mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
77b78935f2
Reviewed By: kevinbchen Differential Revision: D6300944 fbshipit-source-id: e915c3f3d6b475752d8b7df82ec467d86f88a7c7
272 lines
8.5 KiB
Python
272 lines
8.5 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import unittest
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import numpy as np
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import copy
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from hypothesis import given
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import hypothesis.strategies as st
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from caffe2.python.model_helper import ModelHelper
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from caffe2.python.models import resnet
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from caffe2.python import workspace, brew
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.mkl.rewrite_graph as rewrite_graph
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def deterministic_io(model):
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model = copy.deepcopy(model)
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for i, op in enumerate(model.InitProto().op):
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op.device_option.random_seed = i + 1
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if not model.Proto().external_output:
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model.Proto().external_output.extend([model.Proto().op[-1].output[0]])
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return model
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def simple_fc():
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model = ModelHelper(name="r")
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brew.fc(model, "data", "fc", 10, 10)
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return model, [(1, 10)]
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def double_matmul():
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model = ModelHelper(name="r")
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fc0 = brew.fc(model, "data", "fc0", 10, 10)
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fc1 = brew.fc(model, fc0, "fc1", 10, 10)
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model.Proto().external_output[:] = [str(fc0), str(fc1)]
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return model, [(1, 10)]
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def simple_relu():
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model = ModelHelper(name="r")
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brew.relu(model, "data", "fc")
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return model, [(1, 10)]
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def simple_mlp():
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model = ModelHelper(name="r")
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brew.relu(
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model,
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brew.fc(
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model,
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brew.relu(
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model,
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brew.fc(
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model,
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"data",
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"fc1",
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10,
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10),
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"rl1"),
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"fc2",
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10,
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10),
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"rl2")
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return model, [(1, 10)]
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def simple_cnn():
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model = ModelHelper(name="r", arg_scope={"order": "NCHW", "is_test": True})
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brew.conv(
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model, "data", 'conv1', 3, 16, kernel=3, stride=1
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)
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brew.spatial_bn(
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model, 'conv1', 'conv1_spatbn', 16, epsilon=1e-3
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)
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brew.relu(model, 'conv1_spatbn', 'relu1')
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return model, [(1, 3, 32, 32)]
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def alexnet():
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model = ModelHelper(name="r", arg_scope={"order": "NCHW", "is_test": True})
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conv1 = brew.conv(
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model,
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"data",
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"conv1",
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3,
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64,
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11, ('XavierFill', {}), ('ConstantFill', {}),
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stride=4,
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pad=2
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)
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relu1 = brew.relu(model, conv1, "conv1")
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pool1 = brew.max_pool(model, relu1, "pool1", kernel=3, stride=2, pad=0,
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legacy_pad=3)
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lrn1 = brew.lrn(
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model, pool1, "pool1_lrn", size=5, alpha=1.0e-4, beta=0.75, bias=1.0)
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conv2 = brew.conv(
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model,
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lrn1,
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"conv2",
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64,
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192,
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5,
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('XavierFill', {}),
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('ConstantFill', {}),
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pad=2
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)
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relu2 = brew.relu(model, conv2, "conv2")
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pool2 = brew.max_pool(model, relu2, "pool2", kernel=3, stride=2)
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lrn2 = brew.lrn(
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model, pool2, "pool2_lrn", size=5, alpha=1.0e-4, beta=0.75, bias=1.0)
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conv3 = brew.conv(
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model,
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lrn2,
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"conv3",
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192,
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384,
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3,
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('XavierFill', {}),
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('ConstantFill', {}),
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pad=1
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)
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relu3 = brew.relu(model, conv3, "conv3")
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conv4 = brew.conv(
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model,
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relu3,
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"conv4",
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384,
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256,
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3,
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('XavierFill', {}),
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('ConstantFill', {}),
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pad=1
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)
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relu4 = brew.relu(model, conv4, "conv4")
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conv5 = brew.conv(
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model,
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relu4,
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"conv5",
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256,
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256,
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3,
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('XavierFill', {}),
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('ConstantFill', {}),
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pad=1
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)
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relu5 = brew.relu(model, conv5, "conv5")
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pool5 = brew.max_pool(model, relu5, "pool5", kernel=3, stride=2)
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fc6 = brew.fc(
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model,
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pool5, "fc6", 256 * 6 * 6, 4096, ('XavierFill', {}),
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('ConstantFill', {})
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)
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relu6 = brew.relu(model, fc6, "fc6")
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fc7 = brew.fc(
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model, relu6, "fc7", 4096, 4096, ('XavierFill', {}), ('ConstantFill', {})
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)
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relu7 = brew.relu(model, fc7, "fc7")
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drop7 = brew.dropout(model, relu7, "fc7_dropout", is_test=1, ratio=0.5)
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fc8 = brew.fc(
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model, drop7, "fc8", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {})
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)
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relu8 = brew.relu(model, fc8, "fc8")
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_ = brew.dropout(model, relu8, "fc8_dropout", is_test=1, ratio=0.5)
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return model, [(1, 3, 224, 224)]
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def simple_resnet():
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model = ModelHelper(name="r", arg_scope={"order": "NCHW", "is_test": True})
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resnet.create_resnet_32x32(
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model, "data", num_input_channels=1, num_groups=1, num_labels=5,
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is_test=True)
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return model, [(1, 1, 32, 32)]
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def complex_resnet():
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model = ModelHelper(name="r", arg_scope={"order": "NCHW", "is_test": True})
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resnet.create_resnet50(
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model, "data", num_input_channels=1, num_labels=5, is_test=True,
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no_loss=True)
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return model, [(1, 1, 224, 224)]
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@unittest.skipIf(not workspace.C.has_mkldnn,
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"Skipping as we do not have mkldnn.")
