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new 26f7c0d [Relay testing] densenet implementation fix (#8704)
26f7c0d is described below
commit 26f7c0d7c1959bc1fe37915abe26db5c080dbb57
Author: Jaehun Ryu <[email protected]>
AuthorDate: Wed Aug 18 02:14:05 2021 +0900
[Relay testing] densenet implementation fix (#8704)
* Fixed testing densenet bug
* Fixed code format using black
---
python/tvm/relay/testing/densenet.py | 16 ++++++++++------
1 file changed, 10 insertions(+), 6 deletions(-)
diff --git a/python/tvm/relay/testing/densenet.py
b/python/tvm/relay/testing/densenet.py
index 1ceb626..d621249 100644
--- a/python/tvm/relay/testing/densenet.py
+++ b/python/tvm/relay/testing/densenet.py
@@ -1,4 +1,3 @@
-# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
@@ -44,9 +43,12 @@ def _make_dense_layer(data, growth_rate, bn_size, index):
def _make_dense_block(data, num_layers, bn_size, growth_rate, index):
"""Makes a block of dense layers of the specified size."""
layer_out = data
+ blocks = []
for i in range(num_layers):
layer_out = _make_dense_layer(layer_out, growth_rate, bn_size, "%s_%s"
% (index, i))
- return layer_out
+ blocks.append(layer_out)
+ block_out = relay.concatenate(blocks, 1)
+ return block_out
def _make_transition(data, num_output_features, index):
@@ -63,7 +65,9 @@ def _make_dense_net(
num_init_features, growth_rate, block_config, data_shape, data_dtype,
bn_size=4, classes=1000
):
"""Builds up a densenet."""
- data = relay.Var("data", relay.TensorType(data_shape, data_dtype)) #
(bn_size, 3, 224, 224)))
+ data = relay.Var(
+ "data", relay.TensorType(data_shape, data_dtype)
+ ) # (batch_size, 3, 224, 224)))
conv1 = layers.conv2d(
data,
channels=num_init_features,
@@ -79,7 +83,7 @@ def _make_dense_net(
num_features = num_init_features
layer_out = mp
for i, num_layers in enumerate(block_config):
- layer_out = _make_dense_block(layer_out, num_layers, growth_rate,
bn_size, i)
+ layer_out = _make_dense_block(layer_out, num_layers, bn_size,
growth_rate, i)
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
layer_out = _make_transition(layer_out, num_features // 2, i)
@@ -131,10 +135,10 @@ def get_workload(
169: (69, 32, [6, 12, 32, 32]),
201: (64, 32, [6, 12, 48, 32]),
}
-
+ bn_size = 4
num_init_features, growth_rate, block_config = specs[densenet_size]
data_shape = tuple([batch_size] + list(image_shape))
net = _make_dense_net(
- num_init_features, growth_rate, block_config, data_shape, dtype,
batch_size, classes
+ num_init_features, growth_rate, block_config, data_shape, dtype,
bn_size, classes
)
return create_workload(net)
