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Enable Detectron model inference for CPU and MKL-DNN paths (pytorch#1…
…0157) Summary: 1. Support ops needed for inference of Faster-RCNN/Mask-RCNN needed in Detectron, mostly direct fallbacks. 2. Use CPU device to hold 0-dim tensors and integer tensors in both fallback op and blob feeder, needed by Detectron models. 3. Ignore 0-dim tensor in MKL-DNN concat operator. 4. Generate dynamic library of Detectron module for CPU device. This PR obsoletes pytorch#9164. Pull Request resolved: pytorch#10157 Differential Revision: D9276837 Pulled By: yinghai fbshipit-source-id: dc364932ae4a2e7fcefdee70b5fce3c0cee91b6f
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
| from __future__ import unicode_literals | ||
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| import unittest | ||
| import hypothesis.strategies as st | ||
| from hypothesis import given | ||
| import numpy as np | ||
| from caffe2.python import core, workspace | ||
| from caffe2.proto import caffe2_pb2 | ||
| import caffe2.python.hypothesis_test_util as hu | ||
| import caffe2.python.ideep_test_util as mu | ||
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| @unittest.skipIf(not workspace.C.use_ideep, "No IDEEP support.") | ||
| class TestFallbackOps(hu.HypothesisTestCase): | ||
| @given(stride=st.integers(1, 3), | ||
| pad=st.integers(0, 3), | ||
| kernel=st.integers(3, 5), | ||
| size=st.integers(8, 10), | ||
| input_channels=st.integers(1, 3), | ||
| output_channels=st.integers(1, 5), | ||
| batch_size=st.integers(1, 3), | ||
| use_bias=st.booleans(), | ||
| **mu.gcs) | ||
| def test_in_place(self, stride, pad, kernel, size, | ||
| input_channels, output_channels, | ||
| batch_size, use_bias, gc, dc): | ||
| # To expose fallback in-place potential issue, the fallback op | ||
| # following ideep op must be run at least two iterations. | ||
| conv = core.CreateOperator( | ||
| "Conv", | ||
| ["X", "w", "b"] if use_bias else ["X", "w"], | ||
| ["Y"], | ||
| stride=stride, | ||
| pad=pad, | ||
| kernel=kernel, | ||
| device_option=dc[0] | ||
| ) | ||
| X = np.random.rand( | ||
| batch_size, input_channels, size, size).astype(np.float32) - 0.5 | ||
| w = np.random.rand(output_channels, input_channels, kernel, kernel) \ | ||
| .astype(np.float32) - 0.5 | ||
| b = np.random.rand(output_channels).astype(np.float32) - 0.5 | ||
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| old_ws_name = workspace.CurrentWorkspace() | ||
| workspace.SwitchWorkspace("_device_check_", True) | ||
| workspace.FeedBlob('X', X, dc[0]) | ||
| workspace.FeedBlob('w', w, dc[0]) | ||
| workspace.FeedBlob('b', b, dc[0]) | ||
| workspace.RunOperatorOnce(conv) | ||
| Y = workspace.FetchBlob('Y') | ||
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| scale = np.random.randn(Y.shape[1]).astype(np.float32) | ||
| bias = np.random.randn(Y.shape[1]).astype(np.float32) | ||
| ac = core.CreateOperator( | ||
| "AffineChannel", | ||
| ["Y", "scale", "bias"], | ||
| ["Y"], | ||
| is_learnable=False, | ||
| device_option=dc[0] | ||
| ) | ||
| workspace.FeedBlob('scale', scale, dc[0]) | ||
| workspace.FeedBlob('bias', bias, dc[0]) | ||
| workspace.RunOperatorOnce(ac) | ||
| workspace.RunOperatorOnce(conv) | ||
| workspace.RunOperatorOnce(ac) | ||
| Y0 = workspace.FetchBlob('Y') | ||
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| workspace.ResetWorkspace() | ||
| dev_net = caffe2_pb2.NetDef() | ||
| conv_dev = caffe2_pb2.OperatorDef() | ||
| conv_dev.CopyFrom(conv) | ||
| conv_dev.device_option.CopyFrom(dc[1]) | ||
| ac_dev = caffe2_pb2.OperatorDef() | ||
| ac_dev.CopyFrom(ac) | ||
| ac_dev.device_option.CopyFrom(dc[1]) | ||
| dev_net.op.extend([conv_dev, ac_dev]) | ||
| workspace.FeedBlob('X', X, dc[1]) | ||
| workspace.FeedBlob('w', w, dc[1]) | ||
| workspace.FeedBlob('b', b, dc[1]) | ||
| workspace.FeedBlob('scale', scale, dc[1]) | ||
| workspace.FeedBlob('bias', bias, dc[1]) | ||
| workspace.RunNetOnce(dev_net) | ||
| workspace.RunNetOnce(dev_net) | ||
| Y1 = workspace.FetchBlob('Y') | ||
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| if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01): | ||
| print(Y1.flatten()) | ||
| print(Y0.flatten()) | ||
| print(np.max(np.abs(Y1 - Y0))) | ||
| self.assertTrue(False) | ||
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| workspace.SwitchWorkspace(old_ws_name) | ||
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| if __name__ == "__main__": | ||
| unittest.main() |
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