Ankle/yolov5_ZWgra
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Add pre-commit CI actions (#4982)

Browse Source 
* define pre-commit

* add CI code

* configure

* apply pre-commit

* fstring

* apply MD

* pre-commit

* Update torch_utils.py

* Update print strings

* notes

* Cleanup code-format.yml

* Update setup.cfg

* Update .pre-commit-config.yaml

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Jirka BorovecandGlenn Jocher authored and GitHub committed 2021-10-28 18:35:01 +02:00
1 parent a4fece8c14
commit ed887b5976
30 files changed
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models/common.py
+5 -5
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@@ -79,7 +79,7 @@ class TransformerBlock(nn.Module):
if c1 != c2:
self.conv = Conv(c1, c2)
self.linear = nn.Linear(c2, c2) # learnable position embedding
self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
self.c2 = c2
def forward(self, x):
@@ -114,7 +114,7 @@ class BottleneckCSP(nn.Module):
self.cv4 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
self.act = nn.LeakyReLU(0.1, inplace=True)
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
def forward(self, x):
y1 = self.cv3(self.m(self.cv1(x)))
@@ -130,7 +130,7 @@ class C3(nn.Module):
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c1, c_, 1, 1)
self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
# self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
def forward(self, x):
@@ -158,7 +158,7 @@ class C3Ghost(C3):
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e) # hidden channels
self.m = nn.Sequential(*[GhostBottleneck(c_, c_) for _ in range(n)])
self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
class SPP(nn.Module):
@@ -362,7 +362,7 @@ class Detections:
def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
super().__init__()
d = pred[0].device # device
gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1., 1.], device=d) for im in imgs] # normalizations
self.imgs = imgs # list of images as numpy arrays
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
self.names = names # class names
models/experimental.py
-1
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@@ -97,7 +97,6 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
else:
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse
# Compatibility updates
for m in model.modules():
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
models/hub/yolov5-bifpn.yaml
+1 -1
View File
@@ -18,7 +18,7 @@ backbone:
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]]
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, C3, [1024, False]], # 9
models/tf.py
+20 -20
View File
@@ -40,7 +40,7 @@ LOGGER = logging.getLogger(__name__)
class TFBN(keras.layers.Layer):
# TensorFlow BatchNormalization wrapper
def __init__(self, w=None):
super(TFBN, self).__init__()
super().__init__()
self.bn = keras.layers.BatchNormalization(
beta_initializer=keras.initializers.Constant(w.bias.numpy()),
gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
@@ -54,7 +54,7 @@ class TFBN(keras.layers.Layer):
class TFPad(keras.layers.Layer):
def __init__(self, pad):
super(TFPad, self).__init__()
super().__init__()
self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
def call(self, inputs):
@@ -65,7 +65,7 @@ class TFConv(keras.layers.Layer):
# Standard convolution
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
# ch_in, ch_out, weights, kernel, stride, padding, groups
super(TFConv, self).__init__()
super().__init__()
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
assert isinstance(k, int), "Convolution with multiple kernels are not allowed."
# TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
@@ -96,7 +96,7 @@ class TFFocus(keras.layers.Layer):
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
# ch_in, ch_out, kernel, stride, padding, groups
super(TFFocus, self).__init__()
super().__init__()
self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c)
@@ -110,7 +110,7 @@ class TFFocus(keras.layers.Layer):
class TFBottleneck(keras.layers.Layer):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion
super(TFBottleneck, self).__init__()
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
@@ -123,7 +123,7 @@ class TFBottleneck(keras.layers.Layer):
class TFConv2d(keras.layers.Layer):
# Substitution for PyTorch nn.Conv2D
def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
super(TFConv2d, self).__init__()
super().__init__()
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
self.conv = keras.layers.Conv2D(
c2, k, s, 'VALID', use_bias=bias,
@@ -138,7 +138,7 @@ class TFBottleneckCSP(keras.layers.Layer):
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
# ch_in, ch_out, number, shortcut, groups, expansion
super(TFBottleneckCSP, self).__init__()
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
@@ -158,7 +158,7 @@ class TFC3(keras.layers.Layer):
# CSP Bottleneck with 3 convolutions
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
# ch_in, ch_out, number, shortcut, groups, expansion
super(TFC3, self).__init__()
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
@@ -172,7 +172,7 @@ class TFC3(keras.layers.Layer):
class TFSPP(keras.layers.Layer):
# Spatial pyramid pooling layer used in YOLOv3-SPP
def __init__(self, c1, c2, k=(5, 9, 13), w=None):
super(TFSPP, self).__init__()
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
@@ -186,7 +186,7 @@ class TFSPP(keras.layers.Layer):
class TFSPPF(keras.layers.Layer):
# Spatial pyramid pooling-Fast layer
def __init__(self, c1, c2, k=5, w=None):
super(TFSPPF, self).__init__()
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
@@ -201,7 +201,7 @@ class TFSPPF(keras.layers.Layer):
class TFDetect(keras.layers.Layer):
def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer
super(TFDetect, self).__init__()
super().__init__()
self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor
@@ -249,7 +249,7 @@ class TFDetect(keras.layers.Layer):
class TFUpsample(keras.layers.Layer):
def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w'
super(TFUpsample, self).__init__()
super().__init__()
assert scale_factor == 2, "scale_factor must be 2"
self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
# self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
@@ -263,7 +263,7 @@ class TFUpsample(keras.layers.Layer):
class TFConcat(keras.layers.Layer):
def __init__(self, dimension=1, w=None):
super(TFConcat, self).__init__()
super().__init__()
assert dimension == 1, "convert only NCHW to NHWC concat"
self.d = 3
@@ -272,7 +272,7 @@ class TFConcat(keras.layers.Layer):
def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
LOGGER.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
@@ -299,7 +299,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
elif m is Detect:
args.append([ch[x + 1] for x in f])
if isinstance(args[1], int): # number of anchors
@@ -312,11 +312,11 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \
else tf_m(*args, w=model.model[i]) # module
torch_m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace('__main__.', '') # module type
np = sum([x.numel() for x in torch_m_.parameters()]) # number params
np = sum(x.numel() for x in torch_m_.parameters()) # number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
LOGGER.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
ch.append(c2)
@@ -325,7 +325,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
class TFModel:
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes
super(TFModel, self).__init__()
super().__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
@@ -336,7 +336,7 @@ class TFModel:
# Define model
if nc and nc != self.yaml['nc']:
print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc))
print(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
self.yaml['nc'] = nc # override yaml value
self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
models/yolo.py
+5 -5
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@@ -247,7 +247,7 @@ class Model(nn.Module):
def parse_model(d, ch): # model_dict, input_channels(3)
LOGGER.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
@@ -275,7 +275,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum([ch[x] for x in f])
c2 = sum(ch[x] for x in f)
elif m is Detect:
args.append([ch[x] for x in f])
if isinstance(args[1], int): # number of anchors
@@ -287,11 +287,11 @@ def parse_model(d, ch): # model_dict, input_channels(3)
else:
c2 = ch[f]
m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace('__main__.', '') # module type
np = sum([x.numel() for x in m_.parameters()]) # number params
np = sum(x.numel() for x in m_.parameters()) # number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
LOGGER.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n_, np, t, args)) # print
LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
if i == 0: