Minor fixes
This commit is contained in:
Marcos Luciano
@@ -7,6 +7,57 @@ import torch
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from utils.torch_utils import select_device
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class YoloLayers():
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def get_route(self, n, layers):
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route = 0
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for i, layer in enumerate(layers):
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if i <= n:
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route += layer[1]
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else:
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break
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return route
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def route(self, layers=""):
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return "\n[route]\n" + \
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"layers=%s\n" % layers
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def shortcut(self, route=-1, activation="linear"):
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return "\n[shortcut]\n" + \
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"from=%d\n" % route + \
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"activation=%s\n" % activation
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def maxpool(self, stride=1, size=1):
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return "\n[maxpool]\n" + \
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"stride=%d\n" % stride + \
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"size=%d\n" % size
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def upsample(self, stride=1):
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return "\n[upsample]\n" + \
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"stride=%d\n" % stride
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def convolutional(self, bn=False, size=1, stride=1, pad=1, filters=1, groups=1, activation="linear"):
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b = "batch_normalize=1\n" if bn is True else ""
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g = "groups=%d\n" % groups if groups > 1 else ""
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return "\n[convolutional]\n" + \
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b + \
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"filters=%d\n" % filters + \
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"size=%d\n" % size + \
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"stride=%d\n" % stride + \
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"pad=%d\n" % pad + \
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g + \
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"activation=%s\n" % activation
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def yolo(self, mask="", anchors="", classes=80, num=3):
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return "\n[yolo]\n" + \
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"mask=%s\n" % mask + \
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"anchors=%s\n" % anchors + \
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"classes=%d\n" % classes + \
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"num=%d\n" % num + \
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"scale_x_y=2.0\n" + \
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"beta_nms=0.6\n" + \
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"new_coords=1\n"
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def parse_args():
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parser = argparse.ArgumentParser(description="PyTorch YOLOv5 conversion")
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parser.add_argument("-w", "--weights", required=True, help="Input weights (.pt) file path (required)")
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@@ -77,10 +128,10 @@ with open(wts_file, "w") as f:
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cv3_idx = 0
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sppf_idx = 11 if p6 else 9
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for k, v in model.state_dict().items():
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if not "num_batches_tracked" in k and not "anchors" in k and not "anchor_grid" in k:
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if "num_batches_tracked" not in k and "anchors" not in k and "anchor_grid" not in k:
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vr = v.reshape(-1).cpu().numpy()
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idx = int(k.split(".")[1])
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if ".cv1." in k and not ".m." in k and idx != sppf_idx:
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if ".cv1." in k and ".m." not in k and idx != sppf_idx:
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cv1 += "{} {} ".format(k, len(vr))
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for vv in vr:
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cv1 += " "
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@@ -102,7 +153,7 @@ with open(wts_file, "w") as f:
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wts_write += cv3
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cv3 = ""
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cv3_idx = 0
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if not ".cv3." in k and not (".cv1." in k and not ".m." in k and idx != sppf_idx):
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if ".cv3." not in k and not (".cv1." in k and ".m." not in k and idx != sppf_idx):
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wts_write += "{} {} ".format(k, len(vr))
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for vv in vr:
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wts_write += " "
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@@ -125,219 +176,117 @@ with open(wts_file, "w") as f:
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with open(cfg_file, "w") as c:
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with open(yaml_file, "r", encoding="utf-8") as f:
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nc = 0
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depth_multiple = 0
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width_multiple = 0
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detections = []
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layers = []
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f = yaml.load(f,Loader=yaml.FullLoader)
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c.write("[net]\n")
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c.write("width=%d\n" % model_width)
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c.write("height=%d\n" % model_height)
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c.write("channels=%d\n" % model_channels)
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for l in f:
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if l == "nc":
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nc = f[l]
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elif l == "depth_multiple":
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depth_multiple = f[l]
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elif l == "width_multiple":
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width_multiple = f[l]
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elif l == "backbone" or l == "head":
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for v in f[l]:
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nc = 0
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depth_multiple = 0
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width_multiple = 0
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layers = []
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yoloLayers = YoloLayers()
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f = yaml.load(f, Loader=yaml.FullLoader)
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for topic in f:
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if topic == "nc":
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nc = f[topic]
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elif topic == "depth_multiple":
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depth_multiple = f[topic]
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elif topic == "width_multiple":
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width_multiple = f[topic]
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elif topic == "backbone" or topic == "head":
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for v in f[topic]:
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if v[2] == "Conv":
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layer = ""
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layer = "\n# Conv\n"
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blocks = 0
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % get_width(v[3][0], width_multiple)
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layer += "size=%d\n" % v[3][1]
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layer += "stride=%d\n" % v[3][2]
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple), size=v[3][1],
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stride=v[3][2], activation="silu")
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "C3":
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layer = ""
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layer = "\n# C3\n"
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blocks = 0
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layer += "\n# C3\n"
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# SPLIT
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % get_width(v[3][0] / 2, width_multiple)
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layer += "size=1\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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blocks += 1
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layer += "\n[route]\n"
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layer += "layers=-2\n"
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layer += yoloLayers.route(layers="-2")
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blocks += 1
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % get_width(v[3][0] / 2, width_multiple)
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layer += "size=1\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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blocks += 1
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# Residual Block
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if len(v[3]) == 1 or v[3][1] == True:
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if len(v[3]) == 1 or v[3][1] is True:
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for _ in range(get_depth(v[1], depth_multiple)):
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % get_width(v[3][0] / 2, width_multiple)
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layer += "size=1\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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blocks += 1
