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Small fix of the Depthwise Convolution example in python3 #224

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Merged
tqchen merged 3 commits into from
Jul 7, 2017
Merged

Small fix of the Depthwise Convolution example in python3 #224

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4e0e654
fix for python3
sxjscience Jul 7, 2017
1b51be9
Update depthwise_conv2d_map_test.py
sxjscience Jul 7, 2017
c403817
Merge branch 'master' into small_fix
sxjscience Jul 7, 2017
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11 changes: 8 additions & 3 deletions topi/recipe/conv/depthwise_conv2d_map_test.py
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Original file line number Diff line number Diff line change
@@ -1,9 +1,12 @@
import os
import sys
import tvm
import numpy as np
from scipy import signal
from tvm.contrib import nvcc_compiler

DIR_PATH = os.path.realpath(os.path.dirname(__file__))
sys.path.append(os.path.join(DIR_PATH, '..', '..', 'python'))
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@tqchen tqchen Jul 7, 2017

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We can assume topi is in path, so this is not needed

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@tqchen tqchen Jul 7, 2017

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@sxjscience can you remove this line so that it can be merged? thanks!

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@sxjscience sxjscience Jul 7, 2017

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OK, done.

import topi
from topi.nn.util import get_const_tuple
from topi.cuda.depthwise_conv2d_map import schedule_depthwise_conv2d_map
Expand Down Expand Up @@ -78,12 +81,14 @@ def depthwise_conv2d_map_scipy(input_np, filter_np, scale_np, shift_np):
index_w = pad_left_scipy - pad_left_tvm
for i in range(batch):
for j in range(out_channel):
depthwise_conv2d_scipy[i,j,:,:] = signal.convolve2d(input_np[i,j/channel_multiplier,:,:], np.rot90(filter_np[j/channel_multiplier,j%channel_multiplier,:,:], 2),
depthwise_conv2d_scipy[i,j,:,:] = signal.convolve2d(input_np[i,j // channel_multiplier,:,:],
np.rot90(filter_np[j // channel_multiplier,j%channel_multiplier,:,:], 2),
mode='same')[index_h:in_height:stride_h, index_w:in_width:stride_w]
if padding == 'VALID':
for i in range(batch):
for j in range(out_channel):
depthwise_conv2d_scipy[i,j,:,:] = signal.convolve2d(input_np[i,j/channel_multiplier,:,:], np.rot90(filter_np[j/channel_multiplier,j%channel_multiplier,:,:], 2),
depthwise_conv2d_scipy[i,j,:,:] = signal.convolve2d(input_np[i,j // channel_multiplier,:,:],
np.rot90(filter_np[j // channel_multiplier,j%channel_multiplier,:,:], 2),
mode='valid')[0:(in_height - filter_height + 1):stride_h, 0:(in_width - filter_height + 1):stride_w]
for c in range(out_channel):
scale_shift_scipy[:,c,:,:] = depthwise_conv2d_scipy[:,c,:,:] * scale_np[c] + shift_np[c]
Expand Down Expand Up @@ -132,7 +137,7 @@ def check_device(device):
np.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5)
np.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5)
np.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5)
print "success"
print("success")

with tvm.build_config(auto_unroll_max_step=32,
auto_unroll_min_depth=0,
Expand Down