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added check to avoid IR generation in case of wrong input shape
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@sadolini
sadolini committed Sep 11, 2020
1 parent ba254d7 commit c43bc86
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Showing 4 changed files with 70 additions and 10 deletions.
6 changes: 5 additions & 1 deletion model-optimizer/mo/ops/convolution.py
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Expand Up @@ -20,7 +20,6 @@

from mo.front.common.partial_infer.utils import int64_array, float_array, mark_input_bins, assign_dims_to_weights, \
tf_window_op_pad_infer
from mo.front.extractor import spatial_getter
from mo.front.onnx.extractors.utils import get_backend_pad
from mo.graph.graph import Node, Graph
from mo.ops.op import Op, PermuteAttrs
Expand Down Expand Up @@ -72,6 +71,11 @@ def calc_convolution(input_spatial_shape, stride_spatial_shape, pad_spatial_shap
Verified to be applicable for both Caffe and ONNX.
'''
spatial_val_wo_stride = input_spatial_shape + pad_spatial_shape - kernel_extent

if np.any(spatial_val_wo_stride < 0):
raise Error("Data after padding has dimension less than window size. " +
"Possible reason of error in incorrect input_shape")

float_spatial_val_wo_stride = float_array(spatial_val_wo_stride)
return float_spatial_val_wo_stride / stride_spatial_shape + 1

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29 changes: 29 additions & 0 deletions model-optimizer/mo/ops/convolution_test.py
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Expand Up @@ -21,6 +21,7 @@
from mo.front.common.partial_infer.utils import int64_array
from mo.graph.graph import Node
from mo.ops.convolution import Convolution
from mo.utils.error import Error
from mo.utils.unittest.extractors import FakeValue
from mo.utils.unittest.graph import build_graph

Expand Down Expand Up @@ -378,3 +379,31 @@ def test_conv_infer_3D_convolution(self):
self.assertTrue(np.array_equal(int64_array([[0, 0], [0, 0], [0, 0]]), conv_node.pad_spatial_shape))
# Check resulting output shape
self.assertTrue(np.array_equal(int64_array([1, 64, 16, 218, 218]), conv_output.shape))

def test_caffe_conv2d_infer_wrong_input_shape(self):
graph = build_graph(nodes_attributes,
[('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{'conv_output': {'shape': None},
'conv_input': {'shape': np.array([1, 3, 1, 1])},
'conv_weights': {'shape': np.array([64, 3, 3, 3]),
'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']},
'conv_node': {'pad_spatial_shape': np.array([[0, 0], [0, 0]]),
'conv_pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]]),
'dilation': np.array([1, 1, 1, 1]), 'bias_addable': True, 'bias_term': False,
'output_spatial_shape': None, 'output_shape': None,
'stride': np.array([1, 1, 1, 1]), 'group': 1,
'kernel_spatial_idx': np.array([2, 3]),
'input_feature_channel': 1,
'output_feature_channel': 0,
'output': 64, 'kernel_spatial': np.array([3, 3]),
'spatial_dims': np.array([2, 3]), 'channel_dims': np.array([1]),
'batch_dims': np.array([0])}
})

conv_node = Node(graph, 'conv_node')
with self.assertRaises(Error):
Convolution.infer(conv_node)
21 changes: 12 additions & 9 deletions model-optimizer/mo/ops/pooling.py
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Expand Up @@ -17,23 +17,21 @@
import numpy as np

from mo.front.common.partial_infer.utils import tf_window_op_pad_infer
from mo.front.extractor import attr_getter
# from mo.front.common.partial_infer.pooling import pool_explicit_padding_infer
from mo.front.extractor import spatial_getter
from mo.front.onnx.extractors.utils import get_backend_pad
from mo.graph.graph import Node, Graph
from mo.ops.op import Op, PermuteAttrs
from mo.utils.error import Error


class Pooling(Op):
op = 'Pooling'

def __init__(self, graph: Graph, attrs: dict):
super().__init__(graph, {
'type': __class__.op,
'op': __class__.op,
'type': self.op,
'op': self.op,
'version': 'opset1',
'infer': __class__.infer,
'infer': self.infer,
'in_ports_count': 1,
'out_ports_count': 1,
}, attrs)
Expand Down Expand Up @@ -96,9 +94,14 @@ def infer(node: Node):
rounding = np.floor
if node.soft_get('pooling_convention') == 'full' or node.soft_get('rounding_type') == 'ceil':
rounding = np.ceil
output_spatial_shape = np.array(rounding(
np.array(input_spatial_shape + pad_spatial_shape - window_spatial_shape,
dtype=np.float) / stride_spatial), dtype=np.int64) + 1

padded_spatial_shape = input_spatial_shape + pad_spatial_shape - window_spatial_shape
if np.any(padded_spatial_shape < 0):
raise Error("Data after padding has dimension less than window size. " +
"Possible reason of error in incorrect input_shape")

output_spatial_shape = np.array(rounding(np.array(padded_spatial_shape, dtype=np.float) / stride_spatial),
dtype=np.int64) + 1

original_pads = np.array([i[1] for i in node.pad_spatial_shape])

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24 changes: 24 additions & 0 deletions model-optimizer/mo/ops/pooling_test.py
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Expand Up @@ -21,6 +21,7 @@
from mo.graph.graph import Node
from mo.ops.pooling import Pooling
from mo.utils.unittest.graph import build_graph
from mo.utils.error import Error

nodes_attributes = {'node_1': {'value': None, 'kind': 'data'},
'pool': {'type': 'Pooling', 'value': None, 'kind': 'op'},
Expand Down Expand Up @@ -129,3 +130,26 @@ def test_pooling_infer_no_shape(self):
Pooling.infer(pool_node)
res_shape = graph.node['node_2']['shape']
self.assertIsNone(res_shape)

def test_pooling_infer_wrong_input_shape(self):
graph = build_graph(nodes_attributes,
[('node_1', 'pool'),
('pool', 'node_2'),
('node_2', 'op_output')
],
{'node_2': {'shape': None},
'node_1': {'shape': np.array([1, 3, 1, 1])},
'pool': {'window': np.array([1, 1, 5, 5]), 'stride': np.array([1, 1, 2, 2]),
'pad': np.array([[0, 0], [0, 0], [1, 1], [1, 1]]),
'pad_spatial_shape': np.array([[1, 1], [1, 1]]),
'pool_method': 'avg', 'exclude_pad': 'false', 'global_pool': 0,
'output_spatial_shape': None, 'output_shape': None,
'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]),
'channel_dims': np.array([1]), 'batch_dims': np.array([0]),
'pooling_convention': 'full'}
})

pool_node = Node(graph, 'pool')

with self.assertRaises(Error):
Pooling.infer(pool_node)

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