsm_util.py	2259	0644	edit dl rm
hypothesis_test.py	105762	0644	edit dl rm
hypothesis_test_util.py	26853	0644	edit dl rm
ideep_test_util.py	998	0644	edit dl rm
layers_test.py	92931	0644	edit dl rm
layer_model_helper.py	29340	0644	edit dl rm
layer_model_instantiator.py	3935	0644	edit dl rm
layer_parameter_sharing_test.py	9148	0644	edit dl rm
layer_test_util.py	4875	0644	edit dl rm
lazy.py	277	0644	edit dl rm
lazy_dyndep.py	2562	0644	edit dl rm
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lengths_reducer_fused_8bit_rowwise_ops_test.py	7575	0644	edit dl rm
lengths_reducer_rowwise_8bit_ops_test.py	5710	0644	edit dl rm
lstm_benchmark.py	10649	0644	edit dl rm
memonger.py	34041	0644	edit dl rm
memonger_test.py	36910	0644	edit dl rm
mkl_test_util.py	1142	0644	edit dl rm
model_device_test.py	4777	0644	edit dl rm
model_helper.py	23492	0644	edit dl rm
model_helper_test.py	2336	0644	edit dl rm
modifier_context.py	1772	0644	edit dl rm
muji.py	8131	0644	edit dl rm
muji_test.py	3058	0644	edit dl rm
net_builder.py	27679	0644	edit dl rm
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net_drawer.py	14264	0644	edit dl rm
net_printer.py	12704	0644	edit dl rm
net_printer_test.py	3190	0644	edit dl rm
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nomnigraph_transformations_test.py	5767	0644	edit dl rm
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normalizer_context.py	1007	0644	edit dl rm
normalizer_test.py	487	0644	edit dl rm
numa_benchmark.py	2230	0644	edit dl rm
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observer_test.py	5316	0644	edit dl rm
operator_fp_exceptions_test.py	1248	0644	edit dl rm
optimizer.py	78813	0644	edit dl rm
optimizer_context.py	1462	0644	edit dl rm
optimizer_test.py	30705	0644	edit dl rm
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parallelize_bmuf_distributed_test.py	9908	0644	edit dl rm
parallel_workers.py	7682	0644	edit dl rm
parallel_workers_test.py	3501	0644	edit dl rm
pipeline.py	17283	0644	edit dl rm
pipeline_test.py	2542	0644	edit dl rm
predictor_constants.py	198	0644	edit dl rm
python_op_test.py	9169	0644	edit dl rm
queue_util.py	4459	0644	edit dl rm
record_queue.py	4453	0644	edit dl rm
recurrent.py	13297	0644	edit dl rm
regularizer.py	21120	0644	edit dl rm
regularizer_context.py	1013	0644	edit dl rm
regularizer_test.py	10266	0644	edit dl rm
rnn_cell.py	68233	0644	edit dl rm
schema.py	45621	0644	edit dl rm
schema_test.py	15754	0644	edit dl rm
scope.py	3623	0644	edit dl rm
scope_test.py	5249	0644	edit dl rm
session.py	7642	0644	edit dl rm
session_test.py	2078	0644	edit dl rm
sparse_to_dense_mask_test.py	6565	0644	edit dl rm
sparse_to_dense_test.py	3556	0644	edit dl rm
task.py	24274	0644	edit dl rm
task_test.py	870	0644	edit dl rm
test_util.py	3524	0644	edit dl rm
text_file_reader.py	1990	0644	edit dl rm
timeout_guard.py	4054	0644	edit dl rm
toy_regression_test.py	2822	0644	edit dl rm
transformations.py	1832	0644	edit dl rm
transformations_test.py	11960	0644	edit dl rm
tt_core.py	9349	0644	edit dl rm
tt_core_test.py	2518	0644	edit dl rm
utils.py	14181	0644	edit dl rm
utils_test.py	1399	0644	edit dl rm
visualize.py	6315	0644	edit dl rm
workspace.py	25263	0644	edit dl rm
workspace_test.py	34844	0644	edit dl rm
_import_c_extension.py	2250	0644	edit dl rm
__init__.py	3925	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/convnet_benchmarks.py (20533B)

