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Extend support to Python3 and PyTorch 0.4.1 #30

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Extend support to Python3 and PyTorch 0.4.1 #30

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a4e0add
Extend support to Python3 and PyTorch 0.4.1 for the validation routine
SergioRAgostinho Aug 6, 2018
47f60c4
Add Python3 and PyTorch 0.4.1 support to training procedure
SergioRAgostinho Aug 7, 2018
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6 changes: 3 additions & 3 deletions cfg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ def load_conv(buf, start, conv_model):
num_w = conv_model.weight.numel()
num_b = conv_model.bias.numel()
conv_model.bias.data.copy_(torch.from_numpy(buf[start:start+num_b])); start = start + num_b
conv_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w])); start = start + num_w
conv_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w]).view_as(conv_model.weight.data)); start = start + num_w
return start

def save_conv(fp, conv_model):
Expand All @@ -172,7 +172,7 @@ def load_conv_bn(buf, start, conv_model, bn_model):
bn_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_b])); start = start + num_b
bn_model.running_mean.copy_(torch.from_numpy(buf[start:start+num_b])); start = start + num_b
bn_model.running_var.copy_(torch.from_numpy(buf[start:start+num_b])); start = start + num_b
conv_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w])); start = start + num_w
conv_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w]).view_as(conv_model.weight.data)); start = start + num_w
return start

def save_conv_bn(fp, conv_model, bn_model):
Expand All @@ -193,7 +193,7 @@ def load_fc(buf, start, fc_model):
num_w = fc_model.weight.numel()
num_b = fc_model.bias.numel()
fc_model.bias.data.copy_(torch.from_numpy(buf[start:start+num_b])); start = start + num_b
fc_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w])); start = start + num_w
fc_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w]).view_as(fc_model.weight.data)); start = start + num_w
return start

def save_fc(fp, fc_model):
Expand Down
10 changes: 5 additions & 5 deletions darknet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,10 +28,10 @@ def forward(self, x):
assert(W % stride == 0)
ws = stride
hs = stride
x = x.view(B, C, H/hs, hs, W/ws, ws).transpose(3,4).contiguous()
x = x.view(B, C, H/hs*W/ws, hs*ws).transpose(2,3).contiguous()
x = x.view(B, C, hs*ws, H/hs, W/ws).transpose(1,2).contiguous()
x = x.view(B, hs*ws*C, H/hs, W/ws)
x = x.view(B, C, H//hs, hs, W//ws, ws).transpose(3,4).contiguous()
x = x.view(B, C, H//hs*W//ws, hs*ws).transpose(2,3).contiguous()
x = x.view(B, C, hs*ws, H//hs, W//ws).transpose(1,2).contiguous()
x = x.view(B, hs*ws*C, H//hs, W//ws)
return x

class GlobalAvgPool2d(nn.Module):
Expand Down Expand Up @@ -146,7 +146,7 @@ def create_network(self, blocks):
kernel_size = int(block['size'])
stride = int(block['stride'])
is_pad = int(block['pad'])
pad = (kernel_size-1)/2 if is_pad else 0
pad = (kernel_size-1)//2 if is_pad else 0
activation = block['activation']
model = nn.Sequential()
if batch_normalize:
Expand Down
40 changes: 20 additions & 20 deletions region_loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@ def build_targets(pred_corners, target, anchors, num_anchors, num_classes, nH, n
gy8 = target[b][t*21+18]*nH