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class MKLRewriteTest(hu.HypothesisTestCase):
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@given(gen=st.sampled_from([simple_relu, simple_fc,
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simple_mlp, simple_cnn]))
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def test_mkl_simple_rewrite(self, gen):
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cpu_model, (shape,) = gen()
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cpu_model = deterministic_io(cpu_model)
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mkl_model = rewrite_graph.rewrite_model_helper_simple(cpu_model)
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X = np.random.randn(*shape).astype(np.float32)
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def run(model):
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self.ws.run(model.InitProto())
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self.ws.create_blob(model.Proto().external_input[0]).feed(X)
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self.ws.run(model.Proto())
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return self.ws.blobs[model.Proto().external_output[0]].fetch()
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np.testing.assert_allclose(run(cpu_model), run(mkl_model),
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atol=1e-4, rtol=1e-4)
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def test_mkl_resnet_rewrite(self):
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cpu_model, (shape,) = complex_resnet()
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cpu_model = deterministic_io(cpu_model)
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mkl_model = rewrite_graph.rewrite_model_helper_simple(cpu_model)
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np.random.seed(1701)
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X = np.random.randn(*shape).astype(np.float32)
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def run(model):
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self.ws.run(model.InitProto())
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self.ws.create_blob(model.Proto().external_input[0]).feed(X)
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self.ws.run(model.Proto())
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return self.ws.blobs[model.Proto().external_output[0]].fetch()
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np.testing.assert_allclose(run(cpu_model), run(mkl_model),
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atol=1e-4, rtol=1e-4)
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def test_mkl_multi_output_rewrite(self):
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cpu_model, shapes = double_matmul()
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cpu_model = deterministic_io(cpu_model)
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mkl_model = rewrite_graph.rewrite_model_helper_simple(cpu_model)
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np.random.seed(1701)
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Xs = [np.random.randn(*shape).astype(np.float32) for shape in shapes]
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def run(model):
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self.ws.run(model.InitProto())
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for (name, X) in zip(model.Proto().external_input, Xs):
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self.ws.create_blob(name).feed(X)
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print(model.Proto())
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self.ws.run(model.Proto())
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return [self.ws.blobs[name].fetch()
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for name in model.Proto().external_output]
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run(mkl_model)
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np.testing.assert_allclose(run(cpu_model), run(mkl_model),
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atol=1e-4, rtol=1e-4)
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def test_mkl_alexnet_rewrite(self):
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cpu_model, (shape,) = alexnet()
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cpu_model = deterministic_io(cpu_model)
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mkl_model = rewrite_graph.rewrite_model_helper_simple(cpu_model)
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np.random.seed(1701)
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X = np.random.randn(*shape).astype(np.float32)
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def run(model):
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self.ws.run(model.InitProto())
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self.ws.create_blob(model.Proto().external_input[0]).feed(X)
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self.ws.run(model.Proto())
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return self.ws.blobs[model.Proto().external_output[0]].fetch()
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np.testing.assert_allclose(run(cpu_model), run(mkl_model),
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atol=1e-4, rtol=1e-4)
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if __name__ == "__main__":
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import unittest
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unittest.main()
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