## @package convnet_benchmarks
# Module caffe2.python.convnet_benchmarks
"""
Benchmark for common convnets.

Speed on Titan X, with 10 warmup steps and 10 main steps and with different
versions of cudnn, are as follows (time reported below is per-batch time,
forward / forward+backward):

CuDNN V3        CuDNN v4
AlexNet         32.5 / 108.0    27.4 /  90.1
OverFeat       113.0 / 342.3    91.7 / 276.5
Inception      134.5 / 485.8   125.7 / 450.6
VGG (batch 64) 200.8 / 650.0   164.1 / 551.7

Speed on Inception with varied batch sizes and CuDNN v4 is as follows:

Batch Size   Speed per batch     Speed per image
 16             22.8 /  72.7         1.43 / 4.54
 32             38.0 / 127.5         1.19 / 3.98
 64             67.2 / 233.6         1.05 / 3.65
128            125.7 / 450.6         0.98 / 3.52

Speed on Tesla M40, which 10 warmup steps and 10 main steps and with cudnn
v4, is as follows:

AlexNet         68.4 / 218.1
OverFeat       210.5 / 630.3
Inception      300.2 / 1122.2
VGG (batch 64) 405.8 / 1327.7

(Note that these numbers involve a "full" backprop, i.e. the gradient
with respect to the input image is also computed.)

To get the numbers, simply run:

for MODEL in AlexNet OverFeat Inception; do
  PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
    --batch_size 128 --model $MODEL --forward_only True
done
for MODEL in AlexNet OverFeat Inception; do
  PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
    --batch_size 128 --model $MODEL
done
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
  --batch_size 64 --model VGGA --forward_only True
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
  --batch_size 64 --model VGGA

for BS in 16 32 64 128; do
  PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
    --batch_size $BS --model Inception --forward_only True
  PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
    --batch_size $BS --model Inception
done

Note that VGG needs to be run at batch 64 due to memory limit on the backward
pass.
"""

import argparse

from caffe2.python import workspace, brew, model_helper

def MLP(order, cudnn_ws):
    model = model_helper.ModelHelper(name="MLP")
    d = 256
    depth = 20
    width = 3
    for i in range(depth):
        for j in range(width):
            current = "fc_{}_{}".format(i, j) if i > 0 else "data"
            next_ = "fc_{}_{}".format(i + 1, j)
            brew.fc(
                model,
                current,
                next_,
                dim_in=d,
                dim_out=d,
                weight_init=('XavierFill', {}),
                bias_init=('XavierFill', {}),
            )
    brew.sum(
        model, ["fc_{}_{}".format(depth, j) for j in range(width)], ["sum"]
    )
    brew.fc(
        model,
        "sum",
        "last",
        dim_in=d,
        dim_out=1000,
        weight_init=('XavierFill', {}),
        bias_init=('XavierFill', {}),
    )
    xent = model.net.LabelCrossEntropy(["last", "label"], "xent")
    model.net.AveragedLoss(xent, "loss")
    return model, d

def AlexNet(order, cudnn_ws):
    my_arg_scope = {
        'order': order,
        'use_cudnn': True,
        'cudnn_exhaustive_search': True,
    }
    if cudnn_ws:
        my_arg_scope['ws_nbytes_limit'] = cudnn_ws
    model = model_helper.ModelHelper(
        name="alexnet",
        arg_scope=my_arg_scope,
    )
    conv1 = brew.conv(
        model,
        "data",
        "conv1",
        3,
        64,
        11, ('XavierFill', {}), ('ConstantFill', {}),
        stride=4,
        pad=2
    )
    relu1 = brew.relu(model, conv1, "conv1")
    pool1 = brew.max_pool(model, relu1, "pool1", kernel=3, stride=2)
    conv2 = brew.conv(
        model,
        pool1,
        "conv2",
        64,
        192,
        5,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=2
    )
    relu2 = brew.relu(model, conv2, "conv2")
    pool2 = brew.max_pool(model, relu2, "pool2", kernel=3, stride=2)
    conv3 = brew.conv(
        model,
        pool2,
        "conv3",
        192,
        384,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1
    )
    relu3 = brew.relu(model, conv3, "conv3")
    conv4 = brew.conv(
        model,
        relu3,
        "conv4",
        384,
        256,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1
    )
    relu4 = brew.relu(model, conv4, "conv4")
    conv5 = brew.conv(
        model,
        relu4,
        "conv5",
        256,
        256,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1
    )
    relu5 = brew.relu(model, conv5, "conv5")
    pool5 = brew.max_pool(model, relu5, "pool5", kernel=3, stride=2)
    fc6 = brew.fc(
        model,
        pool5, "fc6", 256 * 6 * 6, 4096, ('XavierFill', {}),
        ('ConstantFill', {})
    )
    relu6 = brew.relu(model, fc6, "fc6")
    fc7 = brew.fc(
        model, relu6, "fc7", 4096, 4096, ('XavierFill', {}), ('ConstantFill', {})
    )
    relu7 = brew.relu(model, fc7, "fc7")
    fc8 = brew.fc(
        model, relu7, "fc8", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {})
    )
    pred = brew.softmax(model, fc8, "pred")
    xent = model.net.LabelCrossEntropy([pred, "label"], "xent")
    model.net.AveragedLoss(xent, "loss")
    return model, 224