cur_gt_corners = torch.FloatTensor([gx0/nW,gy0/nH,gx1/nW,gy1/nH,gx2/nW,gy2/nH,gx3/nW,gy3/nH,gx4/nW,gy4/nH,gx5/nW,gy5/nH,gx6/nW,gy6/nH,gx7/nW,gy7/nH,gx8/nW,gy8/nH]).repeat(nAnchors,1).t() # 16 x nAnchors
cur_confs = torch.max(cur_confs, corner_confidences9(cur_pred_corners, cur_gt_corners)) # some irrelevant areas are filtered, in the same grid multiple anchor boxes might exceed the threshold
cur_confs = torch.max(cur_confs, corner_confidences9(cur_pred_corners, cur_gt_corners)).view_as(conf_mask[b]) # some irrelevant areas are filtered, in the same grid multiple anchor boxes might exceed the threshold
conf_mask[b][cur_confs>sil_thresh] = 0
if seen < -1:#6400:
tx0.fill_(0.5)
Expand Down Expand Up @@ -202,24 +202,24 @@ def forward(self, output, target):
pred_corners = torch.cuda.FloatTensor(18, nB*nA*nH*nW)
grid_x = torch.linspace(0, nW-1, nW).repeat(nH,1).repeat(nB*nA, 1, 1).view(nB*nA*nH*nW).cuda()
grid_y = torch.linspace(0, nH-1, nH).repeat(nW,1).t().repeat(nB*nA, 1, 1).view(nB*nA*nH*nW).cuda()
pred_corners[0] = (x0.data + grid_x) / nW
pred_corners[1] = (y0.data + grid_y) / nH
pred_corners[2] = (x1.data + grid_x) / nW
pred_corners[3] = (y1.data + grid_y) / nH
pred_corners[4] = (x2.data + grid_x) / nW
pred_corners[5] = (y2.data + grid_y) / nH
pred_corners[6] = (x3.data + grid_x) / nW
pred_corners[7] = (y3.data + grid_y) / nH
pred_corners[8] = (x4.data + grid_x) / nW
pred_corners[9] = (y4.data + grid_y) / nH
pred_corners[10] = (x5.data + grid_x) / nW
pred_corners[11] = (y5.data + grid_y) / nH
pred_corners[12] = (x6.data + grid_x) / nW
pred_corners[13] = (y6.data + grid_y) / nH
pred_corners[14] = (x7.data + grid_x) / nW
pred_corners[15] = (y7.data + grid_y) / nH
pred_corners[16] = (x8.data + grid_x) / nW
pred_corners[17] = (y8.data + grid_y) / nH
pred_corners[0] = (x0.data.view(-1) + grid_x) / nW
pred_corners[1] = (y0.data.view(-1) + grid_y) / nH
pred_corners[2] = (x1.data.view(-1) + grid_x) / nW
pred_corners[3] = (y1.data.view(-1) + grid_y) / nH
pred_corners[4] = (x2.data.view(-1) + grid_x) / nW
pred_corners[5] = (y2.data.view(-1) + grid_y) / nH
pred_corners[6] = (x3.data.view(-1) + grid_x) / nW
pred_corners[7] = (y3.data.view(-1) + grid_y) / nH
pred_corners[8] = (x4.data.view(-1) + grid_x) / nW
pred_corners[9] = (y4.data.view(-1) + grid_y) / nH
pred_corners[10] = (x5.data.view(-1) + grid_x) / nW
pred_corners[11] = (y5.data.view(-1) + grid_y) / nH
pred_corners[12] = (x6.data.view(-1) + grid_x) / nW
pred_corners[13] = (y6.data.view(-1) + grid_y) / nH
pred_corners[14] = (x7.data.view(-1) + grid_x) / nW
pred_corners[15] = (y7.data.view(-1) + grid_y) / nH
pred_corners[16] = (x8.data.view(-1) + grid_x) / nW
pred_corners[17] = (y8.data.view(-1) + grid_y) / nH
gpu_matrix = pred_corners.transpose(0,1).contiguous().view(-1,18)
pred_corners = convert2cpu(gpu_matrix)
t2 = time.time()
Expand Down Expand Up @@ -248,7 +248,7 @@ def forward(self, output, target):
tx8 = Variable(tx8.cuda())
ty8 = Variable(ty8.cuda())
tconf = Variable(tconf.cuda())
tcls = Variable(tcls.view(-1)[cls_mask].long().cuda())
tcls = Variable(tcls[cls_mask].long().cuda())
coord_mask = Variable(coord_mask.cuda())
conf_mask = Variable(conf_mask.cuda().sqrt())
cls_mask = Variable(cls_mask.view(-1, 1).repeat(1,nC).cuda())
Expand Down
2 changes: 1 addition & 1 deletion train.py
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Expand Up @@ -343,7 +343,7 @@ def truths_length(truths):
init_height = model.height
test_width = 672
test_height = 672
init_epoch = model.seen/nsamples
init_epoch = model.seen//nsamples

# Variable to save
training_iters = []
Expand Down
4 changes: 2 additions & 2 deletions utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -884,7 +884,7 @@ def get_color(c, x, max_val):
def read_truths(lab_path):
if os.path.getsize(lab_path):
truths = np.loadtxt(lab_path)
truths = truths.reshape(truths.size/21, 21) # to avoid single truth problem
truths = truths.reshape(truths.size//21, 21) # to avoid single truth problem
return truths
else:
return np.array([])
Expand Down Expand Up @@ -1008,7 +1008,7 @@ def file_lines(thefilepath):
buffer = thefile.read(8192*1024)
if not buffer:
break
count += buffer.count('\n')
count += buffer.count(b'\n')
thefile.close( )
return count

Expand Down