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % get_width(v[3][0] / 2, width_multiple)
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layer += "size=3\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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size=3, activation="silu")
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blocks += 1
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layer += "\n[shortcut]\n"
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layer += "from=-3\n"
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layer += "activation=linear\n"
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layer += yoloLayers.shortcut(route=-3)
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blocks += 1
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# Merge
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layer += "\n[route]\n"
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layer += "layers=-1, -%d\n" % (3 * get_depth(v[1], depth_multiple) + 3)
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layer += yoloLayers.route(layers="-1, -%d" % (3 * get_depth(v[1], depth_multiple) + 3))
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blocks += 1
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else:
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for _ in range(get_depth(v[1], depth_multiple)):
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % get_width(v[3][0] / 2, width_multiple)
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layer += "size=1\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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blocks += 1
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % get_width(v[3][0] / 2, width_multiple)
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layer += "size=3\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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size=3, activation="silu")
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blocks += 1
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# Merge
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layer += "\n[route]\n"
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layer += "layers=-1, -%d\n" % (2 * get_depth(v[1], depth_multiple) + 3)
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layer += yoloLayers.route(layers="-1, -%d" % (2 * get_depth(v[1], depth_multiple) + 3))
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blocks += 1
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# Transition
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % get_width(v[3][0], width_multiple)
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layer += "size=1\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += "\n##########\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple),
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activation="silu")
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "SPPF":
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layer = ""
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layer = "\n# SPPF\n"
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blocks = 0
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layer += "\n# SPPF\n"
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % (get_width(v[3][0], width_multiple) / 2)
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layer += "size=1\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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blocks += 1
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layer += "\n[maxpool]\n"
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layer += "stride=1\n"
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layer += "size=%d\n" % v[3][1]
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layer += yoloLayers.maxpool(size=v[3][1])
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blocks += 1
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layer += "\n[maxpool]\n"
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layer += "stride=1\n"
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layer += "size=%d\n" % v[3][1]
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layer += yoloLayers.maxpool(size=v[3][1])
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blocks += 1
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layer += "\n[maxpool]\n"
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layer += "stride=1\n"
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layer += "size=%d\n" % v[3][1]
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layer += yoloLayers.maxpool(size=v[3][1])
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blocks += 1
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layer += "\n[route]\n"
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layer += "layers=-4, -3, -2, -1\n"
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layer += yoloLayers.route(layers="-4, -3, -2, -1")
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blocks += 1
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layer += "\n[convolutional]\n"
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layer += "batch_normalize=1\n"
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layer += "filters=%d\n" % get_width(v[3][0], width_multiple)
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layer += "size=1\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "activation=silu\n"
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layer += "\n##########\n"
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple),
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activation="silu")
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "nn.Upsample":
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layer = ""
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layer = "\n# nn.Upsample\n"
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blocks = 0
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layer += "\n[upsample]\n"
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layer += "stride=%d\n" % v[3][1]
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layer += yoloLayers.upsample(stride=v[3][1])
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "Concat":
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layer = ""
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blocks = 0
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route = v[0][1]
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r = 0
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if route > 0:
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for i, item in enumerate(layers):
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if i <= route:
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r += item[1]
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else:
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break
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else:
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route = len(layers) + route
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for i, item in enumerate(layers):
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if i <= route:
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r += item[1]
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else:
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break
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layer += "\n# Concat\n"
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layer += "\n[route]\n"
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layer += "layers=-1, %d\n" % (r - 1)
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layer += "\n##########\n"
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route = yoloLayers.get_route(route, layers) if route > 0 else \
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yoloLayers.get_route(len(layers) + route, layers)
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layer = "\n# Concat\n"
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blocks = 0
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layer += yoloLayers.route(layers="-1, %d" % (route - 1))
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "Detect":
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for i, n in enumerate(v[0]):
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layer = ""
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route = yoloLayers.get_route(n, layers)
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layer = "\n# Detect\n"
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blocks = 0
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r = 0
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for j, item in enumerate(layers):
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if j <= n:
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r += item[1]
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else:
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break
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layer += "\n# Detect\n"
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layer += "\n[route]\n"
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layer += "layers=%d\n" % (r - 1)
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layer += yoloLayers.route(layers="%d" % (route - 1))
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blocks += 1
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layer += "\n[convolutional]\n"
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layer += "size=1\n"
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layer += "stride=1\n"
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layer += "pad=1\n"
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layer += "filters=%d\n" % ((nc + 5) * len(masks[i]))
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layer += "activation=logistic\n"
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layer += yoloLayers.convolutional(filters=((nc + 5) * len(masks[i])), activation="logistic")
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blocks += 1
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layer += "\n[yolo]\n"
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layer += "mask=%s\n" % str(masks[i])[1:-1]
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layer += "anchors=%s\n" % anchors
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layer += "classes=%d\n" % nc
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layer += "num=%d\n" % num
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layer += "scale_x_y=2.0\n"
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layer += "beta_nms=0.6\n"
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layer += "new_coords=1\n"
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layer += "\n##########\n"
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layer += yoloLayers.yolo(mask=str(masks[i])[1:-1], anchors=anchors, classes=nc, num=num)
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blocks += 1
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layers.append([layer, blocks])
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for layer in layers:
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