def OverFeat(order, cudnn_ws):
    my_arg_scope = {
        'order': order,
        'use_cudnn': True,
        'cudnn_exhaustive_search': True,
    }
    if cudnn_ws:
        my_arg_scope['ws_nbytes_limit'] = cudnn_ws
    model = model_helper.ModelHelper(
        name="overfeat",
        arg_scope=my_arg_scope,
    )
    conv1 = brew.conv(
        model,
        "data",
        "conv1",
        3,
        96,
        11,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        stride=4,
    )
    relu1 = brew.relu(model, conv1, "conv1")
    pool1 = brew.max_pool(model, relu1, "pool1", kernel=2, stride=2)
    conv2 = brew.conv(
        model, pool1, "conv2", 96, 256, 5, ('XavierFill', {}),
        ('ConstantFill', {})
    )
    relu2 = brew.relu(model, conv2, "conv2")
    pool2 = brew.max_pool(model, relu2, "pool2", kernel=2, stride=2)
    conv3 = brew.conv(
        model,
        pool2,
        "conv3",
        256,
        512,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu3 = brew.relu(model, conv3, "conv3")
    conv4 = brew.conv(
        model,
        relu3,
        "conv4",
        512,
        1024,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu4 = brew.relu(model, conv4, "conv4")
    conv5 = brew.conv(
        model,
        relu4,
        "conv5",
        1024,
        1024,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu5 = brew.relu(model, conv5, "conv5")
    pool5 = brew.max_pool(model, relu5, "pool5", kernel=2, stride=2)
    fc6 = brew.fc(
        model, pool5, "fc6", 1024 * 6 * 6, 3072, ('XavierFill', {}),
        ('ConstantFill', {})
    )
    relu6 = brew.relu(model, fc6, "fc6")
    fc7 = brew.fc(
        model, relu6, "fc7", 3072, 4096, ('XavierFill', {}), ('ConstantFill', {})
    )
    relu7 = brew.relu(model, fc7, "fc7")
    fc8 = brew.fc(
        model, relu7, "fc8", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {})
    )
    pred = brew.softmax(model, fc8, "pred")
    xent = model.net.LabelCrossEntropy([pred, "label"], "xent")
    model.net.AveragedLoss(xent, "loss")
    return model, 231

def VGGA(order, cudnn_ws):
    my_arg_scope = {
        'order': order,
        'use_cudnn': True,
        'cudnn_exhaustive_search': True,
    }
    if cudnn_ws:
        my_arg_scope['ws_nbytes_limit'] = cudnn_ws
    model = model_helper.ModelHelper(
        name="vgga",
        arg_scope=my_arg_scope,
    )
    conv1 = brew.conv(
        model,
        "data",
        "conv1",
        3,
        64,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu1 = brew.relu(model, conv1, "conv1")
    pool1 = brew.max_pool(model, relu1, "pool1", kernel=2, stride=2)
    conv2 = brew.conv(
        model,
        pool1,
        "conv2",
        64,
        128,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu2 = brew.relu(model, conv2, "conv2")
    pool2 = brew.max_pool(model, relu2, "pool2", kernel=2, stride=2)
    conv3 = brew.conv(
        model,
        pool2,
        "conv3",
        128,
        256,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu3 = brew.relu(model, conv3, "conv3")
    conv4 = brew.conv(
        model,
        relu3,
        "conv4",
        256,
        256,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu4 = brew.relu(model, conv4, "conv4")
    pool4 = brew.max_pool(model, relu4, "pool4", kernel=2, stride=2)
    conv5 = brew.conv(
        model,
        pool4,
        "conv5",
        256,
        512,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu5 = brew.relu(model, conv5, "conv5")
    conv6 = brew.conv(
        model,
        relu5,
        "conv6",
        512,
        512,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu6 = brew.relu(model, conv6, "conv6")
    pool6 = brew.max_pool(model, relu6, "pool6", kernel=2, stride=2)
    conv7 = brew.conv(
        model,
        pool6,
        "conv7",
        512,
        512,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu7 = brew.relu(model, conv7, "conv7")
    conv8 = brew.conv(
        model,
        relu7,
        "conv8",
        512,
        512,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu8 = brew.relu(model, conv8, "conv8")
    pool8 = brew.max_pool(model, relu8, "pool8", kernel=2, stride=2)

fcix = brew.fc(
        model, pool8, "fcix", 512 * 7 * 7, 4096, ('XavierFill', {}),
        ('ConstantFill', {})
    )
    reluix = brew.relu(model, fcix, "fcix")
    fcx = brew.fc(
        model, reluix, "fcx", 4096, 4096, ('XavierFill', {}),
        ('ConstantFill', {})
    )
    relux = brew.relu(model, fcx, "fcx")
    fcxi = brew.fc(
        model, relux, "fcxi", 4096, 1000, ('XavierFill', {}),
        ('ConstantFill', {})
    )
    pred = brew.softmax(model, fcxi, "pred")
    xent = model.net.LabelCrossEntropy([pred, "label"], "xent")
    model.net.AveragedLoss(xent, "loss")
    return model, 231

def _InceptionModule(
    model, input_blob, input_depth, output_name, conv1_depth, conv3_depths,
    conv5_depths, pool_depth
):
    # path 1: 1x1 conv
    conv1 = brew.conv(
        model, input_blob, output_name + ":conv1", input_depth, conv1_depth, 1,
        ('XavierFill', {}), ('ConstantFill', {})
    )
    conv1 = brew.relu(model, conv1, conv1)
    # path 2: 1x1 conv + 3x3 conv
    conv3_reduce = brew.conv(
        model, input_blob, output_name + ":conv3_reduce", input_depth,
        conv3_depths[0], 1, ('XavierFill', {}), ('ConstantFill', {})
    )
    conv3_reduce = brew.relu(model, conv3_reduce, conv3_reduce)
    conv3 = brew.conv(
        model,
        conv3_reduce,
        output_name + ":conv3",
        conv3_depths[0],
        conv3_depths[1],
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    conv3 = brew.relu(model, conv3, conv3)
    # path 3: 1x1 conv + 5x5 conv
    conv5_reduce = brew.conv(
        model, input_blob, output_name + ":conv5_reduce", input_depth,
        conv5_depths[0], 1, ('XavierFill', {}), ('ConstantFill', {})
    )
    conv5_reduce = brew.relu(model, conv5_reduce, conv5_reduce)
    conv5 = brew.conv(
        model,
        conv5_reduce,
        output_name + ":conv5",
        conv5_depths[0],
        conv5_depths[1],
        5,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=2,
    )
    conv5 = brew.relu(model, conv5, conv5)
    # path 4: pool + 1x1 conv
    pool = brew.max_pool(
        model,
        input_blob,
        output_name + ":pool",
        kernel=3,
        stride=1,
        pad=1,
    )
    pool_proj = brew.conv(
        model, pool, output_name + ":pool_proj", input_depth, pool_depth, 1,
        ('XavierFill', {}), ('ConstantFill', {})
    )
    pool_proj = brew.relu(model, pool_proj, pool_proj)
    output = brew.concat(model, [conv1, conv3, conv5, pool_proj], output_name)
    return output

def Inception(order, cudnn_ws):
    my_arg_scope = {
        'order': order,
        'use_cudnn': True,
        'cudnn_exhaustive_search': True,
    }
    if cudnn_ws:
        my_arg_scope['ws_nbytes_limit'] = cudnn_ws
    model = model_helper.ModelHelper(
        name="inception",
        arg_scope=my_arg_scope,
    )
    conv1 = brew.conv(
        model,
        "data",
        "conv1",
        3,
        64,
        7,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        stride=2,
        pad=3,
    )
    relu1 = brew.relu(model, conv1, "conv1")
    pool1 = brew.max_pool(model, relu1, "pool1", kernel=3, stride=2, pad=1)
    conv2a = brew.conv(
        model, pool1, "conv2a", 64, 64, 1, ('XavierFill', {}),
        ('ConstantFill', {})
    )
    conv2a = brew.relu(model, conv2a, conv2a)
    conv2 = brew.conv(
        model,
        conv2a,
        "conv2",
        64,
        192,
        3,
        ('XavierFill', {}),
        ('ConstantFill', {}),
        pad=1,
    )
    relu2 = brew.relu(model, conv2, "conv2")
    pool2 = brew.max_pool(model, relu2, "pool2", kernel=3, stride=2, pad=1)
    # Inception modules
    inc3 = _InceptionModule(
        model, pool2, 192, "inc3", 64, [96, 128], [16, 32], 32
    )
    inc4 = _InceptionModule(
        model, inc3, 256, "inc4", 128, [128, 192], [32, 96], 64
    )
    pool5 = brew.max_pool(model, inc4, "pool5", kernel=3, stride=2, pad=1)
    inc5 = _InceptionModule(
        model, pool5, 480, "inc5", 192, [96, 208], [16, 48], 64
    )
    inc6 = _InceptionModule(
        model, inc5, 512, "inc6", 160, [112, 224], [24, 64], 64
    )
    inc7 = _InceptionModule(
        model, inc6, 512, "inc7", 128, [128, 256], [24, 64], 64
    )
    inc8 = _InceptionModule(
        model, inc7, 512, "inc8", 112, [144, 288], [32, 64], 64
    )
    inc9 = _InceptionModule(
        model, inc8, 528, "inc9", 256, [160, 320], [32, 128], 128
    )
    pool9 = brew.max_pool(model, inc9, "pool9", kernel=3, stride=2, pad=1)
    inc10 = _InceptionModule(
        model, pool9, 832, "inc10", 256, [160, 320], [32, 128], 128
    )
    inc11 = _InceptionModule(
        model, inc10, 832, "inc11", 384, [192, 384], [48, 128], 128
    )
    pool11 = brew.average_pool(model, inc11, "pool11", kernel=7, stride=1)
    fc = brew.fc(
        model, pool11, "fc", 1024, 1000, ('XavierFill', {}),
        ('ConstantFill', {})
    )
    # It seems that Soumith's benchmark does not have softmax on top
    # for Inception. We will add it anyway so we can have a proper
    # backward pass.
    pred = brew.softmax(model, fc, "pred")
    xent = model.net.LabelCrossEntropy([pred, "label"], "xent")
    model.net.AveragedLoss(xent, "loss")
    return model, 224

def AddParameterUpdate(model):
    """ Simple plain SGD update -- not tuned to actually train the models """
    ITER = brew.iter(model, "iter")
    LR = model.net.LearningRate(
        ITER, "LR", base_lr=-1e-8, policy="step", stepsize=10000, gamma=0.999)
    ONE = model.param_init_net.ConstantFill([], "ONE", shape=[1], value=1.0)
    for param in model.params:
        param_grad = model.param_to_grad[param]
        model.net.WeightedSum([param, ONE, param_grad, LR], param)

def Benchmark(model_gen, arg):
    model, input_size = model_gen(arg.order, arg.cudnn_ws)
    model.Proto().type = arg.net_type
    model.Proto().num_workers = arg.num_workers

# In order to be able to run everything without feeding more stuff, let's
    # add the data and label blobs to the parameter initialization net as well.
    if arg.order == "NCHW":
        input_shape = [arg.batch_size, 3, input_size, input_size]
    else:
        input_shape = [arg.batch_size, input_size, input_size, 3]
    if arg.model == "MLP":
        input_shape = [arg.batch_size, input_size]

model.param_init_net.GaussianFill(
        [],
        "data",
        shape=input_shape,
        mean=0.0,
        std=1.0
    )
    model.param_init_net.UniformIntFill(
        [],
        "label",
        shape=[arg.batch_size, ],
        min=0,
        max=999
    )

if arg.forward_only:
        print('{}: running forward only.'.format(arg.model))
    else:
        print('{}: running forward-backward.'.format(arg.model))
        model.AddGradientOperators(["loss"])
        AddParameterUpdate(model)
        if arg.order == 'NHWC':
            print(
                '==WARNING==\n'
                'NHWC order with CuDNN may not be supported yet, so I might\n'
                'exit suddenly.'
            )

if not arg.cpu:
        model.param_init_net.RunAllOnGPU()
        model.net.RunAllOnGPU()

if arg.engine:
        for op in model.net.Proto().op:
            op.engine = arg.engine

if arg.dump_model:
        # Writes out the pbtxt for benchmarks on e.g. Android
        with open(
            "{0}_init_batch_{1}.pbtxt".format(arg.model, arg.batch_size), "w"
        ) as fid:
            fid.write(str(model.param_init_net.Proto()))
        with open("{0}.pbtxt".format(arg.model, arg.batch_size), "w") as fid:
            fid.write(str(model.net.Proto()))

workspace.RunNetOnce(model.param_init_net)
    workspace.CreateNet(model.net)
    workspace.BenchmarkNet(
        model.net.Proto().name, arg.warmup_iterations, arg.iterations,
        arg.layer_wise_benchmark)

def GetArgumentParser():
    parser = argparse.ArgumentParser(description="Caffe2 benchmark.")
    parser.add_argument(
        "--batch_size",
        type=int,
        default=128,
        help="The batch size."
    )
    parser.add_argument("--model", type=str, help="The model to benchmark.")
    parser.add_argument(
        "--order",
        type=str,
        default="NCHW",
        help="The order to evaluate."
    )
    parser.add_argument(
        "--cudnn_ws",
        type=int,
        help="The cudnn workspace size."
    )
    parser.add_argument(
        "--iterations",
        type=int,
        default=10,
        help="Number of iterations to run the network."
    )
    parser.add_argument(
        "--warmup_iterations",
        type=int,
        default=10,
        help="Number of warm-up iterations before benchmarking."
    )
    parser.add_argument(
        "--forward_only",
        action='store_true',
        help="If set, only run the forward pass."
    )
    parser.add_argument(
        "--layer_wise_benchmark",
        action='store_true',
        help="If True, run the layer-wise benchmark as well."
    )
    parser.add_argument(
        "--cpu",
        action='store_true',
        help="If True, run testing on CPU instead of GPU."
    )
    parser.add_argument(
        "--engine",
        type=str,
        default="",
        help="If set, blindly prefer the given engine(s) for every op.")
    parser.add_argument(
        "--dump_model",
        action='store_true',
        help="If True, dump the model prototxts to disk."
    )
    parser.add_argument("--net_type", type=str, default="dag")
    parser.add_argument("--num_workers", type=int, default=2)
    parser.add_argument("--use-nvtx", default=False, action='store_true')
    parser.add_argument("--htrace_span_log_path", type=str)
    return parser

if __name__ == '__main__':
    args, extra_args = GetArgumentParser().parse_known_args()
    if (
        not args.batch_size or not args.model or not args.order
    ):
        GetArgumentParser().print_help()
    else:
        workspace.GlobalInit(
            ['caffe2', '--caffe2_log_level=0'] + extra_args +
            (['--caffe2_use_nvtx'] if args.use_nvtx else []) +
            (['--caffe2_htrace_span_log_path=' + args.htrace_span_log_path]
                if args.htrace_span_log_path else []))

model_map = {
            'AlexNet': AlexNet,
            'OverFeat': OverFeat,
            'VGGA': VGGA,
            'Inception': Inception,
            'MLP': MLP,
        }
        Benchmark(model_map[args.model], args)

cmd: